This article discusses a new computational tool developed by researchers at Case Western Reserve University that aims to improve the identification of genes and genetic changes responsible for diseases.
Here’s a breakdown of the key points:
The Problem: Existing methods like genome-wide association studies (GWAS) can identify DNA regions linked to diseases, but pinpointing the exact gene or genetic change is difficult due to overlapping genes and indirect effects (like turning genes on/off).
The Solution: The researchers created a tool called TGVIS (Tissue-Gene pairs, direct casual Variants, and Infinitesimal Effects Selector).
How TGVIS Works: It combines data from GWAS with other biological information,such as how DNA instructions are used to create proteins and molecules. Advanced mathematical and computational calculations merge this data to identify causal genes and DNA changes.
Focus of the Study: The initial request of TGVIS was on cardiometabolic health, focusing on 45 traits related to the heart and metabolism, using genomic data from 31 diffrent body tissues.
Key Findings: TGVIS helped identify new genes that were previously overlooked, expanding the understanding of the genetic basis of diseases.
Potential Impact: This new approach could lead to earlier detection and treatment of cardiometabolic diseases. The method is also adaptable to studying other diseases like breast cancer, Alzheimer’s disease, and cardiovascular diseases, making research more efficient and focused.
publication: The findings were published in the journal Nature Communications*.
