FOR IMMEDIATE RELEASE
March 14, 2024 Ann Arbor, MI
Genomenon Presents Study Identifying 11,000 Gene Disease Relationships Across the Clinical Exome at the ACMG Annual Clinical Genetics Meeting
The 6-month study combined computational indexing of 9.5 million full-text genomic publications with a systematic literature review by genetic scientists
Genomenon, a leading genomic intelligence company, presented data at the ACMG Annual Clinical Genetics Meeting today demonstrating how computational indexing of millions of published abstracts and full-text references combined with a systematic literature review can be used to rapidly and accurately characterize gene-disease relationships (GDRs) and to resolve variants of uncertain significance (VUS). The study was completed in less than six months and identified 10,745 germline GDRs and 5,973 germline GDRs with positive associations between a disease and gene. Each GDR is accompanied by well-documented scientific evidence curated by Genomenon’s team of genetic scientists. Today’s presentation shares a milestone in the company’s mission to curate the human genome and understand the pathogenicity of any variant for patient diagnosis and precision medicine development.
The need for a robust and efficient method to identify GDRs is essential as it has been estimated that more than 50% of patients with a suspected Mendelian condition lack a precise diagnosis. This need is underscored by the fact that the number of VUS’s is growing exponentially due to increased genetic testing and sequencing. In recent weeks, for example, the NIH’s All of Us research program released nearly a quarter of a million clinical-grade genome sequences along with more than 275 million previously unreported genetic variants.
“With an onslaught of new sequencing data, it is becoming increasingly urgent to rapidly and accurately curate and characterize VUS and GDRs across all genes associated with the clinical exome,” said Mark J. Kiel, MD, PhD, Genomenon’s Chief Scientific Officer. “The results we presented today demonstrate the power of integrating computational indexing with expert curation of scientific evidence to achieve this goal. This approach allowed us to increase the speed and accuracy of defining variant pathogenicity, which is essential to keep pace with the publication of new variants and improve the precision of genetic diagnoses.”
There was substantial agreement between the results of the Genomenon study and ClinGen. Most discrepancies were due to new evidence being published after the last ClinGen curation. This gap reflects the value of combining computational power and human expertise to enable more timely identification of novel GDRs accompanied by well-documented evidence. Comparison of the Genomenon results with aggregated results from twelve submitting groups in GenCC revealed a level of disagreement (14%) that was consistent with internal disagreement among the submitting groups.
Poster Presentations at ACMG Meeting
In a poster presentation, Genomenon, in collaboration with The Broad Institute and the INADcure Foundation, developed new estimates of the global prevalence of PLA2G6-associated neurodegeneration. The study used a literature-based approach that gathered variants through Genomenon’s Mastermind Genomic Intelligence Platform and variant databases. A significant underdiagnosis of PLA2G6-associated neurodegeneration was revealed, as well as a higher carrier frequency of PLA2G6 variants in African and Asian populations was also shown.
The company also presented a poster describing the use of homologous annotation to interpret variants in CALM1, CALM2, and CALM3 genes. These genes encode an identical calmodulin protein, are located on different chromosomes, and are associated with severe calmodulinopathies. The presence of disease-causing genes with homologous counterparts compounds the challenges associated with variant interpretation, evidence curation, and diagnostic interpretation. Curation of the CALM variant dataset using homologous annotation enabled reconciliation of variant annotations across the three genes. The study demonstrated that there is only a 27% match of genetic variants listed in current databases and those found in the literature. This indicates that if there isn’t 100% coverage of the three genes, a variant in one gene that is not present in another poses the risk of a missed diagnosis.
