Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis
Year: 2024
Type: preprint
Abstract: ABSTRACT The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.
Source: medRxiv
Institutions: Vanderbilt University, Vanderbilt University Medical Center
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: Vanderbilt University, Vanderbilt University Medical Center, Georgia Clinical and Translational Science Alliance, National Institutes of Health, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of Neurological Disorders and Stroke, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G1531600799, Genomic and Phenomic Architecture of Heart Failure, G1766209448, Network Analysis for a Data-Driven Approach to Cancer Care, G2103760183, Understanding the genetic risk underlying racial disparities in uterine fibroids, G2615776415, G2683268942, G3395468139, G3619590958, G364208108, G3791753988, G3938985443, G4049315607, Automated Storage and Retrieval of Biological Systems, G4395870004, G4410787187, G560889077, G5679716143, G5717004637, Genetic Epidemiology of Multiple Sclerosis, G6589054092, G659731888, G7163342664, The Vanderbilt Institute for Clinical and Translational Research (VICTR), G8098630230, Epidemiologic Architecture for Genes Linked to Environment (EAGLE), G8332127685, G9000008449, G9000022789, G9000043901, G9000054231, G9000090229, G9000167145, G9000263428
OpenAlex ID: W4394963838