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Benchmark of computational methods to detect digenism in sequencing data

dc.rights.licenseopenen_US
dc.contributor.authorOGLOBLINSKY, M. C.
dc.contributor.authorCONRAD, D. F.
dc.contributor.authorBAUDOT, A.
dc.contributor.authorTOURNIER-LASSERVE, E.
dc.contributor.authorGÉNIN, E.
dc.contributor.authorMARENNE, G.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDARTIGUES, Jean-Francois
ORCID: 0000-0001-9482-5529
IDREF: 058586105
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorLETENNEUR, Luc
dc.date.accessioned2025-05-16T08:04:34Z
dc.date.available2025-05-16T08:04:34Z
dc.date.issued2025-04-09
dc.identifier.issn1018-4813en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/206641
dc.description.abstractEnDigenic inheritance is characterized by the combined alteration of two different genes leading to a disease. It could explain the etiology of many currently undiagnosed rare diseases. With the advent of next-generation sequencing technologies, the identification of digenic inheritance patterns has become more technically feasible, yet still poses significant challenges without any gold standard method. Here, we present a comprehensive overview of the existing methods developed to detect digenic inheritance in sequencing data and provide a classification in cohort-based and individual-based methods. The latter category of methods appeared the most applicable to rare diseases, especially the ones not needing patient phenotypic description as input. We discuss the availability of the different methods, their output and scalability to inform potential users. Focusing on methods to detect digenic inheritance in the case of very rare or heterogeneous diseases, we propose a benchmark using different real-life scenarios involving known digenic and putative neutral pairs of genes. Among these different methods, DiGePred stood out as the one giving the least number of false positives, ARBOCK as giving the greatest number of true positives, and DIEP as having the best balance between both. By synthesizing the state-of-the-art techniques and providing insights into their practical utility, this benchmark serves as a valuable resource for researchers and clinicians in selecting suitable methodologies for detecting digenic inheritance in a wide range of disorders using sequencing data.
dc.language.isoENen_US
dc.title.enBenchmark of computational methods to detect digenism in sequencing data
dc.title.alternativeEur J Hum Geneten_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41431-025-01834-9en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed40204980en_US
bordeaux.journalEuropean Journal of Human Geneticsen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamColl_ACTIVEen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=European%20Journal%20of%20Human%20Genetics&rft.date=2025-04-09&rft.eissn=1018-4813&rft.issn=1018-4813&rft.au=OGLOBLINSKY,%20M.%20C.&CONRAD,%20D.%20F.&BAUDOT,%20A.&TOURNIER-LASSERVE,%20E.&G%C3%89NIN,%20E.&rft.genre=article


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