학술논문
Resource profile and user guide of the Polygenic Index Repository
Document Type
Author
Becker, Joel; Ahlskog, Rafael; Kleinman, Aaron; Goldman, Grant; Wang, Nancy; Jayashankar, Hariharan; Bennett, Michael; Belsky, Daniel W.; Karlsson- Linnér, Richard; Burik, Casper A.P.; Hinds, David A.; Agee, Michelle; Ajnakina, Olesya; Beauchamp, Jonathan P; Caspi, Avshalom; Corcoran, David L.; Moffitt, Terrie E.; Poulton, Richie G.; Sugden, Karen; Williams, Benjamin S.; Harris, Kathleen Mullan; Milani, Lili; Esko, Tõnu; Iacono, William G.; Steptoe, Andrew; McGue, Matt; Magnusson, Patrik K. E.; Mallard, Travis T.; Harden, Kathryn Paige; Tucker-Drob, Elliot M.; Herd, Pamela; Freese, Jeremy; Young, Alexander; Koellinger, Philipp D.; Johannesson, Magnus; Visscher, Peter M.; Meyer, Michelle N; Oskarsson, Sven; Laibson, David; Cesarini, David; Benjamin, Daniel J.; Turley, Patrick; Okbay, Aysu; Bell, Robert K.; Bryc, Katarzyna; Elson, Sarah L.; Furlotte, Nicholas A; Huber, Karen E.; Fontanillas, Pierre; Litterman, Nadia K.; McCreight, Jennifer C.; McIntyre, Matthew H.; Mountain, Joanna L.; Pitts, Steven J.; Sathirapongsasuti, Jarupon Fah; Sazonova, Olga V.; Northover, Carrie A.M.; Shelton, Janie F.; Shringarpure, Suyash S.; Tian, Chao; Alipanahi Ramandi, Babak; Auton, Adam; Vacic, Vladimir; Tung, Joyce Y.; Wilson, Catherine H.
Source
Nature Human Behaviour. 5(12):1744-1758
Subject
Language
English
ISSN
2397-3374
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. © 2021, The Author(s), under exclusive licence to Springer Nature Limited.