Publication Date
2015
Journal Title
Am J Hum Genet
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.
Volume Number
97
Issue Number
4
Pages
576-92
Document Type
Article
EPub Date
2015/10/03
Status
Faculty
Facility
School of Medicine
Primary Department
Psychiatry
Additional Departments
Molecular Medicine
PMID
DOI
10.1016/j.ajhg.2015.09.001