Inside Precision Medicine January 26, 2024
A new statistical tool developed at University of Chicago makes it quicker and easier to find genetic variants underlying disease. The tool, described in a paper today in Nature Genetics, combines data from genome wide association studies (GWAS) and predictions of genetic expression to better identify disease-causal variants. Causal-transcriptome-wide association studies (cTWAS) uses a Bayesian multiple regression model and can account for multiple genes and variants at once.
GWAS is often used to associate genes with human traits, including common diseases. But most human diseases are not caused by a single genetic variation, but rather are the result of a complex interaction of multiple genes, environmental factors, and other variables. GWAS, however, only identifies association, not causality. In a typical...
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