Digital PCR for Copy Number Variation Analysis

Until recently, real-time quantitative PCR (qPCR) assays and microarray hybridization have been the main methods used to determine copy number variation (CNV) in the genome. The advent of digital PCR (dPCR) now permits very high-resolution determination of CNV, often using smaller sample and reagent volumes. Droplet Digital™ PCR technology overcomes a number of inherent limitations of qPCR and microarray techniques for CNV analysis.

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What Is Copy Number Variation?

Copy number variation is defined as the presence of variable numbers of copies of a particular DNA segment relative to a reference genome. Deletions and duplications result in copy number changes. For some variants there can be copy number differences in the hundreds between individuals, however, for most variants copy number differences are much smaller.

Differences in the number of copies of a particular DNA segment may or may not be associated with a detectable phenotype change. CNVs are found throughout the genome, with a relatively higher abundance in noncoding regions. The presence of CNVs contributes to genetic variability among individuals within a species.

The International HapMap Project has estimated that CNVs constitute approximately 12% of the human genome and involve a greater fraction of nucleotides than single-nucleotide polymorphisms (SNPs). As with SNPs, consortiums, such as the CNV Project coordinated by the Wellcome Trust Sanger Institute, are studying CNVs in order to investigate a number of questions including the effects of CNVs on gene expression, their roles in disease and human evolution, and their usefulness in mapping lineages.

Advantages of Droplet Digital PCR Technology for CNV Analysis

When using qPCR assays to estimate differences in CNV, many replicates are required to achieve sensitive discrimination of differences in copy number. For example, to determine the difference between a copy number of four and five using qPCR, as many as 18 replicates could be required. (Karlin-Neumann et al. 2011). Furthermore, the determination of copy number by qPCR provides a relative measure rather than an absolute quantification.

In Bio-Rad's Droplet Digital PCR (ddPCR™) System, each sample is partitioned into 20,000 nanoliter-sized droplets prior to amplification. PCR amplification proceeds to the reaction endpoint followed by determination of whether every droplet in a well is positive or negative for both the target and the reference. The fluorescence in each droplet is measured, and those above a threshold are counted as positive, thus providing a digital (yes/no) result. This differs fundamentally from qPCR, in which the exponential part of the amplification curves for the target and reference are used to calculate copy number via a standard curve. The digital data generated by the ddPCR system yields absolute quantification and fine resolution without requiring standard curves or many replicate reactions. The ddPCR system can easily detect small fold changes such as the 1.2-fold change in CNV from five to six copies (Berman et al. 2012).

DNA microarrays using comparative genomic hybridization (CGH) are frequently used for CNV screening. Generally for CGH, the test and reference are differentially labeled and then hybridized to the clones on the array. The sensitivity of CGH in detecting fine changes in CNV depends on the representation of the genomic sequence of the clones, probe characteristics and signal-to-noise ratios. Multiple replicates or several probes can be needed to increase sensitivity, requiring the averaging of data obtained with multiple probes, which reduces the resolution of an individual array.

Another advantage of ddPCR technology for quantifying both CNVs and point mutations is that single-color detection methods can be used. Using the nonspecific DNA-binding dye EvaGreen, target and reference products can be individually identified and quantified (Miotke et al. 2014). The ddPCR system allows the flexibility of using one or multiple colors for the determination of copy number. The use of an EvaGreen assay increases cost effectiveness.

Accurate Measure of Copy Number Variations in Human Genome


Accurate Measure of Copy Number Variations in Human Genome

The McCarroll Lab uses Droplet Digital PCR technology to study the distribution and molecular properties of the genome and its influence on gene expression, as well as the genetics underlying schizophrenia and bipolar disorder.

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CNV and Disease

The discovery of genome-wide CNVs led to the understanding that in addition to mutations and chromosomal changes, copy number changes play a role in many diseases. It has been demonstrated that some CNVs can cause an increased susceptibility to certain diseases, and other types of CNVs are directly associated with disease. For example, CNV at defined loci have been shown to be associated with insulin resistance (Irvin et al. 2011) and bone mineral density (Chew et al. 2012). Additionally, comparisons of CNV maps of individuals with and without diseases are starting to be used to determine which chromosomal rearrangements are detrimental.

Personalized medicine is becoming a reality in both diagnosis and treatment. ddPCR technology will be an important tool in the validation and analysis of biomarkers, including CNV, and their application to individualized therapy (Day et al. 2013).

Copy Number Variation  across Healthy And Diseased Populations


Copy Number Variation across Healthy and Diseased Populations

The ddPCR System allows the robust identification of copy number variants of genetic factors involved in neurogenetic diseases, such as DUF1220, with greater accuracy and precision while effectively validating array CGH data.

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CNV Mosaicism

The ddPCR System was used to demonstrate that at least 50% of the changes in copy number found in human-induced pluripotent stem cells derived from skin were present at low frequency in the parental fibroblasts (Abyzov et al. 2012). The results indicate that reprogramming cells to become stem cells may not cause as much CNV as previously thought. Further, somatic CNV mosaicism may be more widespread than had previously been suggested. The finding of potentially high levels of somatic mosaicism in some tissues could have major implications for the determination of the etiology of many diseases, particularly developmental diseases and cancers. ddPCR technology will be invaluable in detecting CNV mosaicism due to the ability of this technique to quantify low-frequency CNV differences in a complex background.

CNV in Cancer

The investigation of individual cancers and the potential for personalized therapy is an area where ddPCR technology is starting to play an important role. It has been shown that an increase in the copy number of an oncogene can lead to cancer and that, for many cancers, higher oncogene copy numbers associate with more aggressive tumors. ddPCR technology is being used for both the assessment of cancer risk and design of personalized treatment protocols after detection of a tumor.

The ddPCR System has a number of advantages for determination of CNV in cancer. Tumor tissues are often preserved by formalin fixation and paraffin embedding (FFPE). This processing has been shown to inhibit PCR reactions and damage genomic DNA. Analysis of FFPE tissue by qPCR can result in errors in determination of copy number because qPCR relies on a comparison of PCR reaction rates. Since ddPCR technology does not use the rate of reaction, instead using determination of whether amplification above a threshold has occurred, ddPCR assays can give more accurate results in the presence of inhibitors. Additionally, the sensitivity of ddPCR assays can overcome the problem of the high amounts of poor-quality DNA found in FFPE samples (Nadauld et al. 2012).

A comparison of copy numbers of human epidermal growth factor receptor 2 (HER2) in breast tissue by the standard methods of immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) with ddPCR assays in FFPE breast tissue demonstrated that ddPCR assays gave 100% concordance with both techniques (Belgrader et al. 2013, Heredia et al. 2013). For samples that were ambiguous for IHC, ddPCR agreed with the FISH analysis in assigning samples as either positive or negative (Belgrader et al. 2013). The use of the ddPCR system provides quantitative results and reduces the need for expert pathologists when performing routine screening.

As discussed on the Rare Sequence Detection and Liquid Biopsy pages, the high level of sensitivity of the ddPCR system allows for noninvasive testing by the analysis of cell-free DNA (cfDNA). cfDNA can be obtained from bodily fluids such as urine, sputum, tears, sweat or plasma. Analysis of plasma using ddPCR assays demonstrated that amplification of HER2 can be detected without requiring a tumor sample (Grevensleben et al. 2013).

It has been demonstrated that ddPCR assays can detect CNVs and other changes in tumor-specific cfDNA in a canine model (Beck et al. 2013). Furthermore, the analysis of cfDNA using the ddPCR system detected metastasis one year after surgical removal of a tumor. This model suggests that ddPCR technology is a cost-effective method for the screening and monitoring of cancer biomarkers, both before and after treatment.

CNV and Neurological Disease

Recently, it has been suggested that CNV plays a role in an array of neurological diseases ranging from alcohol dependence to epilepsy. Investigations of CNVs in families with autism spectrum disorders identified CNVs at several loci that may be associated with the etiology of the disease. CNV at some of these loci may be involved in other diseases such as schizophrenia and attention deficit hyperactivity disorder (Marshall and Scherer 2012). As with other types of disease associated with CNV, ddPCR technology will be invaluable for analysis of these loci, quantification of changes in copy number in neurological conditions and, potentially, in disease diagnosis.

CNV and Population and Lineage Studies

Studies of populations, lineage, and heredity are used in a number of fields including health and disease, animal and plant breeding, anthropology, and evolution. In conjunction with SNPs, CNVs are now being used to further refine lineages and relationships. For example, ddPCR has been used to determine copy numbers of multi-allelic CNVs where germline mutations in different ancestors gave rise to a complex pattern of variability within the population (Boettger et al. 2012).

CNV in Plants

In the past few years, analysis of CNVs in plants has shown that, as in animals, CNVs are common in plant genomes, and some of the variation is associated with differences in phenotype. CNV has been suggested to be involved in plant height, responses to stress, and flowering time (Zmieńko et al. 2014, Nitcher et al. 2013). In the future, in addition to studies of the role of CNV in plant physiology, analysis and quantification of CNV in plants will likely be used in plant breeding as part of acquisition of desirable traits.


Analysis of CNV requires high levels of sensitivity to enable discrimination between small changes in copy number and to distinguish changes in a complex background. ddPCR technology provides the level of precision and resolution required for accurate and straightforward CNV analysis.


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Beck J et al. (2013). Genome aberrations in canine mammary carcinomas and their detection in cell-free plasma DNA. PLoS One 8, e75485. PMID: 24098698

Belgrader P et al. (2013). Droplet digital PCR measurement of HER2 copy number alteration in formalin-fixed paraffin-embedded breast carcinoma tissue. Clin Chem 59, 991–994. PMID: 23358413

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Chew S et al. (2012). Copy number variation of the APC gene is associated with regulation of bone mineral density. Bone 51, 939–943. PMID: 22884971

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Heredia NJ et al. (2013). Droplet Digital™ PCR quantitation of HER2 expression in FFPE breast cancer samples. Methods 59, S20–23. PMID: 23036330

Irvin ML et al. (2011). Genome-wide detection of allele specific copy number variation associated with insulin resistance in African Americans from the HyperGEN study. PLos One 6, e24052. PMID: 21901158

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Marshall CR and Scherer SW (2012). Detection and characterization of copy number variation in autism spectrum disorder. Methods Mol Biol 838, 115–135. PMID: 22228009

Miotke LK et al. (2014). High Sensitivity Detection and Quantitation of DNA Copy Number and Single Nucleotide Variants with Single Color Droplet Digital PCR. Anal Chem 862, ,618–2,624. PMID: 24483992

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Nitcher R et al. (2013). Increased copy number at the HvFT1 locus is associated with accelerated flowering time in barley. Mol Genet Genomics 288, 261–275. PMID: 23591592

Zmieńko A et al. (2014). Copy number polymorphism in plant genomes. Theor Appl Genet 127, 1–18. PMID: 23989647

Related Content

International HapMap Project
Wellcome Trust Sanger Institute, The Copy Number Variation (CNV) Project