Bjarni Vilhjalmsson , Jian Yang , Hilary Kiyo Finucane , Alexander Gusev , Sara Lindstrom , Stephan Ripke , Giulio Genovese , Po-Ru Loh , Gaurav Bhatia , Ron Do , Tristian Hayeck , Hong-Hee Won , Schizophrenia Working Group of the Psychiatric Genomics Consortium , the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study , Sekar Kathiresan , Michele Pato , Carlos Pato , Rulla Tamimi , Eli Stahl , Noah Zaitlen , Bogdan Pasaniuc , Mikkel Schierup , Phillip De Jager , Nikolaos Patsopoulos , Steven A McCarroll , Mark Daly , Shaun Purcell , Daniel Chasman , Benjamin Neale , Mike Goddard , Peter M Visscher , Peter Kraft , Nick J Patterson , Alkes L Price
Polygenic risk scores have shown great promise in predicting complex disease risk, and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the pruning/thresholding approach, particularly at large sample sizes. Accordingly, prediction R2 increased from 20.1% to 25.3% in a large schizophrenia data set and from 9.8% to 12.0% in a large multiple sclerosis data set. A similar relative improvement in accuracy was observed for three additional large disease data sets and when predicting in non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.