Resource profile and user guide of the Polygenic Index Repository

23andMe Research Group

doi:10.1038/s41562-021-01119-3

Resource profile and user guide of the Polygenic Index Repository

23andMe Research Group

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Abstract

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Original language	English
Pages (from-to)	1744–1758
Number of pages	15
Journal	Nature Human Behaviour
Volume	5
Issue number	12
Early online date	17 Jun 2021
DOIs	https://doi.org/10.1038/s41562-021-01119-3
Publication status	Published - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

The authors thank C. Shulman for helpful comments. This research was carried out under the auspices of the SSGAC. This research was conducted using the UKB resource under application number 11,425. J.B. was supported by the Pershing Square Fund of the Foundations of Human Behavior, awarded to D.L.; H.J., M.B., D.C. and P.T. by the Ragnar Söderberg Foundation (E42/15), to D.C.; C.A.P.B., P.K. and A.O. by an ERC Consolidator Grant (647648 EdGe), to P.K.; H.J., M.B., A.Y., J.P.B., M.N.M., D.C., D.J.B. and P.T. by Open Philanthropy (010623-00001), to D.J.B.; C.A.P.B., R.A. and S.O. by Riksbankens Jubileumsfond (P18-0782:1), to S.O.; C.A.P.B. and S.O. by the Swedish Research Council (2019-00244), to S.O.; G.G., N.W. and D.J.B. by the NIA/NIH

Funders	Funder number
Australian Research Council
Ministry of Business, Innovation and Employment
National Science Foundation
University of Wisconsin-Madison
Eunice Kennedy Shriver National Institute of Child Health and Human Development
Pershing Square Foundation
National Institutes of Health
Health Research Council of New Zealand
Social Sciences and Humanities Research Council of Canada
Jacobs Foundation
Pershing Square Fund
Open Philanthropy Project
Spencer Foundation
Economic and Social Research Council	ES/K005774/1
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	016.Veni.198.058
Eesti Teadusagentuur	PRG184, PUT1660
National Institute of Child Health and Human Development	R01HD092548, R01HD060726, P01HD031921, R01HD073342, R01HD077482, R01HD083613
National Health and Medical Research Council	1002190, GNT113400
Medical Research Council	MR/P005918, G1002190
National Institute on Aging	R01AG042568, R01AG017644, R01AG032282, R01AG041868, P01AG021079, R56AG058726, R24AG065184, R01AG049789, R01AG009775, K99AG062787, R01AG033285
National Institute on Drug Abuse	U01DA024417
Ragnar Söderberg Foundation	E42/15
Vetenskapsrådet	421-2013-1061, 2019-00244, 2017-00641
Horizon 2020 Framework Programme	MP1GI18418R, MMVCM18418R
National Institute of Mental Health	U01MH109539, R01MH101244
Riksbankens Jubileumsfond	P18-0782:1
H2020 European Research Council	647648
Ragnar Söderbergs stiftelse	E9/11
Ontario Genomics Institute	OGI-152
Engineering Research Centers	010623-00001, 647648 EdGe

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10.1038/s41562-021-01119-3

Resource profile and user guide of the Polygenic Index RepositoryFinal published version, 1.86 MBLicence: Article 25fa Dutch Copyright Act

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@article{af1d9fff6f284afbb646e700454aea6c,

title = "Resource profile and user guide of the Polygenic Index Repository",

abstract = "Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs{\textquoteright} prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the {\textquoteleft}additive SNP factor{\textquoteright}. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.",

author = "Joel Becker and Burik, \{Casper A.P.\} and Grant Goldman and Nancy Wang and Hariharan Jayashankar and Michael Bennett and Belsky, \{Daniel W.\} and \{Karlsson Linn{\'e}r\}, Richard and Rafael Ahlskog and Aaron Kleinman and Hinds, \{David A.\} 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 Huber, \{Karen E.\} 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 Suyash Shringarpure and Chao Tian and Tung, \{Joyce Y.\} and Vladimir Vacic and Wilson, \{Catherine H.\} and Avshalom Caspi and Corcoran, \{David L.\} and Moffitt, \{Terrie E.\} and Richie Poulton and Karen Sugden and Williams, \{Benjamin S.\} and Harris, \{Kathleen Mullan\} and Andrew Steptoe and Olesya Ajnakina and Lili Milani and T{\~o}nu Esko and Iacono, \{William G.\} and Matt McGue and Magnusson, \{Patrik K.E.\} and Mallard, \{Travis T.\} and Harden, \{K. Paige\} and Tucker-Drob, \{Elliot M.\} and Pamela Herd and Jeremy Freese and Alexander Young and Beauchamp, \{Jonathan P.\} and Koellinger, \{Philipp D.\} and Sven Oskarsson and Magnus Johannesson and Visscher, \{Peter M.\} and Meyer, \{Michelle N.\} and David Laibson and David Cesarini and Benjamin, \{Daniel J.\} and Patrick Turley and Aysu Okbay and \{23andMe Research Group\}",

year = "2021",

month = dec,

doi = "10.1038/s41562-021-01119-3",

language = "English",

volume = "5",

pages = "1744–1758",

journal = "Nature Human Behaviour",

issn = "2397-3374",

publisher = "Nature Publishing Group",

number = "12",

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AU - Becker, Joel

AU - Burik, Casper A.P.

AU - Goldman, Grant

AU - Wang, Nancy

AU - Jayashankar, Hariharan

AU - Bennett, Michael

AU - Belsky, Daniel W.

AU - Karlsson Linnér, Richard

AU - Ahlskog, Rafael

AU - Kleinman, Aaron

AU - Hinds, David A.

AU - Agee, Michelle

AU - Alipanahi, Babak

AU - Auton, Adam

AU - Bell, Robert K.

AU - Bryc, Katarzyna

AU - Elson, Sarah L.

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AU - Furlotte, Nicholas A.

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AU - McCreight, Jennifer C.

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AU - Mountain, Joanna L.

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AU - Pitts, Steven J.

AU - Sathirapongsasuti, J. Fah

AU - Sazonova, Olga V.

AU - Shelton, Janie F.

AU - Shringarpure, Suyash

AU - Tian, Chao

AU - Tung, Joyce Y.

AU - Vacic, Vladimir

AU - Wilson, Catherine H.

AU - Caspi, Avshalom

AU - Corcoran, David L.

AU - Moffitt, Terrie E.

AU - Poulton, Richie

AU - Sugden, Karen

AU - Williams, Benjamin S.

AU - Harris, Kathleen Mullan

AU - Steptoe, Andrew

AU - Ajnakina, Olesya

AU - Milani, Lili

AU - Esko, Tõnu

AU - Iacono, William G.

AU - McGue, Matt

AU - Magnusson, Patrik K.E.

AU - Mallard, Travis T.

AU - Harden, K. Paige

AU - Tucker-Drob, Elliot M.

AU - Herd, Pamela

AU - Freese, Jeremy

AU - Young, Alexander

AU - Beauchamp, Jonathan P.

AU - Koellinger, Philipp D.

AU - Oskarsson, Sven

AU - Johannesson, Magnus

AU - Visscher, Peter M.

AU - Meyer, Michelle N.

AU - Laibson, David

AU - Cesarini, David

AU - Benjamin, Daniel J.

AU - Turley, Patrick

AU - Okbay, Aysu

AU - 23andMe Research Group

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AB - Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is growing rapidly. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some not previously published—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the ‘additive SNP factor’. Regressions in which the true regressor is this factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

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Resource profile and user guide of the Polygenic Index Repository

Abstract

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