Dataset Information

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

ABSTRACT:

Purpose

Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.

Methods

We performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples.

Results

A total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS.

Conclusion

The SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

SUBMITTER: Mu W

PROVIDER: S-EPMC6752280 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

Mu Wenbo W Li Bing B Wu Sitao S Chen Jefferey J Sain Divya D Xu Dong D Black Mary Helen MH Karam Rachid R Gillespie Katrina K Farwell Hagman Kelly D KD Guidugli Lucia L Pronold Melissa M Elliott Aaron A Lu Hsiao-Mei HM

Genetics in medicine : official journal of the American College of Medical Genetics 20181219 7

<h4>Purpose</h4>Structural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.<h4>Methods</h4>We performed SV assessment on 60,000 clinical samples teste ...[more]

PMID: 30563988

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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:BackgroundSkeletal dysplasia is typically diagnosed using a combination of radiographic imaging, clinical examinations, and molecular testing. Identifying a molecular diagnosis for an individual with a skeletal dysplasia can lead to improved clinical care, guide future medical management and treatment, and inform assessment of risk for familial recurrence. The molecular diagnostic utility of multi-gene panel testing using next-generation sequencing (NGS) has not yet been characterized for an unselected population of individuals with suspected skeletal dysplasia. In this study, we retrospectively reviewed patient reports to assess the diagnostic yield, reported variant characteristics, impact of copy number variation, and performance in prenatal diagnostics of panel tests for variants in genes associated with skeletal dysplasia and growth disorders.ResultsClinical reports of consecutive patients with a clinical indication of suspected skeletal dysplasia who underwent panel testing were examined. The 543 patients included in the study submitted samples for diagnostic genetic testing with an indication of suspected skeletal dysplasia or growth disorder and received one of three nested panel tests. A molecular diagnosis was established in 42.0% of patients (n = 228/543). Diagnostic variants were identified in 71 genes, nearly half of which (n = 35, 49.3%) contributed uniquely to a molecular diagnosis for a single patient in this cohort. Diagnostic yield was significantly higher among fetal samples (58.0%, n = 51/88) than postnatal samples (38.9%, n = 177/455; z = 3.32, p < 0.0009). Diagnostic variants in fetal cases were identified across 18 genes. Thirteen diagnostic CNVs were reported, representing 5.7% of diagnostic findings and ranging in size from 241-bp to whole chromosome aneuploidy. Additionally, 11.4% (36/315) of non-diagnostic patient reports had suspicious variants of unknown significance (VUS), in which additional family studies that provide segregation data and/or functional characterization may result in reclassification to likely pathogenic.ConclusionsThese findings demonstrate the utility of panel testing for individuals with a suspected skeletal dysplasia or growth disorder, with a particularly high diagnostic yield seen in prenatal cases. Pursuing comprehensive panel testing with high-resolution CNV analysis can provide a diagnostic benefit, given the considerable phenotype overlap amongst skeletal dysplasia conditions.

Dataset Information

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

Purpose

Methods

Results

Conclusion

Publications

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

Similar Datasets

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