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

Abstract

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 novel 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 BMI, 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. Polygenic transmission disequilibrium test showed a significant over-transmission 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.

GWAS on Birth Year Infant Mortality Rates Provides New Evidence of Recent Natural Selection

“…Recall that Sanjak et al (2018) reported inconsistent findings between reproductive success and age at menarche (positive) and age at menopause (negative), which the authors label as “less explicable” than other results 10 . Similar to earlier findings 6,8 , we show correlations with EA and cognition, but extend this finding by showing these results are driven by direct-EA component and not by indirect-EA component (i.e., genetic nurture) using methods in Wu et al (2020) 42 . The difference in these findings suggest a broader need for caution when examining the genetic correlation findings, as we cannot decouple parental and child genetics in these results.…”

Estimating direct and indirect genetic effects on offspring phenotypes using genome-wide summary results data: a comparison of multivariate methods

“…Both previous studies observed significant genetic correlations for the number of children between men and women (Barban and colleagues 23 : r g =0.97, SE=0.095; Mathieson and colleagues 29 : r g =0.74, 95%CI=0.66-0.82), which our findings from the conditional analysis are consistent with (r g =0.871, SE=0.023). We also identified a strong negative genetic correlation between both male/female fertility and sibling effects; this is likely to be due to a technical artefact of the analyses as described by Wu and colleagues 30 . We replicate the negative genetic correlation between years of education and fertility described in Barban et al 23 .…”

Geographic Confounding in Genome-Wide Association Studies

“…One efficient way to control for SES-related gene-environment correlations is by conducting within family GWAS analyses. [23][24][25] Within family analyses, however, do not allow us to identify and study the specific source of gene-environment correlations, where our analyses do. In addition, while family-datasets are growing, large sample sizes of genotyped families are harder to attain, which results in less powerful within-family GWASs with noisier estimates of genetic effects (Figure 4).…”