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oai:repisalud.isciii.es:20.500.12105/79012024-11-29T16:33:20Zcom_20.500.12105_2173com_20.500.12105_2051col_20.500.12105_19597

00925njm 22002777a 4500 dc Shi, Jianxin author Park, Ju-Hyun author Duan, Jubao author Berndt, Sonja T author Moy, Winton author Yu, Kai author Song, Lei author Wheeler, William author Hua, Xing author Silverman, Debra author Garcia-Closas, Montserrat author Hsiung, Chao Agnes author Figueroa, Jonine D author Cortessis, Victoria K author Malats, Nuria author Karagas, Margaret R author Vineis, Paolo author Chang, I-Shou author Lin, Dongxin author Zhou, Baosen author Seow, Adeline author Matsuo, Keitaro author Hong, Yun-Chul author Caporaso, Neil E author Wolpin, Brian author Jacobs, Eric author Petersen, Gloria M author Klein, Alison P author Li, Donghui author Risch, Harvey author Sanders, Alan R author Hsu, Li author Schoen, Robert E author Brenner, Hermann author Stolzenberg-Solomon, Rachael author Gejman, Pablo author Lan, Qing author Rothman, Nathaniel author Amundadottir, Laufey T author Landi, Maria Teresa author Levinson, Douglas F author Chanock, Stephen J author Chatterjee, Nilanjan author 2016-12 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. PLoS Genet. 2016;12(12):e1006493. 10.1371/journal.pgen.1006493 1553-7404 1553-7404 PLoS genetics 28036406 http://hdl.handle.net/20.500.12105/7901 Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data