Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Psychosis Endophenotypes International Consortium, Wellcome Trust Case Control Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study, Hereditary Breast and Ovarian Cancer Research Group Netherlands (HEBON)

研究成果: Article査読

382 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)576-592
ページ数17
ジャーナルAmerican Journal of Human Genetics
97
4
DOI
出版ステータスPublished - 2015

All Science Journal Classification (ASJC) codes

  • 遺伝学
  • 遺伝学(臨床)

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