ArticleAmerican journal of human genetics2021
Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network.
Article in American journal of human genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02450851 (Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network), which is not on this map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network
Who cites it
23 citing papers in PubMed, 59 citations in OpenAlex.
- VUStruct: A compute pipeline for high throughput and personalized structural biology.PLoS computational biology · 2026Article
- Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations.Briefings in bioinformatics · 2026Article
- Genomic rare variant mechanisms for congenital cardiac laterality defect: A digenic model approach.American journal of human genetics · 2025Article
- 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
- Development of multiplexed orthogonal base editor (MOBE) systems.Nature biotechnology · 2025Article
- Phenotypic spectrum of dual diagnoses in developmental disorders.American journal of human genetics · 2024Article
- Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards.Journal of neurology, neurosurgery, and psychiatry · 2024Review
- The co-occurrence of genetic variants in the TYR and OCA2 genes confers susceptibility to albinism.Nature communications · 2024Article
- Oligogenic inheritance in severe adult obesity.International journal of obesity (2005) · 2024Article
- Digenic Inheritance in Rare Disorders and Mitochondrial Disease-Crossing the Frontier to a More Comprehensive Understanding of Etiology.International journal of molecular sciences · 2024Review
- Probable digenic inheritance of Diamond-Blackfan anemia.American journal of medical genetics. Part A · 2024Article
- Oligogenic basis of premature ovarian insufficiency: an observational study.Journal of ovarian research · 2024Observational
- Strategies for dissecting the complexity of neurodevelopmental disorders.Trends in genetics : TIG · 2024Review
- A knowledge graph approach to predict and interpret disease-causing gene interactions.BMC bioinformatics · 2023Article
- Faster and more accurate pathogenic combination predictions with VarCoPP2.0.BMC bioinformatics · 2023Article
- Allelic overload and its clinical modifier effect in Bardet-Biedl syndrome.NPJ genomic medicine · 2022Article
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- Rare disorders have many faces: in silico characterization of rare disorder spectrum.Orphanet journal of rare diseases · 2022Article
- Co-occurrence of mutations inFrontiers in endocrinology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.