Imputation of race and ethnicity categories using genetic ancestry from real-world genomic testing data

This study introduces two methods-one heuristic and the other machine learning-based-to impute race and ethnicity from genetic ancestry using tumor profiling data. Analyzing de-identified data from over 100,000 cancer patients sequenced with the Tempus xT panel, we demonstrate that both methods outperform existing geolocation and surname-based methods, with the machine learning approach achieving high recall (range: 0.859-0.993) and precision (range: 0.932-0.981) across four mutually exclusive race and ethnicity categories. This work presents a novel pathway to enhance RWD utility in studying racial disparities in healthcare.PMID:38160297

Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Brooke Rhead Paige E Haffener Yannick Pouliot Francisco M De La Vega Source Type: research