Journal Article

DKFZ-2026-00155

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome.

Bundalian, L.DKFZ* ; Strnadová, M. S. ; Garten, F. ; Horn, S. ; Stenzel, U. ; Popp, D. ; Lemke, J. R. ; Biskup, S. ; Schulte, B. ; May, P. ; Bösebeck, F. ; Garten, A. ; Thor, D. ; Schulz, A. ; Hentschel, J. ; Kelso, J. ; Schöneberg, T. ; Le Duc, D.DKFZ*

2026
Oxford University Press Oxford

Abstract: Synonymous single nucleotide variants (sSNVs), traditionally seen as neutral, are now recognized for their biological impact. To assess their relevance, we developed SyMetrics, a framework that integrates predictors of splicing, RNA stability, evolutionary conservation, codon usage, synonymous variation effects, sequence properties, and allele frequency. We analyzed all possible sSNVs across the human genome, and our machine-learning model achieved 97% accuracy in distinguishing deleterious from benign variants, with a ROC-AUC of 0.89, outperforming individual predictors. Our estimates indicate that about 1.98 ± 0.17% of sSNVs absent from population databases are damaging (roughly 900 000 sSNVs), with an odds ratio of 3.87 for deleteriousness compared to common sSNVs (P < 0.05). To validate predictions, we performed functional assays on selected sSNVs in the AVPR2 gene and additionally used available large scale mutagenesis screens of RAD51C and BAP1 variants. In a clinical cohort, we identified 15 predicted deleterious sSNVs in genes linked to patient phenotypes; 9 were classified as (likely) pathogenic while 6 were variants of uncertain significance (VUS) per American College of Medical Genetics guidelines. For three VUS, segregation data supported their suspected inheritance patterns (de novo, X-linked). Our findings underscore the functional importance of sSNVs. To support further research and clinical applications, we provide a Python package and web application (https://symetrics.org/) for evaluating these variants comprehensively.

Keyword(s): Humans (MeSH) ; Machine Learning (MeSH) ; Genome, Human (MeSH) ; Polymorphism, Single Nucleotide (MeSH) ; Silent Mutation (MeSH) ; Software (MeSH)

Note: #NCTZFB9#

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Contributing Institute(s):

1. Koordinierungsstelle NCT Dresden (DD04)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

Appears in the scientific report 2026

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Database coverage:
; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Emerging Sources Citation Index ; Fees ; SCOPUS ; Web of Science Core Collection

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