Copy number regulation & CNV

Copy number regulation & CNV

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I have some genes which showed copy number loss between two groups. Now I want to see the copy number regulation of those genes. I really don't know about this concept. Can anyone please tell me clearly about this like why we need to check regulation of copy number loss genes? And any idea how to do this?

Thank you

It might be helpful if you showed us what sort of data you are looking at since copy number variations and gene expression can each be detected in similar but distinct ways.

Generally speaking, both gene expression and CNVs are detected by counting the number of reads that map to a given genomic region. The difference is that CNVs are detected using DNA sequencing while gene expression is detected using RNA sequencing. This is an important distinction because CNVs are a more stable genomic feature than gene expression. Reviewing the Central Dogma might help if this is confusing.

In the context of your problem, the main thing to understand is that CNVs can exert an influence on gene expression but gene expression cannot exert an influence on CNVs. Having fewer copies of a gene means that the process of transcribing RNA to express it cannot proceed as quickly as normal. So in an organism with gene X showing copy number loss, it makes sense to expect diminished expression of gene X than in organisms whose genome includes more copies of that gene X. If you're asking for an undergraduate class, chances are this is all you're expected to understand about "copy number regulation": the maximum transcription rate of a gene is dependent on how many copies of that gene are available for transcription.

Gene regulation becomes important for understanding how an organism with fewer than normal copies of gene X in its DNA might have the same amount of gene X RNA expressed as an organism with a normal number of copies of gene X. Suppose that some molecule, M, is necessary for expression of X but degrades after a short time. The cell will send M to the nucleus whenever it needs X to be expressed and the number of X transcripts produced per molecule of M will be dependent on the number of X copies available for transcription. If M is abundant and can be continually sent to the nucleus, then the cell with fewer copies of X can theoretically produce as much X as the cell with a normal number of X copies although it'll take a bit longer. If instead M is a finite resource within the cell, then the amount of X expressed in cells with fewer copies of the gene should be consistently lower than cells with more copies.

Of course if M happens to be produced by a gene exhibiting copy number gain/loss then that could also play an important role in how much X RNA transcripts you ultimately detect (even in the event that X actually has a neutral copy number). Hope this helps.

Copy Number Variation

Copy number variations (CNVs) currently are most often understood as submicroscopic gains or losses of chromosomal material, either connected with a disease or just one of the many possible genetic variants in man. However decades ago, besides such submicroscopic CNVs, chromosome analysis revealed the existence of cytogenetic visible copy number variations (CG-CNVs). In this chapter a short outline of cytogenetic history is given, highlighting the first detection and overinterpretation and possible meanings of CG-CNVs. Also heterochromatic and euchromatic CG-CNVs are distinguished from submicroscopic CNVs and some specific features of each group are introduced.

CNV biology in neurodevelopmental disorders

Pathogenic CNVs are shared among neurodevelopmental and neuropsychiatric disorders.

Understanding epigenetic regulation provides important insights not only into CNV pathophysiology but also therapeutic development.

Quantitative biomarkers are essential for further understanding of CNV pathology.

Copy number variants (CNVs), characterized in recent years by cutting-edge technology, add complexity to our knowledge of the human genome. CNVs contribute not only to human diversity but also to different kinds of diseases including neurodevelopmental delay, autism spectrum disorder and neuropsychiatric diseases. Interestingly, many pathogenic CNVs are shared among these diseases. Studies suggest that pathophysiology of disease may not be simply attributed to a single driver gene within a CNV but also that multifactorial effects may be important. Gene expression and the resulting phenotypes may also be affected by epigenetic alteration and chromosomal structural changes. Combined with human genetics and systems biology, integrative research by multi-dimensional approaches using animal and cell models of CNVs are expected to further understanding of pathophysiological mechanisms of neurodevelopmental disorders and neuropsychiatric disorders.

Copy number variation of bovine SHH gene is associated with body conformation traits in Chinese beef cattle

Sonic Hedgehog (Shh) regulates many key developmental processes during vertebrate limb development, fat formation, and skeletal tissue regeneration. Current whole genome sequencing data have identified a copy number variation mapping to bovine Sonic Hedgehog gene (SHH-CNV). The object of this study was to characterize the SHH-CNV distributions in 648 individuals from 11 Chinese cattle populations and further to investigate the associations of the copy number changes with gene expression and cattle growth traits. The SHH-CNV showed a high variance within Chinese indigenous yellow cattle. Compared to yak and dairy cattle, the beef cattle like Luxi and Xianan breed had significantly higher median copy numbers, suggesting the diversity of SHH-CNV in beef cattle selections. The negative correlation of SHH-CNV with SHH transcriptional level in adult adipose tissue (P < 0.01) indicated the dosage effects of SHH-CNV related to bovine fat formation. Association analysis of SHH-CNV and body size traits was conducted in five breeds. The results revealed that the copy number gain type of SHH-CNV exhibited significantly better chest depth in 24 months old Qinchuan cattle, and better body weight, body length, and chest girth in 18 months old Nanyang cattle, whereas the normal copy number had superior chest girth and body weight in adult Jinnan cattle (P < 0.05 or P < 0.01). In summary, this research uncovered meaningful effects of SHH-CNV on gene expression and cattle phenotypic traits, indicating its potential applications for genetic improvement of beef cattle.

Keywords: Associations Cattle Copy number variation Growth trait Sonic Hedgehog.

Dosage sensitivity shapes the evolution of copy-number varied regions

Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1. A network representation of the…

Figure 1. A network representation of the CORUM database.

Nodes represent complexes and are ordered…

Figure 2. Coefficients of gene expression variation…

Figure 2. Coefficients of gene expression variation (CV), defined as standard deviation normalised to expression…

A) Effects of resolution and dynamic range of expression arrays on CVs. The measurable variation in gene expression is limited by the sensitivity of the employed array technology. Genes which are expressed at extremely low levels, or not expressed at all, cluster in the low expression/low CV region. Shown in grey are genes which were excluded from further calculations (standard deviation ). B) CORUM genes have significantly smaller CVs than non-CORUM genes. Outliers beyond are not shown. C) Large CORUM complexes exhibit lower average CVs of their members.

Figure 3. Difference between deletion (white) and…

Figure 3. Difference between deletion (white) and duplication (black) variations in HapMap individuals.

The histograms show the ratio of average expression levels between individuals with and without the CNV for all genes inside a CNV region. The shift between the two distributions is significantly larger than would be expected by chance (MWU: ).

Figure 4. Ratio of WGTP array hybridisation…

Figure 4. Ratio of WGTP array hybridisation intensity over relative expression level for four example…

Figure 5. Distribution of average Pearson correlation…

Figure 5. Distribution of average Pearson correlation coefficients between all members of known proteins complexes…

Divergent patterns of genic copy number variation in KCNIP1 gene reveal risk locus of type 2 diabetes in Chinese population

Copy number variation (CNV) has emerged as another important genetic marker in addition to SNP for understanding etiology of complex disease. Kv channel interacting protein 1 (KCNIP1) is a Ca 2+ -dependent transcriptional modulator that contributes to the regulation of insulin secretion. Previous genome-wide CNV assay identified the KCNIP1 gene encompassing a CNV region, however, its further effect and risk rate on type 2 diabetes (T2D) have rarely been addressed, especially in Chinese population. The current study aims to detect and excavate genetic distribution profile of KCNIP1 CNV in Chinese T2D and control populations, and further to investigate the associations with clinical characteristics. Divergent patterns of the KCNIP1 CNV were identified (p < 0.01), in which the copy number gain was predominant in T2D, while the copy number normal accounted for the most in control group. Consistently, the individuals with copy number gain showed significant risk on T2D (OR = 4.550, p < 0.01). The KCNIP1 copy numbers presented significantly positive correlations with fasting plasma glucose and glycated hemoglobin in T2D. For OGTT test, the T2D patients with copy number gain had remarkably elevated glucose contents (60, 120, 180-min, p < 0.05 or p < 0.01) and diminished insulin levels (60, 120-min, p < 0.05) than those with copy number loss and normal, which suggested that the KCNIP1 CNV was correlated with the glucose and insulin action. This is the first CNV association study of the KCNIP1 gene in Chinese population, and these data indicated that KCNIP1 might function as a T2D-susceptibility gene whose dysregulation alters insulin production.

Keywords: Association Copy number variation KCNIP1 gene Type 2 diabetes.

A genome wide association study between copy number variation (CNV) and human height in Chinese population

Copy number variation (CNV) is a type of genetic variation which may have important roles in phenotypic variability and disease susceptibility. To hunt for genetic variants underlying human height variation, we performed a genome wide CNV association study for human height in 618 Chinese unrelated subjects using Affymetrix 500K array set. After adjusting for age and sex, we found that four CNVs at 6p21.3, 8p23.3-23.2, 9p23 and 16p12.1 were associated with human height (with borderline significant p value: 0.013, 0.011, 0.024, 0.049 respectively). However, after multiple tests correction, none of them was associated with human height. We observed that the gain of copy number (more than 2 copies) at 8p23.3-23.2 was associated with lower height (normal copy number vs. gain of copy number: 161.2 cm vs. 153.7 cm, p = 0.011), which accounted for 0.9% of height variation. Loss of copy number (less than 2 copies) at 6p21.3 was associated with 0.8% lower height (loss of copy number vs. normal copy number: 154.5 cm vs. 161.1 cm, p = 0.013). Since no important genes influencing height located in CNVs at loci of 8p23.3-23.2 and 6p21.3, the two CNVs may cause the structural rearrangements of neighbored important candidate genes, thus regulates the variation of height. Our results expand our knowledge of the genetic factors underlying height variation and the biological regulation of human height.

Copyright © 2010 Institute of Genetics and Developmental Biology and the Genetics Society of China. Published by Elsevier Ltd. All rights reserved.


We have demonstrated that copy number is a key control parameter in the expression dynamics of simple network motifs. Changing the copy number can make a network switch to an entirely different equilibrium gene expression state and move it to and from an oscillating regime. Our results stand in contrast to previous assertions that target gene expression is proportional to gene copy number (42, 43). Gene expression can be nonlinearly related to gene copy number because of feedbacks found in even the simplest of network motifs. Such nonlinearities are found even when the balance among gene components is maintained. Although not every small-scale CNV will lead to large-scale changes in gene expression, we have found a set of principles to understand when such a link may occur. In the cases of positive feedback, bistable feedback, and toggle switch motifs, we are able to find general conditions for the presence of qualitative sensitivity to copy number. In more technical terms, we have solved for the sufficient conditions for the existence of saddle-node bifurcations within a set of nonlinear dynamical systems. This has dramatic consequences for systematic analysis of the emergence and maintenance of CNV.

Importantly, our findings hold despite significant variation in parameter values associated with the molecular details of regulated recruitment (see SI Appendix). Thus, sensitivity of motifs to CNV may apply to a broad range of cellular contexts. The robustness of genetic regulatory networks to noise (44, 45) and gene duplication (46, 47) have been highlighted. Our findings suggest that there are limits to robustness, particularly with respect to gene duplication. The bifurcation conditions we derived for each motif provide guidance as to the range of kinetic parameters in which network fragility may be expected.

There are many challenges remaining in the study of the link between CNV and phenotypic effects. The networks we have considered are small components of complex gene regulatory networks. It remains an open question whether and to what extent these results scale up to larger, more complex networks (23, 29, 48). For example, how have actual networks evolved with respect to the critical values of copy number which can lead to qualitative shifts in system behavior? Although we have studied the effect of varying the copy number of motifs, it is worthwhile to examine the effects of copy number imbalances in complex motifs. Note that, in this article, we have assumed fully coupled network motifs, whereas the dynamics of intracellular transport of regulatory elements is certainly more complex (38, 49). There are a number of areas where we believe further examination is likely to yield successes in applying the theory presented here: host-phage dynamics, synthetic biology, and evolution via gene duplication. We discuss each of these areas below.

First, in the case of host-phage dynamics, there may be selection pressure favoring sensitivity to copy number, as in the case of temperate viruses whose exploitation strategy depends on the multiplicity of infection (5, 39). In ref. 6, we demonstrated that the number of phage DNA copies inside a bacterial cell has a dynamical effect on the decision making circuit of bacteriophage λ. Hence, coinfecting phages can in principle make collective decisions about a cell's fate. A small number of viruses can direct regulatory machinery toward lysis, whereas the coinfection of a single host by many viruses leads to a latent infection. Different phages differ in their response to coinfection, and so the response to coupling decision modules is likely to be an evolvable feature of phages' life histories. An alternative hypothesis for the link between cell fate and multiple infection is that each injected phage genome experiences a distinct microenvironment (38). Even in such a case, coordination of phage response depends on synchronization of decision modules, although perhaps on different time scales.

Next, of relevance to synthetic biology, CNV may alter dynamics of gene regulatory networks that have been engineered de novo or modified to acquire new functions (50). Here, we briefly discuss two experimental studies in which qualitative changes in gene expression were observed in synthetic networks as a consequence of small scale changes in the copy number of gene regulatory components. In one case, an E. coli gene regulatory circuit was designed to exhibit both sustained oscillations and toggle switch behavior (26). The copy number of a key activator module in the circuit (controlled by the glnAp2 promoter) was increased by inserting it closer to the origin of replication. Comparison of gene expression showed a 20% decrease in the degree of damping of oscillations when the activator was located near the origin as opposed to near the terminus. In another case, a reengineered budding yeast pheromone response pathway was designed to exhibit bistable response to pheromone induction (27). Bistability depended sensitively on the number of positive feedback modules inserted into the yeast cells. A minimum of 3 tandem copies of the PFUS1J1−STE11ΔN construct was necessary for a sustained positive feedback response, whereas 1 or 2 copies did not lead to a sustained response. Although these are only two examples, they both suggest that experimental studies of the sensitivity of small genetic circuits to CNV may be necessary if regulatory motifs are to be used as reliable building blocks of more complex networks (23).

Finally, gene duplication is considered to be a major factor in the evolution of novel phenotypes. According to the theory of neofunctionalization, duplicated genes are initially redundant, and, on occasion, one of a duplicate pair may diverge to perform some new function (8). In fact, the number of retained gene duplicate pairs is unexpectedly high, with extensive experimental evidence that duplicate genes retain functional compensation over long periods of time (4, 51, 52). Duplicated genes or motifs may not be strictly redundant, even initially. The evolution of network motifs subsequent to duplication may depend on global network context (24). In the current theoretical framework, it is apparent that an extra copy of a gene or motif caused by a duplication event can lead to a shift in expression past some functional threshold. Thus, a new feature could emerge immediately, augmenting or modifying previous function. The possibility that duplicated genes are not redundant is supported by a number of evolutionary studies (25, 53). This is not to say that large-scale gene expression given a gene duplication event must be the norm. To the contrary, if the effect of an extra copy was somehow buffered, then the present dynamical framework of gene regulation would be consistent with a model of evolution via neofunctionalization.

These three biological examples reflect a small fraction of ongoing research by scientists from many disciplines to understand how CNV impacts a broad range of biological phenomena. Although our treatment of gene regulation is closest to the mechanisms of regulated recruitment within bacteria and viruses, we envision that a copy number effect may be present from viruses to higher eukaryotes. This effect may have as its hallmark, a dramatic change in gene expression given a small change in copy number. Even if such a dramatic change represents the exception in gene regulatory networks, when such a change does occur it may have exceptional implications in modifying biological function. Whether in the case of genomic structural variation in humans or bacteriophage infections, variation in copy number is ubiquitous. At minimum, we hope to have provided some first steps toward constructing quantitative models of regulated recruitment that take into account CNV.


In this study, we identified 170 human CNVs located within 34 primate hotspot regions of CNV formation. The structurally plastic hotspots appear to have remained active in the three lineages despite being separated by over 25 million years of evolution. The majority of primate hotspots overlap with functional genomic elements, especially genes related to immunity. A significant portion of these genes that overlap primate hotspots appear to have evolved under positive selection (Figure 4c) and some of these genes are also known to be evolving under balancing selection in humans (for example, the HLA, PHDB, and LILR families). As such, the evolution and maintenance of primate CNV hotspots may be a response to diverse environmental pressures acting on the genes residing in these hotspots. The maintained plasticity may then provide the mutational flexibility for these genes to adapt rapidly to changing selective pressures. Therefore, it is not surprising to see that multiple immune system-related genes are variable in copy number across primates, possibly resonating with the 'Red Queen hypothesis': that the constant diversification of the host immune system genes and the parasite defense genes is in response to changes in each other's defenses [21].

For example, we observed a significant enrichment of HCR CNVs in a chromosome 19 region corresponding to the leukocyte receptor cluster (LRC). In humans, this 1 Mb region encompasses several families of immunoglobulin (Ig)-like receptor genes, including gene clusters encoding multiple leukocyte Ig-like receptors (LILRs), leukocyte-associated Ig-like receptors (LAIRs) and killer-cell Ig-like receptors (KIRs). The KIRs have a multifaceted role in two processes, immune defense and reproduction, and interact with cell-surface molecules encoded by the MHC class I locus, another region that displays rapid evolution and copy number variation. These epistatic interactions likely require the co-evolution of MHC and KIR, similar to the co-evolution of parasitic and host defenses described above. Under ever-changing pathogenic pressures, more of this variation could be maintained, especially among primates, which, due to their complex social dynamics, have higher pathogenic transfer rates [22]. Therefore, at least some of these primate CNV hotspots are likely maintained under dynamic selective pressures, allowing for copy number variability at these loci.

Other gene ontological categories are represented, albeit less frequently, in the observed primate CNV hotspots. For instance, the pepsinogens (PGA family) are precursors for pepsin (a major digestive enzyme) and may be involved in local environmental adaptation of primates [23]. Such adaptation would be akin to that of the amylase encoding gene in humans, where different copy numbers of the amylase gene evolved as an adaptation to dietary habits [7]. Similarly, genes such as CHYS1, involved in wound healing, are also noteworthy. More surprising are gene families such as PHDB and CBX, which may be involved in neural function [24] and, among other functions, testis development [25], respectively. These findings provide an initial framework for functional studies to establish the extent to which the variation in these genes has contributed to primate evolution.

In their classic paper, King and Wilson [26] recognized the similarity between the macromolecules in chimpanzees and humans, noting that regulation of the amount of these macromolecules during different developmental phases may account for most of the phenotypic differences. In this theoretical framework, copy number variation may be one of the major mechanisms to regulate the expression levels within and between the species (Figure 5a). Indeed, genes that overlap with HCR CNVs were more likely to be differentially expressed between the three primate species studied here and to have evolved under positive selection in primates (Figures 4c and 5b). Further evidence indicates that intraspecific expression differences are also significantly higher in genes that fall into primate hotspots (Figure 5b Figure S9 in Additional file 2). Not surprisingly, in addition to the HCR CNVs that overlap with coding regions of the genes, we found that at least two HCR CNVs overlap squarely with known enhancer regions that are highly conserved at the sequence level (Figure 5c). The redundancy in enhancers has been related to phenotypic robustness in fruit flies (Drosophila melanogaster), especially when exposed to genetic and environmental variability [27]. Hence, the maintenance of copy number variation in enhancer elements in primates may similarly reflect the evolutionary response to maintain phenotypic robustness in varying and rapidly changing selective pressures. By changing the number and position of genes or regulatory elements present in a single genome, CNVs likely impact gene regulation.

Impact of CNVs on gene regulation. (a) There are multiple ways in which CNVs can impact transcription by overlapping coding regions of the genes. (b) Blekhman et al. (2010) used RNA-seq data to determine whether specific genes are differentially expressed between human, chimpanzee, and macaque [32]. Based on their results, we plotted the proportion of human CNVs (H) and hotspot CNVs (HCR) that are differentially expressed between species. In particular, 3,423 of the Ensembl genes analyzed by Blekhman et al. (2010) overlap with human CNVs. Based on these data, here we plot the proportion of the genes that are differentially expressed between two or all three species, evolved under directional selection in the human lineage (Directional Human) or under stabilizing selection (that is, no expression differences between species). (c) At least three HCR CNVs overlap with regions with clear enhancer and/or promoter signals in the genome. To visualize the enhancer and promoter activity, we used the H3K4Me3 track generated by the ENCODE consortium [33] from the UCSC Genome Browser.

In addition, two recent studies demonstrated that copy number variation in one locus affects the expression levels in other loci. One of these studies showed that the expression level of a gene can be changed through alteration of the copy number variation of another gene that shares the same promoter region [28]. The other study demonstrated that the expressed pseudogene of PTEN acts as a sponge for microRNAs. As such, the deletion of the pseudogene subsequently increased the number of microRNA molecules, which can, in turn, negatively regulate the expression of the parental gene [29].


In conclusion, our CNV study suggests that NPY4R varies in copy number and that the most common gene copy number is four per genome, not two as previously reported by other investigators. A comparative study would require many more individuals to draw conclusions at the population level and, especially, to investigate copy number differences between populations. Due to the CNV and the role of NPY4R and its ligand pancreatic polypeptide in the regulation of food intake, this gene is a strong candidate for contribution to body weight variation and obesity. However its exact role remains to be investigated, as the CNV in this region has shown both a positive and a negative correlation with BMI [5, 11, 13, 14]. We have demonstrated here that the quality of sequencing data plays a crucial role in read depth analysis and that methods for copy number determination can differ in precision. Based on multiple CNV studies [20, 30, 37, 38, 43, 48,49,50] as well as our own results we suggest that ddPCR is a reliable method for CNV determination that can be used to calibrate read depth analysis.

Watch the video: plasmid copy number regulation. What is meant by copy number of plasmid? low copy number plasmid (August 2022).