Abstract
Genome-wide association studies depend on accurate ascertainment of patient phenotype. However, phenotyping is difficult, and it is often treated as an afterthought in these studies because of the expense involved. Electronic health records (EHRs) may provide higher fidelity phenotypes for genomic research than other sources such as administrative data. We used whole genome association models to evaluate different EHR and administrative data-based phenotyping methods in a cohort of 16,858 Caucasian subjects for type 1 diabetes mellitus, type 2 diabetes mellitus, coronary artery disease and breast cancer. For each disease, we trained and evaluated polygenic models using three different phenotype definitions: phenotypes derived from billing data, the clinical problem list, or a curated phenotyping algorithm. We observed that for these diseases, the curated phenotype outperformed the problem list, and the problem list outperformed administrative billing data. This suggests that using advanced EHR-derived phenotypes can further increase the power of genome-wide association studies.
Highlights
A fundamental goal of precision medicine is to use genomic data to explain and predict health status
We applied a series of steps including linkage disequilibrium (LD) pruning to remove the low-quality SNPs before obtained 472,811 autosomal SNPs
Four complex diseases with different genetic heritabilities were chosen for this study: type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), coronary artery disease (CAD) and breast cancer (BC)
Summary
A fundamental goal of precision medicine is to use genomic data to explain and predict health status. Studies have shown that many human complex disorders are driven by genomic factors[1,2,3]. In light of these findings, researchers are trying to further address the causal relationship between genetic variations and specific diseases phenotypes. Many genome-wide association studies (GWAS) use self-reported binary phenotypic descriptions or administrative data to establish phenotypes[10,11]. Prior research has shown that self-reported disease status and administrative data, such as billing data, are often inaccurate[12,13]. Compared with traditional self-reported phenotypes, EHR data can efficiently create standardized phenotypes with refinable definitions in large cohort studies.
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