Modeling linkage disequilibrium increases accuracy of polygenic risk scores

Vilhjálmsson, Bjarni J. and Yang, Jian and Finucane, Hilary K. and Gusev, Alexander and Lindström, Sara and Ripke, Stephan and Genovese, Giulio and Loh, Po-Ru and Bhatia, Gaurav and Do, Ron and Hayeck, Tristan and Won, Hong-Hee and Kathiresan, Sekar and Pato, Michele and Pato, Carlos and Tamimi, Rulla and Stahl, Eli and Zaitlen, Noah and Pasaniuc, Bogdan and Belbin, Gillian and Kenny, Eimear E. and Schierup, Mikkel H. and De Jager, Philip and Patsopoulos, Nikolaos A. and McCarroll, Steve and Daly, Mark and Purcell, Shaun and Chasman, Daniel and Neale, Benjamin and Goddard, Michael and Visscher, Peter M. and Kraft, Peter and Patterson, Nick and Price, Alkes L. and Knight, Jo (2015) Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics, 97 (4). pp. 576-592. ISSN 0002-9297

PDF (final)
final.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (627kB)


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.

Item Type:
Journal Article
Journal or Publication Title:
American Journal of Human Genetics
Additional Information:
This is the author’s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
23 Jun 2016 12:48
Last Modified:
19 Sep 2020 04:03