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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
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Behavioral Genetics and Evolution
The Human Genome Project and Other Genome Projects
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Human Research

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Behavioral Genetics by Lisabeth DiLalla , Matthew Jamnik , Riley Marshall , Emily Pali LAST REVIEWED: 20 February 2024 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.

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Celebrating a Century of Research in Behavioral Genetics

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

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research articles on behavioral genetics

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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.

Challenges and Strategies for Integrating Molecular Genetics into Behavioral Science

Genotype-environment correlation in the era of dna.

Behavioural genetics methods

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.

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.

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

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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

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Overview of Behavioral Genetics Research for Family Researchers

Affiliation.

1 Department of Psychology, N218 Elliot Hall, 75 East River Road, Minneapolis, MN 55108 ( [email protected] ).
PMID: 24073018
PMCID: PMC3780434
DOI: 10.1111/jftr.12013

This article provides an overview of the methods, assumptions, and key findings of behavioral genetics methodology for family researchers with a limited background. We discuss how family researchers can utilize and contribute to the behavioral genetics field, particularly in terms of conducting research that seeks to explain shared environmental effects. This can be done, in part, by theoretically controlling for genetic confounds in research that seeks to determine cause-and-effect relationships among family variables and individual outcomes. Gene-environment correlation and interaction are especially promising areas for the family researcher to address. Given the methodological advancements in the field, we also briefly comment on new methods in molecular genetics for studying psychological mental health disorders.

Keywords: Behavior genetics; genetic relatedness; human development; shared environment.

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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.

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

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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

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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.

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A Clinical Diagnostic Test for Calcium Release Deficiency Syndrome

1 Libin Cardiovascular Institute, Department of Physiology and Pharmacology, University of Calgary, Calgary, Alberta, Canada
2 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
3 Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, and Hebrew University Faculty of Medicine, Jerusalem, Israel
4 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, England
5 Oxford Heart Centre, John Radcliffe Hospital, Oxford, England
6 Department of Cardiology, Faculty of Medicine and Health Sciences, Antwerp University Hospital, Antwerp, Belgium
7 Cardiovascular Research, Departments of Genetics, Pharmacology and Physiopathology of Heart, Blood Vessels and Skeleton, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
8 Member of the European Reference Network for Rare, Low Prevalence, and Complex Diseases of the Heart (ERN GUARD-Heart)
9 Department of Clinical Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
10 Heart Failure and Arrhythmias, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
11 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, Ontario, Canada
12 Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada
13 Department of Cardiology, Aarhus University Hospital, Aarhus N, Denmark
14 Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, Quebec, Canada
15 Department of Cardiac Pacing and Electrophysiology, Hopital Cardiologique du Haut-Leveque, Centre Hospitalier Universitaire de Bordeaux, Pessac, France
16 Division of Cardiology and Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, Canada
17 Department of Molecular Cardiology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
18 Department of Molecular Medicine, University of Pavia, Pavia, Italy
19 Windland Smith Rice Genetic Heart Rhythm Clinic, Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
20 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
21 Section of Cardiac Electrophysiology, Division of Cardiology, University of Washington Medical Center, Seattle
22 Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
23 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, University of California, San Francisco
24 Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
25 Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota
26 Inherited Arrhythmia and Cardiomyopathy Program, Arrhythmia Service, Division of Cardiology, Toronto General Hospital and the University of Toronto, Toronto, Ontario, Canada
27 Leviev Heart Institute, Chaim Sheba Medical Center, Ramat Gan, Israel
28 Tel Aviv University, Tel Aviv, Israel
29 Oxford Biomedical Research Centre and Wellcome Centre for Human Genetics, University of Oxford, Oxford, England
30 Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
31 Heart Institute, Hadassah University Hospital, Jerusalem, Israel
Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? Gregory M. Marcus, MD, MAS; Gregory Curfman, MD; Kirsten Bibbins-Domingo, PhD, MD, MAS JAMA

Question Cardiac arrest frequently occurs without explanation, even after a thorough clinical evaluation. Can a simple maneuver clinically diagnose calcium release deficiency syndrome (CRDS), a newly described cause of sudden death?

Findings In this international, multicenter, case-control study, a provoked measure of T-wave amplitude on an electrocardiogram ascertained cases of CRDS with high accuracy. The genetic mouse models recapitulated the human findings and suggested a pathologically large systolic calcium release from the sarcoplasmic reticulum was responsible.

Meaning These preliminary results suggest that the repolarization response on an electrocardiogram to brief tachycardia followed by a pause may effectively diagnose CRDS. Given the frequency of unexplained cardiac arrest, should these findings be confirmed in larger studies, this readily available maneuver may provide clinically actionable information.

Importance Sudden death and cardiac arrest frequently occur without explanation, even after a thorough clinical evaluation. Calcium release deficiency syndrome (CRDS), a life-threatening genetic arrhythmia syndrome, is undetectable with standard testing and leads to unexplained cardiac arrest.

Objective To explore the cardiac repolarization response on an electrocardiogram after brief tachycardia and a pause as a clinical diagnostic test for CRDS.

Design, Setting, and Participants An international, multicenter, case-control study including individual cases of CRDS, 3 patient control groups (individuals with suspected supraventricular tachycardia; survivors of unexplained cardiac arrest [UCA]; and individuals with genotype-positive catecholaminergic polymorphic ventricular tachycardia [CPVT]), and genetic mouse models (CRDS, wild type, and CPVT were used to define the cellular mechanism) conducted at 10 centers in 7 countries. Patient tracings were recorded between June 2005 and December 2023, and the analyses were performed from April 2023 to December 2023.

Intervention Brief tachycardia and a subsequent pause (either spontaneous or mediated through cardiac pacing).

Main Outcomes and Measures Change in QT interval and change in T-wave amplitude (defined as the difference between their absolute values on the postpause sinus beat and the last beat prior to tachycardia).

Results Among 10 case patients with CRDS, 45 control patients with suspected supraventricular tachycardia, 10 control patients who experienced UCA, and 3 control patients with genotype-positive CPVT, the median change in T-wave amplitude on the postpause sinus beat (after brief ventricular tachycardia at ≥150 beats/min) was higher in patients with CRDS ( P < .001). The smallest change in T-wave amplitude was 0.250 mV for a CRDS case patient compared with the largest change in T-wave amplitude of 0.160 mV for a control patient, indicating 100% discrimination. Although the median change in QT interval was longer in CRDS cases ( P = .002), an overlap between the cases and controls was present. The genetic mouse models recapitulated the findings observed in humans and suggested the repolarization response was secondary to a pathologically large systolic release of calcium from the sarcoplasmic reticulum.

Conclusions and Relevance There is a unique repolarization response on an electrocardiogram after provocation with brief tachycardia and a subsequent pause in CRDS cases and mouse models, which is absent from the controls. If these findings are confirmed in larger studies, this easy to perform maneuver may serve as an effective clinical diagnostic test for CRDS and become an important part of the evaluation of cardiac arrest.

Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? JAMA

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Female contemplating model of a double helix.

Genetic Evidence

Through news accounts and crime stories, we’re all familiar with the fact that the DNA in our cells reflects each individual’s unique identity and how closely related we are to one another. The same is true for the relationships among organisms. DNA, or deoxyribonucleic acid, is the molecule that makes up an organism’s genome in the nucleus of every cell. It consists of genes, which are the molecular codes for proteins – the building blocks of our tissues and their functions. It also consists of the molecular codes that regulate the output of genes – that is, the timing and degree of protein-making. DNA shapes how an organism grows up and the physiology of its blood, bone, and brains.

DNA is thus especially important in the study of evolution. The amount of difference in DNA is a test of the difference between one species and another – and thus how closely or distantly related they are.

While the genetic difference between individual humans today is minuscule – about 0.1%, on average – study of the same aspects of the chimpanzee genome indicates a difference of about 1.2%. The bonobo ( Pan paniscus ), which is the close cousin of chimpanzees ( Pan troglodytes ), differs from humans to the same degree. The DNA difference with gorillas, another of the African apes, is about 1.6%. Most importantly, chimpanzees, bonobos, and humans all show this same amount of difference from gorillas. A difference of 3.1% distinguishes us and the African apes from the Asian great ape, the orangutan. How do the monkeys stack up? All of the great apes and humans differ from rhesus monkeys, for example, by about 7% in their DNA.

Geneticists have come up with a variety of ways of calculating the percentages, which give different impressions about how similar chimpanzees and humans are. The 1.2% chimp-human distinction, for example, involves a measurement of only substitutions in the base building blocks of those genes that chimpanzees and humans share. A comparison of the entire genome, however, indicates that segments of DNA have also been deleted, duplicated over and over, or inserted from one part of the genome into another. When these differences are counted, there is an additional 4 to 5% distinction between the human and chimpanzee genomes.

No matter how the calculation is done, the big point still holds: humans, chimpanzees, and bonobos are more closely related to one another than either is to gorillas or any other primate. From the perspective of this powerful test of biological kinship, humans are not only related to the great apes – we are one. The DNA evidence leaves us with one of the greatest surprises in biology: the wall between human, on the one hand, and ape or animal, on the other, has been breached. The human evolutionary tree is embedded within the great apes.

The strong similarities between humans and the African great apes led Charles Darwin in 1871 to predict that Africa was the likely place where the human lineage branched off from other animals – that is, the place where the common ancestor of chimpanzees, humans, and gorillas once lived. The DNA evidence shows an amazing confirmation of this daring prediction. The African great apes, including humans, have a closer kinship bond with one another than the African apes have with orangutans or other primates. Hardly ever has a scientific prediction so bold, so ‘out there’ for its time, been upheld as the one made in 1871 – that human evolution began in Africa.

The DNA evidence informs this conclusion, and the fossils do, too. Even though Europe and Asia were scoured for early human fossils long before Africa was even thought of, ongoing fossil discoveries confirm that the first 4 million years or so of human evolutionary history took place exclusively on the African continent. It is there that the search continues for fossils at or near the branching point of the chimpanzee and human lineages from our last common ancestor.

Primate Family Tree

Due to billions of years of evolution, humans share genes with all living organisms. The percentage of genes or DNA that organisms share records their similarities. We share more genes with organisms that are more closely related to us.

Humans belong to the biological group known as Primates, and are classified with the great apes, one of the major groups of the primate evolutionary tree. Besides similarities in anatomy and behavior, our close biological kinship with other primate species is indicated by DNA evidence. It confirms that our closest living biological relatives are chimpanzees and bonobos, with whom we share many traits. But we did not evolve directly from any primates living today.

DNA also shows that our species and chimpanzees diverged from a common ancestor species that lived between 8 and 6 million years ago. The last common ancestor of monkeys and apes lived about 25 million years ago.

Woman sitting crossed leg in front of a world map with a timeline from 200-50 thousand years ago

One Species, Living Worldwide

The amazing story of adaptation and survival in our species, Homo sapiens , is written in the language of our genes, in every cell of our bodies—as well as in the fossil and behavioral evidence. Explore the African origins of modern humans about 200,000 years ago and celebrate our species’ epic journey around the world in this video: “One Species, Living Worldwide".

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Astrocytes modulate a specific paraventricular thalamus-prefrontal cortex projection to enhance consciousness recovery from anesthesia

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Current anesthetic theory is mostly based on neurons and/or neuronal circuits. A role for astrocytes also has been shown in promoting recovery from volatile anesthesia, while the exact modulatory mechanism and/or the molecular target in astrocytes is still unknown. In this study, by animal models in male mice and electrophysiological recordings in vivo and in vitro, we found that activating astrocytes of paraventricular thalamus (PVT) and/or knocking down PVT astrocytic Kir4.1 promoted the consciousness recovery from sevoflurane anesthesia. Single-cell RNA sequencing of PVT reveals two distinct cellular subtypes of glutamatergic neurons: PVT GRM and PVT ChAT neurons. Patch-clamp recording results proved astrocytic Kir4.1-mediated modulation of sevoflurane on PVT mainly worked on PVT ChAT neurons, which projected mainly to the mPFC. In summary, our findings support the novel conception that there is a specific PVT-prefrontal cortex projection involved in consciousness recovery from sevoflurane anesthesia, which mediated by the inhibition of sevoflurane on PVT astrocytic Kir4.1 conductance.

Significance Statement How volatile anesthetics work is not fully understood. Here, we demonstrate that the commonly used volatile anesthetic sevoflurane can inhibit astrocytic Kir4.1 conductance in PVT, which enhances neuronal firing of PVT neurons. Additionally, by single-cell sequencing, cholinergic neurons in the PVT (PVT ChAT ) are the neuronal substrates for astrocytic modulation in volatile anesthesia, which directly project to prefrontal cortex. Behaviorally, the modulation of astrocytes on PVT ChAT promotes electroencephalogram (EEG) transition of prefrontal cortex; and then accelerates emergence from sevoflurane anesthesia. In summary, this study is the first to identify that astrocytic Kir4.1 in wakeful nuclei is involved in consciousness recovery from volatile anesthetics, as well as the subcellular mechanism.

The authors declare no competing interests.

We thank Prof. Xia Zhang from Department of Neurology, West China Hospital of Sichuan University for his critical discussion; and Prof. Daniel K. Mulkey (Departments of Physiology and Neurobiology, University of Connecticut) for his technical help in electrophysiological recordings.

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Beyond CRISPR: seekRNA delivers a new pathway for accurate gene editing

SeekRNA a 'real game-changer'

Courtesy Sky News Australia

Scientists at the University of Sydney have developed a gene-editing tool with greater accuracy and flexibility than the industry standard, CRISPR, which has revolutionised genetic engineering in medicine, agriculture and biotechnology.

SeekRNA uses a programmable ribonucleic acid (RNA) strand that can directly identify sites for insertion in genetic sequences, simplifying the editing process and reducing errors.

The new gene-editing tool is being developed by a team led by Dr Sandro Ataide in the School of Life and Environmental Sciences. Their findings have been published in Nature Communications .

“We are tremendously excited by the potential for this technology. SeekRNA’s ability to target selection with precision and flexibility sets the stage for a new era of genetic engineering, surpassing the limitations of current technologies,” Dr Ataide said.

“With CRISPR you need extra components to have a ‘cut-and-paste tool’, whereas the promise of seekRNA is that it is a stand-alone ‘cut-and-paste tool’ with higher accuracy that can deliver a wide range of DNA sequences.”

CRISPR relies on creating a break in both strands of target DNA, the double-helix genetic code of life, and needs other proteins or the DNA repair machinery to insert the new DNA sequence. This can introduce errors.

Lead author Rezwan Siddiquee (centre) with Dr Sandro Ataide (left) and Caitlin McCormack in the Ataide Laboratory. Photo: Fiona Wolf/University of Sydney

Dr Ataide said: “SeekRNA can precisely cleave the target site and insert the new DNA sequence without the use of any other proteins.

“This allows for a much cleaner editing tool with higher accuracy and fewer errors.”

Gene-editing has opened completely new areas of research and application since the development of CRISPR more than 10 years ago. It has led to improvements in disease resistance in fruit and crops, reduced the cost and speed of human disease detection, helped in the search for a cure for sickle cell disease and allowed for the development of revolutionary cancer treatment known as (CAR) T-cell therapy.

“We are very much in the early days of what gene editing can do. We hope that by developing this new approach to gene editing, we can contribute to advances in health, agriculture and biotechnology,” said joint author Professor Ruth Hall from the University of Sydney.

Precise genetic targeting

SeekRNA is derived from a family of naturally occurring insertion sequences known as IS 1111 and IS 110 , discovered in bacteria and archaea (cells without a nucleus). Most insertion sequence proteins exhibit little or no target selectivity, however these families exhibit high target specificity.

It is this accuracy that seekRNA has used to achieve its promising results to date.

Using the accuracy from this insertion sequence family, seekRNA can be modified to any genomic sequence and insert the new DNA in a precise orientation.

Dr Sandro Ataide at the University of Sydney. Photo: Fiona Wolf/University of Sydney

“In the laboratory we have successfully tested seekRNA in bacteria. Our next steps will be to investigate if the technology can be adapted for the more complex eukaryotic cells found in humans,” Dr Ataide said.

An advantage of the system reported in this study is that it can be applied using only a single protein of modest size plus a short seekRNA strand, to efficiently move genetic cargo. SeekRNA is made up of a small protein of 350 amino acids and an RNA strand of between 70 and 100 nucleotides.

A system of this size could be packed into biological nanoscale delivery vehicles (vesicles or lipid nanoparticles) for delivery to cells of interest.

Direct insertion to DNA

Another point of differentiation is this technology’s ability to insert DNA sequences in the desired location by itself, a feat not possible with many current editing tools.

“Current CRISPR technology has limitations on the size of genetic sequences that can be introduced,” said University of Sydney research associate Rezwan Siddiquee , lead author of the paper. “This restricts the scope of application.”

Globally, other teams are pursuing similar research into the gene-editing potential of the IS 1111 and IS 110 family. However, Dr Ataide says they only have shown results for one member of the IS 110 family and rely on a much larger RNA version. The team at Sydney is advancing its technique through direct laboratory sampling and application of the shorter seekRNA itself.

Siddiquee, R. et. al. ‘A programmable seekRNA guide target selection by IS 111 1 and IS 110 type insertion sequences’. ( Nature Communications ) DOI: 10.1038/s41467-024-49474-9

Declaration

Dr Sandro Ataide, Professor Ruth Hall and Rezwan Siddiquee are inventors of patent applications related to this work. Research was supported by the University of Sydney Deputy Vice-Chancellor (Research) Strategic Research Impact Fund and a National Health and Medical Research Council (NHMRC) Investigator grant.

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A car speeds past a woman holding two children's hands, standing at a crosswalk

Traffic engineers build roads that invite crashes because they rely on outdated research and faulty data

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“Can you name the truck with four-wheel drive, smells like a steak, and seats 35?”

Back in 1998, “The Simpsons” joked about the Canyonero, an SUV so big that they were obviously kidding. At that time, it was preposterous to think anyone would drive something that was “12 yards long, two lanes wide, 65 tons of American Pride.”

In 2024, that joke isn’t far from reality.

And our reality is one where more pedestrians and bicyclists are getting killed on U.S. streets than at any time in the past 45 years – over 1,000 bicyclists and 7,500 pedestrians in 2022 alone.

Vehicle size is a big part of this problem. A recent paper by urban economist Justin Tyndall found that increasing the front-end height of a vehicle by roughly 4 inches (10 centimeters) increases the chance of a pedestrian fatality by 22% . The risk increases by 31% for female pedestrians or those over 65 years, and by 81% for children.

It’s hard to argue with physics, so there is a certain logic in blaming cars for rising traffic deaths. In fact, if a bicyclist is hit by a pickup truck instead of a car, Tyndall suggests that they are 291% more likely to die.

Yet automakers have long asserted that if everyone simply followed the rules of the road, nobody would die. Vehicle size is irrelevant to that assertion.

My discipline, traffic engineering , acts similarly. We underestimate our role in perpetuating bad outcomes, as well as the role that better engineering can play in designing safer communities and streets.

A bicycle, painted white and decorated with flowers, attached to a street pole at an urban intersection.

Millions of road deaths

How bad are the bad outcomes? The U.S. has been tracking car-related road deaths since 1899. As a country, we hit the threshold of 1 million cumulative deaths in 1953, 2 million in 1975 and 3 million in 1998. While the past several years of data have not yet been released, I estimate that the U.S. topped 4 million total road deaths sometime in the spring of 2024.

How many of those are pedestrians and bicyclists? Analysts didn’t do a great job of separating out the pedestrian and cyclist deaths in the early years , but based on later trends, my estimate is that some 930,000 pedestrians and bicyclists have been killed by automobiles in the U.S.

How many of those deaths do we blame on big cars or bad streets? The answer is, very few.

As I show in my new book, “ Killed by a Traffic Engineer: Shattering the Delusion that Science Underlies our Transportation System ,” the National Highway Traffic Safety Administration calls road user error the “ critical reason” behind 94% of crashes, injuries and deaths .

Crash data backs that up.

Police investigate crashes and inevitably look to see which road users, including drivers, pedestrians and cyclists, are most at fault. It’s easy to do because in almost any crash, road user error appears to be the obvious problem.

This approach helps insurance companies figure out who needs to pay. It also helps automakers and traffic engineers rationalize away all these deaths. Everyone – except the families and friends of these 4 million victims – goes to sleep at night feeling good that bad-behaving road users just need more education or better enforcement.

But road user error only scratches the surface of the problem.

Who creates dangerous streets?

When traffic engineers build an overly wide street that looks more like a freeway , and a speeding driver in a Canyonero crashes, subsequent crash data blames the driver for speeding.

When traffic engineers provide lousy crosswalks separated by long distances , and someone jaywalks and gets hit by that speeding Canyonero driver, one or both of these road users will be blamed in the official crash report.

And when automakers build gargantuan vehicles that can easily go double the speed limit and fill them with distracting touchscreens , crash data will still blame the road users for almost anything bad that happens.

These are the sorts of systemic conditions that lead to many so-called road user errors. Look just below the surface, though, and it becomes clear that many human errors represent the typical, rational behaviors of typical, rational road users given the transportation system and vehicle options we put in front of them.

Look more deeply, and you can start to see how our underlying crash data gives everyone a pass but the road users themselves. Everyone wants a data-driven approach to road safety, but today’s standard view of crash data lets automakers, insurance companies and policymakers who shape vehicle safety standards off the hook for embiggening these ever-larger cars and light-duty trucks.

It also absolves traffic engineers, planners and policymakers of blame for creating a transportation system where for most Americans, the only rational choice for getting around is a car .

Understanding road behavior

Automakers want to sell cars and make money. And if bigger SUVs seem safer to potential customers, while also being much more profitable , it’s easy to see how interactions between road users and car companies – making seemingly rational decisions – have devolved into an SUV arms race.

Even though these same vehicles are less safe for pedestrians, bicyclists and those in opposing vehicles , the current data-driven approach to road safety misses that part of the story.

This can’t all be fixed at once. But by pursuing business as usual, automakers and traffic engineers will continue wasting money on victim-blaming campaigns or billboards placed high over a road telling drivers to pay attention to the road .

A better starting point would be remaking the U.S.’s allegedly data-driven approach to road safety by reinventing our understanding of the crash data that informs it all.

The key is starting to ask why. Why did these road users act as they did? Why didn’t they follow the rules that were laid out for them? Bad road user behavior shouldn’t be excused, but a bit of digging below the surface of crash data unearths a completely different story.

Figuring out which road user is most at fault may be useful for law enforcement and insurance companies, but it doesn’t give transportation engineers, planners, policymakers or automakers much insight into what they can do better. Even worse, it has kept them from realizing that they might be doing anything wrong.

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Published: 17 June 2024

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging

Chao-Jie Ye ORCID: orcid.org/0009-0009-7565-5048 1 , 2 na1 ,
Dong Liu 1 , 2 na1 ,
Ming-Ling Chen ORCID: orcid.org/0000-0001-7992-1838 1 , 2 na1 ,
Li-Jie Kong 1 , 2 ,
Chun Dou 1 , 2 ,
Yi-Ying Wang ORCID: orcid.org/0000-0002-1252-7788 1 , 2 ,
Min Xu ORCID: orcid.org/0000-0003-3930-8718 1 , 2 ,
Yu Xu 1 , 2 ,
Mian Li ORCID: orcid.org/0000-0001-6514-2729 1 , 2 ,
Zhi-Yun Zhao ORCID: orcid.org/0000-0001-5950-2732 1 , 2 ,
Rui-Zhi Zheng 1 , 2 ,
Jie Zheng 1 , 2 ,
Jie-Li Lu ORCID: orcid.org/0000-0003-1317-0896 1 , 2 ,
Yu-Hong Chen 1 , 2 ,
Guang Ning 1 , 2 ,
Wei-Qing Wang ORCID: orcid.org/0000-0001-6027-3084 1 , 2 ,
Yu-Fang Bi ORCID: orcid.org/0000-0002-4829-5915 1 , 2 &
Tian-Ge Wang ORCID: orcid.org/0000-0003-0723-489X 1 , 2

Nature Human Behaviour ( 2024 ) Cite this article

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Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms ( n = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity ( n = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z -score higher) was causally associated with a higher aging-GIP ( β [95% confidence interval (CI)] in different models ranging from 1.00 [0.82–1.18] to 1.07 [0.91–1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience ( β [95% CI] ranging from 0.97 [0.82–1.12] to 1.04 [0.91–1.17] s.d.), self-rated health (0.61 [0.43–0.79] to 0.76 [0.59–0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02–1.48] to 1.35 [1.11–1.65]) and parental lifespan (1.77 [0.010–3.54] to 2.95 [1.13–4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

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Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Genome-wide association studies

Temporal dynamics of the multi-omic response to endurance exercise training

Data availability.

All GWAS summary statistics analysed in this study are publicly available as shown in Table 1 and Supplementary Table 1 for download by qualified researchers. The GWAS data for mental well-being traits can be obtained from the GWAS catalogue 38 ( https://www.ebi.ac.uk/gwas/publications/30643256 ). The GWAS data for aging phenotypes can be retrieved or requested from the study authors at https://doi.org/10.7488/ds/2972 (the aging-GIP 14 ), https://doi.org/10.6084/m9.figshare.9204998.v3 (frailty index 42 ), http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST006001-GCST007000/GCST006620 (self-rated health 43 ), https://doi.org/10.5281/zenodo.1302861 (healthspan 44 ), https://doi.org/10.7488/ds/2463 (parental lifespan 45 ) and https://www.longevitygenomics.org/downloads (longevity 46 ). All data generated in this study are included in the Supplementary Information .

Code availability

All the MR analyses were conducted using R packages TwoSampleMR (version 0.5.7), MVMR (version 0.4), MRPRESSO (version 1.0) and MRlap (version 0.0.3.0) in R software (version 4.3.1). Custom code that supports the findings of this study is available at https://github.com/yechaojie/mental_aging .

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Acknowledgements

This work was supported by the grants from the National Natural Science Foundation of China (82370820, 82088102, 91857205, 823B2014 and 81930021), the ‘Shanghai Municipal Education Commission–Gaofeng Clinical Medicine Grant Support’ from Shanghai Jiao Tong University School of Medicine (20171901 Round 2), and the Innovative Research Team of High-level Local Universities in Shanghai. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors are grateful to the participants of all the GWASs used in this manuscript and the investigators who made these GWAS data publicly available.

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These authors contributed equally: Chao-Jie Ye, Dong Liu, Ming-Ling Chen.

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Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Chao-Jie Ye, Dong Liu, Ming-Ling Chen, Li-Jie Kong, Chun Dou, Yi-Ying Wang, Min Xu, Yu Xu, Mian Li, Zhi-Yun Zhao, Rui-Zhi Zheng, Jie Zheng, Jie-Li Lu, Yu-Hong Chen, Guang Ning, Wei-Qing Wang, Yu-Fang Bi & Tian-Ge Wang

Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

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C.-J.Y. and T.-G.W. contributed to the conception and design of the study. C.-J.Y. performed statistical analyses and drafted the manuscript. T.-G.W. critically revised the manuscript. D.L., M.-L.C. and T.-G.W. checked the statistical analysis and proofread the manuscript. T.-G.W., G.N., W.-Q.W. and C.-J.Y. obtained funding. All authors contributed to the acquisition or interpretation of data, proofreading of the manuscript for important intellectual content and the final approval of the version to be published. T.-G.W. is the guarantor of this work and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Wei-Qing Wang , Yu-Fang Bi or Tian-Ge Wang .

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Ye, CJ., Liu, D., Chen, ML. et al. Mendelian randomization evidence for the causal effect of mental well-being on healthy aging. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01905-9

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