TY - JOUR
T1 - Resource Profile and User Guide of the Polygenic Index Repository
AU - Becker, Joel
AU - Burik, Casper
AU - Goldman, Grant
AU - Wang, Nancy S.
AU - Jayashankar, Hariharan
AU - Bennett, Michael
AU - Belsky, Daniel
AU - Linnér, Richard Karlsson
AU - Ahlskog, Rafael
AU - Kleinman, Aaron
AU - Hinds, David A.
AU - Corcoran, David
AU - Moffitt, Terrie
AU - Poulton, Richie
AU - Sugden, Karen
AU - Williams, Benjamin
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
N1 - Funding Information:
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
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4/14
Y1 - 2021/4/14
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85108188484&partnerID=8YFLogxK
U2 - 10.1038/s41562-021-01119-3
DO - 10.1038/s41562-021-01119-3
M3 - Article
SN - 2397-3374
JO - Nature Human Behaviour
JF - Nature Human Behaviour
ER -