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, 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 HaywardArchie Campbell, David J. Porteous, Jeremy Freese, Pamela Herd, Alexander I. Young

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

10 Citaten (Scopus)
9 Downloads (Pure)


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.

Originele taal-2English
Pagina's (van-tot)437-449
Aantal pagina's13
TijdschriftNature genetics
Nummer van het tijdschrift4
StatusPublished - apr-2022

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