TY - JOUR
T1 - Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
AU - Okbay, Aysu
AU - Wu, Yeda
AU - Wang, Nancy
AU - Jayashankar, Hariharan
AU - Bennett, Michael
AU - Nehzati, Seyed Moeen
AU - Sidorenko, Julia
AU - Kweon, Hyeokmoon
AU - Goldman, Grant
AU - Gjorgjieva, Tamara
AU - Jiang, Yunxuan
AU - Hicks, Barry
AU - Tian, Chao
AU - Hinds, David A
AU - Ahlskog, Rafael
AU - Magnusson, Patrik K E
AU - Oskarsson, Sven
AU - Hayward, Caroline
AU - Campbell, Archie
AU - Porteous, David J
AU - Freese, Jeremy
AU - Herd, Pamela
AU - Watson, Chelsea
AU - Jala, Jonathan
AU - Conley, Dalton
AU - Koellinger, Philipp D
AU - Johannesson, Magnus
AU - Laibson, David
AU - Meyer, Michelle N
AU - Lee, James J
AU - Kong, Augustine
AU - Yengo, Loic
AU - Cesarini, David
AU - Turley, Patrick
AU - Visscher, Peter M
AU - Beauchamp, Jonathan P
AU - Benjamin, Daniel J
AU - Young, Alexander I
AU - 23andMe Research Team
AU - Social Science Genetic Association Consortium
A2 - Ahluwalia, Tarunveer Singh
A2 - Bønnelykke, Klaus
A2 - Waage, Johannes
A2 - Bisgaard, Hans
A2 - Sørensen, Thorkild Ingvor A
N1 - © 2022. The Author(s).
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Genome-Wide Association Study
KW - Humans
KW - Multifactorial Inheritance/genetics
KW - Polymorphism, Single Nucleotide/genetics
UR - https://www.scopus.com/pages/publications/85127422477
U2 - 10.1038/s41588-022-01016-z
DO - 10.1038/s41588-022-01016-z
M3 - Journal article
C2 - 35361970
SN - 1061-4036
VL - 54
SP - 437
EP - 449
JO - Nature Genetics
JF - Nature Genetics
IS - 4
ER -