Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

American journal of human genetics
Authors
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.

Year of Publication
2015
Journal
American journal of human genetics
Volume
97
Issue
4
Pages
576-92
Date Published
2015/10/01
ISSN
0002-9297
URL
DOI
10.1016/j.ajhg.2015.09.001
PubMed ID
26430803
Links