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Recent advances in understanding the molecular genetic basis of mitochondrial disease
Author(s) -
Thompson Kyle,
Collier Jack J.,
Glasgow Ruth I. C.,
Robertson Fiona M.,
Pyle Angela,
Blakely Emma L.,
Alston Charlotte L.,
Oláhová Monika,
McFarland Robert,
Taylor Robert W.
Publication year - 2020
Publication title -
journal of inherited metabolic disease
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.462
H-Index - 102
eISSN - 1573-2665
pISSN - 0141-8955
DOI - 10.1002/jimd.12104
Subject(s) - exome sequencing , computational biology , biology , exome , human genetics , genomics , disease , dna sequencing , mitochondrial disease , genome , molecular diagnostics , whole genome sequencing , bioinformatics , genetics , mitochondrial dna , gene , medicine , mutation , pathology
Mitochondrial disease is hugely diverse with respect to associated clinical presentations and underlying genetic causes, with pathogenic variants in over 300 disease genes currently described. Approximately half of these have been discovered in the last decade due to the increasingly widespread application of next generation sequencing technologies, in particular unbiased, whole exome—and latterly, whole genome sequencing. These technologies allow more genetic data to be collected from patients with mitochondrial disorders, continually improving the diagnostic success rate in a clinical setting. Despite these significant advances, some patients still remain without a definitive genetic diagnosis. Large datasets containing many variants of unknown significance have become a major challenge with next generation sequencing strategies and these require significant functional validation to confirm pathogenicity. This interface between diagnostics and research is critical in continuing to expand the list of known pathogenic variants and concomitantly enhance our knowledge of mitochondrial biology. The increasing use of whole exome sequencing, whole genome sequencing and other “omics” techniques such as transcriptomics and proteomics will generate even more data and allow further interrogation and validation of genetic causes, including those outside of coding regions. This will improve diagnostic yields still further and emphasizes the integral role that functional assessment of variant causality plays in this process—the overarching focus of this review.