Nature Genetics

, volume 54 , issue 5 , pages 581-592

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects

Laura Howe ^{1, 2}

M.J.M Nivard ³

Tim T. Morris ^{1, 2}

Humaira Rasheed ^{1, 4}

Yoonsu Cho ^{1, 2}

Penelope A. Lind ^{5, 6, 7}

Teemu Palviainen ⁸

Matthijs D. van der Zee ³

Massimo Mangino ^{9, 10}

Yunzhang Wang ¹¹

Lucija Klarić ¹²

Lawrence F Bielak ¹³

Marianne Nygaard ^{14, 15}

Alexandros Giannelis ¹⁶

Emily A. Willoughby ¹⁶

Chandra A. Reynolds ¹⁷

Jared Balbona ^{18, 19}

Ole A. Andreassen ^{20, 21}

Helga Ask ²²

Aris Baras ²³

Christopher R Bauer ^{24, 25}

Dorret I. Boomsma ^{3, 26}

Archie Campbell ²⁷

Z. Chen ^{28, 29}

Elizabeth Corfield ^{22, 30}

Luke T. Evans ^{19, 31}

Scott E. Gordon ³²

Kathryn Paige Harden ³³

Amanda Hughes ^{1, 2}

Shona M. Kerr ¹²

Hyeokmoon Kweon ³⁴

Debbie A Lawlor ^{1, 2, 35}

P Magnusson ¹¹

Travis T. Mallard ³³

Pekka Martikainen ^{36, 37, 38}

Pål R. Njølstad ^{39, 40}

John D Overton ²³

David J. Porteous ²⁷

J.B. Reid ²³

Melissa C Southey ^{41, 42, 43}

Camilla Stoltenberg ^{44, 45}

Elliot M. Tucker-Drob ³³

Margaret L. Wright ⁴⁶

Hyeokmoon Kweon ^{18, 19}

Philipp D Koellinger ^{18, 19}

Daniel J. Benjamin ^{18, 19}

Laurence J. Howe ^{14, 15, 47}

Michel G Nivard ¹³

Tim T Morris ¹³

AILIN F. HANSEN ^{13, 48}

Humaira Rasheed ^{12, 49}

Yoonsu Cho ⁵⁰

Geetha Chittoor ¹¹

Rafael Ahlskog ⁹

Penelope A. Lind ^{51, 52}

TEEMU PALVIAINEN ⁵¹

MATTHIJS D. VAN DER ZEE ^{22, 30, 53}

Rosa Cheesman ³

Massimo Mangino ³²

Yunzhang Wang ⁵⁴

Shuai Li ⁵⁵

Lucija Klarić ^{28, 29}

Scott M Ratliff ^{4, 56}

Lawrence F. Bielak ^{44, 57}

Marianne Nygaard ^{4, 58, 59}

Alexandros Giannelis ^{4, 56, 60}

Emily A. Willoughby ^{34, 61}

Chandra A. Reynolds ⁸

Jared V Balbona ^{5, 7, 62}

Ole A Andreassen ^{28, 29}

Helga Ask ^{63, 64, 65}

Dorret I. Boomsma ^{66, 67}

Archie Campbell ^{1, 68, 69}

Harry Campbell ^{1, 2}

Zhengming Chen ¹²

Paraskevi Christofidou ^{1, 4, 56}

Elizabeth Corfield ^{1, 2}

Christina C. Dahm ^{1, 2, 4}

Hide authors affiliations Show authors affiliations: 69 affiliations

Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK |

Population Health Sciences, Bristol Medical School, University Of Bristol, Bristol, UK |

Department of Biological Psychology, Netherlands Twin Register, Vrije Universiteit, Amsterdam, The Netherlands |

⁴

K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway |

⁵

Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia |

⁶

School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia |

⁷

Faculty of Medicine, University of Queensland, Brisbane, Australia |

⁸

Institute for Molecular Medicine FIMM, University of Helsinki, Helsinki, Finland

University of Helsinki

Institute for Molecular Medicine Finland

⁹

Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK |

¹⁰

NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK |

¹¹

Department of medical epidemiology and biostatistics, Karolinska Institutet, Stockholm, Sweden |

¹²

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK |

¹³

Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, USA |

¹⁴

The Danish Twin Registry, Department of Public Health, University of Southern Denmark, Odense, Denmark |

¹⁵

Department of Clinical Genetics, Odense University Hospital, Odense, Denmark |

¹⁶

Department of Psychology, University of Minnesota, Minneapolis, USA |

¹⁷

Department of Psychology, University of California, Riverside, Riverside, USA |

¹⁸

Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, USA |

¹⁹

Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, USA |

²⁰

NORMENT Centre, University of Oslo, Oslo, Norway |

²¹

Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway |

²²

Department of mental disorders, Norwegian Institute of Public Health, Oslo, Norway |

²³

Regeneron Genetics Center, Tarrytown, USA |

²⁴

Biomarin Pharmaceutical Inc., Novato, USA |

²⁵

Biomedical and Translational Informatics, Geisinger Health, Danville, USA |

²⁶

Amsterdam Public Health (APH) and Amsterdam Reproduction and Development (AR&D), Amsterdam, the Netherlands |

²⁷

Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK |

²⁸

Nuffield Department of Population Health, University of Oxford, Oxford, UK |

²⁹

MRC Population Health Research Unit, University of Oxford, Oxford, UK |

³⁰

Nic Waals institute, Lovisenberg Diaconal Hospital, Oslo, Norway |

³¹

Department of Ecology & Evolutionary Biology, University of Colorado at Boulder, Boulder, USA |

³²

Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Australia |

³³

Department of Psychology and Population Research Center, University of Texas at Austin, Austin, USA |

³⁴

Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands |

³⁵

Bristol NIHR Biomedical Research Centre, Bristol, UK |

³⁶

Population Research Unit, Faculty of Social Sciences, University of Helsinki, Helsinki, Finland |

³⁷

The Max Planck Institute for Demographic Research, Rostock, Germany |

³⁸

Department of Public Health Sciences, Stockholm University, Stockholm, Sweden |

³⁹

Department of clinical science, University of Bergen, Bergen, Norway |

⁴⁰

Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway |

⁴¹

Precision Medicine, School of Clinical Sciences At Monash Health, Monash University, Clayton, Australia

Monash University

Monash Health

⁴²

Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Australia |

⁴³

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia |

⁴⁴

Norwegian Institute of Public health, Oslo, Norway |

⁴⁵

Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway |

⁴⁶

Queensland Brain Institute, the University of Queensland, Brisbane, Australia |

⁴⁷

Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark |

⁴⁸

Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, USA |

⁴⁹

Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK |

⁵⁰

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia |

⁵¹

Social Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK |

⁵²

Department of Clinical, Educational and Health Psychology, University College London, London, UK |

⁵³

Promenta Research Center, Department of Psychology, University of Oslo, Oslo, Norway |

⁵⁴

Department of Government, Uppsala university, Uppsala, Sweden |

⁵⁵

Department of Population Health Sciences, Geisinger Health, Danville, USA |

⁵⁶

HUNT Research Center, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway |

⁵⁷

Institute of Health and Society, University of Oslo, Oslo, Norway |

⁵⁸

Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, USA |

⁵⁹

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA |

⁶⁰

Department of Endocrinology, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway |

⁶¹

La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, USA |

⁶²

School of Psychology, University of Queensland, Brisbane, Australia |

⁶³

UCLA Anderson School of Management, Los Angeles, USA |

⁶⁴

Human Genetics Department, UCLA David Geffen School of Medicine, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Los Angeles, USA |

⁶⁵

National Bureau of Economic Research, Cambridge, USA |

⁶⁶

Center for Economic and Social Research, University of Southern California, Los Angeles, USA |

⁶⁷

Department of Economics, University of Southern California, Los Angeles, USA |

⁶⁸

University of Queensland Diamantina Institute, University of Queensland, Brisbane, Australia |

⁶⁹

Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia |

Publication type: Journal Article

Publication date: 2022-05-09

Springer Nature

Nature Genetics

scimago Q1

wos Q1

SJR: 16.586

CiteScore: 45.1

Impact factor: 29.0

ISSN: 10614036, 15461718

DOI: 10.1038/s41588-022-01062-7

Copy DOI

PubMed ID: 35534559

Genetics

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. Within-sibship genome-wide association analyses using data from 178,076 siblings illustrate differences between population-based and within-sibship GWAS estimates for phenotypes influenced by demographic and indirect genetic effects.

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Howe L. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects // Nature Genetics. 2022. Vol. 54. No. 5. pp. 581-592.

GOST all authors (up to 50) Copy

Howe L. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects // Nature Genetics. 2022. Vol. 54. No. 5. pp. 581-592.

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BibTex (up to 50 authors) Copy

@article{2022_Howe,

author = {Laura Howe and M.J.M Nivard and Tim T. Morris and Humaira Rasheed and Yoonsu Cho and Penelope A. Lind and Teemu Palviainen and Matthijs D. van der Zee and Massimo Mangino and Yunzhang Wang and Lucija Klarić and Lawrence F Bielak and Marianne Nygaard and Alexandros Giannelis and Emily A. Willoughby and Chandra A. Reynolds and Jared Balbona and Ole A. Andreassen and Helga Ask and Aris Baras and Christopher R Bauer and Dorret I. Boomsma and Archie Campbell and Z. Chen and Elizabeth Corfield and Luke T. Evans and Scott E. Gordon and Kathryn Paige Harden and Amanda Hughes and Shona M. Kerr and Hyeokmoon Kweon and Debbie A Lawlor and P Magnusson and Travis T. Mallard and Pekka Martikainen and Pål R. Njølstad and John D Overton and David J. Porteous and J.B. Reid and Melissa C Southey and Camilla Stoltenberg and Elliot M. Tucker-Drob and Margaret L. Wright and Hyeokmoon Kweon and Philipp D Koellinger and Daniel J. Benjamin and Laurence J. Howe and Michel G Nivard and Tim T Morris and AILIN F. HANSEN and others},

title = {Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects},

journal = {Nature Genetics},

year = {2022},

volume = {54},

publisher = {Springer Nature},

month = {may},

url = {https://doi.org/10.1038/s41588-022-01062-7},

number = {5},

pages = {581--592},

doi = {10.1038/s41588-022-01062-7}

}

MLA

Cite this

MLA Copy

Howe, Laura, et al. “Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.” Nature Genetics, vol. 54, no. 5, May. 2022, pp. 581-592. https://doi.org/10.1038/s41588-022-01062-7.

Publisher

Springer Nature

Journal

Nature Genetics

scimago Q1

wos Q1

SJR

16.586

CiteScore

45.1

Impact factor

29.0

ISSN

10614036 (Print)

15461718 (Electronic)