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 R2from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2to 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.
| Original language | English (US) |
|---|---|
| Article number | e1006493 |
| Journal | PLoS Genetics |
| Volume | 12 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 1 2016 |
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ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Molecular Biology
- Genetics
- Genetics(clinical)
- Cancer Research
Cite this
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data. / MGS (Molecular Genetics of Schizophrenia) GWAS Consortium; GECCO (The Genetics and Epidemiology of Colorectal Cancer Consortium); The GAME-ON/TRICL (Transdisciplinary Research in Cancer of the Lung) GWAS Consortium; PRACTICAL (PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations) Consortium; PanScan Consortium; The GAME-ON/ELLIPSE Consortium.
In: PLoS Genetics, Vol. 12, No. 12, e1006493, 01.12.2016.Research output: Contribution to journal › Article
}
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
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 Consortium
AU - The GAME-ON/ELLIPSE Consortium
AU - Shi, Jianxin
AU - Park, Ju Hyun
AU - Duan, Jubao
AU - Berndt, Sonja T.
AU - Moy, Winton
AU - Yu, Kai
AU - Song, Lei
AU - Wheeler, William
AU - Hua, Xing
AU - Silverman, Debra
AU - Garcia-Closas, Montserrat
AU - Hsiung, Chao Agnes
AU - Figueroa, Jonine D.
AU - Cortessis, Victoria K.
AU - Malats, Núria
AU - Karagas, Margaret R.
AU - Vineis, Paolo
AU - Chang, I. Shou
AU - Lin, Dongxin
AU - Zhou, Baosen
AU - Seow, Adeline
AU - Matsuo, Keitaro
AU - Hong, Yun Chul
AU - Caporaso, Neil E.
AU - Wolpin, Brian
AU - Jacobs, Eric
AU - Petersen, Gloria M
AU - Klein, Alison P.
AU - Li, Donghui
AU - Risch, Harvey
AU - Sanders, Alan R.
AU - Hsu, Li
AU - Schoen, Robert E.
AU - Brenner, Hermann
AU - Stolzenberg-Solomon, Rachael
AU - Gejman, Pablo
AU - Lan, Qing
AU - Rothman, Nathaniel
AU - Amundadottir, Laufey T.
AU - Landi, Maria Teresa
AU - Levinson, Douglas F.
AU - Chanock, Stephen J.
AU - Chatterjee, Nilanjan
PY - 2016/12/1
Y1 - 2016/12/1
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 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 R2from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2to 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.
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 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 R2from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2to 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.
UR - http://www.scopus.com/inward/record.url?scp=85007574079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007574079&partnerID=8YFLogxK
U2 - 10.1371/journal.pgen.1006493
DO - 10.1371/journal.pgen.1006493
M3 - Article
C2 - 28036406
AN - SCOPUS:85007574079
VL - 12
JO - PLoS Genetics
JF - PLoS Genetics
SN - 1553-7390
IS - 12
M1 - e1006493
ER -