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

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; 23andMe Research Team; LifeLines Cohort Study; 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

doi:10.1038/s41588-022-01016-z

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

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. PorteousJeremy Freese, Pamela Herd, 23andMe Research Team, , LifeLines Cohort Study, 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

Population Health and Genomics

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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.

Original language	English
Pages (from-to)	437-449
Number of pages	13
Journal	Nature Genetics
Volume	54
Issue number	4
DOIs	https://doi.org/10.1038/s41588-022-01016-z
Publication status	Published - 31 Mar 2022

Keywords

Behavioural genetics
Genome-wide association studies

ASJC Scopus subject areas

Genetics

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10.1038/s41588-022-01016-zLicence: CC BY

Final Published VersionFinal published version, 6.84 MBLicence: CC BY

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Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., Jiang, Y., Hicks, B., Tian, C., Hinds, D. A., Ahlskog, R., Magnusson, P. K. E., Oskarsson, S., Hayward, C., Campbell, A., ... Young, A. I. (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449. https://doi.org/10.1038/s41588-022-01016-z

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

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{\textquoteright}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.",

keywords = "Behavioural genetics, Genome-wide association studies",

author = "Aysu Okbay and Yeda Wu and Nancy Wang and Hariharan Jayashankar and Michael Bennett and Nehzati, {Seyed Moeen} and Julia Sidorenko and Hyeokmoon Kweon and Grant Goldman and Tamara Gjorgjieva and Yunxuan Jiang and Barry Hicks and Chao Tian and Hinds, {David A.} and Rafael Ahlskog and Magnusson, {Patrik K.E.} and Sven Oskarsson and Caroline Hayward and Archie Campbell and Porteous, {David J.} and Jeremy Freese and Pamela Herd and {23andMe Research Team} and {LifeLines Cohort Study} and Smith, {Blair H.} and Chelsea Watson and Jonathan Jala and Dalton Conley and Koellinger, {Philipp D.} and Magnus Johannesson and David Laibson and Meyer, {Michelle N.} and Lee, {James J.} and Augustine Kong and Loic Yengo and David Cesarini and Patrick Turley and Visscher, {Peter M.} and Beauchamp, {Jonathan P.} and Benjamin, {Daniel J.} and Young, {Alexander I.}",

note = "{\textcopyright} 2022, The Author(s).",

year = "2022",

month = mar,

day = "31",

doi = "10.1038/s41588-022-01016-z",

language = "English",

volume = "54",

pages = "437--449",

journal = "Nature Genetics",

issn = "1061-4036",

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Okbay, A, Wu, Y, Wang, N, Jayashankar, H, Bennett, M, Nehzati, SM, Sidorenko, J, Kweon, H, Goldman, G, Gjorgjieva, T, Jiang, Y, Hicks, B, Tian, C, Hinds, DA, Ahlskog, R, Magnusson, PKE, Oskarsson, S, Hayward, C, Campbell, A, Porteous, DJ, Freese, J, Herd, P, 23andMe Research Team, LifeLines Cohort Study, Watson, C, Jala, J, Conley, D, Koellinger, PD, Johannesson, M, Laibson, D, Meyer, MN, Lee, JJ, Kong, A, Yengo, L, Cesarini, D, Turley, P, Visscher, PM, Beauchamp, JP & Benjamin, DJ & Young, AI 2022, 'Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals', Nature Genetics, vol. 54, no. 4, pp. 437-449. https://doi.org/10.1038/s41588-022-01016-z

TY - JOUR

T1 - Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

AU - Okbay, Aysu

AU - Wu, Yeda

AU - Wang, Nancy

AU - Jayashankar, Hariharan

AU - Bennett, Michael

AU - Nehzati, Seyed Moeen

AU - Sidorenko, Julia

AU - Kweon, Hyeokmoon

AU - Goldman, Grant

AU - Gjorgjieva, Tamara

AU - Jiang, Yunxuan

AU - Hicks, Barry

AU - Tian, Chao

AU - Hinds, David A.

AU - Ahlskog, Rafael

AU - Magnusson, Patrik K.E.

AU - Oskarsson, Sven

AU - Hayward, Caroline

AU - Campbell, Archie

AU - Porteous, David J.

AU - Freese, Jeremy

AU - Herd, Pamela

AU - 23andMe Research Team

AU - LifeLines Cohort Study

AU - Smith, Blair H.

AU - Watson, Chelsea

AU - Jala, Jonathan

AU - Conley, Dalton

AU - Koellinger, Philipp D.

AU - Johannesson, Magnus

AU - Laibson, David

AU - Meyer, Michelle N.

AU - Lee, James J.

AU - Kong, Augustine

AU - Yengo, Loic

AU - Cesarini, David

AU - Turley, Patrick

AU - Visscher, Peter M.

AU - Beauchamp, Jonathan P.

AU - Benjamin, Daniel J.

AU - Young, Alexander I.

PY - 2022/3/31

Y1 - 2022/3/31

N2 - 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.

AB - 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.

KW - Behavioural genetics

KW - Genome-wide association studies

UR - http://www.scopus.com/inward/record.url?scp=85127422477&partnerID=8YFLogxK

U2 - 10.1038/s41588-022-01016-z

DO - 10.1038/s41588-022-01016-z

M3 - Article

C2 - 35361970

AN - SCOPUS:85127422477

SN - 1061-4036

VL - 54

SP - 437

EP - 449

JO - Nature Genetics

JF - Nature Genetics

IS - 4

ER -

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

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