TY - JOUR T1 - Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores JF - bioRxiv DO - 10.1101/015859 SP - 015859 AU - Bjarni J. Vilhjálmsson AU - Jian Yang AU - Hilary Finucane AU - Alexander Gusev AU - Sara Lindström AU - Stephan Ripke AU - Giulio Genovese AU - Po-Ru Loh AU - Gaurav Bhatia AU - Ron Do AU - Tristan Hayeck AU - Hong-Hee Won AU - Schizophrenia Working Group of the Psychiatric Genomics Consortium AU - the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study AU - Sekar Kathiresan AU - Michele Pato AU - Carlos Pato AU - Rulla Tamimi AU - Eli Stahl AU - Noah Zaitlen AU - Bogdan Pasaniuc AU - Mikkel H. Schierup AU - Philip De Jager AU - Nikolaos A. Patsopoulos AU - Steve McCarroll AU - Mark Daly AU - Shaun Purcell AU - Daniel Chasman AU - Benjamin Neale AU - Michael Goddard AU - Peter Visscher AU - Peter Kraft AU - Nick Patterson AU - Alkes L. Price Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/03/01/015859.abstract N2 - 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. ER -