Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni J Vilhjálmsson, Jian Yang, Hilary K Finucane, Alexander Gusev, Sara Lindström, Stephan Ripke, Giulio Genovese, Po-Ru Loh, Gaurav Bhatia, Ron Do, Tristan Hayeck, Hong-Hee Won, Sekar Kathiresan, Michele Pato, Carlos Pato, Rulla Tamimi, Eli Stahl, Noah Zaitlen, Bogdan Pasaniuc, Gillian BelbinEimear E Kenny, Mikkel H Schierup, Philip De Jager, Nikolaos A Patsopoulos, Steve McCarroll, Mark Daly, Shaun Purcell, Daniel Chasman, Benjamin Neale, Michael Goddard, Peter M Visscher, Peter Kraft, Nick Patterson, Alkes L Price, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Francis O'Neill

Research output: Contribution to journalArticlepeer-review

419 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)576-92
Number of pages17
JournalThe American Journal of Human Genetics
Issue number4
Publication statusPublished - 01 Oct 2015


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