Resource profile and user guide of the Polygenic Index Repository.
Journal Article (Journal Article)
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.
- Becker, J; Burik, CAP; Goldman, G; Wang, N; Jayashankar, H; Bennett, M; Belsky, DW; Karlsson Linnér, R; Ahlskog, R; Kleinman, A; Hinds, DA; 23andMe Research Group, ; Caspi, A; Corcoran, DL; Moffitt, TE; Poulton, R; Sugden, K; Williams, BS; Harris, KM; Steptoe, A; Ajnakina, O; Milani, L; Esko, T; Iacono, WG; McGue, M; Magnusson, PKE; Mallard, TT; Harden, KP; Tucker-Drob, EM; Herd, P; Freese, J; Young, A; Beauchamp, JP; Koellinger, PD; Oskarsson, S; Johannesson, M; Visscher, PM; Meyer, MN; Laibson, D; Cesarini, D; Benjamin, DJ; Turley, P; Okbay, A
- December 2021
Volume / Issue
- 5 / 12
Start / End Page
- 1744 - 1758
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
International Standard Serial Number (ISSN)
Digital Object Identifier (DOI)