Within-sibship GWAS improve estimates of direct genetic effects
Authors: Howe Laurence J , Nivard Michel G , Morris Tim T , Hansen Ailin F , Rasheed Humaira , Cho Yoonsu , Chittoor Geetha , Lind Penelope A , Palviainen Teemu , van der Zee Matthijs D , Cheesman Rosa , Mangino Massimo , Wang Yunzhang , Li Shuai , Klaric Lucija , Ratliff Scott M , Bielak Lawrence F , Nygaard Marianne , Reynolds Chandra A , Balbona Jared V , Bauer Christopher R , Boomsma Dorret I , Baras Aris , Campbell Archie , Campbell Harry , Chen Zhengming , Christofidou Paraskevi , Dahm Christina C , Dokuru Deepika R , Evans Luke M , de Geus Eco JC , Giddaluru Sudheer , Gordon Scott D , Harden K. Paige , Havdahl Alexandra , Hill W. David , Kerr Shona M , Kim Yongkang , Kweon Hyeokmoon , Latvala Antti , Li Liming , Lin Kuang , Martikainen Pekka , Magnusson Patrik KE , Mills Melinda C , Lawlor Deborah A , Overton John D , Pedersen Nancy L , Porteous David J , Reid Jeffrey , Silventoinen Karri , Southey Melissa C , Mallard Travis T , Tucker-Drob Elliot M , Wright Margaret J ,   Social Science Genetic Association Consortium,   Within Family Consortium, Hewitt John K , Keller Matthew C , Stallings Michael C , Christensen Kaare , Kardia Sharon LR , Peyser Patricia A , Smith Jennifer A , Wilson James F , Hopper John L , Hägg Sara , Spector Tim D , Pingault Jean-Baptiste , Plomin Robert , Bartels Meike , Martin Nicholas G , Justice Anne E , Millwood Iona Y , Hveem Kristian , Naess Øyvind , Willer Cristen J , Åsvold Bjørn Olav , Koellinger Philipp D , Kaprio Jaakko , Medland Sarah E , Walters Robin G , Benjamin Daniel J , Turley Patrick , Evans David M , Smith George Davey , Hayward Caroline , Brumpton Ben , Hemani Gibran , Davies Neil M 
Published: March 2021

AbstractEstimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.