ArticleProceedings of the National Academy of Sciences of the United States of America2019
Predicting disease-causing variant combinations.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.Briefings in bioinformatics · 2026Article
- Additive effect of multiple genetic variants inHemaSphere · 2026Article
- Benchmark of computational methods to detect digenism in sequencing data.European journal of human genetics : EJHG · 2025Review
- Digenic variant interpretation with hypothesis-driven explainable AI.NAR genomics and bioinformatics · 2025Article
- Oligogenic analysis across broad phenotypes of 46,XY differences in sex development associated with NR5A1/SF-1 variants: findings from the international SF1next study.EBioMedicine · 2025Article
- Review
- Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders.Genome research · 2024Article
- MDVarP: modifier ~ disease-causing variant pairs predictor.BioData mining · 2024Article
- The co-occurrence of genetic variants in the TYR and OCA2 genes confers susceptibility to albinism.Nature communications · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Oligogenic inheritance in severe adult obesity.International journal of obesity (2005) · 2024Article
- Modulating autophagy to treat diseases: A revisited review on in silico methods.Journal of advanced research · 2024Review
- Prioritization of oligogenic variant combinations in whole exomes.Bioinformatics (Oxford, England) · 2024Article
- Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders.medRxiv : the preprint server for health sciences · 2024Article
- Oligogenic basis of premature ovarian insufficiency: an observational study.Journal of ovarian research · 2024Observational
- Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations.ACS omega · 2024Review
- The genetic landscape of mitochondrial diseases in the next-generation sequencing era: a Portuguese cohort study.Frontiers in cell and developmental biology · 2024Article
- Clinical and genetic characteristics of a large international cohort of individuals with rare NR5A1/SF-1 variants of sex development.EBioMedicine · 2024Article
Corrections and comments
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.
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