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.
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Data availability
For how to access the repository PGIs and other data from each participating dataset, see Supplementary Note; an up-to-date list of participating datasets and data access procedures is maintained at https://www.thessgac.org/pgi-repository. For each phenotype that we analyse, we report GWAS and MTAG summary statistics and PGI (LDpred) weights for all SNPs from the largest discovery sample for that analysis, unless the sample includes 23andMe. SNP-level summary statistics from analyses based entirely or in part on 23andMe data can only be reported for up to 10,000 SNPs. Therefore, if the largest GWAS or MTAG analysis for a phenotype includes 23andMe, we report summary statistics for only the genome-wide significant SNPs from that analysis. In addition, we report summary statistics for all SNPs from a version of the largest GWAS analysis that excludes 23andMe. Finally, we also report summary statistics and PGI (LDpred) weights on which the ‘public PGIs’ are based. These summary statistics and PGI weights can be downloaded from https://www.thessgac.org/pgi-repository. The data underlying Fig. 1 are also available at https://www.thessgac.org/pgi-repository. Researchers at non-profit institutions can obtain access to the genome-wide summary statistics from 23andMe used in this paper by completing the 23andMe publication dataset access request form, available at https://research.23andme.com/dataset-access/. Source data are provided with this paper.
Code availability
The software used for the measurement-error correction is available at https://github.com/JonJala/pgi_correct. The code for constructing PGIs and principal components, the code for the illustrative application and the code for analysing the data displayed in Fig. 1 are at https://www.thessgac.org/pgi-repository.
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Acknowledgements
The authors thank C. Shulman for helpful comments. This research was carried out under the auspices of the SSGAC. This research was conducted using the UKB resource under application number 11,425. J.B. was supported by the Pershing Square Fund of the Foundations of Human Behavior, awarded to D.L.; H.J., M.B., D.C. and P.T. by the Ragnar Söderberg Foundation (E42/15), to D.C.; C.A.P.B., P.K. and A.O. by an ERC Consolidator Grant (647648 EdGe), to P.K.; H.J., M.B., A.Y., J.P.B., M.N.M., D.C., D.J.B. and P.T. by Open Philanthropy (010623-00001), to D.J.B.; C.A.P.B., R.A. and S.O. by Riksbankens Jubileumsfond (P18-0782:1), to S.O.; C.A.P.B. and S.O. by the Swedish Research Council (2019-00244), to S.O.; G.G., N.W. and D.J.B. by the NIA/NIH (R24-AG065184 and R01-AG042568), to D.J.B.; D.J.B. by the NIA/NIH (R56-AG058726), to T. Galama; T.T.M., K.P.H., and E.M.T.-D. by the NIH/NICHD R01-HD083613 to E.M.T.-D. and R01-HD092548 to K.P.H.; P.T. by the NIA/NIMH (R01-MH101244-02 and U01-MH109539-02), to B. Neale. The study was also supported by the NIA/NIH (K99-AG062787-01, P.T.); Netherlands Organisation for Scientific Research VENI (016.Veni.198.058, A.O.); the Swedish Research Council (421-2013-1061, M.J.); the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-152, J.P.B.); the Social Sciences and Humanities Research Council of Canada (J.P.B.); the European Union (MP1GI18418R, T.E.); the Estonian Research Council (PRG1291, T.E.); the National Health and Medical Research Council (GNT113400, P.M.V.); and the Australian Research Council (P.M.V.). The authors thank the following consortia for sharing GWAS summary statistics: Reproductive Genetics (ReproGen) Consortium for age at first menses; Genetics of Personality Consortium (GPC) for neuroticism, extraversion and openness; Psychiatric Genomics Consortium (PGC) for ADHD and depressive symptoms; Tobacco and Alcohol Genetics (TAG) Consortium for cigarettes per day and ever smoker; International Genomics of Alzheimer’s Project (IGAP) for Alzheimer’s disease; GWAS & Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) for cigarettes per day, ever smoker and drinks per week; Genetic Investigation of Anthropometric Traits (GIANT) Consortium for height and body mass index; and Cognitive Genomics (COGENT) Consortium for cognitive performance. The authors thank the Neale Lab for making UKB GWAS results available for asthma, cannabis use, COPD, hayfever, life satisfaction (family, finance, friend and work), loneliness, migraine, nearsightedness, number ever born (men, women), religious attendance, self-rated health and subjective well-being. The authors thank the research participants and employees of 23andMe for making this work possible. A full list of acknowledgements is provided in the Supplementary Note.
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Contributions
D.J.B., D.C., A.O., and P.T. designed and oversaw the study. A.O. supervised all analyses and led the writing of the manuscript. J.B. was the lead analyst, responsible for the GWAS and MTAG analyses, quality control of GWAS summary statistics and the PGI validation analyses. C.A.P.B. was responsible for quality control of genotype data and the construction of PGIs. G.G., N.W., H.J. and M.B. assisted with analyses. G.G. conducted the illustrative application and wrote the Python code. N.W. designed and implemented the algorithm used to generate Fig. 1. R.K.L. ran a meta-analysis of general risk tolerance omitting validation datasets. P.T. derived the measurement-error-correction estimator. A.K., D.A.H. and the 23andMe Research Group conducted genome-wide association analyses for 23andMe. The following authors shared genotype data to enable dataset participation in the Repository: K.M.H. for Add Health; D.W.B., A.C., D.L.C., T.E.M., R.P., K.S. and B.S.W. for Dunedin and E-Risk; A.S. and O.A. for ELSA; L.M. and T.E. for ECGUT; W.G.I. and M.M. for MCTFR; R.A. and P.K.E.M. for STR; T.T.M., K.P.H. and E.M.T.-D. for TTP; and P.H. and J.F. for WLS. More details about dataset-level contributions are given in the Supplementary Note. D.W.B. conducted PGI validations analyses in Dunedin and E-Risk. R.A., A.Y., J.P.B., P.K., S.O., M.J., P.M.V., M.N.M., and D.L. contributed to study design. All authors contributed to and critically reviewed the manuscript. D.J.B., A.O., D.C. and P.T. made especially major contributions to the writing and editing.
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D.A.H., A.K. and members of the 23andMe Research Team are current or former employees of 23andMe, Inc. and hold stock or stock options in 23andMe. The authors declare no other competing interests.
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Supplementary Methods, Supplementary Note and Supplementary Fig. 1.
Supplementary data 1
Source data for Becker et al. Supplementary Fig. 1 (predictive power of repository multi-trait PGIs).
Supplementary Table 1
Supplementary Tables 1–13 for Becker et al.
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Statistical source data.
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Statistical source data.
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Becker, J., Burik, C.A.P., Goldman, G. et al. Resource profile and user guide of the Polygenic Index Repository. Nat Hum Behav 5, 1744–1758 (2021). https://doi.org/10.1038/s41562-021-01119-3
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DOI: https://doi.org/10.1038/s41562-021-01119-3
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