Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
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.
Source: The American Journal of Human Genetics - Category: Genetics & Stem Cells Authors: 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, Tags: Article Source Type: research
More News: Genetics | Psychiatry | Schizophrenia | Statistics | Training | Universities & Medical Training