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

Aysu OkbayYeda WuNancy WangHariharan JayashankarMichael BennettSeyed Moeen NehzatiJulia SidorenkoHyeokmoon KweonGrant GoldmanTamara GjorgjievaYunxuan JiangBarry HicksChao TianDavid A HindsRafael AhlskogPatrik K E MagnussonSven OskarssonCaroline HaywardArchie I CampbellDavid J PorteousJeremy FreesePamela Herdnull nullnull nullChelsea WatsonJonathan JalaDalton ConleyPhilipp D KoellingerMagnus JohannessonDavid LaibsonMichelle N MeyerJames J LeeAugustine KongLoic YengoDavid CesariniPatrick TurleyPeter M VisscherJonathan P BeauchampDaniel J BenjaminAlexander I Young
Published in: Nature genetics (2022)
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.
Keyphrases
  • genome wide
  • genome wide association study
  • dna methylation
  • copy number
  • genome wide association
  • gene expression