@article{discovery10131355,
       publisher = {NATURE RESEARCH},
          volume = {5},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           pages = {1744--1758},
           title = {Resource profile and user guide of the Polygenic Index Repository},
         journal = {Nature Human Behaviour},
           month = {December},
            year = {2021},
          author = {Becker, J and Burik, CAP and Goldman, G and Wang, N and Jayashankar, H and Bennett, M and Belsky, DW and Linner, RK and Ahlskog, R and Kleinman, A and Hinds, DA and Caspi, A and Corcoran, DL and Moffitt, TE and Poulton, R and Sugden, K and Williams, BS and Harris, KM and Steptoe, A and Ajnakina, O and Milani, L and Esko, T and Iacono, WG and McGue, M and Magnusson, PKE and Mallard, TT and Harden, KP and Tucker-Drob, EM and Herd, P and Freese, J and Young, A and Beauchamp, JP and Koellinger, P and Oskarsson, S and Johannesson, M and Visscher, PM and Meyer, MN and Laibson, D and Cesarini, D and Benjamin, DJ and Turley, P and Okbay, A},
             url = {https://doi.org/10.1038/s41562-021-01119-3},
        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.},
        keywords = {Behavioural genetics, Economics, Genome-wide association studies, Human behaviour}
}