Genome–wide association studies (GWAS) have discovered thousands of replicable genetic associations, guiding drug target discovery and powering genetic prediction of human phenotypes and diseases. However, genetic associations can be affected by gene–environment correlations and non–random mating, which can lead to biased inferences in downstream analyses. Family–based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations. Here, we report results from an FGWAS meta–analysis of 34 phenotypes from 17 cohorts. We found evidence that factors uncorrelated with DGEs make substantial contributions to genetic associations for 27 phenotypes, with population stratification confounding — a form of gene–environment correlation — likely the major cause. By estimating SNP heritability and genetic correlations using DGEs, we found evidence that assortative mating has led to overestimation of SNP heritability for 5 phenotypes and overestimation of the degree of shared genetic effects (pleiotropy) between 22 pairs of phenotypes. Polygenic predictors constructed from DGEs are particularly useful for studying natural selection, assortative mating, and indirect genetic effects (effects of relatives′ genes mediated through the family environment). We validate our meta–analysis results by predicting phenotypes in hold–out samples using polygenic predictors constructed from DGEs, achieving statistically significant out–of–sample prediction for 24 phenotypes with little attenuation of predictive power within–families. We provide FGWAS summary statistics for 34 phenotypes that can be used for downstream analyses. Our study provides both a template for performing FGWAS and an argument for its value for debiasing inferences and understanding the impact of environment and mating patterns.

The authors have declared no competing interest.

The study was supported by Open Philanthropy and the National Institute on Aging/National Institutes of Health through grants R24-AG065184, R01-AG042568, R01-AG083379 (to the University of California, Los Angeles) and R00-AG062787 (to the University of Southern California). This research has been conducted using the UK Biobank Resource under Application Number 11425. See Supplementary Note Section 5 for additional acknowledgements.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

We used only previously collected data from 17 cohorts and have included links to relevant publications and ethics approvals for the contributing cohorts in Supplementary Note Section 5.

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Meta-analysis summary statistics are available for download from the SSGAC data portal: https://thessgac.com/. Summary statistics from HUNT were excluded from the public release for blood pressure (diastolic), EA, neuroticism, height, BMI, HDL cholesterol, blood pressure (systolic), depressive symptoms, and non-HDL cholesterol. We will update the publicly available summary statistics with the HUNT summary statistics following publication of relevant HUNT studies.

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