Research News

A Cleveland Clinic Research team has developed a new genomic analysis method that uses artificial intelligence to identify disease-associated genes and potential drug targets. The method, called GenT and published in Nature Communications, offers an alternative to decades-old approaches of interpreting DNA sequences, advancing laboratory discoveries, drug development and clinical diagnostics. 

For twenty years, researchers have used a technique called GWAS (genome-wide association studies) to identify DNA mutations, called variants, associated with specific diseases. GWAS works by comparing genetic sequences from multiple people to find DNA regions that are unique to individuals with a certain disease.  

While powerful, GWAS identifies millions of genetic regions that could potentially cause a disease. Many of these regions don’t fall in one specific gene. Instead, they span multiple genes or fall in noncoding sequences in. Determining the gene a variant affects and its relevance to disease takes a long time, which slows progress towards new therapies. 

GenT takes a different approach. Instead of testing millions of variants individually, it groups variants around different genes into sets. GenT also integrates multi-omics data like RNA sequences, protein activity and ancestry analysis. The shift provides a clearer picture of which genes influence which disease, and which genes could be targeted by drugs. 

“I don’t use the word ‘innovation’ lightly, but this method can really change how people analyze genetic data,” says Dr. Feixiong Cheng, the director of the Cleveland Clinic Genome Center and the Dr. Keyhan and Dr. Jafar Mobasseri Endowed Chair for Innovative Research. “GWAS gave us the map, and now GenT helps us find the landmarks—the genes that matter for disease pathogenesis, progression and drug development.” 

Dr. Cheng and his laboratory have already used their new approach to identify dozens of druggable genes linked to Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), depression, schizophrenia and type 2 diabetes. 

In one experiment, researchers blocked a gene identified through GenT called ntrk1 in cell cultures derived from brain cells of patients with Alzheimer’s disease. Levels of tau phosphorylation, a key marker of Alzheimer’s disease, dropped.  

Dr. Cheng and his team are now using GenT to pinpoint new drug targets for Alzheimer’s disease by analyzing datasets from the Alzheimer’s Disease Sequencing Project. He says that pairing advanced AI tools like GenT with emerging human model systems, including brain organoids, could improve Alzheimer’s treatments in the near future. 

The Cheng Laboratory has published the GenT software and results online, inviting other genetic researchers to explore their new method for drug discovery. 

This work was supported by the National Institute on Aging (U01AG073323).