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Joel Becker, Casper Burik, Grant Goldman, Nancy S. Wang, Hariharan Jayashankar, Michael Bennett, Daniel Belsky, Richard Karlsson Linnér, Rafael Ahlskog, Aaron Kleinman, David A. Hinds, David Corcoran, Terrie Moffitt, Richie Poulton, Karen Sugden, Benjamin Williams, Kathleen Mullan Harris, Andrew Steptoe, Olesya Ajnakina, Lili Milani & 21 more Tõnu Esko, William G. Iacono, Matt McGue, Patrik K.E. Magnusson, Travis T. Mallard, K. Paige Harden, Elliot M. Tucker-drob, Pamela Herd, Jeremy Freese, Alexander Young, Jonathan P Beauchamp, Philipp D Koellinger, Sven Oskarsson, Magnus Johannesson, Peter M. Visscher, Michelle N Meyer, David Laibson, David Cesarini, Daniel J Benjamin, Patrick Turley, Aysu Okbay

Original languageEnglish
JournalNature Human Behaviour
Published14 Apr 2021

Bibliographical note

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.


King's Authors


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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