Case Western Reserve University’s innovative computational tool, TGVIS, is revolutionizing how researchers identify genes linked to complex diseases, offering hope for more effective treatments and earlier detection of conditions like heart disease and diabetes.
A team of researchers at Case Western Reserve University has devised an innovative computational method that holds promise for revolutionizing how genes and genetic changes responsible for diseases are identified. Their tool could significantly advance the early detection and treatment of cardiometabolic diseases, such as heart disease and diabetes.
Genetic changes can signal the presence of diseases, but determining which specific genes and changes are responsible can be challenging due to the complexity and interconnected nature of genetic information.
In a recently published study in Nature Communications, the researchers introduced a new computational tool named TGVIS that could transform this landscape.
“We have been able to identify new genes that were previously overlooked, expanding our knowledge of the genetic basis of diseases,” lead researcher Xiaofeng Zhu, a professor in the Department of Population and Quantitative Health Sciences at the Case Western Reserve School of Medicine, said in a news release.
A Novel Approach to Complex Diseases
The study focused on traits that offer clues about cardiovascular health, such as lipid and glucose levels and inflammation.
By leveraging existing genome-wide association studies (GWAS), the team enhanced the precision of identifying genes responsible for these traits.
While GWAS can link DNA regions to disease-associated traits, pinpointing the causative gene or genetic change remained difficult due to overlaps and indirect gene interactions.
Building upon GWAS, the researchers created TGVIS (Tissue-Gene pairs, direct causal Variants, and Infinitesimal Effects Selector), a tool designed to more accurately identify the genes and changes in a person’s DNA that may cause disease.
“We used TGVIS to study 45 traits related to heart and metabolism, using genomic data from 31 different types of body tissues,” Zhu added. “This helped us better identify which genes are likely causing these traits. We even found new genes that previous studies missed.”
How TGVIS Works
The TGVIS tool integrates information from GWAS with other biological data, such as how the body uses DNA instructions to produce proteins and other essential molecules.
Advanced mathematical models and computational algorithms were employed to pinpoint the genes and DNA changes potentially responsible for specific diseases.
The method was initially applied to cardiometabolic traits, which encompass the health of the heart and blood vessels and the body’s energy metabolism.
However, Zhu aims to adapt this approach to extend its utility to other diseases, including breast cancer, Alzheimer’s and additional cardiovascular conditions.
“We can also now prioritize which genes to study further, making research more efficient and focused, which can accelerate the pace of scientific discoveries and innovations,” added Zhu.
Source: Case Western Reserve University