Researchers Develop New Statistical Tool to Uncover Genetic Causes of Disease

University of Chicago scientists have created a statistical tool called cTWAS to identify disease-causing genetic variants. Published in Nature Genetics, cTWAS combines genome-wide association study (GWAS) data with genetic expression predictions. This helps pinpoint causal genes and variants underlying complex traits.

GWAS finds genetic associations but not causality, as most diseases involve multiple genes, environment, and other factors. Nearby variants are also highly correlated, creating uncertainty over the true causal variant.

As senior author Xin He explains, “You may have many correlated variants associated with disease risk, but you don’t know which one actually causes it – that’s the fundamental challenge of GWAS.”

Most variants reside in non-coding regions, further complicating interpretation. A common strategy uses expression quantitative trait loci (eQTLs) to nominate risk genes. However, existing methods struggle with confounding from neighboring associations.

cTWAS employs a Bayesian regression model to analyze multiple genes and variants simultaneously. This reduces false positives by accounting for confounding effects. The tool identifies 35 causal genes for LDL cholesterol, over half novel findings, illuminating new treatment targets.

The software is available for download from He’s website. He aims to expand cTWAS by incorporating diverse omics data across tissues. This will provide researchers with an advanced capability to connect genotypes to disease phenotypes.

By leveraging statistical techniques to sift causal signals from association noise, cTWAS represents an important step for unraveling the complex genetics of common diseases. It demonstrates the power of innovative computational methods to translate massive genomic data into biomedical insights.