Behavior Genetics

Parental genotypes may influence offspring outcomes through the environments parents provide, a process known as genetic nurture, but estimating such effects from polygenic scores is complicated jointly by measurement error, unobserved genetic variation, and assortative mating. Here, we introduce RAVEL, a structural equation modeling framework that uses two independently constructed polygenic scores for the same trait to estimate latent genetic liability associations in parent-offspring trios and to distinguish direct and non-transmitted genetic associations. Through analytic derivations and simulations, we show that naive trio regressions can produce spurious non-zero parental coefficients, attenuate genuine genetic nurture effects, and obscure asymmetric parental effects, whereas RAVEL recovers unbiased estimates of the underlying coefficients when the assortative mating history and the extent of unobserved genetic variation are correctly specified. Applying RAVEL to national test scores in the Norwegian Mother, Father and Child Cohort Study using polygenic scores for educational attainment, we find that parental genetic liabilities explain less than 0.5% as much variance in childhood cognitive performance as the child's own genetic liability (substantially lower than the 10% observed with naive trio regressions) once assortative mating is modeled, with only a small paternal association significantly surviving the correction. In contrast, we find a maternal-specific non-transmitted association with offspring premature birth. RAVEL provides a general framework for interpreting trio polygenic score analyses of direct and non-transmitted genetic effects, clarifying the contribution of parental genetic liabilities to offspring traits.

Studies of resemblance for disorders and other traits measured at the binary (yes/no) level between relatives frequently contain individuals who are currently in the negative category but who will become positive in future. For example, a 10-year-old may develop depression in the future, but is as yet unaffected. Such censoring can substantially bias estimates of correlation between relatives. To overcome this problem we develop a model for the association between liability to a disorder, and its age at onset. The model is designed for data from pairs of relatives to enable estimation of the correlation between an individuals' liability to disorder and their age at onset. Usually, such information is not available at the individual level, because age at onset is uniquely available when onset has occurred. Lacking variation in disorder status, data from non-related persons cannot estimate the covariance between liability and age at onset. Data from relatives can resolve this issue when there is a correlation in liability between the relatives, because different age at onset distributions would be expected in concordant vs. discordant pairs of relatives. Greater severity and worse outcomes are often observed among those with earlier onset, so a correlation between disorder liability and age at onset seems likely in many cases. In this article we present the basic theory of the model, implemented as a mixture distribution, and an application to cannabis use in a Virginia Twin Study of Adolescent Behavioral Development. A negative association of (-.212) between age at onset an liability was found, with confidence intervals of -.263 to -.152, which do not cross zero. The method contrasts with Cox Proportional Hazards, in which disorder liability and onset timing are treated as a single dimension.

Background: Across phenotypic and genotypic studies, executive functioning (EF) has been shown to be associated with externalizing behaviors, including attention-deficit/hyperactivity disorder (ADHD), aggression, and substance use, and is a proposed underlying mechanism of these behaviors. However, there is limited research on the shared biological mechanisms underpinning EF and externalizing behaviors, including their overlap at the level of genomic regions. Methods: The current study used genome-wide association study summary statistics of EF, ADHD, aggressive behavior, alcohol use, and alcohol use disorder to examine whether the heritability of these traits or the co-heritability between them is enriched for genes expressed in particular tissues or stratified by other annotation types. Additionally, the study examined the genomic regions contributing to the covariation between EF and the externalizing traits to identify genetic variants and genes that may be underlying their overlap. Results: First, across all phenotypes, there was evidence for enrichment of genetic signals in DNase hypersensitive sites and H3K4me1 regions, and significant enrichment among genes expressed in the anterior cingulate cortex and frontal cortex. Second, when combining results across regional methods, there was evidence for associations between EF and each of the externalizing behaviors in regions on chromosomes 1, 11, and 15. Conclusions: These findings highlight biological pathways that may link the relations between EF and externalizing behaviors and provides additional evidence of EF as an underlying mechanism of externalizing behavior.

The phenotype of a focal individual can be affected not only by the individuals own genes (direct genetic effects, DGE) but also by genes of other individuals it interacts with (indirect genetic effects, IGE). Better understanding IGE can help better understand the impact and mechanisms of social effects. Here, we investigated, in laboratory mice, the modulation of IGE by genetic background of the focal individual (social epistasis) and by sex. We focused on IGE arising from Epha4, a gene that has been associated with the individuals own stress-related and social behaviours (i.e. DGE) and the stress-coping behaviour and wound healing of the other mice in the cage (i.e. IGE). Among four different genetic backgrounds for focal mice, significant IGE from Epha4 were detected in CBA females only. A formal model selection analysis was consistent with the modulation of IGE by the similarity of the genetic backgrounds of the focal mouse and its Epha4 cage mate, as well as by sex. Observations of fighting in cages of males with dissimilar genetic backgrounds suggested aggression might underpin these modulatory effects, which prompted to us to expand the conceptual framework describing the mechanisms of social epistasis. Thus, our results strengthen the emerging view that IGE are not fixed properties of social genotypes but can depend on the genetic and sex context of interacting individuals, revealing an underexplored layer of their genetic architecture.

Cocaine Use Disorder (CUD) poses a significant public health and socioeconomic challenge. Determining the genetic basis of predisposition for development of CUD is challenging in human populations but can be studied in Drosophila. We assessed cocaine consumption and cocaine preference of 74,875 flies from 598 sequenced, wild-derived, inbred lines from the expanded Drosophila melanogaster Genetic Reference Panel (DGRP3). We found significant genetic variation, sexual dimorphism, and genetic variation in sexual dimorphism for these traits. Whereas most lines showed cocaine avoidance, ~10% of the lines showed innate cocaine preference in at least one sex. Genome-wide association analyses for cocaine consumption, preference, and micro-environmental variance of these traits identified 2,155 polymorphisms in/near 866 genes that were enriched for Gene Ontology terms associated with neurogenesis, development, and behavior. Many of the associated genes had human orthologs with known associations with CUD and other substance use disorders as well as psychiatric and behavioral traits. We confirmed causal associations with cocaine preference for three polymorphisms with large effect sizes by assessing their effects in DGRP3 lines not included in the initial association analyses. Pairwise associations between these polymorphisms exhibited suppressing epistasis. These polymorphisms are in genes with human orthologs that fulfill essential functions in the nervous system, including the glucose transporter SLC2A8; KCNC2, a subunit of the voltage gated potassium channel; and CHRNA7, a nicotinic cholinergic receptor subunit. Thus, studies on Drosophila can provide insights into the genetic and neural mechanisms of CUD.

Intelligence is often considered to be fundamentally different from other personality traits. Twin studies show that intelligence and the Big Five personality traits are partly influenced by the same genetic factors, but it remains unclear to what degree shared genes, SNPs, and biological pathways influence them. To address these questions, we analyzed the genetic associations of all six traits. First, we calculated their genetic correlations using LD score regression. We then quantified polygenic overlap using a bivariate causal mixture model, finding that approximately 90% of the genetic variants influencing the Big Five personality traits also impact intelligence. Finally, we performed gene prioritization and gene set enrichment analysis on each trait, once again finding substantial overlap. At each level, intelligence was as similar to the Big Five traits as they were to one another. Genetically and biologically, intelligence fits in with the rest of personality.

Background: Attention-deficit/hyperactivity disorder (ADHD) is associated with perinatal and genetic risk factors, including prenatal maternal smoking, pre-pregnancy BMI, gestational age, birth weight, and common genetic variants. These risk factors, as well as ADHD symptoms themselves, have previously been linked to cord blood DNA methylation (DNAm). We tested the hypothesis that cord blood DNAm mediates the effects of these risk factors on ADHD symptoms. Methods: Participants were drawn from two large European population-based cohorts: the Generation R Study and Avon Longitudinal Study of Parents and Children (n=3087). Cord blood DNAm was assessed using Illumina 450k and EPIC v1 arrays. ADHD symptoms were repeatedly measured with parent-based questionnaires between the ages 6 and 10 years. A high-dimensional mediational model based on DNAm principal components mediation analysis (PCMA) estimated the global mediation effect of all tested DNAm sites. Mediation via single principal components and individual DNAm sites was also evaluated using structural equation modeling and Divide-Aggregate Composite-null Test (DACT). Results: DNAm globally mediated the relationships of maternal smoking, low birth weight, and an ADHD polygenic score (PGS) with ADHD symptoms. Specifically, DNAm explained 62% of the total effect for maternal smoking, 56% for birth weight, and 35% for the ADHD-PGS. No association with individual principal components or single DNAm sites survived multiple testing correction. Evidence for mediation was absent for pre-pregnancy BMI and inconsistent for gestational age. Conclusions: In this first epigenome-wide mediation study of ADHD, we demonstrate a role of DNAm at birth in mediating the association of maternal smoking, birth weight and ADHD-related genetic variants with ADHD symptoms. However, lack of individual site-specific findings and the observational design limit causal biological interpretations. We therefore encourage further research of epigenetic pathways for these three risk factors.

The Big 5 personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness to experience) are moderately heritable correlates of behavior, health, and life outcomes. Characterizing childhood expressions of genetic variation associated with adult personality may inform our understanding of personality development and how personality becomes associated with behaviors and environments. Our phenome-wide association study (PheWAS) of Big 5 polygenic indices (PGIs) among children (nEuropean-like ancestry=5,556; nAfrican-like ancestry=1,584) in the Adolescent Brain Cognitive Development StudySM (ABCD Study) across ages 9-11 and 11-13 revealed stable associations between neuroticism PGI and measures of child mental health, openness PGI and cognition, agreeableness PGI and externalizing behaviors, conscientiousness PGI and perseverance, and extraversion PGI and impulsivity. Longitudinal analyses indicated stronger personality PGI associations with some phenotypes (e.g., prosocial behavior) at ages 11-13. Behavioral correspondence to genetic propensity to adult personality is evident in early adolescence while certain correlates of personality PGIs may only manifest later in development.

Polygenic scores (PGS) predict an individual's genetic value for a complex trait of interest by aggregating effect size estimates from genome-wide association studies (GWAS). While powerful, this approach assumes that genetic variants affect the phenotype in an additive way, which might be problematic when dominance is part of the genetic architecture. To address this limitation, we developed PGSa,d, a simple polygenic scoring framework, rooted in quantitative genetics theory, that incorporates both additive (a) and dominance effects (d) directly. We evaluated PGSa,d against the standard GWAS-based PGS (PGS{beta}) in simulations. The results showed that PGSa,d consistently achieved lower root mean squared error (RMSE) and progressively higher R2 as dominance contributed more to the genetic architecture. We then applied the method to 16 blood cell traits, height, and body mass index (BMI) from UK Biobank. PGSa,d showed comparable R2 to PGS{beta} for all traits (including height and BMI whose architecture is predominantly additive) and significantly lower RMSE for four traits after strict Bonferroni correction. Across traits, RMSE improvement from PGSa,d was positively associated with the magnitude of the estimated dominance effects, whereby traits with larger dominance effects displayed lower RMSE from PGSa,d. As a byproduct of our analysis, we also identified 141 variants in 39 genomic regions with significant dominance effects after Bonferroni correction across traits, recapitulating known associations and identifying novel ones. Together, these results show that explicitly modeling additive and dominance effects is a robust strategy that can improve individual-level predictions, especially when dominance contributes to complex trait architecture.

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene-environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

Substance use and psychiatric disorders frequently co-occur, but establishing causality of their relationships is difficult given the multifactorial nature of these traits. To assess these relationships, we applied two genetically informed methods using the largest available European-ancestry genome-wide association studies of six substance use behaviors and twelve psychiatric and personality phenotypes. Using Genomic Structural Equation Modelling, we modelled the latent genetic architecture of the psychiatric and personality traits and examined these factors' correlations with substance use traits. A structure with three factors fit the data best: F1 (PTSD, MDD, GAD, ADHD, agreeableness), F2 (BD, SCZ, OCD, openness), and F3 (OCD, GAD, extraversion, neuroticism), which showed differential correlations with substance use. Smoking correlated significantly which each factor, however, strongest with F1, alcohol use showed weak but significant correlations with F1 and F2, and cannabis use showed moderate correlations with F1 and F2, but strongest with F2. Using Mendelian Randomization, we examined bidirectional causal relationships. We found strong evidence that smoking continuation was causally associated with increased bipolar disorder risk, cigarettes per day with increased schizophrenia, and smoking initiation with increased extraversion and decreased agreeableness. Genetic liability to schizophrenia and PTSD were causally associated with increased risk of cannabis use, and schizophrenia with increased alcohol use. Liability to neuroticism, openness and agreeableness was causally linked to smoking behaviors, and extraversion to cannabis use. Most remaining associations showed weak causal evidence, largely due to heterogeneity and pleiotropy. Together, these findings provide evidence for both shared genetic liability and, for a subset of relationships, direct causal effects between substance use behaviors, psychiatric disorders, and personality traits, which can help inform future intervention and prevention research.

Attention Deficit Hyperactivity Disorder (ADHD) frequently co-occurs with distinct personality profiles, but whether ADHD shapes personality (scar model) or personality influences the development of ADHD (vulnerability model) remains unclear. Leveraging genome-wide association study (GWAS) data, we examined directional associations between ADHD and the Big Five personality traits (Neuroticism, Extraversion, Openness, Agreeableness, and Conscientiousness). We estimated genetic correlations and conducted bidirectional two-sample Mendelian Randomization (MR) analyses between ADHD and the Big Five. Robustness was evaluated using multiple sensitivity tests, including MR-CAUSE, which accounts for correlated and uncorrelated pleiotropy, and colocalization analyses on fine-mapped variants. Higher genetic liability to Neuroticism and Extraversion, alongside lower liability to Conscientiousness, increased ADHD risk, consistent with the vulnerability model. Conversely, ADHD genetic liability was associated with lower Agreeableness, supporting the scar model. Associations were generally robust across sensitivity analyses, with MR-CAUSE supporting the Neuroticism and Agreeableness associations and colocalization providing variant-level support for associations between ADHD and Neuroticism, Conscientiousness, and Agreeableness. These findings provide early, population-level insight into personality-related vulnerability to ADHD and motivate further work on the role of personality traits in ADHD risk.

Background: Psychopathy is a personality construct characterized by affective and interpersonal features coupled with severe antisocial behavior. While strongly associated with extreme behaviors such as violent crime, empirical work suggests that psychopathy exists as a dimensional trait in the general population. However, our understanding of the genetic structure of psychopathy remains limited, with genome-wide association studies (GWAS) having been restricted to broad measures of antisocial behavior. Methods: We perform the first large-scale GWAS of psychopathy, using total and factor scores from the Self-Report Psychopathy scale (SRP) in up to 10,558 participants enriched for mood disorder cases from the AFFECT study. LD score regression (LDSC) was used to characterize the heritability of SRP scores and estimate their genetic correlation (rG) with several externally-measured traits, using the Benjamini-Hochberg approach to perform multiple testing correction. Results: Psychopathy scores showed significant heritability, with 12-14% of phenotypic variance explained by common genetic variants. However, GWAS did not identify any genome-wide significant loci in this sample. The LDSC-estimated genetic correlation with antisocial behavior was only 0.54 (95%CI: 0.21-0.81), in line with prior twin research. Significant genetic correlations were found with a wide range of psychiatric disorders (rG's: 0.21 - 0.72), with the highest observed for borderline personality disorder (95% CI: 0.41-0.72) and substance use disorders (95% CI: 0.43-0.63). Psychopathy was also genetically correlated with lower life-satisfaction, poorer health outcomes, worse cognitive functioning, and lower educational attainment. Conclusions: In the first large-scale GWAS of self-reported psychopathy, we found significant SNP heritability and genetic overlap with risk for psychiatric disorders, and poorer behavioral, cognitive, and physical health outcomes. These findings provide important genetic support for differentiating psychopathy from antisocial behavior. Further, they inform our understanding of how genetic factors may contribute to poor health outcomes in psychopathy, and shed insight into the comorbidity between psychopathy and other disorders.

Local genetic correlation is estimated as a ratio of a local genetic covariance to the geometric mean of two local heritabilities. LAVA, the standard implementation, returns that ratio only where both traits clear a univariate heritability test at P < 0.05, a rule its documentation presents as a stabiliser. We show that it is a selection event acting on the denominator, and characterise what it does to the estimates that survive. Under the noncentral Wishart model LAVA's own interval routine assumes, the gate-conditional mean of the estimator is affine in the true correlation, with slope and intercept available in closed form from quantities every run already reports, that is, the components retained, the two univariate P-values, and the sample-overlap correlation. Attenuation is therefore a property of the analysis, not of the method. Across six psychiatric trait pairs the measured slope runs from 0.579 to 0.923 and the closed form predicts each to within 0.007 with nothing fitted to the panel, while within a single pair it runs from 0.371 to 0.971, so no single factor can correct it. Overlap and the filter jointly manufacture correlation where the truth is zero, most where power is lowest. We invert the closed form into a gate-aware correction, which reduces mean absolute bias from 0.247 to 0.026 over 48 simulation cells, and pair it with a diagnostic that decides whether the correction is identified. Three pairs fail it, all three sharing one trait whose local heritabilities are indistinguishable from a complete local null.

Addiction is a complex and heritable trait which progresses through several developmental stages, each of which is presumably influenced by multiple partially overlapping genetic factors. Cocaine initially produces rewarding effects, followed by aversive effects including anxiety, craving, anhedonia, and withdrawal. These aversive effects have been suggested to contribute to the etiology of cocaine use disorders (CUD), as repeated exposure is thought to desensitize the rewarding effects and sensitize the aversive effects through a process involving both aberrant reward-based learning and aberrant avoidance-based learning. We examined the genetic basis of aversion learning using both food-based and cocaine-based behavioral assays in outbred Heterogenous Stock (HS) rats. A total of 1,074 HS rats (35.3% male) underwent runway operant cocaine-seeking, food-based progressive ratio and punishment testing, and locomotion testing. These phenotypes were significantly heritable (with h2 estimates as high as 0.307) and identified significant (p < 0.05) genetic loci related to avoidance-based learning including from the punishment task on Chromosomes 2, 3, 5, and 6, and the cocaine-operant runway latency task on Chromosome X. 172 positional candidate genes were identified from significant and suggestive loci, including Cdh10, Cdh12, Cdh18 which have previously been associated with smoking initiation from human GWAS, Adcy3, Cfap206, and Drc1 which are associated with primary neuronal cilia, as well as SNPs associated with novelty-related and social interaction phenotypes in independent samples of HS rats. Our results suggest that these aversion learning phenotypes are themselves complex heritable traits influenced by multiple genetic loci, which may pleiotropically affect other aspects of addiction biology.

Partner resemblance influences and reflects the social, genetic and health structure of human populations. Using Danish registers on 787,658 couples, we quantified partner resemblance for ten psychiatric disorders and socioeconomic status (SES), measured up to a year before first childbirth and across the life course. Psychiatric partner correlations ranged from 0.08 for eating disorders to 0.36 for schizophrenia, below those for education (0.63) and income (0.57). Income resemblance fell substantially across the life course (to 0.38), whereas education resemblance was stable. Psychiatric resemblance changed little across windows. Adjusting psychiatric liability for own SES reduced psychiatric partner resemblance, whereas adjusting SES for own psychiatric status barely affected SES partner resemblance. The reduction in psychiatric partner resemblance had a median of ~7% before first childbirth but ~26% across the life course, as diagnosis and SES became increasingly coupled within individuals. What psychiatric partner resemblance represents therefore depends on when it was measured.

Population stratification is a major source of inflated false positive rates in genome wide association studies. However, relatively few studies have examined its impact on gene-gene interaction detection, despite the importance of epistasis for understanding the genetic architecture of complex traits. In this study, we identify scenarios under which population stratification can inflate the interaction test statistics. Through analytical derivations and simulation studies, we show that this inflation is not adequately controlled by including principal components as covariates in the regression model. We then propose an alternative approach that effectively controls the inflation of false-positive rates for interaction test statistics due to population stratification by using single nucleotide polymorphism-by-population structure interaction as an additional covariate term in the regression model.

Anthropogenic copper (Cu) contamination from agriculture, mining, and industrial runoff creates environmental gradients affecting physiology and behavior in wild populations. While Cu toxicity in Drosophila melanogaster is well characterized, it remains unclear whether Cu resistance is one integrated trait or several independently evolving components. Using a subset of recombinant inbred lines (RILs) from the Drosophila Synthetic Population Resource (DSPR), we measured three components of Cu response: feeding avoidance, oviposition avoidance, and physiological tolerance (median lethal time, LT50) under sustained Cu exposure. All three traits showed substantial phenotypic variation among RILs. Feeding and oviposition avoidance were both highly heritable (H 2 ~ 0.88), and RIL identity accounted for 49.5% of the variance in LT50. However, the three traits showed no significant correlation across RILs, indicating distinct genetic architecture. We identified a single male specific quantitative trait locus (QTL) on chromosome 2R that explained 17.7% of the variation in feeding preference; the interval included candidate detoxification genes Jheh1, Jheh2, Jheh3 and sano, the latter of which is associated with olfactory behavior. No significant QTL were detected for oviposition preference, suggesting a highly polygenic structure that may difficult to detect with our limited panel size. Together, these results indicate that Cu resistance in D. melanogaster is genetically modular. Behavioral avoidance during feeding, oviposition, and physiological tolerance are heritable but architecturally distinct components, each with potential to respond to selection independently.

Polyandry, multiple mating by females, both benefits females by increases genetic variation within families and provides an opportunity for sexual selection through sperm competition and cryptic female choice. Estimating polyandry in species with large clutches is challenging because genotyping all progeny individually can be impractical. We present a likelihood-based framework for estimating the number of sires and their relative contributions from the maternal genotype and pooled sequencing of entire progeny pools. The method evaluates the probability of observed pooled offspring read counts under alternative paternal genotype mixtures, accounting for Mendelian segregation and sampling variation from finite sequencing depth. We implement this framework for both biallelic SNPs and short-read mini-haplotype markers constructed from closely linked SNPs, which provide multi-allelic information comparable to traditional microsatellite markers. Simulations show that this approach accurately recovers sire number and sire contributions across a range of conditions. We then applied the method to 275 wild-caught Drosophila melanogaster females and their offspring from a Kansas population using 3,053 biallelic SNPs and 239 four-allele mini-haplotype markers. Estimates from the two marker sets were highly concordant, and combined analyses indicate that 57% of families were sired by a single male. These results demonstrate that pooled progeny sequencing provides an efficient genomic approach for estimating polyandry in species with large families.

The present study examined the relationship between the DSM-5 diagnostic criteria for gambling disorder (GD) and gambling severity via an item response theory (IRT) analysis in two large German population survey data sets (Buth et al. (2022, 2024)). IRT-based person fit analyses may reveal atypical response patterns (e.g. endorsing criteria linked to higher levels of disorder severity, but not criteria linked to lower levels). We examined the link of such atypical response patterns and mental health as measured by the MHI-5, employing a 2-parameter-logistic (2PL) IRT model and a linear mixed model with random intercepts. Results largely replicated previously reported item severity rankings across both samples: GD criteria such as loss chasing and a preoccupation with gambling were generally linked to lower severity levels, whereas criteria such as withdrawal symptoms or job/family problems where generally linked to higher severity levels. Modelling revealed a reduced assessment sensitivity in lower gambling severity ranges. Furthermore, person fit analyses suggest that atypical symptom patterns may be linked to poorer mental health (MHI-5). Implications for the interpretability of total scores of endorsed criteria and the validity of diagnostic practices determining eligibility for treatment and financial compensation are discussed.