Despite the increasing use of genomic sequencing in clinical practice, the interpretation of rare genetic mutations, even among well-studied disease genes, remains difficult. Current predictive models are useful for interpreting such mutations, but they tend to misclassify those that do not cause disease, contributing to false positives. Researchers at the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD) in Germany, and the Harvard Medical School in Boston, USA, have developed a tool called Cell Decryption. Mutations in Actionable Genes (DeMAG) published in the journal Nature Communications. DeMAG is an open source web server (demag.org) that offers an interpretation of the effects of all possible single amino acid mutations that could occur in 316 clinically relevant disease-causing genes for which diagnostics and preventive treatments are already available. DeMAG provides medical professionals with a tool that allows them to more accurately assess the effect of mutations in those genes by reducing the false positive rate, meaning that less benign mutations are predicted to be pathogenic. As a result, the tool can support clinical decision making.
In recent years, genomic sequencing has become less expensive and more advanced. On the one hand, this allows clinicians to increasingly use sequencing for diagnostic purposes, while also allowing scientists to explore more research hypotheses. On the other hand, many detected mutations do not have a clear clinical interpretation. Uncertainty about whether a mutation causes disease can be stressful for patients, leading to the psychological burden, morbidity, and healthcare costs associated with underdiagnosis and overdiagnosis. While existing tools are already used to predict the functional impact of these variants, their performance is skewed by limited clinical data that make it difficult to distinguish between pathogenic (disease-causing) and benign (neutral) variants within a given gene and often lead to misclassification. mutations that do not cause disease as pathogenic. Addressing these difficulties is critical to developing a reliable predictor for clinical applications.
Agnes Toth-Petroczy’s research group at MPI-CBG and CSBD partnered with Christopher Cassa, assistant professor of medicine in the Division of Genetics at Brigham and Women’s Hospital at Harvard Medical School, and Ivan Adzhubei, researcher associate in the Department of Biomedical Informatics, Harvard Medical School, to develop a statistical model and a DeMAG web server that achieves high accuracy in the interpretation of genetic mutations in disease genes. To do this, the researchers carefully selected known benign and pathogenic mutations to train the model. “We use multipopulation and clinical databases. We select only mutations whose clinical interpretation is agreed upon among multiple submitters, such as physicians and genetics laboratories. And we also include ancestry data that is underrepresented in current population databases, such as Korean or Japanese, to make it even more representative and accurate,” explains Federica Luppino, first author of the research paper and a doctoral student in the Toth-Petroczy group. DeMAG includes a novel feature, “partner scoring,” which identifies groups of amino acids in a protein that share the same clinical effect. With partner scoring, DeMAG takes advantage of amino acid relationships based on evolutionary information from the genomes of many organisms and the recent AI (artificial intelligence) revolution of predicting the 3D shapes of proteins using the AlphaFold algorithm developed by Google DeepMind .
Agnes Toth-Petroczy, who supervised the study, concludes: “We provide a basic framework for integrating clinical and protein data to help assess the impact of mutations. We hope that our tool and web server will make it easier to assess the effect of variants.” and clinical decision-making. Furthermore, the newly developed features may be applied to other genes and organisms beyond humans.”
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