Behavior Genetics

BackgroundResearch has begun to explore the effects of parental genetic nurturing on offspring outcomes using polygenic scores (PGSs). However, there are concerns regarding potential biases due to confounding when mediating parental phenotypes are included. MethodsDepression, educational attainment and height PGSs were generated for 2680 biological parent-offspring trios using genome-wide association study (GWAS) meta-analysis summary statistics in a large population study: Generation Scotland. Regression and pathway models were estimated incorporating PGSs for both parents and offspring to explore direct (offspring PGS) and genetic nurturing (parental PGS) effects on psychological distress, educational attainment and height. Genetic nurturing via parental phenotypes were incorporated into the models. To explore sources of bias we conducted simulation analyses of 10,000 trios using combinations of PGS predictive accuracy and accounted variance. ResultsModels incorporating both offspring and parental PGSs suggested positive parental genetic nurturing effects on offspring educational attainment, but not psychological distress or height. In contrast, models additionally incorporating parental phenotypic information suggested positive parent phenotype mediated genetic nurturing effects were at play for all phenotypes explored as well as negative residual genetic nurturing effects for height. 10,000 parent-offspring trio effects (without genetic nurturing effects) were simulated. Simulations demonstrated that models incorporating parent and offspring PGSs resulted in genetic nurturing effects that were unbiased. However, adding parental phenotypes as mediating variables results in biased positive estimates of parent phenotype mediated genetic nurturing effects and negative estimates of residual genetic nurturing effects. Biased effects increased in magnitude as PGS accuracy and accounted variance decreased. These biases were only eliminated when PGSs were simulated to capture the entirety of trait genetic variance. ConclusionResults suggest that in the absence of PGSs that capture all genetic variance, parental phenotypes act as colliders in the same way as heritable environments. Relatively simple models combining parental and offspring PGSs can be used to detect genetic nurturing effects in complex traits. However, our findings suggest alternative methods should be utilised when aiming to identify mediating phenotypes and potentially modifiable parental nurturing effects.

Gene-environment interaction with regards to educational attainment has received increasing attention during the last few years. However, the potential interdependence between different types of environments in gene-environment interaction models has mostly been neglected. Using high-quality register data for an extensive panel of Swedish twins, born during most of the twentieth century, this study explores how genetic propensities for educational attainment, as measured by a polygenic index, interact with both macro-level institutional and sociopolitical context, and with socioeconomic background. The analyses, which combine between-family and causally robust within-family models, suggest that the average association between genetic propensities and educational attainment has increased in Sweden during the twentieth century, along with the expansion of the educational system and decreased economic inequality. There is also evidence of a positive interaction between genetic propensities and socioeconomic background, but only in the oldest cohorts in the sample, and that were born before the Swedish welfare state had been fully established. This implies that micro-level gene-environment interactions can be significantly dependent on macro-level context, an insight that has arguably not yet been given sufficient attention in the literature. Acknowledging limitations of polygenic indices, and the arbitrariness of the genetic lottery, the results may nevertheless indicate a development towards higher equality of opportunity in Sweden during the twentieth century.

Twin and family studies have historically aimed to partition phenotypic variance into components corresponding to additive genetic effects (A), common environment (C), and unique environment (E). Here we present the ACE Model and several extensions in the Adolescent Brain Cognitive Development Study (ABCD Study(R)), employed using the new Fast Efficient Mixed Effects Analysis (FEMA) package. In the twin sub-sample (n = 924; 462 twin pairs), heritability estimates were similar to those reported by prior studies for height (twin heritability = 0.86) and cognition (twin heritability between 0.00 and 0.61), respectively. Incorporating SNP-derived genetic relatedness and using the full ABCD Study(R) sample (n = 9,742) led to narrower confidence intervals for all parameter estimates. By leveraging the sparse clustering method used by FEMA to handle genetic relatedness only for participants within families, we were able to take advantage of the diverse distribution of genetic relatedness within the ABCD Study(R) sample.

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are being increasingly applied to human and animal gene-expression data to estimate module eigengenes (MEs). MEs represent multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. Although this approach has the potential to elucidate the mechanisms by which molecular genomic variations contribute to individual differences in complex traits, the genetic etiology of MEs has never been empirically established. It is unclear if and to what degree individual differences in blood derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N=661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD=2.6 years, range=62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD=0.08, range=35-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

Child educational development is associated with major psychological, social, economic and health milestones throughout the life course. Understanding the early origins of educational inequalities and their reproduction across generations is therefore crucial. Recent genomic studies provide novel insights in this regard, uncovering "genetic nurture" effects, whereby parental genotypes influence offsprings educational development via environmental pathways rather than genetic transmission. These findings have yet to be systematically appraised. We conducted the first systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes and investigate key moderators. Twelve studies comprising 38,654 distinct parent(s)-offspring pairs or trios from eight cohorts were included, from which we derived 22 estimates of genetic nurture effects. Multilevel random effects models showed that the effect of genetic nurture on offsprings educational outcomes ({beta}genetic nurture = 0.08, 95% CI [0.07, 0.09]) was about half the size of direct genetic effects ({beta}direct genetic = 0.17, 95% CI [0.13, 0.20]). Maternal and paternal genetic nurture effects were similar in magnitude, suggesting comparable roles of mothers and fathers in determining their childrens educational outcomes. Genetic nurture effects were largely explained by parental educational level and family socioeconomic status, suggesting that genetically influenced environments play an important role in shaping child educational outcomes. Even after accounting for genetic transmission, we provide evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes. Further exploring these downstream environmental pathways may inform educational policies aiming to break the intergenerational cycle of educational underachievement and foster social mobility. Public Significance StatementThis meta-analysis demonstrates that parents genetics influence their childrens educational outcomes through the rearing environments that parents provide. This "genetic nurture" effect is largely explained by family socioeconomic status and parental education level, is similar for mothers and fathers (suggesting that both parents equally shape their childrens educational outcomes) and is about half the size of direct genetic effects on childrens educational outcomes. Interventions targeting such environmental pathways could help to break the intergenerational cycle of educational underachievement and foster social mobility.

Summary statistics from a genome-wide association study (GWAS) can be used to generate a polygenic score (PGS). For complex, behavioral traits, the correlation between an individuals PGS and their phenotype may contain bias alongside the causal effect of the individuals genes (due to geographic, ancestral, and/or socioeconomic confounding). We formalize the recent introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes, also known as genetic nurture. GWAS do not discriminate between the various pathways through which genes influence outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects. We construct a theoretical model for genetic effects and show that, unlike other sources of bias in PGSs, the presence of genetic nurture biases PGS coefficients from both naive OLS (between-family) and family fixed effects (within-family) regressions. This bias is in opposite directions; while naive OLS estimates are biased upwards, family fixed effects estimates are biased downwards. We quantify this bias for a given trait using two novel parameters that we identify and discuss: (1) the genetic correlation between the direct and nurture effects and (2) the ratio of the SNP heritabilities for the direct and nurture effects.

Contemporary genomic studies of complex traits, such as genome-wide association studies (GWASs) and polygenic index (PGI) analyses, often use the principal components of the genotype matrix (PCs) to adjust for population stratification. In this paper, we explore the extent to which we may be discounting direct genetic effects by adjusting for PCs. Using family-based models that control for parental genotype (obtained via Mendelian imputation), we test whether PCs have a direct genetic effect on nine complex phenotypes in the White British subsample of the UK Biobank. Further, we assess the extent to which estimates of polygenic effects meaningfully change when adjusting for PCs in within-family models. Across the nine traits, within-family effects of the top 40 PCs are highly similar to their population effects, suggesting that standard PC adjustments diminish, albeit to a small degree, detectable signals of direct genetic effects. Within family models also confirm that PCs have significant marginal effects on a few traits, most consistently, height and educational attainment. Nonetheless, the variance explained by the effects of PCs is modest, and adjusting for PCs does not appear to affect the magnitude and significance of PGI effects in within-family models.

The assumption in the twin model that genotypic and environmental variables are uncorrelated is primarily made to ensure parameter identification, not because researchers necessarily think that these variables are uncorrelated. Although the biasing effects of such correlations are well understood, it would be useful to be able to estimate these parameters in the twin model. Here we consider the possibility of relaxing this assumption by adding polygenic score to the (univariate) twin model. We demonstrated numerically and analytically this extension renders the additive genetic (A) - unshared environmental correlation (E) and the additive genetic (A) - shared environmental (C) correlations simultaneously identified. We studied the statistical power to detect A-C and A-E correlations in the ACE model, and to detect A-E correlation in the AE model. The results showed that the power to detect these covariance terms, given 1000 MZ and 1000 DZ twin pairs (=0.05), depends greatly on the parameter settings of the model. We show fixing the estimated percentage of variance in the outcome trait that is due to the polygenic scores greatly increases statistical power.

Polygenic scores for educational attainment and intelligence (eduPGS), genetic ancestry, and cognitive ability have been found to be inter-correlated in some admixed American populations. We argue that this could either be due to causally-relevant genetic differences between ancestral groups or be due to population stratification-related confounding. Moreover, we argue that it is important to determine which scenario is the case so to better assess the validity of eduPGS. We investigate the confounding vs. causal concern by examining, in detail, the relation between eduPGS, ancestry, and general cognitive ability in East Coast Hispanic and non-Hispanic samples. European ancestry was correlated with g in the admixed Hispanic (r = .30, N = 506), European-African (r = .26, N = 228), and African (r = .084, N = 2,179) American samples. Among Hispanics and the combined sample, these associations were robust to controls for racial / ethnic self-identification, genetically predicted color, and parental education. Additionally, eduPGS predicted g among Hispanics (B = 0.175, N = 506) and all other groups (European: B = 0.230, N = 4914; European-African: B = 0.215, N = 228; African: B = 0.126, N = 2179) with controls for ancestry. Path analyses revealed that eduPGS, but not color, partially statistically explained the association between g and European ancestry among both Hispanics and the combined sample. Of additional note, we were unable to account for eduPGS differences between ancestral populations using common tests for ascertainment bias and confounding related to population stratification. Overall, our results suggest that eduPGS derived from European samples can be used to predict g in American populations. However, owing to the uncertain cause of the differences in eduPGS, it is not yet clear how the effect of ancestry should be handled. We argue that more research is needed to determine the source of the relation between eduPGS, genetic ancestry, and cognitive ability.

Genomewide association studies increasingly employ multivariate tests of multiple correlated phenotypes to exploit likely pleiotropy to improve power. Typical multivariate methods produce a global p-value of association between a variant (or set of variants) and multiple phenotypes. When the global test is significant, subsequent interest then focuses on dissecting the signal and, in particular, delineating the set of phenotypes where the genetic variant(s) have a direct effect from the remaining phenotypes where the genetic variant(s) possess either indirect or no effect. While existing techniques like mediation models can be utilized for this purpose, they generally cannot handle high-dimensional phenotypic and genotypic data. To assist in filling this important gap, we propose a modification of a kernel distance-covariance framework for gene mapping of multiple variants with multiple phenotypes to test instead whether the association between the variants and a group of phenotypes is driven through a direct association with just a subset of the phenotypes. We use simulated data to show that our new method controls for type I error and is powerful to detect a variety of models demonstrating different patterns of direct and indirect effects. We further illustrate our method using GWAS data from the Grady Trauma Project and show that an existing signal between genetic variants in the ZHX2 gene and 21 items within the Beck Depression Inventory appears to be due to a direct effect of these variants on only 3 of these items. Our approach scales to genomewide analysis, and is applicable to high-dimensional correlated phenotypes.

Randomized experiments are the "gold standard" for inferring causation and are designed to collect covariate information. Yet, many questions cannot be answered with experiments practically or ethically. Often, potential confounds are controlled statistically as covariates in quasi-experimental designs. The typical use of covariates does not control for many systematically confounded gene-and-environmental effects. Poverty, health, and individual differences all covary with gene-and-environmental effects, so much so that using covariates can create bias. We advocate for using genetically informed designs, which strengthen causal inference by controlling for major genetic and environmental confounds even in the absence of random assignment. We adapted the reciprocal standard dyad model into an analytic method to facilitate sibling comparisons. Differences between kin pairs explicitly distinguish within-family variance from between-family and control for all background variance linked to gene-and-environmental differences. We present four vignettes with individual differences and health outcomes. Although all four illustrations found significant associations when using covariate-based approaches, results diverged after addressing familial confounding with the discordant-kinship model. This divergence highlights the importance of considering familial influences in psychological research, demonstrating the versatility and efficacy of our method in different contexts.

Both genes and the neighborhood are important for life satisfaction; however, there is little research on gene-environment interactions (GxE) that examines how the effect of genetic endowments varies as a function of the environmental context with life satisfaction as the outcome. This study investigated how neighborhood deprivation moderates the effects of genetic predisposition on life satisfaction. Using data from the German Twin Family Panel (TwinLife), we identified 760 dizygotic (DZ) twins and employed twin fixed-effect models to assess the GxE effects on life satisfaction. The findings reveal that the polygenic score (PGS) for subjective well-being is positively associated with life satisfaction. The effect of PGS for subjective well-being on life satisfaction is strongest for individuals living in moderately deprived areas, while it is weaker for those living in highly deprived and less deprived areas. Thus, there are signs of compensation in less deprived areas and, particularly, diathesis-stress/triggering in highly deprived areas.

Moral judgments may be driven by both principled and opportunistic motivations. Being morally principled is to consistently adhere to a single set of rules about morality and justice. Opportunistic morality rather involves selectively enforcing rules when they are beneficial to ones interests. These two kinds of motivations sometimes pull in the same direction, other times not. Prior studies on moral motivations have mostly focused on principled morality. Opportunistic morality, along with its phenotypic and genetic correlates, remains largely unexamined. Here, utilizing a sample from the Norwegian Twin Registry, consisting of 312 monozygotic-and 298 dizygotic twin pairs (N = 1220), we measure peoples propensity to react to injustice as victims, observers, beneficiaries, and perpetrators of injustice, using the Justice Sensitivity scale. Our genetically informative sample allows a biometric modeling approach that provides increased stringency in inferring latent psychological traits. We find evidence for two substantially heritable traits explaining correlations between Justice Sensitivity facets, which we interpret as a principled justice sensitivity (h2 = .45) leading to increased sensitivity to injustices of all categories, and an opportunistic justice sensitivity (h2 = .69) associated with increased victim sensitivity and a decreased propensity to feel guilt from being a perpetrator. These heritable justice traits share a genetic substrate with broad strategies for cooperation (as measured by altruism and trust) and for selectively benefitting oneself over the adaptive interests of others (as measured by social dominance orientation and support for monopolizing territory and resources), and differ genetically and phenotypically from Big Five personality traits.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, which in some cases occurs comorbid with aggressive and antisocial behavior (AGG; ASB). The three externalizing behaviors are moderately to highly heritable and are genetically correlated. However, the genomic regions underlying this correlation are unknown. In this study, we aimed to localize genetic loci shared between ADHD, AGG, and ASB, using two complementary approaches. GWAS summary statistics for ADHD, AGG, and ASB were used for (1) cross-trait gene-based meta-analysis association analyses and (2) local genetic correlation analyses to identify shared genetic loci. Results of both complementary methods were combined to retrieve overlapping genes. Biological functionality of prioritized genes was assessed by exploring gene expression patterns in brain tissues and testing for gene-based association with (subcortical) brain regions. We confirmed previous findings that ADHD, AGG, and ASB were positively genetically correlated at a global level. We identified eleven significant genes in cross-trait gene-based meta-analyses, 31 loci shared between traits; 34 genes were identified when both approaches were combined. This study emphasizes the complex genetic architecture underlying global genetic correlations at the locus level. Converging evidence from these cross-trait analyses highlights novel candidate genes underlying biological mechanisms shared by ADHD, AGG, and ASB.

Mendelian randomization (MR) uses genetic variants as instrumental variables to enhance causal inference. Studies have identified genetic variants related to childhood maltreatment, but interpreting the effects of these variants or assessing the plausibility of MR assumptions is complex. We aim to investigate the feasibility of applying MR to complex social traits using the association between childhood maltreatment and mental health and behavioral outcomes as an example. We explore four potential key concerns: confounding by population phenomena, horizontal and vertical pleiotropy, reverse causality, and selection. For each concern, we demonstrate scenarios where MR studies of childhood maltreatment may be biased using DAGs and critical appraisal of previous MR analyses. For confounding by population phenomena, we further perform within-family genetic analyses in 42,101 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) to address bias due to family-level processes since childhood maltreatment often occurs within households. Our results showed same-trait shrinkage (11% attenuation of the association between childrens polygenic risk scores of childhood maltreatment (PRSCM) and mothers report of childrens physical abuse) but not cross-trait shrinkage (childrens PRSCM and childrens mental health and behavioral outcomes) after adjusting for parental PRSCM. The lack of cross-trait shrinkage suggests that genetic variants related to childhood maltreatment may be capturing other child-level phenotypes, after adjusting for family-level processes. Mothers PRSCM were also associated with mothers own maltreatment experiences in childhood and adulthood with similar magnitudes, suggesting these genetic effects are not specific to childhood maltreatment. Due to the complexity involved in the causal chain of childhood maltreatment and it being reported, the interpretation of MR studies for childhood maltreatment is challenging. Other causal approaches should be considered for observational studies of complex social traits.

As human complex diseases are influenced by the interplay of genes and environment, detecting gene-environment interactions (G x E) can shed light on biological mechanisms of diseases and play an important role in disease risk prediction. Development of powerful quantitative tools to incorporate G x E in complex diseases has potential to facilitate the accurate curation and analysis of large genetic epidemiological studies. However, most of existing methods that interrogate G x E focus on the interaction effects of an environmental factor and genetic variants, exclusively for common or rare variants. In this study, we proposed two tests, MAGEIT_RAN and MAGEIT_FIX, to detect interaction effects of an environmental factor and a set of genetic markers containing both rare and common variants, based on the MinQue for Summary statistics. The genetic main effects in MAGEIT_RAN and MAGEIT_FIX are modeled as random or fixed, respectively. Through simulation studies, we illustrated that both tests had type I error under control and MAGEIT_RAN was overall the most powerful test. We applied MAGEIT to a genome-wide analysis of gene-alcohol interactions on hypertension in the Multi-Ethnic Study of Atherosclerosis. We detected two genes, CCNDBP1 and EPB42, that interact with alcohol usage to influence blood pressure. Pathway analysis identified sixteen significant pathways related to signal transduction and development that were associated with hypertension, and several of them were reported to have an interactive effect with alcohol intake. Our results demonstrated that MAGEIT can detect biologically relevant genes that interact with environmental factors to influence complex traits.

Chronic occupational stress and an individuals reaction to that stress often lead to burnout syndrome. We sought to use genetics to evaluate what, if any, interactions exist between John Henryism (JH) and cynicism in hopes of clarifying holistic risk factors of burnout syndrome. We performed genome-wide association studies in a discover phase with 1,852 samples and validated associations in a replication phase of 465 samples, both from the CARDIA study, and used supervised machine learning to developing genetic risk algorithms. We identified 933 genetic associations and developed a classification algorithm for high cynicism using machine learning with areas under the receiver operator characteristics curve greater than 0.7. We found significant genetic components to these traits but no evidence of an interaction between JH and cynicism, so while there may be a genetic risk component, JH does not appear to contribute to burnout risk.

While genetic and environmental factors have been shown as predictors of childrens reading ability, the interaction effects of identified genetic risk susceptibility and specified environmental for reading ability have rarely been investigated. The current study assessed potential gene-environmental (GxE) interactions on reading ability in 1477 school-aged children. The gene-environment interactions on character recognition were investigated by an exploration analysis between the risk single-nucleotide polymorphisms (SNPs) which were discovered by previous genome-wide association studies of developmental dyslexia (DD), and parental education (PE). The re-parameterized regression analysis suggested that this GxE interaction conformed to the strong differential-susceptibility model. Results showed that rs281238 exhibits a significant interaction with PE on character recognition. Children with "T" genotype profited from high PE, whereas they performed worse in low PE environment, but "CC" genotype children were not malleable in different PE environments. Research HighlightsO_LIWe calculated the Cumulative Genetic Score (CGS) of 9 SNPs related to developmental dyslexia, and found that the interaction between CGS and parental education on reading ability. The GxE comforted to the differential-susceptibility model that those individuals carrying more plasticity alleles were affected more than those carrying more fewer. C_LIO_LIThe interaction between rs281238 and parental education conformed to the strong differential-susceptibility model that children with "T" allele would have highest reading ability in positive environment and lowest reading ability in adversity environment, whereas children without "T" allele would not be affected by parental education. C_LI

The most highly predictive polygenic scores in the behavioural sciences are for cognitive traits, especially general cognitive ability (g) and educational attainment. We combined polygenic scores derived from genome-wide association studies of adult g and educational attainment to create adult 'polygenic g scores' which we used to chart the course of cognitive development of 10,000 white British children from toddlerhood through early adulthood. We integrated cross-sectional regression, latent growth curve, and confirmatory factor analysis to systematically characterise cognitive development. Polygenic g score showed minimal prediction in toddlerhood, modest prediction in childhood, and substantial prediction by early adulthood accounting for 12% of the variance. Higher polygenic g scores were associated with faster cognitive growth in latent growth models. Prediction was strongest for a cross-time latent cognitive factor (15%) capturing cognitive ability across development. By integrating polygenic prediction directly into a structural equation model framework, we provided a theoretical upper bound of genetic influences on g under minimal measurement error. We also examined the polygenic g score's prediction of educational achievement, behaviour problems, and anthropometric outcomes and found similar developmental increases in prediction for educational achievement. Together, our findings demonstrate that adult polygenic g scores can be a useful tool for charting the development of cognitive traits.

Here we examine geographical and historical differences in polygenic associations with educational attainment in East and West Germany around reunification. We test this in n = 1902 25-85-year-olds from the German SOEP-G[ene] cohort. We leverage a DNA-based measure of genetic influence, a polygenic index calculated based on a previous genome-wide association study of educational attainment in individuals living in democratic countries. We find that polygenic associations with educational attainment were significantly stronger among East, but not West, Germans after but not before reunification. Negative control analyses of a polygenic index of height with educational attainment and height indicate that this gene-by-environemt interaction is specific to the educational domain. These findings suggest that the shift from an East German state-socialist to a free-market West German system increased the importance of genetic variants previously identified as important for education. One Sentence SummaryWe find that polygenic associations with educational attainment were significantly stronger among East, but not West, Germans after but not before reunification.