Resource profile and user guide of the Polygenic Index Repository
Joël Becker, Casper A.P. Burik, Grant Goldman, Nancy Wang, Hariharan Jayashankar, Michael Bennett, Daniel W. Belsky, Richard Karlsson Linnér, Rafael Ahlskog, Aaron Kleinman, David A. Hinds, Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, Nicholas A. Furlotte, Karen E. Huber, 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, Suyash Shringarpure, Chao Tian, Joyce Y. Tung, Vladimir Vacic, Catherine H. Wilson, Avshalom Caspi, David L. Corcoran, Terrie E. Moffitt, Richie Poulton, Karen Sugden, Benjamin Williams, Kathleen Mullan Harris, Andrew Steptoe, Olesya Ajnakina, Lili Milani, 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 I. Young, Jonathan Beauchamp, Philipp Koellinger, Sven Oskarsson, Magnus Johannesson, Peter M. Visscher, Michelle N. Meyer, David Laibson, David Cesarini, Daniel J. Benjamin, Patrick Turley, Aysu Okbay
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. Benjamin et al. construct polygenic indexes (DNA-based predictors) for 47 phenotypes and make them available to researchers in 11 datasets. They also present a theoretical framework and estimator to help interpret analyses using polygenic indexes.