{"doi":"10.1101/2024.12.22.24319370","title":"Mitochondrial DNA variant detection in over 6,500 rare disease families by the systematic analysis of exome and genome sequencing data resolves undiagnosed cases","abstract":"ABSTRACT Background Variants in the mitochondrial genome (mtDNA) cause a diverse collection of mitochondrial diseases and have extensive phenotypic overlap with Mendelian diseases encoded on the nuclear genome. The mtDNA is often not specifically evaluated in patients with suspected Mendelian disease, resulting in overlooked diagnostic variants. Methods Using dedicated pipelines to address the technical challenges posed by the mtDNA - circular genome, variant heteroplasmy, and nuclear misalignment - single nucleotide variants, small indels, and large mtDNA deletions were called from exome and genome sequencing data, in addition to RNA-sequencing when available. A cohort of 6,660 rare disease families were analyzed (5,625 genetically undiagnosed, 84%) from the Genomics Research to Elucidate the Genetics of Rare diseases (GREGoR) Consortium as well as other rare disease cohorts. Results Diagnostic mtDNA variants were identified in 10 previously genetically undiagnosed families (one large deletion, eight reported pathogenic variants, one novel pathogenic variant). In one additional undiagnosed proband, the detection of >900 heteroplasmic variants provided functional evidence of pathogenicity to a novel de novo variant in the nuclear gene POLG (DNA polymerase gamma), responsible for mtDNA replication and repair. Conclusion mtDNA variant calling from data generated by exome and genome sequencing for nuclear variant analysis resulted in a genetic diagnosis or detection of a candidate variant for 0.4% of undiagnosed families affected by a broad range of rare diseases.","journal":"medRxiv","year":2024,"id":492756,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9508,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":971,"name":"Kristen M. 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