Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.

Author(s): Vilhjálmsson BJ,  Yang J,  Finucane HK,  Gusev A,  Lindström S,  Ripke S,  Genovese G,  Loh PR,  Bhatia G,  Do R,  Hayeck T,  Won HH,  Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study,  Kathiresan S,  Pato M,  Pato C,  Tamimi R,  Stahl E,  Zaitlen N,  Pasaniuc B,  Belbin G,  Kenny EE,  Schierup MH,  De Jager P,  Patsopoulos NA,  McCarroll S,  Daly M,  Purcell S,  Chasman D,  Neale B,  Goddard M,  Visscher PM,  Kraft P,  Patterson N,  Price AL

Journal: Am J Hum Genet

Date: 2015 Oct 1

Major Program(s) or Research Group(s): PLCO

PubMed ID: 26430803

PMC ID: PMC4596916

Abstract: 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 linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.