The UCLA ATLAS biobank was utilized to integrate genetic data with electronic health records from individuals representing five continental and 36 fine-scale ancestries, identifying genotype-phenotype associations and highlighting populations at risk for disease. Computational predictors mitigated European bias in clinical variant curation predictors, uncovering ancestry-specific disease-gene links, and genetic factors influencing semaglutide-induced weight loss were identified. These findings demonstrate the value of well-curated, ancestrally diverse biobanks in advancing precision medicine.
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Advancing precision health discovery in a genetically diverse health system – Research
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