Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

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Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data

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Title: Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data
Author: Shi, Jianxin; Park, Ju-Hyun; Duan, Jubao; Berndt, Sonja T.; Moy, Winton; Yu, Kai; Song, Lei; Wheeler, William; Hua, Xing; Silverman, Debra; Garcia-Closas, Montserrat; Hsiung, Chao Agnes; Figueroa, Jonine D.; Cortessis, Victoria K.; Malats, Núria; Karagas, Margaret R.; Vineis, Paolo; Chang, I-Shou; Lin, Dongxin; Zhou, Baosen; Seow, Adeline; Matsuo, Keitaro; Hong, Yun-Chul; Caporaso, Neil E.; Wolpin, Brian; Jacobs, Eric; Petersen, Gloria M.; Klein, Alison P.; Li, Donghui; Risch, Harvey; Sanders, Alan R.; Hsu, Li; Schoen, Robert E.; Brenner, Hermann; Stolzenberg-Solomon, Rachael; Gejman, Pablo; Lan, Qing; Rothman, Nathaniel; Amundadottir, Laufey T.; Landi, Maria Teresa; Levinson, Douglas F.; Chanock, Stephen J.; Chatterjee, Nilanjan

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Citation: Shi, J., J. Park, J. Duan, S. T. Berndt, W. Moy, K. Yu, L. Song, et al. 2016. “Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.” PLoS Genetics 12 (12): e1006493. doi:10.1371/journal.pgen.1006493. http://dx.doi.org/10.1371/journal.pgen.1006493.
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Abstract: Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner’s-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner’s curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25–50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner’s curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10−5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.
Published Version: doi:10.1371/journal.pgen.1006493
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5201242/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:30370996
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