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Within-sibship GWAS improve estimates of direct genetic effects

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

Posted 07 Mar 2021
bioRxiv DOI: 10.1101/2021.03.05.433935

Estimates 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.

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