In a major step towards untangling the genetic complexities of rare diseases, King Abdullah University of Science and Technology (KAUST) researchers have unveiled a tool that could aid in the diagnosis of these enigmatic conditions.
Named STARVar, the method leverages a diverse range of data sources, including background information from the scientific literature, genomic information from DNA sequence reads, and clinical symptoms from individual patient records, to precisely identify genetic variants associated with diseases.
The new artificial intelligence–powered resource stands apart from other gene prioritisation tools because of its focus on real-world patient symptoms, regardless of how these clinical descriptions are documented.
“STARVar stands a unique and efficient tool that has the advantage of prioritising genomic variants by using flexibly expressed patient symptoms in free-form text,” said Șenay Kafkas, a bioinformatics researcher at KAUST and the first author of a new report, published in BMC Informatics, that details the innovative tool.
Traditional methods often demand that clinical presentations adhere to standardised vocabularies, impeding a more nuanced and accurate understanding of patient symptoms. The reality, however, is that doctors and researchers frequently convey patient data using terminology that extends beyond predefined terms.
STARVar, which is short for Symptom-based Tool for Automatic Ranking of Variants, now offers a solution that is more dynamic and adaptable. Designed by KAUST computer scientist Robert Hoehndorf and members of his team, the method can interpret symptom data recorded in either standardised or natural language formats.
When evaluated on different genomic datasets, generated using clinical variants collected from patients in Saudi Arabia and other countries, STARVar outperformed several other variant prioritisation tools that can operate with only rigidly represented symptoms. In particular, the algorithm consistently ranked the correct disease-associated variant at or near the top of the list of potential candidate variants in these validation tests.
Illustrating the impact of STARVar in a real-world setting, the researchers also used the tool to help diagnose a young Saudi girl who showed signs of joint stiffness, lumps under the skin, and bone damage. Out of nearly 800 suspect gene variants uncovered by genomic sequencing, STARVar deftly narrowed down the possibilities to a solitary mutation. This mutation, in a gene called MMP2, was already known to be pathogenic and thus was implicated as the likely driver of the girl’s condition.
STARVar is now freely available online, and Kafkas hopes to see the clinical genetics community embracing it and integrating the analytic method into their genomic workflows. “STARVar stands as a unique and efficient tool,” she said, “one that will shed light on rare diseases and provide vital diagnostic support to clinicians and affected families.”