Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Vilhjalmsson, Bjarni J., Yang, Jian, Finucane, Hilary K., Gusev, Alexander, Lindstrom, Sara, Ripke, Stephan, Genovese, Giulio, Loh, Po-Ru, Bhatia, Gaurav, Do, Ron, Hayeck, Tristan, Won, Hong-Hee, Kathiresan, Sekar, Pato, Michele, Pato, Carlos, Tamimi, Rulla, Stahl, Eli, Zaitlen, Noah, Pasaniuc, Bogdan, Belbin, Gillian, Kenny, Eimear E., Schierup, Mikkel H., De Jager, Philip, Patsopouos, Nikolaos A., Mc Carroll, Steve, Daly, Mark, Purce, Shaun, Chasman, Daniel, Neale, Benjamin, Goddard, Michael, Visscher, Peter M., Kraft, Peter, Patterson, Nick and Price, Alkes L. (2015) Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. American Journal of Human Genetics, 97 4: 576-592. doi:10.1016/j.ajhg.2015.09.001

Author Vilhjalmsson, Bjarni J.
Yang, Jian
Finucane, Hilary K.
Gusev, Alexander
Lindstrom, Sara
Ripke, Stephan
Genovese, Giulio
Loh, Po-Ru
Bhatia, Gaurav
Do, Ron
Hayeck, Tristan
Won, Hong-Hee
Kathiresan, Sekar
Pato, Michele
Pato, Carlos
Tamimi, Rulla
Stahl, Eli
Zaitlen, Noah
Pasaniuc, Bogdan
Belbin, Gillian
Kenny, Eimear E.
Schierup, Mikkel H.
De Jager, Philip
Patsopouos, Nikolaos A.
Mc Carroll, Steve
Daly, Mark
Purce, Shaun
Chasman, Daniel
Neale, Benjamin
Goddard, Michael
Visscher, Peter M.
Kraft, Peter
Patterson, Nick
Price, Alkes L.
Title Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Journal name American Journal of Human Genetics   Check publisher's open access policy
ISSN 0002-9297
Publication date 2015-10-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.ajhg.2015.09.001
Open Access Status Not Open Access
Volume 97
Issue 4
Start page 576
End page 592
Total pages 17
Place of publication Cambridge, MA United States
Publisher Cell Press
Language eng
Formatted 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
Keyword Genome wide association
Analysis Identifies 13
Breast Cancer Risk
Susceptibility Loci
Complex Traits
Multiple sclerosis
Genetic Risk
Mixed model
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
UQ Diamantina Institute Publications
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Citation counts: TR Web of Science Citation Count  Cited 39 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 40 times in Scopus Article | Citations
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