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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Case Study Research: Design and Methods (Applied Social Research Methods) Fifth Edition

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• ISBN-10 1452242569
• ISBN-13 978-1452242569
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• Publisher SAGE Publications, Inc
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• Language English
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About the author.

Robert K. Yin is President of COSMOS Corporation, an applied research and social science firm.  Over the years, COSMOS has successfully completed hundreds of projects for federal agencies, state and local agencies, and private foundations.

Outside of COSMOS, Dr. Yin has assisted numerous other research groups, helping to train their field teams or to design research studies. The most recent such engagements have been with The World Bank, the Division of Special Education and disAbility Research at George Mason University, the Department of Nursing Research and Quality Outcomes at the Children’s National Health System (Washington, DC), and the School of Education, Southern New Hampshire University.

Dr. Yin has authored over 100 publications, including authoring or editing 11 books (not counting the multiple editions of any given book). Earlier editions of the present book have been translated into eight languages (Chinese, Japanese, Korean, Swedish, Romanian, Italian, Polish, and Portuguese), and a second book on Qualitative Research from Start to Finish (2016) is in its 2nd edition and has been translated into four languages (Chinese, Korean, Swedish, and Portuguese).  Dr. Yin received his B.A. in history from Harvard College (magna cum laude) and his Ph.D. in brain and cognitive sciences from MIT.

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• Publisher ‏ : ‎ SAGE Publications, Inc; Fifth edition (May 10, 2013)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 312 pages
• ISBN-10 ‏ : ‎ 1452242569
• ISBN-13 ‏ : ‎ 978-1452242569
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• Dimensions ‏ : ‎ 7.25 x 0.5 x 9.25 inches
• #207 in Social Sciences Methodology
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About the author

Robert k. yin.

Robert K. Yin, Ph.D., serves as Chairman of the Board and CEO of COSMOS Corporation, an applied research and social science firm that has been in operation since 1980. Over the years, COSMOS has successfully completed hundreds of projects for government agencies, private foundations, and other entrepreneurial and non-profit organizations. At COSMOS, Dr. Yin actively leads various research projects, including those in which the case study method is used. He has authored numerous books and peer-reviewed articles, including Case Study Research and Applications of Case Study Research. In 1998 he founded the “Robert K. Yin Fund” at M.I.T., which supports seminars on brain sciences, as well as other activities related to the advancement of pre-doctoral students in the Department of Brain and Cognitive Sciences.

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Case Study Research: Methods and Designs

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves…

Case study research is a type of qualitative research design. It’s often used in the social sciences because it involves observing subjects, or cases, in their natural setting, with minimal interference from the researcher.

In the case study method , researchers pose a specific question about an individual or group to test their theories or hypothesis. This can be done by gathering data from interviews with key informants.

Here’s what you need to know about case study research design .

What Is The Case Study Method?

Main approaches to data collection, case study research methods, how case studies are used, case study model.

Case study research is a great way to understand the nuances of a matter that can get lost in quantitative research methods. A case study is distinct from other qualitative studies in the following ways:

• It’s interested in the effect of a set of circumstances on an individual or group.
• It begins with a specific question about one or more cases.
• It focuses on individual accounts and experiences.

Here are the primary features of case study research:

• Case study research methods typically involve the researcher asking a few questions of one person or a small number of people—known as respondents—to test one hypothesis.
• Case study in research methodology may apply triangulation to collect data, in which the researcher uses several sources, including documents and field data. This is then analyzed and interpreted to form a hypothesis that can be tested through further research or validated by other researchers.
• The case study method requires clear concepts and theories to guide its methods. A well-defined research question is crucial when conducting a case study because the results of the study depend on it. The best approach to answering a research question is to challenge the existing theories, hypotheses or assumptions.
• Concepts are defined using objective language with no reference to preconceived notions that individuals might have about them. The researcher sets out to discover by asking specific questions on how people think or perceive things in their given situation.

They commonly use the case study method in business, management, psychology, sociology, political science and other related fields.

A fundamental requirement of qualitative research is recording observations that provide an understanding of reality. When it comes to the case study method, there are two major approaches that can be used to collect data: document review and fieldwork.

A case study in research methodology also includes literature review, the process by which the researcher collects all data available through historical documents. These might include books, newspapers, journals, videos, photographs and other written material. The researcher may also record information using video cameras to capture events as they occur. The researcher can also go through materials produced by people involved in the case study to gain an insight into their lives and experiences.

Field research involves participating in interviews and observations directly. Observation can be done during telephone interviews, events or public meetings, visits to homes or workplaces, or by shadowing someone for a period of time. The researcher can conduct one-on-one interviews with individuals or group interviews where several people are interviewed at once.

Let’s look now at case study methodology.

The case study method can be divided into three stages: formulation of objectives; collection of data; and analysis and interpretation. The researcher first makes a judgment about what should be studied based on their knowledge. Next, they gather data through observations and interviews. Here are some of the common case study research methods:

One of the most basic methods is the survey. Respondents are asked to complete a questionnaire with open-ended and predetermined questions. It usually takes place through face-to-face interviews, mailed questionnaires or telephone interviews. It can even be done by an online survey.

2. Semi-structured Interview

For case study research a more complex method is the semi-structured interview. This involves the researcher learning about the topic by listening to what others have to say. This usually occurs through one-on-one interviews with the sample. Semi-structured interviews allow for greater flexibility and can obtain information that structured questionnaires can’t.

3. Focus Group Interview

Another method is the focus group interview, where the researcher asks a few people to take part in an open-ended discussion on certain themes or topics. The typical group size is 5–15 people. This method allows researchers to delve deeper into people’s opinions, views and experiences.

4. Participant Observation

Participant observation is another method that involves the researcher gaining insight into an experience by joining in and taking part in normal events. The people involved don’t always know they’re being studied, but the researcher observes and records what happens through field notes.

Case study research design can use one or several of these methods depending on the context.

Case studies are widely used in the social sciences. To understand the impact of socio-economic forces, interpersonal dynamics and other human conditions, sometimes there’s no other way than to study one case at a time and look for patterns and data afterward.

It’s for the same reasons that case studies are used in business. Here are a few uses:

• Case studies can be used as tools to educate and give examples of situations and problems that might occur and how they were resolved. They can also be used for strategy development and implementation.
• Case studies can evaluate the success of a program or project. They can help teams improve their collaboration by identifying areas that need improvements, such as team dynamics, communication, roles and responsibilities and leadership styles.
• Case studies can explore how people’s experiences affect the working environment. Because the study involves observing and analyzing concrete details of life, they can inform theories on how an individual or group interacts with their environment.
• Case studies can evaluate the sustainability of businesses. They’re useful for social, environmental and economic impact studies because they look at all aspects of a business or organization. This gives researchers a holistic view of the dynamics within an organization.
• We can use case studies to identify problems in organizations or businesses. They can help spot problems that are invisible to customers, investors, managers and employees.
• Case studies are used in education to show students how real-world issues or events can be sorted out. This enables students to identify and deal with similar situations in their lives.

And that’s not all. Case studies are incredibly versatile, which is why they’re used so widely.

Human beings are complex and they interact with each other in their everyday life in various ways. The researcher observes a case and tries to find out how the patterns of behavior are created, including their causal relations. Case studies help understand one or more specific events that have been observed. Here are some common methods:

1. Illustrative case study

This is where the researcher observes a group of people doing something. Studying an event or phenomenon this way can show cause-and-effect relationships between various variables.

2. Cumulative case study

A cumulative case study is one that involves observing the same set of phenomena over a period. Cumulative case studies can be very helpful in understanding processes, which are things that happen over time. For example, if there are behavioral changes in people who move from one place to another, the researcher might want to know why these changes occurred.

3. Exploratory case study

An exploratory case study collects information that will answer a question. It can help researchers better understand social, economic, political or other social phenomena.

There are several other ways to categorize case studies. They may be chronological case studies, where a researcher observes events over time. In the comparative case study, the researcher compares one or more groups of people, places, or things to draw conclusions about them. In an intervention case study, the researcher intervenes to change the behavior of the subjects. The study method depends on the needs of the research team.

Deciding how to analyze the information at our disposal is an important part of effective management. An understanding of the case study model can help. With Harappa’s Thinking Critically course, managers and young professionals receive input and training on how to level up their analytic skills. Knowledge of frameworks, reading real-life examples and lived wisdom of faculty come together to create a dynamic and exciting course that helps teams leap to the next level.

Explore Harappa Diaries to learn more about topics such as Objectives Of Research , What are Qualitative Research Methods , How To Make A Problem Statement and How To Improve your Cognitive Skills to upgrade your knowledge and skills.

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• Published: 22 April 2024

The design and evaluation of gamified online role-play as a telehealth training strategy in dental education: an explanatory sequential mixed-methods study

• Chayanid Teerawongpairoj 1 ,
• Chanita Tantipoj 1 &
• Kawin Sipiyaruk 2  

Scientific Reports volume  14 , Article number:  9216 ( 2024 ) Cite this article

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• Public health

To evaluate user perceptions and educational impact of gamified online role-play in teledentistry as well as to construct a conceptual framework highlighting how to design this interactive learning strategy, this research employed an explanatory sequential mixed-methods design. Participants were requested to complete self-perceived assessments toward confidence and awareness in teledentistry before and after participating in a gamified online role-play. They were also asked to complete a satisfaction questionnaire and participate in an in-depth interview to investigate their learning experience. The data were analyzed using descriptive statistics, paired sample t-test, one-way analysis of variance, and framework analysis. There were 18 participants who completed self-perceived assessments and satisfaction questionnaire, in which 12 of them participated in a semi-structured interview. There were statistically significant increases in self-perceived confidence and awareness after participating in the gamified online role-play ( P  < 0.001). In addition, the participants were likely to be satisfied with this learning strategy, where usefulness was perceived as the most positive aspect with a score of 4.44 out of 5, followed by ease of use (4.40) and enjoyment (4.03). The conceptual framework constructed from the qualitative findings has revealed five key elements in designing a gamified online role-play, including learner profile, learning settings, pedagogical components, interactive functions, and educational impact. The gamified online role-play has demonstrated its potential in improving self-perceived confidence and awareness in teledentistry. The conceptual framework developed in this research could be considered to design and implement a gamified online role-play in dental education. This research provides valuable evidence on the educational impact of gamified online role-play in teledentistry and how it could be designed and implemented in dental education. This information would be supportive for dental instructors or educators who are considering to implement teledentistry training in their practice.

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Introduction.

Telehealth has gained significant attention from various organization due to its potential to improve healthcare quality and accessibility 1 . It can be supportive in several aspects in healthcare, including medical and nursing services, to enhance continuous monitoring and follow-up 2 . Its adoption has increased substantially during the COVID-19 pandemic, aiming to provide convenient healthcare services 3 . Even though the COVID-19 outbreak has passed, many patients still perceive telehealth as an effective tool in reducing a number of visits and enhancing access to health care services 4 , 5 . This supports the use of telehealth in the post-COVID-19 era.

Teledentistry, a form of telehealth specific to dentistry, has been employed to improve access to dental services 6 . This system offers benefits ranging from online history taking, oral diagnosis, treatment monitoring, and interdisciplinary communication among dental professionals, enabling comprehensive and holistic treatment planning for patients 7 . Teledentistry can also reduce travel time and costs associated with dental appointments 8 , 9 , 10 . There is evidence that teledentistry serves as a valuable tool to enhance access to dental care for patients 11 . Additionally, in the context of long-term management in patients, telehealth has contributed to patient-centered care, by enhancing their surrounding environments 12 . Therefore, teledentistry should be emphasized as one of digital dentistry to enhance treatment quality.

Albeit the benefits of teledentistry, available evidence demonstrates challenges and concerns in the implementation of telehealth. Lack of awareness and knowledge in the use of telehealth can hinder the adoption of telehealth 13 . Legal issues and privacy concerns also emerge as significant challenges in telehealth use 14 . Moreover, online communication skills and technology literacy, including competency in using technological tools and applications, have been frequently reported as challenges in teledentistry 15 , 16 . Concerns regarding limitations stemming from the lack of physical examination are also significant 17 . These challenges and complexities may impact the accuracy of diagnosis and the security and confidentiality of patient information. Therefore, telehealth training for dental professionals emerges as essential prerequisites to effectively navigate the use of teledentistry, fostering confidence and competence in remote oral healthcare delivery.

The feasibility and practicality of telehealth in dental education present ongoing challenges and concerns. Given the limitations of teledentistry compared to face-to-face appointments, areas of training should encompass the telehealth system, online communication, technical issues, confidentiality concerns, and legal compliance 18 . However, there is currently no educational strategy that effectively demonstrates the importance and application of teledentistry 19 . A role-play can be considered as a teaching strategy where learners play a role that closely resembles real-life scenarios. A well-organized storytelling allows learner to manage problematic situations, leading to the development of problem-solving skill 20 , 21 . When compared to traditional lecture-based learning, learners can also enhance their communication skills through conversations with simulated patients 22 , 23 . In addition, they could express their thoughts and emotions during a role-play through experiential learning 20 , 24 , 25 . Role-play through video teleconference would be considered as a distance learning tool for training dental professionals to effectively use teledentistry.

While there have been studies supporting online role-play as an effective learning tool due to its impact of flexibility, engagement, and anonymity 26 , 27 , no evidence has been yet reported whether or not this learning strategy could have potential for training teledentistry. Given the complicated issues in telehealth, role-play for training teledentistry should incorporate different learning aspects compared to face-to-face communication with patients. In addition, game components have proved to be supportive in dental education 28 , 29 . Consequently, this research aimed to evaluate user perceptions and educational impact of gamified online role-play to enhance learner competence and awareness in using teledentistry as well as to construct a conceptual framework highlighting how to design and implement this interactive learning strategy. This research would introduce and promote the design and implementation of gamified online role-play as a learning tool for training teledentistry. To achieve the aim, specific objectives were established as follows:

1. To design a gamified online role-play for teledentistry training.

2. To investigate learner perceptions regarding their confidence and awareness in the use of teledentistry after completing the gamified online role-play.

3. To explore user satisfactions toward the use of gamified online role-play.

4. To develop a conceptual framework for designing and implementing a gamified online role-play for teledentistry training.

Materials and methods

Research design.

This research employed an explanatory sequential mixed-methods design, where a quantitative phase was firstly performed followed by a qualitative phase 30 , 31 . The quantitative phase was conducted based on pre-experimental research using one-group pretest–posttest design. Participants were requested to complete self-perceived assessments toward confidence and awareness in the use of teledentistry before and after participating in a gamified online role-play. They were also asked to complete a satisfaction questionnaire in using a gamified online role-play for training teledentistry. The qualitative phase was afterwards conducted to explore in-depth information through semi-structured interviews, in order to enhance an understanding of the quantitative phase, and to develop a conceptual framework for designing and implementing an online role-play for training teledentistry.

A gamified online role-play for training teledentistry

A gamified online role-play was designed and developed by the author team. To ensure its educational impact was significant, the expected learning outcomes were formulated based on insights gathered from a survey with experienced instructors from the Department of Advanced General Dentistry, Faculty of Dentistry, Mahidol University. These learning outcomes covered areas of online communication skill, technical issues, technology literacy of patients, limitations of physical examination, and privacy concerns of personal information. Learning scenario and instructional content were subsequently designed to support learners in achieving the expected learning outcomes, with their alignments validated by three experts in dental education. A professional actress underwent training to role-play a patient with a dental problem, requesting a virtual consultation or teledentistry. Before conducting data collection, the simulated patient was required to undergo a training and adjusting process with a pilot group under supervision of two experts in advanced general dentistry and dental education who had experience with teledentistry to ensure realism and completeness of learning content.

According to the role-play scenario, an actress was assigned to portray a 34-year-old female with chief complaints of pain around both ears, accompanied by difficulties in chewing food due to tooth loss. She was instructed to express her anxiety and nervousness about addressing these issues. Additionally, it was specified that she could not take a day off from work during this period. Despite this constraint, she required a dental consultation to receive advice for initial self-care, as her symptoms significantly impacted her daily life. Furthermore, she was designated to encounter difficulties with the technological use of the teledentistry platform.

The game components were implemented into the online role-play to enhance motivation and engagement. As challenge and randomness appear to be game elements 32 , 33 , five challenge cards were designed and embedded into the online role-play, where a participant was asked to randomly select one of them before interacting with the simulated patient. The challenging situations were potential technical concerns which could occur frequently during video conferencing, including network problems (e.g., internet disconnection and poor connection) and audiovisual quality issues. The participants were blinded to the selected card, while it was revealed to only the simulated patient. The challenging conditions were mimicked by the organizers and simulated patient, allowing learners to deal with difficulties. Therefore, both challenges and randomness were implemented into this learning intervention not only to create learning situations but also to enhance engagement.

A feedback system was carefully considered and implemented into the gamified online role-play. Immediate feedback appears to be a key feature of interactive learning environments 29 . Formative feedback was instantly delivered to learners through verbal and non-verbal communication, including words (content), tone of voice, facial expressions, and gestures of the simulated patient. This type of feedback allowed participants to reflect on whether or not their inputs were appropriate, enabling them to learn from their mistakes, or so-called the role of failure 34 . Summative feedback was also provided at the end of the role-play through a reflection from a simulated patient and suggestions from an instructor.

Learners were able to interact with the simulated patient using an online meeting room by Cisco WebEx. According to the research setting (Fig.  1 ), a learner was asked to participate in the role-play activity using a computer laptop in a soundproof room, while a simulated patient was arranged in a prepared location showing her residential environment. The researcher and instructor also joined the online meeting room and observed the interaction between the simulated patient and learners during the role-play activity whether or not all necessary information was accurately obtained. The role-play activity took around 30 minutes.

A diagram demonstrating the setting of gamified online role-play.

Research participants

Quantitative phase.

The participants in this research were postgraduate students from the Residency Training Program in Advanced General Dentistry at Mahidol University Faculty of Dentistry in academic year 2022, using a volunteer sampling. This program was selected because its objective was to develop graduates capable of integrating competencies from various dental disciplines to provide comprehensive dental care for both normal patients and those with special needs. Therefore, teledentistry should be a supportive component of their service. The recruitment procedure involved posting a recruiting text in the group chat of the residents. Those interested in participating in the research were informed to directly contact us to request more information, and they were subsequently allowed to decide whether they would like to participate. This approach ensured that participation was voluntary. Although there could be a non-response bias within this non-probability sampling technique 35 , it was considered as appropriate for this study, as participants were willing to have contribution in the learning activity, and therefore accurate and reliable research findings with no dropout could be achieved 36 .

The inclusion and exclusion criteria were established to determine the eligibility of prospective participants for this research. This study included postgraduate students from Years 1 to 3 in the Residency Training Program in Advanced General Dentistry at Mahidol University Faculty of Dentistry, enrolled during the academic year 2022. They were also required to at least complete the first semester to be eligible for this research to ensure familiarity with comprehensive dental care. However, they were excluded if they had previous involvement in the pilot testing of the gamified online role-play or if they were not fluent in the Thai language. The sample size was determined using a formula for two dependent samples (comparing means) 37 . To detect a difference in self-perceived confidence and awareness between pre- and post-assessments at a power of 90% and a level of statistical significance of 1%, five participants were required. With an assumed dropout rate of 20%, the number of residents per year (Year 1–3) was set to be 6. Therefore, 18 residents were required for this research.

Qualitative phase

The participants from the quantitative phase were selected for semi-structured interviews using a purposive sampling. This sampling method involved the selection of information-rich participants based on specific criteria deemed relevant to the research objective and to ensure a diverse representation of perspectives and experiences within the sample group 38 . In this research, the information considered for the purposive sampling included demographic data (e.g., sex and year of study), along with self-perceived assessment scores. By incorporating perceptions from a variety of participants, a broad spectrum of insights from different experiences in comprehensive dental practice and diverse improvement levels in self-perceived confidence and awareness could inform the design and implementation of the training program effectively. The sample size for this phase was determined based on data saturation, wherein interviews continued until no new information or emerging themes were retrieved. This method ensured thorough exploration of the research topic and maximized the richness of the qualitative data obtained.

Outcome assessments

To evaluate the gamified online role-play, a triangular design approach was employed, enabling the researchers to compare the research outcomes from different assessment methods. In this research, self-perceived assessments (confidence and awareness) in teledentistry, satisfactions toward gamified online role-play, and learner experience were assessed to assure the quality and feasibility of the gamified online role-play.

Self-perceived confidence and awareness toward teledentistry

All participants were requested to rate their perceptions of teledentistry before and after participating in the gamified online role-play (Supplementary material 1 ). The self-perceived assessment was developed based on previous literature 39 , 40 , 41 , 42 . The assessment scores would inform whether or not the participants could improve their self-perceived confidence and awareness through a learning activity. The assessment consisted of two parts, which were (1) self-perceived confidence and (2) self-perceived awareness. Each part contained six items, which were similar between the pre- and post-assessments. All items were designed using a 5-point Likert scale, where 1 being ‘strongly disagree’ and 5 being ‘strongly agree’.

Satisfactions toward the gamified online role-play

All participants were asked to complete the satisfaction questionnaire after participating in the gamified online role-play, to investigate whether or not they felt satisfied with their learning (Supplementary material 2 ). The questionnaire was developed based on previous literature regarding gamification and role-play 41 , 42 , 43 , 44 . Most of the items were designed using a 5-point Likert scale, where 1 being ‘very dissatisfied’ and 5 being ‘very satisfied’. They were grouped into three aspects, which were (1) Perceived usefulness, (2) Perceived ease of use, and (3) Perceived enjoyment.

Learner experiences within the gamified online role-play

Semi-structured interviews were conducted with the purposively selected participants to gather in-depth information regarding their learning experiences within the gamified online role-play. This technique allowed researchers to ask additional interesting topics raised from the responses of participants. A topic guide for interviews were constructed based on the findings of previous literature 45 , 46 , 47 . The interview was conducted in a private room by a researcher who was trained in conducting qualitative research including interviews. The interview sessions took approximately 45–60 minutes, where all responses from participants were recorded using a digital audio recorder with their permission. The recorded audios were transcribed using a verbatim technique by a transcription service under a confidential agreement.

Validity and reliability of data collection tools

To enhance the quality of self-perceived assessment and satisfaction questionnaire, they were piloted and revised to assure their validity and reliability. According to the content validity, three experts in advanced general dentistry were asked to evaluate the questionnaire, where problematic items were iteratively revised until they achieved the index of item-objective congruence (IOC) higher than 0.5. To perform a test–retest reliability, the validated versions of both self-perceived assessment and satisfaction questionnaire were afterwards piloted in residents from other programs, and the data were analyzed using an intraclass correlation coefficient (ICC), where the values of all items were 0.7 or greater. The data from the first pilot completion of both data collection tools were analyzed using Cronbach’s alpha to ensure the internal consistency of all constructs. The problematic items were deleted to achieve the coefficient alpha of 0.7 or greater for all constructs, which was considered as acceptable internal consistency.

Data analysis

The quantitative data retrieved from self-perceived assessment and satisfaction questionnaire were analyzed with the Statistical Package for Social Sciences software (SPSS, version 29, IBM Corp.). Descriptive statistics were performed to present an overview of the data. The scores from pre- and post-assessments were analyzed using a paired sample t-test to evaluate whether or not the participants would better self-perceive their confidence and awareness in teledentistry after participating in the gamified online role-play. One-way analysis of variance (ANOVA) was conducted to compare whether or not there were statistically significant differences in self-perceived assessment and satisfaction scores among the three academic years.

The qualitative data retrieved from semi-structured interviews were analyzed using a framework analysis, where its procedure involved transcription, familiarization with the interview data, coding, developing an analytical framework, indexing, charting, and data interpreting qualitative findings 48 . In this research, the initial codes had been pre-defined from previous literature and subsequently adjusted following the analysis of each transcript to develop an analytical framework (themes and subthemes), requiring several iterations until no additional codes emerged. Subsequently, the established categories and codes were applied consistently across all transcripts (indexing). The data from each transcript were then charted to develop a matrix, facilitating the management and summarization of qualitative findings. This method enabled the researchers to compare and contrast differences within the data and to identify connections between categories, thereby exploring their relationships and informing data interpretation.

The procedure of framework analysis necessitated a transparent process for data management and interpretation of emerging themes to ensure the robustness of research 49 . The transparency of this analytic approach enabled two researchers (C.Te. and K.S.) to independently analyze the qualitative data, and the emerging themes afterwards were discussed to obtain consensus among the researchers. This technique can be considered as a triangular approach to assure the intercoder reliability and internal validity of this research. The transparent process also allowed an external expert in dental education to verify the accuracy of the analysis. All emerging themes and the decision on data saturation were based on a discussion of all researchers until an agreement was made. NVivo (version 14, QSR International) was used to performed the qualitative data analysis. Subsequently, a conceptual framework was constructed to demonstrate emerging themes and subthemes together with their relationships.

Ethical consideration

The ethical approval for the study was approved by the Institutional Review Board of Faculty of Dentistry and Faculty of Pharmacy, Mahidol University on 29 th September 2022, the ethical approval number: MU-DT/PY-IRB 2022/049.2909. All methods were performed in accordance with the relevant guidelines and regulations. Although the data were not anonymous in nature as they contained identifiable data, they were coded prior to the analysis to assure confidentiality of participants.

Informed consent

Informed consent was obtained from all participants.

There were 18 residents from Year 1 to 3 of the Residency Training Program in Advanced General Dentistry who participated in this research (six from each year). Of these, there were 14 females and 4 males. There was no participant dropout, as all of them completed all required tasks, including the pre- and post-perceived assessments, gamified online role-play, and satisfaction questionnaire. According to the purposive sampling, the participants from the quantitative phase were selected for semi-structured interviews by considering sex, year of study, and self-perceived assessment scores. Twelve students (ten females and two males) participated in semi-structured interviews, where their characteristics are presented in Table 1 .

Internal consistency of all constructs

The data collected from the research participants, in addition to the pilot samples, were analyzed with Cronbach’s alpha to confirm the internal consistency. The coefficient alpha of all constructs demonstrated high internal consistency, as demonstrated in Table 2 .

Self-perceived assessments toward confidence and awareness of teledentistry

There were statistically significant increases in the assessment scores of self-perceived confidence and awareness after participating in the gamified online role-play ( P  < 0.001). According to Table 3 , there was an increase in self-perceived confidence from 3.38 (SD = 0.68) for the pre-assessment to 4.22 (SD = 0.59) for the post-assessment ( P  < 0.001). The findings of self-perceived awareness also showed score improvement from 4.16 (SD = 0.48) to 4.55 (SD = 0.38) after interacting with the simulated patient ( P  < 0.001).

According to Fig.  2 , participants demonstrated a higher level of self-perceived assessments for both self-confidence and awareness in all aspects after participating in the gamified online role-play for teledentistry training.

Self-perceived assessments toward confidence and awareness of teledentistry.

When comparing the self-perceived assessment scores toward confidence and awareness in the use of teledentistry among the three years of study (Year 1–3), there were no statistically significant differences in the pre-assessment, post-assessment score, and score difference (Table 4 ).

Satisfactions toward the use of gamified online role-play

According to Fig.  3 , participants exhibited high levels of satisfaction with the use of gamified online role-play across all three aspects. The aspect of usefulness received the highest satisfaction rating with a score of 4.44 (SD = 0.23) out of 5, followed by ease of use and enjoyment, scoring 4.40 (SD = 0.23) and 4.03 (SD = 0.21), respectively. Particularly, participants expressed the highest satisfaction levels regarding the usefulness of gamified online role-play for identifying their role (Mean = 4.72, SD = 0.46) and developing problem-solving skills associated with teledentistry (Mean = 4.61, SD = 0.50). Additionally, they reported satisfaction with the learning sequence presented in the gamified online role-play (Mean = 4.61, SD = 0.50). However, participants did not strongly perceive that the format of the gamified online role-play could engage them with the learning task for an extended period (Mean = 3.72, SD = 0.83).

Satisfactions toward the use of gamified online role-play.

When comparing the satisfaction levels perceived by participants from different academic years (Table 5 ), no statistically significant differences were observed among the three groups for all three aspects ( P  > 0.05).

Following the framework analysis of qualitative data, there were five emerging themes, including: (1) learner profile, (2) learning settings of the gamified online role-play, (3) pedagogical components, (4) interactive functions, and (5) educational impact.

Theme 1: Learner profile

Learner experience and preferences appeared to have impact on how the participants perceived the use of gamified online role-play for teledentistry training. When learners preferred role-play or realized benefits of teledentistry, they were likely to support this learning intervention. In addition, they could have seen an overall picture of the assigned tasks before participating in this research.

“I had experience with a role-play activity when I was dental undergraduates, and I like this kind of learning where someone role-plays a patient with specific personalities in various contexts. This could be a reason why I felt interested to participate in this task (the gamified online role-play). I also believed that it would be supportive for my clinical practice.” Participant 12, Year 1, Female “Actually, I' have seen in several videos (about teledentistry), where dentists were teaching patients to perform self-examinations, such as checking their own mouth and taking pictures for consultations. Therefore, I could have thought about what I would experience during the activity (within the gamified online role-play).” Participant 8, Year 2, Female

Theme 2: Learning settings of the gamified online role-play

Subtheme 2.1: location.

Participants had agreed that the location for conducting a gamified online role-play should be in a private room without any disturbances, enabling learners to focus on the simulated patient. This could allow them to effectively communicate and understand of the needs of patient, leading to a better grasp of lesson content. In addition, the environments of both learners and simulated patient should be authentic to the learning quality.

“The room should be a private space without any disturbances. This will make us feel confident and engage in conversations with the simulated patient.” Participant 10, Year 1, Female “… simulating a realistic environment can engage me to interact with the simulated patient more effectively ...” Participant 8, Year 2, Female

Subtheme 2.2: Time allocated for the gamified online role-play

The time allocated for the gamified online role-play in this research was considered as appropriate, as participants believed that a 30-minutes period should be suitable to take information and afterwards give some advice to their patient. In addition, a 10-minutes discussion on how they interact with the patient could be supportive for participants to enhance their competencies in the use of teledentistry.

“… it would probably take about 20 minutes because we would need to gather a lot of information … it might need some time to request and gather various information … maybe another 10-15 minutes to provide some advice.” Participant 7, Year 1, Female “I think during the class … we could allocate around 30 minutes for role-play, … we may have discussion of learner performance for 10-15 minutes ... I think it should not be longer than 45 minutes in total.” Participant 6, Year 2, Female

Subtheme 2.3: Learning consequence within a postgraduate curriculum

Most participants suggested that the gamified online role-play in teledentistry should be arranged in the first year of their postgraduate program. This could maximize the effectiveness of online role-play, as they would be able to implement teledentistry for their clinical practice since the beginning of their training. However, some participants suggested that this learning approach could be rearranged in either second or third year of the program. As they already had experience in clinical practice, the gamified online role-play would reinforce their competence in teledentistry.

"Actually, it would be great if this session could be scheduled in the first year … I would feel more comfortable when dealing with my patients through an online platform." Participant 11, Year 2, Male "I believe this approach should be implemented in the first year because it allows students to be trained in teledentistry before being exposed to real patients. However, if this approach is implemented in either the second or third year when they have already had experience in patient care, they would be able to better learn from conversations with simulated patients." Participant 4, Year 3, Male

Theme 3: Pedagogical components

Subtheme 3.1: learning content.

Learning content appeared to be an important component of pedagogical aspect, as it would inform what participants should learn from the gamified online role-play. Based on the interview data, participants reported they could learn how to use a video teleconference platform for teledentistry. The conditions of simulated patient embedded in an online role-play also allowed them to realize the advantages of teledentistry. In addition, dental problems assigned to the simulated patient could reveal the limitations of teledentistry for participants.

“The learning tasks (within the gamified online role-play) let me know how to manage patients through the teleconference.” Participant 5, Year 2, Female “… there seemed to be limitations (of teledentistry) … there could be a risk of misdiagnosis … the poor quality of video may lead to diagnostic errors … it is difficult for patients to capture their oral lesions.” Participant 3, Year 2, Female

Subtheme 3.2: Feedback

During the use of online role-play, the simulated patient can provide formative feedback to participants through facial expressions and tones of voice, enabling participants to observe and learn to adjust their inquiries more accurately. In addition, at the completion of the gamified online role-play, summative feedback provided by instructors could summarize the performance of participants leading to further improvements in the implementation of teledentistry.

“I knew (whether or not I interacted correctly) from the gestures and emotions of the simulated patient between the conversation. I could have learnt from feedback provided during the role-play, especially from the facial expressions of the patient.” Participant 11, Year 2, Male “The feedback provided at the end let me know how well I performed within the learning tasks.” Participant 2, Year 1, Female

Theme 4: Interactive functions

Subtheme 4.1: the authenticity of the simulated patient.

Most participants believed that a simulated patient with high acting performance could enhance the flow of role-play, allowing learners to experience real consequences. The appropriate level of authenticity could engage learners with the learning activity, as they would have less awareness of time passing in the state of flow. Therefore, they could learn better from the gamified online role-play.

"It was so realistic. ... This allowed me to talk with the simulated patient naturally ... At first, when we were talking, I was not sure how I should perform … but afterwards I no longer had any doubts and felt like I wanted to explain things to her even more." Participant 3, Year 2, Female "At first, I believed that if there was a factor that could influence learning, it would probably be a simulated patient. I was impressed by how this simulated patient could perform very well. It made the conversation flow smoothly and gradually." Participant 9, Year 3, Female

Subtheme 4.2: Entertaining features

Participants were likely to be satisfied with the entertaining features embedded in the gamified online role-play. They felt excited when they were being exposed to the unrevealed challenge which they had randomly selected. In addition, participants suggested to have more learning scenarios or simulated patients where they could randomly select to enhance randomness and excitement.

“It was a playful experience while communicating with the simulated patient. There are elements of surprise from the challenge cards that make the conversation more engaging, and I did not feel bored during the role-play.” Participant 4, Year 3, Male “I like the challenge card we randomly selected, as we had no idea what we would encounter … more scenarios like eight choices and we can randomly choose to be more excited. I think we do not need additional challenge cards, as some of them have already been embedded in patient conditions.” Participant 5, Year 2, Female

Subtheme 4.3: Level of difficulty

Participants suggested the gamified online role-play to have various levels of difficulty, so learners could have a chance to select a suitable level for their competence. The difficulties could be represented through patient conditions (e.g., systemic diseases or socioeconomic status), personal health literacy, and emotional tendencies. They also recommended to design the gamified online role-play to have different levels where learners could select an option that is suitable for them.

“The patient had hidden their information, and I needed to bring them out from the conversation.” Participant 12, Year 1, Female “Patients' emotions could be more sensitive to increase level of challenges. This can provide us with more opportunities to enhance our management skills in handling patient emotions.” Participant 11, Year 2, Male “… we can gradually increase the difficult level, similar to playing a game. These challenges could be related to the simulated patient, such as limited knowledge or difficulties in communication, which is likely to occur in our profession.” Participant 6, Year 2, Female

Theme 5: Educational impact

Subtheme 5.1: self-perceived confidence in teledentistry, communication skills.

Participants were likely to perceive that they could learn from the gamified online role-play and felt more confident in the use of teledentistry. This educational impact was mostly achieved from the online conversation within the role-play activity, where the participants could improve their communication skills through a video teleconference platform.

“I feel like the online role-play was a unique form of learning. I believe that I gained confidence from the online communication the simulated patient. I could develop skills to communicate effectively with real patients.” Participant 11, Year 2, Male “I believe it support us to train communication skills ... It allowed us to practice both listening and speaking skills more comprehensively.” Participant 4, Year 3, Male

Critical thinking and problem-solving skills

In addition to communication skills, participants reported that challenges embedded in the role-play allowed them to enhance critical thinking and problem-solving skills, which were a set of skills required to deal with potential problems in the use of teledentistry.

"It was a way of training before experiencing real situations … It allowed us to think critically whether or not what we performed with the simulated patients was appropriate." Participant 7, Year 1, Female “It allowed us to learn how to effectively solve the arranged problems in simulated situation. We needed to solve problems in order to gather required information from the patient and think about how to deliver dental advice through teledentistry.” Participant 11, Year 2, Male

Subtheme 5.2: Self perceived awareness in teledentistry

Participants believed that they could realize the necessity of teledentistry from the gamified online role-play. The storytelling or patient conditions allowed learners to understand how teledentistry could have both physical and psychological support for dental patients.

“From the activity, I would consider teledentistry as a convenient tool for communicating with patients, especially if a patient cannot go to a dental office”. Participant 5, Year 2, Female “I learned about the benefits of teledentistry, particularly in terms of follow-up. The video conference platform could support information sharing, such as drawing images or presenting treatment plans, to patients.” Participant 8, Year 2, Female

A conceptual framework of learning experience within a gamified online role-play

Based on the qualitative findings, a conceptual framework was developed in which a gamified online role-play was conceptualized as a learning strategy in supporting learners to be able to implement teledentistry in their clinical practice (Fig.  4 ).

The conceptual framework of key elements in designing a gamified online role-play.

The conceptual framework has revealed key elements to be considered in designing a gamified online role-play. Learner profile, learning settings, pedagogical components, and interactive functions are considered as influential factors toward user experience within the gamified online role-play. The well-designed learning activity will support learners to achieve expected learning outcomes, considered as educational impact of the gamified online role-play. The contributions of these five key elements to the design of gamified online role-play were interpreted, as follows:

Learner profile: This element tailors the design of gamified online role-plays for teledentistry training involves considering the background knowledge, skills, and experiences of target learners to ensure relevance and engagement.

Learning settings: The element focuses the planning for gamified online role-plays in teledentistry training involves selecting appropriate contexts, such as location and timing, to enhance accessibility and achieve learning outcomes effectively.

Pedagogical components: This element emphasizes the alignment between learning components and learning outcomes within gamified online role-plays, to ensure that the content together with effective feedback design can support learners in improving their competencies from their mistakes.

Interactive functions: This element highlights interactivity features integrated into gamified online role-plays, such as the authenticity and entertaining components to enhance immersion and engagement, together with game difficulty for optimal flow. All these features should engage learners with the learning activities until the achievement of learner outcomes.

Educational impact: This element represents the expected learning outcomes, which will inform the design of learning content and activities within gamified online role-plays. In addition, this element could be considered to evaluate the efficacy of gamified online role-plays, reflecting how well learning designs align with the learning outcomes.

A gamified online role-play can be considered as a learning strategy for teledentistry according to its educational impact. This pedagogical approach could mimic real-life practice, where dental learners could gain experience in the use of teledentistry in simulated situations before interacting with actual patients. Role-play could provide learners opportunities to develop their required competencies, especially communication and real-time decision-making skills, in a predictable and safe learning environment 20 , 23 , 46 . Potential obstacles could also be arranged for learners to deal with, leading to the enhancement of problem-solving skill 50 . In addition, the recognition of teledentistry benefits can enhance awareness and encourage its adoption and implementation, which could be explained by the technology acceptance model 51 . Therefore, a gamified online role-play with a robust design and implementation appeared to have potential in enhancing self-perceived confidence and awareness in the use of teledentistry.

The pedagogical components comprised learning content, which was complemented by assessment and feedback. Learners could develop their competence with engagement through the learning content, gamified by storytelling of the online role-play 52 , 53 . Immediate feedback provided through facial expression and voice tone of simulated patients allowed participants to learn from their failure, considered as a key feature of game-based learning 29 , 45 . The discussion of summative feedback provided from an instructor at the end of role-play activity could support a debriefing process enabling participants to reflect their learning experience, considered as important of simulation-based game 54 . These key considerations should be initially considered in the design of gamified online role-play.

The interactive functions can be considered as another key component for designing and evaluating the gamified online role-play 45 . Several participants enjoyed with a learning process within the gamified online role-play and suggested it to have more learning scenarios. In other words, this tool could engage learners with an instructional process, leading to the achievement of learning outcomes 29 , 45 . As challenge and randomness appear to be game elements 32 , 33 , this learning intervention assigned a set of cards with obstacle tasks for learners to randomly pick up before interacting with simulated patients, which was perceived by participants as a feature to make the role-play more challenging and engaging. This is consistent with previous research, where challenging content for simulated patients could make learners more engaged with a learning process 55 . However, the balance between task challenges and learner competencies is certainly required for the design of learning activities 56 , 57 . The authenticity of simulated patient and immediate feedback could also affect the game flow, leading to the enhancement of learner engagement 45 . These elements could engage participants with a learning process, leading to the enhancement of educational impact.

The educational settings for implementing gamified online role-play into dental curriculum should be another concern. This aspect has been recognized as significant in existing evidence 45 . As this research found no significant differences in all aspects among the three groups of learners, this learning intervention demonstrated the potential for its implementation at any time of postgraduate dental curriculum. This argument can be supported by previous evidence where a role-play could be adaptable for learning at any time, as it requires a short learning period but provides learners with valuable experience prior to being exposed in real-life scenarios 58 . This strategy also provides opportunities for learners who have any question or concern to seek advice or guidance from their instructors 59 . Although the gamified online role-play can be arranged in the program at any time, the first academic year should be considered, as dental learners would be confidence in implementing teledentistry for their clinical practice.

While a gamified online role-play demonstrated its strengths as an interactive learning strategy specifically for teledentistry, there are a couple of potential drawbacks that need to be addressed. The requirement for synchronous participation could limit the flexibility of access time for learners (synchronous interactivity limitation). With only one learner able to engage with a simulated patient at a time (limited participants), more simulated patients would be required if there are a number of learners, otherwise they would need to wait for their turn. Time and resources are significantly required for preparing simulated patients 60 . Despite the use of trained and calibrated professional actors/actresses, inauthenticity may be perceived during role-plays, requiring a significant amount of effort to achieve both interactional and clinical authenticities 46 . Future research could investigate asynchronous learning approaches utilizing non-player character (NPC) controlled by an artificial intelligence system as a simulated patient. This setup would enable multiple learners to have the flexibility to engage with the material at their own pace and at times convenient to them 29 . While there are potential concerns about using gamified online role-plays, this interactive learning intervention offers opportunities for dental professionals to enhance their teledentistry competency in a safe and engaging environment.

Albeit the robust design and data collection tools to assure reliability and validity as well as transparency of this study, a few limitations were raised leading to a potential of further research. While this research recruited only postgraduate students to evaluate the feasibility of gamified online role-play in teledentistry training, further research should include not only experienced dental practitioners but also undergraduate students to confirm its potential use in participants with different learner profiles. More learning scenarios in other dental specialties should also be included to validate its effectiveness, as different specialties could have different limitations and variations. Additional learning scenarios from various dental disciplines should be considered to validate the effectiveness of gamified online role-plays, as different specialties may present unique limitations and variations. A randomized controlled trial with robust design should be required to compare the effectiveness of gamified online role-play with different approaches in training the use of teledentistry.

Conclusions

This research supports the design and implementation of a gamified online role-play in dental education, as dental learners could develop self-perceived confidence and awareness with satisfaction. A well-designed gamified online role-play is necessary to support learners to achieve expected learning outcomes, and the conceptual framework developed in this research can serve as a guidance to design and implement this interactive learning strategy in dental education. However, further research with robust design should be required to validate and ensure the educational impact of gamified online role-play in dental education. Additionally, efforts should be made to develop gamified online role-play in asynchronous learning approaches to enhance the flexibility of learning activities.

Data availability

The data that support the findings of this study are available from the corresponding author, up-on reasonable request. The data are not publicly available due to information that could compromise the privacy of research participants.

Van Dyk, L. A review of telehealth service implementation frameworks. Int. J. Environ. Res. Public Health 11 (2), 1279–1298 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Bartz, C. C. Nursing care in telemedicine and telehealth across the world. Soins. 61 (810), 57–59 (2016).

Article   PubMed   Google Scholar  

Lin, G.S.S., Koh, S.H., Ter, K.Z., Lim, C.W., Sultana, S., Tan, W.W. Awareness, knowledge, attitude, and practice of teledentistry among dental practitioners during COVID-19: A systematic review and meta-analysis. Medicina (Kaunas). 58 (1), 130 (2022).

Wolf, T.G., Schulze, R.K.W., Ramos-Gomez, F., Campus, G. Effectiveness of telemedicine and teledentistry after the COVID-19 pandemic. Int. J. Environ. Res. Public Health. 19 (21), 13857 (2022).

Gajarawala, S. N. & Pelkowski, J. N. Telehealth benefits and barriers. J. Nurse Pract. 17 (2), 218–221 (2021).

Jampani, N. D., Nutalapati, R., Dontula, B. S. & Boyapati, R. Applications of teledentistry: A literature review and update. J. Int. Soc. Prev. Community Dent. 1 (2), 37–44 (2011).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Khan, S. A. & Omar, H. Teledentistry in practice: literature review. Telemed. J. E. Health. 19 (7), 565–567 (2013).

Baheti, M. J. B. S., Toshniwal, N. G. & Misal, A. Teledentistry: A need of the era. Int. J. Dent. Med. Res. 1 (2), 80–91 (2014).

Google Scholar  

Datta, N., Derenne, J., Sanders, M. & Lock, J. D. Telehealth transition in a comprehensive care unit for eating disorders: Challenges and long-term benefits. Int. J. Eat. Disord. 53 (11), 1774–1779 (2020).

Bursell, S. E., Brazionis, L. & Jenkins, A. Telemedicine and ocular health in diabetes mellitus. Clin. Exp. Optom. 95 (3), 311–327 (2012).

da Costa, C. B., Peralta, F. D. S. & Ferreira de Mello, A. L. S. How has teledentistry been applied in public dental health services? An integrative review. Telemed. J. E. Health. 26 (7), 945–954 (2020).

Heckemann, B., Wolf, A., Ali, L., Sonntag, S. M. & Ekman, I. Discovering untapped relationship potential with patients in telehealth: A qualitative interview study. BMJ Open. 6 (3), e009750 (2016).

Pérez-Noboa, B., Soledispa-Carrasco, A., Padilla, V. S. & Velasquez, W. Teleconsultation apps in the COVID-19 pandemic: The case of Guayaquil City, Ecuador. IEEE Eng. Manag. Rev. 49 (1), 27–37 (2021).

Article   Google Scholar  

Wamsley, C. E., Kramer, A., Kenkel, J. M. & Amirlak, B. Trends and challenges of telehealth in an academic institution: The unforeseen benefits of the COVID-19 global pandemic. Aesthetic Surg. J. 41 (1), 109–118 (2020).

Jonasdottir, S. K., Thordardottir, I. & Jonsdottir, T. Health professionals’ perspective towards challenges and opportunities of telehealth service provision: A scoping review. Int. J. Med. Inform. 167 , 104862 (2022).

Tan, S. H. X., Lee, C. K. J., Yong, C. W. & Ding, Y. Y. Scoping review: Facilitators and barriers in the adoption of teledentistry among older adults. Gerodontology. 38 (4), 351–365 (2021).

Minervini, G. et al. Teledentistry in the management of patients with dental and temporomandibular disorders. BioMed. Res. Int. 2022 , 7091153 (2022).

Edirippulige, S. & Armfield, N. Education and training to support the use of clinical telehealth: A review of the literature. J. Telemed. Telecare. 23 (2), 273–282 (2017).

Article   CAS   PubMed   Google Scholar  

Mariño, R. & Ghanim, A. Teledentistry: A systematic review of the literature. J. Telemed. Telecare. 19 (4), 179–183 (2013).

Article   ADS   PubMed   Google Scholar  

Armitage-Chan, E. & Whiting, M. Teaching professionalism: Using role-play simulations to generate professionalism learning outcomes. J. Vet. Med. Educ. 43 (4), 359–363 (2016).

Spyropoulos, F., Trichakis, I. & Vozinaki, A.-E. A narrative-driven role-playing game for raising flood awareness. Sustainability. 14 (1), 554 (2022).

Jiang, W. K. et al. Role-play in endodontic teaching: A case study. Chin. J. Dent. Res. 23 (4), 281–288 (2020).

PubMed   Google Scholar  

Vizeshfar, F., Zare, M. & Keshtkaran, Z. Role-play versus lecture methods in community health volunteers. Nurse Educ. Today. 79 , 175–179 (2019).

Nestel, D. & Tierney, T. Role-play for medical students learning about communication: Guidelines for maximising benefits. BMC Med. Educ. 7 , 3 (2007).

Gelis, A. et al. Peer role-play for training communication skills in medical students: A systematic review. Simulat. Health. 15 (2), 106–111 (2020).

Cornelius, S., Gordon, C. & Harris, M. Role engagement and anonymity in synchronous online role play. Int. Rev. Res. Open Distrib. Learn. 12 (5), 57–73 (2011).

Bell, M. Online role-play: Anonymity, engagement and risk. Educ. Med. Int. 38 (4), 251–260 (2001).

Sipiyaruk, K., Gallagher, J. E., Hatzipanagos, S. & Reynolds, P. A. A rapid review of serious games: From healthcare education to dental education. Eur. J. Dent. Educ. 22 (4), 243–257 (2018).

Sipiyaruk, K., Hatzipanagos, S., Reynolds, P. A. & Gallagher, J. E. Serious games and the COVID-19 pandemic in dental education: An integrative review of the literature. Computers. 10 (4), 42 (2021).

Morse, J.M., Niehaus, L. Mixed Method Design: Principles and Procedures. (2016).

Creswell, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches 3rd edn. (SAGE Publications, 2009).

Cheng, V. W. S., Davenport, T., Johnson, D., Vella, K. & Hickie, I. B. Gamification in apps and technologies for improving mental health and well-being: Systematic review. JMIR Ment. Health. 6 (6), e13717 (2019).

Gallego-Durán, F. J. et al. A guide for game-design-based gamification. Informatics. 6 (4), 49 (2019).

Gee, J. P. Learning and games. In The Ecology of Games: Connecting Youth, Games, and Learning (ed. Salen, K.) 21–40 (MIT Press, 2008).

Cheung, K. L., ten Klooster, P. M., Smit, C., de Vries, H. & Pieterse, M. E. The impact of non-response bias due to sampling in public health studies: A comparison of voluntary versus mandatory recruitment in a Dutch national survey on adolescent health. BMC Public Health. 17 (1), 276 (2017).

Murairwa, S. Voluntary sampling design. Int. J. Adv. Res. Manag. Social Sci. 4 (2), 185–200 (2015).

Chow, S.-C., Shao, J., Wang, H. & Lokhnygina, Y. Sample Size Calculations in Clinical Research (CRC Press, 2017).

Book   Google Scholar  

Palinkas, L. A. et al. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration Policy Mental Health Mental Health Services Res. 42 (5), 533–544 (2015).

McIlvried, D. E., Prucka, S. K., Herbst, M., Barger, C. & Robin, N. H. The use of role-play to enhance medical student understanding of genetic counseling. Genet. Med. 10 (10), 739–744 (2008).

Schlegel, C., Woermann, U., Shaha, M., Rethans, J.-J. & van der Vleuten, C. Effects of communication training on real practice performance: A role-play module versus a standardized patient module. J. Nursing Educ. 51 (1), 16–22 (2012).

Manzoor, I. M. F. & Hashmi, N. R. Medical students’ perspective about role-plays as a teaching strategy in community medicine. J. Coll. Physicians Surg. Pak. 22 (4), 222–225 (2012).

Cornes, S., Gelfand, J. M. & Calton, B. Foundational telemedicine workshop for first-year medical students developed during a pandemic. MedEdPORTAL. 17 , 11171 (2021).

King, J., Hill, K. & Gleason, A. All the world’sa stage: Evaluating psychiatry role-play based learning for medical students. Austral. Psychiatry. 23 (1), 76–79 (2015).

Arayapisit, T. et al. An educational board game for learning orofacial spaces: An experimental study comparing collaborative and competitive approaches. Anatomical Sci. Educ. 16 (4), 666–676 (2023).

Sipiyaruk, K., Hatzipanagos, S., Vichayanrat, T., Reynolds, P.A., Gallagher, J.E. Evaluating a dental public health game across two learning contexts. Educ. Sci. 12 (8), 517 (2022).

Pilnick, A. et al. Using conversation analysis to inform role play and simulated interaction in communications skills training for healthcare professionals: Identifying avenues for further development through a scoping review. BMC Med. Educ. 18 (1), 267 (2018).

Lane, C. & Rollnick, S. The use of simulated patients and role-play in communication skills training: A review of the literature to August 2005. Patient Educ. Counseling. 67 (1), 13–20 (2007).

Gale, N. K., Heath, G., Cameron, E., Rashid, S. & Redwood, S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med. Res. Methodol. 13 (1), 117 (2013).

Ritchie, J., Lewis, J., Nicholls, C. M. & Ormston, R. Qualitative Research Practice: A Guide for Social Science Students and Researchers (Sage, 2014).

Chen, J. C. & Martin, A. R. Role-play simulations as a transformative methodology in environmental education. J. Transform. Educ. 13 (1), 85–102 (2015).

Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manag. Inform. Syst. Quart. 13 (3), 319–340 (1989).

Novak, E., Johnson, T. E., Tenenbaum, G. & Shute, V. J. Effects of an instructional gaming characteristic on learning effectiveness, efficiency, and engagement: Using a storyline for teaching basic statistical skills. Interact. Learn. Environ. 24 (3), 523–538 (2016).

Marchiori, E. J. et al. A narrative metaphor to facilitate educational game authoring. Comput. Educ. 58 (1), 590–599 (2012).

Luctkar-Flude, M. et al. Effectiveness of debriefing methods for virtual simulation: A systematic review. Clin. Simulat. Nursing. 57 , 18–30 (2021).

Joyner, B. & Young, L. Teaching medical students using role play: Twelve tips for successful role plays. Med. Teach. 28 (3), 225–229 (2006).

Csikszentmihalyi, M. Flow: The Psychology of Optimal Performance (HarperCollins Publishers, 1990).

Buajeeb, W., Chokpipatkun, J., Achalanan, N., Kriwattanawong, N. & Sipiyaruk, K. The development of an online serious game for oral diagnosis and treatment planning: Evaluation of knowledge acquisition and retention. BMC Med. Educ. 23 (1), 830 (2023).

Littlefield, J. H., Hahn, H. B. & Meyer, A. S. Evaluation of a role-play learning exercise in an ambulatory clinic setting. Adv. Health. Sci. Educ. Theory Pract. 4 (2), 167–173 (1999).

Alkin, M. C. & Christie, C. A. The use of role-play in teaching evaluation. Am. J. Evaluat. 23 (2), 209–218 (2002).

Lovell, K. L., Mavis, B. E., Turner, J. L., Ogle, K. S. & Griffith, M. Medical students as standardized patients in a second-year performance-based assessment experience. Med. Educ. Online. 3 (1), 4301 (1998).

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Acknowledgements

The authors would like to express our sincere gratitude to participants for their contributions in this research. We would also like to thank the experts who provided their helpful suggestions in the validation process of the data collection tools.

This research project was funded by the Faculty of Dentistry, Mahidol University. The APC was funded by Mahidol University.

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Conceptualization, C.Te., C.Ta., and K.S.; methodology, C.Te., C.Ta., and K.S.; validation, C.Te., C.Ta., and K.S.; investigation, C.Te. and K.S.; formal analysis, C.Te., C.Ta., and K.S.; resources, C.Te., C.Ta., and K.S.; data curation, C.Ta. and K.S.; writing-original draft preparation, C.Te., C.Ta., and K.S.; writing-review and editing, C.Te., C.Ta., and K.S. All authors have read and agreed to the published version of the manuscript.

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Teerawongpairoj, C., Tantipoj, C. & Sipiyaruk, K. The design and evaluation of gamified online role-play as a telehealth training strategy in dental education: an explanatory sequential mixed-methods study. Sci Rep 14 , 9216 (2024). https://doi.org/10.1038/s41598-024-58425-9

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GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction

• Xinrui Liu 1 , 2 ,
• Mingxiu Wang 1 ,
• Jie Qin 1 ,
• Yaxin Liu 1 ,
• Shikai Wang 1 ,
• Shiyu Wu 1 ,
• Ming Zhang 1 ,
• Jincheng Zhong 1 &
• Jiabo Wang 1  

BMC Genomics volume  25 , Article number:  386 ( 2024 ) Cite this article

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Metrics details

The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS).

Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability.

Conclusions

Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website ( https://github.com/liu-xinrui/GbyE ).

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Genetic by environmental interaction (G × E) is crucial of explaining individual traits and has gained increasing attention in research. It refers to the influence of genetic factors on susceptibility to environmental factors. In-depth study of G × E contributes to a deeper understanding of the relationship between individual growth, living environment and phenotypes. Genetic factors play a role in most human diseases at the molecular or cellular level, but environmental factors also contribute significantly. Researchers aim to uncover the mechanisms behind complex diseases and quantitative traits by investigating the interactions between organisms and their environment. Common, complex, or rare human diseases are often considered as outcomes resulting from the interplay of genes, environmental factors, and their interactions. Analyzing the joint effects of genes and the environment can provide valuable insights into the underlying pathway mechanisms of diseases. For instance, researchers have successfully identified potential loci associated with asthma risk through G × E interactions [ 1 ], and have explored predisposing factors for challenging-to-treat diseases like cancer [ 2 , 3 ], rhinitis [ 4 ], and depression [ 5 ].

However, two main methods are currently being used by breeders in agricultural production to increase crop yields and livestock productivity [ 6 ]. The first is to develop varieties with relatively low G × E effect to ensure stable production performance in different environments. The second is to use information from different environments to improve the statistical power of genome-wide association study (GWAS) to reveal potential loci of complex traits. The first method requires long-term commitment, while the second method clearly has faster returns. In GWAS, the use of multiple environments or phenotypes for association studies has become increasingly important. This not only improves the statistical power of environmental susceptibility traits[ 7 ], but also allows to detect signaling loci for G × E. There are significant challenges when using multiple environments or phenotypes for GWAS, mainly because most diseases and quantitative traits have numerous associated loci with minimal impact [ 8 ], and thus it is impossible to determine the effect size regulated by environment in these loci. The current detection strategy for G × E is based on complex statistical model, often requiring the use of a large number of samples to detect important signals [ 9 , 10 ]. In GS, breeders can use whole genome marker data to identify and select target strains in the early stages of animal and plant production [ 11 , 12 , 13 ]. Initially, GS models, similar to GWAS models, could only analyze a single environment or phenotype [ 14 ]. To improve the predictive accuracy of the models, higher marker densities are often required, allowing the proportion of genetic variation explained by these markers to be increased, indirectly obtaining higher predictive accuracy. It is worth mentioning that the consideration of G × E and multiple phenotypes in GS models [ 15 ] has been widely studied in different plant and animal breeding [ 16 ]. GS models that allow G × E have been developed [ 17 ] and most of them have modeled and interpreted G × E using structured covariates [ 18 ]. In these studies, most of the GS models provided more predictive accuracy when combined with G × E compared to single environment (or phenotype) analysis. Hence, there is need to develop models that leverage G × E information for GWAS and GS studies.

This study developed a novel genotype-by-environment method based on R, termed GbyE, which leverages the interaction among multiple environments or phenotypes to enhance the association study and prediction performance of environmental susceptibility traits. The method enables the identification of mutation sites that exhibit G × E interactions in specific environments. To evaluate the performance of the method, simulation experiments were conducted using a dataset comprising 282 corn samples. Importantly, this method can be seamlessly integrated into any GWAS and GS analysis.

Materials and methods

Support packages.

The development purpose of GbyE is to apply it to GWAS and GS research, therefore it uses the genome association and prediction integrated tool (GAPIT) [ 19 ], Bayesian Generalized Linear Regression (BGLR) [ 20 ], and Ridge Regression Best Linear Unbiased Prediction (rrBLUP) [ 21 ]package as support packages, where GbyE only provides conversion of interactive formats and file generation. In order to simplify the operation of the GbyE function package, the basic calculation package is attached to this package to support the operation of GbyE, including four function packages GbyE.Simulation.R (Dual environment phenotype simulation based on heritability, genetic correlation, and QTL quantity), GbyE.Calculate.R (For numerical genotype and phenotype data, this package can be used to process interactive genotype files of GbyE), GbyE.Power.FDR.R (Calculate the statistical power and false discovery rate (FDR) of GWAS), and GbyE.Comparison.Pvalue.R (GbyE generates redundant calculations in GWAS calculations, and SNP effect loci with minimal p -values can be filtered by this package).

Samples and sequencing data

In this study, a small volume of data was used for software simulation analysis, which is widely used in testing tasks of software such as GAPIT, TASSEL, and rMPV. The demonstration data comes from 282 inbred lines of maize, including 4 phenotypic data. In any case, there are no missing phenotypes in these data, and this dataset can be obtained from the website of GAPIT ( https://zzlab.net/GAPIT/index.html , accessed on May 1, 2022). Among them, our phenotype data was simulated using a self-made R simulation function, and the Mean and GbyE phenotype files were calculated. Convert this format to HapMap format using PLINK v1.09 and scripts written by oneself.

Simulated traits

Phenotype simulation was performed by modifying the GAPIT.Phenotype.Simulation function in the GAPIT. Based on the input parameter NQTN, the random selected markers’ genotype from whole genome were used to simulate genetic effect in the simulated trait. The genotype effects of these selected QTNs were randomly sampled from a multivariate normal distribution, the correlation value between these normal distribution was used to define the genetic relationship between each environments. The additive heritability ( \({{\text{h}}}_{{\text{g}}}^{2}\) ) was used to scale the relationship between additive genetic variance and phenotype variance. The simulated phenotype conditions in this paper are set as follows: 1) The three levels of \({{\text{h}}}_{{\text{g}}}^{2}\) were set at 0.8, 0.5, and 0.2, representing high ( \({{\text{h}}}_{{\text{h}}}^{2}\) ), median ( \({{\text{h}}}_{{\text{m}}}^{2}\) ) and low ( \({{\text{h}}}_{{\text{l}}}^{2}\) ) heritability; 2) Genetic correlation were set three levels 0.8, 0.5, 0.2 representing high ( \({{\text{R}}}_{{\text{h}}}\) ), medium ( \({{\text{R}}}_{{\text{m}}}\) ) and low ( \({{\text{R}}}_{{\text{l}}}\) ) genetic correlation; 3) 20 pre-set effect loci of QTL. The phenotype values in each environment were simulated together following above parameters.

Genetic by environment interaction model

The pipeline analysis process of GbyE includes three steps: data preprocessing, production converted, Association analysis. Normalize the phenotype data matrix Y of the dual environment and perform GbyE conversion to generate phenotype data in GbyE.Y format. The genotype data format, such as hapmap, vcf, bed and other formats firstly need to be converted into numerical genotype format (homozygotes were coded as 0 or 2, heterozygotes were coded as 1) using software or scripts such as GAPIT, PLINK, etc. The environment (E) matrix is environment index matrix. The G (n × m) originally of genotype matrix was converted as GbyE.GD(2n × 2 m) \(\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]\) during the Kronecker product, and the Y vector (n × 1) was also converted as the GbyE.Y vector (2n × 1) after normalization. The duplicated data format indicated different environments, genetic effect, and populations. The genomic data we used in the analysis was still retained the whole genome information. The first column of E is the additive effect, which was the average genetic effect among environments. The others columns of E are the interactive effect, which should be less one column than the number of environments. Because it need to avoid the linear dependent in the model. In the GbyE algorithm, we coded the first environment as background as default, that means the genotype in the first environment are 0, the others are 1. Then the Kronecker product of G and environment index matrix was named as GbyE.GD. The interactive effect part of the GbyE.GD matrix in the GWAS and GS were the relative values based on the first environment (Fig.  1 ). The GbyE environmental interaction matrix can be easily obtained by constructing the interaction matrix E (e.g., Eq. 1 ) such that the genotype matrix G is Kronecker-product with the design interaction matrix E (e.g., Eq. 2 ), in which \(\left[\begin{array}{c}G\\ G\end{array}\right]\) matrix is defined as additive effect and \(\left[\begin{array}{c}0\\ G\end{array}\right]\) matrix is defined as interactive effect. \(\left[\begin{array}{cc}G& 0\\ G& G\end{array}\right]\) matrix is called gene by environment interaction matrix, hereinafter referred to as the GbyE matrix. The phenotype file (GbyE.Y) and genotype file (GbyE.GD) after transformation by GbyE will be inputted into the GWAS and GS models and computed as standard phenotype and genotype files.

where G is the matrix of whole genotype and E is the design matrix for exploring interactive effects. GbyE mainly uses the Kronecker product of the genetic matrix (G) and the environmental matrix (E) as the genotype for subsequent GWAS as a way to distinguish between additive and interactive effects.

The workflow pipeline of GbyE. The GbyE contains three main steps. (Step 1) Preprocessing of phenotype and genotype data,. The phenotype values in each environment was normalized respectively. Meanwhile, all genotype from HapMap, VCF, BED, and other types were converted to numeric genotype; (Step 2) Generate GbyE phenotype and interactive genotype matrix through the transformation of GbyE. In GbyE.GD matrix, the blue characters indicate additive effect, and red ones indicate interactive effect; (Step 3) The MLM and rrBLUP and BGLR were used to perform GWAS and GS

Association analysis model

The mixed linear model (MLM) of GAPIT is used as the basic model for GWAS analysis, and the principal component analysis (PCA) parameter is set to 3. Then the p -values of detection results are sorted and their power and FDR values are calculated. General expression of MLM (Fig.  1 ):

where Y is the vector of phenotypic measures (2n × 1); PCA and SNP i were defined as fixed effects, with a size of (2n × 2 m); Z is the incidence matrix of random effects; μ is the random effect vector, which follows the normal distribution μ ~ N(0, \({\delta }_{G}^{2}\) K) with mean vector of 0 and variance covariance matrix of \({\delta }_{G}^{2}\) K, where the \({\delta }_{G}^{2}\) is the total genetic variance including additive variance and interactive variance, the K is the kinship matrix built with all genotype including additive genotype and interactive genotype; e is a random error vector, and its elements need not be independent and identically distributed, e ~ N(0, \({\delta }_{e}^{2}\) I), where the \({\delta }_{e}^{2}\) is the residual and environment variance, the I is the design matrix.

Detectivity of GWAS

In the GWAS results, the list of markers following the order of P-values was used to evaluate detectivity of GWAS methods. When all simulated QTNs were detected, the power of the GWAS method was considered as 1 (100%). From the list of markers, following increasing of the criterion of real QTN, the power values will be increasing. The FDR indicates the rate between the wrong criterion of real QTNs and the number of all un-QTNs. The mean of 100 cycles was used to consider as the reference value for statistical power comparison. Here, we used a commonly used method in GWAS research with multiple traits or environmental phenotypes as a comparison[ 22 ]. This method obtains the mean of phenotypic values under different conditions as the phenotypic values for GWAS analysis, called the Mean value method, Compare the calculation results of GbyE with the additive and interactive effects of the mean method to evaluate the detection power of the GbyE strategy. Through the comprehensive analysis of these evaluation indicators, we aim to comprehensively evaluate the statistical power of the GbyE strategy in GWAS and provide a reference for future optimization research.

Among them, the formulae for calculating Power and FDR are as follows:

where \({{\text{n}}}_{{\text{i}}}\) indicates whether the i-th detection is true, true is 1, false is 0; \({{\text{m}}}_{{\text{r}}}\) is the total number of all true QTLs in the sample size; the maximum value of Power is 1.

where \({{\text{N}}}_{{\text{i}}}\) represents the i-th true value detected in the pseudogene, true is 1, false is 0. and cumulative calculation; \({{\text{M}}}_{{\text{f}}}\) is the number of all labeled un-QTNs in the total samples; the maximum value of FDR is 1.

Genomic prediction

To comparison the prediction accuracy of different GS models using GbyE, we performed rrBLUP, Bayesian methods using R packages. All phenotype of reference population and genotype of all population were used to train the model and predict genomic estimated breeding value (gEBV) of all individuals. The correlation between real phenotypes and gEBV of inference population was considered as prediction accuracy. fivefold cross-validation and 100 times repeats was performed to avoid over prediction and reduce bias. In order to distinguish the additive and interactive effects in GbyE, we designed two lists of additive and interactive effects in the "ETA" of BGLR, and put the additive and interactive effects into the model as two kinships for random objects. However, it was not possible to load the gene effects of the two lists in rrBLUP, so the additive and interactive genotypes together were used to calculate whole genetic kinship in rrBLUP (Fig.  1 ). Relevant parameters in BGLR are set as follows: 1) model set to "RRB"; 2) nIter is set to "12000"; 3) burnIn is set to "10000". The results of the above operations are averaged over 100 cycles. We also validated the GbyE method using four other Bayesian methods (BayesA, BayesB, BayesCpi, and Bayesian LASSO) in addition to RRB in BGLR.

Partial missing phentoype in the prediction

In this study, we artificially missed phenotype values in the single and double environments in the whole population from 281 inbred maize datasets. In the missing single environment case, the inference set in the cross-validation was selected from whole population, and each individual in the inference were only missed phenotypes in the one environment. The phenotype in the other environment was kept. The genotypes were always kept. In the case of missing double environments, both phenotypes and genotypes of environment 1 and environment 2 are missing, and the model can only predict phenotypic values in the two missing environments through the effects of other markers. In addition, the data were standardized and unstandardized to assess whether standardization had an effect on the estimation of the model. This experiment was tested using the "ML" method in rrBLUP to ensure the efficiency of the model.

GWAS statistical power of models at different heritabilities and genetic correlations

Power-FDR plots were used to demonstrate the detection efficiency of GbyE at three genetic correlation and three genetic power levels, with a total of nine different scenarios simulated (from left to right for high and low genetic correlation and from top to bottom for high and low genetic power). In order to distinguish whether the effect of improving the detection ability of genome-wide association analysis in GbyE is an additive effect or an effect of environmental interactions, we plotted their Power-FDR curves separately and added the traditional Mean method for comparative analysis. As shown in Fig.  2 , GbyE algorithm can detect more statistically significant genetic loci with lower FDR under any genetic background. However, in the combination with low heritability (Fig.  2 A, B, C), the interactive effect detected more real loci than GbyE under low FDR, but with the continued increase of FDR, GbyE detected more real loci than other groups. Under the combination with high heritability, all groups have high statistical power at low FDR, but with the increase of FDR, the statistical effect of GbyE gradually highlights. From the analysis of heritability combinations at all levels, the effect of heritability on interactive effect is not obvious, but GbyE always maintains the highest statistical power. The average detection power of GWAS in GbyE can be increased by about 20%, and with the decrease of genetic correlation, the effect of GbyE gradually highlights, indicating that the G × E plays a role.

The power-FDR testing in simulated traits. Comparing the efficacy of the GbyE algorithm with the conventional mean method in terms of detection power and FDR. From left to right, the three levels of genetic correlation are indicated in order of low, medium and high. From top to bottom, the three levels of heritability, low, medium and high, are indicated in order. (1) Inter: Interactive section extracted from GbyE; (2) AddE: Additive section extracted from GbyE; (3) \({{\text{h}}}_{{\text{l}}}^{2}\) , \({{\text{h}}}_{{\text{m}}}^{2}\) , \({{\text{h}}}_{{\text{g}}}^{2}\) : Low, medium, high heritability; (4) \({{\text{R}}}_{{\text{l}}}\) , \({{\text{R}}}_{{\text{m}}}\) , \({{\text{R}}}_{{\text{l}}}\) : where R stands for genetic correlation, represents three levels of low, medium and high

Resolution of additive and interactive effect

The output results of GbyE could be understood as resolution of additive and interactive genetic effect. Hence, we created a combined Manhattan plots with Mean result from MLM, additive, and interactive results from GbyE. As shown in Fig.  3 , true marker loci were detected on chromosomes 1, 6 and 9 in Mean, and the same loci were detected on chromosomes 1 and 6 for the additive result in GbyE (the common loci detected jointly by the two results were marked as solid gray lines in the figure). All known pseudo QTNs were labeled with gray dots in the circle. Total 20 pseudo QTNs were simulated in such trait (The heritability is set to 0.9, and the genetic correlation is set to 0.1). Although the additive section in GbyE did not catch the locus on chromosome 9 yet (those p-values of markers did not show above the significance threshold (p-value < 3.23 × 10 –6 )), it has shown high significance relative to other markers of the same chromosome. In the reciprocal effect of GbyE, we detected more significant loci on chromosomes 1, 2, 3 and 10, and these loci were not detected in either of the two previous sections. An integrate QQ plot (Fig.  3 D) shows that the overall statistical power of the additive section in Mean and GbyE are close, nevertheless, the interactive section in the GbyE provided a bit of inflation.

Manhattan statistical comparison plot. Manhattan comparison plots of mean ( A ), additive ( B ) and gene-environment interactive sections ( C ) at a heritability of 0.9 and genetic correlation of 0.1. Different colors are used in the diagram to distinguish between different chromosomes (X-axis). Loci with reinforcing circles and centroids are set up as real QTN loci. Consecutive loci found in both parts are shown as id lines, and loci found separately in the reciprocal effect only are shown as dashed lines. Parallel horizontal lines indicate significance thresholds ( p -value < 3.23 × 10 –6 ). D Quantile–quantile plots of simulated phenotypes for demo data from genome-wide association studies. x-axis indicates expected values of log p -values and y-axis is observed values of log p -values. The diagonal coefficients in red are 1. GbyE-inter is the interactive section in GbyE; GbyE-AddE is the additive section in GbyE

Genomic selection in assumption codistribution

The prediction accuracy of GbyE was significantly higher than the Mean value method by model statistics of rrBLUP in most cases of heritability and genetic correlation (Fig.  4 ). The prediction accuracy of the additive effect was close to that of Mean value method, which was consistent with the situation under the low hereditary. The prediction accuracy of interactive sections in GbyE remains at the same level as in GbyE, and interactive section plays an important role in the model. We observed that in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig.  4 C), \({{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig.  4 F), \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig.  4 G), the prediction accuracy of GbyE was slightly higher than the Mean value method, but there was no significant difference overall. In addition, we only observed that the prediction accuracy of GbyE was slightly lower than the Mean value method in \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig.  4 H), but there was still no significant difference between GbyE and Mean value methods. Under the combination of low heritability and genetic correlation, the prediction accuracy of Mean value method and additive effect model remained at a similar level. However, with the continuous increase of heritability and genetic correlation, the difference in prediction accuracy between the two gradually increases. In summary, the GbyE algorithm can improve the accuracy of GS by capturing information on multiple environment or trait effects under the rrBLUP model.

Box-plot of model prediction accuracy. The prediction accuracy (pearson's correlation coefficient) of the GbyE algorithm was compared with the tradition al Mean value method in a simulation experiment of genomic selection under the rrBLUP operating environment. The effect of different levels of heritability and genetic correlation on the prediction accuracy of genomic selection was simulated in this experiment. Each row from top to bottom represents low heritability ( \({{\text{h}}}_{{\text{l}}}^{2}\) ), medium heritability ( \({{\text{h}}}_{{\text{m}}}^{2}\) ) and high heritability ( \({{\text{h}}}_{{\text{h}}}^{2}\) ), respectively; each column from left to right represents low genetic correlation ( \({{\text{R}}}_{{\text{l}}}\) ), medium genetic correlation ( \({{\text{R}}}_{{\text{m}}}\) ) and high genetic correlation ( \({{\text{R}}}_{{\text{h}}}\) ), respectively; The X-axis shows the different test methods and effects, and the Y-axis shows the prediction accuracy

Genomic selection in assumption un-codistribution

The overall performance of GbyE under the 'BRR' statistical model based on the BGLR package remained consistent with rrBLUP, maintaining high predictive accuracy in most cases of heritability and genetic relatedness (Fig. S1 ). However, when the heritability is set to low and medium, the difference between the prediction accuracy of GbyE algorithm and Mean value method gradually decreases with the continuous increase of genetic correlation, and there is no statistically significant difference between the two. The prediction accuracy of the model by GbyE in \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 G) and \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}\) (Fig. S1 I) is significantly higher than that by Mean value method when the heritability is set to be high. On the contrary, when the genetic correlation is set to medium, there is no significant difference between GbyE and Mean value method in improving the prediction accuracy of the model, and the overall mean of GbyE is lower than Mean. When GbyE has relatively high heritability and low genetic correlation, its prediction accuracy is significantly higher than the mean method, such as \({{\text{h}}}_{{\text{m}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 D), \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{l}}}\) (Fig. S1 G), and \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{m}}}\) (Fig. S1 H). Therefore, GbyE is more suitable for situations with high heritability and low genetic correlation.

Adaptability of Bayesian models

Next, we tested a more complex Bayesian model. The GbyE algorithm and Mean value method were combined with five Bayesian algorithms in BGLR for GS analysis, and the computing R script was used for phenotypic simulation test, where heritability and genetic correlation were both set to 0.5. The results indicate that among the three Bayesian models of RRB, BayesA, and BayesLASSO, the predictive accuracy of GbyE is significantly higher than that of Mean value method (Fig.  5 ). In contrast, under the Bayesian models of BayesB and BayesCpi, the prediction accuracy of GbyE is lower than that of the Mean value method. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from G × E and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. However, the predictive accuracy of the BayesB model decreased by 11.3%, while the BayescCpi model decreased by 6%.

Relative prediction accuracy histogram for different Bayesian models. The X-axis is the Bayesian approach based on BGLR, and the Y-axis is the relative prediction accuracy. Where we normalize the prediction accuracy of Mean (the prediction accuracy is all adjusted to 1); the prediction accuracy of GbyE is the increase or decrease value relative to Mean in the same group of models

Impact of all and partial environmental missing

We tested missing the environmental by using simulated data. In the case of the simulated data, we simulated a total of nine situations with different heritability and genetic correlations (Fig.  6 ) and conducted tests on single and dual environment missing. The improvement in prediction accuracy by the GbyE algorithm was found to be significantly higher than the Mean value method in single environment deletion, regardless of the combination of heritability and genetic correlation. In the case of \({{\text{h}}}_{{\text{h}}}^{2}{{\text{R}}}_{{\text{h}}}\) , the prediction accuracy of GbyE is higher than 0.5, which is the highest value among all simulated combinations. When GbyE estimates the phenotypic values of Environment 1 and Environment 2 separately, its predictive accuracy seems too accurate. On the other hand, when the phenotypic values of both environments are missing on the same genotype, the predictive accuracy of GbyE does not show a significant decrease, and even maintains accuracy comparable to that of a single environment missing. However, when GbyE estimates Environment 1 and Environment 2 separately, the prediction accuracy significantly decreases compared to when a single environment is missing, and the prediction accuracy of Environment 1 and Environment 2 in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{m}}}\) is extremely low (Fig.  6 B). In addition, the prediction accuracy of GbyE is lower than Mean values only in \({{\text{h}}}_{{\text{l}}}^{2}{{\text{R}}}_{{\text{h}}}\) , whether it is missing in a single or dual environment.

Prediction accuracy of simulated data in single and dual environment absence. The prediction effect of GbyE was divided into two parts, environment 1 and environment 2, to compare the prediction accuracy of GbyE when predicting these two parts separately. This includes simulations with missing phenotypes and genotypes in environment 1 only ( A ) and simulations with missing in both environments ( B ). The horizontal coordinates of the graph indicate the different combinations of heritabilities and genetic correlations of the simulations

The phenotype of organisms is usually controlled by multiple factors, mainly genetic [ 23 ] and environmental factors [ 24 ], and their interactive factors. The phenotype of quantitative traits is often influenced by these three factors [ 25 , 26 ]. However, based on the computing limitation and lack of special tool, the interactive effect always was ignored in most GWAS and GS research, and it is difficult to distinguish additive and interactive effects. The rate between all additive genetic variance and phenotype variance was named as narrow sense heritability. The accuracy square of prediction of additive GS model is considered that can not surpass narrow sense heritability. In this study, the additive effects in GbyE are essentially equivalent to the detectability of traditional models, the key advantage of GbyE is the interactive section. More significant markers with interactive effects were detected. Detecting two genetic effects (additive and interactive sections) in GWAS and GS is a boost to computational complexity, while obtaining genotypes for genetic interactions by Kronecker product is an efficient means. This allows the estimation of additive and interactive genetic effects separately during the analysis, and ultimately the estimated genetic effects for each GbyE genotype (including additive and interactive genetic effect markers) are placed in a t-distribution for p -value calculation, and the significance of each genotype is considered by multiple testing. The GbyE also expanded the estimated heritability as generalized heritability which could be explained as the rate between total genetics variance and phenotype variance.

The genetic correlation among traits in multiple environments is the major immanent cause of GbyE. When the genetic correlation level is high, then additive genetic effects will play primary impact in the total genetic effect, and interactive genetic effects with different traits or environments are often at lower levels [ 27 ]. Therefore, the statistical power of the GbyE algorithm did not improve significantly compared with the traditional method (Mean value) when simulating high levels of genetic correlation. On the contrary, in the case of low levels of genetic correlation, the genetic variance of additive effects is relatively low and the genetic variance of interactive effects is major. At this time, GbyE utilizes multiple environments or traits to highlight the statistical power. Since the GbyE algorithm obtains additive, environmental, and interactive information by encoding numerical genotypes, it only increases the volume of SNP data and can be applied to any traditional GWAS association statistical model. However, this may slightly increase the correlation operation time of the GWAS model, but compared to other multi environment or trait models [ 28 , 29 ], GbyE only needs to perform a complete traditional GWAS once to obtain the results.

In GS, rrBLUP algorithm is a linear mixed model-based prediction method that assumes all markers provide genetic effects and their values following a normal distribution [ 30 ]. In contrast, the BGLR model is a linear mixed model, which assumes that gene effects are randomly drawn from a multivariate normal distribution and genotype effects are randomly drawn from a multivariate Gaussian process, which takes into account potential pleiotropy and polygenic effects and allows inferring the effects of single gene while estimating genomic values [ 31 ]. The algorithm typically uses Markov Chain Monte Carlo methods for estimation of the ratio between genetic variances and residual variances [ 32 , 33 ]. The model has been able to take into account more biological features and complexity, and therefore the overall improvement of the GbyE algorithm under BGLR is smaller than Mean method. In addition, the length of the Markov chain set on the BGLR package is often above 20,000 to obtain stable parameters and to undergo longer iterations to make the chain stable [ 34 ]. GbyE is effective in improving the statistical power of the model under most Bayesian statistical models. In the case of the phenotypes we simulated, more iterations cannot be provided for the BayesB and BayesCpi models because of the limitation of computation time, which causes low prediction accuracy. It is worth noting that the prediction accuracy of BayesCpi may also be influenced by the number of QTLs [ 35 ], and the prediction accuracy of BayesB is often related to the distribution of different allele frequencies (from rare to common variants) at random loci [ 36 ].

The overall statistical power of GbyE was significantly higher in missing single environment than in missing double environment, because in the case of missing single environment, GbyE can take full advantage of the information from the phenotype in the second environment. And the correlation between two environments can also affect the detectability of the GbyE algorithm in different ways. On the one hand, a high correlation between two environments can improve the predictive accuracy of the GbyE algorithm by using the information from one environment to predict the breeding values in the other environment, even if there is only few relationship with that environment [ 37 , 38 ]. On the other hand, when two environments are extremely uncorrelated, GbyE algorithm trained in one environment may not export well to another environment, which may lead to a decrease in prediction accuracy [ 39 ]. In the testing, we found that when the GbyE algorithm uses a GS model trained in one environment and tested in another environment, the high correlation between environments may result to the model capturing similarities between environments unrelated to G × E information [ 40 ]. However, when estimating the breeding values for each environment separately, GbyE still made effective predictions using the genotypes in that environment and maintained high prediction accuracy. As expected, the additive effect calculates the average genetic effect between environments, and its predictive effect does not differ much from the mean method. The interactive effect, however, has one less column than the number of environments, and it calculates the relative values between environments, a component that has a direct impact on the predictive effect. The correlation between the two environments may have both positive and negative effects on the detectability of the GbyE, so it is important to carefully consider the relationship between the two environments in subsequent in development and testing.

A key advantage of the GbyE algorithm is that it can be applied to almost all current genome-wide association and prediction. However, the focus of GbyE is still on estimating additive and interactive effects separately, so that it is easy to determine which portion of the is playing a role in the computational estimation.. The GbyE algorithm may have implications for the design of future GS studies. For example, the model could be used to identify the best environments or traits to include in GS studies in order to maximize prediction accuracy. It is particularly important to test the model on large datasets with different genetic backgrounds and environmental conditions to ensure that it can accurately predict genome-wide effects in a variety of contexts.

GbyE can simulate the effects of gene-environment interactions by building genotype files for multiple environments or multiple traits, normalizing the effects of multiple environments and multiple traits on marker effects. It also enables higher statistical power and prediction accuracy for GWAS and GS. The additive and interactive effects of genes under genetic roles could be revealed clearly, which makes it possible to utilize environmental information to improve the statistical power and prediction accuracy of traditional models, thus helping us to better understand the interactions between genes and the environment.

Availability of data and materials

The GbyE source code, demo script, and demo data are freely available on the GitHub website ( https://github.com/liu-xinrui/GbyE ).

Abbreviations

• Genome-widely association study

Genome selection

Genetic by environmental interaction

Genome association and prediction integrated tool

Mixed linear model

Bayesian generalized linear regression

Ridge regression best linear unbiased prediction

False discovery rate

Principal component analysis

Genomic estimated breeding value

Maazi H, Hartiala JA, Suzuki Y, Crow AL, Shafiei Jahani P, Lam J, Patel N, Rigas D, Han Y, Huang P. A GWAS approach identifies Dapp1 as a determinant of air pollution-induced airway hyperreactivity. PLoS Genet. 2019;15(12):e1008528.

Article   PubMed   PubMed Central   Google Scholar  

Simonds NI, Ghazarian AA, Pimentel CB, Schully SD, Ellison GL, Gillanders EM, Mechanic LE. Review of the gene-environment interaction literature in cancer: what do we know? Genet Epidemiol. 2016;40(5):356–65.

Wang X, Chen H, Kapoor PM, Su Y-R, Bolla MK, Dennis J, Dunning AM, Lush M, Wang Q, Michailidou K. A Genome-Wide Gene-Based Gene-Environment Interaction Study of Breast Cancer in More than 90,000 Women. Cancer research communications. 2022;2(4):211–9.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Chen R-X, Dai M-D, Zhang Q-Z, Lu M-P, Wang M-L, Yin M, Zhu X-J, Wu Z-F, Zhang Z-D, Cheng L. TLR Signaling Pathway Gene Polymorphisms, Gene-Gene and Gene-Environment Interactions in Allergic Rhinitis. Journal of Inflammation Research. 2022;15:3613–30.

Zhao M-Z, Song X-S, Ma J-S. Gene× environment interaction in major depressive disorder. World Journal of Clinical Cases. 2021;9(31):9368.

Falconer DS. The problem of environment and selection. Am Nat. 1952;86(830):293–8.

Article   Google Scholar  

Kim J, Zhang Y, Pan W. Powerful and adaptive testing for multi-trait and multi-SNP associations with GWAS and sequencing data. Genetics. 2016;203(2):715–31.

Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 years of GWAS discovery: biology, function, and translation. The American Journal of Human Genetics. 2017;101(1):5–22.

Article   CAS   PubMed   Google Scholar  

van Os J, Rutten BP. Gene-environment-wide interaction studies in psychiatry. Am J Psychiatry. 2009;166(9):964–6.

Article   PubMed   Google Scholar  

Winham SJ, Biernacka JM. Gene–environment interactions in genome-wide association studies: current approaches and new directions. Journal of Child Psychology Psychiatry. 2013;54(10):1120–34.

Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink J-L, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K. Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3: Genes|Genomes|Genetics. 2012;2(11):1427–36.

Xu S, Zhu D, Zhang Q. Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc Natl Acad Sci. 2014;111(34):12456–61.

Zhao Y, Mette M, Gowda M, Longin C, Reif J. Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity. 2014;112(6):638–45.

Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity. 2014;112(1):48–60.

Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, De Los CG, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 2017;22(11):961–75.

Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A, Howard R, Srinivasan S, Jain A, Garg V. Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype× environment interaction on prediction accuracy in chickpea. Sci Rep. 2018;8(1):11701.

Burgueño J, de los Campos G, Weigel K, Crossa J. Genomic prediction of breeding values when modeling genotype× environment interaction using pedigree and dense molecular markers. Crop Science. 2012;52(2):707–19.

Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M. A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theoretical applied genetics. 2014;127:595–607.

Wang JB, Zhang ZW. GAPIT Version 3: boosting power and accuracy for genomic association and prediction. Genomics Proteomics Bioinformatics. 2021;19(4):629–40.

Pérez P, de Los CG. Genome-wide regression and prediction with the BGLR statistical package. Genetics. 2014;198(2):483–95.

Endelman JB. Ridge Regression and other kernels for genomic selection with R package rrBLUP. Plant Genome J. 2011;4:250–5.

Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyen-Viet TA, Wedow R, Zacher M. Furlotte NAJNg. Multi-trait analysis of genome-wide association summary statistics using MTAG. 2018;50(2):229–37.

CAS   Google Scholar  

Falconer DS. Introduction to quantitative genetics. Pearson Education India; 1996.

Google Scholar  

Lynch M, Walsh B. Genetics and analysis of quantitative traits, vol. 1: Sinauer Sunderland, MA. 1998.

Mackay TF. The genetic architecture of quantitative traits. Annu Rev Genet. 2001;35(1):303–39.

Visscher PM, Hill WG, Wray NR. Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet. 2008;9(4):255–66.

Van der Sluis S, Posthuma D, Dolan CV. TATES: efficient multivariate genotype-phenotype analysis for genome-wide association studies. PLoS Genet. 2013;9(1):e1003235.

O’Reilly PF, Hoggart CJ, Pomyen Y, Calboli FC, Elliott P, Jarvelin M-R, Coin LJ. MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS. PLoS ONE. 2012;7(5):e34861.

Chung J, Jun GR, Dupuis J, Farrer LA. Comparison of methods for multivariate gene-based association tests for complex diseases using common variants. Eur J Hum Genet. 2019;27(5):811–23.

Pérez-Rodríguez P, Gianola D, González-Camacho JM, Crossa J, Manès Y, Dreisigacker S. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes|Genomes|Genetics. 2012;2(12):1595–16605.

VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91(11):4414–23.

Meuwissen TH, Hayes BJ, Goddard M. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29.

de Los CG, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MP. Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics. 2013;193(2):327–45.

Andrieu C, De Freitas N, Doucet A, Jordan MI. An introduction to MCMC for machine learning. Mach Learn. 2003;50:5–43.

Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010;185(3):1021–31.

Clark SA, Hickey JM, Van der Werf JH. Different models of genetic variation and their effect on genomic evaluation. Genet Sel Evol. 2011;43(1):1–9.

Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42(7):565–9.

González-Recio O, Forni S. Genome-wide prediction of discrete traits using Bayesian regressions and machine learning. Genet Sel Evol. 2011;43:1–12.

Korte A, Farlow A. The advantages and limitations of trait analysis with GWAS: a review. Plant Methods. 2013;9(1):1–9.

Gauderman WJ. Sample size requirements for matched case-control studies of gene–environment interaction. Stat Med. 2002;21(1):35–50.

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Acknowledgements

Thank you to all colleagues in the laboratory for their continuous help.

This project was partially funded by the National Key Research and Development Project of China, China (2022YFD1601601), the Heilongjiang Province Key Research and Development Project, China (2022ZX02B09), the Qinghai Science and Technology Program, China (2022-NK-110), Sichuan Science and Technology Program, China (Award #s 2021YJ0269 and 2021YJ0266), the Program of Chinese National Beef Cattle and Yak Industrial Technology System, China (Award #s CARS-37), and Fundamental Research Funds for the Central Universities, China (Southwest Minzu University, Award #s ZYN2023097).

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Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Sichuan Province and Ministry of Education, Southwest Minzu University, Chengdu, 6110041, China

Xinrui Liu, Mingxiu Wang, Jie Qin, Yaxin Liu, Shikai Wang, Shiyu Wu, Ming Zhang, Jincheng Zhong & Jiabo Wang

Nanchong Academy of Agricultural Sciences, Nanchong, 637000, China

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JW and XL conceived and designed the project. XL managed the entire trial, conducted software code development, software testing, and visualization. MW, JQ, YL, SW, MZ and SW helped with data collection and analysis. JQ, and YL assisted with laboratory analyses. JW, and XL had primary responsibility for the content in the final manuscript. JZ supervised the research. JW designed software and project methodology. All authors approved the final manuscript. All authors have reviewed the manuscript.

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Liu, X., Wang, M., Qin, J. et al. GbyE: an integrated tool for genome widely association study and genome selection based on genetic by environmental interaction. BMC Genomics 25 , 386 (2024). https://doi.org/10.1186/s12864-024-10310-5

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DOI : https://doi.org/10.1186/s12864-024-10310-5

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Transgender athletes could be at a physical disadvantage, new research shows.

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LAUSANNE, SWITZERLAND - JANUARY 11: The Olympic Rings sit on display outside the International ... [+] Olympic Committee (IOC) Headquarters on January 11, 2020 in Lausanne, Switzerland. (Photo by David Ramos/Getty Images)

Transgender athletes could be at a physical disadvantage compared to their cisgender counterparts, challenging claims that serve to exclude transgender athletes from participation in sport spaces that coincide with their gender identity, a new report suggests. A recent cross-sectional study examined the athletic capabilities and potential differences among trans and cisgender athletes. This investigation was the first to be funded by the International Olympic Committee (IOC) on the subject and marks the first analysis on athletes who have undergone gender-affirming hormone therapy.

The study, which was published earlier this month in the British Journal of Sports Medicine , a leading peer-reviewed journal in sports medicine, examined the athletic capabilities of 35 transgender athletes compared to 40 cisgender athletes. The study assessed cardiovascular performance, strength, and lower-body power among 23 transgender women, 12 transgender men, 21 cisgender women, and 19 cisgender men. All transgender participants had undergone hormone therapy for over a year, and both cisgender and transgender participants were actively engaged in competitive sports or underwent physical training at least three times weekly.

Significant Findings:

• Transgender women performed worse than cisgender women in tests measuring lower-body strength.
• Transgender women performed worse than cisgender women in tests measuring lung function.
• Transgender women had a higher percentage of fat mass, lower fat-free mass, and weaker handgrip strength compared to cisgender men.
• Transgender women’s bone density was found to be equivalent to that of cisgender women, which is linked to muscle strength.
• There were no meaningful differences found between the two groups’ hemoglobin profiles. Hemoglobin (Hb) plays a crucial role in athletic performance by facilitating improved oxygen delivery to muscles. Elite endurance athletes may exhibit up to a 40% higher level of Hb compared to untrained individuals. Moreover, heightened levels of Hb typically correlate with enhanced aerobic performance.

Similar findings have been echoed in previous reporting. According to a recent report that generated an in-depth review of all English-language scientific literature (published between 2011-2021) about transgender (trans) women athlete participation in elite sport, several key conclusions coincide with findings from the IOC funded study, including:

• Biomedical factors such as lung size, bone density, and hip-to-knee joint angle (q-angle) are not indicative of athletic prowess.
• Testosterone levels do not predict athletic performance or overall athleticism.
• Conversely, social elements such as nutrition, training regimen, and equipment accessibility significantly influence an athlete's performance, but are frequently disregarded in policy formulation.
• It's imperative to integrate both biomedical and social scientific insights into policy-making processes. However, there's a tendency to prioritize biomedical research excessively, which can compromise the overall well-being of athletes.

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Transgender athletes frequently encounter a range of barriers to inclusion and participation in athletic activities. A notable challenge to inclusion is the absence of comprehensive policies and regulations that facilitate the involvement of transgender athletes in competitive sports. As a result, the research team, among them a member of the IOC’s Medical and Scientific Commission, concluded that sporting federations should refrain from hastily banning transgender women from competing in the women’s category, advocating for additional research tailored to each sport and additional longitudinal investigations. You can read the full open-access publication in the British Journal of Sports Medicine here .

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