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University of South Australia

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Am. J. Hum. Genet., 2023 Feb 2;110(2):349-358 | Significance tests for R2 of out-of-sample prediction using polygenic scores

Md. Moksedul Momin ^1,2,3,4, Soohyun Lee ⁵, Naomi R. Wray ^6,7, S. Hong Lee^1,2,4

¹Australian Centre for Precision Health, University of South Australia, Adelaide, SA 5000, Australia
²UniSA Allied Health and Human Performance, University of South Australia, Adelaide, SA 5000, Australia
³Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram 4225, Bangladesh
⁴South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, SA 5000, Australia
⁵Division of Animal Breeding and Genetics, National Institute of Animal Science (NIAS), Cheonan, South Korea
⁶Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
⁷Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia

Corresponding authors: Md. Moksedul Momin, S. Hong Lee

Abstract

The coefficient of determination (R²) is a well-established measure to indicate the predictive ability of polygenic scores (PGSs). However, the sampling variance of R² is rarely considered so that 95% confidence intervals (CI) are not usually reported. Moreover, when comparisons are made between PGSs based on different discovery samples, the sampling covariance of R² is required to test the difference between them. Here, we show how to estimate the variance and covariance of R² values to assess the 95% CI and p value of the R² difference. We apply this approach to real data calculating PGSs in 28,880 European participants derived from UK Biobank (UKBB) and Biobank Japan (BBJ) GWAS summary statistics for cholesterol and BMI. We quantify the significantly higher predictive ability of UKBB PGSs compared to BBJ PGSs (p value 7.6e-31 for cholesterol and 1.4e-50 for BMI). A joint model of UKBB and BBJ PGSs significantly improves the predictive ability, compared to a model of UKBB PGS only (p value 3.5e-05 for cholesterol and 1.3e-28 for BMI). We also show that the predictive ability of regulatory SNPs is significantly enriched over non-regulatory SNPs for cholesterol (p value 8.9e-26 for UKBB and 3.8e-17 for BBJ). We suggest that the proposed approach (available in R package r2redux) should be used to test the statistical significance of difference between pairs of PGSs, which may help to draw a correct conclusion about the comparative predictive ability of PGSs.

논문정보

형식Research article
게재일2023년 02월 (BRIC 등록일 2023-03-17)
연구진국내+국외 연구진
분야 생명과학 > 유전학

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