Discovery Of Copy Number Variants Research Articles

High positive predictive value of CNVs detected by clinical exome sequencing in suspected genetic diseases

BackgroundGenetic disorders often manifest as abnormal fetal or childhood development. Copy number variations (CNVs) represent a significant genetic mechanism underlying such disorders. Despite their importance, the effectiveness of clinical exome sequencing (CES) in detecting CNVs, particularly small ones, remains incompletely understood. We aimed to evaluate the detection of both large and small CNVs using CES in a substantial clinical cohort, including parent–offspring trios and proband only analysis.MethodsWe conducted a retrospective analysis of CES data from 2428 families, collected from 2018 to 2021. Detected CNV were categorized as large or small, and various validation techniques including chromosome microarray (CMA), Multiplex ligation-dependent probe amplification assay (MLPA), and/or PCR-based methods, were employed for cross-validation.ResultsOur CNV discovery pipeline identified 171 CNV events in 154 cases, resulting in an overall detection rate of 6.3%. Validation was performed on 113 CNVs from 103 cases to assess CES reliability. The overall concordance rate between CES and other validation methods was 88.49% (100/113). Specifically, CES demonstrated complete consistency in detecting large CNV. However, for small CNVs, consistency rates were 81.08% (30/37) for deletions and 73.91% (17/23) for duplications.ConclusionCES demonstrated high sensitivity and reliability in CNV detection. It emerges as an economical and dependable option for the clinical CNV detection in cases of developmental abnormalities, especially fetal structural abnormalities.

VEuPathDB Resources: A Platform for Free Online Data Exploration, Integration, and Analysis.

The Eukaryotic Pathogen, Vector and Host Informatics Resources ( VEuPathDB.org ) provide free online access to omic data from eukaryotic protozoan and fungal pathogens, arthropod vectors of disease, and host responses to pathogen infection. The goal of VEuPathDB is to make data easily accessible, findable, and importantly, re-usable by laboratory scientists. All integrated data and analyses follow standard workflows and methods to ensure data accuracy and enable data interoperability. Integrated data include genomes and annotation, transcriptomic (e.g., single-cell/bulk RNA-sequence and microarray data), proteomic (e.g., mass spectrometry evidence and quantitative data), isolate sequencing data used for variant calling and copy number variation determination, epigenomics, whole-genome phenotyping data (e.g., CRISPR screens and large-scale imaging and subcellular localization data), etc. Standard analyses provide additional data such as InterPro domains, signal peptide and transmembrane domain predictions, and metabolic pathways. Comparative genomic analysis in VEuPathDB is facilitated by leveraging orthology to enable the transformation of results between organisms and identifying genes with specific phyletic patterns. In addition, synteny between genomes is facilitated by shading orthologs across species and strains. Accessibility to and re-usability of the data is made possible through specialized searches and a graphical search strategy system that enables scientists to build in silico experiments combining results from multiple experiments with diverse data types.

Assessment of MYC and TERT copy number variations in lung cancer using digital PCR

ObjectiveLung cancer is the second most frequent cancer type and the most common cause of cancer-related deaths worldwide. Alteration of gene copy numbers are associated with lung cancer and the determination of copy number variations (CNV) is appropriate for the discrimination between tumor and non-tumor tissue in lung cancer. As telomerase reverse transcriptase (TERT) and v-myc avian myelocytomatosis viral oncogene homolog (MYC) play a role in lung cancer the aims of this study were the verification of our recent results analyzing MYC CNV in tumor and non-tumor tissue of lung cancer patients using an independent study group and the assessment of TERT CNV as an additional marker.ResultsTERT and MYC status was analyzed using digital PCR (dPCR) in tumor and adjacent non-tumor tissue samples of 114 lung cancer patients. The difference between tumor and non-tumor samples were statistically significant (p < 0.0001) for TERT and MYC. Using a predefined specificity of 99% a sensitivity of 41% and 51% was observed for TERT and MYC, respectively. For the combination of TERT and MYC the overall sensitivity increased to 60% at 99% specificity. We demonstrated that a combination of markers increases the performance in comparison to individual markers. Additionally, the determination of CNV using dPCR might be an appropriate tool in precision medicine.

Genome-wide copy number variations in Bhutia equine breed using SNP genotyping data

Copy number variants (CNVs) have dynamic potential and evolutionary significance like other genetic variants, namely, single nucleotide polymorphisms, InDels, short tandem repeat polymorphisms, inversion variants, etc. Discovering CNVs leads to further speculation that the genomic DNA contains more changes than previously thought and contributes to the phenotypic variation. CNVs are big DNA fragments (&gt; 1 kb) being duplicated or deleted. A bridge between CNVs and phenotypic variations supports CNVs to be utilized in GWAS, which are currently mostly based on SNPs. CNV, which refers to the structural differences, influence gene expression and can be an indicator of numerous traits for improvement. There is a severe dearth of research on CNVs in animals, especially equine. The present study investigates the genomes of the Bhutia Equine breed for genome-wide discovery of CNVs using theAxiom™ Equine Genotyping Array chip for a better understanding of its traits which had been unexplored till date. A total of 619 CNVs from 20 Bhutia equines were identified with the median and average size as 49.394 kb and 114.955 kb, respectively. Total 225 frequent CNVRs with &gt; 1% CNV frequency were identified among them along with singleton type. These CNVRs contained 361 genes in all. The information obtained on genomic variation could be utilized to identify economically advantageous genetic features in Bhutia equine breed.

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in WES data, with broad applications.

Accidental discovery of copy number variation on chromosome 1 in a fetus with high risk of trisomy 13 suggested by NIPT

To validate a fetus with high risk for trisomy 13 suggested by non-invasive prenatal testing (NIPT). The fetus was selected as the study subject after the NIPT detection at Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences on February 18, 2019. Clinical data of the pregnant woman was collected. Fluorescence in situ hybridization (FISH), chromosomal karyotyping analysis and chromosomal microarray analysis (CMA) were carried out on amniotic fluid and umbilical cord blood and the couple's peripheral blood samples. Copy number variation sequencing (CNV-seq) was also performed on the placental and amniotic fluid samples following induced labor. The pregnant woman, a 38-year-old G4P1 gravida, was found to have abnormal fetal development by prenatal ultrasonography. NIPT test suggested that the fetus has a high risk for trisomy 13. Chromosomal karyotyping analysis of fetal amniotic fluid and umbilical cord blood were 46,XN,add(13)(p10). The result of CMA was arr[hg19]1q41q44(223937972_249224684)×3, with the size of the repeat fragment being approximately 25.29 Mb, the fetal karyotype was thereby revised as 46,XN,der(13)t(1;13)(q41;p10). Chromosomal karyotyping analysis and CMA of the parents' peripheral blood samples showed no obvious abnormality. The CNV-seq analysis of induced placenta revealed mosaicisms of normal karyotype and trisomy 13. The CNV-seq test of induced amniotic fluid confirmed a duplication of chr1:22446001_249220000 region spanning approximately 24.75 Mb, which was in keeping with the CMA results of amniotic fluid and umbilical cord blood samples. NIPT may yield false positive result due to placenta mosaicism. Invasive prenatal diagnosis should be recommended to women with a high risk by NIPT test. And analysis of placenta can explain the inconsistency between the results of NIPT and invasive prenatal diagnosis.

Copy Number Variation Discovery in South African Nguni-Sired and Bonsmara-Sired Crossbred Cattle.

Crossbreeding forms part of Climate-Smart beef production and is one of the strategies to mitigate the effects of climate change. Two Nguni-sired and three Bonsmara-sired crossbred animals underwent whole genome sequencing. Following quality control and file preparation, the sequence data were investigated for genome-wide copy number variation (CNV) using the panelcn.MOPS tool. A total of 355 CNVs were identified in the crossbreds, of which 274 were unique in Bonsmara-sired crossbreds and 81 unique in the Nguni-sired crossbreds. Genes that differed in copy number in both crossbreds included genes related to growth (SCRN2, LOC109572916) and fertility-related factors (RPS28, LOC1098562432, LOC109570037). Genes that were present only in the Bonsmara-sired crossbreds included genes relating to lipid metabolism (MAF1), olfaction (LOC109569114), body size (HES7), immunity (LOC10957335, LOC109877039) and disease (DMBT1). Genes that were present only in the Nguni-sired crossbreds included genes relating to ketosis (HMBOX1) and amino acid transport (LOC109572916). Results of this study indicate that Nguni and Bonsmara cattle can be utilized in crossbreeding programs as they may enhance the presence of economically important traits associated with both breeds. This will produce crossbred animals that are good meat producers, grow faster, have high fertility, strong immunity and a better chance of producing in South Africa's harsh climate conditions. Ultimately, this study provides new genetic insights into the adaptability of Nguni and Bonsmara crossbred cattle.

Whole genome sequencing of simmental cattle for SNP and CNV discovery

BackgroudThe single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are two major genomic variants, which play crucial roles in evolutionary and phenotypic diversity.ResultsIn this study, we performed a comprehensive analysis to explore the genetic variations (SNPs and CNVs) of high sperm motility (HSM) and poor sperm motility (PSM) Simmental bulls using the high-coverage (25×) short-read next generation sequencing and single-molecule long reads sequencing data. A total of ~ 15 million SNPs and 2,944 CNV regions (CNVRs) were detected in Simmental bulls, and a set of positive selected genes (PSGs) and CNVRs were found to be overlapped with quantitative trait loci (QTLs) involving immunity, muscle development, reproduction, etc. In addition, we detected two new variants in LEPR, which may be related to the artificial breeding to improve important economic traits. Moreover, a set of genes and pathways functionally related to male fertility were identified. Remarkably, a CNV on SPAG16 (chr2:101,427,468 − 101,429,883) was completely deleted in all poor sperm motility (PSM) bulls and half of the bulls in high sperm motility (HSM), which may play a crucial role in the bull-fertility.ConclusionsIn conclusion, this study provides a valuable genetic variation resource for the cattle breeding and selection programs.

Genetic variation in placental insufficiency: What have we learned over time?

Genetic variation shapes placental development and function, which has long been known to impact fetal growth and pregnancy outcomes such as miscarriage or maternal pre-eclampsia. Early epidemiology studies provided evidence of a strong heritable component to these conditions with both maternal and fetal-placental genetic factors contributing. Subsequently, cytogenetic studies of the placenta and the advent of prenatal diagnosis to detect chromosomal abnormalities provided direct evidence of the importance of spontaneously arising genetic variation in the placenta, such as trisomy and uniparental disomy, drawing inferences that remain relevant to this day. Candidate gene approaches highlighted the role of genetic variation in genes influencing immune interactions at the maternal-fetal interface and angiogenic factors. More recently, the emergence of molecular techniques and in particular high-throughput technologies such as Single-Nucleotide Polymorphism (SNP) arrays, has facilitated the discovery of copy number variation and study of SNP associations with conditions related to placental insufficiency. This review integrates past and more recent knowledge to provide important insights into the role of placental function on fetal and perinatal health, as well as into the mechanisms leading to genetic variation during development.

Practice of clinical application of noninvasive prenatal testing based on cell-free fetal DNA

To assess the application value of noninvasive prenatal testing (NIPT) based on cell-free fetal DNA. The results of 2777 cases of basic and extended NIPT were retrospectively analyzed. The clinical data and outcome of pregnancy were analyzed, in addition with the diagnosis rate and testing efficiency. Among the 2777 pregnant women, 1192 (42.9%) had accepted basic NIPT and 1585 (57.1%) accepted extended NIPT. With a failure rate of 0.1%, 8 and 6 cases were reported respectively as high-risk pregnancies for trisomy 21 and sex chromosomal abnormalities. Other genetic abnormalities were detected in 32 cases. The positive predictive value for trisomy 21 was 85.7%, and one case of 47,XXX was diagnosed among 3 women with high risks for sex chromosomal abnormalities. For those with a high risk for other genetic abnormalities, pregnant diagnosis rates of basic and extended NIPT were 71.4% (5/7) and 68.2% (15/22), respectively. Seven copy number variations (CNVs) were confirmed, including one pathogenic CNV, one likely pathogenic CNV and 5 variants of unknown significance. Among 6 cases with high-risk of maternal CNVs, 5 fetuses and the mothers were confirmed to be carriers. No CNV was detected in the remainder fetus by chromosomal microarray analysis, while its mother was a carrier of the corresponding CNV. NIPT has shown a relatively high positive predictive value for the screening of trisomy 21 and maternal CNVs but with a limited efficiency for the discovery of fetal CNVs. For other genetic abnormalities signaled by NIPT, informed choice by the pregnant women during pre-testing consultation is recommended. Invasive prenatal diagnosis should be considered in the combination of NIPT reports and fetal ultrasound, while the residual risks should be fully informed.

Discovering Copy Number Variation in Dual-Purpose XinJiang Brown Cattle.

Copy number variants (CNVs), which are a class of structural variant, can be important in relating genomic variation to phenotype. The primary aims of this study were to discover the common CNV regions (CNVRs) in the dual-purpose XinJiang-Brown cattle population and to detect differences between CNVs inferred using the ARS-UCD 1.2 (ARS) or the UMD 3.1 (UMD) genome assemblies based on the 150K SNP (Single Nucleotide Polymorphisms) Chip. PennCNV and CNVPartition methods were applied to calculate the deviation of the standardized signal intensity of SNPs markers to detect CNV status. Following the discovery of CNVs, we used the R package HandyCNV to generate and visualize CNVRs, compare CNVs and CNVRs between genome assemblies, and identify consensus genes using annotation resources. We identified 38 consensus CNVRs using the ARS assembly with 1.95% whole genome coverage, and 33 consensus CNVRs using the UMD assembly with 1.46% whole genome coverage using PennCNV and CNVPartition. We identified 37 genes that intersected 13 common CNVs (>5% frequency), these included functionally interesting genes such as GBP4 for which an increased copy number has been negatively associated with cattle stature, and the BoLA gene family which has been linked to the immune response and adaption of cattle. The ARS map file of the GGP Bovine 150K Bead Chip maps the genomic position of more SNPs with increased accuracy compared to the UMD map file. Comparison of the CNVRs identified between the two reference assemblies suggests the newly released ARS reference assembly is better for CNV detection. In spite of this, different CNV detection methods can complement each other to generate a larger number of CNVRs than using a single approach and can highlight more genes of interest.

Identification of Copy Number Alterations from Next-Generation Sequencing Data.

Copy number variation (CNV), which is deletion and multiplication of segments of a genome, is an important genomic alteration that has been associated with many diseases including cancer. In cancer, CNVs are mostly somatic aberrations that occur during cancer evolution. Advances in sequencing technologies and arrival of next-generation sequencing data (whole-genome sequencing and whole-exome sequencing or targeted sequencing) have opened up an opportunity to detect CNVs with higher accuracy and resolution. Many computational methods have been developed for somatic CNV detection, which is a challenging task due to complexity of cancer sequencing data, high level of noise and biases in the sequencing process, and big data nature of sequencing data. Nevertheless, computational detection of CNV in sequencing data has resulted in the discovery of actionable cancer-specific CNVs to be used to guide cancer therapeutics, contributing to significant progress in precision oncology. In this chapter, we start by introducing CNVs. Then, we discuss the main approaches and methods developed for detecting somatic CNV for next-generation sequencing data, along with its challenges. Finally, we describe the overall workflow for CNV detection and introduce the most common publicly available software tools developed for somatic CNV detection and analysis.

Detecting cryptic clinically relevant structural variation in exome-sequencing data increases diagnostic yield for developmental disorders

SummaryStructural variation (SV) describes a broad class of genetic variation greater than 50 bp in size. SVs can cause a wide range of genetic diseases and are prevalent in rare developmental disorders (DDs). Individuals presenting with DDs are often referred for diagnostic testing with chromosomal microarrays (CMAs) to identify large copy-number variants (CNVs) and/or with single-gene, gene-panel, or exome sequencing (ES) to identify single-nucleotide variants, small insertions/deletions, and CNVs. However, individuals with pathogenic SVs undetectable by conventional analysis often remain undiagnosed. Consequently, we have developed the tool InDelible, which interrogates short-read sequencing data for split-read clusters characteristic of SV breakpoints. We applied InDelible to 13,438 probands with severe DDs recruited as part of the Deciphering Developmental Disorders (DDD) study and discovered 63 rare, damaging variants in genes previously associated with DDs missed by standard SNV, indel, or CNV discovery approaches. Clinical review of these 63 variants determined that about half (30/63) were plausibly pathogenic. InDelible was particularly effective at ascertaining variants between 21 and 500 bp in size and increased the total number of potentially pathogenic variants identified by DDD in this size range by 42.9%. Of particular interest were seven confirmed de novo variants in MECP2, which represent 35.0% of all de novo protein-truncating variants in MECP2 among DDD study participants. InDelible provides a framework for the discovery of pathogenic SVs that are most likely missed by standard analytical workflows and has the potential to improve the diagnostic yield of ES across a broad range of genetic diseases.

A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data.

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

An integrated approach for copy number variation discovery in parent-offspring trios.

Whole-genome sequencing (WGS) of parent-offspring trios has become widely used to identify causal copy number variations (CNVs) in rare and complex diseases. Existing CNV detection approaches usually do not make effective use of Mendelian inheritance in parent-offspring trios and yield low accuracy. In this study, we propose a novel integrated approach, TrioCNV2, for jointly detecting CNVs from WGS data of the parent-offspring trio. TrioCNV2 first makes use of the read depth and discordant read pairs to infer approximate locations of CNVs and then employs the split read and local de novo assembly approaches to refine the breakpoints. We use the real WGS data of two parent-offspring trios to demonstrate TrioCNV2's performance and compare it with other CNV detection approaches. The software TrioCNV2 is implemented using a combination of Java and R and is freely available from the website at https://github.com/yongzhuang/TrioCNV2.

Accurate detection of CNV based on single-nucleotide variants recalibration and image classification from whole genome sequencing

Copy number variations (CNVs) play an important role in the genome aberrations and human diseases. Comprehensive discovery of CNVs from whole genome sequencing data remains difficult because of low sensitivity and high false detective rate (FDR). We presented a novel framework which integrated SNV-based recalibration probabilistic model and image classification architecture (ImageCNV) for CNVs discovery. A Naive Bayesian model and a deep neural network InceptionV3 were adopted to infer candidate CNVs, and we utilize the benchmark datasets to evaluate the performance of our framework. ImageCNV yielded comparable sensitivity and lower FDR, complementing other methods based on different signals and providing a new perspective for the detection of CNVs. ImageCNV is freely available at https://github.com/minqing1/ImageCNV.

Genome-wide detection of copy number variants in European autochthonous and commercial pig breeds by whole-genome sequencing of DNA pools identified breed-characterising copy number states.

In this study, we identified copy number variants (CNVs) in 19 European autochthonous pig breeds and in two commercial breeds (Italian Large White and Italian Duroc) that represent important genetic resources for this species. The genome of 725 pigs was sequenced using a breed-specific DNA pooling approach (30-35 animals per pool) obtaining an average depth per pool of 42×. This approach maximised CNV discovery as well as the related copy number states characterising, on average, the analysed breeds. By mining more than 17.5billion reads, we identified a total of 9592 CNVs (~683 CNVs per breed) and 3710 CNV regions (CNVRs; 1.15% of the reference pig genome), with an average of 77 CNVRs per breed that were considered as private. A few CNVRs were analysed in more detail, together with other information derived from sequencing data. For example, the CNVR encompassing the KIT gene was associated with coat colour phenotypes in the analysed breeds, confirming the role of the multiple copies in determining breed-specific coat colours. The CNVR covering the MSRB3 gene was associated with ear size in most breeds. The CNVRs affecting the ELOVL6 and ZNF622 genes were private features observed in the Lithuanian Indigenous Wattle and in the Turopolje pig breeds respectively. Overall, the genome variability unravelled here can explain part of the genetic diversity among breeds and might contribute to explain their origin, history and adaptation to a variety of production systems.

DpGMM: A Dirichlet Process Gaussian Mixture Model for Copy Number Variation Detection in Low-Coverage Whole-Genome Sequencing Data

Comprehensive identification and cataloging of copy number variation (CNVs) are essential to providing a complete view of human genetic variation and to finding diseased genes. Due to the large-scale sequencing and cost control whole-genome sequencing (WGS) data, low-coverage data is favorably disposed towards CNV identification. However, such low-coverage data is sensitive to noise and sequencing biases, which results in low resolution of CNV detection in past experimental designs for WGS datasets. In this paper, we present a control-free Dirichlet process Gaussian mixture model (dpGMM) based approach, to analyze the read depth (RD) of low-coverage WGS datasets for CNV discovery. First, noise and biases of the RD signals are corrected through the preprocessing step of dpGMM. Then we assume that RD signals across genomic regions follow a Gaussian mixture model (GMM) in which each Gaussian distribution is followed by a copy number state. Without requiring the number of Gaussian distributions, dpGMM builds a Dirichlet process (DP) GMM for RD signals and further uses a DP prior to infer the number of Gaussian models. After that, we apply dpGMM to simulation datasets with different coverages and individual datasets, and compare ours to three widely used RD-based pipelines, CNVnator, GROM-RD, and BIC-seq2. Simulation results demonstrate that our approach, dpGMM, has a high F1 score in both low- and high- coverage sequences. Also, the number of overlaps between CNVs detected in real data by ours and the standard benchmark is twice as much as that detected by other tools such as CNVnator and GROM-RD.

A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data.

Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

Development of an Inexpensive and Rapid Operation Device for High-Throughput Real-Time Quantitative PCR-Based &lt;i&gt;CYP2D6&lt;/i&gt; CNV Genotyping

The CYP2D6 gene is the most well characterized gene involved in drug metabolism and is known to have both gene duplication and deletion variants. We report an optimized method for the determination of copy number variation (CNV) in the CYP2D6 gene by a novel purification process for a real-time quantitative PCR. This high-throughput low-cost method accurately determines CNV in the CYP2D6 gene enabling reliable estimates of disease prediction in large epidemiological samples.