Imprint of assortative mating on the human genome

Phenotypic and ancestry-related assortative mating in autism

BACKGROUND

Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population.

METHODS

To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families).

RESULTS

We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5).

LIMITATIONS

We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification.

CONCLUSIONS

This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

The impact of assortative mating, participation bias and socioeconomic status on the polygenic risk of behavioural and psychiatric traits

To investigate assortative mating (AM), participation bias and socioeconomic status (SES) with respect to the genetics of behavioural and psychiatric traits, we estimated AM signatures using gametic phase disequilibrium and within-spouses and within-siblings polygenic risk score correlation analyses, also performing a SES conditional analysis. The cross-method meta-analysis identified AM genetic signatures for multiple alcohol-related phenotypes, bipolar disorder, major depressive disorder, schizophrenia and Tourette syndrome. Here, after SES conditioning, we observed changes in the AM genetic signatures for maximum habitual alcohol intake, frequency of drinking alcohol and Tourette syndrome. We also observed significant gametic phase disequilibrium differences between UK Biobank mental health questionnaire responders versus non-responders for major depressive disorder and alcohol use disorder. These results highlight the impact of AM, participation bias and SES on the polygenic risk of behavioural and psychiatric traits, particularly in alcohol-related traits.

Confounding Fuels Hereditarian Fallacies

Scientific literature has seen a resurgence of interest in genetic influences on socioeconomic outcomes. Such investigations are often limited by confounding between signals of genetic and non-genetic influences. An illustrative example is Clark (2023), which considers the similarity in socioeconomic status between relatives, drawing on genealogical records spanning four centuries in England. Based on the fit of a quantitative genetics model, it suggests that social status is largely determined by one's DNA; and that, for that reason, contemporary English people "remain correlated in outcomes with their lineage relatives in exactly the same way as in preindustrial England." These conclusions are based on a conflation of genetic and non-genetic transmission (e.g., of wealth) within families. We demonstrate that additional errors and statistical artifacts influenced inferences in Clark (2023). In reality, Clark (2023) provides no information about the relative contribution of genetic and non-genetic factors to social status. We discuss how lessons learned from the failure to account for confounding generalize to contemporary studies that claim to establish genetic underpinnings to social outcomes.

International Cross-Temporal Meta-Analysis of Assortative Mating for Educational Attainment

Previous studies have found a high degree of assortative mating for educational attainment (r = .56). However, this can be confounded by cohort effects or country effects, where certain nations may have more pronounced assortative mating than others. In addition, method variance regarding how educational attainment is measured may also result in heterogeneity of effect sizes. Effect sizes were gathered from various datasets and from academic literature, resulting in a large collection of effect sizes (k = 1498, n = 9,159,098), spanning 84 different countries. Assortative mating for educational attainment was stronger than what previous literature suggested (r = .66, [.64, .68]), largely due to the fact that assortative mating for educational attainment is stronger when latent methods are used. The strongest predictors of assortative mating for education between countries were individualism (r = -.61, p < .001) and HDI (r = -.56, p < .001). Assortative mating over time was found to vary by region. Capitalist Europe experienced an increase in assortative mating for education, while Communist Europe experienced a decrease. The United States had a non-linear trend in assortative mating for educational attainment, as it decreased from 1875 to 1926, increased from 1926 to 1945, decreased from 1945 to 1958, increased from 1958 to 1977, and decreased from 1977 onwards.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Genome-wide association studies have problems due to confounding: Are family-based designs the answer?

Genome-wide association studies (GWASs) can be affected by confounding. Family-based GWAS uses random, within-family genetic variation to avoid this. A study in PLOS Biology details how different sources of confounding affect GWAS and whether family-based designs offer a solution.

Genetic similarity between relatives provides evidence on the presence and history of assortative mating

Assortative mating - the non-random mating of individuals with similar traits - is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities.

More than nature and nurture, indirect genetic effects on children's academic achievement are consequences of dynastic social processes

Families transmit genes and environments across generations. When parents' genetics affect their children's environments, these two modes of inheritance can produce an 'indirect genetic effect'. Such indirect genetic effects may account for up to half of the estimated genetic variance in educational attainment. Here we tested if indirect genetic effects reflect within-nuclear-family transmission ('genetic nurture') or instead a multi-generational process of social stratification ('dynastic effects'). We analysed indirect genetic effects on children's academic achievement in their fifth to ninth years of schooling in N = 37,117 parent-offspring trios in the Norwegian Mother, Father, and Child Cohort Study (MoBa). We used pairs of genetically related families (parents were siblings, children were cousins; N = 10,913) to distinguish within-nuclear-family genetic-nurture effects from dynastic effects shared by cousins in different nuclear families. We found that indirect genetic effects on children's academic achievement cannot be explained by processes that operate exclusively within the nuclear family.

Assortative mating and parental genetic relatedness contribute to the pathogenicity of variably expressive variants

We examined more than 97,000 families from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents contributing to neurodevelopmental disease risk in children. We identified within- and cross-disorder correlations between six phenotypes in parents and children, such as obsessive-compulsive disorder (R = 0.32-0.38, p < 10-126). We also found that measures of sub-clinical autism features in parents are associated with several autism severity measures in children, including biparental mean Social Responsiveness Scale scores and proband Repetitive Behaviors Scale scores (regression coefficient = 0.14, p = 3.38 × 10-4). We further describe patterns of phenotypic similarity between spouses, where spouses show correlations for six neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R = 0.24-0.68, p < 0.001) and a cross-disorder correlation between anxiety and bipolar disorder (R = 0.09-0.22, p < 10-92). Using a simulated population, we also found that assortative mating can lead to increases in disease liability over generations and the appearance of "genetic anticipation" in families carrying rare variants. We identified several families in a neurodevelopmental disease cohort where the proband inherited multiple rare variants in disease-associated genes from each of their affected parents. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse relationship with variant pathogenicity and propose that parental relatedness modulates disease risk by increasing genome-wide homozygosity in children (R = 0.05-0.26, p < 0.05). Our results highlight the utility of assessing parent phenotypes and genotypes toward predicting features in children who carry rare variably expressive variants and implicate assortative mating as a risk factor for increased disease severity in these families.

Scaling the discrete-time Wright-Fisher model to biobank-scale datasets

The discrete-time Wright-Fisher (DTWF) model and its diffusion limit are central to population genetics. These models can describe the forward-in-time evolution of allele frequencies in a population resulting from genetic drift, mutation, and selection. Computing likelihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large samples or in the presence of strong selection. Existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here, we present a scalable algorithm that approximates the DTWF model with provably bounded error. Our approach relies on two key observations about the DTWF model. The first is that transition probabilities under the model are approximately sparse. The second is that transition distributions for similar starting allele frequencies are extremely close as distributions. Together, these observations enable approximate matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the tens of millions, paving the way for rigorous biobank-scale inference. Finally, we use our results to estimate the impact of larger samples on estimating selection coefficients for loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.

Educational attainment, health outcomes and mortality: a within-sibship Mendelian randomization study

BACKGROUND

Previous Mendelian randomization (MR) studies using population samples (population MR) have provided evidence for beneficial effects of educational attainment on health outcomes in adulthood. However, estimates from these studies may have been susceptible to bias from population stratification, assortative mating and indirect genetic effects due to unadjusted parental genotypes. MR using genetic association estimates derived from within-sibship models (within-sibship MR) can avoid these potential biases because genetic differences between siblings are due to random segregation at meiosis.

METHODS

Applying both population and within-sibship MR, we estimated the effects of genetic liability to educational attainment on body mass index (BMI), cigarette smoking, systolic blood pressure (SBP) and all-cause mortality. MR analyses used individual-level data on 72 932 siblings from UK Biobank and the Norwegian HUNT study, and summary-level data from a within-sibship Genome-wide Association Study including >140 000 individuals.

RESULTS

Both population and within-sibship MR estimates provided evidence that educational attainment decreased BMI, cigarette smoking and SBP. Genetic variant-outcome associations attenuated in the within-sibship model, but genetic variant-educational attainment associations also attenuated to a similar extent. Thus, within-sibship and population MR estimates were largely consistent. The within-sibship MR estimate of education on mortality was imprecise but consistent with a putative effect.

CONCLUSIONS

These results provide evidence of beneficial individual-level effects of education (or liability to education) on adulthood health, independently of potential demographic and family-level confounders.

Genotyping, sequencing and analysis of 140,000 adults from Mexico City

The Mexico City Prospective Study is a prospective cohort of more than 150,000 adults recruited two decades ago from the urban districts of Coyoacán and Iztapalapa in Mexico City1. Here we generated genotype and exome-sequencing data for all individuals and whole-genome sequencing data for 9,950 selected individuals. We describe high levels of relatedness and substantial heterogeneity in ancestry composition across individuals. Most sequenced individuals had admixed Indigenous American, European and African ancestry, with extensive admixture from Indigenous populations in central, southern and southeastern Mexico. Indigenous Mexican segments of the genome had lower levels of coding variation but an excess of homozygous loss-of-function variants compared with segments of African and European origin. We estimated ancestry-specific allele frequencies at 142 million genomic variants, with an effective sample size of 91,856 for Indigenous Mexican ancestry at exome variants, all available through a public browser. Using whole-genome sequencing, we developed an imputation reference panel that outperforms existing panels at common variants in individuals with high proportions of central, southern and southeastern Indigenous Mexican ancestry. Our work illustrates the value of genetic studies in diverse populations and provides foundational imputation and allele frequency resources for future genetic studies in Mexico and in the United States, where the Hispanic/Latino population is predominantly of Mexican descent.

Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits

Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. While past studies of similarity between human mating partners have largely found evidence of positive correlations, to our knowledge, no formal meta-analysis has examined human partner correlations across multiple categories of traits. Thus, we conducted systematic reviews and random-effects meta-analyses of human male-female partner correlations across 22 traits commonly studied by psychologists, economists, sociologists, anthropologists, epidemiologists and geneticists. Using ScienceDirect, PubMed and Google Scholar, we incorporated 480 partner correlations from 199 peer-reviewed studies of co-parents, engaged pairs, married pairs and/or cohabitating pairs that were published on or before 16 August 2022. We also calculated 133 trait correlations using up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the 22 mean meta-analysed correlations ranged from rmeta = 0.08 (adjusted 95% CI = 0.03, 0.13) for extraversion to rmeta = 0.58 (adjusted 95% CI = 0.50, 0.64) for political values, with funnel plots showing little evidence of publication bias across traits. The 133 UKB correlations ranged from rUKB = -0.18 (adjusted 95% CI = -0.20, -0.16) for chronotype (being a 'morning' or 'evening' person) to rUKB = 0.87 (adjusted 95% CI = 0.86, 0.87) for birth year. Across analyses, political and religious attitudes, educational attainment and some substance use traits showed the highest correlations, while psychological (that is, psychiatric/personality) and anthropometric traits generally yielded lower but positive correlations. We observed high levels of between-sample heterogeneity for most meta-analysed traits, probably because of both systematic differences between samples and true differences in partner correlations across populations.

Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.

rBahadur: efficient simulation of structured high-dimensional genotype data with applications to assortative mating

Existing methods for generating synthetic genotype data are ill-suited for replicating the effects of assortative mating (AM). We propose rb_dplr, a novel and computationally efficient algorithm for generating high-dimensional binary random variates that effectively recapitulates AM-induced genetic architectures using the Bahadur order-2 approximation of the multivariate Bernoulli distribution. The rBahadur R library is available through the Comprehensive R Archive Network at https://CRAN.R-project.org/package=rBahadur .

Conventional twin studies overestimate the environmental differences between families relevant to educational attainment

Estimates of shared environmental influence on educational attainment (EA) using the Classical Twin Design (CTD) have been enlisted as genetically sensitive measures of unequal opportunity. However, key assumptions of the CTD appear violated for EA. In this study we compared CTD estimates of shared environmental influence on EA with estimates from a Nuclear Twin and Family Design (NTFD) in the same 982 German families. Our CTD model estimated shared environmental influence at 43%. After accounting for assortative mating, our best fitting NTFD model estimated shared environmental influence at 26%, disaggregating this into twin-specific shared environments (16%) and environmental influences shared by all siblings (10%). Only the sibling shared environment captures environmental influences that reliably differ between families, suggesting the CTD substantially overestimates between-family differences in educational opportunity. Moreover, parental education was found to have no environmental effect on offspring education once genetic influences were accounted for.

Estimation of indirect genetic effects and heritability under assortative mating

Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects - effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) - can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or 'genetic nurture' through analysis of parental and offspring polygenic predictors (called polygenic indices or scores - PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075 ) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents' underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.

Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples

Partners resemble each other in health behaviors and outcomes such as alcohol use, smoking, physical activity, and obesity. While this is consistent with social contagion theory suggesting partner influence, it is notoriously difficult to establish causality because of assortative mating and contextual confounding. We offer a novel approach to studying social contagion in health in long-term partnerships by combining genetic data of both partners in married/cohabiting couples with longitudinal data on their health behaviors and outcomes. We examine the influence of the partner's genetic predisposition for three health outcomes and behaviors (BMI, smoking, and drinking) among married/cohabiting couples. We use longitudinal data from the Health and Retirement Study and the English Longitudinal Study of Ageing with data on health outcomes and genotypes for both partners. Results show that changes over time in BMI, smoking, and drinking depend on the partner's genetic predispositions to these traits. These findings underline the importance of people's social surroundings for their health and highlight the potential of targeting health interventions at couples.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Assortative mating and parental genetic relatedness drive the pathogenicity of variably expressive variants

We examined more than 38,000 spouse pairs from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents associated with neurodevelopmental disease risk in children. We identified correlations between six phenotypes in parents and children, including correlations of clinical diagnoses such as obsessive-compulsive disorder (R=0.31-0.49, p<0.001), and two measures of sub-clinical autism features in parents affecting several autism severity measures in children, such as bi-parental mean Social Responsiveness Scale (SRS) scores affecting proband SRS scores (regression coefficient=0.11, p=0.003). We further describe patterns of phenotypic and genetic similarity between spouses, where spouses show both within- and cross-disorder correlations for seven neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R=0.25-0.72, p<0.001) and a cross-disorder correlation between schizophrenia and personality disorder (R=0.20-0.57, p<0.001). Further, these spouses with similar phenotypes were significantly correlated for rare variant burden (R=0.07-0.57, p<0.0001). We propose that assortative mating on these features may drive the increases in genetic risk over generations and the appearance of "genetic anticipation" associated with many variably expressive variants. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse correlations with burden and pathogenicity of rare variants and propose that parental relatedness drives disease risk by increasing genome-wide homozygosity in children (R=0.09-0.30, p<0.001). Our results highlight the utility of assessing parent phenotypes and genotypes in predicting features in children carrying variably expressive variants and counseling families carrying these variants.

Scaling the Discrete-time Wright Fisher model to biobank-scale datasets

The Discrete-Time Wright Fisher (DTWF) model and its large population diffusion limit are central to population genetics. These models describe the forward-in-time evolution of the frequency of an allele in a population and can include the fundamental forces of genetic drift, mutation, and selection. Computing like-lihoods under the diffusion process is feasible, but the diffusion approximation breaks down for large sample sizes or in the presence of strong selection. Unfortunately, existing methods for computing likelihoods under the DTWF model do not scale to current exome sequencing sample sizes in the hundreds of thousands. Here we present an algorithm that approximates the DTWF model with provably bounded error and runs in time linear in the size of the population. Our approach relies on two key observations about Binomial distributions. The first is that Binomial distributions are approximately sparse. The second is that Binomial distributions with similar success probabilities are extremely close as distributions, allowing us to approximate the DTWF Markov transition matrix as a very low rank matrix. Together, these observations enable matrix-vector multiplication in linear (as opposed to the usual quadratic) time. We prove similar properties for Hypergeometric distributions, enabling fast computation of likelihoods for subsamples of the population. We show theoretically and in practice that this approximation is highly accurate and can scale to population sizes in the billions, paving the way for rigorous biobank-scale population genetic inference. Finally, we use our results to estimate how increasing sample sizes will improve the estimation of selection coefficients acting on loss-of-function variants. We find that increasing sample sizes beyond existing large exome sequencing cohorts will provide essentially no additional information except for genes with the most extreme fitness effects.

Genetic basis of STEM occupational choice and regional economic performance: a UK biobank genome-wide association study

BACKGROUND

Science, technology, engineering, and mathematics (STEM) professionals are regarded as the highly skilled labor force that fosters economic productivity, enterprise innovation, and international competitiveness of a country. This study aims to understand the genetic predisposition to STEM occupations and investigate its associations with regional economic performance. We conducted a genome-wide association study on the occupational choice of STEM jobs based on a sample of 178,976 participants from the UK Biobank database.

RESULTS

We identified two genetic loci significantly associated with participants' STEM job choices: rs10048736 on chromosome 2 and rs12903858 on chromosome 15. The SNP heritability of STEM occupations was estimated to be 4.2%. We also found phenotypic and genetic evidence of assortative mating in STEM occupations. At the local authority level, we found that the average polygenic score of STEM is significantly and robustly associated with several metrics of regional economic performance.

CONCLUSIONS

The current study expands our knowledge of the genetic basis of occupational choice and potential regional disparities in socioeconomic developments.

Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity

Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity-partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity.

Celebrating a Century of Research in Behavioral Genetics

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

Disentangling the influences of parental genetics on offspring's cognition, education, and psychopathology via genetic and phenotypic pathways

BACKGROUND

Specific pathways of intergenerational transmission of behavioral traits remain unclear. Here, we aim to investigate how parental genetics influence offspring cognition, educational attainment, and psychopathology in youth.

METHODS

Participants for the discovery sample were 2,189 offspring (aged 6-14 years), 1898 mothers and 1,017 fathers who underwent genotyping, psychiatric, and cognitive assessments. We calculated polygenic scores (PGS) for cognition, educational attainment, attention-deficit hyperactivity disorder (ADHD), and schizophrenia for the trios. Phenotypes studied included educational and cognitive measures, ADHD and psychotic symptoms. We used a stepwise approach and multiple mediation models to analyze the effect of parental PGS on offspring traits via offspring PGS and parental phenotype. Significant results were replicated in a sample of 1,029 adolescents, 363 mothers, and 307 fathers.

RESULTS

Maternal and paternal PGS for cognition influenced offspring general intelligence and executive function via offspring PGS (genetic pathway) and parental education (phenotypic pathway). Similar results were found for parental PGS for educational attainment and offspring reading and writing skills. These pathways fully explained associations between parental PGS and offspring phenotypes, without residual direct association. Associations with maternal, but not paternal, PGS were replicated. No associations were found between parental PGS for psychopathology and offspring specific symptoms.

CONCLUSIONS

Our findings indicate that parental genetics influences offspring cognition and educational attainment by genetic and phenotypic pathways, suggesting the expression of parental phenotypes partially explain the association between parental genetic risk and offspring outcomes. Multiple mediations might represent an effective approach to disentangle distinct pathways for intergenerational transmission of behavioral traits.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

15 years of GWAS discovery: Realizing the promise

It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.

Genetic footprints of assortative mating in the Japanese population

Assortative mating (AM) is a pattern characterized by phenotypic similarities between mating partners. Detecting the evidence of AM has been challenging due to the lack of large-scale datasets that include phenotypic data on both partners, especially in populations of non-European ancestries. Gametic phase disequilibrium between trait-associated alleles is a signature of parental AM on a polygenic trait, which can be detected even without partner data. Here, using polygenic scores for 81 traits in the Japanese population using BioBank Japan Project genome-wide association studies data (n = 172,270), we found evidence of AM on the liability to type 2 diabetes and coronary artery disease, as well as on dietary habits. In cross-population comparison using United Kingdom Biobank data (n = 337,139) we found shared but heterogeneous impacts of AM between populations.

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate human traits has been interpreted as evidence of widespread pleiotropy. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation. We observe that xAM affects many phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R²=74%). We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates and that previously reported genetic correlation estimates between some pairs of psychiatric disorders are congruent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

Rare genetic variants explain missing heritability in smoking

Common genetic variants explain less variation in complex phenotypes than inferred from family-based studies, and there is a debate on the source of this 'missing heritability'. We investigated the contribution of rare genetic variants to tobacco use with whole-genome sequences from up to 26,257 unrelated individuals of European ancestries and 11,743 individuals of African ancestries. Across four smoking traits, single-nucleotide-polymorphism-based heritability ([Formula: see text]) was estimated from 0.13 to 0.28 (s.e., 0.10-0.13) in European ancestries, with 35-74% of it attributable to rare variants with minor allele frequencies between 0.01% and 1%. These heritability estimates are 1.5-4 times higher than past estimates based on common variants alone and accounted for 60% to 100% of our pedigree-based estimates of narrow-sense heritability ([Formula: see text], 0.18-0.34). In the African ancestry samples, [Formula: see text] was estimated from 0.03 to 0.33 (s.e., 0.09-0.14) across the four smoking traits. These results suggest that rare variants are important contributors to the heritability of smoking.

Culture and causal inference: The impact of cultural differences on the generalisability of findings from Mendelian randomisation studies

Cultural effects can influence the results of causal genetic analyses, such as Mendelian randomisation, but the potential influences of culture on genotype-phenotype associations are not currently well understood. Different genetic variants could be associated with different phenotypes in different populations, or culture could confound or influence the direction of the association between genotypes and phenotypes in different populations.

Genetic assortative mating for schizophrenia and bipolar disorder

BACKGROUND

Psychiatric disorders are highly polygenic and show patterns of partner resemblance. Partner resemblance has direct population-level genetic implications if it is caused by assortative mating, but not if it is caused by convergence or social homogamy. Using genetics may help distinguish these different mechanisms. Here, we investigated whether partner resemblance for schizophrenia and bipolar disorder is influenced by assortative mating using polygenic risk scores (PRSs).

METHODS

PRSs from The Danish High-Risk and Resilience Study-VIA 7 were compared between parents in three subsamples: population-based control parent pairs (N=198), parent pairs where at least one parent had schizophrenia (N=193), and parent pairs where at least one parent had bipolar disorder (N=115).

RESULTS

The PRS for schizophrenia was predictive of schizophrenia in the full sample and showed a significant correlation between parent pairs (r=0.121, p=0.0440), indicative of assortative mating. The PRS for bipolar disorder was also correlated between parent pairs (r=0.162, p=0.0067), but it was not predictive of bipolar disorder in the full sample, limiting the interpretation.

CONCLUSIONS

Our study provides genetic evidence for assortative mating for schizophrenia, with important implications for our understanding of the genetics of schizophrenia.

Estimating effects of parents' cognitive and non-cognitive skills on offspring education using polygenic scores

Understanding how parents' cognitive and non-cognitive skills influence offspring education is essential for educational, family and economic policy. We use genetics (GWAS-by-subtraction) to assess a latent, broad non-cognitive skills dimension. To index parental effects controlling for genetic transmission, we estimate indirect parental genetic effects of polygenic scores on childhood and adulthood educational outcomes, using siblings (N = 47,459), adoptees (N = 6407), and parent-offspring trios (N = 2534) in three UK and Dutch cohorts. We find that parental cognitive and non-cognitive skills affect offspring education through their environment: on average across cohorts and designs, indirect genetic effects explain 36-40% of population polygenic score associations. However, indirect genetic effects are lower for achievement in the Dutch cohort, and for the adoption design. We identify potential causes of higher sibling- and trio-based estimates: prenatal indirect genetic effects, population stratification, and assortative mating. Our phenotype-agnostic, genetically sensitive approach has established overall environmental effects of parents' skills, facilitating future mechanistic work.

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.

Evidence of partner similarity for autistic traits, systemizing, and theory of mind via facial expressions

It has been hypothesised that romantic partners are more similar than chance in relation to autistic traits. To test this theory, we recruited n = 105 heterosexual couples and examined within-couple correlations for autistic traits [measured using the Autism Spectrum Quotient (AQ)], empathizing [measured using the Empathy Quotient (EQ)], and systemizing [measured using the Systemizing Quotient-Revised (SQ-R)]. For a subsample that attended the lab (n = 58 couples), we also investigated theory of mind via facial expressions using the Reading the Mind in the Eyes Test (RMET) and attention to detail, a component within systemizing, using the Embedded Figures Task (EFT). Variable-centred analyses revealed positive within-couple correlations for all measures except EQ, although these effects were only statistically significant for unmarried couples and not for married/engaged couples. Follow-up analyses indicated that the observed couple similarity effects are likely consistent with people pairing with those more similar than chance (initial assortment) rather than becoming alike over time (convergence), and to seeking out self-resembling partners (active assortment) rather than pairing in this manner via social stratification processes (social homogamy). Additionally, a significant within-couple correlation for autistic traits was observed at the meta-analytic level. However, it should be noted that the meta-analytic effect size estimate was small (r = 0.153) and indicates that only ~ 2% of variance in a person’s score on a phenotypic measure of autistic traits can be predicted by that of their partner.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Modeling assortative mating and genetic similarities between partners, siblings, and in-laws

Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners (n = 26,681), siblings (n = 2,170), siblings-in-law (n = 3,905), and co-siblings-in-law (n = 1,763) in the Norwegian Mother, Father and Child Cohort Study. Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment (r_g = 0.37), height (r_g = 0.13), and depression (r_g = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (r_g = 0.68) and between siblings-in-law (r_g = 0.25) and co-siblings-in-law (r_g = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.

Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

Assortative mating biases marker-based heritability estimators

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% - 23% higher than corrected estimates using UK Biobank data.

Polygenic scores: prediction versus explanation

During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people's genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype-environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems.

Resource profile and user guide of the Polygenic Index Repository

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs' prediction accuracies, we constructed them using genome-wide association studies-some not previously published-from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the 'additive SNP factor'. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Why do we pick similar mates, or do we?

Humans often mate with those resembling themselves, a phenomenon described as positive assortative mating (PAM). The causes of this attract broad interest, but there is little agreement on the topic. This may be because empirical studies and reviews sometimes focus on just a few explanations, often based on disciplinary conventions. This review presents an interdisciplinary conceptual framework on the causes of PAM in humans, drawing on human and non-human biology, the social sciences, and the humanities. Viewing causality holistically, we first discuss the proximate causes (i.e. the 'how') of PAM, considering three mechanisms: stratification, convergence and mate choice. We also outline methods to control for confounders when studying mate choice. We then discuss ultimate explanations (i.e. 'the why') for PAM, including adaptive and non-adaptive processes. We conclude by suggesting a focus on interdisciplinarity in future research.

Assortative mating and within-spouse pair comparisons

Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.

Genetic and shared couple environmental contributions to smoking and alcohol use in the UK population

Alcohol use and smoking are leading causes of death and disability worldwide. Both genetic and environmental factors have been shown to influence individual differences in the use of these substances. In the present study we tested whether genetic factors, modelled alongside common family environment, explained phenotypic variance in alcohol use and smoking behaviour in the Generation Scotland (GS) family sample of up to 19,377 individuals. SNP and pedigree-associated effects combined explained between 18 and 41% of the variance in substance use. Shared couple effects explained a significant amount of variance across all substance use traits, particularly alcohol intake, for which 38% of the phenotypic variance was explained. We tested whether the within-couple substance use associations were due to assortative mating by testing the association between partner polygenic risk scores in 34,987 couple pairs from the UK Biobank (UKB). No significant association between partner polygenic risk scores were observed. Associations between an individual's alcohol PRS (b = 0.05, S.E. = 0.006, p < 2 × 10-16) and smoking status PRS (b = 0.05, S.E. = 0.005, p < 2 × 10-16) were found with their partner's phenotype. In support of this, G carriers of a functional ADH1B polymorphism (rs1229984), known to be associated with greater alcohol intake, were found to consume less alcohol if they had a partner who carried an A allele at this SNP. Together these results show that the shared couple environment contributes significantly to patterns of substance use. It is unclear whether this is due to shared environmental factors, assortative mating, or indirect genetic effects. Future studies would benefit from longitudinal data and larger sample sizes to assess this further.

Estimating genetic nurture with summary statistics of multigenerational genome-wide association studies

Marginal effect estimates in genome-wide association studies (GWAS) are mixtures of direct and indirect genetic effects. Existing methods to dissect these effects require family-based, individual-level genetic, and phenotypic data with large samples, which is difficult to obtain in practice. Here, we propose a statistical framework to estimate direct and indirect genetic effects using summary statistics from GWAS conducted on own and offspring phenotypes. Applied to birth weight, our method showed nearly identical results with those obtained using individual-level data. We also decomposed direct and indirect genetic effects of educational attainment (EA), which showed distinct patterns of genetic correlations with 45 complex traits. The known genetic correlations between EA and higher height, lower body mass index, less-active smoking behavior, and better health outcomes were mostly explained by the indirect genetic component of EA. In contrast, the consistently identified genetic correlation of autism spectrum disorder (ASD) with higher EA resides in the direct genetic component. A polygenic transmission disequilibrium test showed a significant overtransmission of the direct component of EA from healthy parents to ASD probands. Taken together, we demonstrate that traditional GWAS approaches, in conjunction with offspring phenotypic data collection in existing cohorts, could greatly benefit studies on genetic nurture and shed important light on the interpretation of genetic associations for human complex traits.

A model and test for coordinated polygenic epistasis in complex traits

Interactions between genetic variants-epistasis-is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work, we develop a model for structured polygenic epistasis, called coordinated epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CE in 18 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.

Widespread signatures of natural selection across human complex traits and functional genomic categories

Understanding how natural selection has shaped genetic architecture of complex traits is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level GWAS data to estimate multiple genetic architecture parameters including selection signature. Here, we present a method (SBayesS) that only requires GWAS summary statistics. We analyse data for 155 complex traits (n = 27k–547k) and project the estimates onto those obtained from evolutionary simulations. We estimate that, on average across traits, about 1% of human genome sequence are mutational targets with a mean selection coefficient of ~0.001. Common diseases, on average, show a smaller number of mutational targets and have been under stronger selection, compared to other traits. SBayesS analyses incorporating functional annotations reveal that selection signatures vary across genomic regions, among which coding regions have the strongest selection signature and are enriched for both the number of associated variants and the magnitude of effect sizes.

Methods to study how natural selection shapes genetic architecture of complex traits rely on individual level genome-wide association study (GWAS) data. Here, the authors present a Bayesian method using GWAS summary statistics to study genetic architecture and apply this to 155 complex traits.