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Search: WFRF:(Watson P) > Journal article > Stockholm School of Economics

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  • Lee, James J, et al. (author)
  • Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.
  • 2018
  • In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 50:8, s. 1112-1121
  • Journal article (peer-reviewed)abstract
    • Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1million individuals and identify 1,271independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.
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2.
  • Okbay, Aysu, et al. (author)
  • Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals.
  • 2022
  • In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 54:4, s. 437-449
  • Journal article (peer-reviewed)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.
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