Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
Authors: Laurence J. Howe, Michel G. Nivard, Tim T. Morris, Ailin F. Hansen, Humaira Rasheed, Yoonsu Cho, Geetha Chittoor, Rafael Ahlskog, 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, Alexandros Giannelis, Emily A. Willoughby, Chandra A. Reynolds, Jared V. Balbona, Ole A. Andreassen, Helga Ask, Aris Baras, Christopher R. Bauer, Dorret I. Boomsma, Archie Campbell, Harry Campbell, Zhengming Chen, Paraskevi Christofidou, Elizabeth Corfield, Christina C. Dahm, Deepika R. Dokuru, Luke M. Evans, Eco J. C. de Geus, Sudheer Giddaluru, Scott D. Gordon, K. Paige Harden, W. David Hill, Amanda Hughes, Shona M. Kerr, Yongkang Kim, Hyeokmoon Kweon, Antti Latvala, Deborah A. Lawlor, Liming Li, Kuang Lin, Per Magnus, Patrik K. E. Magnusson, Travis T. Mallard, Pekka Martikainen, Melinda C. Mills, Pål Rasmus Njølstad, John D. Overton, Nancy L. Pedersen, David J. Porteous, Jeffrey Reid, Karri Silventoinen, Melissa C. Southey, Camilla Stoltenberg, Elliot M. Tucker-Drob, Margaret J. Wright, Hyeokmoon Kweon, Philipp D. Koellinger, Daniel J. Benjamin, Patrick Turley, Laurence J. Howe, Michel G. Nivard, Tim T. Morris, Ailin F. Hansen, Humaira Rasheed, Yoonsu Cho, Geetha Chittoor, Rafael Ahlskog, 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, Alexandros Giannelis, Emily A. Willoughby, Chandra A. Reynolds, Jared V. Balbona, Ole A. Andreassen, Helga Ask, Dorret I. Boomsma, Archie Campbell, Harry Campbell, Zhengming Chen, Paraskevi Christofidou, Elizabeth Corfield, Christina C. Dahm, Deepika R. Dokuru, Luke M. Evans, Eco J. C. de Geus, Sudheer Giddaluru, Scott D. Gordon, K. Paige Harden, W. David Hill, Amanda Hughes, Shona M. Kerr, Yongkang Kim, Antti Latvala, Deborah A. Lawlor, Liming Li, Kuang Lin, Per Magnus, Patrik K. E. Magnusson, Travis T. Mallard, Pekka Martikainen, Melinda C. Mills, Pål Rasmus Njølstad, Nancy L. Pedersen, David J. Porteous, Karri Silventoinen, Melissa C. Southey, Camilla Stoltenberg, Elliot M. Tucker-Drob, Margaret J. Wright, John K. Hewitt, Matthew C. Keller, Michael C. Stallings, James J. Lee, Kaare Christensen, Sharon L. R. Kardia, Patricia A. Peyser, Jennifer A. Smith, James F. Wilson, John L. Hopper, Sara Hägg, Tim D. Spector, Jean-Baptiste Pingault, Robert Plomin, Alexandra Havdahl, Meike Bartels, Nicholas G. Martin, Sven Oskarsson, Anne E. Justice, Iona Y. Millwood, Kristian Hveem, Øyvind Naess, Cristen J. Willer, Bjørn Olav Åsvold, Jaakko Kaprio, Sarah E. Medland, Robin G. Walters, David M. Evans, George Davey Smith, Caroline Hayward, Ben Brumpton, Gibran Hemani, Neil M. Davies, John K. Hewitt, Matthew C. Keller, Michael C. Stallings, James J. Lee, Kaare Christensen, Sharon L. R. Kardia, Patricia A. Peyser, Jennifer A. Smith, James F. Wilson, John L. Hopper, Sara Hägg, Tim D. Spector, Jean-Baptiste Pingault, Robert Plomin, Alexandra Havdahl, Meike Bartels, Nicholas G. Martin, Sven Oskarsson, Anne E. Justice, Iona Y. Millwood, Kristian Hveem, Øyvind Naess, Cristen J. Willer, Bjørn Olav Åsvold, 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 Brumpton, Gibran Hemani, Neil M. Davies
Journal: Nature Genetics (2022)

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.