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(https://directorsblog.nih.gov/2024/05/16/speeding-the-diagnosis-of-rare-genetic-disorders-with-the-help-of-artificial-intelligence/) Speeding the Diagnosis of Rare Genetic Disorders with the Help of Artificial Intelligence
May 16th 2024, 09:00

Credit: Donny Bliss/NIH, Qpt/Shutterstock, taka/Adobe Stock

Millions of children around the world are born each year with severe genetic disorders. Many of these are Mendelian disorders, which are rare genetic conditions caused by mutations in a single gene. But pinpointing the specific gene responsible for a disorder to get a clear diagnosis for an individual can be labor-intensive, and reanalysis of undiagnosed cases is also difficult. As a result, only about 30% of people with a rare genetic disorder get a definitive diagnosis, and on average, it takes 6 years from symptom onset to diagnosis.

Progress is needed to get accurate diagnoses to individuals and families more often and faster, and to create more efficient ways to update genetic diagnoses as new discoveries are made. As an important step in this direction, a team funded in part by NIH has developed a new artificial intelligence (AI) system called AI-MARRVEL (AI-Model organism Aggregated Resources for Rare Variant ExpLoration).1 

As reported in (https://ai.nejm.org/doi/full/10.1056/AIoa2300009) NEJM AI by a research team led by (https://www.bcm.edu/people-search/pengfei-liu-25661) Pengfei Liu, (https://www.bcm.edu/people-search/hugo-bellen-18243) Hugo Bellen, and (https://www.bcm.edu/people-search/zhandong-liu-25684) Zhandong Liu at the Baylor College of Medicine and the Jan and Dan Duncan Neurological Research Institute at Texas Children’s Hospital in Houston, AI-MARRVEL relies on a machine learning approach. Machine learning involves using vast quantities of data to train computer systems to become increasingly better at recognizing patterns.

The AI-MARRVEL system was trained using a compendium of data called MARRVEL, (https://www.cell.com/ajhg/fulltext/S0002-9297(17)30154-4) previously developed by the research team. MARRVEL integrates genetic information from six human databases and seven model organism databases into one site and includes more than 3.5 million known genetic variants from thousands of healthy individuals as well as those with diagnosed cases of genetic disorders. Using what it has learned from that compendium of data, AI-MARRVEL uses a person’s symptoms and protein-coding genome sequences to narrow down the most likely variants responsible for that person’s genetic condition.

To find out how well it works, the researchers compared the results from AI-MARRVEL to other previously published tools for genetic diagnosis based on three different databases containing established molecular diagnoses from a clinical diagnostic laboratory: Baylor Genetics, the NIH-funded (https://undiagnosed.hms.harvard.edu/) Undiagnosed Diseases Network (UDN), and the (https://www.sanger.ac.uk/collaboration/deciphering-developmental-disorders-ddd/) Deciphering Developmental Disorders (DDD) project. Overall, the researchers found that AI-MARRVEL consistently made accurate diagnoses in twice as many cases as these other tools.

While hundreds of new disease-causing variants are discovered each year, there’s currently no streamlined way to determine which cases should be reanalyzed when previous sequencing and interpretation failed to identify the cause.2 To see how well AI-MARRVEL does at identifying diagnosable cases from pools of unsolved cases, the researchers designed a confidence metric and found the tool achieved a precision rate of 98% and correctly identified 57% of diagnosable cases out of a collection of 871 cases. The researchers also suggest that AI-MARRVEL could help identify short lists of possible gene candidates in even more potentially solvable cases and then send them on to a panel of experts for follow-up review.

There is some early evidence that AI-MARRVEL could also be put to work in making new discoveries that link novel gene variants to diseases for the first time. In fact, the model already correctly identified two recently reported disease genes in a list of top candidates.

These findings suggest a promising path forward where machine learning could one day make diagnostic decisions in a way that’s comparable to experts, only more efficiently. What’s especially exciting is AI-MARRVEL could have the potential for solving rare disease cases, including those that have remained a mystery for years. The hope is that, by combining the power of AI tools together with the latest sequencing data in the years to come, doctors will be able to get faster diagnoses to many more people with rare genetic disorders.

References:

[1] Mao, D, et al. (https://ai.nejm.org/doi/full/10.1056/AIoa2300009) AI-MARRVEL: A Knowledge-Driven Artificial Intelligence for Molecular Diagnostics of Mendelian Disorders. NEJM AI. DOI: 10.1056/AIoa2300009 (2024).

[2] Liu, P, et al. (https://pubmed.ncbi.nlm.nih.gov/31216405/) Reanalysis of Clinical Exome Sequencing Data. N Engl J Med. DOI: 10.1056/NEJMc1812033 (2019).

NIH Support: NIH Common Fund, National Human Genome Research Institute, National Institute of Neurological Disorders and Stroke, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Forwarded by:
Michael Reeder LCPC
Baltimore, MD

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