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; Chelsea Watson; Jonathan Jala; Philipp D. Koellinger; Magnus Johannesson; David Laibson; Michelle N Meyer; James J. Lee; David Cesarini; Augustine Kong; Dalton Conley; Peter M. Visscher; Patrick Turley; Loïc Yengo; Jonathan P Beauchamp; Daniel J. Benjamin; Alexander Young

doi:10.1038/s41588-022-01016-z

Back

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

Journal article

Open access

Peer reviewed

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, …

Nature Genetics, Vol.54(4), pp.437-437

2022

DOI: https://doi.org/10.1038/s41588-022-01016-z

Abstract

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.

Files and links (1)

url

https://doi.org/10.1038/s41588-022-01016-zView

Published (Version of record) Open

Metrics

5 Record Views

157 Times Cited - Web of Science

Details

Title: Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
Creators - without role: Aysu Okbay - Vrije Universiteit Amsterdam (NL)
Yeda Wu - University of Queensland (AU)
Nancy Wang - National Bureau of Economic Research (US)
Hariharan Jayashankar - National Bureau of Economic Research (US)
Michael Bennett - National Bureau of Economic Research (US)
Seyed Moeen Nehzati - UCLA Anderson School of Management
Julia Sidorenko - University of Queensland (AU)
Hyeokmoon Kweon - Vrije Universiteit Amsterdam (NL)
Grant Goldman - National Bureau of Economic Research (US)
Tamara Gjorgjieva - National Bureau of Economic Research (US)
Yunxuan Jiang - 23andMe
Barry Hicks - 23andMe
Chao Tian - 23andMe
David A. Hinds - 23andMe
Rafael Ahlskog - Uppsala University (SE)
Patrik K. E. Magnusson - Karolinska Institutet (SE)
Sven Oskarsson - Uppsala University (SE)
Caroline Hayward - University of Edinburgh
Archie Campbell - University of Edinburgh
David J. Porteous - University of Edinburgh
Jeremy Freese - Stanford University (US)
Pamela Herd - Georgetown University (US)
Chelsea Watson - UCLA Anderson School of Management
Jonathan Jala - UCLA Anderson School of Management
Philipp D. Koellinger - Vrije Universiteit Amsterdam (NL)
Magnus Johannesson - Stockholm School of Economics, Department of Economics
David Laibson - Harvard University (US)
Michelle N Meyer - Geisinger Health System
James J. Lee - University of Minnesota Twin Cities (US)
David Cesarini - Center for Experimental Social Science
Augustine Kong - University of Oxford (GB)
Dalton Conley - Princeton University (US)
Peter M. Visscher - University of Queensland (AU)
Patrick Turley - University of Southern California (US)
Loïc Yengo - University of Queensland (AU)
Jonathan P Beauchamp - George Mason University
Daniel J. Benjamin - National Bureau of Economic Research (US)
Alexander Young - UCLA Anderson School of Management
Publication Details: Nature Genetics, Vol.54(4), pp.437-437
Publisher: Nature Research (part of Springer Nature)
Number of pages: 1
Identifiers: 991001480671706056
Academic Unit: Department of Economics
Language: English
Resource Type: Journal article

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

Abstract

Files and links (1)

Metrics

Details

Stockholm School of Economics Social media