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

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 the mtDNA genome on a large unspecific cohort 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 out of 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 out of 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.

Increased diagnostic and new genes identification outcome using research reanalysis of singleton exome sequencing.

In clinical exome sequencing (cES), the American College of Medical Genetics and Genomics recommends limiting variant interpretation to established human-disease genes. The diagnostic yield of cES in intellectual disability and/or multiple congenital anomalies (ID/MCA) is currently about 30%. Though the results may seem acceptable for rare diseases, they mean that 70% of affected individuals remain genetically undiagnosed. Further analysis extended to all mutated genes in a research environment is a valuable strategy for improving diagnostic yields. This study presents the results of systematic research reanalysis of negative cES in a cohort of 313 individuals with ID/MCA. We identified 17 new genes not related to human disease, implicated 22 non-OMIM disease-causing genes recently or previously rarely related to disease, and described 1 new phenotype associated with a known gene. Twenty-six candidate genes were identified and are waiting for future recurrence. Overall, we diagnose 15% of the individuals with initial negative cES, increasing the diagnostic yield from 30% to more than 40% (or 46% if strong candidate genes are considered). This study demonstrates the power of such extended research reanalysis to increase scientific knowledge of rare diseases. These novel findings can then be applied in the field of diagnostics.

2.5 years' experience of GeneMatcher data-sharing: a powerful tool for identifying new genes responsible for rare diseases.

PURPOSE: Exome sequencing (ES) powerfully identifies the molecular bases of heterogeneous conditions such as intellectual disability and/or multiple congenital anomalies (ID/MCA). Current ES analysis, combining diagnosis analysis restricted to disease-causing genes reported in OMIM database and subsequent research investigation extended to other genes, indicated causal and candidate genes around 40% and 10%. Nonconclusive results are frequent in such ultrarare conditions that recurrence and genotype-phenotype correlations are limited. International data-sharing permits the gathering of additional patients carrying variants in the same gene to draw definitive conclusions on their implication as disease causing. Several web-based tools have been developed and grouped in Matchmaker Exchange. In this study, we report our current experience as a regional center that has implemented ES as a first-line diagnostic test since 2013, working with a research laboratory devoted to disease gene identification. METHODS: We used GeneMatcher over 2.5 years to share 71 novel candidate genes identified by ES. RESULTS: Matches occurred in 60/71 candidate genes allowing to confirm the implication of 39% of matched genes as causal and to rule out 6% of them. CONCLUSION: The introduction of user-friendly gene-matching tools, such as GeneMatcher, appeared to be an essential step for the rapid identification of novel disease genes responsible for ID/MCA.