Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
- Okbay, Aysu
- Wu, Yeda
- Wang, Nancy
- Jayashankar, Hariharan
- Bennett, Michael
- Nehzati, Seyed Moeen
- Sidorenko, Julia
- Kweon, Hyeokmoon
- Goldman, Grant
- Gjorgjieva, Tamara
- Jiang, Yunxuan
- Hicks, Barry
- Tian, Chao
- Hinds, David A.
- Ahlskog, Rafael
- Magnusson, Patrik K. E.
- Oskarsson, Sven
- Hayward, Caroline
- Campbell, Archie
- Porteous, David J.
- Freese, Jeremy
- Herd, Pamela
- Watson, Chelsea
- Jala, Jonathan
- Conley, Dalton
- Koellinger, Philipp D.
- Johannesson, Magnus
- Laibson, David
- Meyer, Michelle N.
- Lee, James J.
- Kong, Augustine
- Yengo, Loic
- Cesarini, David
- Turley, Patrick
- Visscher, Peter M.
- Beauchamp, Jonathan P.
- Benjamin, Daniel J.
- Young, Alexander, I
DOIAbstract
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of similar to 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 additiv...
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