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Autism Spectrum Disorder

What is asd.

Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave. Although autism can be diagnosed at any age, it is described as a “developmental disorder” because symptoms generally appear in the first 2 years of life.

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) , a guide created by the American Psychiatric Association that health care providers use to diagnose mental disorders, people with ASD often have:

  • Difficulty with communication and interaction with other people
  • Restricted interests and repetitive behaviors
  • Symptoms that affect their ability to function in school, work, and other areas of life

Autism is known as a “spectrum” disorder because there is wide variation in the type and severity of symptoms people experience.

People of all genders, races, ethnicities, and economic backgrounds can be diagnosed with ASD. Although ASD can be a lifelong disorder, treatments and services can improve a person’s symptoms and daily functioning. The American Academy of Pediatrics recommends that all children receive screening for autism. Caregivers should talk to their child’s health care provider about ASD screening or evaluation.

What are the signs and symptoms of ASD?

The list below gives some examples of common types of behaviors in people diagnosed with ASD. Not all people with ASD will have all behaviors, but most will have several of the behaviors listed below.

Social communication / interaction behaviors may include:

  • Making little or inconsistent eye contact
  • Appearing not to look at or listen to people who are talking
  • Infrequently sharing interest, emotion, or enjoyment of objects or activities (including infrequent pointing at or showing things to others)
  • Not responding or being slow to respond to one’s name or to other verbal bids for attention
  • Having difficulties with the back and forth of conversation
  • Often talking at length about a favorite subject without noticing that others are not interested or without giving others a chance to respond
  • Displaying facial expressions, movements, and gestures that do not match what is being said
  • Having an unusual tone of voice that may sound sing-song or flat and robot-like
  • Having trouble understanding another person’s point of view or being unable to predict or understand other people’s actions
  • Difficulties adjusting behaviors to social situations
  • Difficulties sharing in imaginative play or in making friends

Restrictive / repetitive behaviors may include:

  • Repeating certain behaviors or having unusual behaviors, such as repeating words or phrases (a behavior called echolalia)
  • Having a lasting intense interest in specific topics, such as numbers, details, or facts
  • Showing overly focused interests, such as with moving objects or parts of objects
  • Becoming upset by slight changes in a routine and having difficulty with transitions
  • Being more sensitive or less sensitive than other people to sensory input, such as light, sound, clothing, or temperature

People with ASD may also experience sleep problems and irritability.

People on the autism spectrum also may have many strengths, including:

  • Being able to learn things in detail and remember information for long periods of time
  • Being strong visual and auditory learners
  • Excelling in math, science, music, or art

What are the causes and risk factors for ASD?

Researchers don’t know the primary causes of ASD, but studies suggest that a person’s genes can act together with aspects of their environment to affect development in ways that lead to ASD. Some factors that are associated with an increased likelihood of developing ASD include:

  • Having a sibling with ASD
  • Having older parents
  • Having certain genetic conditions (such as Down syndrome or Fragile X syndrome)
  • Having a very low birth weight

How is ASD diagnosed?

Health care providers diagnose ASD by evaluating a person’s behavior and development. ASD can usually be reliably diagnosed by age 2. It is important to seek an evaluation as soon as possible. The earlier ASD is diagnosed, the sooner treatments and services can begin.

Diagnosis in young children

Diagnosis in young children is often a two-stage process.

Stage 1: General developmental screening during well-child checkups

Every child should receive well-child check-ups with a pediatrician or an early childhood health care provider. The American Academy of Pediatrics recommends that all children receive screening for developmental delays at their 9-, 18-, and 24- or 30-month well-child visits, with specific autism screenings at their 18- and 24-month well-child visits. A child may receive additional screening if they have a higher likelihood of ASD or developmental problems. Children with a higher likelihood of ASD include those who have a family member with ASD, show some behaviors that are typical of ASD, have older parents, have certain genetic conditions, or who had a very low birth weight.

Considering caregivers’ experiences and concerns is an important part of the screening process for young children. The health care provider may ask questions about the child’s behaviors and evaluate those answers in combination with information from ASD screening tools and clinical observations of the child. Read more about screening instruments   on the Centers for Disease Control and Prevention (CDC) website.

If a child shows developmental differences in behavior or functioning during this screening process, the health care provider may refer the child for additional evaluation.

Stage 2: Additional diagnostic evaluation

It is important to accurately detect and diagnose children with ASD as early as possible, as this will shed light on their unique strengths and challenges. Early detection also can help caregivers determine which services, educational programs, and behavioral therapies are most likely to be helpful for their child.

A team of health care providers who have experience diagnosing ASD will conduct the diagnostic evaluation. This team may include child neurologists, developmental pediatricians, speech-language pathologists, child psychologists and psychiatrists, educational specialists, and occupational therapists.

The diagnostic evaluation is likely to include:

  • Medical and neurological examinations
  • Assessment of the child’s cognitive abilities
  • Assessment of the child’s language abilities
  • Observation of the child’s behavior
  • An in-depth conversation with the child’s caregivers about the child’s behavior and development
  • Assessment of age-appropriate skills needed to complete daily activities independently, such as eating, dressing, and toileting

Because ASD is a complex disorder that sometimes occurs with other illnesses or learning disorders, the comprehensive evaluation may include:

  • Blood tests
  • Hearing test

The evaluation may lead to a formal diagnosis and recommendations for treatment.

Diagnosis in older children and adolescents

Caregivers and teachers are often the first to recognize ASD symptoms in older children and adolescents who attend school. The school’s special education team may perform an initial evaluation and then recommend that a child undergo additional evaluation with their primary health care provider or a health care provider who specialize in ASD.

A child’s caregivers may talk with these health care providers about their child’s social difficulties, including problems with subtle communication. For example, some children may have problems understanding tone of voice, facial expressions, or body language. Older children and adolescents may have trouble understanding figures of speech, humor, or sarcasm. They also may have trouble forming friendships with peers.

Diagnosis in adults

Diagnosing ASD in adults is often more difficult than diagnosing ASD in children. In adults, some ASD symptoms can overlap with symptoms of other mental health disorders, such as anxiety disorder or attention-deficit/hyperactivity disorder (ADHD).

Adults who notice signs of ASD should talk with a health care provider and ask for a referral for an ASD evaluation. Although evaluation for ASD in adults is still being refined, adults may be referred to a neuropsychologist, psychologist, or psychiatrist who has experience with ASD. The expert will ask about:

  • Social interaction and communication challenges
  • Sensory issues
  • Repetitive behaviors
  • Restricted interests

The evaluation also may include a conversation with caregivers or other family members to learn about the person’s early developmental history, which can help ensure an accurate diagnosis.

Receiving a correct diagnosis of ASD as an adult can help a person understand past challenges, identify personal strengths, and find the right kind of help. Studies are underway to determine the types of services and supports that are most helpful for improving the functioning and community integration of autistic transition-age youth and adults.

What treatment options are available for ASD?

Treatment for ASD should begin as soon as possible after diagnosis. Early treatment for ASD is important as proper care and services can reduce individuals’ difficulties while helping them build on their strengths and learn new skills.

People with ASD may face a wide range of issues, which means that there is no single best treatment for ASD. Working closely with a health care provider is an important part of finding the right combination of treatment and services.

A health care provider may prescribe medication to treat specific symptoms. With medication, a person with ASD may have fewer problems with:

  • Irritability
  • Repetitive behavior
  • Hyperactivity
  • Attention problems
  • Anxiety and depression

Read more about the latest medication warnings, patient medication guides, and information on newly approved medications at the Food and Drug Administration (FDA) website  .

Behavioral, psychological, and educational interventions

People with ASD may be referred to a health care provider who specializes in providing behavioral, psychological, educational, or skill-building interventions. These programs are often highly structured and intensive, and they may involve caregivers, siblings, and other family members. These programs may help people with ASD:

  • Learn social, communication, and language skills
  • Reduce behaviors that interfere with daily functioning
  • Increase or build upon strengths
  • Learn life skills for living independently

Other resources

Many services, programs, and other resources are available to help people with ASD. Here are some tips for finding these additional services:

  • Contact your health care provider, local health department, school, or autism advocacy group to learn about special programs or local resources.
  • Find an autism support group. Sharing information and experiences can help people with ASD and their caregivers learn about treatment options and ASD-related programs.
  • Record conversations and meetings with health care providers and teachers. This information may help when it’s time to decide which programs and services are appropriate.
  • Keep copies of health care reports and evaluations. This information may help people with ASD qualify for special programs.

How can I find a clinical trial for ASD?

Clinical trials are research studies that look at new ways to prevent, detect, or treat diseases and conditions. The goal of clinical trials is to determine if a new test or treatment works and is safe. Although individuals may benefit from being part of a clinical trial, participants should be aware that the primary purpose of a clinical trial is to gain new scientific knowledge so that others may be better helped in the future.

Researchers at NIMH and around the country conduct many studies with patients and healthy volunteers. We have new and better treatment options today because of what clinical trials uncovered years ago. Be part of tomorrow’s medical breakthroughs. Talk to your health care provider about clinical trials, their benefits and risks, and whether one is right for you.

To learn more or find a study, visit:

  • NIMH’s Clinical Trials webpage : Information about participating in clinical trials
  • Clinicaltrials.gov: Current Studies on ASD  : List of clinical trials funded by the National Institutes of Health (NIH) being conducted across the country

Where can I learn more about ASD?

Free brochures and shareable resources.

  • Autism Spectrum Disorder : This brochure provides information about the symptoms, diagnosis, and treatment of ASD. Also available  en español .
  • Digital Shareables on Autism Spectrum Disorder : Help support ASD awareness and education in your community. Use these digital resources, including graphics and messages, to spread the word about ASD.

Federal resources

  • Eunice Kennedy Shriver National Institute of Child Health and Human Development  
  • National Institute of Neurological Disorders and Stroke  
  • National Institute on Deafness and Other Communication Disorders  
  • Centers for Disease Control and Prevention   (CDC)
  • Interagency Autism Coordinating Committee  
  • MedlinePlus   (also available en español  )

Research and statistics

  • Science News About Autism Spectrum Disorder : This NIMH webpage provides press releases and announcements about ASD.
  • Research Program on Autism Spectrum Disorders : This NIMH program supports research focused on the characterization, pathophysiology, treatment, and outcomes of ASD and related disorders.
  • Statistics: Autism Spectrum Disorder : This NIMH webpage provides information on the prevalence of ASD in the U.S.
  • Data & Statistics on Autism Spectrum Disorder   : This CDC webpage provides data, statistics, and tools about prevalence and demographic characteristics of ASD.
  • Autism and Developmental Disabilities Monitoring (ADDM) Network   : This CDC-funded program collects data to better understand the population of children with ASD.
  • Biomarkers Consortium - The Autism Biomarkers Consortium for Clinical Trials (ABC-CT)   : This Foundation for the National Institutes of Health project seeks to establish biomarkers to improve treatments for children with ASD.

Last Reviewed:  February 2024

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Autism Spectrum Disorder

The  National Institute of Mental Health (NIMH) , a component of the National Institutes of Health ( NIH ), is a leading federal funder of research on ASD . 
What is autism spectrum disorder? 

Autism spectrum disorder (ASD) refers to a group of complex neurodevelopment disorders caused by differences in the brain that affect communication and behavior. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)—a guide created by the American Psychiatric Association used to diagnose health conditions involving changes in emotion, thinking, or behavior (or a combination of these)—people with ASD can experience: 

  • Challenges or differences in communication and interaction with other people
  • Restricted interests and repetitive behaviors
  • Symptoms that may impact the person's ability to function in school, work, and other areas of life 

ASD can be diagnosed at any age but symptoms generally appear in early childhood (often within the first two years of life). Doctors diagnose ASD by looking at a person's behavior and development. The American Academy of Pediatrics recommends that all children get screened for developmental delays and behaviors often associated with ASD at their 18- and 24-month exams.  

The term “spectrum” refers to the wide range of symptoms, skills, and levels of ability in functioning that can occur in people with ASD. ASD affects every person differently; some may have only a few symptoms and signs while others have many. Some children and adults with ASD are fully able to perform all activities of daily living and may have gifted learning and cognitive abilities while others require substantial support to perform basic activities. A diagnosis of ASD includes Asperger syndrome, autistic disorder, childhood disintegrative disorder, and pervasive developmental disorder not otherwise specified that were once diagnosed as separate disorders.  

In addition to differences or challenges with behavior and difficulty communicating and interacting with others, early signs of ASD may include, but are not limited to: 

  • Avoiding direct eye contact
  • Delayed speech and language skills
  • Challenges with nonverbal cues such as gestures or body language
  • Showing limited interest in other children or caretakers
  • Experiencing stress when routines change 

Scientists believe that both genetics and environment likely play a role in ASD. ASD occurs in every racial and ethnic group, and across all socioeconomic levels. Males are significantly more likely to develop ASD than females.  

People with ASD also have an increased risk of having epilepsy. Children whose language skills regress early in life—before age 3—appear to have a risk of developing epilepsy or seizure-like brain activity. About 20 to 30 percent of children with ASD develop epilepsy by the time they reach adulthood.  

Currently, there is no cure for ASD. Symptoms of ASD can last through a person's lifetime, and some may improve with age, treatment, and services. Therapies and educational/behavioral interventions are designed to remedy specific symptoms and can substantially improve those symptoms. While currently approved medications cannot cure ASD or even treat its main symptoms, there are some that can help with related symptoms such as anxiety, depression, and obsessive-compulsive disorder. Medications are available to treat seizures, severe behavioral problems, and impulsivity and hyperactivity. 

How can I or my loved one help improve care for people with autism spectrum disorder?

Consider participating in a clinical trial so clinicians and scientists can learn more about ASD and related conditions. Clinical trials are studies that use human volunteers to look for new or better ways to diagnose, treat, or cure diseases and conditions. 

All types of volunteers are needed—people with ASD, at-risk individuals, and healthy volunteers—of all different ages, sexes, races, and ethnicities to ensure that study results apply to as many people as possible, and that treatments will be safe and effective for everyone who will use them.

For information about participating in clinical research visit NIH Clinical Research Trials and You . Learn about clinical trials currently looking for people with ASD at Clinicaltrials.gov .

Where can I find more information about autism spectrum disorder?  The following resources offer information about ASD and current research: American Academy of Pediatrics   Centers for Disease Control and Prevention (CDC) Eunice Kennedy Shriver National Institute of Child Health and Human Development   Interagency Autism Coordinating Committee (IACC) National Center for Advancing Translational Sciences   National Institute on Deafness and Other Communication Disorders   National Institute of Environmental Health Sciences   The National Task Group on Intellectual Disabilities and Dementia Practices (NTG) Additional organizations offer information, research news, and other resources about ASD for individuals and caregivers, such as support groups. These organizations include: Association for Science in Autism Treatment     Autism National Committee (AUTCOM)     Autism Network International (ANI)     Autism Research Institute (ARI)   Autism Science Foundation     Autism Society     Autism Speaks, Inc.  

Publications

Prenatal ultrasound use and risk of autism spectrum disorder: Findings from the case‐control Study to Explore Early Development (SEED)

Doctor using ultrasound equipment screening of pregnant woman

Prior studies have found no connection between prenatal ultrasound use and ASD. Through an updated study approach, findings from a 2023 SEED study confirm the previous research, showing no association between prenatal ultrasound use and increased risk for ASD. CDC remains committed to exploring potential risk factors for ASD by using world-class data and analytics.

Key Findings

ADDM Network Expands Surveillance to Identify Healthcare Needs and Transition Planning for Youth Five of CDC’s ADDM Network sites (Arkansas, Georgia, Maryland, Utah, and Wisconsin) began monitoring autism spectrum disorder (ASD) in 2018 among 16-year-old adolescents who were initially identified as having characteristics of ASD in 2010. (Published: February 25, 2023)

Study Shows Linking Statewide Data for ASD Prevalence is Effective Linking statewide health and education data is an effective way for states to have actionable local autism spectrum disorder (ASD) prevalence estimates when resources are limited. (Published: January 18, 2023)

Transitioning from Pediatric to Adult Health Care is Often Difficult for Adolescents with ASD Only 1 in 13 adolescents with Autism Spectrum Disorder (ASD) received the recommended guidance to move from pediatric to adult health care. Greater coordination among healthcare programs and interdisciplinary training for providers could expand access to services and increase provider comfort in treating the unique healthcare needs of adolescents with ASD, and support healthcare planning as they transition from pediatric to adult health care. (Published: April 29, 2021)

CDC Releases First Estimates of the Number of Adults Living with Autism Spectrum Disorder in the United States This study fills a gap in data on adults living with ASD in the United States because there is not an existing surveillance system to collect this information. (Published May 10, 2020)

SEED Research

Many additional studies are underway.  We will provide summaries of those studies in the future.

Community-based service use in preschool children with autism spectrum disorder and associations with insurance status

Rubenstein E, Croen L, Lee LC, Moody, E, Schieve LA, Soke GN, Thomas K, Wiggins L, Daniels J

Research in Autism Spectrum Disorders, 2019

This study examined the association between insurance status and community-based services received outside of school among preschool-aged children with a prior autism spectrum disorder (ASD) diagnosis. Children eligible for autism-related special education services are required by law to receive individualized ASD services in school (“Individuals with Disabilities Education Improvement Act of 2004,” 2004). However, additional community-based services such as behavioral therapy, speech therapy, and occupational therapy are often needed. The Study to Explore Early Development (SEED) provides important information, not available in previous studies, on the use of community-based services by insurance status in preschool-aged children. In this report, insurance status was categorized as private insurance alone, public insurance alone, both private and public insurance, or uninsured. The results showed that about 35% of the children with a prior ASD diagnosis had public insurance alone and 51% had private insurance alone. In addition, 13% had both types of insurance, while few children (1%) were uninsured. The most commonly received services were speech therapy and occupational therapy. Nearly 40% of children received no community-based services at all. After adjusting for sociodemographic variables, insurance status was not associated with the number of different types of community-based services received. However, children with public insurance alone were the least likely to receive behavioral therapy and the most likely to receive psychotropic medication. These findings suggest that many preschool-aged children do not receive community-based services, and the receipt of certain important services varies by insurance type. Increasing access and availability for evidence-based services, especially for children with public insurance only, may improve service use and outcomes for children with ASD.

Assessment of demographic and perinatal predictors of non-response and impact of non-response on measures of association in a population-based case control study: findings from the Georgia Study to Explore Early Development

Schieve LA, Harris S, Maenner MJ, Alexander A, Dowling NF

Emerg Themes Epidemiol., 2018

This report describes characteristics of those who did or did not participate in and complete Georgia SEED between 2007 and 2012. The population (POP) sample was recruited from birth certificates. The autism spectrum disorder (ASD) and developmental disability (DD) groups were recruited from health and special education sources servicing children with developmental disabilities. Children who enrolled in the study were given a comprehensive evaluation to confirm case status. Using birth certificate data, demographics of mothers and children who completed SEED were compared with those who were invited but did not complete the study. Those who did not complete the study included those not located, those located but who declined to participate, or those enrolled in SEED but who dropped out before completing most study steps. Researchers found that all mothers who completed the study tended to be older and had more education than mothers who did not complete the study; yet, they were similar on other demographic factors, such as race/ethnicity and marital status.  Mothers in the ASD group, in particular, were more like to have more education than potential ASD cases invited but who did not complete the study. In two risk factor analyses, associations between the ASD and POP groups were not influenced by the differences in the characteristics of those who participated and completed the study. Assessment of non-response has not yet been done for the other SEED sites. For GA SEED, these findings suggest that differences in participation and completion rates do not appear to affect the study results examined. The information in this report is useful to other researchers conducting epidemiologic studies, especially those seeking to enroll large, diverse population-based samples.

Demographic and Operational Factors Predicting Study Completion in a Multisite Case-Control Study of Preschool Children

Bradley CB, Browne EN, Alexander AA, Collins J, Dahm JL, DiGuiseppi CG, Levy SE, Moody EJ, Schieve LA, Windham GC, Young L, Daniels JL

American Journal of Epidemiology , 2018

This report describes study completion among 3,769 families who enrolled in the first phase of SEED between 2007 and 2011. Families were asked to complete multiple steps for SEED, including phone interviews, filling out forms, participating in an in-person visit to check a child’s development, and providing biological specimens (such as cheek swabs and blood). Researchers found that completion was generally 70% or higher for each study step and 58% of participants completed all key study steps. Researchers found that completion rates varied by families’ demographic characteristics and also the distance they had to travel to the study clinic.  This information is important in helping researchers understand the SEED data already collected and in planning future SEED phases. These study findings also inform researchers on possible ways to improve participation in other future studies.

Demographic Profile of Families and Children in the Study to Explore Early Development (SEED): Case-control Study of Autism Spectrum Disorder.

DiGuiseppi CG, Daniels JL, Fallin DM, Rosenberg SA, Schieve LA, Thomas KC, Windham GC, Goss CW, Soke GN, Currie DW, Singer AB, Lee LC, Bernal P, Croen LA, Miller LA, Pinto-Martin JA, Young LM, Schendel DE.

Disability and Health Journal, 2016

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on sociodemographic characteristics. SEED successfully enrolled a highly diverse sample of participants, including minorities and low socioeconomic status families. The SEED population sample represents racial, ethnic, and demographic diversity in the United States. SEED improves upon other ASD risk factor studies in that it does not rely on administrative data sources, which lack many important details of both child development and maternal risk factors. Nor does it rely on small samples from only a few clinics or schools. SEED collects detailed data in a large and diverse sample.  This provides unique opportunities for researchers to learn more about how socioeconomic characteristics relate to risk factors for ASD and health outcomes in children with ASD.

Autism Spectrum Disorder Symptoms among Children Enrolled in the Study to Explore Early Development (SEED).

Wiggins LD, Levy SE, Daniels J, Schieve L, Croen LA, DiGuiseppi C, Blaskey L, Giarelli E, Lee LC, Pinto-Martin J, Reynolds A, Rice C, Rosenberg CR, Thompson P, Yeargin-Allsopp M, Young L, Schendel D.

Journal of Autism and Developmental Disorders, 2015

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on developmental characteristics. Children enrolled in SEED are divided into four groups: three with children who have varying types of developmental delays and disabilities, including ASD, and one with children from the general population. The report describes how various facets of children’s development vary across these four groups and highlights the many needs of children with ASD and other developmental disabilities.

Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development.

Wiggins LD, Reynolds A, Rice CE, Moody EJ, Bernal P, Blaskey L, Rosenberg SA, Lee LC, Levy SE.

This report describes the SEED process for determining whether a child enrolled in the study will be classified as an ASD case. This classification is based on an in-person assessment given by trained SEED clinicians. Children enrolled in the study are screened for autism symptoms by asking their mothers to respond to a brief questionnaire.  Children with an indication of possible autism symptoms are assessed further during an in-person visit.  Clinicians give these children a more in-depth developmental evaluation known as Autism Diagnostic Observation Schedule and ask their mothers or other caregivers to participate in an interview known as the Autism Diagnostic Interview – Revised. Besides providing clinicians with information to determine a child’s ASD classification, these assessments provide valuable information on ASD-specific behaviors and traits, allowing researchers to better understand the different characteristics among children with ASD.

The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network.

Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M.

Journal of Autism and Developmental Disorders, 2012

This report describes SEED methods. SEED is one of the largest studies investigating genetic and environmental risk factors for autism spectrum disorder (ASD) and child health and behavioral traits associated with ASD. SEED enrolls preschool-aged children with ASD and other developmental disabilities and children from the general population in six sites across the United States. SEED methods focus on enrolling families from diverse populations in each area. A key strength of SEED includes the collection of in-depth information on child development, which allows researchers to more rigorously classify children into various study groups (ASD, other developmental disabilities, or population controls) than what is done in many other ASD research studies.  In SEED, researchers use standardized assessment tools to determine a children’s final study group and to assess specific behavioral traits among children with ASD. Another key strength is the collection of comprehensive data on child health and potential risk factors for ASD. SEED’s large and diverse sample of study participants allows researchers to analyze data in greater detail than most other ASD studies and answer many important questions about ASD.   Top of Page

Maternal Psychiatric Conditions, Treatment with SSRIs, and Neurodevelopmental Disorders

Ames JL, Ladd-Acosta C, Fallin MD, Qian Y, Schieve LA, DiGuiseppi, C, Lee LC, Kasten EP, Zhou G, MPH, MD, PhD, Pinto-Martin J, Howerton E, Eaton, CL, Croen LA, PhD

Biological Psychiatry, 2021

A study published online in Biological Psychiatry looked at whether psychiatric conditions during pregnancy, like depression, and the use of selective serotonin reuptake inhibitors (SSRIs) were associated with autism spectrum disorder (ASD) among the children of mothers who were treated. The study found ASD was more common among children of mothers who had psychiatric conditions during pregnancy. However, among the subset of children whose mothers had psychiatric conditions, ASD was not more common among those treated with SSRIs. The authors conclude that this study provides evidence that maternal psychiatric conditions during pregnancy, but not the use of SSRIs, are associated with increased risk of ASD. These findings have implications for clinical decision-making regarding the continuation of SSRI treatment during pregnancy.

Maternal Pre-Pregnancy Weight and Gestational Weight Gain in Association with Autism and Developmental Disorders in Offspring

Susana L. Matias, Michelle Pearl, Kristen Lyall, Lisa A. Croen, Tanja V. E. Kral, Daniele Fallin, Li-Ching Lee, Chyrise B. Bradley, Laura A. Schieve, Gayle C. Windham

Obesity, 2021

A study published online explored whether obesity in mothers prior to pregnancy or weight gain during pregnancy was associated with autism spectrum disorder (ASD) or other developmental disorders in their children.  Mothers classified as having severe obesity (body mass index ≥35 kg/m) prior to pregnancy had a significantly higher risk of having children with ASD and other developmental disorders. The largest amounts of weight gain during pregnancy were associated with ASD, particularly among male children. Since pre-pregnancy weight and weight gain during pregnancy are regularly measured and potentially modifiable, these findings could assist targeting high-risk mothers for early interventions.

Infection and Fever in Pregnancy and Autism Spectrum Disorders: Findings from the Study to Explore Early Development

Croen LA, Qian Y, Ashwood P, Ousseny Z, Schendel D, Pinto-Martin J, Fallin D, Levy S, Schieve LA, Yeargin-Allsopp M, Sabourin KR

Autism Research, 2019

This study evaluated the associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DD), and whether the child’s mother had an infection during her pregnancy. The Study to Explore Early Development’s (SEED’s) detailed data on type and timing of a mother’s infection and whether the mother also had a fever allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that overall maternal infections during pregnancy were common, occurring in approximately 60% of women in this study, and were not associated with having a child with ASD or DD. Certain infections – those that occurred in the second trimester and were accompanied by fever (7% of mothers) – were associated with ASD in children. These study findings add to other studies of risk factors that highlight the potential association between maternal health during pregnancy and ASD.

Neonatal jaundice in association with autism spectrum disorder and developmental disorder

Cordero C, Schieve LA, Croen LA, Engel SM, Siega-Riz AM, Herring AH, Vladutiu CJ, Seashore CJ, Daniels JL

Journal of Perinatology, 2019

This study examines the association between a child having jaundice just after birth and autism spectrum disorder (ASD) and other developmental disorders (DDs). Jaundice is a yellow discoloration of the skin and eyes that occurs in some newborns because of a build-up of bilirubin, a substance that forms when blood cells are broken down. While most jaundice lasts only a short time, high levels of bilirubin can affect the developing brain. The Study to Explore Early Development’s (SEED’s) detailed data on the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that among children who had been born too early (or preterm), newborn jaundice was associated with both ASD and other DDs. However, among children born on time, newborn jaundice was not associated with either ASD or other DDs. This study highlights the association between newborn health and ASD and other DDs.

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study

McGuinn LA, Windham GC, Messer LC, Di Q, Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Epidemiology, 2020

This study used Study to Explore Early Development (SEED) data to examine the association between autism spectrum disorder (ASD) and exposure to air pollutants during key periods of brain development. Particulate matter (PM), or tiny particles of air pollution, and ozone are common air pollutants. Previous studies have found an association between ASD and exposure to these air pollutants; however, previous studies have been unable to look at exposure to these air pollutants in relation to key periods of brain development or account for potential differences in pollutants in regions of the United States. This study looked at air pollutant exposure among participants living in six different areas of the United States (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) during several critical periods: 3 months before pregnancy, each trimester of pregnancy, the entire pregnancy, and the first year of life. Study findings showed an association between air pollution and ASD by period of exposure; ASD was associated with ozone exposure during the third trimester and with PM exposure during the first year of life. These findings support previous studies of a positive association between ASD and potential exposure to air pollution during the late prenatal period and early postnatal period. Further investigation into these findings may be helpful in increasing our understanding of these association

Air pollution, neighborhood deprivation, and autism spectrum disorder in the Study to Explore Early Development

McGuinn LA, Windham GC, Messer LC, Di Q; Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Environmental Epidemiology, 2019

This study used Study to Explore Early Development (SEED) data to examine whether the association between autism spectrum disorder (ASD) and early exposure to air pollution is modified by neighborhood deprivation.  Previous research, including studies using SEED data, have found an association between ASD and exposure to particulate matter (PM), or tiny particles of air pollution, during the first year of life; however, these studies did not look at different measures of neighborhood deprivation, which may also be associated with ASD and are often geographically correlated with air pollution. This study went beyond prior studies by combining data on pollution, roadway proximity, and neighborhood deprivation at the census tract level in six different areas of the United States. Study findings showed that the association between ASD and PM exposure during the first year of life was stronger for children living in neighborhoods of high deprivation, as compared to neighborhoods of moderate or low deprivation. Confirmation of these preliminary findings may be useful in future studies.

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Association Between Breastfeeding Initiation and Duration and Autism Spectrum Disorder in Preschool Children Enrolled in the Study to Explore Early Development

Soke GN, Maenner M, Windham G, Moody E, Kaczaniuk J, DiGuiseppi C, Schieve LA

Autism Res, 2019

This study compared breastfeeding initiation and duration among children with autism spectrum disorder (ASD) to children from the general population without ASD. SEED’s large sample size and diverse sample allowed researchers to conduct a more thorough assessment than previous studies. Study findings showed mothers of children with ASD were just as likely as mothers of children from the general population to initiate breastfeeding. However, among mothers who initiated breastfeeding, those who had children with ASD were less likely than those who had children without ASD to continue breastfeeding for longer than 6 months. The reasons for shorter breastfeeding duration among children with ASD are unclear. A longer duration of breastfeeding might protect a child from developing ASD, but it is also possible that early discontinuation of breastfeeding is related to underlying developmental conditions in children with ASD, such as child irritability, sensory, or health issues. To better understand why the duration of breastfeeding might be shorter among mothers of children with ASD compared to those without ASD, future studies should consider evaluating the reasons women discontinue breastfeeding.

Maternal diabetes and hypertensive disorders in association with autism spectrum disorder

Cordero C, Windham GC, Schieve LA, Fallin MD, Croen LA, Siega-Rizf AM, Engel SM, Herring AH, Stuebe AM, Vladutiu CJ, Daniels JL

This study examined associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DDs) and whether the child’s mother had diabetes or hypertension during pregnancy. Diabetes and hypertension are among the most common complications experienced by women during pregnancy. SEED’s large sample size and detailed data about the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings showed that having hypertension during pregnancy was associated with both ASD and other DDs in children compared with not having hypertension during pregnancy. Diabetes during pregnancy was not associated with ASD, but was associated with other DDs. This study highlights the relationship between maternal health during pregnancy and children with ASD and other DDs.

Maternal Pre-pregnancy Body Mass Index (BMI) and Gestational Weight Gain in Relation to Autism Spectrum Disorder (ASD) and Other Developmental Disorders in Offspring

Windham GC, Anderson M, Lyall K, Daniels JL, Kral TV, Croen LA, Levy SE, Bradley CB, Cordero C, Young L, Schieve LA

This study examined the relationship between mother’s body mass index (BMI) before pregnancy, mother’s weight gain during pregnancy, and associations with ASD and other developmental disabilities (DDs). Although previous studies have reported an association between higher maternal BMI and ASD, having this information, along with weight gain during pregnancy in SEED, allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings show an association between higher pregnancy weight gain and having a child with ASD, and this association was even stronger when the mother was overweight or obese before becoming pregnant. On the other hand, while maternal BMI before pregnancy was associated with having a child with a DD, mother’s weight gain during pregnancy was not. This study highlights the possible effects of maternal weight on child having ASD or DDs and the importance of maintaining a heathy weight before and during pregnancy.

Brief Report: Maternal Opioid Prescription from Preconception through Pregnancy and the Odds of Autism Spectrum Disorder and Autism Features in Children

Rubenstein E, Young JC, Croen LA, DiGuiseppi C, Dowling NF, Lee LC, Schieve L, Wiggins LD, Daniels J

Journal of Autism and Developmental Disorders, 2018

This study examined possible associations between prescription of opioid medications just before and during pregnancy and ASD and other developmental disabilities (DDs). Currently, the information available on this topic is very limited. SEED collects detailed information about mothers’ health histories, including prescribed medication, which allowed researchers to conduct this exploratory analysis. Illicit opioid use was not included in this analysis. The study findings show that approximately 8% of mothers reported receiving an opioid prescription just before or during pregnancy; of these mothers, the majority (76%) received only one prescription. The most common reasons for opioid prescriptions were migraine headaches, injury, and back pain. Mothers who were prescribed opioids just before becoming pregnant were more likely to have a child with ASD or a child with DDs and some autism symptoms. Researchers were limited by small sample sizes; thus, they were not able to conduct a detailed assessment of whether the associations found were related to the medication itself, the reason the mother took the medication, or some other unknown factors that may be associated with opioid use. This study is among the first to assess possible associations between prescription of opioids just before or during pregnancy and ASD and other DDs. More research is needed to understand how opioid use before and during early pregnancy may impact a child’s development.

Family History of Immune Conditions and Autism Spectrum and Developmental Disorders: Findings from the Study to Explore Early Development

Croen, LA, Qian Y, Ashwood, P, Daniels JL, Fallin D, Schendel D, Schieve LA, Singer AB, Zerbo O

Autism Research, 2018

This study examined the relationship between autism spectrum disorder (ASD) and other developmental disorders (DDs) and having a family history of conditions related to immune system functioning. Such conditions include asthma, allergies, and autoimmune disorders such as eczema or psoriasis. Previous studies have suggested some association, but the results about specific conditions varied. SEED’s large sample size and detailed data on specific types of immune disorders allowed researchers to conduct an in-depth analysis on this topic and examine the associations with ASD alongside associations with other DDs. The study findings show that maternal history of eczema or psoriasis and asthma are associated with both ASD and other DDs in children. Researchers also found that children with ASD are more likely to have eczema or psoriasis and allergies than children without ASD. Autoimmune disorders were not notably increased among children with other DDs. This study highlights the relationship between maternal health before and during pregnancy and ASD and other DDs, and provides researchers more information about the health of children with ASD.

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, Chopra P, Alisch RS, Newschaffer CJ, Warren ST, Feinberg AP, Fallin MD, Ladd-Acosta C

Molecular Autism, 2018

In this study, researchers used SEED data and data from another study of children and adolescents with and without ASD to learn more about how genes are regulated in children with ASD. Many genes are turned on or off by a process called “methylation.”  Although methylation does not change a person’s actual genes (or genetic code), methylation helps different types of cells do their specific jobs by affecting which genes are turned on and which genes are not. The researchers examined children’s DNA to look for differences in the methylation of genes between children with and without ASD. Previous studies of methylation in relation to ASD were limited by small sample sizes. This study is one of the largest so far to look broadly at methylation patterns in children with and without ASD. The study showed several potential differences in methylation between children in the two groups. Some of the differences suggest links to brain function, and they were consistent with results from previous studies. These findings provide clues as to how genes might be related to ASD in children.

Associations Between the 2nd to 4th Digit Ratio and Autism Spectrum Disorder in Population-Based Samples of Boys and Girls: Findings from the Study to Explore Early Development.

Schieve LA, Tian L, Dowling N, Croen L, Hoover-Fong J, Alexander A, Shapira SK.

This study examined associations between ASD and the ratio of children’s index (2nd) finger length to their ring (4th) finger length. The ratio of finger lengths (or digit ratio) has been linked to the level of sex hormones a child was previously exposed to during pregnancy. Researchers study digit ratios because they rarely have direct measurements of fetal exposure to hormones.  Study findings in boys showed that digit ratio was associated with ASD, but only in certain subgroups, such as children who had ASD and also a birth defect or genetic syndrome. This suggests the association might not have been related to hormone levels, but might instead be explained by genetics.  Study findings in girls showed that digit ratio was associated with ASD and that the association was not limited to certain subgroups of children.  There has been little past study of the association between digit ratio and ASD, particularly in girls.  The findings in this report suggest that hormone exposures during pregnancy might be related to ASD in girls, but many gaps remain in our understanding of the underlying reasons for this association and further research is needed.

Autism Spectrum Disorder and Birth Spacing: Findings from the Study to Explore Early Development (SEED).

Schieve LA, Tian LH, Drews-Botsch C, Windham GC, Newschaffer C, Daniels JL, Lee LC, Croen LA, Danielle Fallin M.

Autism Research, 2017

This study examined whether the amount of time between pregnancies was associated with ASD or other developmental disabilities in children. SEED’s detailed data on ASD subgroups and other developmental disabilities allowed researchers to conduct a more in depth analysis on this topic than those that have been done previously. The study findings show that both shorter and longer time periods between births are associated with having a child with ASD. Children conceived less than 18 months after their mother’s previous birth and children conceived 60 or more months after their mother’s previous birth were more likely to have ASD than children conceived between 18 to 59 months after their mother’s previous birth. The relationship was stronger in children with more severe ASD symptoms. Also, the association between birth spacing and ASD appeared to be unique to ASD, as there was no association found between birth spacing and having children with other developmental disabilities. The association between birth spacing and ASD was not explained by unplanned pregnancy, an underlying fertility disorder in the mother, or high blood pressure or diabetes during pregnancy. The findings from this study can help healthcare providers counsel their patients on pregnancy spacing.

Prenatal Alcohol Exposure in Relation to Autism Spectrum Disorder: Findings from the Study to Explore Early Development (SEED).

Singer AB, Aylsworth AS, Cordero C, Croen LA, DiGuiseppi C, Fallin MD, Herring AH, Hooper SR, Pretzel RE, Schieve LA, Windham GC, Daniels JL.

Paediatric and Perinatal Epidemiology, 2017

This study examined associations between alcohol use just before and during pregnancy and ASD or other developmental disabilities (DDs). Previous studies have shown that high levels of alcohol use in pregnancy are associated with child developmental effects, such as decreased intellectual ability, hyperactivity, learning difficulties, and autism-like traits. This study investigated whether lower levels of alcohol use before and during pregnancy were associated with developmental outcomes. Most mothers of children in SEED reported no or low levels of alcohol use before or during their pregnancies.  In fact, nearly all mothers reported no alcohol use in the second month of pregnancy or later (93-98% depending on month). Therefore, a main focus of the study was on alcohol use in the three months prior to pregnancy or the first month of pregnancy. The study findings show that modest alcohol use during these four months was not associated with increased risk for either ASD or other DDs.  Although this study did not find an association between ASD or other DDs and modest alcohol use before or during pregnancy, women who are pregnant or planning to become pregnant should continue to follow recommendations to avoid alcohol use because of other known effects on infant and child health.

Maternal and Paternal Infertility Disorders and Treatments and Autism Spectrum Disorder: Findings from the Study to Explore Early Development.

Schieve LA, Drews-Botsch C, Harris S, Newschaffer C, Daniels J, DiGuiseppi C, Croen LA, Windham GC.

Journal of Autism and Developmental Disorders, 2017

This study examined associations between ASD and whether, prior to becoming pregnant, a child’s mother had a condition that might have affected her ability to get pregnant (i.e., infertility). The study also looked at whether the mother had received any medical treatments to help her become pregnant or to prevent miscarriage during early pregnancy. SEED’s detailed data on specific types of infertility disorders and treatments allowed researchers to conduct a much more in depth analysis on this topic than those that have been done previously. The study findings show that several infertility disorders in the mother — including blocked tubes, uterine conditions such as fibroids, endometriosis, and polycystic ovarian syndrome — are associated with ASD in children. However, treatments for infertility or to prevent miscarriage were not associated with ASD.  While the reasons for the associations with infertility conditions could not be studied, possible explanations include increased inflammation during pregnancy or problems with the mother’s immune system. The findings from this study add to studies of other risk factors highlighting the relationship between maternal health before and during pregnancy and ASD.

Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA, Hendren RL, Traglia M, Lavillaureix A, Zaitlen N, Oldham MC, Levitt P, Nelson S, Amaral DG, Hertz-Picciotto I, Fallin MD, Weiss LA.

PLOS Genetics, 2016

In this study, researchers used SEED data and data from other studies to investigate sex-specific genetic effects for ASD. The findings indicate involvement of genes on the X chromosome. These findings help us better understand how ASD might differ in girls and boys.

Presence of an Epigenetic Signature of Prenatal Cigarette Smoke Exposure in Childhood.

Ladd-Acosta C, Shu C, Lee BK, Gidaya N, Singer A, Schieve LA, Schendel DE, Jones N, Daniels JL, Windham GC, Newschaffer CJ, Croen LA, Feinberg AP, Daniele Fallin M.

Environmental Research, 2016

This study examined how environmental exposures, such as smoking during pregnancy, may impact gene regulation in children. Gene regulation is the process by which genes in a cell are turned on or off, and it is important for child development. Like other studies, researchers found that smoking during pregnancy affected gene regulation in children. However, while other studies have assessed these effects in children at the time of birth, the SEED sample provided an opportunity to look at gene regulation in older children. This study showed that the same pattern of gene effects was present in older children whose mothers had smoked in pregnancy as had been previously observed in newborns. These findings suggest that smoking during pregnancy may have lasting effects on child health and development.

Maternal Exposure to Occupational Asthmagens During Pregnancy and Autism Spectrum Disorder in the Study to Explore Early Development.

Singer AB, Windham GC, Croen LA, Daniels JL, Lee BK, Qian Y, Schendel DE, Fallin MD, Burstyn I.

Journal of Autism and Developmental Disorders, 2016

This study examined whether ASD was associated with the mother’s workplace exposure to certain chemicals or other substances during pregnancy. Because previous studies have shown associations between maternal asthma and allergy and ASD, researchers were particularly interested in exposure to substances that are known to trigger asthma symptoms, called asthmagens.  Examples of asthmagens include latex, certain drugs and chemicals such as dyes, and some cleaning products. The findings show that mothers of children with ASD had been exposed to slightly higher levels of workplace asthmagens than mothers of children in the general population. However, the difference was small and could have been due to chance. Many gaps remain in our understanding of how environmental exposures might impact the risk for ASD, and further research is needed.   Top of Page

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study

Lisa D. Wiggins, PhD, Cy Nadler, PhD, Steven Rosenberg, PhD, Eric Moody, PhD, Nuri Reyes, PhD, Ann Reynolds, MD, Aimee Alexander, MS, Julie Daniels, PhD, Kathleen Thomas, PhD, Ellen Giarelli, PhD, and Susan E. Levy, MD, MPH

Pediatrics, 2021

A study published online in The Journal of Pediatrics explored the rates of psychotropic medication use among preschool-aged children (ages 2-5 years) with autism spectrum disorder (ASD).  While there are no medications to treat core symptoms of ASD, some medications may treat co-occurring symptoms such as attention problems, anxiety, aggression, and self-injurious behaviors.  However, The American Academy of Pediatrics recommends behavior therapy before medication is tried. In the study sample, 37 of 62 (59.7%) children with ASD who used psychotropic medications did not receive the behavior therapy prior to receiving medications.  Pediatricians are an important resource for children and families and can help facilitate behavioral treatment for children with ASD and other behavioral and developmental disorders.

Gastrointestinal Symptoms in 2- to 5-Year-Old Children in the Study to Explore Early Development

Reynolds AM, Soke GN, Sabourin KR, Croen LA, Daniels JL, Fallin MD, Kral TVE, Lee LC, Newschaffer CJ, Pinto-Martin JA, Schieve LA, Sims A, Wiggins LD, Levy SE

Journal of Autism and Developmental Disorders, 2021

This study compared gastrointestinal (GI) symptoms in 2,461 preschool children aged 30–68 months with autism spectrum disorder (ASD) to children with other developmental disabilities (DDs) and children from the general population (POP). Previous studies have shown that GI symptoms are common among children with ASD, but those studies have been limited by small sample sizes and lack of standard measures and comparison groups. Researchers used information from the Study to Explore Early Development (SEED)—including detailed information on GI symptoms, developmental level, and other problems such as anxiety (worry), aggression, and problems related to sleep and attention—to fill these gaps. Parents were asked to complete a detailed questionnaire on GI symptoms and a stool diary for their child. Based on these two instruments, 50.4% of children with ASD had GI symptoms, compared to 42.6% of children with other DDs and 30.6% of POP children. Among children with ASD, researchers also compared children who had lost skills they had previously developed (developmental regression) with those who had not lost previously developed skills and found that more children with developmental regression had GI symptoms (42.9%) than those without regression (31.8%).  Across all three study groups, GI symptoms were related to problems with sleep, attention, anxiety, and aggression.  These findings suggest that GI issues may be more common among children with ASD and are an important healthcare need to address.

Pica, Autism, and Other Disabilities

Fields VL, Soke GN, Reynolds A, Tian LH, Wiggins L, Maenner M, DiGuiseppi C, Kral TVE, Hightshoe K, Schieve LA

This study examined pica in preschool-aged children with autism spectrum disorder (ASD), other developmental disabilities (DDs), and children from the general population (POP). Pica is when a person eats non-food items with no nutritional value—such as paper, hair, paint, or dirt—which can result in medical problems. Previous research on pica in children with ASD and other DDs has been limited by small, non-representative samples, and has lacked a general POP comparison group. Researchers from the Study to Explore Early Development (SEED) examined pica in 4,739 preschool children aged 30–68 months with ASD, other DDs, and from the general population (POP).  Children with ASD and other DDs were further classified according to whether they had co-occurring intellectual disability (ID), and among children in the DD group, whether they had some ASD characteristics, for a total of 6 subgroups (ASD without ID, ASD with ID, DD with ASD characteristics, DD with ASD characteristics and ID, DD without ASD characteristics and with ID, and DD without ASD characteristics and without ID). Study results found that 23.2%, 8.4%, and 3.5% of children in the ASD, DD, and POP groups, respectively, had pica. Within the ASD group, pica was reported in 28.1% of children with ID and 14.0% of children without ID. Within the DD group, pica was reported in 26.3% of children with both ID and some ASD characteristics, 12.0% with some ASD characteristics but without ID, 9.7% with ID but without ASD characteristics, and 3.2% with neither ID nor ASD characteristics. These results show that pica may be common in young children with ASD, ASD characteristics, and/or ID, and suggest that young children in these groups can benefit from careful monitoring and safety precautions to prevent pica.  Parent prevention measures can include closely monitoring children, keeping items out of reach, using childproof locks, finding activities that occupy children’s attention, and informing other caregivers of concerns.

Mapping the Relationship Between Dysmorphology and Cognitive, Behavioral, and Developmental Outcomes in Children with Autism Spectrum Disorder

Tian LH, Wiggins LD, Schieve LA, 1, Yeargin-Allsopp M, Dietz P, Aylsworth AS, Elias ER, Julie E. Hoover‑Fong JE, Meeks NJL, Souders MC, Tsai ACH, Zackai EH, Alexander AA, Dowling NF, Shapira SK

Autism Research, 2020

This study looked at whether having more unusual physical traits (dysmorphic features (DFs)) was related to developmental problems and focused on children with autism spectrum disorder (ASD) compared to children from the general population (POP). Previous studies only looked at whether children with ASD and developmental problems had DFs; these studies did not always include a group of children without ASD. In this study, researchers used information from 881 preschool-aged children 2–5 years old enrolled in the Study to Explore Early Development (SEED). The study included an in-person physical examination where photographs, measurements, and hand scans were taken; these items were reviewed by clinical geneticists to determine the number of DFs in each child. This enabled researchers to ask whether a greater number of DFs was related to more developmental problems. The study found that children with ASD and ID had more language, movement, and learning issues as the number of DFs increased. Children with ASD but without ID had more movement and learning issues as the number of DFs increased. These relationships were not observed in the POP group. These findings suggest that DFs may be linked to the cognitive (learning and memory) problems of children with ASD. Additional studies on groups of children with ASD who do or do not have ID could help explain the findings.

Expressive Dominant Versus Receptive Dominant Language Patterns in Young Children: Findings from the Study to Explore Early Development

Reinhartsen DB, Tapia AL, Watson L, Crais E, Bradley C, Fairchild J, Herring AH, Daniels J

Journal of Autism and Developmental Disorders, 2019

This study examined language skills in children with autism spectrum disorder (ASD), children with other developmental disabilities (DD), and typically developing children from the general population (POP). Previous research has shown that children typically understand more vocabulary and complex language than they can express. However, some studies on the language patterns of children with ASD suggest they may be better at expressing than understanding language. Researchers used information from the Study to Explore Early Development (SEED) to categorize 2,571 children aged 30–68 months according to whether they understood or expressed language better or had similar language skills in both areas.  Study findings showed that all three groups of children were better able to understand than express language.  However, 23.6% of children in the ASD group were better at expressing language, as compared to 11.5% of children in the DD group and 10.8% of children in the POP group. Children in the ASD group who were better at expressing than understanding language typically had noticeable problems understanding language and were younger, had lower nonverbal cognitive skills, and had more serious social symptoms of ASD. These findings highlight the need to consider the type of language deficits when designing clinical interventions or treatment programs for children with ASD.

Wandering Among Preschool Children With and Without Autism Spectrum Disorder

Wiggins LD, DiGuiseppi C, Schieve L, Moody E, Gnakub Soke, Giarelli E, Levy S

Journal of Developmental and Behavioral Pediatrics, 2020

This study describes wandering in children ages 4–5 years with a confirmed autism spectrum disorder (ASD) diagnosis, children with a previous but unconfirmed ASD diagnosis (DDprevASD), children with other developmental disabilities (DD), and children from the general population (POP). Wandering, or leaving a supervised space or care of a responsible person, is common among toddlers who are exploring their environment and learning to be independent. Wandering typically becomes much less common after 4 years of age; however, some studies suggest that wandering may be more common among children with ASD than children with other DD and could compromise child safety and increase parental stress. In this study, researchers described 3,896 parent reports of wandering among their 4–5-year-old children enrolled in the Study to Explore Early Development (SEED) between 2007 and 2016. The researchers also examined the relationship between a child’s likelihood to wander and certain behavioral, developmental, and other factors. Study findings showed that wandering in children aged 4–5 years was reported in 60.4% of children with ASD, compared with 41.3% of children with DDprevASD, 22.3% of children with DD, and 12.4% of children in the POP group. Findings also showed that mood, anxiety, attention, and oppositional problems were all associated with wandering behavior, independent of ASD status. These results provide important information for parents and providers on the occurrence of wandering among children with and without ASD and associated conditions (such as anxiety and attention problems) that may place children at increased risk for wandering from safe environments. Moreover, these results may facilitate discussions between parents and providers about safety, prevention, and interventions that may improve the lives of children who wander and their families.

Injury-related treatments and outcomes in preschool children with autism spectrum disorder: Study to Explore Early Development (SEED)

DiGuiseppi C, Sabourin KR, Levy SE, Soke GN, Lee LC, Wiggins L, Schieve LA

This study examines the parent-reported treatments and outcomes of medically attended injuries among children with autism spectrum disorder (ASD) living in six different areas (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) in the United States in 2003–2006, compared to children with other developmental disabilities (DDs) and children from the general population (POP). The Study to Explore Early Development’s (SEED’s) in-depth data on the health of preschool children aged 2–5 years provided researchers with key information on these injuries. For each reported injury, parents were asked whether the injury resulted in loss of consciousness, an emergency department (ED) visit, hospitalization, surgery, or long-term behavior change. Parents were also asked if their child received any medication or injections for each medically attended injury reported. Study results showed that 30% of children in SEED had at least one medically attended injury. Of those children, 83% had at least one injury-related ED visit or hospitalization. Children with ASD were more likely than children from the POP group to have had a surgical procedure for an injury. Children with ASD were also less likely than those with DDs to receive medication or injections to treat injuries. These differences may be a result of characteristics of the child or injury or may reflect the clinicians’ ability to provide certain treatments or judge the severity of the child’s pain due to challenging behaviors associated with ASD. Further research may aid in understanding the differences in treatments prescribed to children with ASD compared to those prescribed to children with DDs or from the general population.

Early life influences on child weight outcomes in the Study to Explore Early Development

Kral TV, Chittams J, Bradley CB, Daniels JL, DiGuiseppi CG, Johnson SL, Pandey J, Pinto-Martin JA, Rahai N, Ramirez A, Schieve LA, Thompson A, Windham G, York W, Young L, Levy SE

Autism, 2019

This study examined overweight and obesity at age 2–5 years in children with and without autism spectrum disorder (ASD) or other developmental disorders (DDs). Obesity rates among U.S. children have increased markedly in recent decades and children with ASD have previously been shown to be at particularly high risk for obesity. SEED’s large sample and detailed data on children with ASD and other DDs allowed researchers to conduct a more in depth analysis on this topic than done previously. Study findings show that children born to mothers who were overweight/obese before becoming pregnant, or gained more weight than recommended during their pregnancies, were more likely to be overweight or obese between the ages of 2–5 years compared with children born to mothers who were underweight or normal weight prior to pregnancy and gained the recommended amount of weight during their pregnancies. These findings were similar for children with ASD, children with other DDs, and children without DDs. However, children with ASD were more likely than children in the other groups to have rapid weight gain in infancy; rapid weight gain was also associated with increased risk for being overweight or obese between ages 2–5 years. This study highlights the importance of maintaining a heathy weight before and during pregnancy and fostering healthy growth during infancy, among all children, including those with and without ASD.

Sleep Problems in 2- to 5-Year-Olds with Autism and Other Developmental Delays

This study assessed sleep problems, such as difficulties going to sleep or staying asleep through the night, in preschool-aged children with ASD, in comparison to children with other developmental disabilities (DDs) and children in the general population. SEED’s large sample and detailed data on preschoolers allowed researchers to conduct a more in-depth analysis on this topic than in previous studies. Study findings show that children with ASD and children with other DDs who have some ASD symptoms have more sleep problems than children with DDs without ASD symptoms and children in the general population. Even when researchers used a conservative definition to classify children as having sleep problems, 47% of children with ASD and 57% of children with other DDs who had some ASD symptoms were reported to have sleep problems, compared to 29% of children with DDs but no ASD symptoms and 25% of children in the general population. Sleep is important for development in young children. Addressing sleep problems among children with ASD and children with other DDs who have ASD symptoms is an important component of healthcare needs in this population.

A Novel Approach to Dysmorphology to Enhance the Phenotypic Classification of Autism Spectrum Disorder in the Study to Explore Early Development

This study used data from SEED to develop a new method to systematically classify certain physical features in children. The purpose of this system is to evaluate dysmorphology, which is the assessment of physical features that do not follow the typical pattern of growth and development. Children with multiple dysmorphic features often have an underlying genetic condition or had early pregnancy exposures that affected their development during the pregnancy.  The SEED dysmorphology classification method is more comprehensive than that used in previous studies. The findings from this study indicate that approximately 17% of children with ASD have a high number of dysmorphic features, and hence, meet the criteria for classification as dysmorphic. In contrast, approximately 5% of children from the general population control group met the criteria for classification as dysmorphic. Some, but not all, of the dysmorphology differences between children with and without ASD were explained by previously recognized and diagnosed genetic conditions and birth defects, which both occur more commonly in children with ASD. This is the first report of dysmorphology among children with ASD in a diverse U.S. population.

Relationship of Weight Outcomes, Co-occurring Conditions, and Severity of Autism Spectrum Disorder in the Study to Explore Early Development

Levy SE, Pinto-Martin JA, Bradley CB, Chittams J, Johnson SL, Pandey J, Alison Pomykacz A, Ramirez A, Reynolds A, Rubenstein E, Schieve LA, Shapira SK, Thompson A, Young L, Kral TV

Journal of Pediatrics, 2018

This study examined overweight and obesity among children with ASD, other developmental disabilities (DDs), and children from the general population. Study findings show that children with ASD or DDs were more likely to be overweight or obese than children from the general population. The proportion of children who were either overweight or obese was 28% in those with ASD, 25% in children with another DD, and 20% in children in the general population. Children with ASD or DDs were also more likely to have birth defects, medical disorders, seizure disorders, attention-deficit/hyperactivity disorder (ADHD), and psychiatric disorders than children from the general population. After controlling for these co-occurring conditions, the association between ASD and overweight or obesity was not changed, but the association between overweight and obesity and other DDs was reduced. In addition, among children with ASD, those with moderate or severe symptoms of ASD were more likely to be overweight or obese than children with mild ASD symptoms. Addressing overweight and obesity among children with ASD and other DDs is an important component of healthcare needs in this population.

Infections in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED)

Sabourin KR, Reynolds A,  Schendel D, Rosenberg S, Croen L, Pinto-Martin JA, Schieve LA, Newschaffer C, Lee LC, DiGuiseppi C

This study evaluated the association between early childhood infections and ASD and other developmental disabilities (DDs). SEED’s large sample size allowed researchers to conduct a more in-depth analysis on this topic than previous studies. The study findings show that children with ASD were more likely than children with other DDs and children from the general population to have had an infection in the first 28 days of life (early infection). Overall, 4.9% of children with ASD, 4.2% of children with other DDs, and 2.2% of children in the general population had an early infection recorded in their medical records. Children with ASD were also more likely to have an infection in the first 3 years of life than children in the general population, but children with ASD had similar rates of infection during their first 3 years as children with other DDs. This study highlights that ASD is associated with infections very early in the child’s life.

Brief Report: Self-Injurious Behaviors in Preschool Children with Autism Spectrum Disorder Compared to Other Developmental Delays and Disorders.

Soke GN, Rosenberg SA, Rosenberg CR, Vasa RA, Lee LC, DiGuiseppi C.

This study assessed self-injurious behavior, or SIB, among preschool-aged children with ASD in comparison to children with other developmental disabilities (DDs). The study showed that SIB is common in two groups of preschool-aged children – those with ASD and those for whom some autism-related symptoms are reported by their mother or other caregiver, even though they didn’t meet the criteria to be classified as an ASD case.  SIB was much less common in children with other DDs whose mother or caregiver did not report autism-related symptoms. These findings suggest that clinicians working with young children with DDs consider screening for SIB, even in children who do not have an ASD diagnosis.

Associations between Parental Broader Autism Phenotype and Child Autism Spectrum Disorder Phenotype in the Study to Explore Early Development.

Rubenstein E, Wiggins LD, Schieve LA, Bradley C, DiGuiseppi C, Moody E, Pandey J, Pretzel RE, Howard AG, Olshan AF, Pence BW, Daniels J.

Autism, 2018

This study assessed how the variation in developmental features among children with ASD was related to their parents’ own autism-related traits.  The presence of autism traits in family members of children with ASD is commonly referred to as the “broader autism phenotype” or BAP. The study findings show that if one or both parents have traits consistent with BAP, the child’s ASD is more likely to fall within a certain clinical presentation than if neither parent has traits consistent with BAP.  This clinical presentation in the child is characterized by average nonverbal abilities, mild language and motor delays, and increased frequency of other co-occurring developmental difficulties such as anxiety, depression, aggression, and attention difficulties.  The findings reported in this study could help better our understanding of the genetics of ASD.

The Prevalence of Gluten Free Diet Use among Preschool Children with Autism Spectrum Disorder.

Rubenstein E, Schieve L, Bradley C, DiGuiseppi C, Moody E, Thomas K, Daniels J.

This study estimated the proportion of children with ASD who had been on a gluten free diet. Altogether, 20% of preschool-aged children with ASD were currently or previously using a gluten free diet. In contrast, only 1% of children in the general population control group were using a gluten free diet. Children with ASD who also had gastrointestinal problems or had previously had a developmental regression were more likely to use a gluten free diet. This study demonstrates that gluten free diets are commonly used among children with ASD. More research is needed on the effectiveness of a gluten free diet in managing both gastrointestinal and behavioral symptoms related to ASD.

Injuries in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED).

DiGuiseppi C, Levy SE, Sabourin KR, Soke GN, Rosenberg S, Lee LC, Moody E, Schieve LA.

This study evaluated injuries in preschool-aged children with and without ASD and other developmental disabilities (DDs). Parents of children were asked whether their child had ever had an injury that required medical attention, and what types of injuries had occurred. The study findings showed that injuries were common in all groups of children and there was little difference between groups. Parents reported injuries for 32% of children with ASD, 28% of children with other DDs, and 30% of children in the general population. The most common injuries were open wounds and fractures and the most common reason for injuries was falls. While there was a slight difference in injuries between children with ASD and other DDs, further study found that this was largely explained by a higher level of attention problems in the children with ASD.

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development.

Wiggins LD, Tian LH, Levy SE, Rice C, Lee LC, Schieve L, Pandey J, Daniels J, Blaskey L, Hepburn S, Landa R, Edmondson-Pretzel R, Thompson W.

This study used a complex computer program to assess the wide range of developmental characteristics among children with ASD.  Researchers identified four subgroups of children within the ASD group: 1) children with mild language delay and average cognitive functioning, but increased cognitive rigidity (or difficulty changing behaviors); 2) children with significant developmental delay, below average cognitive functioning, and repetitive motor behaviors; 3) children with general developmental delay, below average cognitive functioning, and moderate to highly severe autism symptoms; and 4) children with mild language and motor delays, but increased cognitive rigidity and high rates of problem behaviors. This study shows how information on developmental characteristics can be studied using advanced statistical methods to better understand ASD.  This information might also be useful in understanding children’s future health and development.

Self-injurious Behaviors in Children with Autism Spectrum Disorder Enrolled in the Study to Explore Early Development.

Autism, 2017

This study assessed self-injurious behavior, or SIB, among children with ASD. SIB includes head-banging, hair-pulling, arm-biting, scratching, and hitting oneself. SIB is usually mild, but can be severe in some children and may result in injuries requiring medical care. Children with severe SIB may miss out on educational and social activities. This study showed that in the SEED sample, about 28% of preschool-aged children with ASD displayed SIB currently, and 47% had previously displayed SIB. Researchers found SIB was more common in children with low adaptive behavior scores and gastrointestinal, sleep, and behavioral problems. While its causes are not completely understood, identifying SIB early is helpful because it may reduce the likelihood of more severe SIB later.   Top of Page

Temperament Similarities and Differences: A Comparison of Factor Structures from the Behavioral Style Questionnaire in Children with and Without Autism Spectrum Disorder

Barger B, Moody EJ, Ledbetter C, D’Abreu L, Hepburn S, Rosenberg SA

Journal of Autism and Developmental, 2019

This study assessed the performance of the Behavioral Style Questionnaire (BSQ), a commonly used measure of temperament, in children aged 2–5 years with and without autism spectrum disorder (ASD). The BSQ contains 100 questions designed to measure nine different behavioral tendencies, or temperaments, that affect how well children respond to their environment. Previous research has suggested that the BSQ may function differently for children with ASD compared with typically developing children. As such, researchers used Study to Explore Early Development (SEED) data to compare the behavioral tendencies the BSQ identified among children diagnosed with ASD and among children from the general population. Study findings showed that the BSQ did not identify the behavioral tendencies that it was originally designed to measure. Moreover, while the BSQ measured certain behavioral tendencies similarly among children with ASD and children from the general population, for other behavioral tendencies it did not. One behavioral tendency, “Negative Social Interactions”, was unique among children with ASD, and was not found among children from the general population. These findings suggest that more research may help us better understand how the BSQ performs in different groups of children, including children with ASD.

ASD Screening with the Child Behavior Checklist/1.5-5 in the Study to Explore Early Development

Levy SE, Rescorla LA, Chittams JL, Kral TJ, Moody EJ, Pandey J, Pinto-Martin JA, Pomykacz A, Ramirez A, Reyes N, Rosenberg CR, Schieve LA, Thompson A, Young L, Zhang J, Wiggins L

J Autism Dev Disord., 2019

This study assessed the performance of a general developmental assessment tool, known as the Child Behavior Checklist (CBCL), as a screening tool for autism spectrum disorder (ASD) symptoms in preschool-aged children. The CBCL is a broad-spectrum checklist that includes 99 items completed by a parent or a caregiver. Researchers in this study were interested in a subset of 13 items related to pervasive developmental problems. Previous research on this topic produced inconsistent results. SEED’s large sample of children with and without ASD and other developmental disabilities (DDs) allowed for a more thorough assessment. The study results showed that scores from the 13-item subscale were significantly different for children in the ASD group and the DD with ASD features group, compared to children in the DD without ASD features group and the population control group. These findings suggest that this CBCL subscale was effective at identifying children with ASD features needing further evaluation and supports its use as an ASD screening tool. The findings are particularly noteworthy because the CBCL is already widely used by schools and health professionals to screen for other developmental issues such as attention, anxiety, and depression.

DSM-5 criteria for autism spectrum disorder maximizes diagnostic sensitivity and specificity in preschool children

Wiggins LD, Rice CE, Barger B, Soke GN, Lee LC, Moody E, Edmondson-Pretzel R, Levy SE

Soc Psychiatry Psychiatr Epidemiol, 2019

The Diagnostic and Statistical Manual of Mental Disorders (DSM) specifies standardized criteria for diagnosing individuals with autism spectrum disorder (ASD) and other conditions. Criteria for diagnosing ASD were revised between the fourth (DSM-IV-TR) and the fifth edition of the manual (DSM-5). The purpose of this study was to compare DSM-IV-TR and DSM-5 definitions of ASD using information from comprehensive developmental evaluations completed with preschool children enrolled in the Study to Explore Early Development (SEED). This study was important because it compared DSM-IV-TR and DSM-5 definitions of ASD by evaluating children at a time when they often are first diagnosed, using both criteria in a single clinic visit. Study findings showed that DSM-5 criteria had the best balance between identifying true ASD cases, while ruling out children with other developmental disorders, when compared to DSM-IV-TR criteria. Researchers also found good agreement between DSM-5 and DSM-IV-TR for autistic disorder and moderate agreement for a less stringent definition of ASD known as pervasive developmental disorder not otherwise specified (PDD-NOS). These findings support the DSM-5 criteria for ASD in preschool-aged children.

Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder

Singer AB, Fallin MD, Burstyn I

Current Environmental Health Reports, 2018

In this study, researchers used SEED data and data from another study of children with and without ASD to assess how potential errors in coding the data for certain risk factors might influence the findings of epidemiologic studies. Researchers often want to study the effects of certain exposures during pregnancy but may not have the exact data they need. It is rare to have biologic measurements of the chemicals women were exposed to during pregnancy.  Therefore, studies often rely on related information to classify study participants as “likely exposed” or “not exposed” to certain chemicals. For example, studies often use information on a person’s job — such as type of job and industry where the person worked — to estimate possible chemical exposures from their workplace. In this study, researchers used a statistical method to address the possibility that certain job coding schemes could result in errors when evaluating associations between workplace exposures and ASD. They propose a way researchers might use this method in future studies to assess, and possibly correct, exposure classification errors.

Influence of Family Demographic Factors on Social Communication Questionnaire Scores.

Rosenberg SA, Moody EJ, Lee LC, DiGuiseppi C, Windham GC, Wiggins LD, Schieve LA, Ledbetter CM, Levy SE, Blaskey L, Young L, Bernal P, Rosenberg CR, Fallin MD.

This study assessed how the responses to a standardized questionnaire to screen for autism symptoms varied by family demographic characteristics. The study findings indicate that test performance was different in families with an indication of low versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools might perform differently in various sociodemographic groups of children and their parents.

The Broader Autism Phenotype in Mothers is Associated with Increased Discordance Between Maternal-Reported and Clinician-Observed Instruments that Measure Child Autism Spectrum Disorder.

Rubenstein E, Edmondson Pretzel R, Windham GC, Schieve LA, Wiggins LD, DiGuiseppi C, Olshan AF, Howard AG, Pence BW, Young L, Daniels J.

This study assessed whether parents who have autism traits reported their children’s potential autism symptoms in a similar way as parents without an indication of autism traits. The findings indicate that parents with autism traits report more autism traits in their children compared to parents without autism traits, but parent reports do not always match clinician assessments based on observed behaviors in the child. It is possible that parents with some autism traits are more adept at identifying subtle characteristics of autism in their child. Another possible explanation for the study findings is that questions on various child behaviors could be interpreted differently by parents with and without autism traits. Further study is needed. The findings reported in this study could help better our understanding of developmental assessment results in young children.

Screening for Autism with the SRS and SCQ: Variations across Demographic, Developmental and Behavioral Factors in Preschool Children.

Moody EJ, Reyes N, Ledbetter C, Wiggins L, DiGuiseppi C, Alexander A, Jackson S, Lee LC, Levy SE, Rosenberg SA.

This study assessed and compared the performance of two standardized questionnaires to screen for autism symptoms. The accuracy of each questionnaire varied depending on the child’s level of developmental functioning and family sociodemographic traits. For example, the instruments were less accurate when children had high levels of challenging behaviors or lower levels of developmental functioning. Test performance also varied in families with indication of lower versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools perform differently in various sociodemographic groups of children and their parents.

Brief Report: The ADOS Calibrated Severity Score Best Measures Autism Diagnostic Symptom Severity in Pre-School Children.

Wiggins LD, Barger B, Moody E, Soke GN, Pandey J, Levy S.

This report describes SEED methodology for assessing autism symptom severity among children with ASD. Measuring a child’s autism symptoms is often challenging because many children with ASD also have other developmental conditions. This can make it difficult to separate a child’s social and communication challenges from the child’s other developmental delays or conditions. Researchers evaluated several measures of autism severity and found that the Autism Diagnostic Observation Schedule (ADOS) calibrated severity score best measured the severity of core autism symptoms in a way that did not include symptoms of other developmental conditions. Because of findings from this study, the ADOS calibrated severity score will be used in other SEED research to help scientists better understand how the severity of autism symptoms relates to ASD risk factors and health outcomes.

Cross-tissue Integration of Genetic and Epigenetic Data Offers Insight into Autism Spectrum Disorder.

Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Arking DE, Ladd-Acosta C, Fallin MD.

Nature Communications, 2017

In this study, researchers used SEED data and data from other studies to learn more about genetics and genetic regulation in children with ASD. While it is well-understood that genetics are related to ASD, many unanswered questions remain, such as how certain genes are turned on or off. The information from this study provides insights about how certain genes might be related to ASD.

“Gap Hunting” to Characterize Clustered Probe Signals in Illumina Methylation Array Data.

Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD.

Epigenetics & Chromatin, 2016

This study assessed new laboratory approaches to analyzing information on genetics collected through SEED. The findings contribute to the growing literature on how genes and environmental factors might interact in a way that increases the risk for ASD. While this study does not directly study these interactions, researchers describe and demonstrate how new laboratory approaches could help identify genetic associations.   Top of Page

Feature Articles

Autism Research and Resources from CDC April is Autism Acceptance Month. The recognition raises awareness about autism acceptance and promotes inclusion and connectedness for people with autism.

Higher Autism Prevalence and COVID-19 Disruptions Autism spectrum disorder (ASD) continues to affect many children and families. The COVID-19 pandemic brought disruptions to early ASD identification among young children. These disruptions may have long-lasting effects as a result of delays in identification and initiation of services.

Past, Present, and Future Impact of SEED Since the launch of SEED in 2003, CDC has worked with its partners to learn more about the needs of children with autism spectrum disorder (ASD) and other developmental disabilities so that families, communities, and healthcare providers can deliver the supports and services needed to thrive.

Why Act Early if You’re Concerned about Development? Act early on developmental concerns to make a real difference for your child and you! If you’re concerned about your child’s development, don’t wait. You know your child best.

Early Identification and Prevalence of Autism Among 4-year-old and 8-year-old Children: An Easy Read Summary This is an Easy-Read Summary of two reports. The first report is about identifying autism early among 4-year-old children. The second report is on the number of 8-year-old children with autism. (Published December 2, 2021)

Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood: An Easy-Read Summary The is an Easy-Read Summary (Published April 30, 2021)

Identifying Autism Among Children: An Easy-Read Summary This is an Easy-Read Summary of two reports. The first report is about the number of 8-year-old children with autism. The second report is about identifying autism early among 4-year-old children. (Published March 27, 2020)

Articles by Year

Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018. Annals of Epidemiology, 2023. Shaw KA, Williams S, Hughes MM, et al. [ Read article ]

The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016. Public Health Reports, 2023. Hughes MM, Shaw KA, DiRienzo M, et al. [ Read article ]

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (2): p. 1. Maenner MJ, Warren Z, Williams AR, et al. [ Read article ] [ Easy Read Summary ]

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (1): p. 1. Shaw KA, Bilder DA, McArthur D, et al. [ Read article ] [ Easy Read Summary ]

Social Vulnerability and Prevalence of Autism, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP). Annals of Epidemiology, 2023. Patrick ME, Hughes MM, Ali A, et al. [ Read article ]

Individualized Education Programs and Transition Planning for Adolescents With Autism. Pediatrics, 2023. Hughes MM, Kirby AV, Davis J, et al. [ Read article ]

Defining in Detail and Evaluating Reliability of DSM-5 Criteria for Autism Spectrum Disorder (ASD) Among Children Journal of Autism and Developmental Disorders, 2022: p. 1-13. Rice CE, Carpenter LA, Morrier MJ, et al. [ Read article ]

Reasons for participation in a child development study: Are cases with developmental diagnoses different from controls? Paediatric and Perinatal Epidemiology, 2022. Bradley CB, Tapia AL, DiGuiseppi CG, et al. [ Read article ]

Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 2021. 70 (10): p. 1. Shaw KA, Maenner MJ, Baikan AV, et al. [ Read article ] [Easy Read Summary]

Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 2021. 70 (11): p. 1. Maenner MJ, Shaw KA, Bakian AV, et al. [ Read article ] [Easy Read Summary]

Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002-2016. Journal of the American Academy of Child & Adolescent Psychiatry, 2021. Shaw KA, McArthur D, Hughes MM, et al. [ Read article ]

Peri-Pregnancy Cannabis Use and Autism Spectrum Disorder in the Offspring: Findings from the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 2021: p. 1-8. DiGuiseppi C, Crume T, Van Dyke J, et al. [ Read article ]

Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children. Am J Epidemiol, 2021. 190 (10): p. 2198-2207. Maenner MJ, Graves SJ, Peacock G, et al. [ Read article ]

Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses. Journal of Autism and Developmental Disorders, 2021: p. 1-14. Grosse SD, Nichols P, Nyarko K, et al. [ Read article ]

Maternal prepregnancy weight and gestational weight gain in association with autism and developmental disorders in offspring. Obesity, 2021. 29 (9): p. 1554-1564. Matias SL., Pearl M, Lyall K, et al. [ Read article ]

Maternal psychiatric conditions, treatment with selective serotonin reuptake inhibitors, and neurodevelopmental disorders. Biological psychiatry, 2021. 90 (4): p. 253-262. Ames JL, Ladd-Acosta C, Fallin MD,  et al. [ Read article ]

A preliminary epidemiologic study of social (pragmatic) communication disorder relative to autism spectrum disorder and developmental disability without social communication deficits. Journal of autism and developmental disorders, 2021. 51 (8): p. 2686-2696. Ellis Weismer S, Tomblin JB, Durkin MS, et al. [ Read article ]

Healthcare costs of pediatric autism spectrum disorder in the United States, 2003–2015. Journal of autism and developmental disorders, 2021. 51 (8): p. 2950-2958. Zuvekas SH, Grosse SD, Lavelle TA, et al. [ Read article ]

Association between pica and gastrointestinal symptoms in preschoolers with and without autism spectrum disorder: Study to Explore Early Development. Disability and Health Journal, 2021. 14 (3): p. 101052. Fields VL., Soke GN, Reynolds A, et al. [ Read article ]

Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood—Four US Sites, 2018–2020. Morbidity and Mortality Weekly Report, 2021. 70 (17): p. 605. Powell PS, Pazol K, Wiggins LD, et al. [ Read article ] [Easy Read Summary]

Evaluation of sex differences in preschool children with and without autism spectrum disorder enrolled in the study to explore early development. Res Dev Disabil, 2021. 112 : p. 103897. Wiggins L.D, Rubenstein E, Windham G, et al. [ Read article ]

Pica, Autism, and Other Disabilities. Pediatrics, 2021. 147 (2). Fields VL., Soke GN, Reynolds A, et al. [ Read article ]

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study. J Pediatr, 2021. 232 : p. 264-271. Wiggins LD, Nadler C, Rosenberg S, et al. [ Read article ]

Gastrointestinal Symptoms in 2-to 5-Year-Old Children in the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 2021. 51 (11): p. 3806-3817. Reynolds AM, Soke GN, Sabourin KR, et al. [ Read article ]

A Distinct Three-Factor Structure of Restricted and Repetitive Behaviors in an Epidemiologically Sound Sample of Preschool-Age Children with Autism Spectrum Disorder. J Autism Dev Disord, 2021. 51 (10): p. 3456-3468. Hiruma L, Edmondson Pretzel R, Tapia AL, et al. [ Read article ]

Spending on young children with autism spectrum disorder in employer-sponsored plans, 2011–2017. Psychiatric Services, 2021. 72 (1): p. 16-22. Grosse SD, Ji X, Nichols P, et al. [ Read article ]

CE: From the CDC: Understanding Autism Spectrum Disorder. AJN The American Journal of Nursing, 2020. 120 (10):p. 30-37. Christensen D, Zubler J [ Read article ]

Wandering Among Preschool Children with and Without Autism Spectrum Disorder. J Dev Behav Pediatr, 2020. 41( 4): p. 251-257. Wiggins L, DiGuiseppi C, Schieve L, et al. [ Read article ]

Early identification of autism spectrum disorder among children aged 4 years—Early Autism and Developmental Disability Monitoring Network, six sites, United States, 2016. MMWR Surveillance Summaries, 2020. 69( 3): p. 1. Shaw KA, Maenner MJ, Baio J, et al. [ Read article ] [Easy Read Summary]

Prevalance of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveillance Summaries, 2020. 69 (4): p. 1. Maenner MJ, Shaw KA, Baio J, et al. [ Read article ] [Easy Read Summary]

Disparities in documented diagnoses of autism spectrum disorder based on demographic, individual, and service factors. Autism Research, 2020. 13 (3): p. 464-473. Wiggins LD, Durkin M, Esler A, et al. [ Read article ]

Neonatal jaundice in association with autism spectrum disorder and developmental disorder. J Perinatol, 2020. 40 (2): p. 219-225. Cordero C, Schieve LA, Croen LA, et al. [ Read article ]

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study. Epidemiology, 2020. 31 (1): p. 103-114. McGuinn LA, Windham GC, Kalkbrenner AE, et al. [ Read article ]

Mapping the Relationship between Dysmorphology and Cognitive, Behavioral, and Developmental Outcomes in Children with Autism Spectrum Disorder. Autism Res, 2020. 13 (7): p. 1227-1238. Tian LH, Wiggins LD, Schieve LA, et al. [ Read article ]

Search a database of articles that have been published by CDC authors within the National Center on Birth Defects and Developmental Disabilities from 1990 to present.

2023 Community Report on Autism. The latest ADDM Network Data

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  • Autism spectrum disorder

Autism spectrum disorder is a condition related to brain development that impacts how a person perceives and socializes with others, causing problems in social interaction and communication. The disorder also includes limited and repetitive patterns of behavior. The term "spectrum" in autism spectrum disorder refers to the wide range of symptoms and severity.

Autism spectrum disorder includes conditions that were previously considered separate — autism, Asperger's syndrome, childhood disintegrative disorder and an unspecified form of pervasive developmental disorder. Some people still use the term "Asperger's syndrome," which is generally thought to be at the mild end of autism spectrum disorder.

Autism spectrum disorder begins in early childhood and eventually causes problems functioning in society — socially, in school and at work, for example. Often children show symptoms of autism within the first year. A small number of children appear to develop normally in the first year, and then go through a period of regression between 18 and 24 months of age when they develop autism symptoms.

While there is no cure for autism spectrum disorder, intensive, early treatment can make a big difference in the lives of many children.

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Some children show signs of autism spectrum disorder in early infancy, such as reduced eye contact, lack of response to their name or indifference to caregivers. Other children may develop normally for the first few months or years of life, but then suddenly become withdrawn or aggressive or lose language skills they've already acquired. Signs usually are seen by age 2 years.

Each child with autism spectrum disorder is likely to have a unique pattern of behavior and level of severity — from low functioning to high functioning.

Some children with autism spectrum disorder have difficulty learning, and some have signs of lower than normal intelligence. Other children with the disorder have normal to high intelligence — they learn quickly, yet have trouble communicating and applying what they know in everyday life and adjusting to social situations.

Because of the unique mixture of symptoms in each child, severity can sometimes be difficult to determine. It's generally based on the level of impairments and how they impact the ability to function.

Below are some common signs shown by people who have autism spectrum disorder.

Social communication and interaction

A child or adult with autism spectrum disorder may have problems with social interaction and communication skills, including any of these signs:

  • Fails to respond to his or her name or appears not to hear you at times
  • Resists cuddling and holding, and seems to prefer playing alone, retreating into his or her own world
  • Has poor eye contact and lacks facial expression
  • Doesn't speak or has delayed speech, or loses previous ability to say words or sentences
  • Can't start a conversation or keep one going, or only starts one to make requests or label items
  • Speaks with an abnormal tone or rhythm and may use a singsong voice or robot-like speech
  • Repeats words or phrases verbatim, but doesn't understand how to use them
  • Doesn't appear to understand simple questions or directions
  • Doesn't express emotions or feelings and appears unaware of others' feelings
  • Doesn't point at or bring objects to share interest
  • Inappropriately approaches a social interaction by being passive, aggressive or disruptive
  • Has difficulty recognizing nonverbal cues, such as interpreting other people's facial expressions, body postures or tone of voice

Patterns of behavior

A child or adult with autism spectrum disorder may have limited, repetitive patterns of behavior, interests or activities, including any of these signs:

  • Performs repetitive movements, such as rocking, spinning or hand flapping
  • Performs activities that could cause self-harm, such as biting or head-banging
  • Develops specific routines or rituals and becomes disturbed at the slightest change
  • Has problems with coordination or has odd movement patterns, such as clumsiness or walking on toes, and has odd, stiff or exaggerated body language
  • Is fascinated by details of an object, such as the spinning wheels of a toy car, but doesn't understand the overall purpose or function of the object
  • Is unusually sensitive to light, sound or touch, yet may be indifferent to pain or temperature
  • Doesn't engage in imitative or make-believe play
  • Fixates on an object or activity with abnormal intensity or focus
  • Has specific food preferences, such as eating only a few foods, or refusing foods with a certain texture

As they mature, some children with autism spectrum disorder become more engaged with others and show fewer disturbances in behavior. Some, usually those with the least severe problems, eventually may lead normal or near-normal lives. Others, however, continue to have difficulty with language or social skills, and the teen years can bring worse behavioral and emotional problems.

When to see a doctor

Babies develop at their own pace, and many don't follow exact timelines found in some parenting books. But children with autism spectrum disorder usually show some signs of delayed development before age 2 years.

If you're concerned about your child's development or you suspect that your child may have autism spectrum disorder, discuss your concerns with your doctor. The symptoms associated with the disorder can also be linked with other developmental disorders.

Signs of autism spectrum disorder often appear early in development when there are obvious delays in language skills and social interactions. Your doctor may recommend developmental tests to identify if your child has delays in cognitive, language and social skills, if your child:

  • Doesn't respond with a smile or happy expression by 6 months
  • Doesn't mimic sounds or facial expressions by 9 months
  • Doesn't babble or coo by 12 months
  • Doesn't gesture — such as point or wave — by 14 months
  • Doesn't say single words by 16 months
  • Doesn't play "make-believe" or pretend by 18 months
  • Doesn't say two-word phrases by 24 months
  • Loses language skills or social skills at any age

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Autism spectrum disorder has no single known cause. Given the complexity of the disorder, and the fact that symptoms and severity vary, there are probably many causes. Both genetics and environment may play a role.

  • Genetics. Several different genes appear to be involved in autism spectrum disorder. For some children, autism spectrum disorder can be associated with a genetic disorder, such as Rett syndrome or fragile X syndrome. For other children, genetic changes (mutations) may increase the risk of autism spectrum disorder. Still other genes may affect brain development or the way that brain cells communicate, or they may determine the severity of symptoms. Some genetic mutations seem to be inherited, while others occur spontaneously.
  • Environmental factors. Researchers are currently exploring whether factors such as viral infections, medications or complications during pregnancy, or air pollutants play a role in triggering autism spectrum disorder.

No link between vaccines and autism spectrum disorder

One of the greatest controversies in autism spectrum disorder centers on whether a link exists between the disorder and childhood vaccines. Despite extensive research, no reliable study has shown a link between autism spectrum disorder and any vaccines. In fact, the original study that ignited the debate years ago has been retracted due to poor design and questionable research methods.

Avoiding childhood vaccinations can place your child and others in danger of catching and spreading serious diseases, including whooping cough (pertussis), measles or mumps.

Risk factors

The number of children diagnosed with autism spectrum disorder is rising. It's not clear whether this is due to better detection and reporting or a real increase in the number of cases, or both.

Autism spectrum disorder affects children of all races and nationalities, but certain factors increase a child's risk. These may include:

  • Your child's sex. Boys are about four times more likely to develop autism spectrum disorder than girls are.
  • Family history. Families who have one child with autism spectrum disorder have an increased risk of having another child with the disorder. It's also not uncommon for parents or relatives of a child with autism spectrum disorder to have minor problems with social or communication skills themselves or to engage in certain behaviors typical of the disorder.
  • Other disorders. Children with certain medical conditions have a higher than normal risk of autism spectrum disorder or autism-like symptoms. Examples include fragile X syndrome, an inherited disorder that causes intellectual problems; tuberous sclerosis, a condition in which benign tumors develop in the brain; and Rett syndrome, a genetic condition occurring almost exclusively in girls, which causes slowing of head growth, intellectual disability and loss of purposeful hand use.
  • Extremely preterm babies. Babies born before 26 weeks of gestation may have a greater risk of autism spectrum disorder.
  • Parents' ages. There may be a connection between children born to older parents and autism spectrum disorder, but more research is necessary to establish this link.

Complications

Problems with social interactions, communication and behavior can lead to:

  • Problems in school and with successful learning
  • Employment problems
  • Inability to live independently
  • Social isolation
  • Stress within the family
  • Victimization and being bullied

More Information

  • Autism spectrum disorder and digestive symptoms

There's no way to prevent autism spectrum disorder, but there are treatment options. Early diagnosis and intervention is most helpful and can improve behavior, skills and language development. However, intervention is helpful at any age. Though children usually don't outgrow autism spectrum disorder symptoms, they may learn to function well.

  • Autism spectrum disorder (ASD). Centers for Disease Control and Prevention. https://www.cdc.gov/ncbddd/autism/facts.html. Accessed April 4, 2017.
  • Uno Y, et al. Early exposure to the combined measles-mumps-rubella vaccine and thimerosal-containing vaccines and risk of autism spectrum disorder. Vaccine. 2015;33:2511.
  • Taylor LE, et al. Vaccines are not associated with autism: An evidence-based meta-analysis of case-control and cohort studies. Vaccine. 2014;32:3623.
  • Weissman L, et al. Autism spectrum disorder in children and adolescents: Overview of management. https://www.uptodate.com/home. Accessed April 4, 2017.
  • Autism spectrum disorder. In: Diagnostic and Statistical Manual of Mental Disorders DSM-5. 5th ed. Arlington, Va.: American Psychiatric Association; 2013. http://dsm.psychiatryonline.org. Accessed April 4, 2017.
  • Weissman L, et al. Autism spectrum disorder in children and adolescents: Complementary and alternative therapies. https://www.uptodate.com/home. Accessed April 4, 2017.
  • Augustyn M. Autism spectrum disorder: Terminology, epidemiology, and pathogenesis. https://www.uptodate.com/home. Accessed April 4, 2017.
  • Bridgemohan C. Autism spectrum disorder: Surveillance and screening in primary care. https://www.uptodate.com/home. Accessed April 4, 2017.
  • Levy SE, et al. Complementary and alternative medicine treatments for children with autism spectrum disorder. Child and Adolescent Psychiatric Clinics of North America. 2015;24:117.
  • Brondino N, et al. Complementary and alternative therapies for autism spectrum disorder. Evidence-Based Complementary and Alternative Medicine. http://dx.doi.org/10.1155/2015/258589. Accessed April 4, 2017.
  • Volkmar F, et al. Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry. 2014;53:237.
  • Autism spectrum disorder (ASD). Eunice Kennedy Shriver National Institute of Child Health and Human Development. https://www.nichd.nih.gov/health/topics/autism/Pages/default.aspx. Accessed April 4, 2017.
  • American Academy of Pediatrics policy statement: Sensory integration therapies for children with developmental and behavioral disorders. Pediatrics. 2012;129:1186.
  • James S, et al. Chelation for autism spectrum disorder (ASD). Cochrane Database of Systematic Reviews. http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD010766.pub2/abstract;jsessionid=9467860F2028507DFC5B69615F622F78.f04t02. Accessed April 4, 2017.
  • Van Schalkwyk GI, et al. Autism spectrum disorders: Challenges and opportunities for transition to adulthood. Child and Adolescent Psychiatric Clinics of North America. 2017;26:329.
  • Autism. Natural Medicines. https://naturalmedicines.therapeuticresearch.com. Accessed April 4, 2017.
  • Autism: Beware of potentially dangerous therapies and products. U.S. Food and Drug Administration. https://www.fda.gov/ForConsumers/ConsumerUpdates/ucm394757.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery. Accessed May 19, 2017.
  • Drutz JE. Autism spectrum disorder and chronic disease: No evidence for vaccines or thimerosal as a contributing factor. https://www.uptodate.com/home. Accessed May 19, 2017.
  • Weissman L, et al. Autism spectrum disorder in children and adolescents: Behavioral and educational interventions. https://www.uptodate.com/home. Accessed May 19, 2017.
  • Huebner AR (expert opinion). Mayo Clinic, Rochester, Minn. June 7, 2017.

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ADDRP

Autism and Developmental Disorders Research Program

Welcome to the website of the  Autism and Developmental Disorders Research Program (ADDRP) , Lucile Packard Children's Hospital at Stanford University.  This Stanford autism research program is based in the  Department of Psychiatry and Behavioral Sciences  at the Stanford University School of Medicine.

ADDRP focuses on improving the quality of life of individuals with autism spectrum disorder and/or intellectual disabilities. Through research methods that range from clinical trials, neuroimaging investigations, behavioral analysis to basic science methods, the researchers at ADDRP are committed to developing effective treatment strategies and identifying the causes of these conditions.

Our main research aim is to better understand the basic neurobiology of autism and other developmental disorders while furthering our understanding of how genetic and environmental factors may contribute to the onset and progression of these disorders. With this aim in mind, we conduct a variety of research studies and clinical trials of novel behavioral and biological therapies in hopes of developing effective interventions for the treatment of core features of these disorders.

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Acknowledgements

The Stanford Autism and Developmental Disorders Research Program would like to thank the children, as well as their parents and families, for contributing to research. The joint effort to better understand and provide therapies for developmental disorders is not possible without their past and continued involvement.

Stanford ADDRP would also like to ackowledge financial support from the following organizations:

  • National Institutes of Health
  • Autism Speaks
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  • John and Marcia Goldman Foundation
  • Stanford Bio-X
  • Child Health Research Institute
  • The Teresa and Charles Michael Endowed Fund for Autism Research and Education
  • The Mosbacher Family Fund for Autism Research
  • PTEN Research Foundation
  • The Bernard/Fung Family Fund for Autism Research at Stanford

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9/21/2016: The seekers: Why parents try fringe therapies for autism

8/16/2016:  Automating genetic analysis helps keep up with rapid discovery of new diseases

7/22/2015 : Low levels of hormone linked to social deficit in autism

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8/4/2014 : Blood-oxytocin levels in normal range in children with autism, study finds

11/14/2013 : Stanford drug trial seeks participants with autism spectrum disorder

8/13/2012 : Stanford researchers investigate the emotional side of autism

5/29/2012 : Antioxidant Shows Promise as Treatment for Certain Features of Autism, Study Finds (reprinted in ScienceDaily)

Spring 2012 : Autism Answers - Parents run experiments to see what works

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7/30/2011 : Autism Risks: Genes May Not Play Biggest Role

1/25/2010 : Stanford/Packard autism researchers seek twins for brain-imaging study  

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  • Systematic Review
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  • Published: 15 June 2022

Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses

  • Shuang Qiu 1 ,
  • Yingjia Qiu 2 ,
  • Yan Li 3 &
  • Xianling Cong   ORCID: orcid.org/0000-0002-5790-4188 1  

Translational Psychiatry volume  12 , Article number:  249 ( 2022 ) Cite this article

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  • Autism spectrum disorders

Autism spectrum disorder (ASD) is a class of neurodevelopmental conditions with a large epidemiological and societal impact worldwide. To date, numerous studies have investigated the associations between genetic variants and ASD risk. To provide a robust synthesis of published evidence of candidate gene studies for ASD, we performed an umbrella review (UR) of meta-analyses of genetic studies for ASD (PROSPERO registration number: CRD42021221868). We systematically searched eight English and Chinese databases from inception to March 31, 2022. Reviewing of eligibility, data extraction, and quality assessment were performed by two authors. In total, 28 of 5062 retrieved articles were analyzed, which investigated a combined 41 single nucleotide polymorphisms (SNPs) of nine candidate genes. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified, of which associations with suggestive evidence included the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and the rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence included the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), the rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), the C677T polymorphism of MTHFR (under homozygote model), and the rs731236 polymorphism of VDR (under dominant and recessive models). Our UR summarizes research evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risks. This study will provide clinicians and healthcare decision-makers with evidence-based information about the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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Introduction

Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions characterized by early-onset dysfunctions in communication, impairments in social interaction, and repetitive and stereotyped behaviors and interests [ 1 ]. Patients develop ASD-related symptoms when they are 12−18 months of age, and diagnosis is generally made at the age of 2 years [ 2 ]. In 2010, 52 million people had been diagnosed with ASD worldwide, which was equivalent to a population prevalence of 7.6 per 1000 or 1 in 132 persons [ 3 ]. ASD is the leading cause of disability in children under 5 years, and people with ASD may require high levels of support, which is costly and thus leads to substantial economic, emotional, and physical burdens on affected families [ 3 ].

Due to the lack of clinical and epidemiological evidence for an ASD cure, researchers have focused on better understanding ASD and advancing risk prediction and prevention [ 3 ]. The causes of ASD are complex and multifactorial, with several associated genes and environmental risk factors [ 4 ]. A previous umbrella review (UR) of environmental risk factors for ASD showed that several maternal factors, including advanced age (≥35 years), chronic hypertension, preeclampsia, gestational hypertension, and being overweight before or during pregnancy, were significantly associated with ASD risk, without any signs of bias [ 5 , 6 ]. Accumulating twin- and family based studies further indicate that genetic factors play critical roles in ASD, such that the concordance rate among monozygotic twins is higher (60–90%) than that among dizygotic twins (0–30%) [ 7 , 8 ]. The heritability of ASD has been estimated to be 50%, indicating that genetic factors are the main contributors to the etiology of ASD [ 8 ].

To date, numerous studies investigating the association between genetic variants and ASD risk have been published [ 9 , 10 , 11 ]. Most of these studies focused on identifying single nucleotide polymorphisms (SNPs) of candidate genes associated with ASD risk. However, these SNP studies had small sample sizes and, therefore, low statistical power to demonstrate statistically significant effects of low-risk susceptibility genes, leading to inconsistent conclusions. Although meta-analyses have been conducted to resolve this problem, single SNPs or genes have usually been investigated.

An UR collects and evaluates multiple systematic reviews and meta-analyses conducted on a specific research topic, provides a robust synthesis of published evidence, and considers the importance of effects found over time [ 12 ]. In addition, the results of UR studies may increase the predictive power with more precise estimates [ 13 ]. Thus, we aimed to perform an UR study of all the systematic reviews and meta-analyses that have been published, assessing candidate genes associated with ASD risk. This study will provide clinicians and healthcare decision-makers with evidence-based information about candidate genes of ASD and recommendations for future prevention and research in less time than would otherwise be required to locate and examine all relevant research individually.

Literature search strategy and eligibility criteria

We systematically searched the PubMed, EMBASE, PsycINFO, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Sinomed, and Wanfang databases from inception to March 31, 2022. The databases were searched using the following strategy: (autis* [All Fields] OR autism* [All Fields] OR autistic* [All Fields] OR ASD [All Fields] OR autism spectrum disorder* [All Fields] OR PDD-NOS [All Fields] OR PDDNOS [All Fields] OR unspecified PDD [All Fields] OR PDD [All Fields] OR pervasive developmental disorder* [All Fields] OR pervasive developmental disorder not otherwise specified [All Fields] OR Asperger* [All Fields] OR Asperger* syndrome [All Fields]) AND (gene* [All Fields] OR genom* [All Fields]) AND (systematic review [All Fields] OR meta-analysis [All Fields]). Authors S. Qiu and Y. Qiu independently conducted literature searches for potential articles included in this review. The references of the relevant articles were manually searched to identify and incorporate eligible studies.

We included meta-analyses of family based and case-control studies that examined associations between ASD and potential risk genes. We only included meta-analyses that reported either effect estimates of individual study or the data necessary to calculate these estimates. We excluded meta-analyses if (1) risk genes were used for screening, diagnostic, or prognostic purposes; (2) a study examined ASD as a risk factor for other medical conditions; (3) a study included fewer than three original studies investigating the association between risk genes and ASD; and (4) a study with missing information after the corresponding author, whom we contacted through email, failed to provide the required information. All articles retrieved were first organized in the reference manager software (Endnote 9, Clarivate Analytics, New York, NY, USA), and duplicates were deleted. S. Qiu and Y. Qiu chose eligible articles by screening the titles, abstracts, and full article texts independently. Disagreements were resolved through a discussion with a third investigator (Y. Li) until a consensus was reached.

Data extraction and quality assessment

From each eligible meta-analysis, we extracted the first author, publication year, genetic risk factors examined, number of studies, number of ASD cases and participants, study-specific relative risk estimates (odds ratio [ OR ]) with the corresponding 95% confidence interval ( CI ), sample size of cases and controls, genotype and allele counts, and individual study designs (case-control, family based or mixed [case-control and family based]). We used the ‘assessment of multiple systematic reviews’ tool, consisting of 11 items, to assess the methodological quality of the meta-analyses [ 14 ]. Data extraction and quality assessment were independently conducted by S. Qiu and Y. Qiu. Disagreements were resolved via a discussion with a third investigator (Y. Li) until a consensus was reached.

Data analysis

In agreement with previous URs, we performed a statistical analysis using a series of tests that were previously developed and reproduced [ 13 , 15 , 16 ]. If more than one meta-analysis on the same research question was eligible, the most recent meta-analysis was retained for the main analysis. For each eligible meta-analysis, we calculated the summary-effect size with 95% CI [ 17 ]. We also calculated the 95% prediction interval ( PI ) to explain the between-study heterogeneity and to assess the uncertainty of a new study [ 18 , 19 ]. Heterogeneity between studies was assessed using the Chi-squared test based Q-statistic and quantified using the I 2 -statistic [ 20 , 21 ]. If there was no substantial statistical heterogeneity ( P  > 0.10, I 2  ≤ 50%), data were pooled using a fixed-effect model; otherwise, heterogeneity was evaluated using a random-effect model [ 22 ]. The Hardy–Weinberg equilibrium (HWE) of meta-analyses in the control group was analyzed using Chi-squared tests. Additionally, small-study effects were evaluated using Egger’s regression asymmetry test. P -values < 0.10 were considered to indicate the presence of small-study effects [ 23 , 24 ]. The Chi-squared test was used to assess the presence of excess significance, which evaluated whether the observed number of studies with significant results ( P  < 0.05) was greater than the expected number [ 22 , 25 ]. All statistical analyses were performed using RStudio 3.6.2. Statistical significance was set at P  < 0.05, except where otherwise specified.

Determining the credibility of evidence

In line with previous URs, we categorized the strength of the evidence of risk genes for ASD into five levels: convincing (class I), highly suggestive (class II), suggestive (class III), weak (class IV), and not significant [ 5 , 26 , 27 , 28 ]. Criteria for the level of evidence included the number of ASD cases, P -values by random effects model, small-study effects, excess significance bias, heterogeneity ( I² ), and 95% CI .

This review was prospectively registered with PROSPERO (registration number: CRD42021221868).

Description of eligible meta-analyses

A total of 5062 articles were identified through an initial search. After removing duplicates, the titles and abstracts of 3182 articles were screened for eligibility. Of the remaining 66 articles that were reviewed in full, 28 eligible articles were selected for data extraction (Fig. 1 ).

figure 1

Flow chart of literature identification and selection.

The characteristics of the selected studies are presented in Table 1 . Of the 28 included reviews, eight were on methylenetetrahydrofolate reductase ( MTHFR ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]; four each on solute carrier family 6 member 4 ( SLC6A4 ) [ 37 , 38 , 39 , 40 ] and contactin associated protein 2 ( CNTNAP2 ) [ 41 , 42 , 43 , 44 ]; three each on oxytocin receptor ( OXTR ) [ 45 , 46 , 47 ] and reelin ( RELN ) [ 48 , 49 , 50 ]; two each on gamma-aminobutyric acid type A receptor subunit beta3 ( GABRB3 ) [ 51 , 52 ], solute carrier family 25 member 12 ( SLC25A12 ) [ 53 , 54 ], and vitamin D receptor ( VDR ) [ 55 , 56 ]; and one on catechol-o-methyltransferase ( COMT ) [ 39 ] (one meta-analysis was on both COMT and SLC6A4 ). These studies were published from 2008 to 2021 and considered the associations between 41 SNPs in nine candidate genes and ASD risk. For quality assessment, 22 articles that scored 5−8 were rated as ‘moderate quality’, and six that scored < 5 were rated as ‘low quality’. Seventeen studies (60.7%) performed the HWE check (Table 1 ). With respect to the study design, 14 (64.3%) studies synthesized case-control studies, two (7.1%) included family based studies, and eight (28.6%) used both case-control and family based studies (Table 1 ).

Summary-effect sizes and significant findings

The results of the associations between the 41 SNPs and ASD risks reported in the meta-analyses are presented in Table 2 under five different genetic models: allelic model (mutant allele vs. wild-type allele), dominant model (mutant homozygote + heterozygote vs. wild-type homozygote), heterozygote model (heterozygote vs. wild-type homozygote), homozygote model (mutant homozygote vs. wild-type homozygote), and recessive model (mutant homozygote vs. wild-type homozygote + heterozygote).

Only one meta-analysis on the rs2710102 polymorphism of CNTNAP2 showed that the polymorphism was associated with ASD susceptibility in allelic, homozygote, and recessive models [ 44 ]. This meta-analysis also found that the rs7794745 polymorphism of CNTNAP2 was associated with an increased risk of ASD in dominant and heterozygote models [ 44 ].

All four meta-analyses reported no significant association between the A1298C polymorphism of MTHFR and ASD risk. All eight meta-analyses on the C677T polymorphism of MTHFR showed that the polymorphism was associated with ASD susceptibility in allelic and heterozygote models [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Seven meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in dominant [ 29 , 31 , 32 , 33 , 34 , 35 , 36 ] and homozygote [ 29 , 30 , 31 , 33 , 34 , 35 , 36 ] models. Five meta-analyses found that the C677T polymorphism was associated with an increased risk of ASD in the recessive model [ 29 , 30 , 31 , 33 , 34 ].

For OXTR , 19 SNPs were summarized. LoParo et al. [ 45 ] found that the mutant allele of rs2268491, wild-type allele of rs237887, and mutant allele of rs7632287 were risk-inducing SNPs of ASD. In addition, Kranz et al. [ 46 ] found that the mutant allele of rs237889 was associated with ASD risk.

Regarding SLC25A12 , both Aoki et al. [ 53 ] and Liu et al. [ 54 ] found that the mutant alleles of rs2056202 and rs2292813 significantly increased ASD risk in family-based and mixed studies. We excluded the results of the associations between rs2292813 and ASD risk based on the case-control design reported by Liu et al. [ 54 ], as the authors included only two case–control studies.

Sun et al. [ 55 ] found that the rs2228570 polymorphism of VDR was associated with an increased ASD risk in homozygote and recessive models, while Yang et al. [ 56 ] did not find significant associations in any genetic model. Both authors [ 55 , 56 ] found that the rs731236 polymorphism of VDR was significantly associated with ASD risk in allelic, homozygote, and recessive models. Sun et al. [ 55 ] found that the rs731236 polymorphism was significantly associated with ASD risk in the dominant model. Both Sun et al. [ 55 ] and Yang et al. [ 56 ] found that the mutant allele of rs7975232 of VDR was significantly associated with a decreased ASD risk (Table 2 ). There were no significant SNPs in COMT , GABRB3 , RELN , and SLC6A4 .

When more than one meta-analysis on the same research question was eligible, the most recent one was retained for the main analysis. After comparing the publication year and sample size of each meta-analysis, 11 meta-analyses were retained for further analysis, of which two each study were on RELN and MTHFR , and one each was on CNTNAP2 , COMT , GABRB3 , OXTR , SLC25A12 , SLC6A4 , and VDR . We extracted the allele and genotype frequencies of each SNP in case and control groups from the original research for further analysis. However, the allele and genotype frequencies of some SNPs in the compared groups could not be extracted from the original research that did not contain the information, and we could not obtain this information from the corresponding authors of the studies. Finally, we analyzed the data of 20 SNPs with allele frequencies in 10 meta-analyses from 117 original studies and 16 SNPs with genotype frequencies in eight meta-analyses from 101 original studies. Associations were measured using five different genetic models (Tables 3 , 4 ).

We found that the rs2710102 polymorphism of CNTNAP2 was associated with a decreased ASD risk in the allelic ( OR  = 0.849, 95% CI  = 0.734–0.981, P  = 0.0263), homozygote ( OR  = 0.668, 95% CI  = 0.470–0.950, P  = 0.0248), and recessive ( OR  = 0.715, 95% CI  = 0.563–0.909, P  = 0.0062) models. In addition, we found that the mutant allele of rs7794745 ( CNTNAP2 ) increased ASD risk based on the dominant ( OR  = 1.300, 95% CI  = 1.109–1.523, P  = 0.0012) and heterozygote ( OR  = 1.275, 95% CI  = 1.081–1.504, P  = 0.0039) models. The C677T polymorphism of MTHFR was associated with an increased ASD risk in the allelic ( OR  = 1.799, 95% CI  = 1.303–2.483, P  = 0.0004), dominant ( OR  = 1.959, 95% CI  = 1.402–2.738, P  < 0.0001), heterozygote ( OR  = 1.767, 95% CI  = 1.343–2.330, P  < 0.0001), and homozygote ( OR  = 1.795, 95% CI  = 1.158–2.782, P  = 0.0089) models. The rs607755 polymorphism of RELN was associated with an increased ASD risk in the allelic ( OR  = 1.316, 95% CI  = 1.029–1.683, P  = 0.0284), dominant ( OR  = 1.520, 95% CI  = 1.061–2.178, P  = 0.0226), heterozygote ( OR  = 1.483, 95% CI  = 1.016–2.165, P  = 0.0411), and homozygote ( OR  = 1.816, 95% CI  = 1.051–3.136, P  = 0.0324) models. The rs731236 polymorphism of VDR was associated with an increased ASD risk in the allelic ( OR  = 1.297, 95% CI  = 1.125–1.494, P  = 0.0003), dominant ( OR  = 1.304, 95% CI  = 1.082–1.571, P  = 0.0053), homozygote ( OR  = 1.741, 95% CI  = 1.258–2.409, P  = 0.0008), and recessive ( OR  = 1.613, 95% CI  = 1.187–2.190, P  = 0.0022) models. In addition, we found that the mutant allele of rs7975232 ( VDR ) decreased ASD risk ( OR  = 0.823, 95% CI  = 0.681–0.993, P  = 0.0425) based on the allelic model. There was no significant association between the other SNPs and ASD risk (all P  > 0.05; Table 4 ).

As for the results of PI , the null value was excluded in only four SNPs of rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) under the heterozygote model; rs607755 ( RELN ) and rs731236 ( VDR ) under the allelic and homozygote models (Table 4 ). When evaluating small-study effects using Egger’s regression asymmetry test, evidence for statistically significant small-study effects in the meta-analyses was identified in some SNPs. Supporting evidence included a meta-analysis on A1298C ( MTHFR ) under the allelic, dominant, and heterozygote models; a meta-analysis on C677T ( MTHFR ) under the five genetic models; a meta-analysis on rs20317 ( GABRB3 ) under the dominant and heterozygote models; one each on rs736707 ( RELN ) and rs1544410 ( VDR ) under the recessive and allelic models, respectively; and three meta-analyses on rs607755 ( RELN ), 5-HTTLPR ( SLC6A4 ), and rs7975232 ( VDR ) under the heterozygote model ( P  < 0.10).

Hints of excess-statistical-significance bias were observed in rs2710102 ( CNTNAP2 ) under the allelic, homozygote, and recessive models; rs4680 ( COMT ) under the allelic model; rs20317 ( GABRB3 ) under the heterozygote model; A1298C ( MTHFR ) under allelic, dominant, heterozygote, and recessive models; C677T ( MTHFR ) under homozygote and recessive models; rs736707 ( RELN ) under allelic, dominant, and homozygote models; 5-HTTLPR ( SLC6A4 ) under allelic and recessive models; rs11568820 ( VDR ) under the dominant model; and rs731236 ( VDR ) under the heterozygote model, with statistically significant ( P  < 0.05) excess of positive studies (Table 4 ).

We categorized the strength of the evidence of 20 SNPs for ASD into five levels. According to the criteria for the level of evidence, for rs2710102 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under allelic, homozygote, and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI did not exclude the null value, and there was no excess significance bias ( P  > 0.05) under the five genetic models. For rs7794745 ( CNTNAP2 ), the P -value based on the random effects model was significant at P  < 0.05 under dominant and heterozygote models. For C677T ( MTHFR ), there was a total of 2147 ASD cases, which was > 1000, and the P -value based on the random effects model was significant at P  < 10 –3 under allelic, dominant, and heterozygote models. Moreover, it was significant at P  < 0.05 under the homozygote model. Between-study heterogeneity was large ( I²  > 50.0%) under the five genetic models, the 95% PI did not exclude the null value under the five genetic models, and there was no excess significance bias ( P  > 0.05) under allelic, dominant, and heterozygote models. For rs731236 ( VDR ), there was a total of 1088 ASD cases, which was >1000, the P -value based on the random effects model was significant at P  < 10 –3 under allelic and homozygote models, and the P -value was significant at P  < 0.05 under dominant and recessive models. Between-study heterogeneity was not significant ( P  > 0.10, I²  < 50.0%), the 95% PI excluded the null value, and there was no small-study effect ( P  > 0.10) and excess significance bias ( P  > 0.05) under the five genetic models (Table 4 ). Thus, the rs2710102 ( CNTNAP2 ) was graded as weak evidence (class IV) under allelic, homozygote, and recessive models; rs7794745 ( CNTNAP2 ) was graded as weak evidence (class IV) under dominant and heterozygote models; the C677T ( MTHFR ) was graded as suggestive evidence (class III) under allelic, dominant, and heterozygote models; C677T ( MTHFR ) was graded as weak evidence (class IV) under the homozygote model; VDR (rs731236) was graded as suggestive evidence (class III) under allelic and homozygote models; and VDR (rs731236) was graded as weak evidence (class IV) under dominant and recessive models.

This UR summarizes evidence on the genetic basis of ASD. Our study design provides a robust and significant synthesis of published evidence and increases the conclusive power with more precise estimates. Overall, 12 significant SNPs of CNTNAP2 , MTHFR , OXTR , SLC25A12 , and VDR were identified from 41 SNPs of nine candidate genes in 28 meta-analyses. Of those, associations with suggestive evidence (class III) were the C677T polymorphism of MTHFR (under allelic, dominant, and heterozygote models) and rs731236 polymorphism of VDR (under allelic and homozygote models). Associations with weak evidence (class IV) were the rs2710102 polymorphism of CNTNAP2 (under allelic, homozygote, and recessive models), rs7794745 polymorphism of CNTNAP2 (under dominant and heterozygote models), C677T polymorphism of MTHFR (under homozygote model), and rs731236 polymorphism of VDR (under dominant and recessive models).

ASD remains a ‘disease of theories’, as multiple genes and environmental risk factors are probably involved in its pathogenesis. However, to date, the etiology and pathological mechanism of ASD are still unknown [ 57 ]. The genetic architecture of ASD is complex. Moreover, most research in this field has focused on candidate genes, primarily those with a plausible role in the known underlying pathophysiology, including mitochondrial dysfunction, abnormal neurodevelopment, and dysfunction of synapse formation and stability during neurodevelopment [ 58 , 59 ].

CNTNAP2 is a member of neurexin superfamily and is a synaptic protein [ 60 ]. It plays a major role in neural development, crucial for neural circuit assembly [ 61 ]. CNTNAP2 mutations may be linked to the abnormal behavior of ASD by altering synaptic neurotransmission, functional connectivity, and neuronal network activity [ 61 , 62 ]. The rs2710102 and rs7794745 are two common non-coding variants in CNTNAP2 , with four and three meta-analyses reporting the associations with ASD, respectively. The results of the meta-analysis by Uddin et al. were inconsistent with the other authors’ [ 44 ]. We further re-analyzed and categorized the strengths of evidence. Both the rs2710102 and rs7794745 polymorphisms of CNTNAP2 were associated with decreased risk of ASD. The rs2710102 was graded as having a weak association with ASD under allelic, homozygote, and recessive models. The rs7794745 was graded as having a weak association with ASD under dominant and heterozygote models. Therefore, it is likely that the rs2710102 and rs7794745 polymorphisms of CNTNAP2 influence the risk of ASD.

MTHFR is one of the most frequently-researched genes in ASD, with four and eight meta-analyses for A1298C [ 29 , 31 , 32 , 33 ] and C667T [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] polymorphisms, respectively. The A1298C and C667T polymorphisms of MTHFR are associated with reduced enzymatic activity, which affects folate metabolism, and, consequently, fetal brain development [ 29 , 32 , 33 ]. Dysfunction of the brain is indicated in ASD etiology; thus, MTHFR has been the focal point of investigation in this disorder. The meta-analysis by Li et al. was selected because it was the most recent among the examined meta-analyses [ 34 ]. The genotype distributions of the A1298C and C667T polymorphisms of MTHFR in the control group were not found in the HWE, which may be due to selection bias, population stratification, and genotyping errors within the original studies. We found no significant association between the A1298C polymorphism of MTHFR and ASD risk in the five genetic models, which was consistent with the four meta-analyses, indicating that the A1298C polymorphism of MTHFR may not be a risk SNP of ASD. We found that the C667T polymorphism of MTHFR was associated with an increased risk of ASD, graded as having suggestive association under allelic, dominant, and heterozygote models and weak association under the homozygote model. Thus, the C667T polymorphism of MTHFR may confer ASD risk.

OXTR, a neuropeptide gene, is also one of the most frequently-studied genes associated with ASD [ 45 ]. Oxytocin plays an important role in a range of human behaviors, including affiliative behavior to social bonding, and is differentially expressed in the blood of individuals with autism compared to that of non-autistic individuals [ 45 , 63 ]. Three meta-analyses investigated 19 SNPs and ASD risk. Of these, only rs2254298 and rs53576 were analyzed in two meta-analyses [ 45 , 46 ], and the remaining SNPs were unique in one meta-analysis. Three SNPs (rs2268491, rs237887, and rs7632287) were significantly associated with ASD risk [ 45 , 46 ]; however, we failed to determine the credibility of the evidence because of the lack of original data.

RELN encodes a large secreted extracellular matrix protein considered to be involved in neuronal migration, brain structure construction, synapse formation, and stability during neurodevelopment [ 59 ]. Fatemi et al. found decreased levels of reelin mRNA and protein and increased levels of reelin receptors in the brain and plasma of individuals with autism [ 64 ]. Dysfunction of the reelin signaling pathway has been found in ASD, schizophrenia, epilepsy, bipolar disorder, mental retardation, depression, Alzheimer’s disease, and lissencephaly [ 59 , 65 ]. Genetic association studies have been conducted to investigate the associations between SNPs within RELN and ASD with conflicting results. None of the three meta-analyses found significant associations [ 48 , 49 , 50 ]. The meta-analysis by Hernández-García et al. was retained for further analysis of the original studies after comparing publication years and sample sizes of the three meta-analyses [ 50 ]. Hernández-García et al. did not find a significant association between RELN and ASD risk [ 50 ]. In our analysis, because there was no substantial statistical heterogeneity under the five genetic models (all P  > 0.10, I 2  ≤ 50%), a fixed model was applied to pool the effect size. We found that the rs607755 of RELN was associated with ASD risk in allelic, dominant, heterozygote, and homozygote models. This inconsistent result was caused by different pooling methods, indicating that it is necessary to perform an UR to provide a robust synthesis of published evidence and evaluate the importance of genetic factors related to ASD. Our UR results showed that the rs607755 of RELN was not significant when we categorized the strength of the evidence. Thus, it may not be a risk factor for ASD.

SLC25A12 encodes the mitochondrial aspartate/glutamate carrier of the brain, a calcium-binding solute carrier located in the inner mitochondrial membrane that is expressed principally in the heart, brain, and skeletal muscle [ 66 , 67 ]. Rossignol et al. found that individuals with ASD had a significantly higher prevalence of mitochondrial diseases than that of controls, indicating the involvement of mitochondrial dysfunction in ASD [ 58 ]. Thus, an increasing number of genetic studies on ASD have focused on SLC25A12 . However, the results on the association between SNPs of SLC25A12 and ASD risk are inconsistent. Two meta-analyses were performed by Aoki et al. [ 53 ] and Liu et al. [ 54 ], and despite differences in the number of studies between the two meta-analyses, both found a higher risk of ASD in individuals with the mutant allele of rs2056202 or rs2292813. However, we failed to determine the credibility of the evidence because of a lack of original data.

Vitamin D plays a significant role in brain homeostasis, neurodevelopment, and immunological modulation, and its deficiency has been reported in children with ASD [ 68 ]. Hence, changes in the genes involved in the transport or binding of vitamin D may be associated with ASD risk. Notably, vitamin D exerts its effects on genes via the VDR gene, to which changes may be an underlying risk factor for ASD. Sun et al. [ 55 ] and Yang et al. [ 56 ] performed meta-analyses to pool the effect size of inconsistent conclusions from original studies on the associations between SNPs in VDR and ASD risks. We further re-analyzed and categorized the strengths of evidence. The rs731236 polymorphism of VDR was associated with an increased risk of ASD, graded as having a suggestive association under allelic and homozygote models and a weak association under dominant and recessive models without small-study effects, excess significance bias, and large heterogeneity. It is likely that the VDR rs731236 polymorphism influences the risk of ASD.

Our study has some limitations. First, associations between several SNPs and ASD risks under five genetic models or in different populations were not fully assessed in our UR, partly due to insufficient original data. Second, our UR is limited by significant heterogeneity that may be caused by population stratification, study design, and differences in the pattern of linkage disequilibrium structure. Finally, ASD is a complex disorder with different causative factors (multiple genetic and environmental factors). We did not investigate the involvement of environmental factors in ASD. Despite these limitations above, our UR includes its prospective registration with PROSPERO, an extensive search strategy, clear criteria of inclusion and exclusion, duplicated processing by two authors, accurate quality assessment, systematic assessment and critical comparison of meta-analyses, and consistent standards for re-analysis of original data.

In conclusion, our UR summarizes evidence on the genetics of ASD and provides a broad and detailed overview of risk genes for ASD. The rs2710102 and rs7794745 polymorphisms of CNTNAP2 , C677T polymorphism of MTHFR , and rs731236 polymorphism of VDR may confer ASD risk. This study will aid clinicians in decision-making through the use of evidence-based information on the most salient candidate genes relevant to ASD and recommendations for future treatment, prevention, and research.

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Acknowledgements

This study was funded by the Science and Technology Department of Jilin Province (grant number: 20200601010JC).

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Department of Biobank, China-Japan Union Hospital, Jilin University, Changchun, 130033, Jilin, China

Shuang Qiu & Xianling Cong

China-Japan Union Hospital, Jilin University, Changchun, 130033, Jilin, China

Yingjia Qiu

Department of Epidemiology, School of Public Health, Beihua University, Jilin, 132013, Jilin, China

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Contributions

Study design: S.Q. and X.C. Data collection, analysis, and interpretation: S.Q., Y.Q., and Y.L. Drafting of the manuscript: S.Q. Critical revision of the manuscript: X.C. Approval of the final version for publication: all co-authors.

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Correspondence to Xianling Cong .

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Qiu, S., Qiu, Y., Li, Y. et al. Genetics of autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses. Transl Psychiatry 12 , 249 (2022). https://doi.org/10.1038/s41398-022-02009-6

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Autism Research Institute Logo

What is Autism?

Autism is a developmental disorder with symptoms that appear within the first three years of life. Its formal diagnostic name is autism spectrum disorder. The word “spectrum” indicates that autism appears in different forms with varying levels of severity. That means that each individual with autism experiences their own unique strengths, symptoms , and challenges. 

Understanding more about ASD can help you better understand the individuals who are living with it. 

what is autism

How autism spectrum disorders are described

Psychiatrists and other clinicians rely on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) to define autism and its symptoms. The  DSM-5 definition  recognizes two main symptom areas:

  • Deficits in social communication and interaction
  • Restricted, repetitive behaviors, interests, or activities

These symptoms appear early in a child’s development—although diagnosis may occur later. Autism is diagnosed when symptoms cause developmental challenges that are not better explained by other conditions.

The definition of autism has been refined over the years. Between 1995 and 2011, the DSM-IV grouped Asperger’s Syndrome and Pervasive Developmental Disorder Not Otherwise Specified (PDD-NOS) with autism. Asperger’s syndrome was an autism spectrum disorder marked by strong verbal language skills and, often, high intellectual ability. PDD-NOS was a more general diagnosis for people who did not fit clearly into the other two categories. 

However, the DSM-5 no longer recognizes Asperger’s syndrome or PDD-NOS as separate diagnoses. Individuals who would previously have received either of these diagnoses may now receive a diagnosis of autism spectrum disorder instead. 

Autism symptoms and behaviors

Individuals with autism may present a range of symptoms, such as: 

  • Reduced eye contact
  • Differences in body language
  • Lack of facial expressions
  • Not engaging in imaginative play
  • Repeating gestures or sounds
  • Closely focused interests
  • Indifference to temperature extremes

These are just a few examples of the symptoms an individual with autism may experience. Any individual could have some, all, or none of these symptoms. Keep in mind that having these symptoms does not necessarily mean a person has autism. Only a qualified medical professional can diagnose autism spectrum disorder. 

Most importantly, an individual with autism is first and foremost an individual. Learning about the symptoms can help you start to understand the behaviors and challenges related to autism, but that’s not the same as getting to know the individual. Each person with autism has their own strengths, likes, dislikes, interests, challenges, and skills, just like you do. 

How autism is diagnosed

There is no known biological marker for autism. That means that no blood or genetic test can diagnose the disorder. Instead, clinicians rely on observation, medical histories, and questionnaires to determine whether an individual has autism. 

Physicians and specialists may use one or several of the following screening tools : 

  • Modified Checklist for Autism in Toddlers , Revised (M-CHAT), a 20-question test designed for toddlers between 16 and 30 months old. 
  • The Ages and Stages Questionnaire (ASQ) , a general developmental screening tool with sections targeting specific ages used to identify any developmental challenges a child may have. 
  • Screening Tool for Autism in Toddlers and Young Children (STAT) , an interactive screening tool, comprising 12 activities that assess play, communication, and imitation. 
  • Parents’ Evaluation of Developmental Status (PEDS)  is a general developmental parent-interview form that identifies areas of concern by asking parents questions.  

The American Academy of Pediatrics encourages autism screening for all children at their 18 and 24-month well-child checkups. Parents and caregivers can also ask their pediatrician for an autism screening if they have concerns. In rare cases, individuals with autism reach adulthood before receiving a diagnosis. However, most individuals receive an autism diagnosis before the age of 8.

Prevalence of autism

For many years, a diagnosis of autism was rare, occurring in just one child out of 2,000. One reason for this was the diagnostic criteria. Autism was not clearly defined until 1980 when the disorder was included in the DSM-III. Before that time, some cases of autism spectrum disorder may have been mistaken for other conditions. 

Since the ’80s, the rate of autism has increased dramatically around the world. In March 2020, the US Federal Centers for Disease Control announced that  1 in every 54 children  in the United States is affected by autism. 

Although autism is more likely to affect boys than girls, children of all genders have been diagnosed with ASD. Several recent studies investigate the impact of race, ethnicity, and socioeconomic  disparities on the diagnosis of autism spectrum disorder. 1,2,3,4

A short history of autism

Researchers have been working on autism and autism-like disorders since the 1940s. At that time, autism studies tended to be small in scale and used varying definitions of the disorder. Autism was also sometimes lumped in with other conditions.

Focused research into ASD became more common in the 1980s when the DSM-III established autism as a distinct diagnosis. Since then, researchers have explored the causes, symptoms, comorbidities, efficacy of treatments, and many other issues related to autism. 

Researchers have yet to discover a cause for autism. Many of the ideas put forth thus far have been disproven. Likely a combination of genetic , neurological , and environmental factors are at work, which is the case with many psychiatric disorders and conditions. 

Autism Prognosis

Autism is a lifelong condition, and a wide variety of treatments can help support people with ASD. The symptoms and comorbidities—conditions occurring in the same individual—are treatable. Early intervention delivers the best results. Parents and caregivers should seek out the advice of a qualified medical professional before starting any autism treatment. 

Advances in understanding autism, its symptoms, and comorbidities have improved outcomes for individuals with autism. In recent years, more children with autism have attended school in typical classrooms and gone on to live semi-independently. However, the majority remain affected to some degree throughout their lifetime. 

Co-occurring conditions

When a person has more than two or more disorders, these conditions are known as comorbidities. Several comorbidities are common in people with autism. 

These include: 

  • Gastrointestinal and immune function disorders
  • Metabolic disorders
  • Sleep disorders

Identifying co-occurring conditions can sometimes be a challenge because their symptoms may be mimicked or masked by autism symptoms. However, diagnosing and identifying these conditions can help avoid complications and improve the quality of life for individuals with autism. 

Autism in pop culture

Movies and books featuring characters with autism have helped bring autism spectrum disorder into the public consciousness. Some have ignited controversy; others have increased the public’s general understanding of autism. A few have done both. At ARI, we hope that people will rely on evidence-based research to understand autism spectrum disorder better.   

Learn more about autism spectrum disorder by watching one of our expert-led webinars . They help you learn about ASD from clinicians, researchers, and therapists who research autism and support individuals with ASD. 

  • Donohue MR, Childs AW, Richards M, Robins DL. Race influences parent report of concerns about symptoms of autism spectrum disorder. Autism . 2019;23(1):100-111. doi:10.1177/1362361317722030
  • Durkin MS, Maenner MJ, Baio J, et al. Autism Spectrum Disorder Among US Children (2002-2010): Socioeconomic, Racial, and Ethnic Disparities. Am J Public Health . 2017;107(11):1818-1826. doi:10.2105/AJPH.2017.304032
  • Newschaffer CJ. Trends in Autism Spectrum Disorders: The Interaction of Time, Group-Level Socioeconomic Status, and Individual-Level Race/Ethnicity. Am J Public Health . 2017;107(11):1698-1699. doi:10.2105/AJPH.2017.304085
  • Yingling ME, Hock RM, Bell BA. Time-Lag Between Diagnosis of Autism Spectrum Disorder and Onset of Publicly-Funded Early Intensive Behavioral Intervention: Do Race-Ethnicity and Neighborhood Matter?. J Autism Dev Disord . 2018;48(2):561-571. doi:10.1007/s10803-017-3354-3

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A new study from the United Kingdom indicates that sleep problems in infancy may help to predict later social skills deficits, autism traits, and autism diagnoses in children. Jannath Begum-Ali and

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Children with autism spectrum disorders (ASD) who receive intensive early intervention at the age of 18 months fare significantly better than those who begin receiving this type of intervention at 27

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Positive Behaviour Support (PBS) strategies that are adopted school wide help educators set clear expectations that support students to learn and thrive at school. We surveyed educators from Australian autism-specific schools to find out their views and experiences of school-wide PBS and identify areas for improvement. Read more

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Autistic adults have poorer educational, employment and quality of life outcomes than non-Autistic adults. A strengths-based approach to education may better equip Autistic students to transition into adult life. Our research is helping educators develop programs that take into consideration the strengths of Autistic students. Read more

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Teachers often do not have time to explore new teaching practices. This Autism CRC flagship project developed a suite of teaching resources and strategies for educators who teach diverse learners. All the resources can be easily accessed via the online platform, inclusionED. Teachers who participated in the project reported improved confidence in teaching Autistic children. Read more

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Aspect often receives requests from parents who are educating their child on the autism spectrum at home about where to access appropriate educational resources and supports. We surveyed families to find out more about their experiences of home education. The findings informed the development and delivery of Aspect services and resources. Read more

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The strengths and abilities of Autistic students may be overlooked by teachers. This can hinder students during their school years and may affect their quality of life as adults. We asked Autistic adults who are also gifted and /or talented about what helped and hindered the development of their skills. This study will inform autism programs that focus on the inclusion of strengths. Read more

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In response to the COVID-19 pandemic, Aspect introduced an online telehealth delivery model to conduct diagnostic assessments for autism. The project provided information about the suitability of telehealth assessments from the perspectives of Aspect clinicians, Autistic adults and parents of Autistic children. Read more

Identifying difficulties during the COVID-19 pandemic

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ARCAP joined an international collaboration of more than 60 researchers in a project to investigate how Autistic people, people with special needs and their families across the world coped with the COVID-19 pandemic. The findings helped build an evidence base of the needs of Autistic children and their families during crises. Read more

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Understanding and giving voice to people on the autism spectrum about their everyday life experiences is key to Aspect’s work. These studies gave adults and adolescents on the autism spectrum an opportunity to have their say about the awareness, services and support they need to achieve their goals and aspirations, and have since informed the development of Aspect’s services. The project was extended with the publication of the book Shining a Light on the Autism Spectrum: Experiences and Aspirations of Adults. Read more

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This study found a glaring unmet need and a strong desire within the Autistic community for more accessible and inclusive opportunities for physical activities. New insights about the barriers and facilitators will be used by Aspect Autism Friendly to help sporting organisations and groups create better opportunities for Autistic individuals to enjoy the benefits of physical activity. Read more

Tuning in to online conversations about autism disclosure

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Autistic people are more likely to interact with police than non-Autistic people. We asked Autistic adults and parents of Autistic children to share their lived experiences so we could learn more about when, why and how interactions with police occur. ARCAP’s research led to the development of an autism training module that is being used by a number of police forces across Australia. Read more

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Little is known about Autistic people’s interactions with criminal justice professionals on a global scale. The Global Autism and Criminal Justice Consortium , conducted the largest research study to explore Autistic people’s perceptions of their interactions with the criminal justice system. A policy brief was devised to guide criminal justice agencies worldwide in making adaptations for Autistic people. Analysis of the research data will inform policy, strategies and future research directions. Read more

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NIH grant to support research into connections between autism, sensory hypersensitivity

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Supported by a $2 million grant from the National Institutes of Health, the Auerbach Lab will examine how different genes associated with autism spectrum disorders may similarly impact our brain’s neurons, resulting in heightened sensitivity to sounds. 

Autism spectrum disorders are genetically complex, and hundreds of genes are implicated in their development. As a result, some may conclude that autism is a collection of disconnected disorders with comparable symptoms. However, much like how roads converge as they approach a destination, at some level of brain function there may be bottlenecks: points at which different genes lead to the same effects within the brain and ultimately result in similar symptoms.

“You have this really big constellation of clinical symptoms — of phenotypes — on one side, and tons of genes interacting on the other side,” said lead investigator  Benjamin Auerbach , an assistant professor of molecular and integrative physiology at the University of Illinois Urbana-Champaign, and an assistant professor at the Beckman Institute for Advanced Science and Technology . 

“The question is: How do we get from point A to point B? In particular, how many different routes are there to possibly take?”

In previous research, Auerbach found that the two most common genetic mutations associated with ASD have opposite effects at the cellular level despite resulting in similar symptoms. The grant-funded project will explore whether these similarities may instead be due to a shared mechanism at the level of neural circuits.

Auerbach and his team will focus on the auditory system, as sensory hypersensitivities are common in ASD and can strongly affect individuals’ quality of life. 

Someone who experiences auditory hypersensitivity has difficulty processing sound information. This is especially true in settings like shopping malls, schools, or public transportation, which are often busy, loud, and require individuals to filter out an overabundance of noise and other sensory input. Auditory hypersensitivity has been described as physically painful, impairs individuals’ abilities to focus, and can make it difficult to interact with the environment and with other people. 

Groups of neurons connect and communicate with each other by passing signals through synapses, which can be excitatory or inhibitory. Excitatory synapses amplify signals, while inhibitory synapses dampen them. Typically, a precise balance exists between the numbers of excitatory and inhibitory synapses within a neural circuit, and having an imbalance may lead to hyperexcitability — which in the case of auditory circuits could overamplify sound information.

This project will test whether the two most common ASD-related gene mutations lead to this kind of imbalance. 

The project will focus on dysregulation of a specific type of inhibitory interneuron, parvalbumin-positive, or PV+, interneurons, as a potentially shared mechanism. PV+ interneurons are potent regulators of the sensitivity and activity of excitatory neurons. When their function isn’t properly controlled, individuals may be more sensitive to sounds perceived by others at a normal volume.

The researchers will use rat models to explore how the brain reacts to sound stimuli, and how this may change with different ASD-related gene mutations. The team will use in-vivo electrophysiology to record the electrical activity from populations of auditory neurons in these rat models. This activity can be associated with behavioral changes in response to a stimulus such as playing sounds.

Additionally, the group will collaborate with Beckman researcher  Howard Gritton , an assistant professor of comparative biosciences and bioengineering, to use optogenetics: a method to control cell activity with light. Neurons in a specific brain region can be engineered to activate in the presence of blue light. For example, researchers can target and activate PV+ neurons to test whether this alleviates auditory hypersensitivity symptoms in rats.

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If activating PV+ neurons is shown to reduce auditory overload, the researchers hope to use that information to develop treatments. For example, the team aims to show that minocycline, a drug which manipulates PV+ interneurons, may be a potential treatment for sensory hypersensitivity. 

Methods and results from this study may also help with identification and diagnosis of sensory issues. Methods used to gauge the response of rats to sound could be a basis for tools to quantitatively measure sensory hypersensitivity in humans, for use in clinical trials.

In addition, this research seeks to identify a biomarker for sensory hypersensitivity — in this case, a brain signal which could be measured through an EEG — which could be used as a clinical screening tool. Many past studies which identified potential treatments for sensory overload using animal models have not translated well to humans, and finding such a biomarker may assist with this. 

“One reason for this is a lack of these behavioral and electrophysiological biomarkers that can translate between animals and humans in a very straightforward way,” Auerbach said. “Sensory systems have the potential to be a really good tool to try and provide that bridge.”

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Genetic contributions to autism spectrum disorder

1 Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway

2 Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway

3 Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway

M. Niarchou

4 Vanderbilt Genetics Institute, Vanderbilt University Medical Center, TN, USA

A. Starnawska

5 The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark

6 Department of Biomedicine, Aarhus University, Denmark

7 Center for Genomics for Personalized Medicine, CGPM, and Center for Integrative Sequencing, iSEQ, Aarhus, Denmark

8 College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE

C. van der Merwe

9 Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, MA, USA

10 Department of Psychiatry, Autism Research Centre, University of Cambridge, UK

Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism, complemented by epigenetic and transcriptomic findings. The clinical heterogeneity of autism is mirrored by a complex genetic architecture involving several types of common and rare variants, ranging from point mutations to large copy number variants, and either inherited or spontaneous ( de novo ). More than 100 risk genes have been implicated by rare, often de novo , potentially damaging mutations in highly constrained genes. These account for substantial individual risk but a small proportion of the population risk. In contrast, most of the genetic risk is attributable to common inherited variants acting en masse , each individually with small effects. Studies have identified a handful of robustly associated common variants. Different risk genes converge on the same mechanisms, such as gene regulation and synaptic connectivity. These mechanisms are also implicated by genes that are epigenetically and transcriptionally dysregulated in autism. Major challenges to understanding the biological mechanisms include substantial phenotypic heterogeneity, large locus heterogeneity, variable penetrance, and widespread pleiotropy. Considerable increases in sample sizes are needed to better understand the hundreds or thousands of common and rare genetic variants involved. Future research should integrate common and rare variant research, multi-omics data including genomics, epigenomics, and transcriptomics, and refined phenotype assessment with multidimensional and longitudinal measures.

Definition of autism

Kanner defined autism in 1943 with detailed case descriptions of children showing social aloofness, communication impairments, and stereotyped behaviors and interests, often accompanied by intellectual disability (ID) (Kanner, 1943 ). A year later, Asperger independently published an article on children presenting marked difficulties in social communication and unusually circumscribed and intense interests, despite advanced intellectual and language skills (Asperger, 1944 ). Three decades later, Wing and Gould united Asperger and Kanner's descriptions and conceptualized a spectrum of autistic conditions (Wing and Gould, 1978 , 1979 ).

The onset of autism is during the first years of life, although symptoms may not be fully apparent or recognized until later (American Psychiatric Association, 2013 ). Autism is a heterogeneous and complex group of conditions with considerable variation in core symptoms, language level, intellectual functioning, and co-occurring psychiatric and medical difficulties. Subtype diagnoses such as childhood autism and Asperger's syndrome were previously used to specify more homogeneous presentations, but were unstable over time within individuals and used unreliably by clinicians (Lord et al., 2020 ). Current editions of the major diagnostic manuals have replaced the subtypes with an overarching autism spectrum disorder diagnosis and instead require specification of key sources of heterogeneity; language level, intellectual functioning, and co-occurring conditions (APA, 2013 ; World Health Organization, 2018 ).

Epidemiology

Prevalence estimates of autism have steadily increased from less than 0.4% in the 1970s to current estimates of 1–2% (Fombonne, 2018 ; Lyall et al., 2017 ). The increase is largely explained by broadening diagnostic criteria to individuals without ID and with milder impairments, and increased awareness and recognition of autistic traits (Lord et al., 2020 ; Taylor et al., 2020 ). There are marked sex and gender differences in autism (Halladay et al., 2015 ; Warrier et al., 2020 ). The male-to-female ratio is approximately 4:1 in clinical and health registry cohorts but closer to 3:1 in general population studies with active case-finding (Loomes, Hull, & Mandy, 2017 ) and 1–2:1 in individuals with moderate-to-severe ID (Fombonne, 1999 ; Yeargin-Allsopp et al., 2003 ). The mechanisms underlying the sex difference are mostly unknown, and hypotheses include a female protective effect (aspects of the female sex conferring resilience to risk factors for autism), prenatal steroid hormone exposure, and social factors such as underdiagnosis and misdiagnosis in women (Ferri, Abel, & Brodkin, 2018 ; Halladay et al., 2015 ).

Co-occurring conditions are the rule rather than the exception, estimated to affect at least 70% of people with autism from childhood (Lai et al., 2019 ; Simonoff et al., 2008 ). Common co-occurring conditions include attention-deficit hyperactivity disorder (ADHD), anxiety, depression, epilepsy, sleep problems, gastrointestinal and immune conditions (Davignon, Qian, Massolo, & Croen, 2018 ; Warrier et al., 2020 ). There is an elevated risk of premature mortality from various causes, including medical comorbidities, accidental injury, and suicide (Hirvikoski et al., 2016 ).

Autism is also associated with positive traits such as attention to detail and pattern recognition (Baron-Cohen & Lombardo, 2017 ; Bury, Hedley, Uljarević, & Gal, 2020 ). Further, there is wide variability in course and adulthood outcomes with regard to independence, social relationships, employment, quality of life, and happiness (Howlin & Magiati, 2017 ; Mason et al., 2020 ; Pickles, McCauley, Pepa, Huerta, & Lord, 2020 ). Rigorous longitudinal studies and causally informative designs are needed to determine the factors affecting developmental trajectories and outcomes.

Environmental factors

Twin studies suggest that 9–36% of the variance in autism predisposition might be explained by environmental factors (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2016 ). There is observational evidence for association with pre- and perinatal factors such as parental age, asphyxia-related birth complications, preterm birth, maternal obesity, gestational diabetes, short inter-pregnancy interval, and valproate use (Lyall et al., 2017 ; Modabbernia, Velthorst, & Reichenberg, 2017 ). Mixed results are reported for pregnancy-related nutritional factors and exposure to heavy metals, air pollution, and pesticides, while there is strong evidence that autism risk is unrelated to vaccination, maternal smoking, or thimerosal exposure (Modabbernia et al., 2017 ). It is challenging to infer causality from observed associations, given that confounding by lifestyle, socioeconomic, or genetic factors contributes to non-causal associations between exposures and autism. Many putative exposures are associated with parental genotype (e.g. obesity, age at birth) (Gratten et al., 2016 ; Taylor et al., 2019a , Yengo et al., 2018 ), and some are associated both with maternal and fetal genotypes (e.g. preterm birth) (Zhang et al., 2017 ). Studies triangulating genetically informative designs are needed to disentangle these relationships (Davies et al., 2019 ; Leppert et al., 2019 ; Thapar & Rutter, 2019 ).

Twin and pedigree studies

In 1944, Kanner noted that parents shared common traits with their autistic children, introducing the ‘broader autism phenotype’ (i.e. sub-threshold autistic traits) and recognizing the importance of genetics (Harris, 2018 ; Kanner, 1944 ). Thirty years later, twin studies revolutionized the field of autism research (Ronald & Hoekstra, 2011 ).

Twin studies were the first to demonstrate the heritability of autism. In 1977, the first twin-heritability estimate was published, based on a study of 10 dizygotic (DZ) and 11 monozygotic (MZ) pairs (Folstein & Rutter, 1977 ). Four out of the 11 MZ pairs (36%) but none of the DZ pairs were concordant for autism. Subsequently, over 30 twin studies have been published, further supporting the high heritability of autism (Ronald & Hoekstra, 2011 ). A meta-analysis of seven primary twin studies reported that the heritability estimates ranged from 64% to 93% (Tick et al., 2016 ). The correlations for MZ twins were at 0.98 [95% confidence interval (CI) 0.96–0.99], while the correlations for DZ twins were at 0.53 (95% CI 0.44–0.60) when the autism prevalence rate was assumed to be 5% (based on the broader autism phenotype) and increased to 0.67 (95% CI 0.61–0.72) when the prevalence was 1% (based on the stricter definition) (Tick et al., 2016 ). Additionally, family studies have found that the relative risk of a child having autism relates to the amount of shared genome with affected relatives ( Fig. 1 ) (Bai et al., 2019 ; Constantino et al., 2013 ; Georgiades et al., 2013 ; Grønborg, Schendel, & Parner, 2013 ; Risch et al., 2014 ; Sandin et al., 2014 ).

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Relative risk of autism by degree of relatedness with a person with autism. Relative risk for full and half siblings, and full cousins was provided in Hansen et al. ( 2019 ). Relative risk for half first cousins was estimated based on Xie et al. ( 2019 ). GS, genome shared.

Early twin and pedigree studies demonstrated that the biological relatives of individuals with autism who did not meet the criteria for an autism diagnosis themselves commonly showed elevated autistic traits such as communication and social interaction difficulties (Le Couteur et al., 1996 ), indicating that the heritability is not restricted to the traditional diagnostic boundaries of autism. Twin studies also indicate that although social communication and repetitive behavior trait dimensions each show strong heritability, there is a limited genetic correlation between them (e.g. for a review, see Ronald & Hoekstra, 2011 ). Further, twin studies have found substantial genetic overlap between autistic traits and symptoms of other psychiatric conditions, including language delay (e.g. Dworzynski et al., 2008 ), ID (e.g. Nishiyama et al., 2009 ), ADHD (e.g. Ronald, Edelson, Asherson, & Saudino, 2010 ), and anxiety (e.g. Lundström et al., 2011 ) (for a review, see Ronald & Hoekstra, 2014 ). Moreover, twin and family studies indicate that the sibling recurrence rate of autism is lower in female than male siblings (Palmer et al., 2017 ; Werling & Geschwind, 2015 ), suggesting the female protective effect hypothesis as a potential explanation for the male preponderance in the diagnosis of autism. The hypothesis was supported by results showing that the siblings of autistic females had a higher likelihood of high autistic trait scores and autism than the siblings of autistic males (Ferri et al., 2018 ; Palmer et al., 2017 ; Robinson, Lichtenstein, Anckarsäter, Happé, & Ronald, 2013 ), consistent with females having a higher liability threshold.

Genetic variants differ in the frequency at which they occur in the population (e.g. rare v. common), the type (i.e. SNPs/CNVs/translocations and inversions/indels), and whether they are inherited or de novo . Here, we summarize the findings on genetic risk for autism from linkage and candidate gene studies, common and rare genetic variation studies, epigenomics, and transcriptomics. A glossary of important terms is in Box 1 .

Candidate gene association study: A study that examines the association between a phenotype and a genetic variant chosen a priori based on knowledge of the gene's biology or functional impact.

Complex trait: A trait that does not follow Mendelian inheritance patterns, but is likely the result of multiple factors including a complex mixture of variation within multiple genes.

Copy number variant (CNV): Deletion or duplication of large genomic regions.

de novo mutation: A mutation that is present in the offspring but is either absent in parents or is present only in parental germ cells.

DNA methylation (DNAm): Epigenetic modification of DNA characterized by the addition of a methyl group (-CH 3 ) to the 5 th position of the pyrimidine ring of cytosine base resulting in 5-methylcytosine (5mC).

Epigenetics: The science of heritable changes in gene regulation and expression that do not involve changes to the underlying DNA sequence.

Epigenome-Wide Association Study (EWAS): A study that investigates associations between DNA methylation levels quantified at tens/hundreds of thousands of sites across the human genome, and the trait of interest.

Genome-Wide Association Study (GWAS): A study scanning genome-wide genetic variants for associations with a given trait.

Genetic correlation: An estimate of the proportion of variance shared between two traits due to shared genetics.

Heritability: An estimate of the proportion of variation in a given trait that is due to differences in genetic variation between individuals in a given population.

Heritability on the liability scale : A heritability estimate adjusted for the population prevalence of a given binary trait, typically disorders.

Genetic linkage studies: A statistical method of mapping genes of heritable traits to their chromosomal locations by using chromosomal co-segregation with the phenotype.

Mendelian inheritance: When the inheritance of traits is passed down from parents to children and is controlled by a single gene for which one allele is dominant and the other recessive.

Methylation Quantitative Trait Locus (mQTL): A SNP at which genotype is correlated with the variation of DNA methylation levels at a nearby ( cis- mQTL) or distal ( trans- mQTL) site.

Phenotype: The observable characteristics of an individual.

Polygenic risk score (PRS): An estimate of an individual's genetic liability for a condition calculated based on the cumulative effect of many common genetic variants.

Single nucleotide polymorphism (SNP): A single base pair change that is common (>1%) in the population.

Single nucleotide variant (SNV): A variation in a single nucleotide without any limitation of frequency.

SNP heritability: The proportion of variance in a given phenotype in a population that is attributable to the additive effects of all SNPs tested. Typically, SNPs included have a minor allele frequency >1%.

Linkage and candidate gene studies

Initial linkage studies were conducted to identify chromosomal regions commonly inherited in affected individuals. Susceptibility loci implicated a range of regions, but only two have been replicated (Ramaswami & Geschwind, 2018 ): at chromosome 20p13 (Weiss, Arking, Daly, & Chakravarti, 2009 ) and chromosome 7q35 (Alarcón, Cantor, Liu, Gilliam, & Geschwind, 2002 ). Lack of replication and inconsistent findings were largely due to low statistical power (Kim & Leventhal, 2015 ). Candidate gene association studies identified over 100 positional and/or functional candidate genes for associations with autism (Bacchelli & Maestrini, 2006 ). However, there was no consistent replication for any of these findings (Warrier, Chee, Smith, Chakrabarti, & Baron-Cohen, 2015 ), likely due to limitations in study design (e.g. low statistical power, population diversity, incomplete coverage of variation within the candidate genes, and false positives arising from publication bias) (Ioannidis, 2005 ; Ioannidis, Ntzani, Trikalinos, & Contopoulos-Ioannidis, 2001 ). The advancement of genome-wide association studies (GWAS) and next-generation sequencing techniques has significantly enhanced gene and variant discovery.

Common genetic variation

The SNP-heritability (proportion of variance attributed to the additive effects of common genetic variants) of autism ranges from 65% in multiplex families (Klei et al., 2012 ) to 12% in the latest Psychiatric Genomics Consortium GWAS ( Fig. 2 a ) (Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium, 2017 ; Grove et al., 2019 ). Variation is largely attributable to sample heterogeneity and differences in methods used to estimate SNP-heritability.

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Variance explained by different classes of genetic variants in autism. ( a ) Donut chart of the variance explained by different classes of variants. The narrow-sense heritability (82.7%, Nordic average, shades of green) has been estimated using familial recurrence data from Bai et al. ( 2019 ). The total common inherited heritability (12%) has been estimated using LDSC-based SNP-heritability (additive) from Grove et al. ( 2019 ) and the total rare inherited heritability (3%) has been obtained from Gaugler et al. ( 2014 ). The currently unexplained additive heritability is thus 67.7% (total narrow-sense heritability minus common and rare inherited heritabilities combined). This leaves a total of 17.3% of the variance to shared and unique environmental estimates (Bai et al., 2019 ). The term environmental refers to non-additive and non-inherited factors that contribute to variation in autism liability. Of this, de novo missense and protein-truncating variants (Satterstrom et al., 2020 ) and variation in non-genic regions (An et al., 2018 ) together explain 2.5% of the variance. Whilst de novo variation can be inherited in some cases (germline mutation in the parent) and thus shared between siblings, it is unlikely that this will be shared by other related individuals, and thus unlikely to be included in the narrow-sense heritability in Bai et al. ( 2019 ). This is likely to be a lower-bound of the estimate as we have not included the variance explained by de novo structural variants and tandem repeats. Additionally, non-additive variation accounts for ~4% of the total variance (Autism Sequencing Consortium et al., 2019 ). Thus, ~11% of the total variance is currently unaccounted for, though this is likely to be an upper bound. ( b ) The variance explained is likely to change in phenotypic subgroups. For instance, the risk ratio for de novo protein-truncating variants in highly constrained genes (pLI > 0.9) is higher in autistic individuals with ID compared to those without ID (point estimates and 95% confidence intervals provided; Kosmicki et al., 2017 ). ( c ) Similarly, the proportion of the additive variance explained by common genetic variants is higher in autistic individuals without ID compared to autistic individuals with ID (Grove et al., 2019 ). Point estimates and 95% confidence intervals provided.

Early GWASs of autism were underpowered, partly due to overestimating potential effect sizes. Grove et al. ( 2019 ) conducted a large GWAS of autism combining data from over 18 000 autistic individuals and 27 000 non-autistic controls and an additional replication sample. They identified five independent GWAS loci ( Fig. 3 ). Another recent study (Matoba et al., 2020 ) identified a further novel locus by meta-analyzing the results from Grove et al. ( 2019 ) with over 6000 case-pseudocontrol pairs from the SPARK cohort by employing a massively parallel reporter assay to identify a potential causal variant (rs7001340) at this locus which regulates DDH2 in the fetal brain. The sample sizes are still relatively small compared to other psychiatric conditions (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2020 ; Howard et al., 2019 ), though ongoing work aims to double the sample size and identify additional loci.

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Karyogram showing the 102 genes implicated by rare variant findings at a false discovery rate of 0.1 or less (Satterstrom et al., 2020 ) and the five index SNPs identified in GWAS (Grove et al., 2019 ) of autism.

Using genetic correlations and polygenic score analyses, studies have identified modest shared genetics between autism and different definitions of autistic traits in the general population (Askeland et al., 2020 ; Bralten et al., 2018 ; Robinson et al., 2016 ; Taylor et al., 2019 b ). There is some evidence for developmental effects, with greater shared genetics in childhood compared to adolescence (St Pourcain et al., 2018 ). These methods have also identified modest polygenic associations between autism and other neurodevelopmental and mental conditions such as schizophrenia, ADHD, and major depressive disorder, related traits such as age of walking, language delays, neuroticism, tiredness, and self-harm, as well as risk of exposure to childhood maltreatment and other stressful life events (Brainstorm Consortium et al., 2018 ; Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ; Hannigan et al., 2020 ; Lee et al., 2019 , b ; Leppert et al., 2019 ; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 ; Warrier & Baron-Cohen, 2019 ). Notably, autism is positively genetically correlated with measures of intelligence and educational attainment (EA) (Bulik-Sullivan et al., 2015 ; Grove et al., 2019 ), an observation supported by polygenic score association (Clarke et al., 2016 ). Polygenic Transmission Disequilibrium Tests have identified an over-transmission of polygenic scores for EA, schizophrenia, and self-harm from parents to autistic children, but an absence of such over-transmission to non-autistic siblings (Warrier & Baron-Cohen, 2019 ; Weiner et al., 2017 ), suggesting that these genetic correlations are not explained by ascertainment biases or population stratification. However, a genetic correlation does not necessarily imply a causal relationship between the two phenotypes and may simply index biological pleiotropy. Causal inference methods such as Mendelian randomization can be used to disentangle such relationships (Davies et al., 2019 ; Pingault et al., 2018 ).

The relatively low SNP-heritability in autism compared to other psychiatric conditions may partly be due to phenotypic heterogeneity. In an attempt to reduce phenotypic heterogeneity, Chaste et al. ( 2015 ) identified 10 phenotypic combinations to subgroup autistic individuals. Family-based association analyses did not identify significant loci, and SNP-heritability for the subgroups was negligent. It is unclear if reducing phenotypic heterogeneity increases genetic homogeneity, and investigating this in larger samples is warranted. Another study identified no robust evidence of genetic correlation between social and non-social (restricted and repetitive behavior patterns) autistic traits (Warrier et al., 2019 ). A few studies have investigated the common variant genetic architecture of social and non-social autistic traits in individuals with autism (Alarcón et al., 2002 ; Cannon et al., 2010 ; Cantor et al., 2018 ; Lowe, Werling, Constantino, Cantor, & Geschwind, 2015 ; Tao et al., 2016 ; Yousaf et al., 2020 ) and in the general population (St Pourcain et al., 2014 ; Warrier et al., 2018 , 2019 ), but replication of the identified loci is needed.

Diagnostic classification is another source of heterogeneity: SNP-heritability of Asperger's syndrome (ICD-10 diagnosis) was twice (0.097 ± 0.001) that of childhood autism and unspecified pervasive developmental disorders (Grove et al., 2019 ) [due to overlap in subtype diagnoses, a hierarchy was used: childhood autism>atypical autism>Asperger's syndrome>unspecified subtypes (Grove et al., 2019 )]. Supporting this, polygenic scores for intelligence and EA had larger loadings in the Asperger's syndrome and childhood autism subgroups compared to other subgroups (Grove et al., 2019 ). Additionally, the SNP-heritability of autism (all subtypes) without co-occurring ID diagnosis (0.09 ± 0.005) was three times that of autism with ID (Grove et al., 2019 ) ( Fig. 2 c ).

Rare genetic variation

Rare genetic variants confer significant risk in the complex etiology of autism. They are typically non-Mendelian, with substantial effect sizes and low population attributable risk. It is estimated that ~10% of autistic individuals have been diagnosed with an identifiable rare genetic syndrome characterized by dysmorphia, metabolic, and/or neurologic features (Carter & Scherer, 2013 ; Tammimies et al., 2015 ). Associated syndromes include the 15q11-q13 duplication of the Prader-Willi/Angelman syndrome, fragile X syndrome, 16p11.2 deletion syndrome, and 22q11 deletion syndrome (Sztainberg & Zoghbi, 2016 ). Prevalence estimates for autism vary widely between genetic syndromes; for example, 11% in 22q11.2 deletion syndrome and 54% in Cohen's syndrome (Richards, Jones, Groves, Moss, & Oliver, 2015 ). Of note, estimating the prevalence of autism in the context of genetic syndromes is complex (Havdahl et al., 2016 ; Richards et al., 2015 ).

The rate of gene discovery in autism is a linear function of increasing sample size (De Rubeis et al., 2014 ). Early studies implicated nine genes in the first 1000 autism cases (Neale et al., 2012 ; Sanders et al., 2012 ), increasing to 27 and 33 associated genes from separate analyses of Simons Simplex Collection and Autism Sequencing Consortium (ASC) samples (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Integrating these samples using the TADA framework implicated a total of 65 autism genes (Sanders et al., 2015 ).

The MSSNG initiative analyzed whole genomes from 5205 individuals ( N cases  = 2636), and identified 61 autism-risk genes, of which 18 were new candidates (Yuen et al., 2017 ). More recently, the largest whole-exome sequencing analysis to date conducted by the ASC ( N  = 35 584, N cases  = 11 986) identified 102 autism-associated genes ( Fig. 3 ), many of which are expressed during brain development with roles in the regulation of gene expression and neuronal communication (Satterstrom et al., 2020 ). Rare CNVs and SNVs associated with autism have pleiotropic effects, thus increasing the risk for other complex disorders such as schizophrenia, ADHD, ID, and epilepsy (Gudmundsson et al., 2019 ; Satterstrom et al., 2019 , 2020 ).

CNVs can impact one or multiple genes and can occur at common or rare frequencies in a population. All CNVs associated with autism have been rare. Recurrent CNVs are among the most convincing rare inherited risk variations for autism, and have a prevalence of about 3% in affected patients (Bourgeron, 2016 ). In comparison, approximately 4–10% of autistic individuals have de novo deletions or duplications (Bourgeron, 2016 ; Pinto et al., 2010 ; Sebat et al., 2007 ) frequently mapped to established risk loci 1q21.1, 3q29, 7q11.23, 15q11.2-13, and 22q11.2 (Sanders et al., 2015 ). A higher global frequency of de novo CNVs is observed in idiopathic autism cases from simplex families (10%) compared to multiplex families (2%) and controls (1%) (Halladay et al., 2015 ; Itsara et al., 2010 ; Sebat et al., 2007 ). Inherited CNVs can be present in unaffected siblings and parents, suggesting a model of incomplete penetrance dependent on the dosage sensitivity and function of the gene(s) they affect (Vicari et al., 2019 ).

Damaging SNVs include nonsense, frameshift, and splice site mutations (collectively referred to as protein-truncating variants, or PTVs), and missense variants. Rare inherited variants have a smaller average effect size and reduced penetrance compared to de novo pathogenic mutations. Early studies on whole exomes from trios established a key role for de novo germline mutations in autism. Whilst analysis in smaller sample sizes indicated only modest increase in de novo mutation rates in autism cases (Neale et al., 2012 ), the rate rose significantly in excess of expectation as the sample size increased (De Rubeis et al., 2014 ; Iossifov et al., 2014 ). Most recently, the ASC observed a 3.5-fold case enrichment of damaging de novo PTVs and a 2.1-fold enrichment for damaging de novo missense variants (Satterstrom et al., 2020 ), concluding that all exome de novo SNVs explain 1.92% of the variance in autism liability (Satterstrom et al., 2020 ) ( Fig. 2 a ).

Comparatively, the ASC discovered a 1.2-fold enrichment of rare inherited damaging PTVs in cases compared to unaffected siblings (Satterstrom et al., 2020 ). Similarly, recent whole-genome analysis found no excess of rare inherited SNVs, and no difference in the overall rate of these variants in affected subjects compared to unaffected siblings (Ruzzo et al., 2019 ).

New advancements

It is estimated that de novo mutations in protein-coding genes contribute to risk in ~30% of simplex autism cases (Yuen et al., 2017 ; Zhou et al., 2019 ). However, recent work has also shown that de novo mutations in non-coding regions of the genome (particularly gene promoters) contribute to autism (An et al., 2018 ; Zhou et al., 2019 ). Adapting machine learning techniques may be key to providing novel neurobiological insights to the genetic influences on autism in the future (An et al., 2018 ; Ruzzo et al., 2019 ; Zhou et al., 2019 ). Additionally, rare tandem repeat expansions in genic regions are more prevalent among autism cases than their unaffected siblings, with a combined contribution of ~2.6% to the risk of autism (Trost et al., 2020 ).

Common and rare variant interplay

The largest component of genetic risk is derived from common variants of additive effect with a smaller contribution from de novo and rare inherited variation ( Fig. 2 a ) (de la Torre-Ubieta, Won, Stein, & Geschwind, 2016 ; Gaugler et al., 2014 ). Notably, KMT2E was implicated in both the latest GWAS (Grove et al., 2019 ) and exome sequencing (Satterstrom et al., 2020 ) analyses. It is hypothesized that common genetic variation in or near the genes associated with autism influences autism risk, although current sample sizes lack the power to detect the convergence of the two (Satterstrom et al., 2020 ).

Whilst higher SNP-heritability is observed in autistic individuals without ID ( Fig. 2 b ), de novo PTVs in constrained genes are enriched in autistic individuals with ID ( Fig. 2 a ). However, the genetic architecture of autism is complex and diverse. For example, common genetic variants also contribute to risk in autistic individuals with ID and in autistic individuals carrying known large-effect de novo variants in constrained genes (Weiner et al., 2017 ). Furthermore, an excess of disruptive de novo variants is also observed in autistic individuals without co-occurring ID compared to non-autistic individuals (Satterstrom et al., 2020 ).

Epigenetics

DNA methylation (DNAm), an epigenetic modification, allows for both genetic and environmental factors to modulate a phenotype (Martin & Fry, 2018 ; Smith et al., 2014 ). DNAm affects gene expression, regulatory elements, chromatin structure, and alters neuronal development, functioning, as well as survival (Kundaje et al., 2015 ; Lou et al., 2014 ; Peters et al., 2015 ; Sharma, Klein, Barboza, Lohdi, & Toth, 2016 ; Yu et al., 2012 ; Zlatanova, Stancheva, & Caiafa, 2004 ). Additionally, putative prenatal environmental risk factors impact the offspring's methylomic landscape (Anderson, Gillespie, Thiele, Ralph, & Ohm, 2018 ; Cardenas et al., 2018 ; Joubert et al., 2016 ), thus providing a plausible molecular mechanism to modulate the neurodevelopmental origins of autism.

Autism Epigenome-Wide Association Study (EWAS) meta-analysis performed in blood from children and adolescents from SEED and SSC cohorts ( N cases  = 796, N controls  = 858) identified seven differentially methylated positions (DMPs) associated ( p  < 10 × 10 −05 ) with autism, five of them also reported to have brain-based autism associations. The associated DMPs annotated to CENPM , FENDRR , SNRNP200 , PGLYRP4 , EZH1 , DIO3 , and CCDC181 genes, with the last site having the largest effect size and the same direction of association with autism across the prefrontal cortex, temporal cortex, and cerebellum (Andrews et al., 2018 ). The study reported moderate enrichment of methylation Quantitative Trait Loci (mQTLs) among the associated findings, suggesting top autism DMPs to be under genetic control (Andrews et al., 2018 ). These findings were further extended by the MINERvA cohort that added 1263 neonatal blood samples to the meta-analysis. The SEED-SSC-MINERvA meta-EWAS identified 45 DMPs, with the top finding showing the consistent direction of association across all three studies annotated to ITLN1 (Hannon et al., 2018 ). The MINERvA sample was also used for EWAS of autism polygenic score, hypothesizing that the polygenic score-associated DNAm variation is less affected by environmental risk factors, which can confound case–control EWAS. Elevated autism polygenic score was associated with two DMPs ( p  < 10 × 10 −06 ), annotated to FAM167A / C8orf12 and RP1L1 . Further Bayesian co-localization of mQTL results with autism GWAS findings provided evidence that several SNPs on chromosome 20 are associated both with autism risk and DNAm changes in sites annotated to KIZ , XRN2 , and NKX2-4 (Hannon et al., 2018 ). The mQTL effect of autism risk SNPs was corroborated by an independent study not only in blood, but also in fetal and adult brain tissues, providing additional evidence that autism risk variants can act through DNAm to mediate the risk of the condition (Hammerschlag, Byrne, Bartels, Wray, & Middeldorp, 2020 ).

Since autism risk variants impact an individual's methylomic landscape, studies that investigate DNAm in the carriers of autism risk variants are of interest to provide insight into their epigenetic profiles. A small blood EWAS performed in 52 cases of autism of heterogeneous etiology, nine carriers of 16p11.2del, seven carriers of pathogenic variants in CHD8 , and matched controls found that DNAm patterns did not clearly distinguish autism of the heterogeneous etiology from controls. However, the homogeneous genetically-defined 16p11.2del and CHD8 +/− subgroups were characterized by unique DNAm signatures enriched in biological pathways related to the regulation of central nervous system development, inhibition of postsynaptic membrane potential, and immune system (Siu et al., 2019 ). This finding highlights the need to combine genomic and epigenomic information for a better understanding of the molecular pathophysiology of autism.

It must be noted that a very careful interpretation of findings from peripheral tissues is warranted. DNAm is tissue-specific and therefore EWAS findings obtained from peripheral tissues may not reflect biological processes in the brain. Using the mQTL analytical approach may reduce this challenge, as mQTLs are consistently detected across tissues, developmental stages, and populations (Smith et al., 2014 ). However, not all mQTLs will be detected across tissues and will not necessarily have the same direction of effect (Smith et al., 2014 ). Therefore, it is recommended that all epigenetic findings from peripheral tissues are subjected to replication analyses in human brain samples, additional experimental approaches, and/or Mendelian randomization to strengthen causal inference and explore molecular mediation by DNAm (Walton, Relton, & Caramaschi, 2019 ).

EWASs performed in post-mortem brains have typically been conducted using very small sample sizes, due to limited access to brain tissue (Ladd-Acosta et al., 2014 ; Nardone et al., 2014 ). One of the largest autism EWAS performed in post-mortem brains (43 cases and 38 controls) identified multiple DMPs ( p  < 5 × 10 −05 ) associated with autism (31 DMPs in the prefrontal cortex, 52 in the temporal cortex, and two in the cerebellum) (Wong et al., 2019 ), and autism-related co-methylation modules to be significantly enriched for synaptic, neuronal, and immune dysfunction genes (Wong et al., 2019 ). Another post-mortem brain EWAS reported DNAm levels at autism-associated sites to resemble the DNAm states of early fetal brain development (Corley et al., 2019 ). This finding suggests an epigenetic delay in the neurodevelopmental trajectory may be a part of the molecular pathophysiology of autism.

Overall, methylomic studies of autism provide increasing evidence that common genetic risk variants of autism may alter DNAm across tissues, and that the epigenetic dysregulation of neuronal processes can contribute to the development of autism. Stratification of study participants based on their genetic risk variants may provide deeper insight into the role of aberrant epigenetic regulation in subgroups within autism.

Transcriptomics

Transcriptomics of peripheral tissues.

Gene expression plays a key role in determining the functional consequences of genes and identifying genetic networks underlying a disorder. One of the earliest studies on genome-wide transcriptome (Nishimura et al., 2007 ) investigated blood-derived lymphoblastoid cells gene expression from a small set of males with autism ( N  = 15) and controls. Hierarchical clustering on microarray expression data followed by differentially expressed gene (DEG) analysis revealed a set of dysregulated genes in autism compared to controls. This approach was adopted (Luo et al., 2012 ) to investigate DEGs in a cohort of 244 families with autism probands (index autism case in a family) known to carry de novo pathogenic or variants of unknown significance and discordant sibling carriers of non-pathogenic CNVs. From genome-wide microarray transcriptome data, this study identified significant enrichment of outlier genes that are differentially expressed and reside within the proband rare/ de novo CNVs. Pathway enrichment of these outlier genes identified neural-related pathways, including neuropeptide signaling, synaptogenesis, and cell adhesion. Distinct expression changes of these outlier genes were identified in recurrent pathogenic CNVs, i.e. 16p11.2 microdeletions, 16p11.2 microduplications, and 7q11.23 duplications. Recently, multiple independent genome-wide blood-derived transcriptome analysis (Filosi et al., 2020 ; Lombardo et al., 2018 ; Tylee et al., 2017 ) showed the efficiency of detecting dysregulated genes in autism, including aberrant expression patterns of long non-coding RNAs (Sayad, Omrani, Fallah, Taheri, & Ghafouri-Fard, 2019 ).

Transcriptomics of post-mortem brain tissue

Although blood-derived transcriptome can be feasible to study due to easy access to the biological specimen, blood transcriptome results are not necessarily representative of the transcriptional machinery in the brain (GTEx Consortium, 2017 ). Hence, it is extremely hard to establish a causal relationship between blood transcriptional dysregulations and phenotypes in autism. A landmark initiative by Allen Brain Institute to profile human developing brain expression patterns (RNA-seq) from post-mortem tissue enabled neurodevelopmental research to investigate gene expression in the brain (Sunkin et al., 2013 ). Analyzing post-mortem brain tissue, multiple studies identified dysregulation of genes at the level of gene exons impacted by rare/ de novo mutations in autism (Uddin et al., 2014 ; Xiong et al., 2015 ), including high-resolution detection of exon splicing or novel transcript using brain tissue RNA sequencing (RNA-seq). High-resolution RNA-seq enabled autism brain transcriptome analysis on non-coding elements, and independent studies identified an association with long non-coding RNA and enhancer RNA dysregulation (Wang et al., 2015 ; Yao et al., 2015 ; Ziats & Rennert, 2013 ).

Although it is difficult to access post-mortem brain tissue from autistic individuals, studies of whole-genome transcriptome from autism and control brains have revealed significantly disrupted pathways ( Fig. 4 ) related to synaptic connectivity, neurotransmitter, neuron projection and vesicles, and chromatin remodeling pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ). Recently, an integrated genomic study also identified from autism brain tissue a component of upregulated immune processes associated with hypomethylation (Ramaswami et al., 2020 ). These reported pathways are in strong accordance with numerous independent autism studies that integrated genetic data with brain transcriptomes (Courchesne, Gazestani, & Lewis, 2020 ; Uddin et al., 2014 ; Yuen et al., 2017 ). A large-scale analysis of brain transcriptome from individuals with autism identified allele-specific expressions of genes that are often found to be impacted by pathogenic de novo mutations (Lee et al., 2019 a ). The majority of the studies are in consensus that genes that are highly active during prenatal brain development are enriched for clinically relevant mutations in autism (Turner et al., 2017 ; Uddin et al., 2014 ; Yuen et al., 2017 ). Recently, a large number (4635) of expression quantitative trait loci were identified that were enriched in prenatal brain-specific regulatory regions comprised of genes with distinct transcriptome modules that are associated with autism (Walker et al., 2019 ).

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Most commonly reported three pathways (Ayhan & Konopka, 2019 ; Gordon et al., 2019 ; Voineagu et al., 2011 ) associated with autism. ( a ) The synaptic connectivity and neurotransmitter pathway involves genes (yellow rectangular box) within presynaptic and postsynaptic neurons. Neurotransmitter transport through numerous receptors is an essential function of this pathway; ( b ) the chromatin remodeling pathway involves binding of remodeling complexes that initiate the repositioning (move, eject, or restructure) of nucleosomes that potentially can disrupt gene regulation; and ( c ) the neural projection pathway [adapted from Greig, Woodworth, Galazo, Padmanabhan, & Macklis ( 2013 )] involves the projection of neural dendrite into distant regions and the migration of neuronal cells through ventricular (VZ) and subventricular zones (SVZ) into the different cortical layers (I-VI).

Single-cell transcriptomics

Recent advancement of single-cell transcriptomics enables the detection of cell types that are relevant to disorder etiology. A recent case–control study conducted single-cell transcriptomics analysis on 15 autism and 16 control cortical post-mortem brain tissues generating over 100 000 single-cell transcriptomics data (Velmeshev et al., 2019 ). Cell-type analysis revealed dysregulations of a specific group of genes in cortico-cortical projection neurons that correlate with autism severity (Velmeshev et al., 2019 ). Deciphering cell-type identification has future implications, in particular for the implementation of precision medicine. However, single-cell technology is at very early stages of development and computationally it is still very complex to classify cell-type identity.

The emergence of CRISPR/Cas9 genome editing technology can potentially become an effective tool in future therapeutics of genetic conditions associated with autism. Although introducing and reversing DNA mutation is becoming a mature technology within in vitro systems, much work needs to be done for in vivo use of genome editing. Single-cell OMICs is another emerging field that has the potential to decipher developmental (spatio-temporally) brain cell types that are associated with autism. Identifying cell clusters and defining cell identity is a major computational challenge. Artificial intelligence can significantly improve these computational challenges to identify the molecular associations of autism at the single-cell level.

Clinical and therapeutic implications

In some, but not all, best practice clinical guidelines, genetic tests such as fragile X testing, chromosomal microarray, and karyotype testing are part of the standard medical assessment in a diagnostic evaluation of autism to identify potentially etiologically relevant rare genetic variants (Barton et al., 2018 ). The guidelines vary with respect to whether genetic testing is recommended for all people with autism, or based on particular risk factors, such as ID, seizures, or dysmorphic features. The DSM-5 diagnosis of autism includes a specifier for associated genetic conditions (APA, 2013 ). Although genetic test results may not usually have consequences for treatment changes, the results could inform recurrence risk and provide families with access to information about symptoms and prognosis. In the future, gene therapy, CRISPR/Cas9, and genome editing technologies may lead to the gene-specific design of precision medicine for rare syndromic forms of autism (Benger, Kinali, & Mazarakis, 2018 ; Gori et al., 2015 ).

Given that a substantial proportion of the genetic liability to autism is estimated to be explained by the cumulative effect of a large number of common SNPs, polygenic scores have gained traction as potential biomarkers. However, the predictive ability of polygenic scores from the largest autism GWAS to date is too low to be clinically useful. The odds ratio when comparing the top and bottom polygenic score decile groups is only 2.80 (95% CI 2.53–3.10) (Grove et al., 2019 ). Additionally, polygenic scores based on the samples of European ancestry do not translate well in populations with diverse ancestry (Palk, Dalvie, de Vries, Martin, & Stein, 2019 ).

Genetic testing can in the future become useful for informing screening or triaging for diagnostic assessments or identifying who may be more likely to respond to which type of intervention (Wray et al., 2021 ). Genetics may also help identify individuals with autism who are at a high risk of developing co-occurring physical and mental health conditions or likely to benefit from treatments of such conditions. A top research priority for autistic people and their families is addressing co-occurring mental health problems (Autistica, 2016 ), which may sometimes be the primary treatment need as opposed to autism per se . Genomics may also be helpful to repurpose existing treatments and better identify promising treatments. There are active clinical trials to repurpose drugs in autism (Hong & Erickson, 2019 ). Moreover, genetics can be used to identify social and environmental mediating and moderating factors (Pingault et al., 2018 ), which could inform interventions to improve the lives of autistic people.

Notably, there are important ethical challenges related to clinical translation of advances in genetics, including concerns about discriminatory use, eugenics concerning prenatal genetic testing, and challenges in interpretation and feedback (Palk et al., 2019 ). People with autism and their families are key stakeholders in genetic studies of autism and essential to include in discussions of how genetic testing should be used.

Conclusions and future directions

Recent large-scale and internationally collaborative investigations have led to a better understanding of the genetic contributions to autism. This includes identifying the first robustly associated common genetic variants with small individual effects (Grove et al., 2019 ) and over 100 genes implicated by rare, mostly de novo , variants of large effects (Sanders et al., 2015 ; Satterstrom et al., 2020 ). These and other findings show that the genetic architecture of autism is complex, diverse, and context-dependent, highlighting a need to study the interplay between different types of genetic variants, identify genetic and non-genetic factors influencing their penetrance, and better map the genetic variants to phenotypic heterogeneity within autism.

Immense collaborative efforts are needed to identify converging and distinct biological mechanisms for autism and subgroups within autism, which can in turn inform treatment (Thapar & Rutter, 2020 ). It is crucial to invest in multidimensional and longitudinal measurements of both core defining traits and associated traits such as language, intellectual, emotional, and behavioral functioning, and to collaboratively establish large omics databases including genomics, epigenomics, transcriptomics, proteomics, and brain connectomics (Searles Quick, Wang, & State, 2020 ). Indeed, large-scale multi-omic investigations are becoming possible in the context of large population-based family cohorts with rich prospective and longitudinal information on environmental exposures and developmental trajectories of different neurodevelopmental traits. Finally, novel methods (Neumeyer, Hemani, & Zeggini, 2020 ) can help investigate causal molecular pathways between genetic variants and autism and autistic traits.

Acknowledgements

We thank the Psychiatric Genomics Consortium, Anders Børglum, and Elise Robinson for their support and advice.

Financial support

Alexandra Havdahl was supported by the South-Eastern Norway Regional Health Authority (#2018059, career grant #2020022) and the Norwegian Research Council (#274611 PI Ted Reichborn-Kjennerud and #288083 PI Espen Røysamb). Maria Niarchou was supported by Autism Speaks (#11680). Anna Starnawska was supported by The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Denmark (R155-2014-1724). Varun Warrier is supported by the Bowring Research Fellowship (St. Catharine's College, Cambridge), the Templeton World Charity Foundation, Inc., the Autism Research Trust, and the Wellcome Trust. Celia van der Merwe is supported by the Simons Foundation NeuroDev study (#599648) and the NIH R01MH111813 grant.

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For children on the autism spectrum, having a service dog can lead to sleep improvements

by Stephanie Baum , Medical Xpress

service dog

Service dogs, originally trained to assist physically disabled individuals with guidance, physical tasks, and medical alerts, have more recently begun to provide psychiatric assistance to individuals with conditions such as schizophrenia, and post-traumatic stress, obsessive-compulsive, and—especially in children—autism spectrum disorders (also known as ASD or autism).

Children with autism may exhibit difficulty with verbal and/or non-verbal communication and social interaction . In some individuals, the condition may also drive repetitive actions or lead to restricted behaviors. However, until now there has been little research to quantify the kinds of benefits children with autism spectrum disorder and their caregivers derive from their service dogs .

To expand the literature, a team of researchers from the University of Arizona, the University of Virginia, and Purdue University has conducted a study to explore this topic in more detail. The work appears in Frontiers in Psychiatry .

The researchers hypothesized that "compared to those on the waitlist to receive a service dog, families with a service dog in the home would exhibit superior functioning in [standardized] measured domains" of psychosocial functioning for individuals on the autism spectrum.

The team also sought to understand how families' time spent living with service dogs, bonds between children and service dogs as well as between caregivers and service dogs, and families' perceived costs of service dogs might play a part in outcomes for children and caregivers.

In addition to need-specific educational interventions, some families arrange animal-assisted interventions (AAIs), in which a child can interact with a dog, horse, or other domestic animal that helps to create a general calming atmosphere. This may help improve children's abilities to communicate and interact socially, improve their attention span and emotions, and reduce their stress, negative arousal tendencies and aggressive behavior .

On this basis, some families opt to have a service dog placed in their homes. According to the research, as of 2022, there were 64 organizations placing service dogs throughout the world with accreditation from Assistance Dog International (ADI) to specifically assist individuals with autism.

Such dogs can be specifically trained to help relieve sensory overload; interrupt repetitive behaviors; deliver deep, calming pressure; assist with certain daily activities and development of motor skills; and smooth the way for social interactions.

Qualitative results from previous studies also report that autism service dogs have "helped prevent or interrupt tantrums, improve sleep behaviors, prevent elopement behavior in public, and act as a calming and comforting presence," according to this new research paper. Moreover, there is evidence to suggest that service dogs provide stress relief and a sense of security for caregivers of children with autism (often their parents), as well as reducing isolation and making it easier to have longer and more frequent family outings.

For this study, the researchers recruited families from the database of Canine Companions, an ADI-accredited nonprofit organization that trains and provides cost-free service and assistance dogs to families in need throughout the U.S. Each eligible family had a child between 5 and 18 years old with an autism diagnosis. The study group included 39 families whose dogs had been in their homes for at least six months before the study, and the control group included 36 families meeting the eligibility criteria who were on the waitlist to have a dog placed in their homes.

Of the study children, 72% were male between ages 5 and 17. Three-quarters of the study children had limited verbal ability and 60% had developmental delays. Learning disabilities and attention deficits were present in 49% of these children.

Among the group, treatments included applied behavior analysis, language and speech therapy, occupational therapy, social skills training, and various measures implemented by parents. Many of the children took one or more medications.

Participating families in both the study group and the control group completed questionnaires assessing the children's social communication, sleep habits, behaviors, and peer relationships. In both groups, caregivers completed questionnaires about caregiver strain, sleep disturbances, caregiver depression, and family functioning with respect to daily activities and family relationships.

Families in the study group additionally provided their perceptions of the costs of having a service dog and of the bonds between both the children and the dog and the caregivers and the dog.

Among the notable results, the researchers found an association between the presence of service dogs and better sleep behaviors in the study group children, including positive sleep initiation, longer sleep, and reduced sleep anxiety and co-sleeping behaviors [with caregivers]. The team notes that these findings "support the hypothesis that service dogs provide a sense of security and comfort to a child with autism at night, which may translate into exhibiting less sleep anxiety and co-sleeping behavior with a caregiver."

Interestingly, the findings also showed that children with higher measures of social functioning were more strongly bonded to their service dogs. "It may be that children/adolescents with more verbal and nonverbal communication skills tend to interact with or talk to their service dog more, leading to higher caregiver perceptions of the child-dog bond," the researchers write.

However, contrary to their hypothesis, the team did not find appreciable associations between having a service dog and the children's peer relationships or their emotional and social behaviors; nor between having a service dog and caregiver sleep and strain; nor between having a service dog and family functioning.

Nevertheless, the caregivers who felt that having the dog translated to higher costs for them—in the areas of finance, responsibility, and restrictions associated with having the dog—reported increased strain.

Among caregivers who were more closely bonded with the service dogs, the team found increased negative effects from the children's condition on family activities and relationships. "This may be due to the possibility that caregivers experiencing familial difficulties may be more likely to turn to the service dog as a source of support," the researchers note.

The team cautions that as this study was not longitudinal and did not include systematic demographic matching between groups, further research should include these features. Caregiver reporting may have been subject to self-reporting biases, and the children in the study could not self-report; thus future work should include objective measures and methodologies.

Additionally, the sample size was low and did not represent the general population of families with children on the autism spectrum; and the study occurred during the COVID-19 pandemic, which may have affected the results.

On a positive note, the researchers conclude, "This exploratory cross-sectional study found that having a service dog was associated with better child sleep behaviors , suggesting that this should be a focus of increased research in this area. Specifically, research should further explore the effects of service dogs on child sleep quality, quantity, and disturbances using objective methods."

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Supporting People with Autism Spectrum Disorders in the Exploration of PoIs: An Inclusive Recommender System

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1. Introduction

2. spatial needs of autistic people, 3. related work, 4. preliminary study setup, 5. recommendation model, 6. validation methodology, 7. evaluation results, 8. discussion, 9. conclusion, acknowledgments.

autism puzzle with point-of-interest icon

The suggestion of Points of Interest (PoIs) to people with autism spectrum disorders challenges the research about recommender systems by introducing an explicit need to consider both user preferences and aversions in item evaluation. The reason is that autistic users’ perception of places is influenced by sensory aversions, which can cause stress and anxiety when they visit the suggested PoIs. Therefore, the management of individual preferences is not enough to provide these people with suitable recommendations.

To address this issue, we propose a Top-N recommendation model that combines information about the user’s idiosyncratic aversions with her/his preferences in a personalized way. The goal is that of suggesting the places that (s)he can like and smoothly experience at the same time. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account.

We tested our model on 148 adults, 20 of which were people with autism spectrum disorders. The evaluation results show that, on both groups, our model achieves superior accuracy and ranking results than the recommender systems based on item compatibility, on user preferences, or which integrate these aspects using a uniform evaluation model. These findings encourage us to use our model as a basis for the development of inclusive recommender systems.

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The personalized suggestion of Points of Interest (PoIs) to fragile users challenges the development of recommender systems 19 by broadening the factors to be taken into account in the identification of the most suitable items for the individual user. For instance, people with autism spectrum disorders, who are the main target of this work, have idiosyncratic sensory aversions to noise, brightness, and other sensory features, which influence the way they perceive items, especially places. 20 Thus, a recommender system that overlooks these aversions could suggest PoIs that cause a high level of stress and anxiety on the user. 7 In order to address this issue, the preference data traditionally used to personalize item recommendation should be combined with information about people’s aversions to estimate the likelihood that, rather than only being interested in exploring the suggested places, they can serenely experience them.

Starting from Multi-Criteria Decision Analysis, 25 which provides techniques for the evaluation of multiple dimensions of items, and on match-making models based on user-to-item similarity, 12 most recommender systems assume that the attributes of an item contribute to its utility to the user in an additive way. However, depending on individual idiosyncrasies and their strength, problematic features might make an item unsuitable, even though it meets the user’s preferences. Moreover, the impact of compatibility on decision-making varies individually and it cannot be separately managed. For instance, some people with autism are determined to visit noisy and crowded places if they like them very much. Therefore, inclusive recommendation models must reflect personal evaluation criteria by balancing feature compatibility and preference satisfaction at the individual level. In the present work, we investigate the role of these two types of information in the personalized suggestion of PoIs to users with, or without, autism spectrum disorders (neurotypical users). We propose a novel Top-N recommender system that applies heterogeneous evaluation criteria to take user preferences and compatibility requirements into account, by exploiting feature-based user profiles for the specification of individual needs.

Our work has two key aspects. Firstly, we acquire data about people’s aversion to sensory features of places in terms of disturbance caused by low or high feature values, for example, darkness or strong light. In this task, we try to limit the amount of information elicited from people as much as possible. For this purpose, we employ a questionnaire derived from Tavassoli, 23 which provides data about a user’s aversion to a subset of the values that each feature can take. Then, we interpolate her/his aversion to the whole range of values and we derive the compatibility of the feature as the complement of aversion. Secondly, for the estimation of item ratings, we distinguish user preferences for broad categories of places from idiosyncratic sensory aversions. Moreover, as users might balance differently these aspects in item evaluation, we combine preferences and compatibility by applying user-specific weights, which we acquire by analyzing users’ ratings, in conjunction with their declared preferences and idiosyncrasies.

An important challenge in the development of this type of system is that it must work under data scarcity because few users can be studied to learn their interests. Research studies indicate that autism spectrum disorders affect around 1 in 100 people in Europe. 5 Moreover, these people can be hardly contacted because they have interaction problems and a tendency to avoid new experiences. Finally, their attention problems cause difficulties in providing detailed feedback about items. 13

We tested our model on 148 adults: 20 of them were people with autism, while we did not have any information about the others. However, we can reasonably expect that the second sample respected the proportion of the entire population, including at most 1 or 2 autistic people. On both groups of participants, the accuracy and ranking capability achieved by our model was higher than that of a set of baseline recommender systems that singularly take item compatibility, or user preferences, into account. Moreover, our system outperformed baseline models that uniformly manage compatibility and preference information, without differentiating their contributions.

The approach presented in this paper is part of the Personalized Interactive Urban Maps for Autism project (PIUMA), which aims at developing novel digital solutions to help people with autism in their everyday movements. 18 PIUMA involves a collaboration among the Computer Science and Psychology Departments of the University of Torino, and the Adult Autism Center of the city of Torino, Italy. The result of this project is a mobile app that manages dynamic geographical maps specifically conceived for users with autism spectrum disorders, but which target neurotypical people, as well. 3

The remainder of this paper is organized as follows: first, we discuss the spatial needs of autistic people (Section 2), and we position our work in the related one (Section 3). Section 4 outlines how we gather data about users and PoIs, and Section 5 presents our model. Section 6 describes the validation methodology we applied to test our model. Sections 7 and 8 present and discuss the evaluation results. At last, Section 9 concludes the paper.

Symptoms of autism span from severe language and intellectual disabilities to the absence of disabilities, and an Intelligence Quotient above the average. Autism entails an atypical sensory perception in over 90% of individuals, 23 who can be overwhelmed by environmental factors that are easily managed by neurotypical subjects. At least in part because of these characteristics, people with autism spectrum disorders actively avoid places that may negatively impact their senses. 22 Sight, smell, and hearing are relevant to mobility in urban environments, and high sensory stimulation negatively influences individuals in their movements. Further relevant environmental dimensions that could impact the sense of safeness are the temperature, openness, and crowding of a place. These idiosyncratic sensory aversions may result in anxiety, fatigue, disgust, sense of oppression, or distraction. 18

In order to address this issue, there is a strong need for technological support able to satisfy the spatial needs of people with autism, focusing on aversions derived from their high sensitivity to sensory stimulation. Moreover, as these aversions seem to be highly idiosyncratic, there are no features of places that may reassure the entire autistic population, and the peculiarities of each person have to be considered. 16 Therefore, the provision of personalized solutions that adapt to the individual user is extremely important.

As people with autism spectrum disorders commonly exhibit an affinity with technology, Information and Communication Technologies are largely used to support them in the management of daily activities. 16 , 17 However, the research about autism tends to pay more attention to children, and it overlooks adults’ needs. This might be a consequence of the “medical model”, which promotes the intervention toward a school-aged target. Moreover, the Human–Computer Interaction community seems to prefer addressing social interaction problems, 8 , 16 instead of dealing with spatial difficulties.

Most applications investigate the adoption of personalization strategies targeted to autism in the educational domain. For example, Judy et al. 11 present a personalized e-learning system that provides learning paths having different difficulty levels, based on the user’s past performance. The authors define ontologies to describe learning materials, annotation schemas, and services, and they use a genetic algorithm as an optimization technique, by representing a set of learning objects as chromosomes.

García et al. 6 propose an adaptive Web-based application that helps students with autism spectrum disorders overcome the challenges they might have to face when they attend university. The system adapts the presentation of the information site to autistic and neurotypical students, but the information is the same for everyone. The adaptive functionality is based on learning styles (visual vs. verbal, global vs. analytical, active vs. reflective) and on the user’s history. For example, if the user is more visual than verbal, the video version of content is shown at the top of the learning object. Otherwise, it is moved to the bottom of the object. Hong et al. 10 propose to provide users with suggestions within a social network aimed at supporting young adults’ independence. However, they focus on the organization of the social network, by relying on peer suggestions, rather than automatically generating recommendations.

Differently, Costa et al. 4 develop a task recommendation system that uses a machine learning technique to supplement the child’s regular therapy. The system suggests the daily activities to be performed (related to eating, keeping clean, getting dressed, and so forth) based on age, gender, and time of day. It does not consider the child’s preferences, and the difficulty level of the activities is manually set by the therapist. Moreover, Ng and Pera 15 propose a hybrid game recommender for adult people with autism spectrum disorders, based on collaborative and graph-based recommendation techniques.

Our work differs from the previously listed ones in several aspects. Firstly, we focus on a different domain, that is, spatial support. Secondly, we evaluated our model with autistic people. This has rarely, if ever, been done in the related research. Thirdly, our approach employs personal preferences for item categories, and aversions to sensory features, to steer recommendation in a context where a limited amount of feedback about items can practically be collected.

Our work also differs from general content-based, 12 feature-based, 9 collaborative and multi-criteria 1 recommender systems, because we treat sensory features as sources of discomfort for users, rather than liking or disliking factors. In other words, we separately model the influence of idiosyncratic sensory aversions, which determine the compatibility of items with the user, from her/his preferences for different types of items. Notice that this separation also distinguishes our model from recommender systems that deal with negative preferences, such as, 14 because we support the management of heterogeneous criteria to deal with user preferences and sensory idiosyncrasies. Previously, the INTRIGUE 2 tourist guide introduced the notion of compatibility requirements in PoI recommendation. However, it did not investigate their different meaning and impact on the evaluation of items, with respect to user preferences.

It is worth mentioning that, while constraint-based recommender systems are too knowledge-intensive for our purposes (we are not suggesting item bundles with constraint satisfaction requirements), the optimization of soft constraints for path finding under suitability criteria is relevant to extend PoI suggestion with instructions for reaching the target places. This type of technique has been explored in recommender systems for routing, such as the work by Verma et al. 24

This section describes how we gathered data about users and places to validate our model. Moreover, it describes the samples of users we involved.

The acquisition of individual user profiles is a key step to personalize recommendations because it makes it possible to explicitly represent the user preferences and requirements to be considered in item evaluation. User profiles can be explicitly elicited from users, or they can be unobtrusively learned by tracking and analyzing user behavior. 19 In this work, we adopted the former technique, which makes it possible to initialize the user profile before starting to use the mobile guide, and thus supports the identification of unsuitable PoIs since the beginning of the interaction with the user. This approach does not preclude the adoption of dynamic user modeling techniques to update the user profiles while people use the mobile guide, and we have recently extended our work in this direction.

Our questionnaire, shown in Table 1 , includes two sections. In the first one (left column of the table), it elicits user preferences about categories of PoIs such as restaurants, parks, etc., in order to learn which ones users like or dislike. In the second section (right column), questions concern users’ aversions to sensory features of places.

t1.jpg

The information about sensory aversions is hard to obtain: usually, very long and complex surveys have to be completed for this purpose. 20 Moreover, asking people with autism for such data is challenging because they have difficulties in social interactions and they tend to avoid new experiences. 21 Given our users’ attention problems, 13 and considering the application context of our project, which is not a clinical setting, we decided to avoid long and detailed surveys. Therefore, we carefully prepared with psychologists a short list of questions to capture such information.

We defined the questions about aversions by adapting a subset of the Sensory Perception Quotient (SPQ) test 23 on the basis of the findings reported by Rapp et al. 18 SPQ is a standard sensory questionnaire for adults that assesses basic sensory hyper- and hyposensitivity. We would have liked to directly use it since it is part of the battery of assessment tests proposed to the patients of the Autistic Adult center in Torino. However, it includes 92 items, too many to be proposed when bootstrapping a mobile guide. As shown in Table 1 , our questionnaire is aimed at acquiring aversion information more quickly. Specifically, for some features (brightness and space), the user is asked to evaluate two extreme conditions, that is, low or high levels, assuming that the middle ones are less problematic. In other cases (crowding, noise, and smell), the user is asked about her/his annoyance concerning the highest level, because low levels of these features are neutral.

In our experiment, users filled in the survey of Table 1 , possibly in the presence of an operator (when needed), and they answered questions using the [1, 5] Likert scale. Then, we asked users to evaluate 50 specific PoIs located in Torino city center (e.g., How much do you like Castle Square?) in order to collect a dataset of user ratings to test our model. We used the same [1, 5] Likert scale as above, but we included the “I don’t know this place” choice to support opting out.

Knowledge about PoIs. We used the Maps4All crowd-sourcing platform ( https://maps4all.firstlife.org/ ) as a source of information about places. Specifically, the 50 PoIs mentioned in Section `Knowledge about users’ are representative of all the categories of places defined in that platform. We selected those PoIs with the requirement that they had previously been mapped with the contribution of at least three different crowdsourcers each.

The reason for exploiting an ad-hoc platform as a source of information about PoIs, instead of relying on a public Open Data source, is the fact that Maps4All was explicitly designed to support the crowdsourcing of sensory features of places. In contrast, Open Data sources such as OpenStreetMap ( https://www.openstreetmap.org ) fail to provide the sensory information we needed for our experiment. In particular, for each place, Maps4All enables the user to rate in the [1, 5] scale the level of (i) brightness, (ii) crowding, (iii) noise, (iv) smell, (v) openness, and (vi) temperature. These sensory features have been defined based on the findings of the user study presented by Rapp et al., 18 and of state-of-art research. 20 Notice that, by interacting with Maps4All, the user can also provide a global rating of the place.

We populated the Maps4All platform through two experimental crowdsourcing sessions, during two lessons at the Master’s degree in Social Innovation and ICT at the University of Torino, in May and December 2019. About 120 students participated in the crowdsourcing tasks. In order to guarantee the collection of a reasonable amount of data about places, we asked each of them to provide evaluations for at least three PoIs in Torino city center. In total, during the two sessions, we collected the evaluations of 282 items.

For our study, we involved two groups of users:

  • 20 adults with autism spectrum disorders (from 22 to 40 years old, mean age: 26.3, median 28; 11 men, 9 women), who are patients of the Autistic Adult Center of Torino, medium and high functioning.
  • 128 neurotypical subjects (from 19 to 71 years old, mean age: 28.1, median 23; 63 men, 65 women), who are university students or contacts of the authors of this paper.

All participants signed a privacy consensus according to General Data Protection Regulation. Moreover, we obtained approval for the study from the research ethics committee of the University of Torino.

As far as the 50 PoIs we selected, the mean number of evaluations we obtained is 31 for autistic participants and 39 for neurotypical ones.

As previously discussed, we assume that both user preferences and item compatibility should be taken into account to identify the most relevant items that a user can smoothly experience and like, at the same time. However, evaluation criteria might be personal. Moreover, these aspects can be weighted differently in decision-making processes. For instance, in contrast to the tendency of people with autism spectrum disorders to visit places in which they feel comfortable, during our participatory design interview sessions we encountered a few subjects who face the challenges of noisy and crowded environments in order to be able to carry out the activities they like very much. We thus propose a recommendation model that, based on the observed item evaluations, can weigh the contribution of compatibility and preferences in rating prediction, on a user-specific basis.

For clarity purposes, we split the presentation of our model as follows. In Section 5.1, we describe the input data for recommendation. In Section 5.2, we specify how we estimate the compatibility of the individual features of an item with the user. Then, we present the estimation of the overall compatibility of the item with the user (Section 5.3) and the preference-based item evaluation (Section 5.4). In Section 5.5, we describe how we combine compatibility and preference-based evaluation to predict item ratings.

Before describing our model, we introduce the notation we use:

  • U is the set of users and I the set of items of the domain.
  • C is the set of item categories, such as shops and cinemas.
  • L is a Likert scale in [1, υ max ]. In this work, υ max = 5.
  • F = F ↑ ∪ F V is the set of sensory features defined in our domain. We assume that each feature f ∈ F takes values in L.   Specifically, F ↑ is the set of features f such that, the higher the value of f , the stronger its negative impact on the user. For instance, noise belongs to this class. Differently, F V denotes features whose extreme values make users uncomfortable, while the middle ones are less problematic, for example, brightness.   In our domain, there are no features such that people are expected to feel comfortable with high values and uncomfortable with low ones. Thus, we omit this class.

Our model takes the user and item profiles as input. The profile of u ∈ U , extracted from the questionnaire data, specifies:

  • The ratings r j in L that (s)he provided for a set of items j ∈ I.
  • Her/his declared preferences for the categories c ∈ C , each one expressed in the L scale.
  • Her/his declared sensory aversion to specific values of item features, expressed in L. We denote u ‘s aversion to a value υ of a feature f ∈ F as a ufυ . For example, a uf 5 = 4 means that u is fairly disturbed by items having f = 5.   For each feature f ∈ F ↑ , we assume by default that a uf 1 = 1. Therefore, the user profile stores a single value, a ufυ max , which specifies u ‘s aversion to the maximum value of f. We denote the maximum value of f as υ max .   For each feature f in F V , the user profile stores two values which express u ‘s aversion to the minimum and maximum values of f , respectively, for example, { a uf 1 = 3, a ufυ max = 4}

We can define compatibility as the opposite of aversion to feature values. However, user profiles only include one or two aversion values declared by users for each feature. Thus, the missing ones have to be interpolated. In the following, we describe the patterns we apply to approximate a user’s aversion to item features, starting from the values stored in her/his profile.

For each f ∈ F ↑ , we approximate aversion as a linearly increasing function. Let us represent feature values in the X axis, and user aversion in the Y axis of a plane. Then, we can define this function as a line that connects point (1, 1) to point (υ max , a ufυ max ), as in Figure 1 :

f1.jpg

We thus estimate u ‘s aversion to f in i ( ea ufi ) as follows:

f2.jpg

Differently, for each f ∈ F V , and given { a uf 1 , a ufυ max } in u ‘s profile, we interpolate aversion by means of a concave function on the range of f. The aversion function has a “V” shape, which we approximate by drawing two lines, as in Figure 3 :

f3.jpg

  • line ↑ connects points (1, 1) and (υ max , a ufυ max ) to represent the increment of aversion toward the maximum value of f.
  • line ↓ connects points (1, a uf 1 ) and ( υ max , 1) to represent the decrease in aversion while f takes higher values than its minimum:

We estimate u ‘s aversion to f in i by selecting the maximum values of the two lines:

f4.jpg

Notice that ea ufi takes values in the [1, υ max ] interval. Moreover, higher values of this measure mean that the feature generates more discomfort to u.

Given ea ufi , the compatibility of f with u in i , denoted as comp ufi , can thus be defined as:

For example, if ea ufi = 2.5 and υ max = 5, comp ufi = 3.5.

We propose alternative aggregation measures to compute the overall compatibility of an item i with a user u ( comp ui ) by modeling different types of influence of individual features.

In Section 7, we evaluate their performance, in combination with diverse recommendation algorithms.

f5.jpg

The two vector-based aggregation measures for the computation of the overall compatibility of i with u are

While compatibility indicates whether the user can smoothly experience an item, it does not mean that (s)he will like it. User preferences have to be taken into account for this purpose. In our domain, the only preference that we consider is the interest in the category of the item to be evaluated. Thus, the preference value of a user u for an item of category c ∈ C corresponds to the value of u ‘s preference for c stored in u ‘s profile. We denote this value as p uc .

It is worth mentioning that, if more preferences had to be modeled, a Multi-Criteria Decision Analysis approach might be applied to compute an overall preference estimation as a weighted function of preferences for individual attributes. However, this is out of the scope of the present work.

In order to balance compatibility and preferences in a personalized way, we propose to identify user-dependent evaluation criteria by exploiting the user’s idiosyncrasies and preferences, in combination with the ratings of items (s)he provides. Specifically, we estimate the rating that a user u will give to an item i as a weighted mean of overall compatibility and user preferences:

where α takes values in the [0, 1] interval, and p uci ∈ L is the preference-based evaluation of i , given u ‘s profile. This model, henceforth, referred as Ind (that is, Individual), identifies a specific α value for each user to optimize item recommendation to her/him. We identify the value of α for each u ∈ U as the one that minimizes the distance between estimated ratings and ground-truth ones.

We aim at assessing whether a recommendation model that takes both item compatibility and user preferences into account is more effective than an approach based on a single type of information. Moreover, we aim at evaluating the usefulness of a personalized balance of these aspects, as specified by the α parameter of Equation (10). For these purposes, we compare our model to a set of recommender systems that (i) uniformly manage compatibility and user preferences, ignoring their possibly different impact on decision-making, or (ii) focus either on compatibility or on preferences. We consider the following baselines:

  • C-only. This is a configuration of our recommendation model in which α = 1. In this case, items are evaluated exclusively on the basis of their compatibility with the user.
  • Pref-only. In this configuration of our model, we set α = 0 to evaluate items on the exclusive basis of the user’s preferences.

We did not select as baselines any collaborative or feature-based recommenders such as those proposed by Adomavicius 1 and Han, 9 because the data about users is too small to train those algorithms.

We separately compare our model to the above baselines on the dataset of the users with autism spectrum disorders (henceforth denoted as AUT), and on the one regarding neurotypical users (NEU). For the comparison, we configure all the algorithms on each aggregation measure of Section 5.3. The resulting configurations are named by appending the name of the selected measure to that of the applied algorithm. For instance, Ind Cos represents the application of the Cos aggregation measure to model Ind.

To evaluate recommendation performance, we consider ranking capability (MRR and MAP), accuracy (Precision, Recall, and F1), error minimization (MAE and RMSE), and user coverage. However, consistently with recent trends in the evaluation of recommender systems, we pay special attention to ranking metrics because they help understand whether the items that the user likes are placed in the first positions of the suggestion list, or not.

We perform 5-fold cross-validation in which, for every fold, we use 80% as training set and 20% as test set. As the Ind models have to optimize the α parameter, we train each of them to Find the best user-specific setting by optimizing its results with respect to MAP. Moreover, to be sure that the baselines are consistently evaluated, we run the other algorithms (MC, C-only, and Pref-only, which do not need any training) on the same test sets used for Ind.

Tables 2 and 3 show the Top-N evaluation results with N = 5. That is, the list of suggested items has length = 5. The tables omit the results concerning user coverage because it is 100% in all the cases.

t2.jpg

We consider two categories of algorithms, that is, the configurations of our model on the various aggregation measures, and the corresponding ones of the baselines. In the tables, we show the best value across all algorithms in bold. Moreover, the best value obtained by the other category of algorithms is underlined (when our model obtains the best value, we underline the best value achieved by the baselines, and vice versa). Stars indicate significant differences according to a Student T-Test between the best performing algorithm from each category; ** : p < 0.01; * : p < 0.05.

The evaluation results suggest that Ind Cos is the best recommender system because it achieves good accuracy and ranking capability. On both datasets, it outperforms all the other algorithms (baselines and own category) in F1 and MAP. Moreover, it has the best Recall of its own category. As a matter of fact, Ind Min achieves better error minimization than Ind Cos on both datasets. Specifically, it obtains the best MAE of all models, and it achieves the best RMSE in AUT. Furthermore, in NEU, it obtains better results than the other algorithms of its own category. However, as previously discussed, our primary evaluation criterion is ranking capability.

Interestingly, Ind RMSD is the worst configuration of our model. On the AUT dataset, it obtains the lowest results of its own category on all evaluation metrics. However, it achieves better results than several baselines in MAP and other metrics, supporting the superiority of our model. It is also worth noting that Pref-only is the best baseline regarding MAP. Moreover, C – only Cos has a lower ranking capability than Pref-only, but it has fairly good accuracy. It is the best or second-best baseline on the various measures.

Unfortunately, the low size of the AUT and NEU datasets does not support the statistical significance of results for several evaluation metrics. However, the results concerning MAP and RMSE on the AUT dataset are significant. This is important because our recommendation model is especially targeted to users with autism spectrum disorders. Thus, we can rely on the ranking capability of our model when recommending items to them. At the same time, the results are encouraging for neurotypical users. Therefore, it is worth investigating performance within a larger experiment that will possibly provide more statistically relevant results on both groups of people.

From the evaluation results, we draw two conclusions. The first one is that a customized model of item evaluation, which balances feature compatibility and preference satisfaction in a personalized way, achieves better performance than the recommender systems that manage only one of these aspects. As far as F1 and ranking capability are concerned, the configurations of the Ind model that take both preferences and compatibility into account (and, specifically, Ind Cos ) obtain higher results than Pref-only, which only employs user preferences in item suggestion. Moreover, they achieve better results than the C-only algorithms, which only use compatibility data. The performance of these algorithms is poorer than that of Pref-only, as well. This means that, not surprisingly, compatibility information alone is not enough to generate relevant recommendations for the user.

The second conclusion we draw is that a customized model of item evaluation, which balances feature compatibility and preference satisfaction in a personalized way, outperforms the recommender systems, which uniformly manage both aspects. Specifically, the Ind configurations outperform the MC ones, regardless of the applied aggregation measure, in most evaluation metrics, and especially in ranking and F1 measures.

To summarize, preference information is useful to suggest relevant PoIs in Top-N recommendation. However, better results can be achieved by combining it with a compatibility evaluation aimed at assessing whether the user can smoothly experience the recommended items. Interestingly, a uniform management of compatibility and preference information, which does not distinguish the possibly heterogeneous evaluation criteria concerning them, does not bring good results. Conversely, the acquisition of user-specific weights to balance the impact of compatibility and interests improves item suggestion.

Users with autism spectrum disorders are a challenging target of PoI recommender systems because of their spatial needs. In order to suggest suitable solutions, which the user can like and serenely experience, her/his preferences for PoI categories, traditionally analyzed by researchers, and her/his aversions to sensory features, have to be jointly considered. The reason is that aversions can seriously affect the visit experience, causing negative feelings on the user.

In this paper, we presented a novel Top-N recommender of Points of Interest especially targeted to these people. The peculiarity of our model is that it takes the individual user’s idiosyncratic aversions to sensory features into account to generate suggestions that (s)he is expected to like and smoothly experience at the same time. We tested our model on autistic and neurotypical people. The evaluation results show that, on both user groups, our model achieves higher accuracy and ranking capability than baseline recommenders, which (i) evaluate items on the sole basis of how closely they meet the user’s preferences, or how compatible they are with her/his idiosyncratic aversions to sensory features, and (ii) uniformly manage compatibility and preference information without distinguishing the different contributions of these aspects to item evaluation. We thus conclude that the integration of heterogeneous evaluation criteria about user interests and aversions is a promising approach to make recommender systems more inclusive.

This work is supported by the Compagnia di San Paolo Foundation. We thank Stefano Cocomazzi, Stefania Brighenti, and Claudio Mattutino for their contributions to the work. We are also grateful to the Adult Autism Center of the city of Torino for their participation in the PIUMA project.

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  5. Autism Spectrum Disorder

    Last reviewed on December 19, 2023. Autism spectrum disorder (ASD) refers to a group of complex neurodevelopment disorders caused by differences in the brain that affect communication and behavior. The term "spectrum" refers to the wide range of symptoms, skills, and levels of ability in functioning that can occur in people with ASD.

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  7. Autism spectrum disorders

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  9. Research, Clinical, and Sociological Aspects of Autism

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  10. Advances in autism research, 2021: continuing to decipher the ...

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  11. Autism research: Recent findings

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  13. Autism and Developmental Disorders Research Program

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    The promise of telehealth in autism diagnoses. by Lydia Denworth / 15 February 2023. The COVID-19 pandemic forced a reckoning, in which autism clinicians had to redefine best practices and expand how children are evaluated. The remote assessments they developed may help solve a persistent problem: the long wait families endure to get a ...

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  18. Genetics of autism spectrum disorder: an umbrella review of ...

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  19. DNA methylation in autism, explained

    For example, autistic people are more likely to demonstrate hypermethylation of genes such as MECP2 and UBE3A; loss-of-function mutations in these genes are strongly linked to autism. Other autism-related patterns include higher levels of methylation marks in genes involved in regulating the immune system, neurons and synaptic signaling.

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  21. Autism Research

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  24. NIH grant to support research into connections between autism, sensory

    Autism spectrum disorders are genetically complex, and hundreds of genes are implicated in their development. As a result, some may conclude that autism is a collection of disconnected disorders with comparable symptoms. ... In addition, this research seeks to identify a biomarker for sensory hypersensitivity — in this case, a brain signal ...

  25. Genetic contributions to autism spectrum disorder

    Abstract. Autism spectrum disorder (autism) is a heterogeneous group of neurodevelopmental conditions characterized by early childhood-onset impairments in communication and social interaction alongside restricted and repetitive behaviors and interests. This review summarizes recent developments in human genetics research in autism ...

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    According to the research, as of 2022, there were 64 organizations placing service dogs throughout the world with accreditation from Assistance Dog International (ADI) to specifically assist ...

  27. Supporting People with Autism Spectrum Disorders in the Exploration of

    As people with autism spectrum disorders commonly exhibit an affinity with technology, Information and Communication Technologies are largely used to support them in the management of daily activities. 16, 17 However, the research about autism tends to pay more attention to children, and it overlooks adults' needs. This might be a consequence ...