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Exome Sequencing Covers Specific Gene Sets Ineffectually

By LabMedica International staff writers
Posted on 09 Jun 2014
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Image: Exome sequencing workflow: Double-stranded genomic DNA is fragmented by sonication. Linkers are then attached to the DNA fragments, which are then hybridized to a capture microarray designed to target only the exons (Photo courtesy of Sarah Kusala).
Image: Exome sequencing workflow: Double-stranded genomic DNA is fragmented by sonication. Linkers are then attached to the DNA fragments, which are then hybridized to a capture microarray designed to target only the exons (Photo courtesy of Sarah Kusala).
Clinical diagnosis can be made using exome sequencing, but may be regularly missed as there appears to be a high false-negative rate using existing sequencing kits.

The exome is the DNA sequence of genes that are translated into protein and these protein-coding regions contain most of the currently-known disease-causing genetic mutations. Explicitly, mutations of 56 specific genes with known clinical importance should be reported even when they are incidental to the patient's current medical condition.

Scientists at Thomas Jefferson University (Philadelphia, PA, USA) analyzed 44 exome datasets from four different testing kits, which showed that they missed a high proportion of clinically relevant regions in the 56 genes. The American College of Medical Genetics and Genomics (ACMG; Bethesda, MD, USA) has recommended the reporting to patients of clinically actionable incidental genetic findings in the course of clinical exome testing.

A total of 17,774 disease-causing genetic variants are annotated in the Human Gene Mutation Database (HGMD) for the 56 genes mentioned in the ACMG recommendations. The scientists examined the coverage of the exome datasets for the locations where the 17,774 disease-causing variants can occur. Although the exome datasets are comparable in quality to other published clinical and research exome data sets, the coverage of the disease-causing locations was very heterogeneous and often poor.

The study also found that exome datasets generated from low amounts of sequence data with fewer than six gigabases, performed much worse than datasets that were generated from higher amounts of sequence data with more than 10 gigabases. This finding is consistent with previous studies showing that exome methods do not have a linear relationship between sequence-generated and nucleotide coverage. Instead, a minimum threshold of sequencing data needs to be met before optimum nucleotide coverage is obtained.

Eric R. Londin PhD, the senior author of the study, said, “At least one gene in each exome method was missing more than 40% of disease-causing genetic variants, and we found that the worst-performing method missed more than 90% of such variants in 4 of the 56 genes. Current consensus and regulatory guidelines do not prescribe a minimum data requirement for clinical exome tests. The result is that when a causative variant cannot be identified it does not necessarily imply that the variant is not present, rather that there may be a technical issue with the exome technology used.” The study was presented at the annual conference of the European Society of Human Genetics held May 31–June 3, 2014, in Milan (Italy). 

Related Links:

Thomas Jefferson University
The American College of Medical Genetics and Genomics


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