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Is copy number variation dynamic?

Is copy number variation dynamic?


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Is there any evidence showing that copy number variation changes over time? I'm wanting to model interactions in expression level as a dynamic bayesian network, but an assumption my approach will need to make is that it is static.


Your question could be phrased more specifically to avoid ambiguity, but rephrasing it the way that I suspect you mean it, ("Is there any evidence showing that [the rate of] copy number variation changes over time?"), then yes, there is indeed.

The rate depends on many factors including which mechanism and which organism and which region of the organism's genome and as @Michael wrote, also what scale (et. al.) is under consideration too.

So your assumption that the rate is static should probably be stated explicitly.

Nature Reviews Genetics 10, 551-564 (August 2009) | doi:10.1038/nrg2593


Highly dynamic temporal changes of TSPY gene copy number in aging bulls

The Y-chromosomal TSPY gene is one of the highest copy number mammalian protein coding gene and represents a unique biological model to study various aspects of genomic copy number variations. This study investigated the age-related copy number variability of the bovine TSPY gene, a new and unstudied aspect of the biology of TSPY that has been shown to vary among cattle breeds, individual bulls and somatic tissues. The subjects of this prospective 30-month long study were 25 Holstein bulls, sampled every six months. Real-time quantitative PCR was used to determine the relative TSPY copy number (rTSPY CN) and telomere length in the DNA samples extracted from blood. Twenty bulls showed an altered rTSPY CN after 30 months, although only 9 bulls showed a significant change (4 significant increase while 5 significant decrease, P<0.01). The sequential sampling provided the flow of rTSPY CN over six observations in 30 months and wide-spread variation of rTSPY CN was detected. Although a clear trend of the direction of change was not identifiable, the highly dynamic changes of individual rTSPY CN in aging bulls were observed here for the first time. In summary we have observed a highly variable rTSPY CN in bulls over a short period of time. Our results suggest the importance of further long term studies of the dynamics of rTSPY CN variablility.

Conflict of interest statement

Competing Interests: The commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. TK participated in providing samples. The commercial affiliation has no competing interest in any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products.

Figures

Fig 1. Pie chart representation of change…

Fig 1. Pie chart representation of change in TSPY copy number in the 25 bulls…

Fig 2. Relative TSPY copy number change…

Fig 2. Relative TSPY copy number change over the 30 months sampling period (rTSPY CN…

Fig 3. rTSPY CN measured by qPCR…

Fig 3. rTSPY CN measured by qPCR at every six months (T0-T30) during the 30…


Spectrum of large copy number variations in 26 diverse Indian populations: potential involvement in phenotypic diversity

Copy number variations (CNVs) have provided a dynamic aspect to the apparently static human genome. We have analyzed CNVs larger than 100 kb in 477 healthy individuals from 26 diverse Indian populations of different linguistic, ethnic and geographic backgrounds. These CNVRs were identified using the Affymetrix 50K Xba 240 Array. We observed 1,425 and 1,337 CNVRs in the deletion and amplification sets, respectively, after pooling data from all the populations. More than 50% of the genes encompassed entirely in CNVs had both deletions and amplifications. There was wide variability across populations not only with respect to CNV extent (ranging from 0.04-1.14% of genome under deletion and 0.11-0.86% under amplification) but also in terms of functional enrichments of processes like keratinization, serine proteases and their inhibitors, cadherins, homeobox, olfactory receptors etc. These did not correlate with linguistic, ethnic, geographic backgrounds and size of populations. Certain processes were near exclusive to deletion (serine proteases, keratinization, olfactory receptors, GPCRs) or duplication (homeobox, serine protease inhibitors, embryonic limb morphogenesis) datasets. Populations having same enriched processes were observed to contain genes from different genomic loci. Comparison of polymorphic CNVRs (5% or more) with those cataloged in Database of Genomic Variants revealed that 78% (2473) of the genes in CNVRs in Indian populations are novel. Validation of CNVs using Sequenom MassARRAY revealed extensive heterogeneity in CNV boundaries. Exploration of CNV profiles in such diverse populations would provide a widely valuable resource for understanding diversity in phenotypes and disease.


Is copy number variation dynamic? - Biology

A curated catalogue of structural variation in the human genome

  • How much copy number variation (CNV) exists between human genomes?
  • How best can CNVs be incorporated into whole genome association studies?
  • What is the contribution of copy number variation to genetic disease?
  • What is the relative contribution of different mutational mechanisms to CNV?
  • What is the genomic impact of CNV on gene expression?
  • What role has copy number variation played in recent human evolution?
  • Increasing medical and scientific knowledge about chromosomal microdeletions/duplications
  • Improving medical care and genetic advice for individuals/families with submicroscopic chromosomal imbalance
  • Facilitating research into the study of genes which affect human development and health

Large-scale copy number variants (CNVs): Distribution in normal subjects and FISH/real-time qPCR analysis.
Ying Qiao, Xudong Liu, Chansonette Harvard, Sarah L Nolin, W Ted Brown, Maryam Koochek, Jeanette JA Holden, ME Suzanne Lewis, and Evica Rajcan-Separovic
BMC Genomics 2007, 8: 167-177

Global variation in copy number in the human genome.
Richard Redon, Shumpei Ishikawa, Karen R. Fitch, Lars Feuk, George H. Perry, T. Daniel Andrews, Heike Fiegler, Michael H. Shapero, Andrew R. Carson, Wenwei Chen4, Eun Kyung Cho, Stephanie Dallaire, Jennifer L. Freeman, Juan R. Gonzalez, Monica Gratacos, Jing Huang, Dimitrios Kalaitzopoulos, Daisuke Komura, Jeffrey R. MacDonald, Christian R. Marshall, Rui Mei, Lyndal Montgomery, Kunihiro Nishimura, Kohji Okamura, Fan Shen, Martin J. Somerville, Joelle Tchinda, Armand Valsesia, Cara Woodwark, Fengtang Yang, Junjun Zhang, Tatiana Zerjal, Jane Zhang, Lluis Armengol, Donald F. Conrad, Xavier Estivill, Chris Tyler-Smith, Nigel P. Carter, Hiroyuki Aburatani, Charles Lee, Keith W. Jones, Stephen W. Scherer & Matthew E. Hurles
Nature (2006) Vol 444. 444-454

Copy number variation and evolution in humans and chimpanzees.
Perry GH, Yang F, Marques-Bonet T, Murphy C, Fitzgerald T, Lee AS, Hyland C, Stone AC, Hurles ME, Tyler-Smith C, Eichler EE, Carter NP, Lee C, Redon R.
Genome Res. 2008 18(11): 1698-1710

Simultaneous mutation and copy number variation (CNV) detection by multiplex PCR-based GS-FLX sequencing.
Goossens D, Moens LN, Nelis E, Lenaerts AS, Glassee W, Kalbe A, Frey B, Kopal G, De Jonghe P, De Rijk P, Del-Favero J.
Hum Mutat. 2009 30(3): 472-476

Genome-wide analysis of transcript isoform variation in humans.
Kwan T, Benovoy D, Dias C, Gurd S, Provencher C, Beaulieu P, Hudson TJ, Sladek R, Majewski J.
Nat Genet. 2008 40(2): 225-231.

Transcript copy number estimation using a mouse whole-genome oligonucleotide microarray.
Mark G Carter, Alexei A Sharov, Vincent VanBuren, Dawood B Dudekula, Condie E Carmack, Charlie Nelson and Minoru S H Ko
Genome Biology 2005, 6:R61

Genome-wide copy-number-variation study identified a susceptibility gene, UGT2B17, for osteoporosis.
Yang TL, Chen XD, Guo Y, Lei SF, Wang JT, Zhou Q, Pan F, Chen Y, Zhang ZX, Dong SS, Xu XH, Yan H, Liu X, Qiu C, Zhu XZ, Chen T, Li M, Zhang H, Zhang L, Drees BM, Hamilton JJ, Papasian CJ, Recker RR, Song XP, Cheng J, Deng HW.
Am J Hum Genet. 2008 83(6): 663-674

Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR.
Rose-Zerilli MJ, Barton SJ, Henderson AJ, Shaheen SO, Holloway JW.
Clin Chem. 2009 55(9): 1680-1685

Statistical tools for transgene copy number estimation based on real-time PCR.
Joshua S Yuan, Jason Burris, Nathan R Stewart, Ayalew Mentewab and C Neal Stewart
BMC Bioinformatics 2007, 8(): S6

Copy number variation goes clinical.
A meeting report
Le Caignec C, Redon R.
Genome Biol. 200910(1): 301-303

Copy number variation: New insights in genome diversity.
Jennifer L. Freeman, George H. Perry, Lars Feuk, Richard Redon, Steven A. McCarroll, David M. Altshuler, Hiroyuki Aburatani, Keith W. Jones, Chris Tyler-Smith, Matthew E. Hurles, Nigel P. Carter, Stephen W. Scherer, and Charles Lee
Genome Research (2006) 16:949�

Real-Time Quantitative PCR as an Alternative to Southern Blot or Fluorescence In Situ Hybridization for Detection of Gene Copy Number Changes.
Jasmien Hoebeeck, Frank Speleman, and Jo Vandesompele
Methods in Molecular Biology, vol. 353: 205-226
Protocols for Nucleic Acid Analysis by Nonradioactive Probes, Second Edition Edited by: E. Hilario and J. Mackay

An accurate method for quantifying and analyzing copy number variation in porcine KIT by an oligonucleotide ligation assay.
Bo-Young Seo, Eung-Woo Park, Sung-Jin Ahn, Sang-Ho, Jae-Hwan Kim, Hyun-Tae Im, Jun-Heon Lee, In-Cheol Cho, Il-Keun Kong and Jin-Tae Jeon
BMC Genetics (2007) 8:81

Large-Scale Copy Number Polymorphism in the Human Genome.
Jonathan Sebat, B. Lakshmi, Jennifer Troge, Joan Alexander, Janet Young, Par Lundin, Susanne Maner, Hillary Massa, Megan Walker, Maoyen Chi, Nicholas Navin, Robert Lucito, John Healy, James Hicks, Kenny Ye, Andrew Reiner, T. Conrad Gilliam, Barbara Trask, Nick Patterson, Anders Zetterberg, Michael Wigler
SCIENCE (2004) VOL 305 525-528

Accurate and reliable high-throughput detection of copy number variation in the human genome.
Heike Fiegler, Richard Redon, Dan Andrews, Carol Scott, Robert Andrews, Carol Carder, Richard Clark, Oliver Dovey, Peter Ellis, Lars Feuk, Lisa French, Paul Hunt,1 Dimitrios Kalaitzopoulos, James Larkin, Lyndal Montgomery, George H. Perry, Bob W. Plumb, Keith Porter, Rachel E. Rigby, Diane Rigler, Armand Valsesia, Cordelia Langford, Sean J. Humphray, Stephen W. Scherer, Charles Lee, Matthew E. Hurles, and Nigel P. Carter
Genome Research (2006) 16:1566�

Detection of large-scale variation in the human genome.
A John Iafrate, Lars Feuk, Miguel N Rivera, Marc L Listewnik, Patricia K Donahoe, Ying Qi, Stephen W Scherer & Charles Lee
NATURE GENETICS (2004) VOLUME 36 NUMBER 9 949-951

Genome assembly comparison identifies structural variants in the human genome.
Razi Khaja, Junjun Zhang, Jeffrey R MacDonald, Yongshu He, Ann M Joseph-George, John Wei, Muhammad A Rafiq, Cheng Qian, Mary Shago, Lorena Pantano, Hiroyuki Aburatani, Keith Jones, Richard Redon, Matthew Hurles, Lluis Armengol, Xavier Estivill, Richard J Mural, Charles Lee, Stephen W Scherer & Lars Feuk
NATURE GENETICS (2006) VOLUME 38 NUMBER 12 1413-1418

Stochastic mRNA Synthesis in Mammalian Cells.
Arjun Raj, Charles S. Peskin, Daniel Tranchina, Diana Y. Vargas, Sanjay Tyagi
PLOS (2006) Volume 4 Issue 10 e309


Discussion and conclusions

Our results establish a scalable statistical framework for assigning cells measured using scRNA-seq to cancer clones measured independently using shallow scDNA-seq. We expect this approach can be used ubiquitously in the field of single-cell biology including extensions for other multi-modal approaches such as methylation-transcription and chromatin accessibility-transcription.

However, there are certain situations in which clonealign cannot be applied. While it is estimated that 60–80% of cancers exhibit the complex structural genomic rearrangements required to apply clonealign [26, 27], some cancers have quiescent genomes and are devoid of copy number changes. For example, cancers such as karyotypically normal AML, sarcomas, and other pediatric malignancies without genomic instability would not generate the genomic/transcriptomic signals modeled by clonealign [28].

Furthermore, the focus of this work has been on linking transcriptional measurements to genomically defined clones assuming only a copy-number dosage effect on transcript abundance. While the clonealign model allows for integration of allelic imbalance information caused by clone-specific LOH events, the sparse expression of germline heterozygous variants detected by the 10X chromium 3 ′ assay demonstrated here makes such information uninformative (Additional file 2: Supplementary text section 3). However, full-transcript-length single-cell RNA sequencing technologies such as Smart-seq2 [29] would allow for further refinement of clonal assignment and represent the appropriate use-case of clonealign ’s incorporation of allelic imbalance information.

However, the concepts introduced in the clonealign model provide a basis for future studies of the integration of genomic data from independently sampled assays. At the edge of the field, sparse in situ measurements of transcription integrated with independent disaggregated sampling of single-cell genomes are providing a route to studying spatial context of co-located cell populations [30]. Finally, there is an emergence of commercial platforms whereby single-cell, kit-based assays for methylation, transcription, and genome copy number are becoming widely available to the research community. In all of these settings, clonealign and future derivatives will provide a statistical framework to help interpret the cellular constituents of cancer, their fitness, and their phenotypes.


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Availability of data and materials

The assembly of the Ler genome has been submitted to the European Nucleotide Archive (http://www.ebi.ac.uk) and is publicly available under the accession number GCA_900660825 [56]. The reads are available as part of a separate study under the project ID PRJEB31147 (preprint [46]). All other assemblies are publicly available at NCBI (https://www.ncbi.nlm.nih.gov/), and their accession numbers are GCA_000001735.3 [57], GCA_000001405.27 [25], GCA_002077035.3 [22], GCA_001524155.4 [22], GCA_000146045.2 [27], GCA_000977955.2 [26], GCA_000001215.4 [29], GCA_002300595.1 [28], GCA_000005005.6 [31], and GCA_002237485.1 [30]. Further details about the assemblies are in Additional file 2: Table S1. BAM files for the 50 F2 recombinant genomes are available at European Nucleotide Archive under the project ID PRJEB29265 [33]. SyRI is freely available under the MIT license and is available online [58]. The version of SyRI used in this work is available at doi.org/10.5281/zenodo.3555197 [59]. SyRI is developed using Python3.5 on Linux and can run on other operating systems as well.


Extensive copy-number variation of young genes across stickleback populations

Duplicate genes emerge as copy-number variations (CNVs) at the population level, and remain copy-number polymorphic until they are fixed or lost. The successful establishment of such structural polymorphisms in the genome plays an important role in evolution by promoting genetic diversity, complexity and innovation. To characterize the early evolutionary stages of duplicate genes and their potential adaptive benefits, we combine comparative genomics with population genomics analyses to evaluate the distribution and impact of CNVs across natural populations of an eco-genomic model, the three-spined stickleback. With whole genome sequences of 66 individuals from populations inhabiting three distinct habitats, we find that CNVs generally occur at low frequencies and are often only found in one of the 11 populations surveyed. A subset of CNVs, however, displays copy-number differentiation between populations, showing elevated within-population frequencies consistent with local adaptation. By comparing teleost genomes to identify lineage-specific genes and duplications in sticklebacks, we highlight rampant gene content differences among individuals in which over 30% of young duplicate genes are CNVs. These CNV genes are evolving rapidly at the molecular level and are enriched with functional categories associated with environmental interactions, depicting the dynamic early copy-number polymorphic stage of genes during population differentiation.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1. Phylogenomic relationships among samples.

Figure 1. Phylogenomic relationships among samples.

( A ) Phylogenomic network of the 66 genomes…

Figure 2. Frequency and occurrence of CNVs…

Figure 2. Frequency and occurrence of CNVs across individuals and populations.

Figure 3. CNV proportions across genomic regions…

Figure 3. CNV proportions across genomic regions and homozygous deletions.


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Keywords: copy number variation, transcription, evolution, embryonic development, senescence, oncogenesis, homologous recombination, retrotransposons

Citation: Sui Y and Peng S (2021) A Mechanism Leading to Changes in Copy Number Variations Affected by Transcriptional Level Might Be Involved in Evolution, Embryonic Development, Senescence, and Oncogenesis Mediated by Retrotransposons. Front. Cell Dev. Biol. 9:618113. doi: 10.3389/fcell.2021.618113

Received: 16 October 2020 Accepted: 11 January 2021
Published: 11 February 2021.

Valentina Massa, University of Milan, Italy

Pedro P. Rocha, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), United States
Fang Bai, Nankai University, China

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