Innovative tool enhances disease-causing gene discovery

Enhanced gene discovery with genetic confounder adjustment.


A new tool from the University of Chicago improves finding disease-causing genes. It combines data from genome studies to reduce errors and accurately pinpoint genes linked to diseases. The agency focuses on comparing genetic sequences in people with a specific disease to those without, aiming to identify variants that increase disease risk for further investigation.

Most diseases result from a mix of genes, environment, and other factors. Genome studies find many associated variants but don’t confirm causation. Genes near each other tend to be linked, posing a challenge in pinpointing the exact cause.

Xin He, Ph.D., Associate Professor of Human Genetics and senior author of the new study, said, “You may have many genetic variants in a block that correlate with disease risk, but you don’t know which one is the causal variant. That’s the fundamental challenge of GWAS: how we go from association to causality.”

Finding the cause of diseases is challenging because many gene changes are in non-coding areas. Using gene expression levels helps, but it has challenges. For example, if a variant is linked to a gene’s expression, it might not be the actual cause of the disease. This is because nearby variants and terms of other genes can also be linked, causing mistakes in identifying the actual cause. Current methods often give false positive results more than half the time.

In a recent study, Prof. He and Dr. Stephens introduced a new method called cTWAS to improve gene discovery. Unlike traditional approaches, cTWAS considers multiple genes simultaneously, reducing false positives. Using Bayesian regression effectively filters out confounding genes and variants, increasing the likelihood of identifying the actual cause among a group of nearby genes.

The study applies the new cTWAS technique to examine LDL cholesterol genetics. Unlike traditional methods, a different gene variant related to a common cholesterol drug was revealed. It identified 35 potential causal genes for LDL, half unknown. The CTS software is accessible for download, and the researcher plans to enhance its features by integrating various omics data and using eQTLs from different tissues.

He said, “The software will allow people to do analyses that connect genetic variations to phenotypes. That’s the key challenge facing the entire field. We now have a much better tool to make those connections.”

This study marks a significant advancement in gene discovery for disease causation, presenting a powerful tool called cTWAS. The ability to consider multiple genes simultaneously and reduce false positives holds promise for unraveling the complex genetic underpinnings of various diseases, with potential implications for future therapeutic targets and personalized medicine.

Journal reference:

  1. Zhao, S., Crouse, W., Qian, S. et al. Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits. Nature Genetics. DOI: 10.1038/s41588-023-01648-9.


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