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Next Generation Molecular Diagnosis of Mitochondrial Disorders

by: Lee-Jun C. Wong
Mitochondrion (March 2013), doi:10.1016/j.mito.2013.02.001  Key: citeulike:12154641

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Abstract

Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6 kb mitochondrial genome and ~ 1,500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive , dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders. ⺠We describe comprehensive diagnosis of dual genome mitochondrial disorders by NGS. ⺠The whole mitochondrial genome is enriched by a single amplicon long range PCR. ⺠Uniformly deep coverage allows easy detection of single and multiple mtDNA deletions. ⺠parallel sequence analysis of target nuclear genes for mitochondrial disorders ⺠Clinically validated NGS testing of target genes based on recognizable phenotype


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