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A new statistical tool developed by researchers at the University of Chicago improves the ability to find genetic variants that cause disease.
New tool integrates GWAS data with genetic expression predictions, enhancing accurate identification of disease-related genes and variants.
<p>A new statistical tool developed by researchers at the University of Chicago improves the ability to find genetic variants that cause disease. The tool, described in a new paper published January 26, 2024, in <em>Nature Genetics</em>, combines data from genome wide association studies (GWAS) and predictions of genetic expression to limit the number of false positives and more accurately identify causal genes and variants for a disease.</p>
Statistical model developed by University of Chicago researchers incorporates genome and gene expression data to reliably identify causal genes. A new statistical tool developed by researchers at the University of Chicago improves the ability to find genetic variants that cause disease. The tool,