Abstract
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.
Original language | English |
---|---|
Pages (from-to) | 581-592 |
Number of pages | 12 |
Journal | Nature Genetics |
Volume | 54 |
Issue number | 5 |
DOIs | |
Publication status | Published - May 2022 |
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In: Nature Genetics, Vol. 54, No. 5, 05.2022, p. 581-592.
Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects
AU - Howe, Laurence J.
AU - Nivard, Michel G.
AU - Morris, Tim T.
AU - Hansen, Ailin F.
AU - Rasheed, Humaira
AU - Cho, Yoonsu
AU - Chittoor, Geetha
AU - Ahlskog, Rafael
AU - Lind, Penelope A.
AU - Palviainen, Teemu
AU - van der Zee, Matthijs D.
AU - Cheesman, Rosa
AU - Mangino, Massimo
AU - Wang, Yunzhang
AU - Li, Shuai
AU - Klaric, Lucija
AU - Ratliff, Scott M.
AU - Bielak, Lawrence F.
AU - Nygaard, Marianne
AU - Giannelis, Alexandros
AU - Willoughby, Emily A.
AU - Reynolds, Chandra A.
AU - Balbona, Jared V.
AU - Andreassen, Ole A.
AU - Ask, Helga
AU - Baras, Aris
AU - Bauer, Christopher R.
AU - Boomsma, Dorret I.
AU - Campbell, Archie
AU - Campbell, Harry
AU - Chen, Zhengming
AU - Christofidou, Paraskevi
AU - Corfield, Elizabeth
AU - Dahm, Christina C.
AU - Dokuru, Deepika R.
AU - Evans, Luke M.
AU - de Geus, Eco J.C.
AU - Giddaluru, Sudheer
AU - Gordon, Scott D.
AU - Harden, K. Paige
AU - Hill, W. David
AU - Hughes, Amanda
AU - Kerr, Shona M.
AU - Kim, Yongkang
AU - Kweon, Hyeokmoon
AU - Latvala, Antti
AU - Lawlor, Deborah A.
AU - Li, Liming
AU - Lin, Kuang
AU - Magnus, Per
AU - Magnusson, Patrik K.E.
AU - Mallard, Travis T.
AU - Martikainen, Pekka
AU - Mills, Melinda C.
AU - Njølstad, Pål Rasmus
AU - Overton, John D.
AU - Pedersen, Nancy L.
AU - Porteous, David J.
AU - Reid, Jeffrey
AU - Silventoinen, Karri
AU - Southey, Melissa C.
AU - Stoltenberg, Camilla
AU - Tucker-Drob, Elliot M.
AU - Wright, Margaret J.
AU - Hewitt, John K.
AU - Keller, Matthew C.
AU - Stallings, Michael C.
AU - Lee, James J.
AU - Christensen, Kaare
AU - Kardia, Sharon L.R.
AU - Peyser, Patricia A.
AU - Smith, Jennifer A.
AU - Wilson, James F.
AU - Hopper, John L.
AU - Hägg, Sara
AU - Spector, Tim D.
AU - Pingault, Jean Baptiste
AU - Plomin, Robert
AU - Havdahl, Alexandra
AU - Bartels, Meike
AU - Martin, Nicholas G.
AU - Oskarsson, Sven
AU - Justice, Anne E.
AU - Millwood, Iona Y.
AU - Hveem, Kristian
AU - Naess, Øyvind
AU - Willer, Cristen J.
AU - Åsvold, Bjørn Olav
AU - Kaprio, Jaakko
AU - Medland, Sarah E.
AU - Walters, Robin G.
AU - Evans, David M.
AU - Smith, George Davey
AU - Hayward, Caroline
AU - Brumpton, Ben
AU - Hemani, Gibran
AU - Davies, Neil M.
AU - Keller, Matthew C.
AU - Stallings, Michael C.
AU - Lee, James J.
AU - Christensen, Kaare
AU - Kardia, Sharon L.R.
AU - Peyser, Patricia A.
AU - Smith, Jennifer A.
AU - Wilson, James F.
AU - Hägg, Sara
AU - Spector, Tim D.
AU - Pingault, Jean Baptiste
AU - Plomin, Robert
AU - Havdahl, Alexandra
AU - Bartels, Meike
AU - Martin, Nicholas G.
AU - Oskarsson, Sven
AU - Justice, Anne E.
AU - Millwood, Iona Y.
AU - Hveem, Kristian
AU - Naess, Øyvind
AU - Willer, Cristen J.
AU - Åsvold, Bjørn Olav
AU - Koellinger, Philipp D.
AU - Kaprio, Jaakko
AU - Medland, Sarah E.
AU - Walters, Robin G.
AU - Benjamin, Daniel J.
AU - Turley, Patrick
AU - Evans, David M.
AU - Hayward, Caroline
AU - Brumpton, Ben
AU - Hemani, Gibran
AU - Davies, Neil M.
AU - Social Science Genetic Association Consortium
AU - Within Family Consortium
N1 - Funding Information: L.J.H., T.T.M., Y.C., D.A.L., G.D.S., G.H. and N.M.D. work in a unit that receives support from the University of Bristol and the UK MRC (grant nos. MC_UU_00011/1 & 6). N.M.D. is supported by a Norwegian Research Council Grant (no. 295989). G.H. is supported by the Wellcome Trust and Royal Society (grant no. 208806/Z/17/Z). B.M.B., B.O.Å., H.R., A.F.H. and K.H. work in a research unit funded by Stiftelsen Kristian Gerhard Jebsen, the Liaison Committee for education, research and innovation in Central Norway and the Joint Research Committee between St. Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. Funding information for other co-authors is contained in the Supplementary information. We thank H. Mostafavi and J. Pritchard for helpful suggestions and guidance relating to the polygenic adaptation analyses. Funding Information: L.J.H., T.T.M., Y.C., D.A.L., G.D.S., G.H. and N.M.D. work in a unit that receives support from the University of Bristol and the UK MRC (grant nos. MC_UU_00011/1 & 6). N.M.D. is supported by a Norwegian Research Council Grant (no. 295989). G.H. is supported by the Wellcome Trust and Royal Society (grant no. 208806/Z/17/Z). B.M.B., B.O.Å., H.R., A.F.H. and K.H. work in a research unit funded by Stiftelsen Kristian Gerhard Jebsen, the Liaison Committee for education, research and innovation in Central Norway and the Joint Research Committee between St. Olavs Hospital and the Faculty of Medicine and Health Sciences, NTNU. Funding information for other co-authors is contained in the . We thank H. Mostafavi and J. Pritchard for helpful suggestions and guidance relating to the polygenic adaptation analyses. Publisher Copyright: © 2022, The Author(s).
PY - 2022/5
Y1 - 2022/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85130637219&partnerID=8YFLogxK
U2 - 10.1038/s41588-022-01062-7
DO - 10.1038/s41588-022-01062-7
M3 - Article
C2 - 35534559
AN - SCOPUS:85130637219
SN - 1061-4036
VL - 54
SP - 581
EP - 592
JO - Nature Genetics
JF - Nature Genetics
IS - 5
ER -