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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
10 Downloads (Pure)

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 languageEnglish
Pages (from-to)437-449
Number of pages13
JournalNature genetics
Volume54
Issue number4
DOIs
Publication statusPublished - Apr-2022

Cite this