Family-GWAS reveals effects of environment and mating on genetic associations
Authors: Tan Tammy , Jayashankar Hariharan , Guan Junming , Nehzati Seyed Moeen , Mir Mahdi , Bennett Michael , Agerbo Esben , Ahlskog Rafael , Pinto de Andrade Anapaz Ville , Asvold Bjorn Olav , Benonisdottir Stefania , Bhatta Laxmi , Boomsma Dorret I. , Brumpton Ben , Campbell Archie , Chabris Christopher F. , Cheesman Rosa , Chen Zhengming ,   China Kadoorie Biobank Collaborative Group, de Geus Eco , Ehli Erik A. , Elnahas Abdelrahman G. ,   Estonian Biobank Research Team,   Finngen Authors, Ganna Andrea , Giannelis Alexandros , Hakaste Liisa , Hansen Ailin Falkmo , Havdahl Alexandra , Hayward Caroline , Hottenga Jouke-Jan , Houmark Mikkel Aagaard , Hveem Kristian , Kaprio Jaakko , Langhammer Arnulf , Latvala Antti , Lee James J. , Lehtovirta Mikko , Li Liming ,   LifeLines Cohort Study, Lin Kuang , Linner Richard Karlsson , Lombardi Stefano , Martin Nicholas G. , McGue Matt , Medland Sarah E. , Metspalu Andres , Mitchell Brittany L. , Ni Guiyan , Nolte Ilja M. , Oetjens Matthew T. Oetjens , Oskarsson Sven , Palviainen Teemu , Prasad Rashmi B. , Reigo Anu , Reis Kadri , Sidorenko Julia , Silventoinen Karri , Snieder Harold , Tuomi Tiinamaija , Vilhjalmsson Bjarni J. , Walters Robin G. , Willoughby Emily A. , Winsvold Bendik S. , Ystrom Eivind , Flint Jonathan , Yengo Loic , Visscher Peter M. , Kong Augustine , Tucker-Drob Elliot M. , Border Richard , Cesarini David , Turley Patrick , Okbay Aysu , Benjamin Daniel J. , Young Alexander Strudwick 
Published: October 2024

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.