Machine Learning for Interpretation of DNA Variants of Maturity-Onset Diabetes of the Young Genes Based on ACMG Criteria
Background: Maturity-onset diabetes of the young (MODY) is a group of dominantly inherited monogenic diabetes, with HNF4A-MODY, GCK-MODY and HNF1A-MODY being the three most common genes responsible. Molecular diagnosis of MODY is important for precise treatment. While a DNA variant causing MODY can be assessed by the criteria of the American College of Medical Genetics and Genomics (ACMG) guidelines, gene-specific assessment of disease-causing mutations is important to differentiate between the MODY subtypes. As the ACMG criteria were not originally designed for machine learning algorithms, they are not true independent variables. Methods: In this study, we applied machine learning models for interpretation of DNA variants in MODY genes defined by the ACMG criteria based on Human Gene Mutation Database (HGMD) and ClinVar. Results: The results show highly predictive abilities with accuracy over 95%, suggest that this model could serve as a fast, gene-specific method for physicians or genetic counselors assisting with diagnosis and reporting, especially when confronted by contradictory ACMG criteria. Also, the weight of the ACMG criteria shows gene specificity which advocates for the application of machine learning methods with the ACMG criteria to capture the most relevant information for each disease-related variant. Conclusion: Our results highlight the need for different weights of the ACMG criteria in relation with different MODY genes for accurate functional classification. For proof of principle, we applied the ACMG criteria as feature vectors in a machine learning model obtaining precision-based result.
- Downloaded 300 times
- Download rankings, all-time:
- Site-wide: 84,909
- In genetic and genomic medicine: 308
- Year to date:
- Site-wide: 42,512
- Since beginning of last month:
- Site-wide: 33,275
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!