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Publication Detail

Title: Phenotype validation in electronic health records based genetic association studies.

Authors: Wang, Lu; Damrauer, Scott M; Zhang, Hong; Zhang, Alan X; Xiao, Rui; Moore, Jason H; Chen, Jinbo

Published In Genet Epidemiol, (2017 12)

Abstract: The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation. We develop a closed-form maximum-likelihood estimator for the odds ratio parameters and a score statistic for testing genetic association using the combined validated and error-prone EHR-derived phenotype data, and assess the extent of power improvement provided by this approach. Compared with case-control sampling based only on EHR-derived phenotype data, our genotype stratified strategy maintains nominal type I error rates, and result in higher power for detecting associations. It also corrects the bias in the odds ratio parameter estimates, and reduces the corresponding variance especially when the minor allele frequency is small.

PubMed ID: 29023970 Exiting the NIEHS site

MeSH Terms: Electronic Health Records; Gene Frequency; Genome-Wide Association Study*; Genotype; Humans; Models, Genetic*; Odds Ratio; Phenotype; Polymorphism, Single Nucleotide

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