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Whole-genome and exome sequencing are standard tools for diagnosing rare and other genetic disorders. However, interpreting the tens of thousands of variants identified in these tests remains a major challenge. This article explores how linking gene expression patterns across multiple tissues to disease phenotypes can aid in identifying disease-causing variants.
To test this hypothesis, classifiers are developed to learn associations between tissue-specific gene expression and disease phenotypes. Using Genotype-Tissue Expression (GTEx) data alongside disease-agnostic variant prioritization methods (CADD or MetaSVM) consistently improves classification accuracy. This approach highlights a previously overlooked way to leverage existing expression data for clinical diagnostics and paves the way for integrating other functional genomic datasets into variant prioritization. Read the full article here.