cTWAS: University of Chicago's Innovative Software Revolutionizes Disease Gene Detection

cTWAS: University of Chicago’s Innovative Software Revolutionizes Disease Gene Detection

Scientists at the University of Chicago have developed some slick new software that can pinpoint disease-causing genes with much greater accuracy. It’s called cTWAS, and it outperforms existing methods by looking at groups of genes together, rather than just one at a time.

See, a person’s risk of getting sick is influenced by a mix of factors – their environment, diet, mental health, and unfortunately, some key genes they’re born with. Doctors would love to ID those troublemaker genes so they could head off diseases, or at least minimize their impacts on patients.

The catch is, looking at single genes in isolation results in a bunch of false positives, according to the researchers. But cTWAS cleverly analyzes bunches of related genes and gene variants at the same time. This way, it can reliably zero in on the specific genes actually driving disease.

Here’s how it works: cTWAS combines data from two existing techniques – GWAS and eQTL mapping – using some sophisticated statistics. While powerful individually, together they can lead to misleading results. But cTWAS runs the data through complex Bayesian regression models to weed out the noise.

The team has made their new software available for other scientists to use in identifying gene-disease links. To test it out, they looked at genes tied to LDL cholesterol levels, which can lead to cardiovascular disease if too high. cTWAS found 35 causal genes, over half of which were completely new discoveries!

This suggests some exciting new targets for potential LDL therapies down the line. The researchers plan to keep enhancing cTWAS and exploring other applications for it. But already it represents a major upgrade in our ability to pinpoint exactly which genes contribute to complex diseases.