There is considerable genomic variability among humans that is not associated with a recognizable clinical phenotype. This variability is evident at both the chromosomal level (as microscopically visible gains or losses of chromosomal bands or regions) [1] and at the single nucleotide level (as single nucleotide polymorphisms (SNPs)) [2]. The gains and losses of sub-microscopic DNA segments larger than 1 kb are termed copy number variants (CNVs) [3]. They represent a newly recognized class of DNA variation, identified as a result of the introduction of comparative genomic hybridization (CGH) array technology that enables the study of variation in the number of copies of specific DNA segments among individuals [4, 5]. The widespread presence of CNVs in normal individuals has now been documented using not only array CGH-containing BAC clones [6, 7] but also oligonucleotide arrays at high resolution as well SNP data analysis [8–12] and DNA sequence comparisons between individuals [13].

The discovery of CNVs presents investigators with a number of challenges as CNVs complicate the interpretation of array data and efforts to attribute microdeletions and microduplications identified in individuals with constitutional disorders or in cancerous tissues to the disease processes. The role of CNVs in causing or influencing the susceptibility to disease and genome evolution remains largely unknown. A catalogue of published CNVs can be found in the public database [14], and helps to guide the interpretation of array CGH findings. However, the number and identity of polymorphic loci detected in different studies varies considerably [15], likely because of differences across various array platforms, analytical methods and the populations investigated. In addition, copy number differences for many of the CNVs listed in the above database are not confirmed using secondary independent quantification methods (FISH or quantitative DNA methods). Therefore, further work to identify and characterize CNVs in human populations and confirm copy number variability is essential in order to better understand the significance of CNVs and to determine their role in common disorders.

We studied 27 phenotypically normal individuals and detected 42 different sub-microscopic CNVs using the 1 Mb resolution whole genome array-CGH. We used real-time quantitative PCR (RT-qPCR) and FISH to confirm CNVs. The results of these studies are presented.

Our study of 27 normal subjects revealed a total of 42 CNVs: 26 previously described CNVs [14] and 16 (38%) novel CNVs. A higher number of known than novel CNVs showed recurrence in the subjects tested (7/8 recurrent clones were previously described -Table 1). Similarly, evidence of segmental duplications was found in 6/8 previously described CNVs. This confirms the observation that recurring CNVs are more prevalent in the human population, and tend to be associated with segmental duplications [7], while our novel CNVs are possibly less frequent and individual specific.

The number of CNVs detected in our controls is comparable to the number obtained by other investigators using the same commercial array-CGH and cut-off levels [17–19]. However, the number is significantly smaller than that observed by Iafrate et al [6] who reported 255 CNVs in 59 individuals. Although they used the same array-CGH platform, a different dynamic website-based analytical method was applied, instead of our fixed cut-off levels of 1.2 for duplications and 0.8 for deletions, suggesting that the analytical method used plays a significant role in the number of apparent CNVs detected among individuals. Recently, a number of studies addressed the question of global genomic variation using different approaches including tiling BAC array [7, 12, 20], SNP polymorphisms and oligo arrays [9–11]. The number of CNVs and chromosomal regions affected varied among studies even when the same array platform was used. For example, the BAC tiling arrays detected 3654 autosomal segmental CNVs in 95 controls [7], 913 CNVs in 270 controls [12] and 258 CNVs in 100 individuals with intellectual disability and their phenotypically normal parents [20]. The differences in the populations studied may have contributed to the observed discrepancies, however, even when the same individuals were examined using a different approach (BAC array vs. SNPs) less than half (43%) of the CNVs were detected on both platforms [12]. It is now evident that none of the existing technologies can capture all human variation.

One of the pre-requisites for understanding global human variation is the confirmation of CNVs using alternative methods. Their recurrence and presence as detected using different platforms supports that these are true differences among individuals. However, a large number of CNVs are still "unique", i.e. specific for a control subject/family or study. Independent quantification methods such as FISH or RT-qPCR should ideally be performed on many CNVs, particularly those appearing in one individual, as these are the most likely ones to be false positives [7]. Considering the large number of CNVs reported (>6000 entries in the database of human variation) the number of validated CNVs using independent quantification methods such as RT-qPCR or FISH is still proportionally very small due to the time consuming or limited throughput of single locus analysis. For example, in two recent larger studies reporting a total of >5000 CNVs, less than 300 CNVs were validated using quantification methods [7, 12]. Using FISH we have confirmed array-detected copy number changes of 3/6 selected CNVs (Table 2), while for 3/6 CNVs a normal two signal FISH pattern was seen. We tested two FISH-confirmed CNVs using RT-qPCR, one partially and one fully deleted (RP11-89M16 and RP5-1011O17, respectively), and observed concordance between all 3 methods (array-CGH, FISH and RT-qPCR). As two of the three FISH non-confirmed CNVs (RP11-125A5 and RP11-100C24) are recognized as being very common and recurrent on multiple platforms [6, 12], we further evaluated RP11-100C24 using RT-qPCR but failed to achieve confirmation of both gain and loss. It is possible that this CNV is composed of tightly packed repeats which can be discerned only by fiber FISH, as noted for clone RP11-259N12 from chromosome 1 [6]. Additional cause of the array-CGH vs. FISH/RT-qPCR discrepancy may be due to the fact that array-CGH assays evaluate the relative ratio of segmental DNA copy number in the test DNA vs. reference DNA, and do not provide an absolute number of copies, as explained in Figure 5.

The information on new CNVs is expanding dramatically, and cataloguing clones for which independent quantification is performed are desirable, as only detailed analysis of a large number of CNVs will help better understand their basic structure, DNA content and reasons for variability. Currently, the significance of CNVs remains puzzling, as many of these genomic regions contain genes and coding sequences associated with known genetic disorders [6, 8, 21]. In our subjects 13/42 different CNVs were associated with OMIM genes; the number was usually not higher than 2 genes/CNV, except for CNV RP11-144O23 which had 9 genes involved in sensory perception, cell adhesion-mediated signaling, immune and defense processes (Table 1A). This clone was noted in one of our Hispanic individuals and was reported as one of the more divergent clones in the 4 populations reported by Redon et al [12]. Many of the genes in CNVs are described as "environmental sensor genes" and are associated with mechanisms mediating immune responsiveness (defensin, interferon regulatory factor 4, etc.), cellular metabolism (cytochrome P450 genes and carboxyesterase gene families), and membrane surface interactions (Rhesus blood group gene families, melanoma antigen gene). It is now established that the copy number variability of some genes can influence susceptibility to some diseases [22, 23]. For example, it has been reported that people with fewer copy numbers of CCL3L1, a gene involved in immunity, are more susceptible to HIV infection [22]. The extent of associations of CNVs with disease susceptibility will become clearer as we learn more about the distribution of well characterized CNVs in individuals whose health and medical histories are fully evaluated.

Submicroscopic CNVs are a common form of human genomic variation, which can be readily identified by array-CGH technology in phenotypically normal individuals. The number of CNVs detected in each study is influenced by several factors, especially the array platform and method of analysis. Our results confirm the wide distribution of CNVs in three different ethnic populations and expand the number of recognized CNVs. Cataloguing of confirmed CNVs, quantified using independent methods, would facilitate their interpretation and understanding of their significance in the future.