Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Modeling linkage disequilibrium increases accuracy of polygenic risk scores

Vilhjálmsson, Bjarni J., Yang, Jian, Finucane, Hilary K., Gusev, Alexander, Lindström, 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, Patsopoulos, Nikolaos A., McCarroll, Steve, Daly, Mark, Purcell, Shaun, Chasman, Daniel, Neale, Benjamin, Goddard, Michael, Visscher, Peter M., Kraft, Peter, Patterson, Nick, Price, Alkes L., Holmans, Peter Alan ORCID:, Escott-Price, Valentina ORCID:, Hamshere, Marian L. ORCID: and O'Donovan, Michael Conlon ORCID: 2015. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. American Journal of Human Genetics 97 (4) , pp. 576-592. 10.1016/j.ajhg.2015.09.001

Full text not available from this repository.


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: Article
Date Type: Publication
Status: Published
Schools: Medicine
Advanced Research Computing @ Cardiff (ARCCA)
MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects: R Medicine > R Medicine (General)
Additional Information: Peter Holmans, Valentina Escott-Price, Marian Hamshere and Michael O'Donovan are collaborators on this article.
Publisher: Elsevier (Cell Press)
ISSN: 0002-9297
Date of Acceptance: 1 September 2015
Last Modified: 31 Oct 2022 10:40

Citation Data

Cited 588 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item