|Title:||Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores|
|Journal:||Am J Hum Genet|
|Alternate Journal:||American journal of human genetics|
|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 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.|
Am J Hum Genet. 2015 Oct 1
97(4):576-92. doi: 10.1016/j.ajhg.2015.09.001.
|Authors Address:||Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA|
Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
Bioinformatics Research Centre, Aarhus University, 8000 Aarhus, Denmark. Electronic address: email@example.com.
|Appears in Collections:||2015|
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