The recent implication of genomic copy number variations (CNVs) in multiple human genetic disorders has led to increased interest in CNV discovery technologies. There is a growing consensus that, in addition to the method used for detection, at least one additional technology should be employed for validation. Real-time quantitative polymerase chain reaction (qPCR) analysis, incorporating a normal (2N) copy number standard, is commonly used as a means of validating CNVs. Whereas it has previously been reported that formalin-fixed paraffin-embedded (FFPE) DNA samples can yield spurious CNV calls in real-time qPCR assays, here we report that sample degradation under standard laboratory storage conditions generates a significant increase in false-positive CNV results. Results suggest the possibility of biased degradation among genomic regions and emphasize the need to assess sample integrity immediately prior to real-time qPCR experiments.
"Several studies have suggested that the biological properties of the DNA including protein content, shearing, methods of extraction and storage conditions are potential contributors to the poor performance of real-time QPCR for analyzing multi-allelic CNPs
[2, 14, 15, 17]. Additionally, it has been suggested that real-time QPCR will perform comparably to PRT if the concentration of DNA samples is normalized to a uniform concentration, and the efficiency between the test and reference loci differ by less than 5%
[Show abstract][Hide abstract] ABSTRACT: Background
Intrachromosomal segmental duplications provide the substrate for non-allelic homologous recombination, facilitating extensive copy number variation in the human genome. Many multi-copy gene families are embedded within genomic regions with high levels of sequence identity (>95%) and therefore pose considerable analytical challenges. In some cases, the complexity involved in analyzing such regions is largely underestimated. Rapid, cost effective analysis of multi-copy gene regions have typically implemented quantitative approaches, however quantitative data are not an absolute means of certainty. Therefore any technique prone to degrees of measurement error can produce ambiguous results that may lead to spurious associations with complex disease.
In this study we have focused on testing the accuracy and reproducibility of quantitative analysis techniques. With reference to the C-C Chemokine Ligand-3-like-1 (CCL3L1) gene, we performed analysis using real-time Quantitative PCR (QPCR), Multiplex Ligation-dependent Probe Amplification (MLPA) and Paralogue Ratio Test (PRT). After controlling for potential outside variables on assay performance, including DNA concentration, quality, preparation and storage conditions, we find that real-time QPCR produces data that does not cluster tightly around copy number integer values, with variation substantially greater than that of the MLPA or PRT systems. We find that the method of rounding real-time QPCR measurements can potentially lead to mis-scoring of copy number genotypes and suggest caution should be exercised in interpreting QPCR data.
We conclude that real-time QPCR is inherently prone to measurement error, even under conditions that would seem favorable for association studies. Our results indicate that potential variability in the physicochemical properties of the DNA samples cannot solely explain the poor performance exhibited by the real-time QPCR systems. We recommend that more robust approaches such as PRT or MLPA should be used to genotype multi-allelic copy number variation in disease association studies and suggest several approaches which can be implemented to ensure the quality of the copy number typing using quantitative methods.
"Therefore, robust validation experiments are essential for discovery of somatic CNVs. Non-random DNA sample degradation can lead to false positive CNVs in quantitative PCR33. This may be particularly problematic in the quantitative comparison of target DNA and reference DNA from human post-mortem tissue that is often stored in the freezer for extended periods of time. "
[Show abstract][Hide abstract] ABSTRACT: While somatic DNA copy number variations (CNVs) have been identified in multiple tissues from normal people, they have not been well studied in brain tissues from individuals with psychiatric disorders. With ultrahigh depth sequencing data, we developed an integrated pipeline for calling somatic deletions using data from multiple tissues of the same individual or a single tissue type taken from multiple individuals. Using the pipelines, we identified 106 somatic deletions in DNA from prefrontal cortex (PFC) and/or cerebellum of two normal controls subjects and/or three individuals with schizophrenia. We then validated somatic deletions in 18 genic and in 1 intergenic region. Somatic deletions in BOD1 and CBX3 were reconfirmed using DNA isolated from non-pyramidal neurons and from cells in white matter using laser capture microdissection (LCM). Our results suggest that somatic deletions may affect metabolic processes and brain development in a region specific manner.
"At a single locus level, candidate gene studies based on single-probe quantitative polymerase chain reaction analysis with an internal diploid standard have implicated common CN variation (CNV) at a limited number of genes in auto-inflammatory diseases –. However, even these single gene studies are beset by technical challenges , –, meaning that there is not widespread confidence in these data , . Despite these challenges, meta-analysis using studies employing more robust methodologies provides strong evidence supporting a role for FCGR3B deletion in systemic autoimmunity . "
[Show abstract][Hide abstract] ABSTRACT: The extent of contribution from common gene copy number (CN) variants in human disease is currently unresolved. Part of the reason for this is the technical difficulty in directly measuring CN variation (CNV) using molecular methods, and the lack of single nucleotide polymorphisms (SNPs) that can tag complex CNV that has arisen multiple times on different SNP haplotypes. One CNV locus implicated in human disease is FCGR. Here we aimed to use next-generation sequencing (NGS) data from the 1000 Genomes Project to assign CN at FCGR3A and FCGR3B and to comprehensively assess the ability of SNPs to tag specific CN variants. A read-depth algorithm was developed (CNVrd) and validated on a subset of HapMap samples using CN assignments that had previously been determined using molecular and microarray methods. At 7 out of 9 other complex loci there was >90% concordance with microarray data. However, given that some prior knowledge of CN is required, the generalizability of CNVrd is limited and should be applied to other complex CNV loci with caution. Subsequently, CN was assigned et FCGR3B using CNVrd in a total of 952 samples from the 1000 Genomes Project, using three classes and SNPs that correlated with duplication were identified. The best tag SNP was observed in the Mexican-American sample set for duplication at FCGR3B. This SNP (rs117435514, r(2) = 0.79) also tagged similar duplication in Chinese and Japanese (r(2) = 0.35-0.60), but not in Caucasian or African. No tag SNP for duplication at FCGR3A or deletion at FCGR3B was identified in any population. We conclude that it is possible to tag CNV at the FCGR locus, but CN and SNPs have to be characterized and correlated on a population-specific basis.
PLoS ONE 04/2013; 8(4):e63219. DOI:10.1371/journal.pone.0063219 · 3.23 Impact Factor
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