RT Journal Article
SR Electronic
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
FD Cold Spring Harbor Laboratory
SP 034082
DO 10.1101/034082
A1 Jianxin Shi
A1 Ju-Hyun Park
A1 Jubao Duan
A1 Sonja Berndt
A1 Winton Moy
A1 Kai Yu
A1 Lei Song
A1 William Wheeler
A1 Xing Hua
A1 Debra Silverman
A1 Montserrat Garcia-Closas
A1 Chao Agnes Hsiung
A1 Jonine D Figueroa
A1 Victoria K Cortessis
A1 Núria Malats
A1 Margaret R Karagas
A1 Paolo Vineis
A1 I-Shou Chang
A1 Dongxin Lin
A1 Baosen Zhou
A1 Adeline Seow
A1 Keitaro Matsuo
A1 Yun-Chul Hong
A1 Neil E. Caporaso
A1 Brian Wolpin
A1 Eric Jacobs
A1 Gloria Petersen
A1 Alison P. Klein
A1 Donghui Li
A1 Harvey Risch
A1 Alan R. Sanders
A1 Li Hsu
A1 Robert E. Schoen
A1 Hermann Brenner
A1 MGS (Molecular Genetics of Schizophrenia) GWAS Consortium
A1 GECCO (The Genetics and Epidemiology of Colorectal Cancer Consortium)
A1 The GAME-ON/TRICL (Transdisciplinary Research in Cancer of the Lung) GWAS Consortium
A1 PRACTICAL (PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations) Consortium
A1 PanScan and PanC4 Consortium
A1 The GAMEON/ELLIPSE Consortium
A1 Rachael Stolzenberg-Solomon
A1 Pablo Gejman
A1 Qing Lan
A1 Nathaniel Rothman
A1 Laufey T. Amundadottir
A1 Maria Teresa Landi
A1 Douglas F. Levinson
A1 Stephen J. Chanock
A1 Nilanjan Chatterjee
YR 2016
UL http://biorxiv.org/content/early/2016/01/10/034082.abstract
AB 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.