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

23andMe Research Team; Social Science Genetic Association Consortium; Lifelines Cohort Study

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

23andMe Research Team
, Social Science Genetic Association Consortium
, Lifelines Cohort Study

Vrije Universiteit Amsterdam
University of Queensland
National Bureau of Economic Research
University of California at Los Angeles
23andMe Inc.
Uppsala University
Karolinska Institutet
University of Edinburgh
Stanford University
Georgetown University
New York University
University of Southern California
Hospital for Special Surgery - New York
University of Copenhagen
Statens Serum Institut
Erasmus University Rotterdam
Icelandic Heart Association
University of Iceland
University of Amsterdam
New York Genome Cente
Novo Nordisk Foundation
University of Gothenburg
Helmholtz Zentrum München - German Research Center for Environmental Health
deCODE Genetics
National Research Council of Italy
University of Illinois at Urbana-Champaign
Radboud University Nijmegen
University of Trieste
University of Helsinki
University of Leicester
Medical University of Graz
University of Oxford
Harokopio University
Folkhalsan
Queensland Institute of Medical Research
Max Planck Institute for Human Development
King's College London
Guy's and St Thomas' NHS Foundation Trust
University of Tartu
University of Minnesota Twin Cities
University of Groningen
Hunter Medical Research Institute, Australia
University of Newcastle
University of Manchester
National Institute for Health and Welfare
VU University Medical Center
National Institutes of Health
University of Turku
University of Lausanne
Swiss Institute of Bioinformatics
University of Milan
University of Duisburg-Essen
University of Bristol
University of Greifswald
University of Münster
Harvard University
Broad Institute
Rush University
Rush University Medical Center
University of Michigan, Ann Arbor
University of Regensburg
University of Warwick
Queen Mary University of London
King Abdulaziz University
Charité – Universitätsmedizin Berlin
German Institute for Economic Research
Washington University St. Louis
University of Bonn
Reykjavík University
University of Aberdeen
Brigham and Women’s Hospital
Norwegian Institute of Public Health
Imperial College London
Tampere University
University of Split
University of Toronto
Tema BV
University Hospitals of Leicester NHS Trust
Queensland University of Technology
Massachusetts General Hospital
IRCCS Ospedale Infantile Burlo Garofolo - Trieste
Social Impact
NorthShore University HealthSystem
The University of Chicago
University of Dundee
KU Leuven
Maastricht University
Ludwig Maximilian University of Munich
Florida State University
University of Lübeck
Geisinger Medical Center
Sidra
HELIOS-Hospital Stralsund
Interuniversity Cardiology Institute of the Netherlands
Adelaide University
South Australian Health And Medical Research Institute
University College London
University of Oulu
Fimlab Laboratories
Queen's University Kingston
New York University
University of Tartu
Hope Hospital
The Capital Region
Montpellier Business School
Panteia
Princeton University
University of Wisconsin-Madison
Stockholm School of Economics
George Mason University

Research output: Contribution to journal › Article › peer-review

423 Citations (Scopus)

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
Early online date	Mar 2022
DOIs	https://doi.org/10.1038/s41588-022-01016-z
Publication status	Published - Apr 2022
Externally published	Yes

Keywords

Accuracy
Biobank
Cohort profile
Genetics
Gwas
Health
Human complex traits
Loci
Models
Scores

Access to Document

10.1038/s41588-022-01016-z

Cite this

@article{ac5bc51b37084ea4ba09b6afba3b9749,

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 \textasciitilde{}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 = "Accuracy, Biobank, Cohort profile, Genetics, Gwas, Health, Human complex traits, Loci, Models, Scores",

author = "\{23andMe Research Team\} and \{Social Science Genetic Association Consortium\} and \{Lifelines Cohort Study\} and 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 Michelle Agee and Babak Alipanahi and Adam Auton and Bell, \{Robert K.\} and Katarzyna Bryc and Elson, \{Sarah L.\} and Pierre Fontanillas and Furlotte, \{Nicholas A.\} and Hinds, \{David A.\} and Huber, \{Karen E.\} and Aaron Kleinman and Litterman, \{Nadia K.\} and McCreight, \{Jennifer C.\} and McIntyre, \{Matthew H.\} and Mountain, \{Joanna L.\} and Northover, \{Carrie A.M.\} and Pitts, \{Steven J.\} and Sathirapongsasuti, \{J. Fah\} and Sazonova, \{Olga V.\} and Shelton, \{Janie F.\} and Suyash Shringarpure and Tung, \{Joyce Y.\} and Vladimir Vacic and Wilson, \{Catherine H.\} and Fontana, \{Mark Alan\} and Pers, \{Tune H.\} and Rietveld, \{Cornelius A.\} and Hottenga, \{Jouke Jan\}",

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

year = "2022",

month = apr,

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

language = "English",

volume = "54",

pages = "437--449",

journal = "Nature Genetics",

issn = "1061-4036",

publisher = "Nature Research",

number = "4",

}

TY - JOUR

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

AU - 23andMe Research Team

AU - Social Science Genetic Association Consortium

AU - Lifelines Cohort Study

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 - Agee, Michelle

AU - Alipanahi, Babak

AU - Auton, Adam

AU - Bell, Robert K.

AU - Bryc, Katarzyna

AU - Elson, Sarah L.

AU - Fontanillas, Pierre

AU - Furlotte, Nicholas A.

AU - Hinds, David A.

AU - Huber, Karen E.

AU - Kleinman, Aaron

AU - Litterman, Nadia K.

AU - McCreight, Jennifer C.

AU - McIntyre, Matthew H.

AU - Mountain, Joanna L.

AU - Northover, Carrie A.M.

AU - Pitts, Steven J.

AU - Sathirapongsasuti, J. Fah

AU - Sazonova, Olga V.

AU - Shelton, Janie F.

AU - Shringarpure, Suyash

AU - Tung, Joyce Y.

AU - Vacic, Vladimir

AU - Wilson, Catherine H.

AU - Fontana, Mark Alan

AU - Pers, Tune H.

AU - Rietveld, Cornelius A.

AU - Hottenga, Jouke Jan

PY - 2022/4

Y1 - 2022/4

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 - Accuracy

KW - Biobank

KW - Cohort profile

KW - Genetics

KW - Gwas

KW - Health

KW - Human complex traits

KW - Loci

KW - Models

KW - Scores

UR - https://www.scopus.com/pages/publications/85127422477

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

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this