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

Aysu Okbay; Yeda Wu; Nancy Wang; Hariharan Jayashankar; Michael Bennett; Seyed Moeen Nehzati; Julia Sidorenko; Hyeokmoon Kweon; Grant Goldman; Tamara Gjorgjieva; Yunxuan Jiang; Barry Hicks; Chao Tian; David A. Hinds; Rafael Ahlskog; Patrik K.E. Magnusson; Sven Oskarsson; Caroline Hayward; Archie Campbell; David J. Porteous; Jeremy Freese; Pamela Herd; Michelle Agee; Babak Alipanahi; Adam Auton; Robert K. Bell; Katarzyna Bryc; Sarah L. Elson; Pierre Fontanillas; Nicholas A. Furlotte; David A. Hinds; Karen E. Huber; Aaron Kleinman; Nadia K. Litterman; Jennifer C. McCreight; Matthew H. McIntyre; Joanna L. Mountain; Carrie A.M. Northover; Steven J. Pitts; J. Fah Sathirapongsasuti; Olga V. Sazonova; Janie F. Shelton; Tune H. Pers; Pascal Timshel; Tarunveer S. Ahluwalia; Klaus Bønnelykke; Hans Bisgaard; Thorkild I.A. Sørensen; 23andMe Research Team; Social Science Genetic Association Consortium; LifeLines Cohort Study

doi:10.1038/s41588-022-01016-z

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

Aysu Okbay^*, Yeda Wu, Nancy Wang, Hariharan Jayashankar, Michael Bennett, Seyed Moeen Nehzati, Julia Sidorenko, Hyeokmoon Kweon, Grant Goldman, Tamara Gjorgjieva, Yunxuan Jiang, Barry Hicks, Chao Tian, David A. Hinds, Rafael Ahlskog, Patrik K.E. Magnusson, Sven Oskarsson, Caroline Hayward, Archie Campbell, David J. PorteousJeremy Freese, Pamela Herd, Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, David A. Hinds, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Jennifer C. McCreight, Matthew H. McIntyre, Joanna L. Mountain, Carrie A.M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Tune H. Pers, Pascal Timshel, Tarunveer S. Ahluwalia, Klaus Bønnelykke, Hans Bisgaard, Thorkild I.A. Sørensen, 23andMe Research Team, Social Science Genetic Association Consortium, LifeLines Cohort Study

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

259 Citations (Scopus)

19 Downloads (Pure)

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.

Original language	English
Journal	Nature Genetics
Volume	54
Issue number	4
Pages (from-to)	437-449
Number of pages	13
ISSN	1061-4036
DOIs	https://doi.org/10.1038/s41588-022-01016-z
Publication status	Published - 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Access to Document

10.1038/s41588-022-01016-zLicence: CC BY

FulltextFinal published version, 6.84 MBLicence: CC BY

Cite this

Okbay, A., Wu, Y., Wang, N., Jayashankar, H., Bennett, M., Nehzati, S. M., Sidorenko, J., Kweon, H., Goldman, G., Gjorgjieva, T., Jiang, Y., Hicks, B., Tian, C., Hinds, D. A., Ahlskog, R., Magnusson, P. K. E., Oskarsson, S., Hayward, C., Campbell, A., ... LifeLines Cohort Study (2022). Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. Nature Genetics, 54(4), 437-449. https://doi.org/10.1038/s41588-022-01016-z

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; Agee, Michelle; Alipanahi, Babak; Auton, Adam; Bell, Robert K.; Bryc, Katarzyna; Elson, Sarah L.; Fontanillas, Pierre; Furlotte, Nicholas A.; Hinds, David A.; Huber, Karen E.; Kleinman, Aaron; Litterman, Nadia K.; McCreight, Jennifer C.; McIntyre, Matthew H.; Mountain, Joanna L.; Northover, Carrie A.M.; Pitts, Steven J.; Sathirapongsasuti, J. Fah; Sazonova, Olga V.; Shelton, Janie F.; Pers, Tune H.; Timshel, Pascal; Ahluwalia, Tarunveer S.; Bønnelykke, Klaus; Bisgaard, Hans; Sørensen, Thorkild I.A.; 23andMe Research Team; Social Science Genetic Association Consortium; LifeLines Cohort Study.

In: Nature Genetics, Vol. 54, No. 4, 2022, p. 437-449.

Research output: Contribution to journal › Journal article › Research › peer-review

Okbay, A, Wu, Y, Wang, N, Jayashankar, H, Bennett, M, Nehzati, SM, Sidorenko, J, Kweon, H, Goldman, G, Gjorgjieva, T, Jiang, Y, Hicks, B, Tian, C, Hinds, DA, Ahlskog, R, Magnusson, PKE, Oskarsson, S, Hayward, C, Campbell, A, Porteous, DJ, Freese, J, Herd, P, Agee, M, Alipanahi, B, Auton, A, Bell, RK, Bryc, K, Elson, SL, Fontanillas, P, Furlotte, NA, Hinds, DA, Huber, KE, Kleinman, A, Litterman, NK, McCreight, JC, McIntyre, MH, Mountain, JL, Northover, CAM, Pitts, SJ, Sathirapongsasuti, JF, Sazonova, OV, Shelton, JF, Pers, TH, Timshel, P, Ahluwalia, TS , Bønnelykke, K, Bisgaard, H, Sørensen, TIA, 23andMe Research Team, Social Science Genetic Association Consortium & LifeLines Cohort Study 2022, 'Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals', Nature Genetics, vol. 54, no. 4, pp. 437-449. https://doi.org/10.1038/s41588-022-01016-z

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 ; Agee, Michelle ; Alipanahi, Babak ; Auton, Adam ; Bell, Robert K. ; Bryc, Katarzyna ; Elson, Sarah L. ; Fontanillas, Pierre ; Furlotte, Nicholas A. ; Hinds, David A. ; Huber, Karen E. ; Kleinman, Aaron ; Litterman, Nadia K. ; McCreight, Jennifer C. ; McIntyre, Matthew H. ; Mountain, Joanna L. ; Northover, Carrie A.M. ; Pitts, Steven J. ; Sathirapongsasuti, J. Fah ; Sazonova, Olga V. ; Shelton, Janie F. ; Pers, Tune H. ; Timshel, Pascal ; Ahluwalia, Tarunveer S. ; Bønnelykke, Klaus ; Bisgaard, Hans ; Sørensen, Thorkild I.A. ; 23andMe Research Team ; Social Science Genetic Association Consortium ; LifeLines Cohort Study. / Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals. In: Nature Genetics. 2022 ; Vol. 54, No. 4. pp. 437-449.

@article{8e8e0cd75ac64b0bbde214b5bc12e3ed,

title = "Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals",

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{\textquoteright}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.",

author = "Aysu Okbay and Yeda Wu and Nancy Wang and Hariharan Jayashankar and Michael Bennett and Nehzati, {Seyed Moeen} and Julia Sidorenko and Hyeokmoon Kweon and Grant Goldman and Tamara Gjorgjieva and Yunxuan Jiang and Barry Hicks and Chao Tian and Hinds, {David A.} and Rafael Ahlskog and Magnusson, {Patrik K.E.} and Sven Oskarsson and Caroline Hayward and Archie Campbell and Porteous, {David J.} and Jeremy Freese and Pamela Herd and Michelle Agee and Babak Alipanahi and Adam Auton and Bell, {Robert K.} and Katarzyna Bryc and Elson, {Sarah L.} and Pierre Fontanillas and Furlotte, {Nicholas A.} and Hinds, {David A.} and Huber, {Karen E.} and Aaron Kleinman and Litterman, {Nadia K.} and McCreight, {Jennifer C.} and McIntyre, {Matthew H.} and Mountain, {Joanna L.} and Northover, {Carrie A.M.} and Pitts, {Steven J.} and Sathirapongsasuti, {J. Fah} and Sazonova, {Olga V.} and Shelton, {Janie F.} and Pers, {Tune H.} and Pascal Timshel and Ahluwalia, {Tarunveer S.} and Klaus B{\o}nnelykke and Hans Bisgaard and S{\o}rensen, {Thorkild I.A.} and {23andMe Research Team} and {Social Science Genetic Association Consortium} and {LifeLines Cohort Study}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

doi = "10.1038/s41588-022-01016-z",

language = "English",

volume = "54",

pages = "437--449",

journal = "Nature Genetics",

issn = "1061-4036",

publisher = "nature publishing group",

number = "4",

}

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 - Agee, Michelle

AU - Alipanahi, Babak

AU - Auton, Adam

AU - Bell, Robert K.

AU - Bryc, Katarzyna

AU - Elson, Sarah L.

AU - Fontanillas, Pierre

AU - Furlotte, Nicholas A.

AU - Hinds, David A.

AU - Huber, Karen E.

AU - Kleinman, Aaron

AU - Litterman, Nadia K.

AU - McCreight, Jennifer C.

AU - McIntyre, Matthew H.

AU - Mountain, Joanna L.

AU - Northover, Carrie A.M.

AU - Pitts, Steven J.

AU - Sathirapongsasuti, J. Fah

AU - Sazonova, Olga V.

AU - Shelton, Janie F.

AU - Pers, Tune H.

AU - Timshel, Pascal

AU - Ahluwalia, Tarunveer S.

AU - Bønnelykke, Klaus

AU - Bisgaard, Hans

AU - Sørensen, Thorkild I.A.

AU - 23andMe Research Team

AU - Social Science Genetic Association Consortium

AU - LifeLines Cohort Study

PY - 2022

Y1 - 2022

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.

U2 - 10.1038/s41588-022-01016-z

DO - 10.1038/s41588-022-01016-z

M3 - Journal article

C2 - 35361970

AN - SCOPUS:85127422477

VL - 54

SP - 437

EP - 449

JO - Nature Genetics

JF - Nature Genetics

SN - 1061-4036

IS - 4

ER -