• Subject List
  • Take a Tour
  • For Authors
  • Subscriber Services
  • Publications
  • African American Studies
  • African Studies
  • American Literature
  • Anthropology
  • Architecture Planning and Preservation
  • Art History
  • Atlantic History
  • Biblical Studies
  • British and Irish Literature
  • Childhood Studies
  • Chinese Studies
  • Cinema and Media Studies
  • Communication
  • Criminology
  • Environmental Science
  • Evolutionary Biology
  • International Law
  • International Relations
  • Islamic Studies
  • Jewish Studies
  • Latin American Studies
  • Latino Studies
  • Linguistics
  • Literary and Critical Theory
  • Medieval Studies
  • Military History
  • Political Science
  • Public Health
  • Renaissance and Reformation
  • Social Work
  • Urban Studies
  • Victorian Literature
  • Browse All Subjects

How to Subscribe

  • Free Trials

In This Article Expand or collapse the "in this article" section Behavioral Genetics

Introduction, general overviews.

  • Developmental Behavioral Genetics
  • Genetics and Parenting
  • Genetics and Sexuality
  • Social Behaviors
  • Intelligence
  • Psychopathology
  • Health Behaviors, Gender, and Religion
  • Behavioral Genetics and Evolution
  • The Human Genome Project and Other Genome Projects
  • Molecular Genetics and the Future
  • Human Research

Related Articles Expand or collapse the "related articles" section about

About related articles close popup.

Lorem Ipsum Sit Dolor Amet

Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae; Aliquam ligula odio, euismod ut aliquam et, vestibulum nec risus. Nulla viverra, arcu et iaculis consequat, justo diam ornare tellus, semper ultrices tellus nunc eu tellus.

  • Abnormal Psychology
  • Alzheimer’s Disease
  • Biological Psychology
  • Personality Disorders
  • Psychological Perspectives on Taste
  • Psychotic Disorders
  • Schizophrenic Disorders
  • Trait Perspective

Other Subject Areas

Forthcoming articles expand or collapse the "forthcoming articles" section.

  • Data Visualization
  • Remote Work
  • Workforce Training Evaluation
  • Find more forthcoming articles...
  • Export Citations
  • Share This Facebook LinkedIn Twitter

Behavioral Genetics by Lisabeth DiLalla , Matthew Jamnik , Riley Marshall , Emily Pali LAST REVIEWED: 29 September 2017 LAST MODIFIED: 20 February 2024 DOI: 10.1093/obo/9780199828340-0010

Behavioral genetics is the study of genetic and environmental influences on behaviors. By examining genetic influence, more information can be gleaned about how both genes and the environment operate to affect behavior. Almost all behaviors studied by psychologists are affected by our genetic makeup, and so the question is not whether genes are important, but how do they affect these behaviors? The old nature–nurture debate has been laid to rest. We know, from thousands of studies using many different methodologies, that both genes and environment are important to understand if we hope to untangle the mysteries of virtually any behavior. Among the interesting questions to be asked now: How do genes and environments work together to influence behaviors? What specific genes might be responsible for various types of behaviors and what is their mechanism of action? The field of behavioral genetics is moving forward and changing so rapidly that many of the articles included here are from relatively recent work. Some essential mainstays are included that all students of behavioral genetics should read and that both help to explain the history of this field and also represent seminal papers that still hold true. However, a large number of the articles are representative of many comparable articles. This selection is intended to get the reader started on a foray into the area. It should be noted that most research articles in this field are quantitatively quite complicated. A reading knowledge of path analysis and structural equation modeling would be beneficial. However, even readers without this knowledge can glean sufficient information from these articles by skimming the results sections and concentrating instead on the literature reviews and discussion summaries.

There are several texts that provide an interesting overview of the field of behavioral genetics at large and some recent books that focus on topics relevant for specific subgroups. Kim 2009 is intended to be fairly general and cover a broad array of behaviors. Plomin 2018 , written for a lay audience, is accessible and presents important food for thought about the future of DNA in our everyday lives. DiLalla 2004 and McCartney and Weinberg 2009 are edited texts resulting from Festschrifts that present chapters broadly reviewing the behavioral genetics realm with a focus on work by Irving I. Gottesman (in DiLalla) and Sandra Wood Scarr (in McCartney and Weinberg), both of whom were seminal behaviors genetics researchers. Dick 2021 summarizes behavior genetics research as it relates specifically to parenting in a book written for a lay audience, and Harden 2021 provides a general discussion of how genetics research can benefit society in terms of justice and equality. Two books by Nancy Segal ( Segal 2005 and Segal 2017 ) provide information about twins specifically. Although not recent, these are included because they provide an excellent background into research on twins.

Dick, Danielle M. 2021. The child code . New York: Avery.

This book, written for parents, discusses parenting from the perspective of each child’s unique genetic make-up, or “code.” It clarifies the importance of each individual child’s contribution to the parent-child relationship and suggests ways to parent accordingly.

DiLalla, Lisabeth Fisher, ed. 2004. Behavior genetics principles: Perspectives in development, personality, and psychopathology . Washington, DC: American Psychological Association.

Resulted from a festschrift for Professor Irving I. Gottesman, a pioneer in behavioral genetics research. This book presents research spawned by Gottesman’s work and ideas, with a specific focus on development, personality, and psychopathology. Geared to researchers and students in the field.

Harden, Kathryn Paige. 2021. The genetic lottery: Why DNA matters for social equality . Princeton, NJ, and Oxford: Princeton Univ. Press.

DOI: 10.2307/j.ctv1htpf72

This book should be read with caution, but importantly attempts to clarify to introductory readers that genetic make-up accounts for socioeconomic inequality while simultaneously trying to discredit eugenics as a pseudoscience. Harden states that awareness of human genetic variability across individuals actually should lead to a more fair, equitable society.

Kim, Yong-Kyu. 2009. Handbook of behavior genetics . New York: Springer.

DOI: 10.1007/978-0-387-76727-7

Intended for students of genetics, psychology, and psychiatry. Chapters describe research in various areas of behavior including psychopathology, intelligence, and personality. Behavioral genetic relevance is discussed, as are cutting-edge methodologies and the directions these fields will take in the future.

McCartney, Kathleen, and Richard A. Weinberg. 2009. Experience and development: A Festschrift in honor of Sandra Wood Scarr . New York: Psychology Press.

Resulted from a Festschrift for Dr. Sandra Wood Scarr, an eminent developmental behavior geneticist. Chapters written by her students and colleagues cover topics based on Scarr’s research, such as heritability of cognitive ability in impoverished children, sibling relationships, and adoption. Intended for researchers of psychology, behavior genetics, and childcare.

Plomin, Robert. 2018. Blueprint: How DNA makes us who we are . Cambridge, MA: Massachusetts Institute of Technology Press.

Written for a lay audience, Plomin uses accessible terminology to explain complicated concepts and to tease apart the roles of genes and environment as they affect behaviors. Mostly based on evidence from his own research and large, genome-wide research projects. Bottom line: children’s development is primarily a function of their genetic make-up.

Segal, Nancy L. 2005. Indivisible by two: Lives of extraordinary twins . Cambridge, MA: Harvard Univ. Press.

An arresting book by Nancy Segal. She describes several sets of twins, triplets, and quadruplets to demonstrate how both genes and environment play critical roles in behavioral development.

Segal, Nancy L. 2017. Twin mythconceptions: False beliefs, fables, and facts about twins . London: Academic Press.

In this fun book, intended for professionals, parents, and others interested in twins, Segal identifies over seventy common misconceptions about twins and twinning. She explains each one using known scientific findings, with appendixes explaining some topics in more detail.

back to top

Users without a subscription are not able to see the full content on this page. Please subscribe or login .

Oxford Bibliographies Online is available by subscription and perpetual access to institutions. For more information or to contact an Oxford Sales Representative click here .

  • About Psychology »
  • Meet the Editorial Board »
  • Academic Assessment
  • Acculturation and Health
  • Action Regulation Theory
  • Action Research
  • Addictive Behavior
  • Adolescence
  • Adoption, Social, Psychological, and Evolutionary Perspect...
  • Advanced Theory of Mind
  • Affective Forecasting
  • Affirmative Action
  • Ageism at Work
  • Allport, Gordon
  • Ambulatory Assessment in Behavioral Science
  • Analysis of Covariance (ANCOVA)
  • Animal Behavior
  • Animal Learning
  • Anxiety Disorders
  • Art and Aesthetics, Psychology of
  • Artificial Intelligence, Machine Learning, and Psychology
  • Assessment and Clinical Applications of Individual Differe...
  • Attachment in Social and Emotional Development across the ...
  • Attention-Deficit/Hyperactivity Disorder (ADHD) in Adults
  • Attention-Deficit/Hyperactivity Disorder (ADHD) in Childre...
  • Attitudinal Ambivalence
  • Attraction in Close Relationships
  • Attribution Theory
  • Authoritarian Personality
  • Bayesian Statistical Methods in Psychology
  • Behavior Therapy, Rational Emotive
  • Behavioral Economics
  • Behavioral Genetics
  • Belief Perseverance
  • Bereavement and Grief
  • Birth Order
  • Body Image in Men and Women
  • Bystander Effect
  • Categorical Data Analysis in Psychology
  • Childhood and Adolescence, Peer Victimization and Bullying...
  • Clark, Mamie Phipps
  • Clinical Neuropsychology
  • Clinical Psychology
  • Cognitive Consistency Theories
  • Cognitive Dissonance Theory
  • Cognitive Neuroscience
  • Communication, Nonverbal Cues and
  • Comparative Psychology
  • Competence to Stand Trial: Restoration Services
  • Competency to Stand Trial
  • Computational Psychology
  • Conflict Management in the Workplace
  • Conformity, Compliance, and Obedience
  • Consciousness
  • Coping Processes
  • Correspondence Analysis in Psychology
  • Counseling Psychology
  • Creativity at Work
  • Critical Thinking
  • Cross-Cultural Psychology
  • Cultural Psychology
  • Daily Life, Research Methods for Studying
  • Data Science Methods for Psychology
  • Data Sharing in Psychology
  • Death and Dying
  • Deceiving and Detecting Deceit
  • Defensive Processes
  • Depressive Disorders
  • Development, Prenatal
  • Developmental Psychology (Cognitive)
  • Developmental Psychology (Social)
  • Diagnostic and Statistical Manual of Mental Disorders (DSM...
  • Discrimination
  • Dissociative Disorders
  • Drugs and Behavior
  • Eating Disorders
  • Ecological Psychology
  • Educational Settings, Assessment of Thinking in
  • Effect Size
  • Embodiment and Embodied Cognition
  • Emerging Adulthood
  • Emotional Intelligence
  • Empathy and Altruism
  • Employee Stress and Well-Being
  • Environmental Neuroscience and Environmental Psychology
  • Ethics in Psychological Practice
  • Event Perception
  • Evolutionary Psychology
  • Expansive Posture
  • Experimental Existential Psychology
  • Exploratory Data Analysis
  • Eyewitness Testimony
  • Eysenck, Hans
  • Factor Analysis
  • Festinger, Leon
  • Five-Factor Model of Personality
  • Flynn Effect, The
  • Forensic Psychology
  • Forgiveness
  • Friendships, Children's
  • Fundamental Attribution Error/Correspondence Bias
  • Gambler's Fallacy
  • Game Theory and Psychology
  • Geropsychology, Clinical
  • Global Mental Health
  • Habit Formation and Behavior Change
  • Health Psychology
  • Health Psychology Research and Practice, Measurement in
  • Heider, Fritz
  • Heuristics and Biases
  • History of Psychology
  • Human Factors
  • Humanistic Psychology
  • Implicit Association Test (IAT)
  • Industrial and Organizational Psychology
  • Inferential Statistics in Psychology
  • Insanity Defense, The
  • Intelligence, Crystallized and Fluid
  • Intercultural Psychology
  • Intergroup Conflict
  • International Classification of Diseases and Related Healt...
  • International Psychology
  • Interviewing in Forensic Settings
  • Intimate Partner Violence, Psychological Perspectives on
  • Introversion–Extraversion
  • Item Response Theory
  • Law, Psychology and
  • Lazarus, Richard
  • Learned Helplessness
  • Learning Theory
  • Learning versus Performance
  • LGBTQ+ Romantic Relationships
  • Lie Detection in a Forensic Context
  • Life-Span Development
  • Locus of Control
  • Loneliness and Health
  • Mathematical Psychology
  • Meaning in Life
  • Mechanisms and Processes of Peer Contagion
  • Media Violence, Psychological Perspectives on
  • Mediation Analysis
  • Memories, Autobiographical
  • Memories, Flashbulb
  • Memories, Repressed and Recovered
  • Memory, False
  • Memory, Human
  • Memory, Implicit versus Explicit
  • Memory in Educational Settings
  • Memory, Semantic
  • Meta-Analysis
  • Metacognition
  • Metaphor, Psychological Perspectives on
  • Microaggressions
  • Military Psychology
  • Mindfulness
  • Mindfulness and Education
  • Minnesota Multiphasic Personality Inventory (MMPI)
  • Money, Psychology of
  • Moral Conviction
  • Moral Development
  • Moral Psychology
  • Moral Reasoning
  • Nature versus Nurture Debate in Psychology
  • Neuroscience of Associative Learning
  • Nonergodicity in Psychology and Neuroscience
  • Nonparametric Statistical Analysis in Psychology
  • Observational (Non-Randomized) Studies
  • Obsessive-Complusive Disorder (OCD)
  • Occupational Health Psychology
  • Olfaction, Human
  • Operant Conditioning
  • Optimism and Pessimism
  • Organizational Justice
  • Parenting Stress
  • Parenting Styles
  • Parents' Beliefs about Children
  • Path Models
  • Peace Psychology
  • Perception, Person
  • Performance Appraisal
  • Personality and Health
  • Personality Psychology
  • Phenomenological Psychology
  • Placebo Effects in Psychology
  • Play Behavior
  • Positive Psychological Capital (PsyCap)
  • Positive Psychology
  • Posttraumatic Stress Disorder (PTSD)
  • Prejudice and Stereotyping
  • Pretrial Publicity
  • Prisoner's Dilemma
  • Problem Solving and Decision Making
  • Procrastination
  • Prosocial Behavior
  • Prosocial Spending and Well-Being
  • Protocol Analysis
  • Psycholinguistics
  • Psychological Literacy
  • Psychological Perspectives on Food and Eating
  • Psychology, Political
  • Psychoneuroimmunology
  • Psychophysics, Visual
  • Psychotherapy
  • Publication Bias in Psychology
  • Reasoning, Counterfactual
  • Rehabilitation Psychology
  • Relationships
  • Reliability–Contemporary Psychometric Conceptions
  • Religion, Psychology and
  • Replication Initiatives in Psychology
  • Research Methods
  • Risk Taking
  • Role of the Expert Witness in Forensic Psychology, The
  • Sample Size Planning for Statistical Power and Accurate Es...
  • School Psychology
  • School Psychology, Counseling Services in
  • Self, Gender and
  • Self, Psychology of the
  • Self-Construal
  • Self-Control
  • Self-Deception
  • Self-Determination Theory
  • Self-Efficacy
  • Self-Esteem
  • Self-Monitoring
  • Self-Regulation in Educational Settings
  • Self-Report Tests, Measures, and Inventories in Clinical P...
  • Sensation Seeking
  • Sex and Gender
  • Sexual Minority Parenting
  • Sexual Orientation
  • Signal Detection Theory and its Applications
  • Simpson's Paradox in Psychology
  • Single People
  • Single-Case Experimental Designs
  • Skinner, B.F.
  • Sleep and Dreaming
  • Small Groups
  • Social Class and Social Status
  • Social Cognition
  • Social Neuroscience
  • Social Support
  • Social Touch and Massage Therapy Research
  • Somatoform Disorders
  • Spatial Attention
  • Sports Psychology
  • Stanford Prison Experiment (SPE): Icon and Controversy
  • Stereotype Threat
  • Stereotypes
  • Stress and Coping, Psychology of
  • Student Success in College
  • Subjective Wellbeing Homeostasis
  • Taste, Psychological Perspectives on
  • Teaching of Psychology
  • Terror Management Theory
  • Testing and Assessment
  • The Concept of Validity in Psychological Assessment
  • The Neuroscience of Emotion Regulation
  • The Reasoned Action Approach and the Theories of Reasoned ...
  • The Weapon Focus Effect in Eyewitness Memory
  • Theory of Mind
  • Therapies, Person-Centered
  • Therapy, Cognitive-Behavioral
  • Thinking Skills in Educational Settings
  • Time Perception
  • Trauma Psychology
  • Twin Studies
  • Type A Behavior Pattern (Coronary Prone Personality)
  • Unconscious Processes
  • Video Games and Violent Content
  • Virtues and Character Strengths
  • Women and Science, Technology, Engineering, and Math (STEM...
  • Women, Psychology of
  • Work Well-Being
  • Wundt, Wilhelm
  • Privacy Policy
  • Cookie Policy
  • Legal Notice
  • Accessibility

Powered by:

  • [66.249.64.20|185.80.149.115]
  • 185.80.149.115

Behavioural genetics: An introduction

  • PMID: 26976249
  • DOI: 10.1017/S0924270800036115

Behavioural genetics is the study of the hereditary influence on behaviour, and can therefore be regarded as the intersection between behavioural sciences and genetics. As with most other fields of research it is difficult to exactly pinpoint when behavioural genetics started. In fact, one might say that the notion behavioural traits can be inherited may have appeared in human thought as early at 8000 BC, when the domestication of the dog began. The scientific era of behavioural genetics is generally considered to start with Charles Darwin. In his famous book On the Origin of Species by Means of natural Selection, or the Preservation of favoured Races in the Struggle for Life, published in 1859 (and sold out the first day), he devoted an entire chapter on instinctive behavioural patterns. Some years later, in his book The Descent of Man and Selection in Relation to Sex, he clearly stated that the difference between the mind of a human being and the mind of an animal 'is certainly one of degree and not of kind'. Moreover he gave considerable thought that mental powers (and insanity) are heritable aspects.

research articles on behavioral genetics

The Genetics of Human Behavior

  • Katherine Kim + −
  • David Streid + −

Behavioral disorders arise from environmental, lifestyle, and genetic factors. Past studies have shown evidence for the hereditability of several major behavioral neuropsychiatric disorders, such as schizophrenia, depression, and bipolar disorder. In these cases, certain genetic defects are passed down from parental generations and increase an offspring’s risk of inheriting a specific disorder. While neuropsychiatric disease susceptibility cannot be attributed solely to genetics, it is important to study how one’s genetic makeup can affect various facets of human behavior. Uncovering this link between genes and behavior could lead to the discovery of new biological factors involved in the development of highly prevalent neurological responses and disorders.

A recent study in behavioral genetics has shown that there may be a genetic basis for irrational phobias. It highlights the possibility that phobias are a form of inherited defense mechanism passed down through familial genes. In this study, researchers Dias and Ressler from the Emory School of Medicine subjected mice to fear conditioning by exposing them to the scent of chemical acetophenone, which smells like cherry blossoms, before administering electric shocks to the mice. Offspring of these mice (which were not exposed to the same conditioning as their parents) showed fearful responses to the odor of acetophenone, even when smelling it for the first time. This demonstrated that they had acquired a phobia of the chemical odor.

Structural abnormalities were also discovered in the olfactory bulbs of the offspring mice. Upon sequencing the mice’s sperm DNA, Dias found that the gene encoding M71, an odo receptor activated by acetophenone, was methylated in the conditioned parental and direct offspring generations. However, it is unknown whether this epigenetic alteration in sperm DNA was responsible for the offspring’s heightened odor sensitivity. It is possible that different biological mechanisms worked in conjunction to translate the inherited ancestral experiences to irrational phobias in the offspring.

Other studies in behavioral genetics have shown that some neuropsychiatric disorders are less heritable—or have a weaker genetic component—than others. For example, while genes may account for more than half of the risk for certain neuropsychiatric disorders, such as schizophrenia or bipolar disorder, the hereditability of anxiety and depression appear to be lower. According to Dr. Pine at the Cold Spring Harbor Laboratory, approximately 30-50% of the risk for anxiety and depression is genetic, while the other 50% to 70% of the risk may be attributed to environmental factors, such as substance use, stress, diet, and childhood experiences.

Anxiety disorders are the most common form of mental illness in the U.S., affecting 18% of the total population. Depression is also common, with around 10% of Americans experiencing a major depressive disorder at some point in their lives. Despite the high prevalence, genetic disposition for anxiety and depression is weak when compared to other neuropsychiatric disorders. As scientists, we must determine why this is the case. Is it due to a difference in the number of gene defects? For example, are there less genetic variations linked to anxiety and depression than to other more heritable diseases? Or are depression/anxiety genes less evolutionally conserved? Only by answering these questions can we get a firm understanding of the genetic root of these conditions and develop ways to prevent or fight the disorders.

We must examine the gene defects themselves. Perhaps, in behavioral disorders with relatively low heritability, the gene variations only minimally disrupt the major pathways of the brain. In such cases, it would be wise to study non-genetic factors that trigger the behavioral response. In addition, a psychodynamic treatment approach – alleviating a patient’s mental tension with the help of a psychiatrist—may be more helpful than invasive medical procedures. On the other hand, personalized medicine, such as gene therapy, may be the best option for treating significantly inheritable disorders, like schizophrenia. Through advancements in gene testing, doctors are able to conduct pre-symptomatic diagnostic tests to see the risk for patients with a family history of inherited neurological disorders. Tests can detect abnormalities, which may include missing or heavily altered sections of a gene, or genes that are inactive or lost, in DNA or RNA samples of patients. In other cases, a test may detect excessive RNA from a single gene, indicating that it is overexpressed in the body. Identifying and fixing these problematic sequences in the genetic code requires extensive knowledge of the human genome. Physicians providing personalized medicine must take into account a patient’s genetic makeup to determine the best form of targeted treatment for an illness.

Through genetic research, we are slowly beginning to unravel the biological basis for many neuropsychiatric disorders. Understanding the role of genes in highly prevalent neurological responses, like anxiety and phobias, is crucial for designing effective treatments tailored to patients who are suffering these conditions. Specifically, by identifying the genetic markers associated with inheritable neuropsychiatric diseases, we can analyze a patient’s risk of disease inheritance and responsiveness to existing medical treatment. This knowledge will make a powerful impact on the medical community and the future of medicine.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License .

Celebrating a Century of Research in Behavioral Genetics

  • Open access
  • Published: 20 January 2023
  • Volume 53 , pages 75–84, ( 2023 )

Cite this article

You have full access to this open access article

  • Robert Plomin 1  

6946 Accesses

2 Citations

53 Altmetric

Explore all metrics

A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to slow down in the 1990s, molecular genetics made it possible to assess DNA variation directly. From a rocky start with candidate gene association research, by 2005 the technological advance of DNA microarrays enabled genome-wide association studies, which have successfully identified some of the DNA variants that contribute to the ubiquitous heritability of behavioral traits. The ability to aggregate the effects of thousands of DNA variants in polygenic scores has created a DNA revolution in the behavioral sciences by making it possible to use DNA to predict individual differences in behavior from early in life.

Similar content being viewed by others

research articles on behavioral genetics

Challenges and Strategies for Integrating Molecular Genetics into Behavioral Science

Genotype-environment correlation in the era of dna.

Robert Plomin

research articles on behavioral genetics

Behavioural genetics methods

Emily A. Willoughby, Tinca J. C. Polderman & Brian B. Boutwell

Avoid common mistakes on your manuscript.

Introduction

Although the history of heredity and behavior can be traced back to ancient times (Loehlin 2009 ), the first human behavioral genetic research was reported in the 1920s, which applied quantitative genetic twin and adoption designs to assess genetic influence on newly developed measures of intelligence. The 1920s also marked the beginning of single-gene research that led to molecular genetics. The goal of this review is to outline 100 years of progress in quantitative genetic and molecular genetic research on behavior, a whistle-stop tour of a few of the major milestones in the journey. The review focuses on human research even though non-human animal research played a major role in the first 50 years (Maxson 2007 ). It uses intelligence as a focal example because intelligence was the target of much human research, even though a similar story could be told for other areas of behavioral genetics such as psychopathology.

The Two Worlds of Genetics

The most important development during this century of behavioral genetic research has been the synthesis of the two worlds of genetics, quantitative genetics and molecular genetics. Quantitative genetics and molecular genetics both have their origins in the 1860s with Francis Galton (Galton 1865 , 1869 ) and Gregor Mendel (Mendel 1866 ), respectively. Not much happened until the 1900s when Galton’s insights led to methods to study genetic influence on complex traits and when Mendel’s work was re-discovered. The two worlds clashed as Mendelians looked for 3:1 segregation ratios indicative of single-gene traits, whereas Galtonians assumed that Mendel’s laws of heredity were specific to pea plants because they knew that complex traits are distributed continuously.

Antipathy between the two worlds of genetics followed because of the different goals of Mendelians and Galtonians. Mendelians, the predecessors of molecular geneticists, wanted to understand how genes work, which led to the use of induced mutations and a focus on dichotomous traits that were easily assessed such as physical characteristics rather than behavioral traits. In contrast, Galtonians, whose descendants are quantitative geneticists, used genetics as a tool to understand the etiology of naturally occurring variation in complex traits selected for their intrinsic interest and importance, with behavioral traits, especially intelligence, high on the list. The resolution to the conflict could be seen in Ronald Fisher’s 1918 paper, which showed that Mendelian inheritance is compatible with quantitative traits if the assumption is made that several genes affect a trait (Fisher 1918 ). Nonetheless, the two worlds of genetics went their own way for most of the century.

The synthesis of the two worlds of genetics began in the 1980s with the technological advances of DNA sequencing, polymerase chain reaction, and DNA microarrays that enabled genome-wide association (GWA) studies of complex traits. In addition to finding DNA variants associated with complex traits, GWA genotypes led to three far-reaching advances in genetic research. First, GWA genotypes were used to estimate directly the classical quantitative genetic parameters of heritability and genetic correlation, which could be called quantitative genomics . Second, the results of GWA studies were used to create polygenic scores that predict individual differences for complex traits. Third, GWA genotypes facilitated new approaches to causal modeling of the interplay between genes and environment. Together, when applied to behavioral traits, these advances could be called behavioral genomics . This synthesis of the two worlds of genetics, the journey from behavioral genetics to behavioral genomics, is the overarching theme of this whistle-stop tour celebrating a century of research in behavioral genetics. (See Fig.  1 .) The itinerary begins with milestones in quantitative genetics and then molecular genetics, concluding with behavioral genomics.

figure 1

Synthesis of the two worlds of genetics: from behavioral genetics to behavioral genomics.

Quantitative Genetics

The first 50 years of quantitative genetic research, from 1920 to 1970, started off well with family studies (Jones 1928 ; Thorndike 1928 ), twin studies (Holzinger 1929 ; Lauterbach 1925 ; Merriman 1924 ; Tallman 1928 ) and adoption studies (Burks 1928 ; Freeman et al. 1928 ) using the recently devised IQ test. However, this nascent research was squelched with the emergence of Nazi eugenic policies (McGue 2008 ). The void was filled with behaviorism (Watson 1930 ), which led to environmentalism, the ‘blank slate’ view that we are what we learn (Pinker 2003 ).

Nonetheless, a few studies of IQ appeared in the 1930 and 1940 s, such as the first study of identical twins reared apart (Newman et al. 1937 ) and the first adoption study that assessed birth parents (Skodak and Skeels 1949 ). Both indicated substantial genetic influence on IQ, as did a review of all available IQ data (Woodworth 1941 ).

In 1960, the field-defining book, Behavior Genetics (Fuller and Thompson 1960 ), was published. It mostly reviewed research on nonhuman animals. In their preface, the authors noted that “we considered omitting human studies completely” (p. vi); even their chapter on cognitive abilities primarily reviewed nonhuman research. An earlier influential review began by saying, “In the writer’s opinion, the genetics of behavior must be worked out on species that can be subjected to controlled breeding. At the present time this precludes human subjects” (Hall 1951 ).

In 1963, a milestone review was published in Science of 52 family, twin and adoption studies of IQ (Erlenmeyer-Kimling and Jarvik 1963 ). Although the studies were very small by modern standards and heritability was not calculated, the average results from the different designs suggested substantial heritability. For example, the average MZ and DZ twin correlations were 0.87 and 0.53, respectively, suggesting a heritability of 68%. However, despite being published in Science , the paper was largely ignored; it was cited only 22 times in five years.

The pace of behavioral genetic research picked up in the 1960s, once again primarily research on non-human animals (Lindzey et al. 1971 ; McClearn 1971 ), although some twin studies on cognitive abilities were also published (Nichols 1965 ; Schoenfeldt 1968 ). However, the first 50 years of quantitative genetic research ended badly with the publication in 1969 of Arthur Jensen’s paper, How Much Can We Boost IQ and Scholastic Achievement? (Jensen 1969 ). The paper touched on ethnic differences, which made it one of the most controversial papers in the behavioral sciences, with 900 citations in the first five years and more than 6200 citations in total.

1970 was a watershed year marking the second 50 years of behavioral genetic research. It was the year that the Behavior Genetics Association was launched and the first issue of its journal, Behavior Genetics , was published. Another 1970 milestone was the publication of the foundational paper for model-fitting analysis of quantitative genetic designs (Jinks and Fulker 1970 ).

The 1970s and 1980s yielded most of the major discoveries for quantitative genetics as applied to behavioral traits, discoveries that are listed as landmarks in the following paragraphs. Nonetheless, in the aftermath of Jensen’s 1969 paper, behavioral genetic research, especially on intelligence, was highly controversial (Scarr and Carter-Saltzman 1982 ). Most notably, Leon Kamin severely criticized the politics as well as science of behavioral genetic research on intelligence in his book, The Science and Politics of I.Q. (Kamin 1974 ). He concluded that “There exist no data which should lead a prudent man to accept the hypothesis that I.Q. test scores are in any degree heritable” (p. 1). The book was cited more than 2000 times and stoked antipathy towards genetic research. It also impugned the motivation of genetic researchers, saying that they are ‘committed to the view that those on the bottom are genetically inferior victims of their own immutable defects’ (p. 2).

All Traits are Heritable

Despite this hostility, genetic research grew exponentially in the 1970s and created a seismic shift from the prevailing view that behavioral traits like intelligence are not “in any degree heritable”. In 1978, a review of 30 twin studies of intelligence yielded an average heritability estimate of 46% (Nichols 1978 ). Moreover, the conclusion began to emerge that all traits show substantial heritability. This conclusion, which has been called the first law of behavioral genetics (Turkheimer 2000 ), was first observed in 1976 in a twin study of cognitive data for 3000 twin pairs, which also included extensive data on personality and interests for 850 twin pairs (Loehlin and Nichols 1976 ). The authors noted “the curious uniformity of identical-fraternal differences both within and across trait domains” (p. 89). A 2015 meta-analysis of all published twin studies showed that behavioral traits are about 50% heritable on average (Polderman et al. 2015 ). Demonstrating the ubiquitous importance of genetics was the fundamental accomplishment of behavioral genetics.

No Traits are 100% Heritable

The flip side of the finding of 50% heritability was just as important: no traits are 100% heritable. It is ironic that, after a century of environmentalism, genetic research provided the strongest evidence for the importance of the environment; previous environmental research was confounded because it ignored genetics. Moreover, investigating environmental influences in genetically sensitive designs led to two of the most important discoveries about the environment: nonshared environment and the nature of nurture.

Nonshared Environment

Quantitative genetic research showed that environmental influences work very differently from the way they were assumed to work. A second discovery by Loehlin and Nichols ( 1976 ) was that salient environmental influences are not shared by twins growing up in the same family: “Environment carries substantial weight in determining personality – it appears to account for at least half the variance – but that environment is one for which twin pairs are correlated close to zero” (p. 92). This phenomenon has come to be known as nonshared environment (Plomin and Daniels 1987 ).

Loehlin and Nichols suggested that cognitive abilities are an exception to the rule that environmental influences make children in a family different from, not similar to, one another. Their twin study suggested that about 25% of the variance of cognitive abilities could be attributed to shared environment. A direct test of shared environmental influence is the correlation between adoptive siblings, genetically unrelated children adopted into the same family. Seven small studies of adoptive siblings yielded an average IQ correlation of 0.25, which seemed to precisely confirm the twin estimate (McGue et al. 1993 ).

However, in 1978, a study of 100 pairs of adoptive siblings reported an IQ correlation of -0.03 (Scarr and Weinberg 1978 ). This is a good example of the progressive nature of behavioral genetic research (Urbach 1974 ). Scarr and Weinberg noted that previous studies involved children, whereas theirs was the first study of post-adolescent adoptive siblings aged 16 to 22, and they hypothesized that the effect of shared environmental influence on cognitive development diminishes after adolescence as young adults make their own way in the world. Their hypothesis was confirmed in two additional studies of post-adolescent adoptive siblings that yielded an average IQ correlation of -0.01 (McGue et al. 1993 ). Evidence that shared environmental influence declines after adolescence to negligible levels for cognitive abilities has also emerged from twin studies (Briley and Tucker-Drob 2013 ; Haworth et al. 2010 ). However, one of the biggest mysteries about nonshared environment remains: what are these environmental influences that make children growing up in the same family so different (Plomin 2011 )?

The Nature of Nurture

Another milestone was the revelation that environmental measures widely used in the behavioral sciences, such as parenting, social support, and life events, show genetic influence (Plomin and Bergeman 1991 ), with heritabilities of about 25% on average (Kendler and Baker 2007 ). This finding emerged in the 1980s as measures of the environment were included in quantitative genetic designs, which also led to the discovery that associations between environmental measures and psychological traits are significantly mediated genetically (Plomin et al. 1985 ). The nature of nurture is one of the major directions for research in behavioral genomics, as discussed later.

Heritability Increases During Development

Another milestone in the 1970s was the Louisville Twin Study in which mental development of 500 pairs of twins was assessed longitudinally and showed that the heritability of intelligence increases from infancy to adolescence (Wilson 1983 ). In light of the replication crisis in science (Ritchie 2021 ), a cause for celebration is that this counterintuitive finding of increasing heritability of intelligence – from about 40% in childhood to more than 60% in adulthood -- has consistently replicated, as seen in cross-sectional (Haworth et al. 2010 ) and longitudinal (Briley and Tucker-Drob 2013 ) mega-analyses.

In 1977, a landmark paper showed how univariate analysis of variance can be extended to multivariate analysis of covariance in a model-fitting framework (Martin and Eaves 1977 ). They applied their approach to cognitive abilities and found an average genetic correlation of 0.52, indicating that many genes affect diverse traits, called pleiotropy . Subsequent studies also yielded genetic correlations greater than 0.50 between diverse cognitive abilities (Plomin and Kovas 2005 ).

In the 1970s and 1980s, bigger and better studies made most of the major quantitative genetic discoveries, going far beyond merely estimating heritability. But it was not all smooth sailing. Most notably, The Bell Curve resurrected many of the issues that followed Jensen’s 1969 paper (Herrnstein and Murray 1996 ). Nonetheless, by the 1990s, quantitative genetic research had convinced most scientists of the importance of genetics for behavioral traits, including intelligence (Snyderman and Rothman 1990 ). One symbol of this change was that the 1992 Centennial Conference of the American Psychological Association chose behavioral genetics as one of two themes that best represented the past, present, and future of psychology (Plomin and McClearn 1993 ). Then, just as quantitative genetic discoveries began to slow, the synthesis with molecular genetics began, which led to the DNA revolution and behavioral genomics.

Molecular Genetics

During its first 50 years, molecular genetics focused on single-gene disorders. In 1933, a Nobel prize was awarded to Thomas Hunt Morgan for mapping genes responsible for single-gene mutations in fruit flies (Morgan et al. 1923 ), but human mapping was stymied because only a few single-gene markers such as blood types were available – variants in DNA itself were not available for another fifty years. Research on single-gene effects discovered in pedigree studies only incidentally involved behavioral traits. For example, phenylketonuria, the most common single-gene metabolic disorder, was discovered in 1934 (Folling 1934 ) and shown to be responsible for 1% of the population institutionalized for severe intellectual disability.

In the 1940s, it became clear that DNA is the mechanism of heredity, culminating in the most famous paper in biology which proposed the double-helix structure of DNA (Watson and Crick 1953 ). An important milestone for human behavioral genetics was the discovery in 1959 that the most common form of intellectual disability, Down syndrome, was due to a trisomy of chromosome 21 (Lejeune et al. 1959 ).

In 1961, the genetic code was cracked showing that three-letter sequences of the four-letter alphabet of DNA coded for the 20 amino acids (Crick et al. 1961 ). Just as with quantitative genetics, the 1970s was a watershed decade that ushered in the second 50 years, the genomics era.

The Genomics Era

The era of genomics began in the 1970s when methods were developed to sequence DNA’s nucleotide bases (Sanger et al. 1977 ). In 2003, fifty years after the discovery of the double helix structure of DNA, the Human Genome Project identified the sequence of 92% of the three billion nucleotide bases in the human genome (Collins et al. 2003 ).

In the 1980s, the first common variants in DNA itself were discovered, restriction fragment length polymorphisms (RFLPs) (Botstein et al. 1980 ). RFLPs enabled linkage mapping for single-gene disorders and were the basis for DNA fingerprinting, which revolutionized forensics (Jeffreys 1987 ). Polymerase chain reaction (PCR) was also developed which facilitated genotyping by rapidly amplifying DNA fragments (Mullis et al. 1986 ). In the 1980s, these developments increased the pace of linkage mapping of single-gene disorders, many of which had cognitive consequences, such as phenylketonuria (Woo et al. 1983 ) and Huntington disease (Gusella et al. 1983 ). In the 1990s, DNA sequencing revealed thousands of single-nucleotide polymorphisms (SNPs), the most common DNA variant (Collins et al. 1997 ).

In the 1990s, linkage was also attempted for complex traits that did not show single-gene patterns of transmission, such as reading disability (Cardon et al. 1994 ), but these were unsuccessful because linkage, which traces chromosomal recombination between disease genes and DNA variants within families, is unable to detect small effect sizes (Plomin et al. 1994 ). Researchers then pivoted towards allelic association in unrelated individuals, which is much more powerful in detecting DNA variants of small effect size. An early example of association was an allele of the apolipoprotein E gene on chromosome 19 that was found in 40% of individuals with late-onset Alzheimer disease as compared to 15% in controls (Corder et al. 1993 ).

The downside of allelic association is that an association can only be detected if a DNA variant is itself the functional gene or very close to it. For this reason, and because genotyping each DNA variant was slow and expensive, the 1990s became the decade of candidate gene studies in which thousands of studies reported associations between complex behavioral traits and a few ‘candidate’ genes, typically neurotransmitter genes thought to be involved in behavioral pathways. However, these candidate-gene associations failed to replicate because these studies committed most of the sins responsible for the replication crisis (Ioannidis 2005 ). For example, when 12 candidate genes reported to be associated with intelligence were tested in three large samples, none replicated (Chabris et al. 2012 ).

Genome-wide Association

In 1996, an idea emerged that was the opposite of the candidate-gene approach: using thousands of DNA variants to systematically assess associations across the genome in large samples of unrelated individuals (Risch and Merikangas 1996 ). However, genome-wide association (GWA) seemed a dream because genotyping was slow and expensive.

The problem of genotyping each DNA variant in large samples was solved in the 2000s by the commercial availability of DNA microarrays, called SNP chips , which genotype hundreds of thousands of SNPs for an individual quickly, accurately, and inexpensively. SNP chips paved the way for GWA analyses. In 2007, the first major GWA analysis included 2000 cases for each of seven major disorders and compared SNP allele frequencies for these cases with controls (The Wellcome Trust Case Control Consortium 2007 ). Replicable associations were found but they were few in number and extremely small in effect size. Hundreds of GWA reports appeared in the next decade with similarly small effect sizes across the behavioral and biological sciences (Visscher et al. 2017 ), including cognitive traits such as educational attainment (Rietveld et al. 2013 ) and intelligence in childhood (Benyamin et al. 2014 ) and adulthood (Davies et al. 2011 ).

These GWA studies led to the realization that the biggest effect sizes were much smaller than anyone anticipated. For case-control studies, risk ratios were less than 1.1, and for dimensional traits, variance explained was less than 0.001. This meant that huge sample sizes would be needed to detect these miniscule effects, and thousands of these associations would be needed to account for heritability, which is usually greater than 50% for cognitive traits. Ever larger GWA samples scooped up more of these tiny effects. Most recently, a GWA meta-analysis with a sample size of 3 million netted nearly four thousand independent significant associations after correction for multiple testing, but the median effect size of these SNPs accounted for less than 0.0001 of the variance (Okbay et al. 2022 ).

A century after Fisher’s 1918 paper, the discovery of such extreme polygenicity (Boyle et al. 2017 ; Visscher et al. 2021 ) was a turning point in the voyage from behavioral genetics to behavioral genomics. GWA genotypes brought the two worlds of genetics together by making it possible to use GWA genotypes to create three sets of tools to investigate highly polygenic traits: quantitative genomics, polygenic scores, and causal modeling (see Fig.  1 ). When applied to behavioral traits, these tools constitute the new field of behavioral genomics.

Quantitative Genomics

What good are SNP associations that account for such tiny effects? The molecular genetic goal of tracking effects from genes to brain to behavior is daunting when the effects are so small. However, in contrast to this bottom-up approach from genes to behavior, the top-down perspective of behavioral genetics answered this question by using GWA genotypes to estimate quantitative genetic parameters of heritability and genetic correlations, which could be called quantitative genomics . The journey picked up speed as quantitative genomics led to three new milestones.

Genome-wide Complex Trait Analysis (GCTA). In 2011, the first new method was devised to estimate heritability and genetic correlations since twin and adoption designs in the early 1900s. GCTA (originally called GREML) uses GWA genotypes for large samples of unrelated individuals to compare overall SNP similarity to phenotypic similarity pair by pair for all pairs of individuals (Yang et al. 2011 ). The extent to which SNP similarity explains trait similarity is called SNP heritability because it is limited to heritability estimated by the SNPs on the SNP chip. Genetic correlations are estimated by comparing each pair’s SNP similarity to their cross-trait phenotypic similarity.

SNP heritability estimates are about half the heritability estimated by twin studies (Plomin and von Stumm 2018 ). This ‘missing heritability’ occurs because SNP heritability is limited to the common SNPs genotyped on current SNP chips, which also creates a ceiling for discovery in GWA research. Most SNPs are not common, and rare SNPs appear to be responsible for much of the missing heritability, at least for height (Wainschtein et al. 2022 ). Importantly, quantitative genomic estimates of genetic correlations are not limited in this way and thus provide estimates of genetic correlations similar to those from twin studies (Trzaskowski et al. 2013 ).

Linkage Disequilibrium Score (LDSC) Regression. In 2015, a second quantitative genomic method, LDSC, was published which estimates heritability and genetic correlations from GWA summary effect size statistics for each SNP, corrected for linkage disequilibrium between SNPs (Bulik-Sullivan et al. 2015 ). LDSC estimates of heritability and genetic correlations are similar to GCTA estimates, although GCTA estimates are generally more accurate (Evans et al. 2018 ; Ni et al. 2018 ). The advantage of LDSC is that it can be applied to published GWA summary statistics in contrast to GCTA which requires access to GWA data for individuals in the GWA study.

Genomic Structural Equation Modeling (Genomic SEM). In 2019, a third quantitative genomic analysis completed the arc from quantitative genetics to quantitative genomics by combining quantitative genetic structural equation model-fitting, routinely used in twin analyses, to LDSC heritabilities and genetic correlations (Grotzinger et al. 2019 ). Genomic SEM provides insights into the multivariate genetic architecture of cognitive traits (Grotzinger et al. 2019 ) and psychopathology (Grotzinger et al. 2022 ).

The second answer to the question about what to do with SNP associations that have such small effect sizes is the creation of polygenic scores.

Polygenic Scores

A milestone that marks the spot where the DNA revolution began to transform the behavioral sciences is polygenic scores. Rather than using GWA genotypes to estimate SNP heritabilities and genetic correlations, polygenic scores use GWA genotypes to create a single score for each individual that aggregates, across all SNPs on a SNP chip, an individual’s genotype for each SNP (0, 1 or 2) weighted by the SNP’s effect size on the target trait as indicated by GWA summary statistics. In 2001, polygenic scores were introduced in plant and animal breeding (Meuwissen et al. 2001 ) and later in cognitive abilities (Harlaar et al. 2005 ) and psychopathology (Purcell et al. 2009 ). GWA summary statistics needed to create polygenic scores are now publicly available for more than 500 traits, including dozens for psychiatric disorders and other behavioral traits including cognitive traits (PGS Catalog 2022 ).

The most predictive polygenic scores in the behavioral sciences are for cognitive traits, especially educational attainment and intelligence. Early GWA studies of cognitive traits were underpowered to detect the small effects that we now know are responsible for heritability (Plomin and von Stumm 2018 ). In 2013, a landmark was a GWA study of educational attainment with a sample size exceeding 100,000 (Rietveld et al. 2013 ). A polygenic score derived from its GWA summary statistics predicted 2% of the variance of educational attainment in independent samples. The finding that the biggest effects accounted for only 0.0002 of the variance of educational attainment made it clear that much larger samples would be needed to scoop up more of the tiny effects responsible for the twin heritability estimate of about 40%. In the past decade, the predictive power of polygenic scores for educational attainment has increased with increasing sample sizes from 2% (Rietveld et al. 2013 ) to 5% (Okbay et al. 2016 ) to 10% (Lee et al. 2018 ) to 14% in a GWA study with a sample size of three million (Okbay et al. 2022 ). The current polygenic score for intelligence, derived from a GWA study with a sample of 280,000, predicted 4% of the variance (Savage et al. 2018 ), but, together, the polygenic scores for educational attainment and intelligence predicted 10% of the variance of intelligence test scores (Allegrini et al. 2019 ).

The next milestone will be to narrow the gap between heritability explained by polygenic scores and SNP heritability. A more daunting challenge will be to break through the ceiling of SNP heritability to reach the heritability estimated by twin studies. Reaching both of these destinations will be facilitated by even larger GWA studies and whole-genome sequencing (Wainschtein et al. 2022 ).

Polygenic scores are unique predictors because inherited DNA variations do not change systematically during life – there is no backward causation in the sense that nothing in the brain, behavior or environment changes inherited differences in DNA sequence. For this reason, polygenic scores can predict behavioral traits from early in life without knowing anything about the intervening pathways between genes, brain, and behavior.

Polygenic scores have brought behavioral genetics to the forefront of research in many areas of the life sciences because polygenic scores can be created in any sample of unrelated individuals for whom GWA genotype data are available. No special samples of twins or adoptees are needed, nor is it necessary to assess behavioral traits in order to use polygenic scores to predict them.

Although the implications and applications of polygenic scores derive from its power to predict behavioral traits without regard to explanation (Plomin and von Stumm 2022 ), another milestone on the road to behavioral genomics has been the leverage provided by GWA genotypes for causal modeling.

Causal Modeling

A final milestone on the journey from behavioral genetics to behavioral genomics is a suite of new approaches that use GWA genotypes in causal models that attempt to dissect sources of genetic influence on behavioral traits (Pingault et al. 2018 ). Although traditional quantitative genetic models are causal models, GWA genotypes have enhanced causal modeling in research on assortative mating (Border et al. 2021 ; Yengo et al. 2018 ), population stratification (Abdellaoui et al. 2022 ; Lawson et al. 2020 ), and Mendelian randomization (Richmond and Davey Smith 2022 ).

An explosion of research on genotype-environment correlation was ignited by a 2018 paper in Science on the topic of the nature of nurture (Kong et al. 2018 ). The study included both parent and offspring GWA genotypes and showed that a polygenic score computed from non-transmitted alleles from parent to offspring influenced offspring educational attainment; these indirect effects were dubbed genetic nurture . GCTA has also been used to investigate genotype-environment correlation (Eilertsen et al. 2021 ). Although a great strength of behavioral genomics is its ability to investigate genetic influence in samples of unrelated individuals, combining GWA genotypes with traditional quantitative genetic designs has also enriched causal modeling (McAdams et al. 2022 ), for example, by comparing results within and between families (Brumpton et al. 2020 ; Howe et al. 2022 ).

This whistle-stop tour has highlighted some of the milestones in a century of research in behavioral genetics. The progress is unmatched in the behavioral sciences and its discoveries have been transformative. The most exciting development is the synthesis of quantitative genetics and molecular genetics into behavioral genomics. The energy from this fusion will propel the field far into the future.

Abdellaoui A, Dolan CV, Verweij KJH, Nivard MG (2022) Gene–environment correlations across geographic regions affect genome-wide association studies. Nat Genet 54:1345–1354. https://doi.org/10.1038/s41588-022-01158-0

Article   PubMed   PubMed Central   Google Scholar  

Allegrini AG, Selzam S, Rimfeld K et al (2019) Genomic prediction of cognitive traits in childhood and adolescence. Mol Psychiatry 24:819–827. https://doi.org/10.1038/s41380-019-0394-4

Benyamin B, Pourcain Bs, Davis OS et al (2014) Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry 19:253–258. https://doi.org/10.1038/mp.2012.184

Article   PubMed   Google Scholar  

Border R, O’Rourke S, de Candia T et al (2021) Assortative mating biases marker-based heritability estimators. Nat Genet 13:660. https://doi.org/10.1038/s41467-022-28294-9

Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331

PubMed   PubMed Central   Google Scholar  

Boyle EA, Li YI, Pritchard JK (2017) An expanded view of complex traits: from polygenic to omnigenic. Cell 169:1177–1186. https://doi.org/10.1016/j.cell.2017.05.038

Briley DA, Tucker-Drob EM (2013) Explaining the increasing heritability of cognitive ability across development: a meta-analysis of longitudinal twin and adoption studies. Psychol Sci 24:1704–1713. https://doi.org/10.1177/0956797613478618

Brumpton B, Sanderson E, Heilbron K et al (2020) Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses. Nat Commun 11:3519. https://doi.org/10.1038/s41467-020-17117-4

Bulik-Sullivan B, ReproGen C, Psychiatric Genomics Consortium et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241. https://doi.org/10.1038/ng.3406

Burks BS (1928) The relative influence of nature and nurture upon mental development; a comparative study of foster parent-foster child resemblance and true parent-true child resemblance. Teachers Coll Record 29:219–316. https://doi.org/10.1177/016146812802900917

Article   Google Scholar  

Cardon LR, Smith SD, Fulker DW et al (1994) Quantitative trait locus for reading disability on chromosome 6. Science 266:276–279. https://doi.org/10.1126/science.7939663

Chabris CF, Hebert BM, Benjamin DJ et al (2012) Most reported genetic associations with general intelligence are probably false positives. Psychol Sci 23:1314–1323. https://doi.org/10.1177/0956797611435528

Collins FS, Green ED, Guttmacher AE, Guyer MS (2003) A vision for the future of genomics research. Nature 422:835–847. https://doi.org/10.1038/nature01626

Collins FS, Guyer MS, Chakravarti A (1997) Variations on a theme: cataloging human dna sequence variation. Science 278:1580–1581. https://doi.org/10.1126/science.278.5343.1580

Corder EH, Saunders AM, Strittmatter WJ et al (1993) Gene dose of apolipoprotein e type 4 allele and the risk of alzheimer’s disease in late onset families. Science 261:921–923. https://doi.org/10.1126/science.8346443

Crick FHC, Barnett L, Brenner S, Watts-Tobin RJ (1961) General nature of the genetic code for proteins. Nature 192:1227–1232. https://doi.org/10.1038/1921227a0

Davies G, Tenesa A, Payton A et al (2011) Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Mol Psychiatry 16:996–1005. https://doi.org/10.1038/mp.2011.85

Eilertsen EM, Jami ES, McAdams TA et al (2021) Direct and indirect effects of maternal, paternal, and offspring genotypes: Trio-GCTA. Behav Genet 51:154–161. https://doi.org/10.1007/s10519-020-10036-6

Erlenmeyer-Kimling L, Jarvik LF (1963) Genetics and intelligence: a review. Science 142:1477–1479. https://doi.org/10.1126/science.142.3598.1477

Evans LM, Tahmasbi R, Vrieze SI et al (2018) Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nat Genet 50:737–745. https://doi.org/10.1038/s41588-018-0108-x

Fisher RA (1918) The correlation between relatives on the supposition of mendelian inheritance. Trans R Soc Edinb 52:399–433. https://doi.org/10.1017/S0080456800012163

Folling A (1934) Excretion of phenylpyruvic acid in urine as a metabolicnomaly in connection with imbecility.Nord Med Tidskr 8:1054-1059

Freeman FN, Holzinger KJ, Mithell BC (1928) The influence of environment on the intelligence, school achievement, and conduct of foster children. Yearbook of the National Society for the Study of Education 1:102–217

Google Scholar  

Fuller JL, Thompson WR (1960) Behavior genetics. Wiley, New York

Book   Google Scholar  

Galton F (1865) Hereditary talent and character. Macmillan’s Magazine 12:157–166

Galton F (1869) Hereditary genius: an inquiry into its laws and consequences. Collins, London

Grotzinger AD, Mallard TT, Akingbuwa WA et al (2022) Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat Genet 54:548–559. https://doi.org/10.1038/s41588-022-01057-4

Grotzinger AD, Rhemtulla M, de Vlaming R et al (2019) Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Nat Hum Behav 3:513–525. https://doi.org/10.1038/s41562-019-0566-x

Gusella JF, Wexler NS, Conneally PM et al (1983) A polymorphic DNA marker genetically linked to Huntington’s disease. Nature 306:234–238. https://doi.org/10.1038/306234a0

Hall CS (1951) The genetics of behavior. In: Stevens SS (ed) Handbook of experimental psychology. Wiley, New York, pp 304–329

Harlaar N, Butcher LM, Meaburn E et al (2005) A behavioural genomic analysis of DNA markers associated with general cognitive ability in 7-year-olds. J Child Psychol & Psychiat 46:1097–1107. https://doi.org/10.1111/j.1469-7610.2005.01515.x

Haworth CMA, Wright MJ, Luciano M et al (2010) The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry 15:1112–1120. https://doi.org/10.1038/mp.2009.55

Herrnstein RJ, Murray CA (1996) The bell curve: intelligence and class structure in American life, 1st Free Press pbk. ed. Simon & Schuster, New York

Holzinger KJ (1929) The relative effect of nature and nurture influences on twin differences. J Educ Psychol 20:241–248. https://doi.org/10.1037/h0072484

Howe LJ, Nivard MG, Morris TT et al (2022) Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat Genet 54:581–592. https://doi.org/10.1038/s41588-022-01062-7

Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2:e124. https://doi.org/10.1371/journal.pmed.0020124

Jeffreys AJ (1987) Highly variable minisatellites and DNA fingerprints. Biochem Soc Trans 15:309–317. https://doi.org/10.1042/bst0150309

Jensen A (1969) How much can we boost IQ and scholastic achievement? Harv Educational Rev 39:1–123. https://doi.org/10.17763/haer.39.1.l3u15956627424k7

Jinks JL, Fulker DW (1970) Comparison of the biometrical genetical, MAVA, and classical approaches to the analysis of the human behavior. Psychol Bull 73:311–349. https://doi.org/10.1037/h0029135

Jones HE (1928) A first study of parent-child resemblance in intelligence. Yearbook of the National Society for the Study of Education Part 1:61–72

Kamin LJ (1974) The science and politics of I.Q. Routledge, New York, NY

Kendler KS, Baker JH (2007) Genetic influences on measures of the environment: a systematic review. Psychol Med 37:615–626. https://doi.org/10.1017/S0033291706009524

Kong A, Thorleifsson G, Frigge ML et al (2018) The nature of nurture: Effects of parental genotypes. Science 359:424–428. https://doi.org/10.1126/science.aan6877

Lauterbach CE (1925) Studies in twin resemblance. Genetics 10:525–568. https://doi.org/10.1093/genetics/10.6.525

Lawson DJ, Davies NM, Haworth S et al (2020) Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity? Hum Genet 139:23–41. https://doi.org/10.1007/s00439-019-02014-8

Lee JJ, Wedow R, Okbay A et al (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50:1112–1121. https://doi.org/10.1038/s41588-018-0147-3

Lejeune J, Gautier M, Turpin R (1959) [Study of somatic chromosomes from 9 mongoloid children]. C R Hebd Seances Acad Sci 248:1721–1722

PubMed   Google Scholar  

Lindzey G, Loehlin J, Manosevitz M, Thiessen D (1971) Behavioral genetics. Annu Rev Psychol 22:39–94. https://doi.org/10.1146/annurev.ps.22.020171.000351

Loehlin JC (2009) History of behavior genetics. In: Kim Y-K (ed) Handbook of behavior genetics. Springer New York, New York, NY, pp 3–11

Chapter   Google Scholar  

Loehlin JC, Nichols RC (1976) Heredity, environment, and personality: a study of 850 sets of twins. University of Texas Press, Austin, Texas

Martin NG, Eaves LJ (1977) The genetical analysis of covariance structure. Heredity 38:79–95. https://doi.org/10.1038/hdy.1977.9

Maxson SC (2007) A history of behavior genetics. In: Jones BC, Mormee P (eds) Neurobehavioral genetics: methods and applications, 2nd edn. CRC Press, New York, pp 1–16

McAdams TA, Cheesman R, Ahmadzadeh YI (2022) Annual Research Review: towards a deeper understanding of nature and nurture: combining family-based quasi‐experimental methods with genomic data. Child Psychol Psychiatry jcpp 13720. https://doi.org/10.1111/jcpp.13720

McClearn GE (1971) Behavioral genetics. Syst Res 16:64–81. https://doi.org/10.1002/bs.3830160106

McGue M (2008) The end of behavioral genetics? Acta Physiol Sinica 40:1073–1087. https://doi.org/10.3724/sp.j.1041.2008.01073

McGue M, Bouchard TJ, Iacono WG, Lykken (1993) Behavioral genetics of cognitive ability: a life-span perspective. In: Plomin R, McClearn GE, McGuffin P (eds) Nature, nurture and psychology. American Psychological Association, Washington, DC, pp 59–76

Mendel G (1866) Versuche ueber Pflanzenhybriden. Verhandllungen des Naturforschhunden Vereines in Bruenn 4:3–47

Merriman C (1924) The intellectual resemblance of twins. Psychol Monogr 33:i–57. https://doi.org/10.1037/h0093212

Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829. https://doi.org/10.1093/genetics/157.4.1819

Morgan TH, Sturtevant AH, Muller HJ, Bridges CB (1923) The mechanism of Mendelian heredity. H. Holt and Company

Mullis K, Faloona F, Scharf S et al (1986) Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb Symp Quant Biol 51:263–273. https://doi.org/10.1101/SQB.1986.051.01.032

Newman HH, Freeman FN, Holzinger KJ (1937) Twins: a study of heredity and environment. University of Chicago Press

Ni G, Moser G, Wray NR et al (2018) Estimation of genetic correlation via linkage disequilibrium score regression and genomic restricted maximum likelihood. Am J Hum Genet 102:1185–1194. https://doi.org/10.1016/j.ajhg.2018.03.021

Nichols RC (1965) The National Merit Twin Study. In: Vandenberg SG (ed) Methods and goals of human behavior genetics. Academic Press, New York, pp 231–243

Nichols RC (1978) Twin studies of ability, personality, and interests. Homo 29:158–173

Okbay A, Beauchamp JP, Fontana MA et al (2016) Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533:539–542. https://doi.org/10.1038/nature17671

Okbay A, Wu Y, Wang N et al (2022) Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat Genet 54:437–449. https://doi.org/10.1038/s41588-022-01016-z

PGS Catalog (2022) PGS Catalog. http://www.pgscatalog.org/

Pingault J-B, O’Reilly PF, Schoeler T et al (2018) Using genetic data to strengthen causal inference in observational research. Nat Rev Genet 19:566–580. https://doi.org/10.1038/s41576-018-0020-3

Pinker S (2003) The blank slate: the modern denial of human nature. Penguin, London

Plomin R (2011) Commentary: why are children in the same family so different? Non-shared environment three decades later. Int J Epidemiol 40:582–592. https://doi.org/10.1093/ije/dyq144

Plomin R, Bergeman CS (1991) The nature of nurture: genetic influence on “environmental” measures. Behav Brain Sci 14:373–386. https://doi.org/10.1017/S0140525X00070278

Plomin R, Daniels D (1987) Why are children in the same family so different from one another? Behav Brain Sci 10:1–16. https://doi.org/10.1017/S0140525X00055941

Plomin R, Kovas Y (2005) Generalist genes and learning disabilities. Psychol Bull 131:592–617. https://doi.org/10.1037/0033-2909.131.4.592

Plomin R, Loehlin JC, DeFries JC (1985) Genetic and environmental components of “environmental” influences. Dev Psychol 21:391–402. https://doi.org/10.1037/0012-1649.21.3.391

Plomin R, McClearn GE (eds) (1993) Nature, nurture, & psychology. American Psychological Association, Washington, DC

Plomin R, Owen M, McGuffin P (1994) The genetic basis of complex human behaviors. Science 264:1733–1739. https://doi.org/10.1126/science.8209254

Plomin R, von Stumm S (2018) The new genetics of intelligence. Nat Rev Genet 19:148–159. https://doi.org/10.1038/nrg.2017.104

Plomin R, von Stumm S (2022) Polygenic scores: prediction versus explanation. Mol Psychiatry 27:49–52. https://doi.org/10.1038/s41380-021-01348-y

Polderman TJC, Benyamin B, de Leeuw CA et al (2015) Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet 47:702–709. https://doi.org/10.1038/ng.3285

Purcell SM, Wray NR, Stone JL et al (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748–752. https://doi.org/10.1038/nature08185

Richmond RC, Davey Smith G (2022) Mendelian randomization: concepts and scope. Cold Spring Harb Perspect Med 12:a040501. https://doi.org/10.1101/cshperspect.a040501

Rietveld CA, Medland SE, Derringer J et al (2013) GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340:1467–1471. https://doi.org/10.1126/science.1235488

Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517. https://doi.org/10.1126/science.273.5281.1516

Ritchie S (2021) Science fictions: exposing fraud, bias, negligence and hype in science. Vintage, London

Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA 74:5463–5467. https://doi.org/10.1073/pnas.74.12.5463

Savage JE, Jansen PR, Stringer S et al (2018) Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Genet 50:912–919. https://doi.org/10.1038/s41588-018-0152-6

Scarr S, Carter-Saltzman L (1982) Genetics and intelligence. In: Sternberg RJ (ed) Handbook of human intelligence. Cambridge University Press, Cambridge, pp 792–895

Scarr S, Weinberg RA (1978) The influence of “family background” on intellectual attainment. Am Sociol Rev 43:674. https://doi.org/10.2307/2094543

Schoenfeldt LF (1968) The hereditary components of the Project TALENT two-day test battery. Meas Evaluation Guidance 1:130–140. https://doi.org/10.1080/00256307.1968.12022379

Skodak M, Skeels HM (1949) A final follow-up study of one hundred adopted children. The Pedagogical Seminary and Journal of Genetic Psychology 75:85–125. https://doi.org/10.1080/08856559.1949.10533511

Snyderman M, Rothman S (1990) The IQ controversy, the media and public policy. Transaction, New Brunswick, NJ

Tallman GG (1928) A comparative study of identical and non-identical twins with respect to intelligence resemblances. Teachers Coll Record 29:83–86. https://doi.org/10.1177/016146812802900912

The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678. https://doi.org/10.1038/nature05911

Article   PubMed Central   Google Scholar  

Thorndike EL (1928) The resemblance of siblings in intelligence. Teachers Coll Record 29:41–53. https://doi.org/10.1177/016146812802900904

Trzaskowski M, Davis OSP, DeFries JC et al (2013) DNA evidence for strong genome-wide pleiotropy of cognitive and learning abilities. Behav Genet 43:267–273. https://doi.org/10.1007/s10519-013-9594-x

Turkheimer E (2000) Three laws of behavior genetics and what they mean. Curr Dir Psychol Sci 9:160–164. https://doi.org/10.1111/1467-8721.00084

Urbach P (1974) Progress and degeneration in the “IQ debate” I and II. Br J Philosophical Sci 25:99–135

Visscher PM, Wray NR, Zhang Q et al (2017) 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet 101:5–22. https://doi.org/10.1016/j.ajhg.2017.06.005

Visscher PM, Yengo L, Cox NJ, Wray NR (2021) Discovery and implications of polygenicity of common diseases. Science 373:1468–1473. https://doi.org/10.1126/science.abi8206

Wainschtein P, Jain D, Zheng Z et al (2022) Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat Genet 54:263–273. https://doi.org/10.1038/s41588-021-00997-7

Watson JB (1930) Behaviorism, revised. University of Chicago Press, Chicago

Watson JD, Crick FHC (1953) Genetical implications of the structure of deoxyribonucleic acid. Nature 171:964–967. https://doi.org/10.1038/171964b0

Wilson RS (1983) The Louisville Twin Study: developmental synchronies in behavior. Child Dev 54:298. https://doi.org/10.2307/1129693

Woo SLC, Lidsky AS, Güttler F et al (1983) Cloned human phenylalanine hydroxylase gene allows prenatal diagnosis and carrier detection of classical phenylketonuria. Nature 306:151–155. https://doi.org/10.1038/306151a0

Woodworth RS (1941) Heredity and environment: a critical survey of recently published material on twins and foster children. Social Science Research Council, New York

Yang J, Manolio TA, Pasquale LR et al (2011) Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43:519–525. https://doi.org/10.1038/ng.823

Yengo L, Robinson MR, Keller MC et al (2018) Imprint of assortative mating on the human genome. Nat Hum Behav 2:948–954. https://doi.org/10.1038/s41562-018-0476-3

Download references

Acknowledgements

This work was supported in part by the UK Medical Research Council (MR/V012878/1 and previously MR/M021475/1).

Author information

Authors and affiliations.

Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

The author wrote, reviewed, and submitted the manuscript.

Corresponding author

Correspondence to Robert Plomin .

Ethics declarations

Conflict of interest.

The author has no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This review was based on a talk given at the 52nd Behavior Genetics Association Annual Meeting, Los Angeles, California, June 25, 2022.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Plomin, R. Celebrating a Century of Research in Behavioral Genetics. Behav Genet 53 , 75–84 (2023). https://doi.org/10.1007/s10519-023-10132-3

Download citation

Received : 28 December 2022

Revised : 28 December 2022

Accepted : 04 January 2023

Published : 20 January 2023

Issue Date : March 2023

DOI : https://doi.org/10.1007/s10519-023-10132-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Quantitative genetics
  • Molecular genetics
  • Genome-wide association
  • Polygenic scores
  • Intelligence
  • Find a journal
  • Publish with us
  • Track your research

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

  • Advanced Search
  • Journal List
  • HHS Author Manuscripts

Logo of nihpa

THE BIOLOGY OF RELATIONSHIPS: WHAT BEHAVIORAL GENETICS TELLS US ABOUT INTERACTIONS AMONG FAMILY MEMBERS

Laura a. baker.

* Department of Psychology, University of Southern California

Introduction

Human behavior is subject to genetic variations. The ways in which individuals differ in their intellectual abilities, personalities, and mental health are, to a large extent, functions of their inherited genetic predispositions. Decades of research on twins, adoptees, and families have led to the inescapable conclusion that most reliably measured psychological characteristics are influenced to some degree by genes. Behavior also shows signs of genetic influence; the way one experiences stressful life events, for example, shows some genetic influence. Even personal aspects of individuals, such as spirituality and political ideology, are affected to an extent by genes. 1 It should come as no surprise, then, that genes influence the ways in which families function and how family members relate to one another. 2 Familial relationships of all kinds—parent-child, sibling, and spousal—can be shown to be at least partially the product of genetic factors.

This Article discusses a behavioral genetic perspective that provides insight into the biological factors that influence family relationships. Part II presents a brief overview of the research methods used to understand both genetic and environmental influences on human behavior. Part III then discusses several key findings from the field of behavioral sciences, particularly how they pertain to the ways in which family members relate to one another. It focuses on the following: (1) characteristics of parents and variations in the ways they treat their own children; (2) characteristics of children and how they may react to their parents’ behavior; and (3) the interactive processes that occur between parents and children. While the primary focus of Part III is on parent-child relationships, Part IV considers sibling and spousal relationships. Part V discusses the general interpretation of family relationships from a behavioral genetic perspective.

II. Twin, Family, and Adoption Studies

What is the evidence for the overwhelming influence of genetic factors on human psychological function? What does it mean to say that psychological dimensions of family relations are a function of genes? Answering these questions requires a basic understanding of behavioral genetic studies, which help to separate the effects of genes and environment in human behavior.

The general strategy in behavioral-genetic research designs involves the study of family members with varying degrees of genetic and environmental relatedness. 3 For example, genetic influences in a trait are evident if pairs of monozygotic (MZ) twins (who are genetically identical) are more similar to one another than dizygotic (DZ) twins (who share only about 50% of their genes), or if pairs of biological siblings raised together resemble one another more than unrelated (e.g., adoptive) siblings raised together. In general, if psychological traits and observed behavior have a genetic component, then genetically similar relatives should resemble one another more closely than individuals who share fewer genes. 4

Regarding environmental influences, researchers in behavioral genetics typically distinguish between two broad classes of effects: (1) environmental factors shared by relatives that cause them to behave similarly; and (2) unique, individual environments that are not shared by relatives, which cause them to be different from one another. These are referred to, respectively, as shared and nonshared environments. Shared environmental influences are evident when greater trait similarity is observed for those relatives who share more experiences (e.g., siblings raised together rather than apart), or when twins are more similar to one another than their genetic relatedness would predict. Evidence for nonshared environment often stems from differences observed between genetic relatives—that is, their lack of resemblance. Differences between MZ co-twins, for example, must stem from nonshared environments. The study of the similarities and differences between relatives of varying degrees of genetic and environmental relatedness provide the basic data for understanding the effects of genes, and thus the influence of shared and nonshared environments on behavior. 5

Within a few decades of the earliest twin, family, and adoption studies (which grew immensely from the 1970s onward), genetic factors were implicated in a wide range of human behaviors, such as cognitive ability and personality, as well as most major psychological disorders, such as depression and schizophrenia. 6 Collectively, these studies show that family members who are more closely related genetically demonstrate greater similarity than unrelated individuals for measured aspects of personality (e.g., extraversion or neuroticism), intellectual function (e.g., verbal skills and spatial ability), and likelihood of being diagnosed with a psychological disorder (e.g., depression or schizophrenia). 7

For a while, it was considered a challenge to find an enduring aspect of behavior that did not appear to be influenced by genes. Constructs such as religious behaviors and political attitudes, which had traditionally been understood to be strictly the product of culture, became the subject of behavioral genetic studies. Somewhat surprisingly, even these culturally defined behaviors appear to be influenced by genetic variations, at least within groups of individuals. For example, although one’s religion may be culturally defined and thus independent of genetic influences, the degree to which one engages in the rituals or adheres to the tenets of a particular religion appear to be affected by one’s genetic inheritance. 8 Indeed, even the degree to which an individual may endorse highly liberal or conservative ideals (e.g., abortion rights or gay rights) has been shown to be influenced by genetic factors; MZ twins are much more similar than DZ twins, and biological siblings are more similar than adoptive siblings in conservative attitudes from adolescence onward. 9

Around the same time these culturally defined behaviors became the subject of behavioral genetic research, investigators began to study other variables that were traditionally viewed as entirely “environmental” factors. This research challenged a long-standing social learning perspective in developmental psychology. What were traditionally considered to be “environmental” measures—including aspects of parenting—came to be understood as products of both genes and environment. Thus, we turn back now to the issue at hand: the various aspects of family relationships and how they are influenced by the complex interplay between genes and environment.

III. How the Behavior of Parents and children Is Influenced by Genes and Environment

Genes influence each individual’s behavioral and psychological characteristics, including intellectual ability, personality, and risk for mental illness—all of which have bearing on both parents and children within a family. The ways in which genes and environment can affect parent-child relationships can be seen in Figure 1 . This model represents a standard way in which behavioral geneticists think about human behavior in the context of family relationships. Parents’ genes influence their own behavior (including the ways they parent their children) and children’s genes influence their own behavior (including the ways they respond to their parents). The transmission of genes from parent to child is one important link that will lead to similarities between the behavior of a parent and a child. For example, to the extent that genes predispose an individual toward aggressive behavior, including violence toward others, parents and children will show similarities in this area of behavior. This might offer another explanation for the “cycle of violence” in which abusive parenting is related to aggression and other antisocial behaviors in children. 10 Antisocial behavior does, in fact, show moderate genetic influence in a wide range of studies. 11

An external file that holds a picture, illustration, etc.
Object name is nihms741938f1.jpg

Behavioral Genetic Model of Parent-Child Behavior

Besides direct genetic transmission, the model in Figure 1 indicates two other important ways in which the behavior of parents and children may be linked. First, parental behavior may itself be an important aspect of the child’s environment, which may be considered a form of “cultural transmission.” For example, a mother’s intelligence, personality, and mental health may have an impact on the child’s environment; mothers with higher intelligence and education spend more time reading to their children and engaging them in stimulating activities. 12 Importantly, however, these characteristics may each be influenced by the mother’s genetic makeup, and thus it can be seen how the mother’s genotype may ultimately be associated with the child’s environment. The association between genes and environment is generally referred to as a genotype-environment correlation (r GE ). One way in which r GE may arise is through this passive form of cultural transmission, which is referred to as a passive r GE . 13

The third link between the behavior of parents and children is established through the “evocative responses” that children’s behavior may elicit from their parents. Because a child’s behavior is itself influenced by the child’s genes, genetically different children living in the same family may elicit different parenting responses. This may result in another form of a genotype-environment correlation, an r GE of an evocative form. 14 That is, genetically based differences among children (e.g., temperament characteristics) may evoke different responses from their parents (e.g., disciplinary styles). Thus, genes and environment may be intertwined in complex ways within parent-child relationships.

These complexities can be unraveled by twin, family, and adoption studies. Genetic influences on parenting behavior can be understood by examining the similarities and differences in adult twins’ parenting styles. The parenting styles of adult twins—as measured by positivity, negativity, and monitoring of their children—were more highly correlated for MZ than DZ twins. 15 Reviews of other studies show similar patterns, in which parents’ genes influence the ways in which they parent their children. 16

Evidence of parental behaviors evoked by children has been demonstrated by studying how parents respond differently to two or more children in the same family, such as twins and other siblings. DZ twin children, for example, have reported more differences than MZ twins in levels of affect and warmth received by their parents, 17 a finding that has been replicated by using reports from parents about their own behavior, as well as by observing parents interacting with their different children. 18 Studies of adopted children have also revealed evocative responses in the rearing parents as a function of the child’s genetic predispositions, as measured by characteristics in their birth parents. More coercive parenting and negative affect were reported by the adoptive parents of children born to more antisocial parents. 19 These genetically high risk children displayed more conduct problems as children and adolescents, 20 and thus may have elicited more negative parenting. The key point is that the direction of causality may not necessarily run from parent to child; when children elicit parental behaviors, it can move in the reverse direction.

Passive r GE effects are best understood in studies comparing parent-child relationships in adopted and nonadopted children. Since adopted children are not genetically related to their rearing parents, the passive r GE does not influence their similarity, because the parents’ genes are not linked to the children’s environments. If passive r GE effects arise, whether through cultural transmission effects or other mechanisms, 21 correlations between parenting characteristics and child outcomes should be stronger when parents are raising their own genetic children. In fact, one study of adoptive and nonadoptive families found that parents’ ratings of family cohesion, low conflict, and open communication about feelings in early childhood were associated with lower ratings of aggression at age seven, but only for nonadopted children. 22 This link between early environment and child outcome was not found for adopted children, suggesting that passive gene-environment correlations may exist in nonadoptive families that have increased similarity compared to adoptive families. 23

Like other areas of human behavior, parenting itself is subject to genetic influence. This means that “bad parenting” may itself be influenced by the parents’ genetic inheritance. Negative affect, over-control, and even abuse and neglect could be related to the genetic makeup of the parents. This does not mean that environmental factors are unimportant, nor does it make such behavior excusable. It just means that genes can explain parenting behavior to some degree.

It is almost certain that parenting has an environmental influence on children. The fact that parental behavior—including parenting style—may be influenced by genes does not imply that such behaviors have no environmental impact on the children that receive such parenting. What are the best methods for testing the true environmental mediation of the relationship between parent and child behavior? Behavioral genetic designs—adoption and extended twin studies—actually provide the ideal methods for identifying environmental effects while controlling for genetic factors. 24 Behavioral genetic studies have helped resolve the issue of genetic and environmental effects in abusive parenting and its relationship to later behavior problems in children by studying, for example, differences in the physical maltreatment of co-twins. Twin resemblance for maltreatment was substantial and equal for MZ pairs and DZ pairs, suggesting that children’s genetic differences did not elicit abusive parenting. This does not rule out the possibility, however, that parents’ genes may have influenced their abusive parenting. Most importantly, associations between abusive parenting and a child’s later antisocial behavior remained significant even after controlling for genetic differences in the children. 25 It is noteworthy that this genetically informative study provided convincing evidence of an environmental effect of abusive parenting on child outcomes.

The environmental effects of abuse on child development have also been shown to be exacerbated by a child’s genetic predispositions. Children who inherited a deleterious gene that causes a deficiency in monoamine oxidase (MAO-A) appear particularly vulnerable to physical maltreatment, compared to children with a normal MAO-A gene. 26 These findings underscore the importance of genotype X environment interactions, 27 in which genetic predispositions amplify environmental vulnerabilities and vice versa. We can expect that a more detailed understanding of this complex interplay between specific genetic mechanisms and measured environments will emerge over the next few years, as more studies begin to obtain DNA markers of genetic variations.

IV. Other Family Relationships

Behavioral geneticists have also studied family relationships beyond that of the parent and child. Sibling interactions, for example, have been examined in both twin and non-twin siblings. Unlike parents and children, who always share exactly half of their genes, siblings vary in their degree of genetic relatedness. MZ twins are genetically identical; DZ twins and non-twin siblings share about half of their genes, although some pairs may share more or less genetic material. This variation in genetic relatedness could explain why some siblings have a more cooperative and close relationship than others. Genetic similarity among siblings has been shown to affect both their positive and negative interactions with one another, 28 as well as levels of mutual competition and cooperation. 29 In general, siblings who share a stronger genetic makeup demonstrate a closer, more cooperative and positive relationship with one another.

Genetic variations among siblings living in the same family have also been suggested as an important source of differential parenting. The differential parenting of two siblings, albeit stemming originally from their genetic differences, has an environmental effect on the children’s psychological outcomes and may amplify sibling differences over time.

The quality of the relationship between marital partners has also been a subject of behavioral genetic studies. Twin similarity for marital satisfaction has been reported to be greater for MZ pairs than for DZ pairs, 30 suggesting the importance of individual genetic factors in determining the success of a marriage. Indeed, twin studies have also shown significantly greater concordance for divorce among MZ pairs than among DZ pairs, suggesting a substantial genetic effect on the likelihood of a failed marriage. 31 Genetically influenced personality traits, such as negative emotionality (i.e., neuroticism), are also predictive of divorce, and may explain much of the genetic risk for divorce. 32

V. Conclusion

One lesson to be learned from behavioral genetic studies of parenting and other types of family relationships is that one must be careful in drawing conclusions based on findings of family resemblance in nuclear, nonadoptive families. Consider the well-known finding that children of abusers are likely to become aggressive and violent, and perhaps even become abusive parents themselves later in life. 33 Although it is tempting to assume such resemblance is a function of learning and experience, it is possible that inherited genetic factors could explain the transmission of abuse across generations. Family resemblance for a given characteristic does not necessarily imply either genetic or environmental influence, since either could explain observed similarity among family members. Thus, the mere fact that children who are abused by their parents are more likely to become abusive themselves does not prove a causal relationship between parenting behaviors and child outcome. Through genetically controlled studies, we have come to understand that both genes and environment play a role in the cycle of violence. 34 Genes may predispose certain adults toward violence and aggression, even toward their own children. Such behaviors can in turn have a real environmental impact on the child’s mental health and on behavioral outcomes. Children’s genes may also predispose them toward oppositional and other antisocial behaviors, which may elicit negative parenting from the adults who are raising them.

The fact that genetic influences are crucially important for most areas of behavior does not mean that environmental influences are unimportant. Genes typically account for no more than one-half to two-thirds of the variation seen in most individual’s psychological traits. But most environmental influences are based on individual experiences and exposures that are not shared by family members. The implication for families is that most observed resemblance among its individual members is a function of their genetic similarity—not their shared experiences.

Finally, behavioral genetic studies of family relationships provide the valuable information required to develop effective programs of intervention and prevention of serious mental health and behavioral problems. Establishing that environmental effects unequivocally mediate links between parents’ and children’s behavior is a step toward ensuring the success of treatment programs that target either parents or children.

NIMH Logo

Transforming the understanding and treatment of mental illnesses.

Información en español

Celebrating 75 Years! Learn More >>

  • About the Director
  • Advisory Boards and Groups
  • Strategic Plan
  • Offices and Divisions
  • Careers at NIMH
  • Staff Directories
  • Getting to NIMH

Photo of Joshua A. Gordon, M.D., Ph.D.

Decoding the Mind: Basic Science Revolutionizes Treatment of Mental Illnesses

By Linda Brady, Margaret Grabb, Susan Koester, Yael Mandelblat-Cerf, David Panchision, Jonathan Pevsner, Ashlee Van’t-Veer, and Aleksandra Vicentic on behalf of the NIMH Division of Neuroscience and Basic Behavioral Science

March 21, 2024 • 75th Anniversary

Follow the NIMH Director on

For 75 years, NIMH has transformed the understanding and treatment of mental illnesses through basic and clinical research—bringing hope to millions of people. This Director’s Message, guest written by NIMH’s Division of Neuroscience and Basic Behavioral Science , is part of an anniversary series celebrating this momentous milestone.

The Division of Neuroscience and Basic Behavioral Science (DNBBS) at the National Institute of Mental Health (NIMH) supports research on basic neuroscience, genetics, and basic behavioral science. These are foundational pillars in the quest to decode the human mind and unravel the complexities of mental illnesses.

At NIMH, we are committed to supporting and conducting genomics research as a priority research area . As the institute celebrates its 75th Anniversary , we are spotlighting DNBBS-supported efforts connecting genes to cells to circuits to behavior that have led to a wealth of discoveries and knowledge that can improve the diagnosis, treatment, and prevention of mental illnesses.

Making gene discoveries

Illustration of a human head showing a brain and DNA.

Medical conditions often run in families. For instance, if someone in your immediate family has high blood pressure, you are more likely to have it too. It is the same with mental disorders—often they run in families. NIMH is supporting research into human genetics to better understand why this occurs. This research has already led to the discovery of hundreds of gene variants that make us more or less likely to develop a mental disorder.

There are two types of genetic variation: common and rare. Common variation refers to DNA changes often seen in the general population, whereas rare variation is DNA changes found in only a small proportion of the population. Individually, most common gene variants have only a minor impact on the risk for a mental disorder. Instead, most disorders result from many common gene variants that, together, contribute to the risk for and severity of that disorder.

NIMH is committed to uncovering the role of genes in mental disorders with the aim of improving the lives of people who experience them. One of the many ways NIMH contributes to the discovery of common gene variants is by supporting the Psychiatric Genomics Consortium (PGC)   . The consortium of almost 1,000 scientists across the globe, including ones in the NIMH Intramural Research Program and others conducting NIMH-supported research, is one of the largest and most innovative biological investigations in psychiatry.

Global collaborations such as the PGC are critical to amassing the immense sample sizes needed to identify common gene variants. Data from the consortium’s almost one million participants have already led to transformative insights about genetic contributors to mental illnesses and the genetic relationships of these illnesses to each other. To date, studies conducted as part of the consortium have uncovered common variation in over a dozen mental illnesses.

In contrast to common gene variants, rare gene variants are very uncommon in the general population. When they do occur, they often have a major impact on the occurrence of an illness, particularly when they disrupt gene function or regulation. Rare variants involving mutations in a single gene have been linked to several mental disorders, often through NIMH-supported research. For instance, a recent NIMH-funded study found that rare variation in 10 genes substantially increased the risk for schizophrenia. However, it is important to note that genetics is not destiny; even rare variants only raise the risk for mental disorders, but many other factors, including your environment and experiences, play important roles as well.

Because of the strong interest among researchers and the public in understanding how genes translate to changes in the brain and behavior, NIMH has developed a list of human genes associated with mental illnesses. These genes were identified through rare variation studies and are meant to serve as a resource for the research community. The list currently focuses on rare variants, but NIMH plans to continue expanding it as evidence accumulates for additional gene variants (rare or common).

Moreover, mental illnesses are a significant public health burden worldwide . For this reason, NIMH investments in genomics research extend across the globe. NIMH has established the Ancestral Populations Network (APN) to make genomics studies more diverse and shed light on how genetic variation contributes to mental disorders across populations. APN currently includes seven projects with more than 100 researchers across 25 sites worldwide.

World map showing the location of projects in the Ancestral Populations Network: USA, Mexico, Ecuador, Peru, Chile, Colombia, Brazil, Argentina, Nigeria, South Africa, Uganda, Ethiopia, Kenya, Pakistan, India, Singapore, Taiwan, and South Korea.

Connecting biology to behavior

While hundreds of individual genes have been linked to mental illnesses, the function of most of these genes in the brain remains poorly understood. But high-tech advances and the increased availability of computational tools are enabling researchers to begin unraveling the intricate roles played by genes.

In addition to identifying genetic variation that raises the risk for mental illnesses, NIMH supports research that will help us understand how genes contribute to human behavior. This information is critical to discovering approaches to diagnose, treat, and ultimately prevent or cure mental illnesses.

An NIMH-funded project called the PsychENCODE consortium   focuses on understanding how genes impact brain function. PsychENCODE is furthering knowledge of how gene risk maps onto brain function and dysfunction by cataloging genomic elements in the human brain and studying the actions of different cell types. The PsychENCODE dataset currently includes multidimensional genetic data from the postmortem brains of thousands of people with and without mental disorders.

Findings from the first phase of PsychENCODE were published as a series of 11 papers   examining functional genomics in the developing and adult brains and in mental disorders. A second batch of PsychENCODE papers will be published later this year. These findings help clarify the complex relationships between gene variants and the biological processes they influence.

PsychENCODE and other NIMH-supported projects are committed to sharing biospecimens quickly and openly to help speed research and discovery.

Logo for the NIMH Repository and Genomics Resource showing a brain and a test tube.

Facilitating these efforts is the NIMH Repository and Genomics Resource (NRGR)   , where samples are stored and shared. NRGR includes hundreds of thousands of samples, such as DNA, RNA, and cell lines, from people with and without mental disorders, along with demographic and diagnostic information.

Logo for the Scalable and Systematic Neurobiology of Psychiatric and Neurodevelopmental Disorder Risk Genes (SSPsyGene) showing a brain made of puzzle pieces.

Another NIMH initiative to connect risk genes to brain function is Scalable and Systematic Neurobiology of Psychiatric and Neurodevelopmental Disorder Risk Genes (SSPsyGene) . This initiative uses cutting-edge techniques to characterize the biological functions of 250 mental health risk genes—within the cells where they are expressed—to better understand how those genes contribute to mental illnesses. By systematically characterizing the biological functions of risk genes in cells, SSPsyGene will empower researchers to learn about biological pathways that may serve as new targets for treatment.

Genes also affect behavior by providing the blueprint for neurons, the basic units of the nervous system. Neurons communicate with each other via circuits in the brain, which enables us to process, integrate, and convey information. NIMH supports many initiatives to study the foundational role of neural networks and brain circuits in shaping diverse mental health-related behaviors like mood, learning, memory, and motivation.

For instance, studies supported through a basic-to-translational science initiative at NIMH focus on modifying neural activity to improve cognitive, emotional, and social processing  . Similarly, another new funding opportunity encourages studies in humans and animals examining how emotional and social cues are represented across brain circuits  to help address a core deficit in many mental disorders. These studies will increase understanding of the biological mechanisms that support behavior throughout life and offer interventions to improve these functions in healthy and clinical populations.

Developing treatments and therapeutics

The gene discovery and biology-to-behavior programs described here will lay the foundation for delivering novel therapeutics. To be prepared to rapidly implement findings from this research, NIMH supports several initiatives to identify behavioral and biological markers for use in clinical studies and increase our ability to translate research into practice.

Through its therapeutics discovery research programs , NIMH advances early stage discovery and development studies in humans and early efficacy trials for mental disorders. Taking these efforts a step further, NIMH supports the National Cooperative Drug Discovery/Development Groups for the Treatment of Mental Disorders , which encourage public–private partnerships to accelerate the discovery and development of novel therapeutics and new biomarkers for use in human trials. Moreover, NIMH is one of several institutes and centers in the NIH Blueprint Neurotherapeutics Network  , launched to enable neuroscientists in academia and biotechnology companies to develop new drugs for nervous system disorders.

Graphic showing advancing pathway from exploratory and hit-to lead to lead optimization to scale up and manufacturing to IND enabling, to Phase 1 clinical trial and with exit outcomes of external funding and partnerships, other grants, and attrition.

For the treatments of tomorrow, NIMH is building a new research program called Pre-Clinical Research on Gene Therapies for Rare Genetic Neurodevelopmental Disorders  , which encourages early stage research to optimize gene therapies to treat disorders with prominent cognitive, social, or affective impairment. In parallel, NIMH’s Planning Grants for Natural History Studies of Rare Genetic Neurodevelopmental Disorders  encourage the analysis of pre-existing data from people with rare disorders to learn about disease progression and enable future clinical trials with these populations.

NIMH's Division of Neuroscience and Basic Behavioral Science supports many different research projects that help us learn about genes and gene functions, how the brain develops and works, and impacts on behavior. By investing in basic neuroscience, genetics, and behavioral research, we're trying to find new targets for treatment and develop better therapies for mental disorders. We're hopeful these efforts will lead to new ways to treat and prevent mental illnesses in the near future and, ultimately, improve the lives of people in this country and across the globe.

  • Download PDF
  • Share X Facebook Email LinkedIn
  • Permissions

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample

  • 1 Division of Research, Kaiser Permanente Division of Research, Oakland, California
  • 2 Department of Psychology, University of Hawaiʻi at Mānoa, Honolulu
  • 3 The Permanente Medical Group, Kaiser Permanente, San Jose, California

Question   Can a model predicting suicide attempts accurately stratify suicide risk among individuals scheduled for an intake visit to outpatient mental health care?

Findings   In this prognostic study testing a previously validated model of suicide attempts using a sample of 1 623 232 mental health intake appointments scheduled during the past decade, the model showed good overall classification performance. The 10% of appointments at the highest risk level accounted for 48.8% of the appointments followed by a suicide attempt within 90 days.

Meaning   These findings suggest that risk for suicidal behavior may be accurately stratified for mental health care intake appointments to facilitate targeted preventive interventions for individuals who are seeking to initiate an episode of care.

Importance   Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined.

Objective   To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care.

Design, Setting, and Participants   This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023.

Main Outcome and Measures   Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records.

Results   The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt.

Conclusions and Relevance   In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Read More About

Papini S , Hsin H , Kipnis P, et al. Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample. JAMA Psychiatry. Published online March 27, 2024. doi:10.1001/jamapsychiatry.2024.0189

Manage citations:

© 2024

Artificial Intelligence Resource Center

Psychiatry in JAMA : Read the Latest

Browse and subscribe to JAMA Network podcasts!

Others Also Liked

Select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

  • Academic Medicine
  • Acid Base, Electrolytes, Fluids
  • Allergy and Clinical Immunology
  • American Indian or Alaska Natives
  • Anesthesiology
  • Anticoagulation
  • Art and Images in Psychiatry
  • Artificial Intelligence
  • Assisted Reproduction
  • Bleeding and Transfusion
  • Caring for the Critically Ill Patient
  • Challenges in Clinical Electrocardiography
  • Climate and Health
  • Climate Change
  • Clinical Challenge
  • Clinical Decision Support
  • Clinical Implications of Basic Neuroscience
  • Clinical Pharmacy and Pharmacology
  • Complementary and Alternative Medicine
  • Consensus Statements
  • Coronavirus (COVID-19)
  • Critical Care Medicine
  • Cultural Competency
  • Dental Medicine
  • Dermatology
  • Diabetes and Endocrinology
  • Diagnostic Test Interpretation
  • Drug Development
  • Electronic Health Records
  • Emergency Medicine
  • End of Life, Hospice, Palliative Care
  • Environmental Health
  • Equity, Diversity, and Inclusion
  • Facial Plastic Surgery
  • Gastroenterology and Hepatology
  • Genetics and Genomics
  • Genomics and Precision Health
  • Global Health
  • Guide to Statistics and Methods
  • Hair Disorders
  • Health Care Delivery Models
  • Health Care Economics, Insurance, Payment
  • Health Care Quality
  • Health Care Reform
  • Health Care Safety
  • Health Care Workforce
  • Health Disparities
  • Health Inequities
  • Health Policy
  • Health Systems Science
  • History of Medicine
  • Hypertension
  • Images in Neurology
  • Implementation Science
  • Infectious Diseases
  • Innovations in Health Care Delivery
  • JAMA Infographic
  • Law and Medicine
  • Leading Change
  • Less is More
  • LGBTQIA Medicine
  • Lifestyle Behaviors
  • Medical Coding
  • Medical Devices and Equipment
  • Medical Education
  • Medical Education and Training
  • Medical Journals and Publishing
  • Mobile Health and Telemedicine
  • Narrative Medicine
  • Neuroscience and Psychiatry
  • Notable Notes
  • Nutrition, Obesity, Exercise
  • Obstetrics and Gynecology
  • Occupational Health
  • Ophthalmology
  • Orthopedics
  • Otolaryngology
  • Pain Medicine
  • Palliative Care
  • Pathology and Laboratory Medicine
  • Patient Care
  • Patient Information
  • Performance Improvement
  • Performance Measures
  • Perioperative Care and Consultation
  • Pharmacoeconomics
  • Pharmacoepidemiology
  • Pharmacogenetics
  • Pharmacy and Clinical Pharmacology
  • Physical Medicine and Rehabilitation
  • Physical Therapy
  • Physician Leadership
  • Population Health
  • Primary Care
  • Professional Well-being
  • Professionalism
  • Psychiatry and Behavioral Health
  • Public Health
  • Pulmonary Medicine
  • Regulatory Agencies
  • Reproductive Health
  • Research, Methods, Statistics
  • Resuscitation
  • Rheumatology
  • Risk Management
  • Scientific Discovery and the Future of Medicine
  • Shared Decision Making and Communication
  • Sleep Medicine
  • Sports Medicine
  • Stem Cell Transplantation
  • Substance Use and Addiction Medicine
  • Surgical Innovation
  • Surgical Pearls
  • Teachable Moment
  • Technology and Finance
  • The Art of JAMA
  • The Arts and Medicine
  • The Rational Clinical Examination
  • Tobacco and e-Cigarettes
  • Translational Medicine
  • Trauma and Injury
  • Treatment Adherence
  • Ultrasonography
  • Users' Guide to the Medical Literature
  • Vaccination
  • Venous Thromboembolism
  • Veterans Health
  • Women's Health
  • Workflow and Process
  • Wound Care, Infection, Healing
  • Register for email alerts with links to free full-text articles
  • Access PDFs of free articles
  • Manage your interests
  • Save searches and receive search alerts

ORIGINAL RESEARCH article

Use of whole genome sequence data for fine mapping and genomic prediction of sea lice resistance in atlantic salmon.

Olumide V. Onabanjo

  • 1 Department of Animal and Aquacultural Sciences, Faculty of Biosciences, Norwegian University of Life Sciences, Akershus, Norway
  • 2 Department of Animal Sciences, Faculty of Agricultural Sciences, University of Göttingen, Göttingen, Lower Saxony, Germany
  • 3 Department of Breeding and Genetics, Nofima, Aas, Norway
  • 4 Center for Integrated Breeding Research, Faculty of Agricultural Sciences, University of Göttingen, Göttingen, Lower Saxony, Germany

The final, formatted version of the article will be published soon.

Select one of your emails

You have multiple emails registered with Frontiers:

Notify me on publication

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Sea lice (Lepeophtheirus salmonis) infestation of Atlantic salmon (Salmo salar) is a significant challenge facing aquaculture. Over the years, this parasite has developed immunity to medicinal control compounds, and non-medicinal control methods have proven to be stressful, hence the need to study the genomic architecture of salmon resistance to sea lice. Thus, this research used whole-genome sequencing (WGS) to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. MOWI Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts belonging to 191 full-sib families. The 50k SNP genotype was imputed to WGS using the information from 197 closely related individuals with sequencing data. The challenged population's WGS and 50k SNPs were then used to estimate genetic parameters, perform a genome-wide association study (GWAS), predict genomic breeding values, and estimate its accuracy for host resistance to sea lice. The heritability of host resistance to sea lice was estimated to be 0.21 and 0.22, while the accuracy of genomic prediction was estimated to be 0.65 and 0.64 for array and WGS data, respectively. In addition, the association test using both array and WGS data did not identify any marker associated with sea lice resistance at the genome-wide level. We conclude that sea lice resistance is a polygenic trait that is moderately heritable. The genomic predictions using medium-density SNP genotyping array were equally good or better than those based upon WGS data.

Keywords: Atlantic salmon, sea lice infestation, WGS, Imputation accuracy, GWAS, Genomic prediction

Received: 03 Feb 2024; Accepted: 25 Mar 2024.

Copyright: © 2024 Onabanjo, Meuwissen, Aslam, Schmitt and Dagnachew. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Binyam Dagnachew, Department of Breeding and Genetics, Nofima, Aas, Norway

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Published: 09 February 2023

Behavioural genetics methods

  • Emily A. Willoughby   ORCID: orcid.org/0000-0001-7559-1544 1 ,
  • Tinca J. C. Polderman   ORCID: orcid.org/0000-0001-5564-301X 2 , 3 &
  • Brian B. Boutwell 4 , 5  

Nature Reviews Methods Primers volume  3 , Article number:  10 ( 2023 ) Cite this article

1412 Accesses

9 Citations

33 Altmetric

Metrics details

  • Behavioural genetics
  • Human behaviour

The question of why people show individual differences in their behaviours and capacities has intrigued researchers for centuries. Behaviour genetics offers us various methods to address this question. The answers are interesting for a range of research fields, varying from medicine to psychology, economics and neuroscience. Starting with twin and family studies in the late 1970s, the field of behaviour genetics has rapidly developed by applying molecular genetic techniques next to, and sometimes combined with, family data. The overarching conclusion at this point in time is that all measured human traits are to some extent heritable, and that many genetic variants, with each exerting a small effect, explain this heritability. Against this backdrop, we offer readers who might be less familiar with behaviour genetics a brief Primer on the topic. Sitting atop our list of goals is to be a resource for scholars interested in applying the widely useful techniques of the field to their particular specialty, regardless of what that might be.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 1 digital issues and online access to articles

92,52 € per year

only 92,52 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

research articles on behavioral genetics

Similar content being viewed by others

research articles on behavioral genetics

Genome-wide association studies

Emil Uffelmann, Qin Qin Huang, … Danielle Posthuma

research articles on behavioral genetics

The serotonin theory of depression: a systematic umbrella review of the evidence

Joanna Moncrieff, Ruth E. Cooper, … Mark A. Horowitz

research articles on behavioral genetics

The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease

Jordi Manuello, Joosung Min, … Gwenaëlle Douaud

Turkheimer, E. Three laws of behavior genetics and what they mean. Curr. Dir. Psychol. Sci. 9 , 160–164 (2000).

Article   Google Scholar  

Harden, K. P. “Reports of my death were greatly exaggerated”: behavior genetics in the postgenomic era. Annu. Rev. Psychol. 72 , 37–60 (2021).

Barnes, J. C. et al. Demonstrating the validity of twin research in criminology. Criminology 52 , 588–626 (2014).

Friedman, N. P., Banich, M. T. & Keller, M. C. Twin studies to GWAS: there and back again. Trends Cognit. Sci. 25 , 855–869 (2021).

Polderman, T. J. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47 , 702–709 (2015).

Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J. & Laibson, D. I. The fourth law of behavior genetics. Curr. Dir. Psychol. Sci. 24 , 304–312 (2015).

Visscher, PeterM. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2 , e41 (2006).

Plomin, R., DeFries, J. C. & Loehlin, J. C. Genotype–environment interaction and correlation in the analysis of human behavior. Psychol. Bull. 84 , 309–322 (1997).

Scarr, S. & McCartney, K. How people make their own environments: a theory of genotype–environment effects. Child. Dev. 54 , 424 (1983).

Google Scholar  

Fowler-Finn, K. D. & Boutwell, B. B. Using variation in heritability estimates as a test of G × E in behavioral research: a brief research note. Behav. Genet. 49 , 340–346 (2019).

Tucker-Drob, E. M. & Bates, T. C. Large cross-national differences in gene × socioeconomic status interaction on intelligence. Psychol. Sci. 27 , 138–149 (2016).

Loehlin, J., Corley, R., Reynolds, C. & Wadsworth, S. Heritability × SES interaction for IQ: is it present in US adoption studies? Behav. Genet. 52 , 1–8 (2022).

Heath, A. C., Kendler, K. S., Eaves, L. J. & Markell, D. The resolution of cultural and biological inheritance: informativeness of different relationships. Behav. Genet. 15 , 439–465 (1985).

Truett, K. R. et al. A model system for analysis of family resemblance in extended kinships of twins. Behav. Genet. 24 , 35–49 (1994).

Keller, M. C. et al. Modeling extended twin family data I: description of the Cascade model. Twin Res. Hum. Genet. 12 , 8–18 (2009).

Pettersson, E. et al. Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls. Psychol. Med. 49 , 1166–1173 (2019).

Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45 , 1866–1886 (2016).

Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42 , 565–569 (2010).

Young, A. I. Solving the missing heritability problem. PLoS Genet. 15 , e1008222 (2019).

Young, A. I. Discovering missing heritability in whole-genome sequencing data. Nat. Genet. 54 , 224–226 (2022).

Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits’. Nat. Genet. 47 , 1236–1241 (2015).

Burgess, S., Butterworth, A. & Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 37 , 658–665 (2013).

Falconer, D. S. Introduction to quantitative genetics (Longman Group, 1960).

Falconer, D. S. & Mackay, T. F. C. Quantitative Genetics (Longman Group, 1996).

Martin, N. & Eaves, L. The genetical analysis of covariance structure. Heredity 38 , 79–95 (1977).

Neale, M. C. et al. OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81 , 535–549 (2016).

Article   MathSciNet   MATH   Google Scholar  

Burt, S. A., McGue, M. & Iacono, W. G. Nonshared environmental mediation of the association between deviant peer affiliation and adolescent externalizing behaviors over time: results from a cross-lagged monozygotic twin differences design. Dev. Psychol. 45 , 1752–1760 (2009).

Bartels, M., Boomsma, D. I., Hudziak, J. J., van Beijsterveldt, T. C. E. M. & van den Oord, E. J. C. G. Twins and the study of rater (dis)agreement. Psychol. Meth. 12 , 451–466 (2007); erratum 13 , 170 (2008).

Nivard, M. G., Middeldorp, C. M., Dolan, C. V. & Boomsma, D. I. Genetic and environmental stability of neuroticism from adolescence to adulthood. Twin Res. Hum. Genet. 18 , 746–754 (2015).

Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101 , 5–22 (2017).

Willoughby, E. & Lee, J. in The Cambridge Handbook of Intelligence and Cognitive Neuroscience (eds Barbey, A., Karama, S., & Haier, R.) 349–364 (Cambridge Univ. Press, 2021).

Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9 , e1003348 (2013).

Plomin, R. & von Stumm, S. Polygenic scores: prediction versus explanation. Mol. Psychiatry 27 , 49–52 (2022).

Li, M. X. et al. A major gene model of adult height is suggested in Chinese. J. Hum. Genet. 49 , 148–153 (2004).

Roberts, D. F., Billewicz, W. Z. & McGregor, I. A. Heritability of stature in a West African population. Ann. Hum. Genet. 42 , 15–24 (1978).

Tarnoki, A. D., Tarnoki, D. L. & Molnar, A. A. Past, present and future of cardiovascular twin studies. Cor Vasa 56 , e486–e493 (2014).

Pechlivanis, S. et al. Risk prediction for coronary heart disease by a genetic risk score — results from the Heinz Nixdorf Recall study. BMC Med. Genet. 21 , 178 (2020).

Yang, R. et al. A healthy lifestyle mitigates the risk of heart disease related to type 2 diabetes: a prospective nested case–control study in a nationwide Swedish twin cohort. Diabetologia 64 , 530–539 (2021).

McGue, M., Osler, M. & Christensen, K. Causal inference and observational research: the utility of twins. Perspect. Psychol. Sci. 5 , 546–556 (2010).

Lemvigh, C. et al. The relative and interactive impact of multiple risk factors in schizophrenia spectrum disorders: a combined register-based and clinical twin study. Psychol. Med. https://doi.org/10.1017/S0033291721002749 (2021).

Squarcina, L., Fagnani, C., Bellani, M., Altamura, C. A. & Brambilla, P. Twin studies for the investigation of the relationships between genetic factors and brain abnormalities in bipolar disorder. Epidemiol. Psychiatr. Sci. 25 , 515–520 (2016).

Bouchard, T. J. & McGue, M. Genetic and environmental influences on human psychological differences. J. Neurobiol. 54 , 4–45 (2003).

Wilson, R. S. The Louisville twin study: developmental synchronies in behavior. Child. Dev. 54 , 298–316 (1983).

Bouchard, T. J., Lykken, D. T., McGue, M., Segal, N. L. & Tellegen, A. Sources of human psychological differences: the minnesota study of twins reared apart. Science. 250 , 223–228 (1990).

Article   ADS   Google Scholar  

Bouchard, T. The Wilson effect: the increase in heritability of IQ with age. Twin Res. Hum. Genet. 16 , 923–930 (2013).

Deary, I. J., Johnson, W. & Houlihan, L. M. Genetic foundations of human intelligence. Hum. Genetics. 126 , 215–232 (2009).

Jang, K. L., Livesley, W. J. & Vernon, P. A. Heritability of the big five personality dimensions and their facets: a twin study. J. Pers. 64 , 577–591 (1996).

Tellegen, A. & Niels, G. W. Exploring personality through test construction: development of the multidimensional personality questionnaire. SAGE Handb. Pers. Theory Assess. 2 , 261–292 (2008).

Vukasović, T. & Bratko, D. Heritability of personality: a meta-analysis of behavior genetic studies. Psychol. Bull. 141 , 769–785 (2015).

Krueger, R. F., South, S., Johnson, W. & Iacono, W. The heritability of personality is not always 50%: gene–environment interactions and correlations between personality and parenting. J. Pers. 76 , 1485–1522 (2008).

Matteson, L. K., McGue, M. & Iacono, W. G. Shared environmental influences on personality: a combined twin and adoption approach. Behav. Genet. 43 , 491–504 (2013).

Howe, L. J. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. 54 , 581–592 (2022).

Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50 , 1112–1121 (2018).

Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359 , 424–428 (2018).

Belsky, D. W. et al. Genetic analysis of social-class mobility in five longitudinal studies. Proc. Natl Acad. Sci. USA 115 , E7275–E7284 (2018).

Bates, T. C. et al. Social competence in parents increases children’s educational attainment: replicable genetically-mediated effects of parenting revealed by non-transmitted DNA. Twin Res. Hum. Genet. 22 , 1–3 (2019).

Willoughby, E. A., McGue, M., Iacono, W. G., Rustichini, A. & Lee, J. J. The role of parental genotype in predicting offspring years of education: evidence for genetic nurture. Mol. Psychiatry 26 , 3896–3904 (2021).

Loehlin, J. C., Horn, J. M. & Willerman, L. in Intelligence, Heredity, and Environment (eds Sternberg, R. J., & Grigorenko, E. L.) 105–125 (Cambridge Univ. Press, 1997).

Cadoret, R. J. Adoption Studies. Alcohol Health Res. World 19 , 195–200 (1995).

Rhea, S. A., Bricker, J. B., Corley, R. P., DeFries, J. C. & Wadsworth, S. J. Design utility and history of the Colorado Adoption Project: examples involving adjustment interactions. Adopt. Q. 16 , 17–39 (2013).

Willoughby, E. A., McGue, M., Iacono, W. G. & Lee, J. J. Genetic and environmental contributions to IQ in adoptive and biological families with 30-year-old offspring. Intelligence 88 , 101579 (2021).

Baker, M. Reproducibility crisis? Nature 533 , 26 (2016).

Fanelli, D. Opinion: is science really facing a reproducibility crisis, and do we need it to? Proc. Natl Acad. Sci. USA 115 , 2628–2631 (2018).

Plomin, R., DeFries, J. C., Knopik, V. S. & Neiderhiser, J. M. Top 10 replicated findings from behavioral genetics. Perspect. Psychol. Sci. 11 , 3–23 (2016).

Chabris, C. F. et al. Most reported genetic associations with general intelligence are probably false positives. Psychol. Sci. 23 , 1314–1323 (2012).

Johnson, E. C. et al. No evidence that schizophrenia candidate genes are more associated with schizophrenia than noncandidate genes. Biol. Psychiatry 82 , 702–708 (2017).

Border, R. et al. No support for historical candidate gene or candidate gene-by-interaction hypotheses for major depression across multiple large samples. Am. J. Psychiatry   176 , 376–387 (2019).

Lin, X. Learning lessons on reproducibility and replicability in large scale genome-wide association studies. Harvard Data Sci. Rev. https://doi.org/10.1162/99608f92.33703976 (2020).

Røysamb, E., & Tambs, K. The beauty, logic and limitations of twin studies. Nor. Epidemiol . 26 , 35–46 (2016).

van Dongen, J., Slagboom, P. E., Draisma, H. H., Martin, N. G. & Boomsma, D. I. The continuing value of twin studies in the omics era. Nat. Rev. Genet. 13 , 640–653 (2012).

Wilson, S. et al. Minnesota Center for Twin and Family Research. Twin Res. Hum. Genet. 22 , 746–752 (2019).

Hindorff, L. A. et al. Prioritizing diversity in human genomics research. Nat. Rev. Genet. 19 , 175–185 (2017).

Mills, M. C. & Rahal, C. A scientometric review of genome-wide association studies. Commun. Biol. 2 , 1–11 (2019).

George, S., Duran, N. & Norris, K. A systematic review of barriers and facilitators to minority research participation among African Americans, Latinos, Asian Americans, and Pacific Islanders. Am. J. Public Health https://doi.org/10.2105/AJPH.2013.301706 (2014).

Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570 , 514–518 (2019).

Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100 , 635–649 (2017).

Giannakopoulou, O. et al. The genetic architecture of depression in individuals of East Asian ancestry: a genome-wide association study. JAMA Psychiatry 78 , 1258–1269 (2021).

Meng, X. et al. Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference. Preprint at bioRxiv https://doi.org/10.1101/2022.07.20.500802 (2022).

Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51 , 584–591 (2019).

Bigdeli, T. B. et al. Genetic effects influencing risk for major depressive disorder in China and Europe. Transl. Psychiatry 7 , e1074 (2017).

Bigdeli, T. B. et al. Contributions of common genetic variants to risk of schizophrenia among individuals of African and Latino ancestry. Mol. Psychiatry 25 , 2455–2467 (2020).

Hyttinen, V., Kaprio, J., Kinnunen, L., Koskenvuo, M. & Tuomilehto, J. Genetic liability of type 1 diabetes and the onset age among 22,650 young Finnish twin pairs: a nationwide follow-up study. Diabetes 52 , 1052–1055 (2003).

Snieder, H. et al. HbA1c levels are genetically determined even in type 1 diabetes: evidence from healthy and diabetic twins. Diabetes 50 , 2858–2863 (2001).

McAdams, T. A., Rijsdijk, F. V., Zavos, H. M. & Pingault, J. B. Twins and causal inference: leveraging nature’s experiment. Cold Spring Harb. Perspect. Med. 11 , a039552 (2021).

Alberg, A. J., Brock, M. V. & Samet, J. M. in Murray & Nadel’s Textbook of Respiratory Medicine 6th edn, Ch. 52, 927–939 (Saunders Elsevier, 2016).

Poulton, R., Moffitt, T. E. & Silva, P. A. The Dunedin multidisciplinary health and development study: overview of the first 40 years, with an eye to the future. Soc. Psychiatry Psychiatr. Epidemiol., 50 , 679–693 (2015).

Krüger, O., Korsten, P. & Hoffman, J. I. in J. Call, G. M. Burghardt, I. M. Pepperberg, C. T. Snowdon, & T. Zentall (Eds.), APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior (eds Call, J., Burghardt, G. M., Pepperberg, I. M., Snowdon, C. T. & Zentall, T.) 365–379 (American Psychological Association, 2017).

Turkheimer, E. & Harden, K. P. in Handbook of Research Methods in Social and Personality Psychology (eds Reis, H. T. & Judd, C. M.) 159–187 (Cambridge Univ. Press, 2014).

Okbay, A. et al. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nat. Genet. 54 , 437–449 (2022).

Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542 , 186190 (2017).

Wainschtein, P. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet.   54 , 263–273 (2022).

Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10 , 57–63 (2009).

Gupta, S. et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat. Commun. 5 , 5748 (2014).

Pers, T.H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6 , 5890 (2015).

de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11 , 1–19 (2015).

Neale, M. C. & Cardon, L. R. Methodology for Genetic Studies of Twins and Families . (Kluwer Academic/Plenum, 1992).

Vitaro, F., Brendgen, M. & Arseneault, L. Methods and measures: the discordant MZ-twin method: one step closer to the holy grail of causality. Int. J. Behav. Dev. 33 , 376–382 (2009).

Keller, M. C., Medland, S. E. & Duncan, L. E. Are extended twin family designs worth the trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four twin family models. Behav. Genet. 40 , 377–393 (2010).

McAdams, T. A. et al. Revisiting the children-of-twins design: improving existing models for the exploration of intergenerational associations. Behav. Genet. 48 , 397–412 (2018).

Scarr, S. & Weinberg, R. A. The Minnesota Adoption Studies: genetic differences and malleability. Child. Dev. 54 , 260–267 (1983).

Murphy, K. et al. Twins Research Australia: a new paradigm for driving twin research. Twin Res. Hum. Genet. 22 , 438–445 (2019).

Otta, E. et al. The University of São Paulo Twin Panel: current status and prospects for Brazilian twin studies in behavioral research. Twin Res. Hum. Genet. 22 , 467–474 (2019).

Huang et al. The Chinese National Twin Registry: a unique data source for systems epidemiology of complex disease. Twin Res. Hum. Genet. 22 , 482–485 (2019).

Pedersen et al. The Danish Twin Registry: an updated overview. Twin Res. Hum. Genet. 22 , 499–507 (2020).

Bjerregaard-Andersen et al. The Guinea-Bissau Twin Registry update: a platform for studying twin mortality and metabolic disease. Twin Res. Hum. Genet. 22 , 554–560 (2019).

Gharipour et al. Isfahan Twins Registry (ITR): an invaluable platform for epidemiological and epigenetic studies: design and methodology of ITR. Twin Res. Hum. Genet. 22 , 579–582 (2019).

Ligthart et al. The Netherlands Twin Register: longitudinal research based on twin and twin-family designs. Twin Res. Hum. Genet. 22 , 623–636 (2019).

Rimfeld et al. Twins Early Development Study: a genetically sensitive investigation into behavioral and cognitive development from infancy to emerging adulthood. Twin Res. Hum. Genet. 22 , 508–513 (2019).

Pingault, J. B. et al. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19 , 566–580 (2018).

Lee, J. J. Correlation and causation in the study of personality. Eur. J. Personal. 26 , 372–412 (2012).

Vilhjámsson, B. J. et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97 , 576–592 (2015).

de Vlaming, R. & Groenen, P. J. F. The current and future use of ridge regression for prediction in quantitative genetics. BioMed. Res. Int. 2015 , 1–18 (2015).

Vattikuti, S., Lee, J. J., Chang, C. C., Hsu, S. D. H. & Chow, C. C. Applying compressed sensing to genome-wide association studies. GigaScience 3 , 10 (2014).

Lello, L. et al. Accurate genomic prediction of human height. Genetics 210 , 477–497 (2018).

Allegrini, A. G. et al. Genomic prediction of cognitive traits in childhood and adolescence. Mol. Psychiatry 24 , 819–827 (2019).

Download references

Acknowledgements

T.J.C.P. is supported by ZonMw grant 60-63600-98-834. Additionally, all of the authors thank the individuals who together provided incredibly constructive and useful feedback. All of these individuals contributed to the improvement of this Primer, and have their sincere gratitude for this.

Author information

Authors and affiliations.

Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA

Emily A. Willoughby

Department of Clinical Developmental Psychology, Vrije Universiteit, Amsterdam, Netherlands

Tinca J. C. Polderman

Department of Child and Adolescent Psychiatry & Social Care, Amsterdam UMC, Amsterdam, Netherlands

School of Applied Sciences, University of Mississippi, University, MS, USA

  • Brian B. Boutwell

John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA

You can also search for this author in PubMed   Google Scholar

Contributions

Introduction (B.B.B. and T.J.C.P.); Experimentation (E.A.W.); Results (E.A.W.); Applications (E.A.W.); Reproducibility and data deposition (T.J.C.P.); Limitations and optimizations (B.B.B. and T.J.C.P.); Outlook (E.A.W. and B.B.B.); Overview of the Primer (T.J.C.P. and B.B.B.).

Corresponding author

Correspondence to Brian B. Boutwell .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Reviews Methods Primers thanks Thalia Eley, Elliott Rees, Lawrence Wilkinson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

A statistical method for evaluating how well a model fits the given data in which fit is penalized for the number of parameters estimated. In twin family studies, this term is often used to compare different possible models to determine which has the best fit.

A statistical method for evaluating model fit in which a penalty term is introduced for the number of parameters. This is a more explanatory tool that assesses the underlying data.

An extension of the extended twin family design that models covariances of twin pairs in addition to their siblings, spouses, parents and children. The Cascade model relaxes the assumptions of assortative mating and vertical transmission by allowing for the inclusion of latent phenotypes.

The simplest twin design in which pairs of monozygotic and dizygotic twins are compared for similarity on some phenotype, and the observed covariances are used to calculate the relative magnitudes of genetic and environmental sources of variance.

(DZ). Describes a pair of twins derived from two different sperm and two different ova who develop together in utero. DZ twins share approximately 50% of their DNA, similar to regular siblings.

The assumption that monozygotic and dizygotic twin pairs experience the same environmental factors.

A type of twin family design that models observed covariances using the relatives of twins in addition to the focal twin covariances for some variable.

( r GE). A phenomenon in which genes may influence individual variations in exposure to certain types of environment.

An interplay between genes and environments in which different genomes can cause individuals to respond differently to the same environmental exposure.

(MZ). Describes a pair of twins derived from a single sperm and ovum, who are therefore genetically identical.

The observation that the sum total of genetic variants from a genome-wide association study cannot completely explain the heritabilities of complex traits derived from twin family studies.

A type of twin family design that models observed covariances of the parents of twins in addition to the twins themselves.

The non-random placement of children based on traits that are similar between biological and adoptive families.

(PGS). A number generated from genome-wide association data that summarizes the estimated effect of a large number of summed genetic variants on a phenotype of interest.

An extension of the extended twin family design that models covariances of twin pairs in addition to their siblings, spouses, parents and children. The Stealth model relies on primary phenotypic assortment to model assortative mating, and on direct parent to offspring phenotypic transmission to model vertical transmission.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Willoughby, E.A., Polderman, T.J.C. & Boutwell, B.B. Behavioural genetics methods. Nat Rev Methods Primers 3 , 10 (2023). https://doi.org/10.1038/s43586-022-00191-x

Download citation

Accepted : 13 December 2022

Published : 09 February 2023

DOI : https://doi.org/10.1038/s43586-022-00191-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

The challenges and opportunities for mental health twin research in nigeria.

  • Olakunle Ayokunmi Oginni
  • Ayoyinka Ayorinde
  • Kolawole Mosaku

Behavior Genetics (2024)

Genetic and environmental contributions to the subjective burden of social isolation during the COVID-19 pandemic

  • Anita Kottwitz
  • Bastian Mönkediek
  • Jannis Hildebrandt

BMC Psychology (2023)

On the Usefulness of Behavior Genetics: Using Family Studies in Evolutionary Psychological Science to Improve Causal Inference and Sharpen Theory

  • Dario Maestripieri

Adaptive Human Behavior and Physiology (2023)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

research articles on behavioral genetics

  • See us on facebook
  • See us on twitter
  • See us on youtube
  • See us on linkedin
  • See us on instagram

Bengal cat coats are less wild than they look, genetic study finds

Researchers studied hundreds of Bengal cats to uncover the genetic origins of their leopard-like patterns and found that their appearance stems largely from domesticated cats.

March 25, 2024 - By Sarah C.P. Williams

Bengal cat

Researchers have discovered that the spotted coats of Bengal cats are mostly the result of domestic cat genes.  Ermolaev Alexander/Shutterstock.com

Bengal cats are prized for their appearance; the exotically marbled and spotted coats of these domestic pets make them look like small, sleek jungle cats. But the origin of those coats — assumed to come from the genes of Asian leopard cats that were bred with house cats — turns out to be less exotic.

Stanford Medicine researchers, in collaboration with Bengal cat breeders, have discovered that the Bengal cats’ iridescent sheen and leopard-like patterns can be traced to domestic cat genes that were aggressively selected for after the cats were bred with wild cats.

“Most of the DNA changes that underlie the unique appearance of the Bengal cat breed have always been present in domestic cats,” said Gregory Barsh , MD, PhD, an emeritus professor of genetics. “It was really the power of breeding that brought them out.”

For a study published online March 25 in Current Biology , Barsh and his colleagues analyzed genes collected from nearly 1,000 Bengal cats over the course of 15 years. Barsh is the senior author of the paper, and senior scientist  Christopher Kaelin , PhD, is the lead author.

The results shed light not only on the Bengal cat’s coat but also help answer broader questions about how appearance is encoded in genetics and how different genes work together to yield colors, patterns and physical features.

Wild origins

Barsh and his colleagues, including Kaelin, use cats and other animals to study the genetics of physical features. In previous studies, they identified genes responsible for the color coat variation in tabby cats and for the unique markings on the Abyssinian cat.

Gregory Barsh

Gregory Barsh

“The big-picture question is how genetic variation leads to variation in appearance,” Barsh said. “This is a question that has all kinds of implications for different species, but we think that cats offer an especially tractable way to study it.”

From the 1960s through the 1980s, breeders, led by biologist Jean Mills, crossed the wild Asian leopard cat species Prionailurus bengalensis with domestic cats to create a new, visually striking cat breed. Over many generations, the cats with the desired physical characteristics and temperaments were progressively selected and bred. By 1986, the Bengal cat was recognized as its own new breed by the International Cat Association.

Barsh and Kaelin saw Bengals — with their recent genetic origin and unique appearance — as a particularly interesting way to study how genetic variation causes diversity in form, color and pattern. In 2008, they began reaching out to cat breeders, attending cat shows, and collecting cheek swabs and photographs of Bengal cats.

Genetic surprises

The Stanford Medicine team suspected that Bengal cats might give them an accessible way to probe the genetics of wild cat colors and patterns that had evolved naturally. But after sequencing 947 Bengal cat genomes, they found something surprising: There were no parts of the wild Asian leopard cat genomes that were found in all Bengal cats.

“Nearly every Bengal cat breeder and owner has this idea that the distinctive look of the domestic Bengal cat must have come from leopard cats,” Barsh said. “Our work suggests that’s not the case.”

Instead, the genetic signatures suggested that the unique appearance of Bengals was a result of variations in genes that had already been present in domestic cats.

The team found something similar when they looked specifically at “glitter”: About 60% of all Bengal cats have particularly soft, iridescent fur that glitters like gold in the sunlight. A mutation in the gene Fgfr2, they showed, is responsible for glitter and comes not from leopard cats but from domestic cats. Glitter and the underlying Fgfr2 mutation are nearly specific to Bengal cats. Interestingly, the mutation reduces the activity of the protein encoded by Fgfr2, rather than rendering it inactive as many mutations do. This sheds light on how variations in genes can cause subtle changes in appearance, the researchers said.

Christopher Kaelin

Christopher Kaelin

Finally, Barsh and Kaelin’s group analyzed the genetics of “charcoal” Bengals, a rare subset of the breed with darker coloring. They uncovered a leopard cat gene linked to the charcoal color, but only when it was combined with domestic cat genome. The leopard cat gene, known as Asip, essentially doesn’t work as well when it’s mixed with the domestic genes — a phenomenon known as genomic incompatibility. So, in leopard cats, Asip doesn’t cause charcoal coloring, but the same gene in domestic cats does.

“Hybridization between different species can happen naturally and is responsible for the small amount of Neandertal DNA found in many human genomes," Barsh explained. “But the wild leopard cat and the domestic cat are more different from each other than humans are from chimpanzees, and it’s remarkable to see how DNA from these distantly related species can exist and work together in a popular companion animal.”

A boost for biology and breeders

A better understanding of the genetic origins of Bengal cat traits is already helping Bengal breeders fine-tune the way they breed animals to create new colors and patterns. Over the past 15 years, Barsh and Kaelin have worked closely with Bengal cat organizations and given talks at cat shows. They often return ancestry and genetic data to owners to help guide their breeding. 

“Breeders are extremely interested in our data,” Kaelin said. “They not only want to contribute their cats’ DNA but they also want to be involved and help analyze data and hear about our results. It’s been a great collaboration and a true example of citizen science.”

The researchers say there are lessons to be learned in just how powerful artificial selection can be, as the Bengal cat coats could probably have been selected for without the help of the Asian leopard cat.

“People have this idea that we have to get access to these distantly related animals to breed beautiful individuals and designer animals,” Barsh said. “But it turns out all the diversity was already there waiting in the domestic cat genome.”

Scientists from HudsonAlpha Institute of Biotechnology, Gencove Inc., University of Bern, and Texas A&M University were also authors of the paper.

Funding for this research was provided by the HudsonAlpha Institute for Biotechnology and the National Institutes of Health (grant AR082708).

  • Sarah C.P. Williams Sarah C.P. Williams is a freelance science writer.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Artificial intelligence

Exploring ways AI is applied to health care

Stanford Medicine Magazine: AI

ScienceDaily

Sweet success: Sugarcane's complex genetic code cracked

Modern hybrid sugarcane is one of the most harvested crops on the planet, used to make products including sugar, molasses, bioethanol, and bio-based materials. It also has one of the most complex genetic blueprints.

Until now, sugarcane's complicated genetics made it the last major crop without a complete and highly accurate genome. Scientists have developed and combined multiple techniques to successfully map out sugarcane's genetic code. With that map, they were able to verify the specific location that provides resistance to the impactful brown rust disease that, unchecked, can devastate a sugar crop. Researchers can also use the genetic sequence to better understand the many genes involved in sugar production.

The research was conducted as part of the Community Science Program at the U.S. Department of Energy Joint Genome Institute (JGI), a DOE Office of Science user facility at Lawrence Berkeley National Laboratory (Berkeley Lab). The study is published today in the journal Nature , and the genome is available through the JGI's plant portal, Phytozome.

"This was the most complicated genome sequence we've yet completed," said Jeremy Schmutz, Plant Program lead at the JGI and faculty investigator at the HudsonAlpha Institute for Biotechnology. "It shows how far we've come. This is the kind of thing that 10 years ago people thought was impossible. We're able to accomplish goals now that we just didn't think were possible to do in plant genomics."

Sugarcane's genome is so complex both because it is large and because it contains more copies of chromosomes than a typical plant, a feature called polyploidy. Sugarcane has about 10 billion base pairs, the building blocks of DNA; for comparison, the human genome has about 3 billion. Many sections of sugarcane's DNA are identical both within and across different chromosomes. That makes it a challenge to correctly reassemble all the small segments of DNA while reconstructing the full genetic blueprint. Researchers solved the puzzle by combining multiple genetic sequencing techniques, including a newly developed method known as PacBio HiFi sequencing that can accurately determine the sequence of longer sections of DNA.

Having a complete "reference genome" makes it easier to study sugarcane, enabling researchers to compare its genes and pathways with those in other well-studied crops such as sorghum or other biofuel crops of interest, like switchgrass and miscanthus. By comparing this reference to other crops, it becomes easier to understand how each gene influences a trait of interest, such as which genes are highly expressed during sugar production, or which genes are important for disease resistance. This study found that the genes responsible for resistance to brown rust, a fungal pathogen that previously caused millions of dollars of damage to sugarcane crops, are found in only one location in the genome.

"When we sequenced the genome, we were able to fill a gap in the genetic sequence around brown rust disease," said Adam Healey, first author of the paper and a researcher at HudsonAlpha. "There are hundreds of thousands of genes in the sugarcane genome, but it's only two genes, working together, that protect the plant from this pathogen. Across plants, there are only a handful of instances that we know of where protection works in a similar way. Better understanding of how this disease resistance works in sugarcane could help protect other crops facing similar pathogens down the road."

Researchers studied a cultivar of sugarcane known as R570 that has been used for decades around the world as the model to understand sugarcane genetics. Like all modern sugarcane cultivars, R570 is a hybrid made by crossing the domesticated species of sugarcane (which excelled in sugar production) and a wild species (which carried the genes for disease resistance).

"Knowing R570's complete genetic picture will let researchers trace which genes descended from which parent, enabling breeders to more easily identify the genes that control the traits of interest for improved production," said Angélique D'Hont, last author of the paper and a sugarcane researcher at the French Agricultural Research Center for International Development (CIRAD).

Improving future varieties of sugarcane has potential applications in both agriculture and bioenergy. Enhancing how sugarcane produces sugar could increase the yield farmers get from their crops, providing more sugar from the same amount of growing space. Sugarcane is an important feedstock, or starting material, for producing biofuels, particularly ethanol, and other bioproducts. The residues that remain after the pressing of sugarcane, referred to as bagasse, are an important type of agricultural residue that can also be broken down and converted into biofuels and bioproducts.

"We are working to understand how specific genes in plants relate to the quality of the biomass we get downstream, which we can then turn into biofuels and bioproducts," said Blake Simmons, Chief Science and Technology Officer for the Joint BioEnergy Institute, a DOE Bioenergy Research Center led by Berkeley Lab. "With a better understanding of sugarcane genetics, we can better understand and control the plant genotypes needed to produce the sugars and bagasse-derived intermediates we need for sustainable sugarcane conversion technologies at a scale relevant to the bioeconomy."

This study involved collaborations with institutes from around the world, including France (CIRAD, UMR-AGAP, ERCANE); Australia (CSIRO Agriculture and Food, Queensland Alliance for Agriculture and Food Innovation/ARC Centre of Excellence for Plant Success in Nature and Agriculture -- University of Queensland, Sugar Research Australia); Czech Republic (Institute of Experimental Botany of the Czech Academy of Sciences); and the United States (Corteva Agriscience, Joint BioEnergy Institute). The genome was sequenced at the JGI with work completed at the JGI partner laboratories, the Arizona Genomics Institute and the HudsonAlpha Institute for Biotechnology.

  • Agriculture and Food
  • Food and Agriculture
  • Evolutionary Biology
  • Exotic Species
  • Sustainability
  • Plant breeding
  • Genetic code
  • Agriculture
  • Gene therapy
  • Francis Crick

Story Source:

Materials provided by DOE/Lawrence Berkeley National Laboratory . Original written by Lauren Biron. Note: Content may be edited for style and length.

Related Multimedia :

  • Gene order map

Journal Reference :

  • A. L. Healey, O. Garsmeur, J. T. Lovell, S. Shengquiang, A. Sreedasyam, J. Jenkins, C. B. Plott, N. Piperidis, N. Pompidor, V. Llaca, C. J. Metcalfe, J. Doležel, P. Cápal, J. W. Carlson, J. Y. Hoarau, C. Hervouet, C. Zini, A. Dievart, A. Lipzen, M. Williams, L. B. Boston, J. Webber, K. Keymanesh, S. Tejomurthula, S. Rajasekar, R. Suchecki, A. Furtado, G. May, P. Parakkal, B. A. Simmons, K. Barry, R. J. Henry, J. Grimwood, K. S. Aitken, J. Schmutz, A. D’Hont. The complex polyploid genome architecture of sugarcane . Nature , 2024; DOI: 10.1038/s41586-024-07231-4

Cite This Page :

Explore More

  • Illuminating Oxygen's Journey in the Brain
  • DNA Study IDs Descendants of George Washington
  • Heart Disease Risk: More Than One Drink a Day
  • Unlocking Supernova Stardust Secrets
  • Why Do Some Memories Become Longterm?
  • Cell Division Quality Control 'Stopwatch'
  • What Controls Sun's Differential Rotation?
  • Robot, Can You Say 'Cheese'?
  • Researchers Turn Back the Clock On Cancer Cells
  • Making Long-Term Memories: Nerve-Cell Damage

Trending Topics

Strange & offbeat.

IMAGES

  1. (PDF) Behavioral Genetics in Criminal Court

    research articles on behavioral genetics

  2. (PDF) Behavioral Genetics

    research articles on behavioral genetics

  3. Behavior Genetics of Psychopathology

    research articles on behavioral genetics

  4. (PDF) The rise of behavioral genetics and the transition to behavioral

    research articles on behavioral genetics

  5. (PDF) Behavioral genetics of cognitive ability: A life-span perspective

    research articles on behavioral genetics

  6. (PDF) A Behavioral Genetic Analysis of the Co-Occurrence Between

    research articles on behavioral genetics

VIDEO

  1. Curiosities and Breakthroughs: Neuroscience News Top 5 of the Week

  2. Understanding Biopsychology

  3. Journal Club

  4. International Conference on Genetics and Genomics of Disease

  5. Analysis of UK Biobank reveals the similarities and differences in genetic architecture across sexes

  6. Why We are Different? A Behavioral Genetics Perspective by Mehar Khayal

COMMENTS

  1. Behavioural genetics

    Atom. RSS Feed. Behavioural genetics is the interdisciplinary effort to establish causal links between genes and animal (including human) behavioural traits and neural mechanisms. Methods used ...

  2. Celebrating a Century of Research in Behavioral Genetics

    Introduction. Although the history of heredity and behavior can be traced back to ancient times (Loehlin 2009), the first human behavioral genetic research was reported in the 1920s, which applied quantitative genetic twin and adoption designs to assess genetic influence on newly developed measures of intelligence.The 1920s also marked the beginning of single-gene research that led to ...

  3. Behavioural genetics

    For fields such as social and behavioral genomics that are shaped by an ugly history and uncertain future, socially and ethically responsible research and research communication are crucial. FoGS ...

  4. Challenges Faced by Behavioral Genetic Studies: Researchers Perspective

    In the current study, a group of MENA researchers examined ethical challenges related to studies of genetic variations contributing to antisocial behavior in the Arab populations. The main challenges discussed included the recruitment of subjects, informed consent process, risks and benefits, privacy and confidentiality, and interpretations of ...

  5. Behavioural genetics

    Behavioural genetics articles within Nature. Featured. Article | 07 December 2022. ... Research articles News Opinion Research Analysis Careers ...

  6. Articles

    Behavior Genetics is a leading journal concerned with the genetic analysis of behavioral traits. The journal offers the most current original research on ...

  7. Home

    Overview. Behavior Genetics is a leading journal concerned with the genetic analysis of behavioral traits. The journal offers the most current original research on the inheritance and evolution of behavioral characteristics in humans and other species. Contributions focus on both the application of various genetic perspectives to the study of ...

  8. Top 10 Replicated Findings from Behavioral Genetics

    Finally, we note that four of the top-10 findings (2, 7, 8 and 9) are about environmental influences rather than genetic influences. By using genetically sensitive designs such as twin studies, behavioral genetics has revealed almost as much about the environment as about genetics. 1. All psychological traits show significant and substantial ...

  9. The Fourth Law of Behavior Genetics

    Behavior genetics is the study of the relationship between genetic variation and psychological traits. Turkheimer (2000) proposed "Three Laws of Behavior Genetics" based on empirical regularities observed in studies of twins and other kinships. On the basis of molecular studies that have measured DNA variation directly, we propose a Fourth Law of Behavior Genetics: "A typical human ...

  10. Beyond Heritability: Twin Studies in Behavioral Research

    Abstract. The heritability of human behavioral traits is now well established, due in large measure to classical twin studies. We see little need for further studies of the heritability of individual traits in behavioral science, but the twin study is far from having outlived its usefulness. The existence of pervasive familial influences on ...

  11. Behavioral Genetics

    Handbook of behavior genetics. New York: Springer. DOI: 10.1007/978--387-76727-7. Intended for students of genetics, psychology, and psychiatry. Chapters describe research in various areas of behavior including psychopathology, intelligence, and personality.

  12. Frontiers

    Recent years have seen a push for the integration of modern genomic methodologies with sociological inquiry. The inclusion of genomic approaches promises to help address long-standing issues in sociology (e.g., selection effects), as well as open up new avenues for future research. This article reviews the substantive findings of behavior genetic/genomic research, both from the recent past (e ...

  13. Behavioural genetics: An introduction

    Behavioural genetics is the study of the hereditary influence on behaviour, and can therefore be regarded as the intersection between behavioural sciences and genetics. As with most other fields of research it is difficult to exactly pinpoint when behavioural genetics started. In fact, one might say that the notion behavioural traits can be ...

  14. The Genetics of Human Behavior

    Behavioral disorders arise from environmental, lifestyle, and genetic factors. Past studies have shown evidence for the hereditability of several major behavioral neuropsychiatric disorders, such as schizophrenia, depression, and bipolar disorder. In these cases, certain genetic defects are passed down from parental generations and increase an offspring's risk of inheriting a specific disorder.

  15. A Century of Behavioral Genetics at the University of Minnesota

    The strong legacy of behavior-genetic research in Minnesota continues today, with a total of over 9800 individual twins, siblings, parents and adoptees having contributed to the MCTFR's two flagship longitudinal studies (MTFS and SIBS) as of 2022 (Figure 4). The MTFS, having begun in 1989 with 1400 twin pairs, has enrolled an additional 500 ...

  16. Celebrating a Century of Research in Behavioral Genetics

    A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to ...

  17. Using genetics for social science

    Turkheimer, E. & Paige Harden, K. Behavior Genetic Research Methods. in Handbook of Research Methods in Social and Personality Psychology 159-187 (Cambridge University Press, 2014).

  18. Frontiers in Genetics

    Joel Defo. Raj Ramesar. Frontiers in Genetics. doi 10.3389/fgene.2023.1083969. 3,732 views. 2 citations. Explores the interplay between genes and the environment that influence human behavioral traits and the manifestation and pathology of psychiatric illness.

  19. The Biology of Relationships: What Behavioral Genetics Tells Us About

    This Article discusses a behavioral genetic perspective that provides insight into the biological factors that influence family relationships. Part II presents a brief overview of the research methods used to understand both genetic and environmental influences on human behavior. ... The general strategy in behavioral-genetic research designs ...

  20. Decoding the Mind: Basic Science Revolutionizes Treatment of ...

    The Division of Neuroscience and Basic Behavioral Science (DNBBS) at the National Institute of Mental Health (NIMH) supports research on basic neuroscience, genetics, and basic behavioral science. These are foundational pillars in the quest to decode the human mind and unravel the complexities of mental illnesses. At NIMH, we are committed to supporting and conducting genomics research as a ...

  21. A genetic cause of male mate preference

    Related Research Article. Adaptive introgression of a visual preference gene ... but genes that underlie behavioral preferences , and the extent to which color pattern is genetically linked to mate preference in Heliconius, and animals in general, remain to be uncovered. On page 1368 of this issue, Rossi et al. report the genetic basis for ...

  22. Why don't humans have tails?

    Humans' closest primate relatives lost their tails about 25 million years ago, but exactly how has remained a mystery. A breakthrough in genetic research may finally offer answers.

  23. Validation of a Multivariable Model to Predict Suicide Attempt in a

    Key Points. Question Can a model predicting suicide attempts accurately stratify suicide risk among individuals scheduled for an intake visit to outpatient mental health care?. Findings In this prognostic study testing a previously validated model of suicide attempts using a sample of 1 623 232 mental health intake appointments scheduled during the past decade, the model showed good overall ...

  24. Frontiers

    Thus, this research used whole-genome sequencing (WGS) to study the genetic basis of the trait since most research using fewer SNPs did not identify significant quantitative trait loci. MOWI Genetics AS provided the genotype (50k SNPs) and phenotype data for this research after conducting a sea lice challenge test on 3,185 salmon smolts ...

  25. Behavioural genetics methods

    As twins provide such powerful and rich data to conduct behaviour genetic research, various twin registers were created in the second half of the past century. ... Three laws of behavior genetics ...

  26. The behavior of ant queens is shaped by their social environment

    The research was conducted by the Reproduction, Nutrition, and Behavior in Insect Societies group at JGU under the supervision of Dr. Romain Libbrecht, an evolutionary biologist.

  27. Beethoven's genes reveal low predisposition for beat synchronization

    An international team of researchers analyzed Beethoven's DNA to investigate his genetic musical predisposition, an ability closely related to musicality, by using sequences from a 2023 study in ...

  28. Bengal cat coats are less wild than they look, genetic study finds

    Barsh and Kaelin saw Bengals — with their recent genetic origin and unique appearance — as a particularly interesting way to study how genetic variation causes diversity in form, color and pattern. In 2008, they began reaching out to cat breeders, attending cat shows, and collecting cheek swabs and photographs of Bengal cats. Genetic surprises

  29. Daniel Kahneman, pioneering behavioral psychologist, Nobel laureate and

    Daniel Kahneman, the Eugene Higgins Professor of Psychology, Emeritus, professor of psychology and public affairs, emeritus, and a Nobel laureate in economics whose groundbreaking behavioral science research changed our understanding of how people think and make decisions, died on March 27. He was 90. Kahneman joined the Princeton University faculty in 1993, following appointments at Hebrew ...

  30. Sweet success: Sugarcane's complex genetic code cracked

    "Knowing R570's complete genetic picture will let researchers trace which genes descended from which parent, enabling breeders to more easily identify the genes that control the traits of interest ...