Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11130%2F15%3A10316264" target="_blank" >RIV/00216208:11130/15:10316264 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00064203:_____/15:10316264

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ajhg.2015.09.001" target="_blank" >http://dx.doi.org/10.1016/j.ajhg.2015.09.001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ajhg.2015.09.001" target="_blank" >10.1016/j.ajhg.2015.09.001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Popis výsledku v původním jazyce

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 pruningand 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 LDinformation 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 advanta

Název v anglickém jazyce

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Popis výsledku anglicky

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 pruningand 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 LDinformation 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 advanta

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EB - Genetika a molekulární biologie

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

American Journal of Human Genetics

ISSN

0002-9297

e-ISSN

—

Svazek periodika

97

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

576-592

Kód UT WoS článku

000362617300008

EID výsledku v databázi Scopus

2-s2.0-84952665106