Journal article
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
Nature genetics, Vol.54(4), pp.437-449
04/01/2022
DOI: 10.1038/s41588-022-01016-z
PMCID: PMC9005349
PMID: 35361970
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.
Details
- Title: Subtitle
- Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
- Creators
- Aysu Okbay - Vrije Universiteit AmsterdamYeda Wu - The University of QueenslandNancy Wang - National Bureau of Economic ResearchHariharan Jayashankar - National Bureau of Economic ResearchMichael Bennett - National Bureau of Economic ResearchSeyed Moeen Nehzati - Anderson University - South CarolinaJulia Sidorenko - The University of QueenslandHyeokmoon Kweon - Vrije Universiteit AmsterdamGrant Goldman - National Bureau of Economic ResearchTamara Gjorgjieva - National Bureau of Economic ResearchYunxuan Jiang - 23andMeBarry Hicks - 23andMeChao Tian - 23andMeDavid A Hinds - 23andMeRafael Ahlskog - Uppsala UniversityPatrik K E Magnusson - Karolinska InstitutetSven Oskarsson - Uppsala UniversityCaroline Hayward - Institute of Genetics and CancerArchie Campbell - University of EdinburghDavid J Porteous - University of EdinburghJeremy Freese - Stanford UniversityPamela Herd - Georgetown UniversityChelsea Watson - Anderson University - South CarolinaJonathan Jala - Anderson University - South CarolinaDalton Conley - Princeton UniversityPhilipp D Koellinger - University of Wisconsin–MadisonMagnus Johannesson - Stockholm School of EconomicsDavid Laibson - Harvard UniversityMichelle N Meyer - Geisinger Health SystemJames J Lee - University of MinnesotaAugustine Kong - University of OxfordLoic Yengo - The University of QueenslandDavid Cesarini - New York UniversityPatrick Turley - University of Southern CaliforniaPeter M Visscher - The University of QueenslandJonathan P Beauchamp - George Mason UniversityDaniel J Benjamin - University of California, Los AngelesAlexander I Young - University of California, Los Angeles23andMe Research TeamKevin Thom (Contributor) - EconomicsSocial Science Genetic Association Consortium
- Resource Type
- Journal article
- Publication Details
- Nature genetics, Vol.54(4), pp.437-449
- DOI
- 10.1038/s41588-022-01016-z
- PMID
- 35361970
- PMCID
- PMC9005349
- NLM abbreviation
- Nat Genet
- ISSN
- 1061-4036
- eISSN
- 1546-1718
- Grant note
- R00 AG062787 / NIA NIH HHS P30 AG072975 / NIA NIH HHS MC_UU_00007/10 / Medical Research Council R01 MH101244 / NIMH NIH HHS MR/S019669/1 / Medical Research Council R01 AG015819 / NIA NIH HHS R56 AG058726 / NIA NIH HHS K99 AG062787 / NIA NIH HHS R24 AG065184 / NIA NIH HHS R01 AG034374 / NIA NIH HHS
- Language
- English
- Date published
- 04/01/2022
- Academic Unit
- Economics
- Record Identifier
- 9984936834802771
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