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    Rights statement: This is the author’s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001

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Modeling linkage disequilibrium increases accuracy of polygenic risk scores

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  • Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study
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<mark>Journal publication date</mark>1/10/2015
<mark>Journal</mark>American Journal of Human Genetics
Issue number4
Volume97
Number of pages17
Pages (from-to)576-592
Publication statusPublished
Original languageEnglish

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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in American Journal of Human Genetics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in American Journal of Human Genetics, 97, 4, 2015 DOI: 10.1016/j.ajhg.2015.09.001