A genome‐wide scan for copy number variations using high‐density single nucleotide polymorphism array in Simmental cattle

Copy number variations (CNVs) have recently been identified as promising sources of genetic variation, complementary to single nucleotide polymorphisms (SNPs). As a result, detection of CNVs has attracted a great deal of attention. In this study, we performed genome‐wide CNV detection using Illumina Bovine HD BeadChip (770k) data on 792 Simmental cattle. A total of 263 CNV regions (CNVRs) were identified, which included 137 losses, 102 gains and 24 regions classified as both loss and gain, covering 35.48 Mb (1.41%) of the bovine genome. The length of these CNVRs ranged from 10.18 kb to 1.76 Mb, with an average length of 134.78 kb and a median length of 61.95 kb. In 136 of these regions, a total of 313 genes were identified related to biological functions such as transmembrane activity and olfactory transduction activity. To validate the results, we performed quantitative PCR to detect nine randomly selected CNVRs and successfully confirmed seven (77.6%) of them. Our results present a map of cattle CNVs derived from high‐density SNP data, which expands the current CNV map of the cattle genome and provides useful information for investigation of genomic structural variation in cattle.     

Fine mapping of copy number variations on two cattle genome assemblies using high density SNP array

ABSTRACT: BACKGROUND: Btau_4.0 and UMD3.1 are two distinct cattle reference genome assemblies. In our previous study using the low density BovineSNP50 array, we reported a copy number variation (CNV) analysis on Btau_4.0 with 521 animals of 21 cattle breeds, yielding 682 CNV regions with a total length of 139.8 megabases. RESULTS: In this study using the high density BovineHD SNP array, we performed high resolution CNV analyses on both Btau_4.0 and UMD3.1 with 674 animals of 27 cattle breeds. We first compared CNV results derived from these two different SNP array platforms on Btau_4.0. With two thirds of the animals shared between studies, on Btau_4.0 we identified 3,346 candidate CNV regions representing 142.7 megabases (~4.70%) of the genome. With a similar total length but 5 times more event counts, the average CNVR length of current Btau_4.0 dataset is significantly shorter than the previous one (42.7kb vs. 205 kb). Although subsets of these two results overlapped, 64% (91.6 megabases) of current dataset was not present in the previous study. We also performed similar analyses on UMD3.1 using these BovineHD SNP array results. Approximately 50% more and 20% longer CNVs were called on UMD3.1 as compared to those on Btau_4.0. However, a comparable result of CNVRs (3,438 regions with a total length 146.9 megabases) was obtained. We suspect that these results are due to that UMD3.1's efforts of placing unplaced contigs and removing unmerged alleles. Selected CNVs were further experimentally validated, achieving a 73% PCR validation rate, which is considerably higher than the previous validation rate. About 20-45% of CNV regions overlapped with cattle RefSeq genes and Ensembl genes. Panther and IPA analyses indicated that these genes provide a wide spectrum of biological processes involving immune system, lipid metabolism, cell, organism and system development. CONCLUSION: We present a comprehensive result of cattle CNVs at a higher resolution and sensitivity. We identified over 3,000 candidate CNV regions on both Btau_4.0 and UMD3.1, further compared current datasets with previous results, and examined the impacts of genome assemblies on CNV calling.     

Copy number variations identified in the chicken using a 60K SNP BeadChip

Copy number variation (CNV) is considered an important genetic variation, contributing to many economically important traits in the chicken. Although CNVs can be detected using a comparative genomic hybridization array, the high‐density SNP array has provided an alternative way to identify CNVs in the chicken. In the current study, a chicken 60K SNP BeadChip was used to identify CNVs in two distinct chicken genetic lines (White Leghorn and dwarf) using the penncnv program. A total of 209 CNV regions were identified, distributing on chromosomes 1–22 and 24–28 and encompassing 13.55 Mb (1.42%) of chicken autosomal genome area. Three of seven selected CNVs (73.2% individuals) were completely validated by quantitative PCR. To our knowledge, this is the first report in the chicken identifying CNVs using a SNP array. Identification of 190 new identified CNVs illustrates the feasibility of the chicken 60K SNP BeadChip to detect CNVs in the chicken, which lays a solid foundation for future analyses of associations of CNVs with economically important phenotypes in chickens.     

Genomic regions showing copy number variations associate with resistance or susceptibility to gastrointestinal nematodes in Angus cattle

Genomic structural variation is an important and abundant source of genetic and phenotypic variation. We previously reported an initial analysis of copy number variations (CNVs) in Angus cattle selected for resistance or susceptibility to gastrointestinal nematodes. In this study, we performed a large-scale analysis of CNVs using SNP genotyping data from 472 animals of the same population. We detected 811 candidate CNV regions, which represent 141.8 Mb (~4.7%) of the genome. To investigate the functional impacts of CNVs, we created 2 groups of 100 individual animals with extremely low or high estimated breeding values of eggs per gram of feces and referred to these groups as parasite resistant (PR) or parasite susceptible (PS), respectively. We identified 297 (~51 Mb) and 282 (~48 Mb) CNV regions from PR and PS groups, respectively. Approximately 60% of the CNV regions were specific to the PS group or PR group of animals. Selected PR- or PS-specific CNVs were further experimentally validated by quantitative PCR. A total of 297 PR CNV regions overlapped with 437 Ensembl genes enriched in immunity and defense, like WC1 gene which uniquely expresses on gamma/delta T cells in cattle. Network analyses indicated that the PR-specific genes were predominantly involved in gastrointestinal disease, immunological disease, inflammatory response, cell-to-cell signaling and interaction, lymphoid tissue development, and cell death. By contrast, the 282 PS CNV regions contained 473 Ensembl genes which are overrepresented in environmental interactions. Network analyses indicated that the PS-specific genes were particularly enriched for inflammatory response, immune cell trafficking, metabolic disease, cell cycle, and cellular organization and movement.     

Genome‐wide copy number profiling using high‐density SNP array in chickens

Phenotypic diversity is a direct consequence resulting mainly from the impact of underlying genetic variation, and recent studies have shown that copy number variation (CNV) is emerging as an important contributor to both phenotypic variability and disease susceptibility. Herein, we performed a genome‐wide CNV scan in 96 chickens from 12 diversified breeds, benefiting from the high‐density Affymetrix 600 K SNP arrays. We identified a total of 231 autosomal CNV regions (CNVRs) encompassing 5.41 Mb of the chicken genome and corresponding to 0.59% of the autosomal sequence. The length of these CNVRs ranged from 2.6 to 586.2 kb with an average of 23.4 kb, including 130 gain, 93 loss and eight both gain and loss events. These CNVRs, especially deletions, had lower GC content and were located particularly in gene deserts. In particular, 102 CNVRs harbored 128 chicken genes, most of which were enriched in immune responses. We obtained 221 autosomal CNVRs after converting probe coordinates to Galgal3, and comparative analysis with previous studies illustrated that 153 of these CNVRs were regarded as novel events. Furthermore, qPCR assays were designed for 11 novel CNVRs, and eight (72.73%) were validated successfully. In this study, we demonstrated that the high‐density 600 K SNP array can capture CNVs with higher efficiency and accuracy and highlighted the necessity of integrating multiple technologies and algorithms. Our findings provide a pioneering exploration of chicken CNVs based on a high‐density SNP array, which contributes to a more comprehensive understanding of genetic variation in the chicken genome and is beneficial to unearthing potential CNVs underlying important traits of chickens.     

Analysis of copy number variations in Holstein cows identify potential mechanisms contributing to differences in residual feed intake

Genomic structural variation is an important and abundant source of genetic and phenotypic variation. In this study, we performed an initial analysis of copy number variations (CNVs) using BovineHD SNP genotyping data from 147 Holstein cows identified as having high or low feed efficiency as estimated by residual feed intake (RFI). We detected 443 candidate CNV regions (CNVRs) that represent 18.4?Mb (0.6?%) of the genome. To investigate the functional impacts of CNVs, we created two groups of 30 individual animals with extremely low or high estimated breeding values (EBVs) for RFI, and referred to these groups as low intake (LI; more efficient) or high intake (HI; less efficient), respectively. We identified 240 (~9.0?Mb) and 274 (~10.2?Mb) CNVRs from LI and HI groups, respectively. Approximately 30–40?% of the CNVRs were specific to the LI group or HI group of animals. The 240 LI CNVRs overlapped with 137 Ensembl genes. Network analyses indicated that the LI-specific genes were predominantly enriched for those functioning in the inflammatory response and immunity. By contrast, the 274 HI CNVRs contained 177 Ensembl genes. Network analyses indicated that the HI-specific genes were particularly involved in the cell cycle, and organ and bone development. These results relate CNVs to two key variables, namely immune response and organ and bone development. The data indicate that greater feed efficiency relates more closely to immune response, whereas cattle with reduced feed efficiency may have a greater capacity for organ and bone development.     

Copy number variation in the cattle genome

Copy number variations (CNVs) are gains and losses of genomic sequence greater than 50?bp between two individuals of a species. While single nucleotide polymorphisms (SNPs) are more frequent, CNVs impact a higher percentage of genomic sequence and have potentially greater effects, including the changing of gene structure and dosage, altering gene regulation and exposing recessive alleles. In particular, segmental duplications (SDs) were shown to be one of the catalysts and hotspots for CNV formation. Substantial progress has been made in understanding CNVs in mammals, especially in humans and rodents. CNVs have been shown to be important in both normal phenotypic variability and disease susceptibility. Recently, interest in CNV study has extended into domesticated animals, including cattle. Multiple genome-wide cattle CNV studies have been carried out using both microarray and next generation sequencing technologies. Integration of SD and CNV results with SNP and other datasets are beginning to reveal impacts of CNVs on cattle domestication, health, and production traits.     

Copy number variants in a highly inbred Iberian porcine strain

We carried out a comprehensive genomic analysis of porcine copy number variants (CNVs) based on whole‐genome SNP genotyping data and provided new measures of genomic diversity (number, length and distribution of CNV events) for a highly inbred strain (the Guadyerbas strain). This strain represents one of the most ancient surviving populations of the Iberian breed, and it is currently in serious danger of extinction. CNV detection was conducted on the complete Guadyerbas population, adjusted for genomic waves, and used strict quality criteria, pedigree information and the latest porcine genome annotation. The analysis led to the detection of 65 CNV regions (CNVRs). These regions cover 0.33% of the autosomal genome of this particular strain. Twenty‐nine of these CNVRs were identified here for the first time. The relatively low number of detected CNVRs is in line with the low variability and high inbreeding estimated previously for this Iberian strain using pedigree, microsatellite or SNP data. A comparison across different porcine studies has revealed that more than half of these regions overlap with previously identified CNVRs or multicopy regions. Also, a preliminary analysis of CNV detection using whole‐genome sequence data for four Guadyerbas pigs showed overlapping for 16 of the CNVRs, supporting their reliability. Some of the identified CNVRs contain relevant functional genes (e.g., the SCD and USP15 genes), which are worth being further investigated because of their importance in determining the quality of Iberian pig products. The CNVR data generated could be useful for improving the porcine genome annotation.     

Genome-wide copy number variation in Hanwoo, Black Angus, and Holstein cattle

Hanwoo, Korean native cattle, is indigenous to the Korean peninsula. They have been used mainly as draft animals for about 5,000 years; however, in the last 30 years, their main role has been changed to meat production by selective breeding which has led to substantial increases in their productivity. Massively parallel sequencing technology has recently made possible the systematic identification of structural variations in cattle genomes. In particular, copy number variation (CNV) has been recognized as an important genetic variation complementary to single-nucleotide polymorphisms that can be used to account for variations of economically important traits in cattle. Here we report genome-wide copy number variation regions (CNVRs) in Hanwoo cattle obtained by comparing the whole genome sequence of Hanwoo with Black Angus and Holstein sequence datasets. We identified 1,173 and 963 putative CNVRs representing 16.7 and 7.8 Mbp from comparisons between Black Angus and Hanwoo and between Holstein and Hanwoo, respectively. The potential functional roles of the CNVRs were assessed by Gene Ontology enrichment analysis. The results showed that response to stimulus, immune system process, and cellular component organization were highly enriched in the genic-CNVRs that overlapped with annotated cattle genes. Of the 11 CNVRs that were selected for validation by quantitative real-time PCR, 9 exhibited the expected copy number differences. The results reported in this study show that genome-wide CNVs were detected successfully using massively parallel sequencing technology. The CNVs may be a valuable resource for further studies to correlate CNVs and economically important traits in cattle.     

Microarray-based maps of copy-number variant regions in European and sub-Saharan populations

The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N=717; Rwanda, N=450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.     

A map of copy number variations in Chinese populations

It has been shown that the human genome contains extensive copy number variations (CNVs). Investigating the medical and evolutionary impacts of CNVs requires the knowledge of locations, sizes and frequency distribution of them within and between populations. However, CNV study of Chinese minorities, which harbor the majority of genetic diversity of Chinese populations, has been underrepresented considering the same efforts in other populations. Here we constructed, to our knowledge, a first CNV map in seven Chinese populations representing the major linguistic groups in China with 1,440 CNV regions identified using Affymetrix SNP 6.0 Array. Considerable differences in distributions of CNV regions between populations and substantial population structures were observed. We showed that ～35% of CNV regions identified in minority ethnic groups are not shared by Han Chinese population, indicating that the contribution of the minorities to genetic architecture of Chinese population could not be ignored. We further identified highly differentiated CNV regions between populations. For example, a common deletion in Dong and Zhuang (44.4% and 50%), which overlaps two keratin-associated protein genes contributing to the structure of hair fibers, was not observed in Han Chinese. Interestingly, the most differentiated CNV deletion between HapMap CEU and YRI containing CCL3L1 gene reported in previous studies was also the highest differentiated regions between Tibetan and other populations. Besides, by jointly analyzing CNVs and SNPs, we found a CNV region containing gene CTDSPL were in almost perfect linkage disequilibrium between flanking SNPs in Tibetan while not in other populations except HapMap CHD. Furthermore, we found the SNP taggability of CNVs in Chinese populations was much lower than that in European populations. Our results suggest the necessity of a full characterization of CNVs in Chinese populations, and the CNV map we constructed serves as a useful resource in further evolutionary and medical studies.     

The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules’ functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.     

Precision-mapping and statistical validation of quantitative trait loci by machine learning

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.     

Genome-wide mapping of copy number variation in humans: comparative analysis of high resolution array platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications.     

A Definitive Haplotype Map as Determined by Genotyping Duplicated Haploid Genomes Finds a Predominant Haplotype Preference at Copy-Number Variation Events

The majority of complete hydatidiform moles (CHMs) harbor duplicated haploid genomes that originate from sperm. This makes CHMs more advantageous than conventional diploid cells for determining haplotypes of SNPs and copy-number variations (CNVs), because all of the genetic variants in a CHM genome are homozygous. Here we report SNP and CNV haplotype structures determined by analysis of 100 CHMs from Japanese subjects via high-density DNA arrays. The obtained haplotype map should be useful as a reference for the haplotype structure of Asian populations. We resolved common CNV regions (merged CNV segments across the examined samples) into CNV events (clusters of CNV segments) on the basis of mutual overlap and found that the haplotype backgrounds of different CNV events within the same CNV region were predominantly similar, perhaps because of inherent structural instability.     

Identification of copy number variation hotspots in human populations

Copy number variants (CNVs) in the human genome contribute to both Mendelian and complex traits as well as to genomic plasticity in evolution. The investigation of mutational rates of CNVs is critical to understanding genomic instability and the etiology of the copy number variation (CNV)-related traits. However, the evaluation of the CNV mutation rate at the genome level poses an insurmountable practical challenge that requires large samples and accurate typing. In this study, we show that an approximate estimation of the CNV mutation rate could be achieved by using the phylogeny information of flanking SNPs. This allows a genome-wide comparison of mutation rates between CNVs with the use of vast, readily available data of SNP genotyping. A total of 4187 CNV regions (CNVRs) previously identified in HapMap populations were investigated in this study. We showed that the mutation rates for the majority of these CNVRs are at the order of 10⁻⁵ per generation, consistent with experimental observations at individual loci. Notably, the mutation rates of 104 (2.5%) CNVRs were estimated at the order of 10⁻³ per generation; therefore, they were identified as potential hotspots. Additional analyses revealed that genome architecture at CNV loci has a potential role in inciting mutational hotspots in the human genome. Interestingly, 49 (47%) CNV hotspots include human genes, some of which are known to be functional CNV loci (e.g., CNVs of C4 and β-defensin causing autoimmune diseases and CNVs of HYDIN with implication in control of cerebral cortex size), implicating the important role of CNV in human health and evolution, especially in common and complex diseases.     

Genome‐wide genetic variation discovery in Chinese Taihu pig breeds using next generation sequencing

The Chinese Taihu pig breeds are an invaluable component of the world's pig genetic resources, and they are the most prolific breeds of swine in the world. In this study, the genomes of 252 pigs of the six indigenous breeds in the Taihu Lake region were sequenced using the genotyping by genome reducing and sequencing approach. A total of 950 million good reads were obtained using an Illumina Hiseq2000 at an average depth of 13× (for SNP calling) and an average coverage of 2.3%. In total, 122 632 indels, 31 444 insertions, 44 056 deletions and 455 CNVs (copy number variants) were identified in the genomes of the pigs. Approximately 2.3% of these genetic markers were mapped to gene exon regions, and 25% were in QTL regions related to economically important traits. The KEGG pathway or GO enrichment analyses revealed that genetic variants assumed to be large‐effect mutations were significantly overrepresented in 22 SNP, 56 indel, 26 insertion, 28 deletion and three CNV gene sets. A total of 343 breed‐specific SNPs were also identified in the six Chinese indigenous pigs. The findings from this study can contribute to future investigations of the genetic diversity, population structure, positive selection signals and molecular evolutionary history of these pigs at the genome level and can serve as a valuable reference for improving the breeding and cultivation of these pigs.     

Genome-wide analysis of copy number variation in type 1 diabetes

Type 1 diabetes (T1D) tends to cluster in families, suggesting there may be a genetic component predisposing to disease. However, a recent large-scale genome-wide association study concluded that identified genetic factors, single nucleotide polymorphisms, do not account for overall familiality. Another class of genetic variation is the amplification or deletion of >1 kilobase segments of the genome, also termed copy number variations (CNVs). We performed genome-wide CNV analysis on a cohort of 20 unrelated adults with T1D and a control (Ctrl) cohort of 20 subjects using the Affymetrix SNP Array 6.0 in combination with the Birdsuite copy number calling software. We identified 39 CNVs as enriched or depleted in T1D versus Ctrl. Additionally, we performed CNV analysis in a group of 10 monozygotic twin pairs discordant for T1D. Eleven of these 39 CNVs were also respectively enriched or depleted in the Twin cohort, suggesting that these variants may be involved in the development of islet autoimmunity, as the presently unaffected twin is at high risk for developing islet autoimmunity and T1D in his or her lifetime. These CNVs include a deletion on chromosome 6p21, near an HLA-DQ allele. CNVs were found that were both enriched or depleted in patients with or at high risk for developing T1D. These regions may represent genetic variants contributing to development of islet autoimmunity in T1D.     

Insertion-deletions burden in copy number polymorphisms of the Tibetan population

BACKGROUND:

Many studies have been conducted to identify either insertions-deletions (inDels) or copy number variations (CNVs) in humans, but few studies have been conducted to identify both of these forms coexisting in the same region.

AIMS AND OBJECTIVES:

To map the functionally significant sites within human genes that are likely to influence human traits and diseases.

MATERIALS AND METHODS:

In this report, we describe an inDel map in the 1051 Tibetan CNV regions obtained through CNV genotyping using Affymetrix Genome-wide single nucleotide polymorphism 6.0 chip. InDel polymorphisms in these copy number polymorphism regions were identified with a computational approach using the 2500 deoxyribonucleic acid sequences obtained from the 1000 Genome Project.

RESULTS:

The study identified a total of 95935 inDels that range from 1 bp to several bps in length which were found scattered across regulatory regions, exons and in introns of genes underlying the CNVs. A study on the distribution of inDels revealed that the majority of inDels were found in coding regions of the genome than the noncoding, while within the genes, inDels in intron regions were more followed by exonic regions and finally the regulatory regions.

CONCLUSION:

Study of inDels in CNV regions contribute to the enhanced understanding of the role played by the two variations and their collective influence on the genome. Further, a collection of these inDel genetic markers will aid in genetic mapping, further understanding of the phenotypic variability, identification of disease genes and in detecting novel CNVs.