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Hum Mutat. 2019 Aug 5. doi: 10.1002/humu.23885. [Epub ahead of print]

Deciphering exome sequencing data: bringing mitochondrial DNA variants to light.

Author information

1
UMR1231 GAD, Inserm - University of Burgundy-Franche Comté, Dijon, France.
2
Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, Dijon University Hospital, Dijon, France.
3
Laboratoire CERBA, Saint-Ouen l'Aumône, France.
4
Institut MITOVASC, UMR CNRS 6015-INSERM1083, University of Angers, Angers, France.
5
Centre de Référence maladies mitochondriales, CHU Angers, Angers, France.
6
Centre de Référence maladies rares « maladies dermatologiques en mosaïque », service de dermatologie, FHU-TRANSLAD, Dijon University Hospital, Dijon, France.
7
Service Dermatologie, CHU Dijon Bourgogne, Dijon, France.
8
Centre de Référence maladies rares « Anomalies du développement et syndromes malformatifs », centre de génétique, FHU-TRANSLAD, Dijon University Hospital, Dijon, France.
9
Centre de Référence maladies rares « déficience intellectuelle », centre de génétique, FHU-TRANSLAD, Dijon University Hospital, Dijon, France.
10
Service de Pédiatrie, Hôpital d'Enfants Brabois, CHRU Nancy, Vandoeuvre les Nancy, France.
11
UMRS 1256 NGERE, Inserm - University of Lorraine - CHRU Nancy, Nancy, France.
12
Centre de Références des maladies héréditaires du métabolisme, CHRU de Nancy, Nancy, France.
13
Centre de compétences des maladies mitochondriales, Dijon University Hospital, Dijon, France.

Abstract

The expanding use of exome sequencing (ES) in diagnosis generates a huge amount of data, including untargeted mitochondrial DNA (mtDNA) sequences. We developed a strategy to deeply study ES data, focusing on mtDNA genome on a large unspecific cohort in order to increase diagnostic yield. A targeted bioinformatics pipeline assembled mitochondrial genome from ES data to detect pathogenic mtDNA variants in parallel with the "in-house" nuclear exome pipeline. MtDNA data coming from off-target sequences (indirect sequencing) were extracted from the BAM files in 928 individuals with developmental and/or neurological anomalies. The mtDNA variants were filtered out based on database information, cohort frequencies, haplogroups and protein consequences. Two homoplasmic pathogenic variants (m.9035T>C and m.11778G>A) were identified in 2/928 unrelated individuals (0.2%): the m.9035T>C (MT-ATP6) variant in a female with ataxia and the m.11778G>A (MT-ND4) variant in a male with a complex mosaic disorder and a severe ophthalmological phenotype, uncovering undiagnosed Leber's hereditary optic neuropathy (LHON). Seven secondary findings were also found, predisposing to deafness or LHON, in 7/928 individuals (0.75%). This study demonstrates the usefulness of including a targeted strategy in ES pipeline to detect mtDNA variants, improving results in diagnosis and research, without resampling patients and performing targeted mtDNA strategies. This article is protected by copyright. All rights reserved.

KEYWORDS:

ES data; bioinformatics; mtDNA; pipeline

PMID:
31379041
DOI:
10.1002/humu.23885

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