Project description:BACKGROUND: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60 K SNP array, which is a high-throughput method widely used in chicken genomics studies. RESULTS: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. CONCLUSIONS: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken.

Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken.

Project description:BackgroundMarek's disease (MD) is a highly neoplastic disease primarily affecting chickens, and remains as a chronic infectious disease that threatens the poultry industry. Copy number variation (CNV) has been examined in many species and is recognized as a major source of genetic variation that directly contributes to phenotypic variation such as resistance to infectious diseases. Two highly inbred chicken lines, 63 (MD-resistant) and 72 (MD-susceptible), as well as their F1 generation and six recombinant congenic strains (RCSs) with varied susceptibility to MD, are considered as ideal models to identify the complex mechanisms of genetic and molecular resistance to MD.ResultsIn the present study, to unravel the potential genetic mechanisms underlying resistance to MD, we performed a genome-wide CNV detection using next generation sequencing on the inbred chicken lines with the assistance of CNVnator. As a result, a total of 1649 CNV regions (CNVRs) were successfully identified after merging all the nine datasets, of which 90 CNVRs were overlapped across all the chicken lines. Within these shared regions, 1360 harbored genes were identified. In addition, 55 and 44 CNVRs with 62 and 57 harbored genes were specifically identified in line 63 and 72, respectively. Bioinformatics analysis showed that the nearby genes were significantly enriched in 36 GO terms and 6 KEGG pathways including JAK/STAT signaling pathway. Ten CNVRs (nine deletions and one duplication) involved in 10 disease-related genes were selected for validation by using quantitative real-time PCR (qPCR), all of which were successfully confirmed. Finally, qPCR was also used to validate two deletion events in line 72 that were definitely normal in line 63. One high-confidence gene, IRF2 was identified as the most promising candidate gene underlying resistance and susceptibility to MD in view of its function and overlaps with data from previous study.ConclusionsOur findings provide valuable insights for understanding the genetic mechanism of resistance to MD and the identified gene and pathway could be considered as the subject of further functional characterization.

Project description:Background: The chicken (Gallus gallus) is an important model organism that bridges the evolutionary gap between mammals and other vertebrates. Copy number variations (CNVs) are a form of genomic structural variation widely distributed in the genome. CNV analysis has recently gained greater attention and momentum, as the identification of CNVs can contribute to a better understanding of traits important to both humans and other animals. To detect chicken CNVs, we genotyped 475 animals derived from two broiler chicken lines divergently selected for abdominal fat content using chicken 60K SNP array, which is a high-throughput method widely used in chicken genomics studies. Results: Using PennCNV algorithm, we detected 438 and 291 CNVs in the lean and fat lines, respectively, corresponding to 271 and 188 CNV regions (CNVRs), which were obtained by merging overlapping CNVs. Out of these CNVRs, 99% were confirmed also by the CNVPartition program. These CNVRs covered 40.26 and 30.60 Mb of the chicken genome in the lean and fat lines, respectively. Moreover, CNVRs included 176 loss, 68 gain and 27 both (i.e. loss and gain within the same region) events in the lean line, and 143 loss, 25 gain and 20 both events in the fat line. Ten CNVRs were chosen for the validation experiment using qPCR method, and all of them were confirmed in at least one qPCR assay. We found a total of 886 genes located within these CNVRs, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed they could play various roles in a number of biological processes. Integrating the results of CNVRs, known quantitative trait loci (QTL) and selective sweeps for abdominal fat content suggested that some genes (including SLC9A3, GNAL, SPOCK3, ANXA10, HELIOS, MYLK, CCDC14, SPAG9, SOX5, VSNL1, SMC6, GEN1, MSGN1 and ZPAX) may be important for abdominal fat deposition in the chicken. Conclusions: Our study provided a genome-wide CNVR map of the chicken genome, thereby contributing to our understanding of genomic structural variations and their potential roles in abdominal fat content in the chicken. In total, 475 birds (203 and 272 individuals from the lean and fat lines, respectively) from the 11th generation population of Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF) were used. These 475 birds were genotyped by the chicken 60k SNP chip and PennCNV method were used to perform genome-wide CNV detection.

Project description:BACKGROUND: The detection and functional characterization of genomic structural variations are important for understanding the landscape of genetic variation in the chicken. A recently recognized aspect of genomic structural variation, called copy number variation (CNV), is gaining interest in chicken genomic studies. The aim of the present study was to investigate the pattern and functional characterization of CNVs in five characteristic chicken breeds, which will be important for future studies associating phenotype with chicken genome architecture. RESULTS: Using a commercial 385 K array-based comparative genomic hybridization (aCGH) genome array, we performed CNV discovery using 10 chicken samples from four local Chinese breeds and the French breed Houdan chicken. The female Anka broiler was used as a reference. A total of 281 copy number variation regions (CNVR) were identified, covering 12.8 Mb of polymorphic sequences or 1.07% of the entire chicken genome. The functional annotation of CNVRs indicated that these regions completely or partially overlapped with 231 genes and 1032 quantitative traits loci, suggesting these CNVs have important functions and might be promising resources for exploring differences among various breeds. In addition, we employed quantitative PCR (qPCR) to further validate several copy number variable genes, such as prolactin receptor, endothelin 3 (EDN3), suppressor of cytokine signaling 2, CD8a molecule, with important functions, and the results suggested that EDN3 might be a molecular marker for the selection of dark skin color in poultry production. Moreover, we also identified a new CNVR (chr24: 3484617-3512275), encoding the sortilin-related receptor gene, with copy number changes in only black-bone chicken. CONCLUSIONS: Here, we report a genome-wide analysis of the CNVs in five chicken breeds using aCGH. The association between EDN3 and melanoblast proliferation was further confirmed using qPCR. These results provide additional information for understanding genomic variation and related phenotypic characteristics.

Project description:Copy number variations (CNV) include net gains or losses of part or whole chromosomal regions. They differ from copy neutral loss of heterozygosity (cn-LOH) events which do not induce any net change in the copy number and are often associated with uniparental disomy. These phenomena have long been reported to be associated with diseases and particularly in cancer. Losses/gains of genomic regions are often correlated with lower/higher gene expression. On the other hand, loss of heterozygosity (LOH) and cn-LOH are common events in cancer and may be associated with the loss of a functional tumor suppressor gene. Therefore, identifying recurrent CNV and cn-LOH events can be important as they may highlight common biological components and give insights into the development or mechanisms of a disease. However, no currently available tools allow a comprehensive whole-genome visualization of recurrent CNVs and cn-LOH in groups of samples providing absolute quantification of the aberrations leading to the loss of potentially important information.To overcome these limitations, we developed aCNViewer (Absolute CNV Viewer), a visualization tool for absolute CNVs and cn-LOH across a group of samples. aCNViewer proposes three graphical representations: dendrograms, bi-dimensional heatmaps showing chromosomal regions sharing similar abnormality patterns, and quantitative stacked histograms facilitating the identification of recurrent absolute CNVs and cn-LOH. We illustrated aCNViewer using publically available hepatocellular carcinomas (HCCs) Affymetrix SNP Array data (Fig 1A). Regions 1q and 8q present a similar percentage of total gains but significantly different copy number gain categories (p-value of 0.0103 with a Fisher exact test), validated by another cohort of HCCs (p-value of 5.6e-7) (Fig 2B).aCNViewer is implemented in python and R and is available with a GNU GPLv3 license on GitHub https://github.com/FJD-CEPH/aCNViewer and Docker https://hub.docker.com/r/fjdceph/acnviewer/.aCNViewer@cephb.fr.

Dataset Information

Genome-wide identification of copy number variations between two chicken lines that differ in genetic resistance to Marek's disease.

Publications

Genome-wide identification of copy number variations between two chicken lines that differ in genetic resistance to Marek's disease.

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