%D 2021
%I NATURE RESEARCH
%L discovery10131355
%P 1744-1758
%T Resource profile and user guide of the Polygenic Index Repository
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%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.
%V 5
%A J Becker
%A CAP Burik
%A G Goldman
%A N Wang
%A H Jayashankar
%A M Bennett
%A DW Belsky
%A RK Linner
%A R Ahlskog
%A A Kleinman
%A DA Hinds
%A A Caspi
%A DL Corcoran
%A TE Moffitt
%A R Poulton
%A K Sugden
%A BS Williams
%A KM Harris
%A A Steptoe
%A O Ajnakina
%A L Milani
%A T Esko
%A WG Iacono
%A M McGue
%A PKE Magnusson
%A TT Mallard
%A KP Harden
%A EM Tucker-Drob
%A P Herd
%A J Freese
%A A Young
%A JP Beauchamp
%A P Koellinger
%A S Oskarsson
%A M Johannesson
%A PM Visscher
%A MN Meyer
%A D Laibson
%A D Cesarini
%A DJ Benjamin
%A P Turley
%A A Okbay
%K Behavioural genetics, Economics, Genome-wide association studies, Human behaviour
%J Nature Human Behaviour