An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

Yuan, Yangyang; Zhang, Liubin; Long, Qihan; Jiang, Hui; Li, Miaoxin

doi:10.1016/j.csbj.2022.07.011

Cited by 8 publications

(6 citation statements)

References 79 publications

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“…Our findings draw attention to the possible digenic genetic background of albinism. On this ground we recommend re-analysis-possibly utilizing newly developed mathematical models [24,25]-of already existing genetic databases of albinism cohorts to fill the gap of missing heritability of the disease.…”

Section: Discussionmentioning

confidence: 99%

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Nagy,

Pal,

Kun

et al. 2024

IJMS

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Albinism is characterized by a variable degree of hypopigmentation affecting the skin and the hair, and causing ophthalmologic abnormalities. Its oculocutaneous, ocular and syndromic forms follow an autosomal or X-linked recessive mode of inheritance, and 22 disease-causing genes are implicated in their development. Our aim was to clarify the genetic background of a Hungarian albinism cohort. Using a 22-gene albinism panel, the genetic background of 11 of the 17 Hungarian patients was elucidated. In patients with unidentified genetic backgrounds (n = 6), whole exome sequencing was performed. Our investigations revealed a novel, previously unreported rare variant (N687S) of the two-pore channel two gene (TPCN2). The N687S variant of the encoded TPC2 protein is carried by a 15-year-old Hungarian male albinism patient and his clinically unaffected mother. Our segregational analysis and in vitro functional experiments suggest that the detected novel rare TPCN2 variant alone is not a disease-causing variant in albinism. Deep genetic analyses of the family revealed that the patient also carries a phenotype-modifying R305W variant of the OCA2 protein, and he is the only family member harboring this genotype. Our results raise the possibility that this digenic combination might contribute to the observed differences between the patient and the mother, and found the genetic background of the disease in his case.

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Section: Discussionmentioning

confidence: 99%

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Nagy,

Pal,

Kun

et al. 2024

IJMS

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“…ORVAL integrates the pathogenicity predictor VarCoPP2.0 20 DIEP was run locally (8 CPU and 16GB RAM) using the implementation provided by the authors (https://github.com/pmglab/DIEP). The threshold for classification is 0.5, as reported in the paper 24 .…”

Section: Benchmarking With Existing Digenic Interpretation Toolsmentioning

confidence: 99%

“…While DiGePred autonomously classifies gene pairs 23 , it doesn’t provide the variant-specific resolution that ORVAL offers. A more recent ML methodology for predicting digenic interactions at the gene level is DIEP (Digenic Interaction Effect Predictor) 24 . OligoPVP is an automatic variant interpretation tool that can be used to prioritize digenic and oligogenic instances 25 .…”

Section: Introductionmentioning

confidence: 99%

Digenic variant interpretation with hypothesis-driven explainable AI

Paoli,

Nicora,

Berardelli

et al. 2023

Preprint

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MotivationThe digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations based on the proband’s phenotypic profiles and familial information can provide valuable assistance to clinicians during the diagnostic process.ResultsWe have developed diVas, a hypothesis-driven machine learning approach that can effectively interpret genomic variants across different gene pairs. DiVas demonstrates strong performance both in classifying and prioritizing causative pairs, consistently placing them within the top positions across 11 real cases (achieving 73% sensitivity and a median ranking of 3). Additionally, diVas exploits Explainable Artificial Intelligence (XAI) to dissect the digenic disease mechanism for predicted positive pairs.Availability and ImplementationPrediction results of the diVas method on a high-confidence, comprehensive, manually curated dataset of known digenic combinations are available atoliver.engenome.com.

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“…To investigate the possibility of a digenic interaction between her RPS19 and RPL27 variants, we conducted a computational structural biology analysis using the Digenic Interaction Effect Predictor (DIEP) software (Yuan et al, 2022) (see Figure 2). The DIEP software is a machine‐learning‐based method capable of identifying digenic and pseudo‐digenic effects between gene pairs.…”

Section: Case Reportmentioning

confidence: 99%

Probable digenic inheritance of Diamond–Blackfan anemia

Furuta,

Tinker,

Gulsevin

et al. 2023

American J of Med Genetics Pt A

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A 26‐year‐old female proband with a clinical diagnosis and consistent phenotype of Diamond–Blackfan anemia (DBA, OMIM 105650) without an identified genotype was referred to the Undiagnosed Diseases Network. DBA is classically associated with monoallelic variants that have an autosomal‐dominant or ‐recessive mode of inheritance. Intriguingly, her case was solved by a detection of a digenic interaction between non‐allelic RPS19 and RPL27 variants. This was confirmed with a machine learning structural model, co‐segregation analysis, and RNA sequencing. This is the first report of DBA caused by a digenic effect of two non‐allelic variants demonstrated by machine learning structural model. This case suggests that atypical phenotypic presentations of DBA may be caused by digenic inheritance in some individuals. We also conclude that a machine learning structural model can be useful in detecting digenic models of possible interactions between products encoded by alleles of different genes inherited from non‐affected carrier parents that can result in DBA with an unrealized 25% recurrence risk.

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An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

Cited by 8 publications

References 79 publications

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Missing Heritability in Albinism: Deep Characterization of a Hungarian Albinism Cohort Raises the Possibility of the Digenic Genetic Background of the Disease

Digenic variant interpretation with hypothesis-driven explainable AI

Probable digenic inheritance of Diamond–Blackfan anemia

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