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, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE), Sekar Kathiresan, Michele T. Pato, Carlos N. Pato, Rulla Tamimi, Eli A Stahl, Noah ZaitlenBogdan Pasaniuc, Gillian Belbin, Eimear E. Kenny, Mikkel H. Schierup, Philip Laurence De Jager, Nikolaos A Patsopoulos, Steve McCarroll, Mark J Daly, Shaun M. Purcell, Daniel I Chasman, Benjamin M Neale, Michael Goddard, Peter M Visscher, Peter Kraft, Nick Patterson, Alkes L. Price

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221 Citations (Scopus)

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 R2 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)576-592
Number of pages17
JournalAmerican Journal of Human Genetics
Volume97
Issue number4
DOIs
Publication statusPublished - 2015

Cite this

Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P. R., Bhatia, G., Do, R., Hayeck, T., Won, H. H., Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE), Kathiresan, S., Pato, M. T., Pato, C. N., Tamimi, R., Stahl, E. A., ... Price, A. L. (2015). Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. American Journal of Human Genetics, 97(4), 576-592. https://doi.org/10.1016/j.ajhg.2015.09.001