PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes

Roca, Iria; González-Castro, Lorena; Maynou, Joan; Palacios, Lourdes; Fernández, Helena; Couce, Ma Luz; Fernández–Marmiesse, Ana

doi:10.1016/j.ygeno.2019.07.011

Cited by 9 publications

(8 citation statements)

References 46 publications

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“…Reads were aligned to the reference genome GRCh37 with BWA v0.7.17 (Li and Durbin, 2009), and BEDTools 2.27.1 (Quinlan and Hall, 2010) and Picard v2.18.14 (Picard Tools-By Broad Institute, 2018) were used for intermediate steps. VarScan v.2.4.2 (Koboldt et al, 2009), SAMtools v1.9 (Li et al, 2009), GATK v4.0.10 (McKenna et al, 2010), Pindel v0.2.5 (Ye et al, 2009), and Platypus v0.8.1 (Rimmer et al, 2014) software were used for variant detection, SnpEff v4.3 (Cingolani et al, 2012) for variant annotation, and PattRec for CNV detection (Roca et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Rare Variants in 48 Genes Account for 42% of Cases of Epilepsy With or Without Neurodevelopmental Delay in 246 Pediatric Patients

et al. 2019

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In order to characterize the genetic architecture of epilepsy in a pediatric population from the Iberian Peninsula (including the Canary Islands), we conducted targeted exome sequencing of 246 patients with infantile-onset seizures with or without neurodevelopmental delay. We detected 107 variants in 48 different genes, which were implicated in neuronal excitability, neurodevelopment, synaptic transmission, and metabolic pathways. In 104 cases (42%) we detected variant(s) that we classified as pathogenic or likely pathogenic. Of the 48 mutated genes, 32 were dominant, 8 recessive and 8 X-linked. Of the patients for whom family studies could be performed and in whom pathogenic variants were identified in dominant or X-linked genes, 82% carried de novo mutations. The involvement of small copy number variations (CNVs) is 9%. The use of progressively updated custom panels with high mean vertical coverage enabled establishment of a definitive diagnosis in a large proportion of cases (42%) and detection of CNVs (even duplications) with high fidelity. In 10.5% of patients we detected associations that are pending confirmation via functional and/or familial studies. Our findings had important consequences for the clinical management of the probands, since a large proportion of the cohort had been clinically misdiagnosed, and their families were subsequently able to avail of genetic counseling. In some cases, a more appropriate treatment was selected for the patient in question, or an inappropriate treatment discontinued. Our findings suggest the existence of modifier genes that may explain the incomplete penetrance of some epilepsy-related genes. We discuss possible reasons for non-diagnosis and future research directions. Further studies will be required to uncover the roles of structural variants, epimutations, and oligogenic inheritance in epilepsy, thereby providing a more complete molecular picture of this disease. In summary, given the broad phenotypic spectrum of most epilepsy-related genes, efficient genomic tools like the targeted exome sequencing panel described here are essential for early diagnosis and treatment, and should be implemented as first-tier diagnostic tools for children with epilepsy without a clear etiologic basis.

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Section: Methodsmentioning

confidence: 99%

Rare Variants in 48 Genes Account for 42% of Cases of Epilepsy With or Without Neurodevelopmental Delay in 246 Pediatric Patients

et al. 2019

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“…Because MLPA is the most widely used method for detecting CNVs in humans, we validated our method using the largest reported MLPA-confirmed data set. [14][15][16][17][18][19][20][21][22][23][24][25][26] We evaluated the performance of CCR-CNV and compared it with DECoN and Atlas-CNV. CCR-CNV was superior in terms of sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…However, proper diagnoses of exonic deletions or duplications using targeted NGS data have proved to be challenging. [14][15][16][17][18][19][20][21][22][23][24][25][26] In addition, MLPA cannot be performed individually on all genes in an NGS panel because of the associated expenses.…”

Section: Introductionmentioning

confidence: 99%

“…27 Various methods have been developed to identify exonic CNVs in gene panel sequencing data. [14][15][16][17][18][19][20][21][22][23][24][25][26] These methods often use complex data normalization algorithms that reduce sensitivity for exonic CNVs and provide highly segmented copy number regions, resulting in high false positive rates. The use of sophisticated algorithms such as machine learning in clinical settings is difficult.…”

Section: Introductionmentioning

confidence: 99%

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Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Kim¹,

Lee²,

Yun³

et al. 2022

Genetics in Medicine

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Despite the importance of exonic copy number variations (CNVs) in human genetic diseases, reliable next-generation sequencing-based methods for detecting them are unavailable. We developed an expandable and robust exonic CNV detection tool called consistent count region (CCR)-CNV. Methods: In total, about 1000 samples of the truth set were used for validating CCR-CNV. We compared CCR-CNV performance with 2 well-known CNV tools. Finally, to overcome the limitations of CCR-CNV, we devised a combined approach. Results: The mean sensitivity and specificity of CCR-CNV alone were above 95%, which was superior to that of other CNV tools, such as DECoN and Atlas-CNV. However, low covered region and positive predictive value and high false discovery rate act as obstacles to its use in clinical settings. The combined approach showed much improved performance than CCR-CNV alone. Conclusion:In this study, we present a novel diagnostic tool that allows the identification of exonic CNVs with high confidence using various reagents and clinical next-generation sequencing platforms. We validated this method using the largest multiple ligation-dependent probe amplification-confirmed data set, including sufficient copy normal control data. The approach, combined with existing CNV tools, allows the implementation of CCR-CNV in clinical settings.

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“…9 Without this option, as for other pipelines, the minimum limit of CNV detection was at least the smallest wholeexon (lower limit about 300 bp). 8,10 Despite calling CNV at highresolution parameters, no false-positive result was highlighted.…”

Section: Structural Variant Detectionmentioning

confidence: 99%

Development of a new expanded next‐generation sequencing panel for genetic diseases involved in dyslipidemia

et al. 2020

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The aim of this study was to provide an efficient tool: reliable, able to increase the molecular diagnosis performance, to facilitate the detection of copy number variants (CNV), to assess genetic risk scores (wGRS) and to offer the opportunity to explore candidate genes. Custom SeqCap EZ libraries, NextSeq500 sequencing and a homemade pipeline enable the analysis of 311 dyslipidemia-related genes. In the training group (48 DNA from patients with a well-established molecular diagnosis), this nextgeneration sequencing (NGS) workflow showed an analytical sensitivity >99% (n = 532 variants) without any false negative including a partial deletion of one exon. In the prospective group, from 25 DNA from patients without prior molecular analyses, 18 rare variants were identified in the first intention panel genes, allowing the diagnosis of monogenic dyslipidemia in 11 patients. In six other patients, the analysis of minor genes and wGRS determination provided a hypothesis to explain the dyslipidemia. Remaining data from the whole NGS workflow identified four patients with potentially deleterious variants. This NGS process gives a major opportunity to accede to an enhanced understanding of the genetic of dyslipidemia by simultaneous assessment of multiple genetic determinants.

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PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes

Cited by 9 publications

References 46 publications

Rare Variants in 48 Genes Account for 42% of Cases of Epilepsy With or Without Neurodevelopmental Delay in 246 Pediatric Patients

Rare Variants in 48 Genes Account for 42% of Cases of Epilepsy With or Without Neurodevelopmental Delay in 246 Pediatric Patients

Consistent count region–copy number variation (CCR-CNV): an expandable and robust tool for clinical diagnosis of copy number variation at the exon level using next-generation sequencing data

Development of a new expanded next‐generation sequencing panel for genetic diseases involved in dyslipidemia

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