Determination of CYP2D6 gene copy number by multiplex polymerase chain reaction analysis

Cytochrome P450 2D6 (CYP2D6) copy number variation (CNV) influences the metabolism of 15-25% of clinical drugs. Here we describe a novel multiplex polymerase chain reaction (PCR) analysis method that accurately detects CYP2D6 CNV and CYP2D6*9 allele. It includes the amplification of 2 CYP2D6 and 7 control (AQP1, CYP3A4, MDR1, and SDHB) fluorescent PCR products that are separated on a capillary sequencer and normalized using reference samples. The technique was validated using 27 PCR-restriction fragment length polymorphism (RFLP) pregenotyped samples and further tested in 75 Caucasian samples. The method assigns the correct CYP2D6 copy number, independent of already characterized CYP2D6 single nucleotide polymorphisms (SNPs), and could easily be applied to clinical samples.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

Accuracy of CNV Detection from GWAS Data

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two "gold standards," the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a "gold standard" for detection of CNVs remains to be established.     

Identification and functional characterization of copy number variations in diverse chicken breeds

Background

The detection and functional characterization of genomic structural variations are important for understanding the landscape of genetic variation in the chicken. A recently recognized aspect of genomic structural variation, called copy number variation (CNV), is gaining interest in chicken genomic studies. The aim of the present study was to investigate the pattern and functional characterization of CNVs in five characteristic chicken breeds, which will be important for future studies associating phenotype with chicken genome architecture.

Results

Using a commercial 385 K array-based comparative genomic hybridization (aCGH) genome array, we performed CNV discovery using 10 chicken samples from four local Chinese breeds and the French breed Houdan chicken. The female Anka broiler was used as a reference. A total of 281 copy number variation regions (CNVR) were identified, covering 12.8 Mb of polymorphic sequences or 1.07% of the entire chicken genome. The functional annotation of CNVRs indicated that these regions completely or partially overlapped with 231 genes and 1032 quantitative traits loci, suggesting these CNVs have important functions and might be promising resources for exploring differences among various breeds. In addition, we employed quantitative PCR (qPCR) to further validate several copy number variable genes, such as prolactin receptor, endothelin 3 (EDN3), suppressor of cytokine signaling 2, CD8a molecule, with important functions, and the results suggested that EDN3 might be a molecular marker for the selection of dark skin color in poultry production. Moreover, we also identified a new CNVR (chr24: 3484617–3512275), encoding the sortilin-related receptor gene, with copy number changes in only black-bone chicken.

Conclusions

Here, we report a genome-wide analysis of the CNVs in five chicken breeds using aCGH. The association between EDN3 and melanoblast proliferation was further confirmed using qPCR. These results provide additional information for understanding genomic variation and related phenotypic characteristics.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-934) contains supplementary material, which is available to authorized users.     

A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data

Background

It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample.

Methodology and Principal Findings

We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR).

Conclusions and Significance

We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.     

Outlier-Based Identification of Copy Number Variations Using Targeted Resequencing in a Small Cohort of Patients with Tetralogy of Fallot

Copy number variations (CNVs) are one of the main sources of variability in the human genome. Many CNVs are associated with various diseases including cardiovascular disease. In addition to hybridization-based methods, next-generation sequencing (NGS) technologies are increasingly used for CNV discovery. However, respective computational methods applicable to NGS data are still limited. We developed a novel CNV calling method based on outlier detection applicable to small cohorts, which is of particular interest for the discovery of individual CNVs within families, de novo CNVs in trios and/or small cohorts of specific phenotypes like rare diseases. Approximately 7,000 rare diseases are currently known, which collectively affect ∼6% of the population. For our method, we applied the Dixon’s Q test to detect outliers and used a Hidden Markov Model for their assessment. The method can be used for data obtained by exome and targeted resequencing. We evaluated our outlier- based method in comparison to the CNV calling tool CoNIFER using eight HapMap exome samples and subsequently applied both methods to targeted resequencing data of patients with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. In both the HapMap samples and the TOF cases, our method is superior to CoNIFER, such that it identifies more true positive CNVs. Called CNVs in TOF cases were validated by qPCR and HapMap CNVs were confirmed with available array-CGH data. In the TOF patients, we found four copy number gains affecting three genes, of which two are important regulators of heart development (NOTCH1, ISL1) and one is located in a region associated with cardiac malformations (PRODH at 22q11). In summary, we present a novel CNV calling method based on outlier detection, which will be of particular interest for the analysis of de novo or individual CNVs in trios or cohorts up to 30 individuals, respectively.     

A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process

To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.     

Variation of CNV distribution in five different ethnic populations

Recent studies have revealed a new type of variation in the human genome encompassing relatively large genomic segments ( approximately 100 kb-2.5 Mb), commonly referred to as copy number variation (CNV). The full nature and extent of CNV and its frequency in different ethnic populations is still largely unknown. In this study we surveyed a set of 12 CNVs previously detected by array-CGH. More than 300 individuals from five different ethnic populations, including three distinct European, one Asian and one African population, were tested for the occurrence of CNV using multiplex ligation-dependent probe amplification (MLPA). Seven of these loci indeed showed CNV, i.e., showed copy numbers that deviated from the population median. More precise estimations of the actual genomic copy numbers for (part of) the NSF gene locus, revealed copy numbers ranging from two to at least seven. Additionally, significant inter-population differences in the distribution of these copy numbers were observed. These data suggest that insight into absolute DNA copy numbers for loci exhibiting CNV is required to determine their potential contribution to normal phenotypic variation and, in addition, disease susceptibility.     

Comparative Studies of Copy Number Variation Detection Methods for Next-Generation Sequencing Technologies

Copy number variation (CNV) has played an important role in studies of susceptibility or resistance to complex diseases. Traditional methods such as fluorescence in situ hybridization (FISH) and array comparative genomic hybridization (aCGH) suffer from low resolution of genomic regions. Following the emergence of next generation sequencing (NGS) technologies, CNV detection methods based on the short read data have recently been developed. However, due to the relatively young age of the procedures, their performance is not fully understood. To help investigators choose suitable methods to detect CNVs, comparative studies are needed. We compared six publicly available CNV detection methods: CNV-seq, FREEC, readDepth, CNVnator, SegSeq and event-wise testing (EWT). They are evaluated both on simulated and real data with different experiment settings. The receiver operating characteristic (ROC) curve is employed to demonstrate the detection performance in terms of sensitivity and specificity, box plot is employed to compare their performances in terms of breakpoint and copy number estimation, Venn diagram is employed to show the consistency among these methods, and F-score is employed to show the overlapping quality of detected CNVs. The computational demands are also studied. The results of our work provide a comprehensive evaluation on the performances of the selected CNV detection methods, which will help biological investigators choose the best possible method.     

Genome-wide patterns of copy number variations in Spodoptera litura

Spodoptera litura is a polyphagous pest and can feed on more than 100 species of plants, causing great damage to agricultural production. The SNP results showed that there were gene exchanges between different regions. To explore the variations of larger segments in S. litura genome, we used genome resequencing samples from 14 regions of China, India, and Japan to study the copy number variations (CNVs). We identified 3976 CNV events and 1581 unique copy number variation regions (CNVRs) occupying the 108.5 Mb genome of S. litura. A total of 5527 genes that overlapped with CNVRs were detected. Selection signal analysis identified 19 shared CNVRs and 105 group-specific CNVRs, whose related genes were involved in various biological processes in S. litura. We constructed the first CNVs map in S. litura genome, and our findings will be valuable for understanding the genomic variations and population differences of S. litura.     

Diagnostic efficacy and new variants in isolated and complex autism spectrum disorder using molecular karyotyping

Autism spectrum disorder (ASD) is a group of the neurodevelopment disorders presenting as an isolated ASD or more complex forms, where a broader clinical phenotype comprised of developmental delay and intellectual disability is present. Both the isolated and complex forms have a significant causal genetic component and submicroscopic genomic copy number variations (CNV) are the most common identifiable genetic factor in these patients. The data on microarray testing in ASD cohorts are still accumulating and novel loci are often identified; therefore, we aimed to evaluate the diagnostic efficacy of the method and the relevance of implementing it into routine genetic testing in ASD patients. A genome-wide CNV analysis using the Agilent microarrays was performed in a group of 150 individuals with an isolated or complex ASD. Altogether, 11 (7.3%) pathogenic CNVs and 15 (10.0%) variants of unknown significance (VOUS) were identified, with the highest proportion of pathogenic CNVs in the subgroup of the complex ASD patients (14.3%). An interesting case of previously unreported partial UPF3B gene deletion was identified among the pathogenic CNVs. Among the CNVs with unknown significance, four VOUS involved genes with possible correlation to ASD, namely genes SNTG2, PARK2, CADPS2 and NLGN4X. The diagnostic efficacy of aCGH in our cohort was comparable with those of the previously reported and identified an important proportion of genetic ASD cases. Despite the continuum of published studies on the CNV testing in ASD cohorts, a considerable number of VOUS CNVs is still being identified, namely 10.0% in our study.     

Copy number variants in Italian Large White pigs detected using high‐density single nucleotide polymorphisms and their association with back fat thickness

The aim of this study was to identify copy number variants (CNVs) in Italian Large White pigs and test them for association with back fat thickness (BFT). Within a population of 12 000 performance‐tested pigs, two groups of animals with extreme and divergent BFT estimated breeding values (EBVs; 147 with negative and 150 with positive EBVs) were genotyped with the Illumina Porcine SNP60 BeadChip. CNVs were detected with penncnv software. We identified a total of 4146 CNV events in 170 copy number variation regions (CNVRs) located on 15 porcine autosomes. Validation of detected CNVRs was carried out (i) by comparing CNVRs already detected by other studies and (ii) by semiquantitative fluorescent multiplex (SQFM) PCR of a few CNVRs. Most of CNVRs detected in Italian Large White pigs (71.2%) were already reported in other pig breeds/populations, and 82.1% of the CNV events detected by penncnv were confirmed by SQFM PCR. For each CNVR, we compared the occurrence of CNV events between the pigs of the high and low BFT EBV tails. Sixteen regions showed significance at P < 0.10, and seven were significant at P < 0.05 but were not significant after Bonferroni correction (Fisher's exact test). These results indicated that CNVs could explain a limited fraction of the genetic variability of fat deposition in Italian Large White pigs. However, it was interesting to note that one of these CNVRs encompassed the ZPLD1 gene. In humans, a rare CNV event including this gene is associated with obesity. Studies identifying CNVs in pigs could assist in elucidating the genetic mechanisms underlying human obesity.     

Identification of copy number variants from exome sequence data

Background

With advances in next generation sequencing technologies and genomic capture techniques, exome sequencing has become a cost-effective approach for mutation detection in genetic diseases. However, computational prediction of copy number variants (CNVs) from exome sequence data is a challenging task. Whilst numerous programs are available, they have different sensitivities, and have low sensitivity to detect smaller CNVs (1–4 exons). Additionally, exonic CNV discovery using standard aCGH has limitations due to the low probe density over exonic regions. The goal of our study was to develop a protocol to detect exonic CNVs (including shorter CNVs that cover 1–4 exons), combining computational prediction algorithms and a high-resolution custom CGH array.

Results

We used six published CNV prediction programs (ExomeCNV, CONTRA, ExomeCopy, ExomeDepth, CoNIFER, XHMM) and an in-house modification to ExomeCopy and ExomeDepth (ExCopyDepth) for computational CNV prediction on 30 exomes from the 1000 genomes project and 9 exomes from primary immunodeficiency patients. CNV predictions were tested using a custom CGH array designed to capture all exons (exaCGH). After this validation, we next evaluated the computational prediction of shorter CNVs. ExomeCopy and the in-house modified algorithm, ExCopyDepth, showed the highest capability in detecting shorter CNVs. Finally, the performance of each computational program was assessed by calculating the sensitivity and false positive rate.

Conclusions

In this paper, we assessed the ability of 6 computational programs to predict CNVs, focussing on short (1–4 exon) CNVs. We also tested these predictions using a custom array targeting exons. Based on these results, we propose a protocol to identify and confirm shorter exonic CNVs combining computational prediction algorithms and custom aCGH experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-661) contains supplementary material, which is available to authorized users.     

Novel Loci for Non-Syndromic Coarctation of the Aorta in Sporadic and Familial Cases

BackroundCoarctation of the aorta (CoA) accounts for 5-8% of all congenital heart defects. CoA can be detected in up to 20% of patients with Ullrich-Turner syndrome (UTS), in which a part or all of one of the X chromosomes is absent. The etiology of non-syndromic CoA is poorly understood. In the present work, we test the hypothesis that rare copy number variation (CNV) especially on the gonosomes, contribute to the etiology of non-syndromic CoA.MethodsWe performed high-resolution genome-wide CNV analysis using the Affymetrix SNP 6.0 microarray platform for 70 individuals with sporadic CoA, 3 families with inherited CoA (n=13) and 605 controls. Our analysis comprised genome wide association, CNV burden and linkage. CNV was validated by multiplex ligation-dependent probe amplification.ResultsWe identified a significant abundance of large (>100 kb) CNVs on the X chromosome in males with CoA (p=0.005). 11 out of 51 (~ 22%) male cases had these large CNVs. Association analysis in the sporadic cohort revealed 14 novel loci for CoA. The locus on 21q22.3 in the sporadic CoA cohort overlapped with a gene locus identified in all familial cases of CoA (candidate gene TRPM2). We identified one CNV locus within a locus with high multipoint LOD score from a linkage analysis of the familial cases (SEPT9); another locus overlapped with a region implicated in Kabuki syndrome. In the familial cases, we identified a total of 7 CNV loci that were exclusively present in cases but not in unaffected family members.ConclusionOf all candidate loci identified, the TRPM2 locus was the most frequently implicated autosomal locus in sporadic and familial cases. However, the abundance of large CNVs on the X chromosome of affected males suggests that gonosomal aberrations are not only responsible for syndromic CoA but also involved in the development of sporadic and non-syndromic CoA and their male dominance.     

Modeling genetic inheritance of copy number variations

Copy number variations (CNVs) are being used as genetic markers or functional candidates in gene-mapping studies. However, unlike single nucleotide polymorphism or microsatellite genotyping techniques, most CNV detection methods are limited to detecting total copy numbers, rather than copy number in each of the two homologous chromosomes. To address this issue, we developed a statistical framework for intensity-based CNV detection platforms using family data. Our algorithm identifies CNVs for a family simultaneously, thus avoiding the generation of calls with Mendelian inconsistency while maintaining the ability to detect de novo CNVs. Applications to simulated data and real data indicate that our method significantly improves both call rates and accuracy of boundary inference, compared to existing approaches. We further illustrate the use of Mendelian inheritance to infer SNP allele compositions in each of the two homologous chromosomes in CNV regions using real data. Finally, we applied our method to a set of families genotyped using both the Illumina HumanHap550 and Affymetrix genome-wide 5.0 arrays to demonstrate its performance on both inherited and de novo CNVs. In conclusion, our method produces accurate CNV calls, gives probabilistic estimates of CNV transmission and builds a solid foundation for the development of linkage and association tests utilizing CNVs.     

Distinct Genetic Alterations in Colorectal Cancer

Background

Colon cancer (CRC) development often includes chromosomal instability (CIN) leading to amplifications and deletions of large DNA segments. Epidemiological, clinical, and cytogenetic studies showed that there are considerable differences between CRC tumors from African Americans (AAs) and Caucasian patients. In this study, we determined genomic copy number aberrations in sporadic CRC tumors from AAs, in order to investigate possible explanations for the observed disparities.

Methodology/Principal Findings

We applied genome-wide array comparative genome hybridization (aCGH) using a 105k chip to identify copy number aberrations in samples from 15 AAs. In addition, we did a population comparative analysis with aCGH data in Caucasians as well as with a widely publicized list of colon cancer genes (CAN genes). There was an average of 20 aberrations per patient with more amplifications than deletions. Analysis of DNA copy number of frequently altered chromosomes revealed that deletions occurred primarily in chromosomes 4, 8 and 18. Chromosomal duplications occurred in more than 50% of cases on chromosomes 7, 8, 13, 20 and X. The CIN profile showed some differences when compared to Caucasian alterations.

Conclusions/Significance

Chromosome X amplification in male patients and chromosomes 4, 8 and 18 deletions were prominent aberrations in AAs. Some CAN genes were altered at high frequencies in AAs with EXOC4, EPHB6, GNAS, MLL3 and TBX22 as the most frequently deleted genes and HAPLN1, ADAM29, SMAD2 and SMAD4 as the most frequently amplified genes. The observed CIN may play a distinctive role in CRC in AAs.     

Comparative-high resolution melting: a novel method of simultaneous screening for small mutations and copy number variations

Efficient and cost-effective screening for DNA sequence changes, both small mutations and copy number variations (CNVs), is a crucial aspect for routine genetic diagnostics as well as for basic research. In this study we present a development and evaluation of comparative-high resolution melting (C-HRM), a new approach for the simultaneous screening of small DNA changes and gene CNVs. In contrast to other methods, relative quantification in C-HRM is based on the results obtained during the melting process and calculations of the melting peak height ratio in the multiplex reaction. Validation of the method was conducted on DNA samples from 50 individuals from Duchenne muscular dystrophy (DMD) families, 50 probands diagnosed with familial adenomatous polyposis and a control group of 36 women and 36 men. The results of analyses conducted on fragments of the DMD and APC genes correspond completely (100 %) with the results of previous studies. C-HRM sensitivity in CNV detection was assessed through the analysis of mixed DNA samples with different proportions of a deletion carrier and wild type control. The results are presented as a linear regression with R ² of 0.9974 and imply the capability of the method to detect mosaics. C-HRM is an attractive and powerful alternative to other methods of point mutations and CNV detection with 100 % accuracy in our studied group.     

An MLPA-based approach for high-resolution genotyping of disease-related multi-allelic CNVs

Copy number variation has recently been recognized as an important type of genetic variation that modifies human phenotypes. Copy number variants (CNVs) are being increasingly associated with various human phenotypes and diseases. However, the lack of an appropriate method that allows fast, inexpensive and, most importantly, accurate CNVs genotyping significantly hampers CNV analysis. This limitation especially affects the analysis of multi-allelic CNVs that frequently modify various phenotypes. Recently, we developed a multiplex ligation-dependent probe amplification (MLPA)-based strategy for multiplex copy number genotyping and the validation of candidate CNV-miRNAs. Here we present the adaptation and optimization of this recently developed method for high-resolution genotyping of individual disease-related multi-allelic CNVs. We developed appropriate assays for three well-known and extensively studied CNVs: CNV-CCL3L1, CNV-DEFB, and CNV-UGT2B17, which have been associated with various human phenotypes including inflammation-related and infectious diseases. With the use of these assays we identified several general factors that allow to increase the resolution of the copy number genotyping. Performed experiments confirmed the high reproducibility and accuracy of the obtained genotyping results. The reliability of the results and relatively low per-genotype cost makes this strategy an attractive method for large-scale experiments such as genotype–phenotype association studies.     

A Genome Wide Study of Copy Number Variation Associated with Nasopharyngeal Carcinoma in Malaysian Chinese Identifies CNVs at 11q14.3 and 6p21.3 as Candidate Loci

Background

Nasopharyngeal carcinoma (NPC) is a neoplasm of the epithelial lining of the nasopharynx. Despite various reports linking genomic variants to NPC predisposition, very few reports were done on copy number variations (CNV). CNV is an inherent structural variation that has been found to be involved in cancer predisposition.

Methods

A discovery cohort of Malaysian Chinese descent (NPC patients, n = 140; Healthy controls, n = 256) were genotyped using Illumina^® HumanOmniExpress BeadChip. PennCNV and cnvPartition calling algorithms were applied for CNV calling. Taqman CNV assays and digital PCR were used to validate CNV calls and replicate candidate copy number variant region (CNVR) associations in a follow-up Malaysian Chinese (NPC cases, n = 465; and Healthy controls, n = 677) and Malay cohort (NPC cases, n = 114; Healthy controls, n = 124).

Results

Six putative CNVRs overlapping GRM5, MICA/HCP5/HCG26, LILRB3/LILRA6, DPY19L2, RNase3/RNase2 and GOLPH3 genes were jointly identified by PennCNV and cnvPartition. CNVs overlapping GRM5 and MICA/HCP5/HCG26 were subjected to further validation by Taqman CNV assays and digital PCR. Combined analysis in Malaysian Chinese cohort revealed a strong association at CNVR on chromosome 11q14.3 (P_combined = 1.54x10^-5; odds ratio (OR) = 7.27; 95% CI = 2.96–17.88) overlapping GRM5 and a suggestive association at CNVR on chromosome 6p21.3 (P_combined = 1.29x10^-3; OR = 4.21; 95% CI = 1.75–10.11) overlapping MICA/HCP5/HCG26 genes.

Conclusion

Our results demonstrated the association of CNVs towards NPC susceptibility, implicating a possible role of CNVs in NPC development.     

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

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.