Mapping copy number variation by population-scale genome sequencing.
Ontology highlight
ABSTRACT:
SUBMITTER: Mills RE
PROVIDER: S-EPMC3077050 | biostudies-literature | 2011 Feb
REPOSITORIES: biostudies-literature
Publications
Mapping copy number variation by population-scale genome sequencing.
Mills Ryan E RE Walter Klaudia K Stewart Chip C Handsaker Robert E RE Chen Ken K Alkan Can C Abyzov Alexej A Yoon Seungtai Chris SC Ye Kai K Cheetham R Keira RK Chinwalla Asif A Conrad Donald F DF Fu Yutao Y Grubert Fabian F Hajirasouliha Iman I Hormozdiari Fereydoun F Iakoucheva Lilia M LM Iqbal Zamin Z Kang Shuli S Kidd Jeffrey M JM Konkel Miriam K MK Korn Joshua J Khurana Ekta E Kural Deniz D Lam Hugo Y K HY Leng Jing J Li Ruiqiang R Li Yingrui Y Lin Chang-Yun CY Luo Ruibang R Mu Xinmeng Jasmine XJ Nemesh James J Peckham Heather E HE Rausch Tobias T Scally Aylwyn A Shi Xinghua X Stromberg Michael P MP Stütz Adrian M AM Urban Alexander Eckehart AE Walker Jerilyn A JA Wu Jiantao J Zhang Yujun Y Zhang Zhengdong D ZD Batzer Mark A MA Ding Li L Marth Gabor T GT McVean Gil G Sebat Jonathan J Snyder Michael M Wang Jun J Ye Kenny K Eichler Evan E EE Gerstein Mark B MB Hurles Matthew E ME Lee Charles C McCarroll Steven A SA Korbel Jan O JO
Nature 20110201 7332
Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map en ...[more]
Similar Datasets
Project description:BACKGROUND: Copy number variations (CNVs) represent a significant source of genomic structural variation. Their length ranges from approximately one hundred to millions of base pair. Genome-wide screenings have clarified that CNVs are a ubiquitous phenomenon affecting essentially the whole genome. Although Bos taurus is one of the most important domestic animal species worldwide and one of the most studied ruminant models for metabolism, reproduction, and disease, relatively few studies have investigated CNVs in cattle and little is known about how CNVs contribute to normal phenotypic variation and to disease susceptibility in this species, compared to humans and other model organisms. RESULTS: Here we characterize and compare CNV profiles in 2654 animals from five dairy and beef Bos taurus breeds, using the Illumina BovineSNP50 genotyping array (54001 SNP probes). In this study we applied the two most commonly used algorithms for CNV discovery (QuantiSNP and PennCNV) and identified 4830 unique candidate CNVs belonging to 326 regions. These regions overlap with 5789 known genes, 76.7% of which are significantly co-localized with segmental duplications (SD). CONCLUSIONS: This large scale screening significantly contributes to the enrichment of the Bos taurus CNV map, demonstrates the ubiquity, great diversity and complexity of this type of genomic variation and sets the basis for testing the influence of CNVs on Bos taurus complex functional and production traits.
| S-EPMC3618309 | biostudies-literature
Project description:BackgroundCopy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method.Methodology/principal findingsIn our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS.Conclusions/significanceOur study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.
| S-EPMC3897425 | biostudies-literature
Project description:Megabase-scale copy number variants (CNVs) can have profound phenotypic consequences. Germline CNVs of this magnitude are associated with disease and experience negative selection. However, it is unknown whether organismal function requires that every cell maintain a balanced genome. It is possible that large somatic CNVs are tolerated or even positively selected. Single-cell sequencing is a useful tool for assessing somatic genomic heterogeneity, but its performance in CNV detection has not been rigorously tested. Here, we develop an approach that allows for reliable detection of megabase-scale CNVs in single somatic cells. We discover large CNVs in 8%-9% of cells across tissues and identify two recurrent CNVs. We conclude that large CNVs can be tolerated in subpopulations of cells, and particular CNVs are relatively prevalent within and across individuals.
| S-EPMC4772019 | biostudies-literature
Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.
| S-EPMC3481445 | biostudies-literature
Project description:Copy number variation (CNV) is a major genetic polymorphism contributing to genetic diversity and human evolution. Clinical application of CNVs for diagnostic purposes largely depends on sufficient population CNV data for accurate interpretation. CNVs from general population in currently available databases help classify CNVs of uncertain clinical significance, and benign CNVs. Earlier studies of CNV distribution in several populations worldwide showed that a significant fraction of CNVs are population specific. In this study, we characterized and analyzed CNVs in 3,017 unrelated Thai individuals genotyped with the Illumina Human610, Illumina HumanOmniexpress, or Illumina HapMap550v3 platform. We employed hidden Markov model and circular binary segmentation methods to identify CNVs, extracted 23,458 CNVs consistently identified by both algorithms, and cataloged these high confident CNVs into our publicly available Thai CNV database. Analysis of CNVs in the Thai population identified a median of eight autosomal CNVs per individual. Most CNVs (96.73%) did not overlap with any known chromosomal imbalance syndromes documented in the DECIPHER database. When compared with CNVs in the 11 HapMap3 populations, CNVs found in the Thai population shared several characteristics with CNVs characterized in HapMap3. Common CNVs in Thais had similar frequencies to those in the HapMap3 populations, and all high frequency CNVs (>20%) found in Thai individuals could also be identified in HapMap3. The majorities of CNVs discovered in the Thai population, however, were of low frequency, or uniquely identified in Thais. When performing hierarchical clustering using CNV frequencies, the CNV data were clustered into Africans, Europeans, and Asians, in line with the clustering performed with single nucleotide polymorphism (SNP) data. As CNV data are specific to origin of population, our population-specific reference database will serve as a valuable addition to the existing resources for the investigation of clinical significance of CNVs in Thais and related ethnicities.
| S-EPMC4131886 | biostudies-literature
Project description:As one of the major glutathione conjugation enzymes, GSTM1 detoxifies a number of drugs and xenobiotics. Its expression and activity have been shown to correlate both with cancer risks and drug resistance. Through a genome-wide association study, we identified a significant association between HapMap SNP rs366631 and GSTM1 expression. In this study, utilizing lymphoblastoid cell lines derived from International HapMap Consortium CEU and YRI populations, we designed and performed site-specific genotyping assays for both rs366631 and a highly homologous GSTM1 upstream site. Copy number variation (CNV) assays were performed for three different regions of the GSTM1 gene. We demonstrated that HapMap SNP rs366631 is a non-polymorphic site. The false genotyping call arises from sequence homology, a common GSTM1 region deletion and a non-specific genotyping platform used to identify the SNP. However, the HapMap call for rs366631 genotype is an indicator of GSTM1 upstream region deletion. Furthermore, this upstream deletion can be used as a marker of GSTM1 gene deletion. Using a novel GSTM1 CNV assay, we showed a population-specific CNV in this region upstream of the gene. More than 75% of the Caucasian (CEU) samples exhibit GSTM1 deletion and none contain two copies of GSTM1. In contrast, up to 25% of African (YRI) samples were found to have two copies of GSTM1. In conclusion, HapMap rs366631 is a pseudo-SNP that can be used as a GSTM1 deletion marker. Both the pseudo-SNP allele frequency and GSTM1 upstream region CNV show population-specific patterns between CEU and YRI samples.
| S-EPMC2722188 | biostudies-literature
Project description:Improved copy number variation (CNV) detection remains an area of heavy emphasis for algorithm development; however, both CNV curation and disease association approaches remain in its infancy. The current practice of focusing on candidate CNVs, where researchers study specific CNVs they believe to be pathological while discarding others, refrains from considering the full spectrum of CNVs in a hypothesis-free GWAS. To address this, we present a next-generation approach to CNV association by natively supporting the popular VCF specification for sequencing-derived variants as well as SNP array calls using a PennCNV format. The code is fast and efficient, allowing for the analysis of large (>100,000 sample) cohorts without dividing up the data on a compute cluster. The scripts are condensed into a single tool to promote simplicity and best practices. CNV curation pre and post-association is rigorously supported and emphasized to yield reliable results of highest quality. We benchmarked two large datasets, including the UK Biobank (n > 450,000) and CAG Biobank (n > 350,000) both of which are genotyped at >0.5 M probes, for our input files. ParseCNV has been actively supported and developed since 2008. ParseCNV2 presents a critical addition to formalizing CNV association for inclusion with SNP associations in GWAS Catalog. Clinical CNV prioritization, interactive quality control (QC), and adjustment for covariates are revolutionary new features of ParseCNV2 vs. ParseCNV. The software is freely available at: https://github.com/CAG-CNV/ParseCNV2 .
| S-EPMC9995309 | biostudies-literature
Project description:BackgroundCopy number variations (CNVs) represent a major source of genetic diversity and contribute to the phenotypic variation of economically important traits in livestock species. In this study, we report the first genome-wide CNV analysis of American mink using whole-genome sequence data from 100 individuals. The analyses were performed by three complementary software programs including CNVpytor, DELLY and Manta.ResultsA total of 164,733 CNVs (144,517 deletions and 20,216 duplications) were identified representing 5378 CNV regions (CNVR) after merging overlapping CNVs, covering 47.3 Mb (1.9%) of the mink autosomal genome. Gene Ontology and KEGG pathway enrichment analyses of 1391 genes that overlapped CNVR revealed potential role of CNVs in a wide range of biological, molecular and cellular functions, e.g., pathways related to growth (regulation of actin cytoskeleton, and cAMP signaling pathways), behavior (axon guidance, circadian entrainment, and glutamatergic synapse), lipid metabolism (phospholipid binding, sphingolipid metabolism and regulation of lipolysis in adipocytes), and immune response (Wnt signaling, Fc receptor signaling, and GTPase regulator activity pathways). Furthermore, several CNVR-harbored genes associated with fur characteristics and development (MYO5A, RAB27B, FGF12, SLC7A11, EXOC2), and immune system processes (SWAP70, FYN, ORAI1, TRPM2, and FOXO3).ConclusionsThis study presents the first genome-wide CNV map of American mink. We identified 5378 CNVR in the mink genome and investigated genes that overlapped with CNVR. The results suggest potential links with mink behaviour as well as their possible impact on fur quality and immune response. Overall, the results provide new resources for mink genome analysis, serving as a guideline for future investigations in which genomic structural variations are present.
| S-EPMC9468235 | biostudies-literature
Project description:The expression dynamics of interacting genes depends, in part, on the structure of regulatory networks. Genetic regulatory networks include an overrepresentation of subgraphs commonly known as network motifs. In this article, we demonstrate that gene copy number is an omnipresent parameter that can dramatically modify the dynamical function of network motifs. We consider positive feedback, bistable feedback, and toggle switch motifs and show that variation in gene copy number, on the order of a single or few copies, can lead to multiple orders of magnitude change in gene expression and, in some cases, switches in deterministic control. Further, small changes in gene copy number for a 3-gene motif with successive inhibition (the "repressilator") can lead to a qualitative switch in system behavior among oscillatory and equilibrium dynamics. In all cases, the qualitative change in expression is due to the nonlinear nature of transcriptional feedback in which duplicated motifs interact via common pools of transcription factors. We are able to implicitly determine the critical values of copy number which lead to qualitative shifts in system behavior. In some cases, we are able to solve for the sufficient condition for the existence of a bifurcation in terms of kinetic rates of transcription, translation, binding, and degradation. We discuss the relevance of our findings to ongoing efforts to link copy number variation with cell fate determination by viruses, dynamics of synthetic gene circuits, and constraints on evolutionary adaptation.
| S-EPMC2575476 | biostudies-other
Project description:BackgroundCopy number variations (CNVs), which represent a significant source of genetic diversity in mammals, have been shown to be associated with phenotypes of clinical relevance and to be causative of disease. Notwithstanding, little is known about the extent to which CNV contributes to genetic variation in cattle.ResultsWe designed and used a set of NimbleGen CGH arrays that tile across the assayable portion of the cattle genome with approximately 6.3 million probes, at a median probe spacing of 301 bp. This study reports the highest resolution map of copy number variation in the cattle genome, with 304 CNV regions (CNVRs) being identified among the genomes of 20 bovine samples from 4 dairy and beef breeds. The CNVRs identified covered 0.68% (22 Mb) of the genome, and ranged in size from 1.7 to 2,031 kb (median size 16.7 kb). About 20% of the CNVs co-localized with segmental duplications, while 30% encompass genes, of which the majority is involved in environmental response. About 10% of the human orthologous of these genes are associated with human disease susceptibility and, hence, may have important phenotypic consequences.ConclusionsTogether, this analysis provides a useful resource for assessment of the impact of CNVs regarding variation in bovine health and production traits.
| S-EPMC2902221 | biostudies-literature