Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-03-31

AUTHORS

Aysu Okbay, Yeda Wu, Nancy Wang, Hariharan Jayashankar, Michael Bennett, Seyed Moeen Nehzati, Julia Sidorenko, Hyeokmoon Kweon, Grant Goldman, Tamara Gjorgjieva, Yunxuan Jiang, Barry Hicks, Chao Tian, David A. Hinds, Rafael Ahlskog, Patrik K. E. Magnusson, Sven Oskarsson, Caroline Hayward, Archie Campbell, David J. Porteous, Jeremy Freese, Pamela Herd, Chelsea Watson, Jonathan Jala, Dalton Conley, Philipp D. Koellinger, Magnus Johannesson, David Laibson, Michelle N. Meyer, James J. Lee, Augustine Kong, Loic Yengo, David Cesarini, Patrick Turley, Peter M. Visscher, Jonathan P. Beauchamp, Daniel J. Benjamin, Alexander I. Young

ABSTRACT

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57. More... »

PAGES

437-449

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  • Journal

    TITLE

    Nature Genetics

    ISSUE

    4

    VOLUME

    54

    Author Affiliations

  • Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
  • Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
  • National Bureau of Economic Research, Cambridge, MA, USA
  • UCLA Anderson School of Management, Los Angeles, CA, USA
  • 23andMe, Inc., Sunnyvale, CA, USA
  • Department of Government, Uppsala University, Uppsala, Sweden
  • Swedish Twin Registry, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
  • MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK
  • Usher Institute, University of Edinburgh, Edinburgh, UK
  • Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
  • Department of Sociology, Stanford University, Stanford, CA, USA
  • McCourt School of Public Policy, Georgetown University, Washington, DC, USA
  • Department of Sociology, Princeton University, Princeton, NJ, USA
  • Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
  • Department of Economics, Stockholm School of Economics, Stockholm, Sweden
  • Department of Economics, Harvard University, Cambridge, MA, USA
  • Center for Translational Bioethics and Health Care Policy, Geisinger Health System, Danville, PA, USA
  • Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA
  • Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
  • Center for Experimental Social Science, New York University, New York, NY, USA
  • Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
  • Interdisciplinary Center for Economic Science and Department of Economics, George Mason University, Fairfax, VA, USA
  • Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41588-022-01016-z

    DOI

    http://dx.doi.org/10.1038/s41588-022-01016-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1146773406

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/35361970


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    69 polygenic prediction
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