Simplifying causal gene identification in GWAS loci.

Abstract excerpt

Genome-wide association studies (GWAS) help to identify disease-linked genetic variants, but pinpointing the most likely causal genes in GWAS loci remains challenging. Existing GWAS gene prioritization tools are powerful but often use complex black box models trained on datasets containing biases. Here, we used a data-driven approach to construct a truth set of causal genes in 200 GWAS loci. We found that a...

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