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SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics

2018-06-29

Abstract excerpt

<h4>Motivation</h4> Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training data sets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenoty...

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Literature Corpus work
180ddbd2-7f42-516c-a19c-140229ea148b
DOI
10.1101/359463
Open publication

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SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statisticsDOI 10.1101/359463
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