An integrated framework for clinical research: complementing sequencing with high-resolution CNV detection

May 18, 2014

Over the last few decades, it has become increasingly evident that genetic disorders are rarely caused by an isolated mutation within a single gene. In light of advances in DNA analysis technologies, we are instead witnessing a shift away from the “single mutation—single disorder” paradigm, becoming ever more aware of the underlying genetic complexity. One or many genes can contribute to one or multiple disorders, which can give rise to a range of phenotypes, while the causal aberrations can vary widely from larger copy number variations (CNVs), down to single-point mutations (Figure 1). 

Figure 1. Mutation spectrum of genetic disorders. Genetic disorders often arise from a mixture of different aberrations. Image courtesy of Madhuri Hegde, PhD, FACMG, Emory Genetics Lab.

This complex framework demands an assay that is not only able to cover single or multiple genes, but is also capable of detecting the full mutation spectrum, building a complete picture of the disease profile before a sample is deemed positive or negative. In line with this concept, researchers employ a suite of available technologies to provide comprehensive analysis for genetic disorders, guiding basic genetic research as well as driving applied therapeutic strategies of the future.

DNA sequencing for clinical research

The sequencing of genes to identify potentially causative mutations is an established technique, with Sanger sequencing having been the primary technology for the last 30 years. Within the modern clinical research laboratory, Sanger sequencing is ideal for locus-specific full-gene testing on individuals with well-recognized phenotypes. However, as more genetically diverse disorders are described and the number of genes interrogated increases, the usefulness of Sanger sequencing decreases due to its limited throughput.

Such demand for high-throughput, low-cost DNA sequencing has been a driving force behind the development of next generation sequencing (NGS) technologies, and targeted sequencing is becoming an increasingly popular approach. By reducing the target size and focusing on disease-linked regions, targeted sequencing enables increased depth of coverage, improving the chance of variant detection. In fact, targeted approaches have already had a major impact on disease detection by permitting successful identification of causal mutations for a number of genetic disorders,1-5 including cancer.6,7 

The accuracy of NGS data can, however, be compromised by a number of genomic features, and certain DNA sequences exist that cannot be covered using NGS—which is where traditional Sanger sequencing still prevails. As sequencing technology evolves, Sanger sequencing will continue to play an important role in the validation of sequence variants discovered by NGS platforms.8

DNA sequencing: not a standalone approach

Although presenting a powerful technique for genomic analysis, DNA sequencing is limited by the type of mutations that can be detected. Neither Sanger sequencing nor targeted NGS is currently suitable for reliably detecting CNVs. The prevalence of CNVs throughout the general population suggests that they represent a significant proportion of total genomic variation, with estimations that CNVs may affect as much as 4% to 5% of the human genome.9 Beneficial in terms of evolution, those CNVs occurring within coding or regulatory regions of the genome can have an adverse effect on gene expression. Since CNVs play a significant role in many genetic disorders, the inability of targeted sequencing to detect these aberrations remains a barrier to its widespread application, and alternative methods must be employed. 

aCGH for CNV detection

As the gold standard for CNV detection,10 array comparative genomic hybridization (aCGH) essentially detects aberrations through comparison with a normal reference genome. Array CGH has proven to be a specific, sensitive, and fast technique, amenable to automation for high-throughput workflows. Moreover, because the exact genomic position is known, aberrations can be mapped directly onto the chromosomal location. The resolution and therefore application of the array is determined by its design, including probe length and coverage along the genome—consisting of lower density “backbone” coverage probes, or targeted designs focusing on high-density coverage over specific regions of the genome.  

Optimizing the aCGH design facilitates research into a range of genetic disorders, and producing a targeted gene array that also retains backbone coverage generates cleaner results and allows easier data analysis. Furthermore, by utilizing an array platform that allows customization, researchers can easily eliminate poorly performing probes and instead replace them with high-performance, reliable probes. In this way, new target sequences are easily selected and repetitive sequences avoided, thereby decreasing noise and enhancing precision. Guiding the design of molecular testing arrays and panels, clinical researchers have been working closely with Oxford Gene Technology, selecting probes based on direct clinical research experience and ensuring the most insightful coverage with a high signal-to-noise ratio.

A comprehensive approach

The inherent genetic variation underlying clinical disorders calls for comprehensive analysis, rather than reliance on any single method. By using a range of available tools, a more complete picture of the disease is built up, in turn providing additional insight to drive novel discoveries and potential therapeutic strategies. This integrated approach is being utilized by researchers such as Professor Madhuri Hegde at Emory Genetics Laboratory.11 At this facility, when the sample for investigation arrives, it is subjected to concurrent analysis using complementary methods.

Single point mutations are detected using NGS, and any challenging exons/regions present within the region of interest are then filled in using traditional Sanger sequencing. aCGH is employed specifically to detect CNVs, and although single point mutations and CNVs are major contributors to genetic disorders, for some disorders a truly comprehensive investigation requires additional methods. These include methylation assays to profile epigenetic factors, for example employed alongside triplet repeat expansion analysis to form a first-tier test when investigating FRAXE syndrome. This is followed by sequencing and aCGH only if results are inconclusive. 

Within this comprehensive framework, each specific disorder has its own set of analysis procedures, and all data are integrated for use in the final analysis and interpretation (Figure 2). 

Figure 2. The Emory model of integrated analysis. On arrival in the laboratory, the sample is investigated using NGS, aCGH, or traditional Sanger sequencing. From the three data sets, comprehensive analysis provides more insightful interpretation. Image courtesy of Madhuri Hegde, PhD, FACMG, Emory Genetics Lab.

Tackling genomic complexity

Robust and insightful genomic analysis is of paramount importance for clinical genetics research. As molecular techniques become increasingly established within the modern laboratory, an integrated approach provides a framework for detecting the complete range of aberrations underlying a given genetic disorder. 

Where previously viewed as competing, Sanger sequencing, targeted NGS, and aCGH have instead become highly valued by researchers as complementary techniques, together capable of detecting the entire mutation spectrum, including point mutations and CNVs. Optimizing the aCGH platform has proved invaluable to medical researchers in developing an insightful and comprehensive investigation, made possible through a collaborative partnership with industry. Such work in improving the performance of molecular techniques is a vital component of research, accelerating the development of personalized therapeutic strategies. 

Ruth Burton, PhD, serves as product manager for CytoSure Arrays for Oxford Gene Technology (OGT), provider of products and services for genome analysis. Ephrem Chin, MBA, BTech(Hon), MB(ASCP), QLC, is regional sales manager for OGT. His background is in translational research into rare genetic diseases including the implementation and development of high-throughput aCGH and NGS workflows.


  1. Classen CF, Riehmer V, Landwehr C, et al. Dissecting the genotype in syndromic intellectual disability using whole exome sequencing in addition to genome-wide copy number analysis. Hum Genet. 2013;132(7):825-841.
  2. Semler O, Garbes L, Keupp K, et al. A mutation in the 5’-UTR of IFITM5 creates an in-frame start codon and causes autosomal-dominant osteogenesis imperfecta type V with hyperplastic callus. Am J Hum Genet. 2012;10(91):349-357. 
  3. Choi M, Scholl UI, JI W, et al. Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Pro Natl Acad Sci USA.2009;10(106):19096-19101.
  4. Ng SB, Buckingham K, Lee C, et al. Exome sequencing identifies the cause of a mendelian disorder. Nat Genet. 2010;42(1):30-35.  
  5. Ng SB, Turner EH, Robertson P, et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461(7261):272-276. 
  6. Wei X, Walia V, Lin JC, et al. Exome sequencing identifies GRIN2A as frequently mutated in melanoma. Nat Genet. 2011;43:(5)442-446.
  7. Yan XJ, Xu J, Gu ZH, et al. Exome Sequencing identifies somatic mutations of DNA methyltransferase gene DNMT3A in acute monocytic leukemia. Nat Genet. 2011;43(4)309-315. 
  8. Gargis AS, Kalman L, Berry MW, et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nat Biotechnol. 2012;30(11):1033-1036. 
  9. Conrad DF, Pinto D, Redon R, et al. Origins and functional impact of copy number variation in the human genome. Nature. 2010;464:(7289)704-712. 
  10. Curtis C, Lynch AG, Dunning MJ, et al. The pitfalls of platform comparison: DNA copy number array technologies assessed. BMC Genom. 2009;10(12)588-610.  
  11. Askree SH, Chin EL, Bean LH, et al. Detection limit of intragenic deletions with targeted array comparative genomic hybridization. BMC Genet. 2013;14(12):116.