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Validation of copy number variation analysis for next-generation sequencing diagnostics.

ABSTRACT: Although a common cause of disease, copy number variants (CNVs) have not routinely been identified from next-generation sequencing (NGS) data in a clinical context. This study aimed to examine the sensitivity and specificity of a widely used software package, ExomeDepth, to identify CNVs from targeted NGS data sets. We benchmarked the accuracy of CNV detection using ExomeDepth v1.1.6 applied to targeted NGS data sets by comparison to CNV events detected through whole-genome sequencing for 25 individuals and determined the sensitivity and specificity of ExomeDepth applied to these targeted NGS data sets to be 100% and 99.8%, respectively. To define quality assurance metrics for CNV surveillance through ExomeDepth, we undertook simulation of single-exon (n=1000) and multiple-exon heterozygous deletion events (n=1749), determining a sensitivity of 97% (n=2749). We identified that the extent of sequencing coverage, the inter- and intra-sample variability in the depth of sequencing coverage and the composition of analysis regions are all important determinants of successful CNV surveillance through ExomeDepth. We then applied these quality assurance metrics during CNV surveillance for 140 individuals across 12 distinct clinical areas, encompassing over 500 potential rare disease diagnoses. All 140 individuals lacked molecular diagnoses after routine clinical NGS testing, and by application of ExomeDepth, we identified 17 CNVs contributing to the cause of a Mendelian disorder. Our findings support the integration of CNV detection using ExomeDepth v1.1.6 with routine targeted NGS diagnostic services for Mendelian disorders. Implementation of this strategy increases diagnostic yields and enhances clinical care.

SUBMITTER: Ellingford JM

PROVIDER: S-EPMC5427176 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Validation of copy number variation analysis for next-generation sequencing diagnostics.

Ellingford Jamie M JM Campbell Christopher C Barton Stephanie S Bhaskar Sanjeev S Gupta Saurabh S Taylor Rachel L RL Sergouniotis Panagiotis I PI Horn Bradley B Lamb Janine A JA Michaelides Michel M Webster Andrew R AR Newman William G WG Panda Binay B Ramsden Simon C SC Black Graeme Cm GC

European journal of human genetics : EJHG 20170405 6

Although a common cause of disease, copy number variants (CNVs) have not routinely been identified from next-generation sequencing (NGS) data in a clinical context. This study aimed to examine the sensitivity and specificity of a widely used software package, ExomeDepth, to identify CNVs from targeted NGS data sets. We benchmarked the accuracy of CNV detection using ExomeDepth v1.1.6 applied to targeted NGS data sets by comparison to CNV events detected through whole-genome sequencing for 25 ind ...[more]

PMID: 28378820

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Project description:BackgroundDetection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.ResultsOur computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.ConclusionsWith this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Project description:Primary ciliary dyskinesia (PCD) is a rare disorder characterized by extensive genetic heterogeneity. However, in the genetic pathogenesis of PCD, copy number variation (CNV) has not received sufficient attention and has rarely been reported, especially in China. Next-generation sequencing (NGS) followed by targeted CNV analysis was used in patients highly suspected to have PCD with negative results in routine whole-exome sequencing (WES) analysis. Quantitative real-time polymerase chain reaction (qPCR) and Sanger sequencing were used to confirm these CNVs. To further characterize the ciliary phenotypes, high-speed video microscopy analysis (HSVA), transmission electron microscopy (TEM), and immunofluorescence (IF) analysis were used. Patient 1 (F1: II-1), a 0.6-year-old girl, came from a nonconsanguineous family-I. She presented with situs inversus totalis, neonatal respiratory distress, and sinusitis. The nasal nitric oxide level was markedly reduced. The respiratory cilia beat with reduced amplitude. TEM revealed shortened outer dynein arms (ODA) of cilia. chr5:13717907-13722661del spanning exons 71-72 was identified by NGS-based CNV analysis. Patient 2 (F2: IV-4), a 37-year-old man, and his eldest brother Patient 3 (F2: IV-2) came from a consanguineous family-II. Both had sinusitis, bronchiectasis and situs inversus totalis. The respiratory cilia of Patient 2 and Patient 3 were found to be uniformly immotile, with ODA defects. Two novel homozygous deletions chr5:13720087_13733030delinsGTTTTC and chr5:13649539_1 3707643del, spanning exons 69-71 and exons 77-79 were identified by NGS-based CNV analysis. Abnormalities in DNA copy number were confirmed by qPCR amplification. IF showed that the respiratory cilia of Patient 1 and Patient 2 were deficient in dynein axonemal heavy chain 5 (DNAH5) protein expression. This report identified three novel DNAH5 disease-associated variants by WES-based CNV analysis. Our study expands the genetic spectrum of PCD with DNAH5 in the Chinese population.Supplementary informationThe online version contains supplementary material available at 10.1007/s43657-023-00130-0.

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Validation of copy number variation analysis for next-generation sequencing diagnostics.

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Validation of copy number variation analysis for next-generation sequencing diagnostics.

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