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Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.

ABSTRACT: BACKGROUND:Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome sequencing data are not suitable for carrier screening, as they require matched normals, very large cohorts, or extensive gene panels. METHODS:We present a computational software package, geneCNV ( http://github.com/vkozareva/geneCNV ), which can identify exon-level CNVs using exome sequencing data from only a few genes. The tool relies on a hierarchical parametric model trained on a small cohort of reference samples. RESULTS:Using geneCNV, we accurately inferred heterozygous CNVs in the DMD gene across a cohort of 15 test subjects. These results were validated against MLPA, the current standard for clinical CNV analysis in DMD. We also benchmarked the tool's performance against other computational techniques and found comparable or improved CNV detection in DMD using data from panels ranging from 4,000 genes to as few as 8 genes. CONCLUSIONS:geneCNV allows for the creation of cost-effective screening panels by allowing NGS sequencing approaches to generate results equivalent to bespoke genotyping assays like MLPA. By using a parametric model to detect CNVs, it also fulfills regulatory requirements to define a reference range for a genetic test. It is freely available and can be incorporated into any Illumina sequencing pipeline to create clinical assays for detection of exon duplications and deletions.

SUBMITTER: Kozareva V

PROVIDER: S-EPMC6195989 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.

Kozareva Velina V Stroff Clayton C Silver Maxwell M Freidin Jonathan F JF Delaney Nigel F NF

BMC medical genomics 20181020 1

<h4>Background</h4>Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome ...[more]

PMID: 30342520

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Project description:Importance:PTEN is among the most common autism spectrum disorder (ASD)-predisposition genes. Germline PTEN mutation carriers can develop malignant neoplasms and/or neurodevelopmental disorders such as ASD and developmental delay. Why a single gene contributes to disparate clinical outcomes, even in patients with identical PTEN mutations, remains unclear. Objective:To investigate the association of copy number variations (CNVs), altered numbers of copies of DNA sequences within the genome, with specific phenotypes in patients with germline PTEN mutations. Design, Setting, and Participants:This prospective cohort study examined genome-wide microarrays performed on blood-derived DNA to detect germline CNVs from September 1, 2005, through January 3, 2018. Multicenter accrual occurred from community and academic medical centers throughout North America, South America, Europe, Australia, and Asia. Participants included patients with PTEN hamartoma tumor syndrome (PHTS) (n = 481), molecularly defined as carrying germline pathogenic PTEN mutations. Data were analyzed from November 14, 2018, to August 1, 2019. Exposures:Detection of CNVs from patient-derived germline DNA. Main Outcomes and Measures:Prevalence of pathogenic and/or likely pathogenic CNVs in patients with PHTS and association with ASD/developmental delay and/or cancer, ascertained through medical records and pathology reports. Results:The study included 481 patients with PHTS (mean [SD] age, 33.2 [21.6] years; 268 female [55.7%]). The analytic series consisted of 309 patients with PHTS and genetically determined European ancestry. Patients were divided into 3 phenotypic groups, excluding family members within each group. These include 110 patients with ASD/developmental delay, 194 without ASD/developmental delay, and 121 with cancer (of whom 116 were in the no ASD/developmental delay group). Genome-wide evaluation of autosomal CNVs indicated an increased CNV burden, particularly duplications in genic regions, in patients with ASD/developmental delay compared with those without ASD/developmental delay (odds ratio [OR], 1.9; 95% CI, 1.1-3.4; P = .03) and those with cancer (OR, 2.5; 95% CI, 1.3-4.6; P = .003). Eleven of the 110 patients (10.0%) with ASD/developmental delay carried pathogenic and/or likely pathogenic CNVs associated with neurodevelopmental disorders, compared with 5 of 194 (2.6%) without ASD/developmental delay (OR, 4.2; 95% CI, 1.4-13.7; P = .008) and 2 of 121 (1.7%) with cancer (OR, 6.6; 95% CI, 1.6-44.5; P = .007). Evidence of an association between pathogenic and/or likely pathogenic CNVs and PHTS with ASD/developmental delay was further supported in a validation series of 69 patients with PHTS of genetically determined non-European ancestry. Conclusions and Relevance:These findings suggest that copy number variations are associated with the ASD/developmental delay clinical phenotype in PHTS, providing proof of principle for similarly heterogeneous disorders lacking outcome-specific associations.

Project description:BACKGROUND: Some diseases, like tumors, can be related to chromosomal aberrations, leading to changes of DNA copy number. The copy number of an aberrant genome can be represented as a piecewise constant function, since it can exhibit regions of deletions or gains. Instead, in a healthy cell the copy number is two because we inherit one copy of each chromosome from each our parents. Bayesian Piecewise Constant Regression (BPCR) is a Bayesian regression method for data that are noisy observations of a piecewise constant function. The method estimates the unknown segment number, the endpoints of the segments and the value of the segment levels of the underlying piecewise constant function. The Bayesian Regression Curve (BRC) estimates the same data with a smoothing curve. However, in the original formulation, some estimators failed to properly determine the corresponding parameters. For example, the boundary estimator did not take into account the dependency among the boundaries and succeeded in estimating more than one breakpoint at the same position, losing segments. RESULTS: We derived an improved version of the BPCR (called mBPCR) and BRC, changing the segment number estimator and the boundary estimator to enhance the fitting procedure. We also proposed an alternative estimator of the variance of the segment levels, which is useful in case of data with high noise. Using artificial data, we compared the original and the modified version of BPCR and BRC with other regression methods, showing that our improved version of BPCR generally outperformed all the others. Similar results were also observed on real data. CONCLUSION: We propose an improved method for DNA copy number estimation, mBPCR, which performed very well compared to previously published algorithms. In particular, mBPCR was more powerful in the detection of the true position of the breakpoints and of small aberrations in very noisy data. Hence, from a biological point of view, our method can be very useful, for example, to find targets of genomic aberrations in clinical cancer samples.

Dataset Information

Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.

Publications

Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model.

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