MDVarP: modifier ~ disease-causing variant pairs predictor.

Hong Sun, Yunqin Chen, Liangxiao Ma

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Article in BioData mining, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

3 authors.

Hong Sun *Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine, Center for Biomedical Informatics, School of Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, 200062, China. shpolor@163.com.

Yunqin Chen *Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies (SIBPT), Shanghai, 200237, China.

Liangxiao Ma *Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Science, Shanghai, 200031, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundModifiers significantly impact disease phenotypes by modulating the effects of disease-causing variants, resulting in varying disease manifestations among individuals. However, identifying genetic interactions between modifier and disease-causing variants is challenging.

resultsWe developed MDVarP, an ensemble model comprising 1000 random forest predictors, to identify modifier ~ disease-causing variant combinations. MDVarP achieves high accuracy and precision, as verified using an independent dataset with published evidence of genetic interactions. We identified 25 novel modifier ~ disease-causing variant combinations and obtained supporting evidence for these associations. MDVarP outputs a class label ("Associated-pair" or "Nonrelevant-pair") and two prediction scores indicating the probability of a true association.

conclusionsMDVarP prioritizes variant pairs associated with phenotypic modulations, enabling more effective mapping of functional contributions from disease-causing and modifier variants. This framework interprets genetic interactions underlying phenotypic variations in human diseases, with potential applications in personalized medicine and disease prevention.

Indexed as

Interacting unitPhenotypical expressionPredictionVariants

Identifiers

PMID39379981

DOI10.1186/s13040-024-00392-y

PMCPMC11460193

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LicenceCC BY-NC-ND

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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.