New AI Tool Finds Rare Variants Linked to Heart Disease in 17 Genes

Using an advanced artificial intelligence (AI) tool, researchers at the Icahn School of Medicine at Mount Sinai have identified rare coding variants in 17 genes that shed light on the molecular basis of coronary artery disease (CAD), the leading cause of morbidity and mortality worldwide.

The discoveries, detailed in the June 11 online issue of Nature Genetics, reveal genetic factors impacting heart disease that open new avenues for targeted treatments and personalized approaches to cardiovascular care.

The investigators used an in silico, or computer-derived, score for coronary artery disease (ISCAD) that holistically represents CAD, as described in a previous paper by the team in The Lancet. The ISCAD score incorporates hundreds of different clinical features from the electronic health record, including vital signs, laboratory test results, medications, symptoms, and diagnoses. To build the score, they trained machine learning models on the electronic health records of 604,914 individuals across the UK Biobank, All of Us Research Program, and BioMe Biobank in this comprehensive meta-analysis.

The score was then tested for association with rare and ultra-rare coding variants found in the exome sequences of these individuals. In addition, the research team conducted further investigation into the discovered genes to study their roles in causal CAD risk factors, clinical manifestations of CAD, and their connections with CAD status in traditional large-scale genome-wide association studies, among other factors.

"Our findings help us understand how these 17 genes are involved in coronary artery disease. Some of these genes are already known to influence heart disease development, while others have never been linked to it before," says Ron Do, PhD, senior study author and the Charles Bronfman Professor in Personalized Medicine at Icahn Mount Sinai. "Our study shows how machine learning tools can uncover genetic insights that traditional methods might miss when comparing cases and controls. This could lead to new ways to identify biological mechanisms of heart disease or gene targets for treatment."

Because they occur in only a small percentage of individuals, rare coding variants may have a significant impact on disease risk or susceptibility when present. Therefore, studying these variants is essential to understanding the genetic basis of diseases and can inform therapeutic targets.

The study was driven by the challenges faced, over the last decade, in identifying rare coding variants associated with CAD using traditional methods relying on diagnosed cases and controls. Diagnostic codes' limitations in capturing the complexity of CAD prompted the researchers to explore new avenues of investigation.

"Our previous Lancet paper showed that a machine learning model trained with electronic health records can generate an in silico score for coronary artery disease, capturing disease across its spectrum," says lead author Ben Omega Petrazzini, BS, Associate Bioinformatician in Dr. Do's lab at Icahn Mount Sinai. "Based on these findings, we hypothesized that the in-silico score for CAD could reveal novel rare coding variants related to CAD by offering a more holistic view of the disease."

Next, the investigators plan to further investigate the role of the identified genes in CAD biology and explore potential applications of machine learning in the genetic study of other complex diseases, as part of their ongoing efforts to advance understanding of disease mechanisms, discover new treatments, and improve patient outcomes.

Petrazzini BO, Forrest IS, Rocheleau G, Vy HMT, Márquez-Luna C, Duffy Á, Chen R, Park JK, Gibson K, Goonewardena SN, Malick WA, Rosenson RS, Jordan DM, Do R.
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
Nat Genet. 2024 Jun 11. doi: 10.1038/s41588-024-01791-x

Most Popular Now

New Computational Model of Real Neurons …

Nearly all the neural networks that power modern artificial intelligence (AI) tools such as ChatGPT are based on a 1960s-era computational model of a living neuron. A new model developed...

Meet CARMEN, a Robot that Helps People w…

Meet CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation - a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory...

AI Matches Protein Interaction Partners

Proteins are the building blocks of life, involved in virtually every biological process. Understanding how proteins interact with each other is crucial for deciphering the complexities of cellular functions, and...

Mobile Phone Data Helps Track Pathogen S…

A new way to map the spread and evolution of pathogens, and their responses to vaccines and antibiotics, will provide key insights to help predict and prevent future outbreaks. The...

AI Model to Improve Patient Response to …

A new artificial intelligence (AI) tool that can help to select the most suitable treatment for cancer patients has been developed by researchers at The Australian National University (ANU). DeepPT, developed...

Can AI Tell you if You Have Osteoporosis…

Osteoporosis is so difficult to detect in early stage it’s called the "silent disease." What if artificial intelligence could help predict a patient’s chances of having the bone-loss disease before...

Study Reveals Why AI Models that Analyze…

Artificial intelligence (AI) models often play a role in medical diagnoses, especially when it comes to analyzing images such as X-rays. However, studies have found that these models don’t always...

Think You're Funny? ChatGPT might b…

A study comparing jokes by people versus those told by ChatGPT shows that humans need to work on their material. The research team behind the study published on Wednesday, July 3...

Innovative, Highly Accurate AI Model can…

If there is one medical exam that everyone in the world has taken, it's a chest x-ray. Clinicians can use radiographs to tell if someone has tuberculosis, lung cancer, or...

New AI Approach Optimizes Antibody Drugs

Proteins have evolved to excel at everything from contracting muscles to digesting food to recognizing viruses. To engineer better proteins, including antibodies, scientists often iteratively mutate the amino acids -...

AI Speeds Up Heart Scans, Saving Doctors…

Researchers have developed a groundbreaking method for analysing heart MRI scans with the help of artificial intelligence (AI), which could save valuable NHS time and resources, as well as improve...

Young People Believe that AI is a Valuab…

Children and young people are generally positive about artificial intelligence (AI) and think it should be used in modern healthcare, finds the first-of-its-kind survey led by UCL and Great Ormond...