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University of Chicago Researchers Unveil cTWAS, a Breakthrough Method to Identify Causal Genes
A groundbreaking statistical tool, cTWAS (causal-Transcriptome-wide Association studies), developed by researchers at the University of Chicago, promises to enhance the precision of identifying genetic variants responsible for diseases. Published in Nature Genetics on January 26, 2024, the innovative tool integrates data from genome-wide association studies (GWAS) and genetic expression predictions, mitigating false positives and offering a more accurate identification of causal genes and variants linked to diseases.
GWAS has been a pivotal approach to pinpoint genes associated with various human traits and common diseases. By comparing genome sequences from individuals with a specific disease to those without, researchers identify genetic variants that may increase the risk of the disease. However, diseases are typically multifactorial, involving complex interactions of multiple genes and environmental factors. GWAS, while valuable, often identifies numerous variants across the genome associated with a disease without establishing causality.
The challenge arises from the phenomenon of linkage disequilibrium, where variants near each other in the genome are highly correlated. This complicates the determination of the causal variant within a correlated block of variants. Additionally, many disease-associated genetic variants are located in non-coding regions, further complicating interpretation.
To address these challenges, the cTWAS model introduces a novel strategy. Unlike traditional methods that focus on individual genes, cTWAS considers multiple genes and variants simultaneously, employing a Bayesian multiple regression model to minimize confounding variables.
Prof. Xin He, Associate Professor of Human Genetics and senior author of the study, explained, “If you look at one at a time, you’ll have false positives, but if you look at all the nearby genes and variants together, you are much more likely to find the causal gene.”
The research, exemplified by studying the genetics of LDL cholesterol levels, demonstrated the efficacy of cTWAS. Existing eQTL methods suggested a DNA repair gene, while cTWAS pinpointed a variant in the statin-target gene associated with LDL cholesterol. cTWAS identified 35 potential causal genes for LDL, over half of which were previously unreported.
The cTWAS software is now available for download from He’s lab website. Future work aims to expand its capabilities by incorporating other ‘omics data and utilizing eQTLs from various tissue types.
Dr. He emphasized, “The software will allow people to do analyses that connect genetic variations to phenotypes. That’s really the key challenge facing the entire field. We now have a much better tool to make those connections.”
