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Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
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 1...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005349/ https://www.ncbi.nlm.nih.gov/pubmed/35361970 http://dx.doi.org/10.1038/s41588-022-01016-z |
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author | 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. |
author_facet | 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. |
author_sort | Okbay, Aysu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9005349 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-90053492022-04-27 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. Nat Genet Article 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. Nature Publishing Group US 2022-03-31 2022 /pmc/articles/PMC9005349/ /pubmed/35361970 http://dx.doi.org/10.1038/s41588-022-01016-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article 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. Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title | Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title_full | Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title_fullStr | Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title_full_unstemmed | Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title_short | Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
title_sort | polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005349/ https://www.ncbi.nlm.nih.gov/pubmed/35361970 http://dx.doi.org/10.1038/s41588-022-01016-z |
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