TY  - JOUR
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, Nuria
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
DO  - 10.1371/journal.pgen.1006493
SN  - 1553-7404
UR  - http://hdl.handle.net/20.500.12105/7901
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...
LA  - eng
PB  - Public Library of Science (PLOS)
KW  - Algorithms
KW  - Computer Simulation
KW  - Genome-Wide Association Study
KW  - Humans
KW  - Linkage Disequilibrium
KW  - Multifactorial Inheritance
KW  - Polymorphism, Single Nucleotide
KW  - Risk Factors
KW  - Genetic Predisposition to Disease
KW  - Models, Genetic
TI  - Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data
TY  - journal article
ER  -