%0 Journal Article
%A Becker, J
%A Burik, CAP
%A Goldman, G
%A Wang, N
%A Jayashankar, H
%A Bennett, M
%A Belsky, DW
%A Linner, RK
%A Ahlskog, R
%A Kleinman, A
%A Hinds, DA
%A Caspi, A
%A Corcoran, DL
%A Moffitt, TE
%A Poulton, R
%A Sugden, K
%A Williams, BS
%A Harris, KM
%A Steptoe, A
%A Ajnakina, O
%A Milani, L
%A Esko, T
%A Iacono, WG
%A McGue, M
%A Magnusson, PKE
%A Mallard, TT
%A Harden, KP
%A Tucker-Drob, EM
%A Herd, P
%A Freese, J
%A Young, A
%A Beauchamp, JP
%A Koellinger, P
%A Oskarsson, S
%A Johannesson, M
%A Visscher, PM
%A Meyer, MN
%A Laibson, D
%A Cesarini, D
%A Benjamin, DJ
%A Turley, P
%A Okbay, A
%D 2021
%F discovery:10131355
%I NATURE RESEARCH
%J Nature Human Behaviour
%K Behavioural genetics, Economics, Genome-wide association studies, Human behaviour
%P 1744-1758
%T Resource profile and user guide of the Polygenic Index Repository
%U https://discovery.ucl.ac.uk/id/eprint/10131355/
%V 5
%X 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.
%Z This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.