TY - JOUR T1 - Winner’s curse correction and variable thresholding improve performance of polygenic risk modeling based on genome-wide association study summary-level data JF - bioRxiv DO - 10.1101/034082 SP - 034082 AU - Jianxin Shi AU - Ju-Hyun Park AU - Jubao Duan AU - Sonja Berndt AU - Winton Moy AU - Kai Yu AU - Lei Song AU - William Wheeler AU - Xing Hua AU - Debra Silverman AU - Montserrat Garcia-Closas AU - Chao Agnes Hsiung AU - Jonine D Figueroa AU - Victoria K Cortessis AU - Núria Malats AU - Margaret R Karagas AU - Paolo Vineis AU - I-Shou Chang AU - Dongxin Lin AU - Baosen Zhou AU - Adeline Seow AU - Keitaro Matsuo AU - Yun-Chul Hong AU - Neil E. Caporaso AU - Brian Wolpin AU - Eric Jacobs AU - Gloria Petersen AU - Alison P. Klein AU - Donghui Li AU - Harvey Risch AU - Alan R. Sanders AU - Li Hsu AU - Robert E. Schoen AU - Hermann Brenner AU - MGS (Molecular Genetics of Schizophrenia) GWAS Consortium AU - GECCO (The Genetics and Epidemiology of Colorectal Cancer Consortium) AU - The GAME-ON/TRICL (Transdisciplinary Research in Cancer of the Lung) GWAS Consortium AU - PRACTICAL (PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations) Consortium AU - PanScan and PanC4 Consortium AU - The GAMEON/ELLIPSE Consortium AU - Rachael Stolzenberg-Solomon AU - Pablo Gejman AU - Qing Lan AU - Nathaniel Rothman AU - Laufey T. Amundadottir AU - Maria Teresa Landi AU - Douglas F. Levinson AU - Stephen J. Chanock AU - Nilanjan Chatterjee Y1 - 2016/01/01 UR - http://biorxiv.org/content/early/2016/01/10/034082.abstract N2 - 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 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=2χ10-5). 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. ER -