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

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 1...

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Vydané v:	Nature genetics Ročník 54; číslo 4; s. 437 - 449
Hlavní autori:	Okbay, Aysu, Wu, Yeda, Wang, Nancy, Jayashankar, Hariharan, Bennett, Michael, Nehzati, Seyed Moeen, Sidorenko, Julia, Kweon, Hyeokmoon, Goldman, Grant, Gjorgjieva, Tamara, Jiang, Yunxuan, Hicks, Barry, Tian, Chao, Hinds, David A., Ahlskog, Rafael, Magnusson, Patrik K. E., Oskarsson, Sven, Hayward, Caroline, Campbell, Archie, Porteous, David J., Freese, Jeremy, Herd, Pamela, Watson, Chelsea, Jala, Jonathan, Conley, Dalton, Koellinger, Philipp D., Johannesson, Magnus, Laibson, David, Meyer, Michelle N., Lee, James J., Kong, Augustine, Yengo, Loic, Cesarini, David, Turley, Patrick, Visscher, Peter M., Beauchamp, Jonathan P., Benjamin, Daniel J., Young, Alexander I.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.04.2022 Nature Publishing Group
Predmet:	631/208/1515 631/208/205/2138 Agriculture Animal Genetics and Genomics Biomedical and Life Sciences Biomedicine Cancer Research Education Educational attainment Gene Function genetics Genome-wide association studies Genome-Wide Association Study Genomes Genotype & phenotype Gynaecology, Obstetrics and Reproductive Medicine Gynekologi, obstetrik och reproduktionsmedicin Human Genetics Humans Meta-analysis Multifactorial Inheritance Multifactorial Inheritance - genetics Nucleotides Phenotypes Polymorphism Polymorphism, Single Nucleotide - genetics Quantitative genetics Single Nucleotide Single-nucleotide polymorphism X chromosomes
ISSN:	1061-4036, 1546-1718, 1546-1718
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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. A genome-wide association study in ~3 million individuals identifies 3,952 independent variants associated with educational attainment. A polygenic index explains 12–16% of variance for this trait and contributes to risk prediction for ten diseases.
AbstractList	We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of similar to 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. 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. A genome-wide association study in ~3 million individuals identifies 3,952 independent variants associated with educational attainment. A polygenic index explains 12–16% of variance for this trait and contributes to risk prediction for ten diseases. 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. A genome-wide association study in ~3 million individuals identifies 3,952 independent variants associated with educational attainment. A polygenic index explains 12–16% of variance for this trait and contributes to risk prediction for ten diseases. 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. 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 (PGl), 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. 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.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.
Author	Watson, Chelsea Herd, Pamela Wang, Nancy Tian, Chao Bennett, Michael Jala, Jonathan Johannesson, Magnus Beauchamp, Jonathan P. Ahlskog, Rafael Young, Alexander I. Hicks, Barry Laibson, David Yengo, Loic Jayashankar, Hariharan Koellinger, Philipp D. Wu, Yeda Benjamin, Daniel J. Nehzati, Seyed Moeen Freese, Jeremy Okbay, Aysu Kong, Augustine Kweon, Hyeokmoon Visscher, Peter M. Campbell, Archie Porteous, David J. Gjorgjieva, Tamara Turley, Patrick Hinds, David A. Cesarini, David Sidorenko, Julia Jiang, Yunxuan Goldman, Grant Oskarsson, Sven Conley, Dalton Lee, James J. Meyer, Michelle N. Magnusson, Patrik K. E. Hayward, Caroline
Author_xml	– sequence: 1 givenname: Aysu surname: Okbay fullname: Okbay, Aysu email: a.okbay@vu.nl organization: Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam – sequence: 2 givenname: Yeda surname: Wu fullname: Wu, Yeda organization: Institute for Molecular Bioscience, University of Queensland – sequence: 3 givenname: Nancy surname: Wang fullname: Wang, Nancy organization: National Bureau of Economic Research – sequence: 4 givenname: Hariharan surname: Jayashankar fullname: Jayashankar, Hariharan organization: National Bureau of Economic Research – sequence: 5 givenname: Michael orcidid: 0000-0002-0446-1029 surname: Bennett fullname: Bennett, Michael organization: National Bureau of Economic Research – sequence: 6 givenname: Seyed Moeen surname: Nehzati fullname: Nehzati, Seyed Moeen organization: UCLA Anderson School of Management – sequence: 7 givenname: Julia orcidid: 0000-0003-1494-6772 surname: Sidorenko fullname: Sidorenko, Julia organization: Institute for Molecular Bioscience, University of Queensland – sequence: 8 givenname: Hyeokmoon surname: Kweon fullname: Kweon, Hyeokmoon organization: Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam – sequence: 9 givenname: Grant surname: Goldman fullname: Goldman, Grant organization: National Bureau of Economic Research – sequence: 10 givenname: Tamara orcidid: 0000-0002-2514-3580 surname: Gjorgjieva fullname: Gjorgjieva, Tamara organization: National Bureau of Economic Research – sequence: 11 givenname: Yunxuan surname: Jiang fullname: Jiang, Yunxuan organization: 23andMe, Inc – sequence: 12 givenname: Barry surname: Hicks fullname: Hicks, Barry organization: 23andMe, Inc – sequence: 13 givenname: Chao surname: Tian fullname: Tian, Chao organization: 23andMe, Inc – sequence: 14 givenname: David A. orcidid: 0000-0002-4911-803X surname: Hinds fullname: Hinds, David A. organization: 23andMe, Inc – sequence: 15 givenname: Rafael surname: Ahlskog fullname: Ahlskog, Rafael organization: Department of Government, Uppsala University – sequence: 16 givenname: Patrik K. E. orcidid: 0000-0002-7315-7899 surname: Magnusson fullname: Magnusson, Patrik K. E. organization: Swedish Twin Registry, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet – sequence: 17 givenname: Sven orcidid: 0000-0001-8698-2866 surname: Oskarsson fullname: Oskarsson, Sven organization: Department of Government, Uppsala University – sequence: 18 givenname: Caroline orcidid: 0000-0002-9405-9550 surname: Hayward fullname: Hayward, Caroline organization: MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital – sequence: 19 givenname: Archie orcidid: 0000-0003-0198-5078 surname: Campbell fullname: Campbell, Archie organization: Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Usher Institute, University of Edinburgh – sequence: 20 givenname: David J. orcidid: 0000-0003-1249-6106 surname: Porteous fullname: Porteous, David J. organization: Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Usher Institute, University of Edinburgh, Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh – sequence: 21 givenname: Jeremy surname: Freese fullname: Freese, Jeremy organization: Department of Sociology, Stanford University – sequence: 22 givenname: Pamela surname: Herd fullname: Herd, Pamela organization: McCourt School of Public Policy, Georgetown University – sequence: 25 givenname: Chelsea surname: Watson fullname: Watson, Chelsea organization: UCLA Anderson School of Management – sequence: 26 givenname: Jonathan surname: Jala fullname: Jala, Jonathan organization: UCLA Anderson School of Management – sequence: 27 givenname: Dalton surname: Conley fullname: Conley, Dalton organization: Department of Sociology, Princeton University – sequence: 28 givenname: Philipp D. surname: Koellinger fullname: Koellinger, Philipp D. organization: Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Robert M. La Follette School of Public Affairs, University of Wisconsin-Madison – sequence: 29 givenname: Magnus surname: Johannesson fullname: Johannesson, Magnus organization: Department of Economics, Stockholm School of Economics – sequence: 30 givenname: David surname: Laibson fullname: Laibson, David organization: Department of Economics, Harvard University – sequence: 31 givenname: Michelle N. surname: Meyer fullname: Meyer, Michelle N. organization: Center for Translational Bioethics and Health Care Policy, Geisinger Health System – sequence: 32 givenname: James J. surname: Lee fullname: Lee, James J. organization: Department of Psychology, University of Minnesota Twin Cities – sequence: 33 givenname: Augustine surname: Kong fullname: Kong, Augustine organization: Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford – sequence: 34 givenname: Loic surname: Yengo fullname: Yengo, Loic organization: Institute for Molecular Bioscience, University of Queensland – sequence: 35 givenname: David surname: Cesarini fullname: Cesarini, David organization: National Bureau of Economic Research, Department of Economics, New York University, Center for Experimental Social Science, New York University – sequence: 36 givenname: Patrick surname: Turley fullname: Turley, Patrick organization: Department of Economics, University of Southern California, Center for Economic and Social Research, University of Southern California – sequence: 37 givenname: Peter M. surname: Visscher fullname: Visscher, Peter M. email: peter.visscher@uq.edu.au organization: Institute for Molecular Bioscience, University of Queensland – sequence: 38 givenname: Jonathan P. surname: Beauchamp fullname: Beauchamp, Jonathan P. organization: Interdisciplinary Center for Economic Science and Department of Economics, George Mason University – sequence: 39 givenname: Daniel J. orcidid: 0000-0002-2642-5416 surname: Benjamin fullname: Benjamin, Daniel J. email: daniel.benjamin@gmail.com organization: National Bureau of Economic Research, UCLA Anderson School of Management, Human Genetics Department, UCLA David Geffen School of Medicine – sequence: 40 givenname: Alexander I. surname: Young fullname: Young, Alexander I. email: alextisyoung@gmail.com organization: UCLA Anderson School of Management, Human Genetics Department, UCLA David Geffen School of Medicine
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Demuth, Ilja
Copyright	The Author(s) 2022 2022. The Author(s). Copyright Nature Publishing Group Apr 2022
Copyright_xml	– notice: The Author(s) 2022 – notice: 2022. The Author(s). – notice: Copyright Nature Publishing Group Apr 2022
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Snippet	We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately... We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of -3 million individuals and identify 3,952 approximately... We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of similar to 3 million individuals and identify 3,952...
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SubjectTerms	631/208/1515 631/208/205/2138 Agriculture Animal Genetics and Genomics Biomedical and Life Sciences Biomedicine Cancer Research Education Educational attainment Gene Function genetics Genome-wide association studies Genome-Wide Association Study Genomes Genotype & phenotype Gynaecology, Obstetrics and Reproductive Medicine Gynekologi, obstetrik och reproduktionsmedicin Human Genetics Humans Meta-analysis Multifactorial Inheritance Multifactorial Inheritance - genetics Nucleotides Phenotypes Polymorphism Polymorphism, Single Nucleotide - genetics Quantitative genetics Single Nucleotide Single-nucleotide polymorphism X chromosomes
Title	Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
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