Identification and functional characterization of copy number variations in diverse chicken breeds

Genome-wide association study reveals 2 copy number variations associated with the variation of plumage color in the white duck hybrid population

Plumage color is an intuitive external poultry characteristic with rich manifestations and complex genetic mechanisms. In our previous study, we observed that there were more dark variations in plumage color in the F2 population derived from the hybridization of 2 white duck varieties. Therefore, based on the statistics of plumage color of 308 F2 populations, we further used the resequencing data of these individuals to detect copy number variations (CNVs) in the whole genome and conducted genome-wide association studies (GWAS) to determine the genetic basis related to plumage color traits. The CNV detection revealed 9,337 CNVs, with an average length of 15,950 bp and a total length of 142.02 MB, accounting for approximately 12.91% of the reference genome. The CNV distribution on the chromosomes was relatively uniform, and the number of CNVs on each chromosome positively correlated with the length of the chromosome. In the pure black plumage group, 2,101 CNVs were only identified, and 1,714 were specifically identified in the pure white plumage group. Ten CNVs were randomly selected for validation using quantitative real-time PCR, and 9 CNVs had the same CNV types as predicted, with an accuracy of 90%. Based on GWAS, we identified 2 CNVs potentially associated with plumage color variations, with the associated CNV regions covering 9 genes. Enrichment analysis of these 9 candidate genes showed significant enrichment of 3 pathways (ribosome biogenesis in eukaryotes, RNA transport, and protein export) and 17 gene ontology terms. Among these, VWA5A can downregulate MITF by binding to the regulatory factors SOX10. The occurrence of CNV may indirectly contribute to duck plumage color variation by affecting the regulatory factors of the switch gene MITF in the melanogenesis pathway. These findings have improved the understanding of the genetic basis of duck plumage color variation and have been beneficial for developing and using plumage color traits in subsequent poultry breeding.

Genome wide detection of CNV and their association with body size in Danzhou chickens

Copy number variation (CNV) is a crucial component of genetic diversity in the genome, serving as the foundation for the genetic architecture and phenotypic variability of complex traits. In this study, we examined CNVs in the Danzhou (DZ) chicken, an indigenous breed exclusive to Hainan Province, China. By employing whole-genome resequencing data from 200 DZ chickens, we conducted a comprehensive genome-wide analysis of CNVs using CNVpytor and performed CNV-based genome-wide association studies (GWAS) on 6 body size traits, including body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD) utilizing linear mixed model methods considering a genomic relationship matrix. We identified a total of 144,265 autosomal CNVs among the 200 individuals, comprising 67,818 deletions and 76,447 duplications. After merging these variants together, we obtained 4,824 distinct copy number variant regions, which accounted for approximately 20% of the chicken autosomal genome. Furthermore, we discovered several significantly associated CNV segments with body size traits located proximal to genes such as IHH, WNT6, WNT10A, LPR4, FZD2, WNT7B, and GNAS that have been extensively implicated in skeletal development and growth processes. These findings enhance our understanding of CNVs in chickens and their potential impact on body size traits by revealing candidate genes involved in the regulation of these traits. This establishes a solid framework for future studies and may prove particularly beneficial for exploring genetic structural variation in chickens.

Genome-Wide Detection of Copy Number Variations and Their Potential Association with Carcass and Meat Quality Traits in Pingliang Red Cattle

Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program.

Identify Candidate Genes Associated with the Weight and Egg Quality Traits in Wenshui Green Shell-Laying Chickens by the Copy Number Variation-Based Genome-Wide Association Study

Copy number variation (CNV), as an essential source of genetic variation, can have an impact on gene expression, genetic diversity, disease susceptibility, and species evolution in animals. To better understand the weight and egg quality traits of chickens, this paper aimed to detect CNVs in Wenshui green shell-laying chickens and conduct a copy number variation regions (CNVRs)-based genome-wide association study (GWAS) to identify variants and candidate genes associated with their weight and egg quality traits to support related breeding efforts. In our paper, we identified 11,035 CNVRs in Wenshui green shell-laying chickens, which collectively spanned a length of 13.1 Mb, representing approximately 1.4% of its autosomal genome. Out of these CNVRs, there were 10,446 loss types, 491 gain types, and 98 mixed types. Notably, two CNVRs showed significant correlations with egg quality, while four CNVRs exhibited significant associations with body weight. These significant CNVRs are located on chromosome 4. Further analysis identified potential candidate genes that influence weight and egg quality traits, including FAM184B, MED28, LAP3, ATOH8, ST3GAL5, LDB2, and SORCS2. In this paper, the CNV map of the Wenshui green shell-laying chicken genome was constructed for the first time through population genotyping. Additionally, CNVRs can be employed as molecular markers to genetically improve chickens' weight and egg quality traits.

Weighted single-step GWAS identified candidate genes associated with carcass traits in a Chinese yellow-feathered chicken population

Carcass traits in broiler chickens are complex traits that are influenced by multiple genes. To gain deeper insights into the genetic mechanisms underlying carcass traits, here we conducted a weighted single-step genome-wide association study (wssGWAS) in a population of Chinese yellow-feathered chicken. The objective was to identify genomic regions and candidate genes associated with carcass weight (CW), eviscerated weight with giblets (EWG), eviscerated weight (EW), breast muscle weight (BMW), drumstick weight (DW), abdominal fat weight (AFW), abdominal fat percentage (AFP), gizzard weight (GW), and intestine length (IL). A total of 1,338 broiler chickens with phenotypic and pedigree information were included in this study. Of these, 435 chickens were genotyped using a 600K single nucleotide polymorphism chip for association analysis. The results indicate that the most significant regions for 9 traits explained 2.38% to 5.09% of the phenotypic variation, from which the region of 194.53 to 194.63Mb on chromosome 1 with the gene RELT and FAM168A identified on it was significantly associated with CW, EWG, EW, BMW, and DW. Meanwhile, the 5 traits have a strong genetic correlation, indicating that the region and the genes can be used for further research. In addition, some candidate genes associated with skeletal muscle development, fat deposition regulation, intestinal repair, and protection were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses suggested that the genes are involved in processes such as vascular development (CD34, FGF7, FGFR3, ITGB1BP1, SEMA5A, LOXL2), bone formation (FGFR3, MATN1, MEF2D, DHRS3, SKI, STC1, HOXB1, HOXB3, TIPARP), and anatomical size regulation (ADD2, AKT1, CFTR, EDN3, FLII, HCLS1, ITGB1BP1, SEMA5A, SHC1, ULK1, DSTN, GSK3B, BORCS8, GRIP2). In conclusion, the integration of phenotype, genotype, and pedigree information without creating pseudo-phenotype will facilitate the genetic improvement of carcass traits in chickens, providing valuable insights into the genetic architecture and potential candidate genes underlying carcass traits, enriching our understanding and contributing to the breeding of high-quality broiler chickens.

Genome-wide investigation to assess copy number variants in the Italian local chicken population

BACKGROUND

Copy number variants (CNV) hold significant functional and evolutionary importance. Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock. High-density chips have enabled the detection of CNV with increased resolution, leading to the identification of even small CNV. This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome.

RESULTS

Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV. The majority of CNV in the investigated breeds were of types 0 and 1, and the minimum length of CNV was significantly larger than that reported in previous studies. Interestingly, a high proportion of the length of chromosome 16 was covered by copy number variation regions (CNVR), with the major histocompatibility complex being the likely cause. Among the genes identified within CNVR, only those present in at least five animals across breeds (n = 95) were discussed to reduce the focus on redundant CNV. Some of these genes have been associated to functional traits in chickens. Notably, several CNVR on different chromosomes harbor genes related to muscle development, tissue-specific biological processes, heat stress resistance, and immune response. Quantitative trait loci (QTL) were also analyzed to investigate potential overlapping with the identified CNVR: 54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size, carcass characteristics, egg production, egg components, fat deposition, and feed intake.

CONCLUSIONS

The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

Association between the copy number variation of CCSER1 gene and growth traits in Chinese Capra hircus (goat) populations

Recently, Coiled-coil serine-rich protein 1 (CCSER1) gene is reported to be related to economic traits in livestock, and become a hotspot. In our study, we detected CCSER1 gene CNV in 693 goats from six breeds (GZB, GZW, AN, BH, HG, TH) by quantitative real-time PCR (qPCR) and the association analysis between the types of CNV and growth traits. Then, CCSER1 gene expression pattern was discovered in seven tissues from NB goats. Our results showed that the CCSER1 gene copy numbers were distributed differently in the aforementioned six breeds. The type of CCSER1 gene CNV was significantly associated with body weight and heart girth traits in GZW goat, in which individuals with deletion type were dominant in body weight trait (P < 0.05), while the normal type individuals were more advantageous in heart girth trait (P < 0.01); and there was a significant association with heart girth in TH goat (P < 0.05), which normal type was the dominant one. The expression profile revealed that CCSER1 gene has the highest level in the lung, followed by the small intestine and heart. In conclusion, our result is dedicated to an in-depth study of the novel CCSER1 gene CNV site and to provide essential information for Chinese goats molecular selective breeding in the future.

The study of selection signature and its applications on identification of candidate genes using whole genome sequencing data in chicken - a review

Chicken is a major source of protein for the increasing human population and is useful for research purposes. There are almost 1,600 distinct regional breeds of chicken across the globe, among which a large body of genetic and phenotypic variations has been accumulated due to extensive natural and artificial selection. Moreover, natural selection is a crucial force for animal domestication. Several approaches have been adopted to detect selection signatures in different breeds of chicken using whole genome sequencing (WGS) data including integrated haplotype score (iHS), cross-populated extend haplotype homozygosity test (XP-EHH), fixation index (F_ST), cross-population composite likelihood ratio (XP-CLR), nucleotide diversity (Pi), and others. In addition, gene enrichment analyses are utilized to determine KEGG pathways and gene ontology (GO) terms related to traits of interest in chicken. Herein, we review different studies that have adopted diverse approaches to detect selection signatures in different breeds of chicken. This review systematically summarizes different findings on selection signatures and related candidate genes in chickens. Future studies could combine different selection signatures approaches to strengthen the quality of the results thereby providing more affirmative inference. This would further aid in deciphering the importance of selection in chicken conservation for the increasing human population.

Population Genomic Sequencing Delineates Global Landscape of Copy Number Variations that Drive Domestication and Breed Formation of in Chicken

Copy number variation (CNV) is an important genetic mechanism that drives evolution and generates new phenotypic variations. To explore the impact of CNV on chicken domestication and breed shaping, the whole-genome CNVs were detected via multiple methods. Using the whole-genome sequencing data from 51 individuals, corresponding to six domestic breeds and wild red jungle fowl (RJF), we determined 19,329 duplications and 98,736 deletions, which covered 11,123 copy number variation regions (CNVRs) and 2,636 protein-coding genes. The principal component analysis (PCA) showed that these individuals could be divided into four populations according to their domestication and selection purpose. Seventy-two highly duplicated CNVRs were detected across all individuals, revealing pivotal roles of nervous system (NRG3, NCAM2), sensory (OR), and follicle development (VTG2) in chicken genome. When contrasting the CNVs of domestic breeds to those of RJFs, 235 CNVRs harboring 255 protein-coding genes, which were predominantly involved in pathways of nervous, immunity, and reproductive system development, were discovered. In breed-specific CNVRs, some valuable genes were identified, including HOXB7 for beard trait in Beijing You chicken; EDN3, SLMO2, TUBB1, and GFPT1 for melanin deposition in Silkie chicken; and SORCS2 for aggressiveness in Luxi Game fowl. Moreover, CSMD1 and NTRK3 with high duplications found exclusively in White Leghorn chicken, and POLR3H, MCM9, DOCK3, and AKR1B1L found in Recessive White Rock chicken may contribute to high egg production and fast-growing traits, respectively. The candidate genes of breed characteristics are valuable resources for further studies on phenotypic variation and the artificial breeding of chickens.

Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations

BACKGROUND

Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens.

RESULTS

The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP - SNP and DUP - SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays.

CONCLUSIONS

The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.

Copy number variation of bovine DYNC1I2 gene is associated with body conformation traits in chinese beef cattle

Previous, studies have shown that the dynein transporter compound has a role in diseases such as intellectual disability and cerebral malformations. However, the study of CNV in DYNC1I2 gene has not been reported. Q-PCR and data association analysis were used for DYNC1I2 gene copy in this study. In this study, blood samples were collected from five breeds of Chinese cattle (Qingchuan cattle, Xianan cattle, Yunling cattle, Pinan cattle and Guyuan cattle) for DYNC1I2 gene CNV type detection. SPSS 20.0 software and method of ANOVA were used to analyzed the association between types of CNV and growth traits. Results reveal that the distribution of different copy number types in different cattle breeds is different. Association analysis indicate that CNV of DYNC1I2 gene showed a positive effect in cattle growth: in XN cattle, individuals with deletion types showed better performance on height at hip cross (P < 0.05); individuals with duplication types have better performance on body length (P < 0.05) in PN cattle; individuals with deletion types was significantly correlated with chest width and Hucklebone width (P < 0.05) in QC cattle; individuals with duplication types in Yunling cattle were better than the normal types, and there was a significant correlation between copy number variant and chest depth (P < 0.05). The results showed that CNV markers closely related to cattle production traits were detected at DNA level, which could be used as an important candidate molecular marker for marker-assisted selection of growth traits in Chinese cattle, and provided a new research basis for genetics and breeding of Chinese beef cattle.

Breeding history and candidate genes responsible for black skin of Xichuan black-bone chicken

Background

Domesticated chickens have a wide variety of phenotypes, in contrast with their wild progenitors. Unlike other chicken breeds, Xichuan black-bone chickens have blue-shelled eggs, and black meat, beaks, skin, bones, and legs. The breeding history and the economically important traits of this breed have not yet been explored at the genomic level. We therefore used whole genome resequencing to analyze the breeding history of the Xichuan black-bone chickens and to identify genes responsible for its unique phenotype.

Results

Principal component and population structure analysis showed that Xichuan black-bone chicken is in a distinct clade apart from eight other breeds. Linkage disequilibrium analysis showed that the selection intensity of Xichuan black-bone chickens is higher than for other chicken breeds. The estimated time of divergence between the Xichuan black-bone chickens and other breeds is 2.89 ka years ago. Fst analysis identified a selective sweep that contains genes related to melanogenesis. This region is probably associated with the black skin of the Xichuan black-bone chickens and may be the product of long-term artificial selection. A combined analysis of genomic and transcriptomic data suggests that the candidate gene related to the black-bone trait, EDN3, might interact with the upstream ncRNA LOC101747896 to generate black skin color during melanogenesis.

Conclusions

These findings help explain the unique genetic and phenotypic characteristics of Xichuan black-bone chickens, and provide basic research data for studying melanin deposition in animals.

Detection of CNV in the SH3RF2 gene and its effects on growth and carcass traits in chickens

BACKGROUND

The SH3RF2 gene is a protein-coding gene located in a quantitative trait locus associated with body weight, and its deletion has been shown to be positively associated with body weight in chickens.

RESULTS

In the present study, CNV in the SH3RF2 gene was detected in 4079 individuals from 17 populations, including the "Gushi ×Anka" F2 resource population and populations of Chinese native chickens, commercial layers, and commercial broilers. The F2 resource population was then used to investigate the genetic effects of the chicken SH3RF2 gene. The results showed that the local chickens and commercial layers were all homozygous for the wild-type allele. Deletion mutation individuals were detected in all of the commercial broiler breeds except Hubbard broiler. A total of, 798 individuals in the F2 resource group were used to analyze the effects of genotype (DD/ID/II) on chicken production traits. The results showed that CNV was associated with 2-, 6-, 10-, and 12-week body weight (P = 0.026, 0.042, 0.021 and 0.039 respectively) and significantly associated with 8-week breast bone length (P = 0.045). The mutation was significantly associated with 8-week body weight (P = 0.007) and 4-week breast bone length (P = 0.010). CNV was significantly associated with evisceration weight, leg muscle weight, carcass weight, breast muscle weight and gizzard weight (P = 0.032, 0.033, 0.045, 0.004 and 0.000, respectively).

CONCLUSIONS

CNV of the SH3RF2 gene contributed to variation in the growth and weight gain of chickens.

First genome-wide CNV mapping in FELIS CATUS using next generation sequencing data

Background

Copy Number Variations (CNVs) have becoming very significant variants, representing a major source of genomic variation. CNVs involvement in phenotypic expression and different diseases has been widely demonstrated in humans as well as in many domestic animals. However, genome wide investigation on these structural variations is still missing in Felis catus. The present work is the first CNV mapping from a large data set of Next Generation Sequencing (NGS) data in the domestic cat, performed within the 99 Lives Consortium.

Results

Reads have been mapped on the reference assembly_6.2 by Maverix Biomics. CNV detection with cn.MOPS and CNVnator detected 592 CNVs. These CNVs were used to obtain 154 CNV Regions (CNVRs) with BedTools, including 62 singletons. CNVRs covered 0.26% of the total cat genome with 129 losses, 19 gains and 6 complexes. Cluster Analysis and Principal Component Analysis of the detected CNVRs showed that breeds tend to cluster together as well as cats sharing the same geographical origins. The 46 genes identified within the CNVRs were annotated.

Conclusion

This study has improved the genomic characterization of 14 cat breeds and has provided CNVs information that can be used for studies of traits in cats. It can be considered a sound starting point for genomic CNVs identification in this species.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5297-2) contains supplementary material, which is available to authorized users.

Detection of copy number variations in brown and white layers based on genotyping panels with different densities

Background

Copy number variations (CNV) are an important source of genetic variation that has gained increasing attention over the last couple of years. In this study, we performed CNV detection and functional analysis for 18,719 individuals from four pure lines and one commercial cross of layer chickens. Samples were genotyped on four single nucleotide polymorphism (SNP) genotyping platforms, i.e. the Illumina 42K, Affymetrix 600K, and two different customized Affymetrix 50K chips. CNV recovered from the Affymetrix chips were identified by using the Axiom^® CNV Summary Tools and PennCNV software and those from the Illumina chip were identified by using the cnvPartition in the Genome Studio software.

Results

The mean number of CNV per individual varied from 0.50 to 4.87 according to line or cross and size of the SNP genotyping set. The length of the detected CNV across all datasets ranged from 1.2 kb to 3.2 Mb. The number of duplications exceeded the number of deletions for most lines. Between the lines, there were considerable differences in the number of detected CNV and their distribution. Most of the detected CNV had a low frequency, but 19 CNV were identified with a frequency higher than 5% in birds that were genotyped on the 600K panel, with the most common CNV being detected in 734 birds from three lines.

Conclusions

Commonly used SNP genotyping platforms can be used to detect segregating CNV in chicken layer lines. The sample sizes for this study enabled a detailed characterization of the CNV landscape within commercially relevant lines. The size of the SNP panel used affected detection efficiency, with more CNV detected per individual on the higher density 600K panel. In spite of the high level of inter-individual diversity and a large number of CNV observed within individuals, we were able to detect 19 frequent CNV, of which, 57.9% overlapped with annotated genes and 89% overlapped with known quantitative trait loci.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0428-4) contains supplementary material, which is available to authorized users.

Copy number variation in livestock: A mini review

Copy number variation (CNV) is a phenomenon in which sections of the genome, ranging from one kilo base pair (Kb) to several million base pairs (Mb), are repeated and the number of repeats vary between the individuals in a population. It is an important source of genetic variation in an individual which is now being utilized rather than single nucleotide polymorphisms (SNPs), as it covers the more genomic region. CNVs alter the gene expression and change the phenotype of an individual due to deletion and duplication of genes in the copy number variation regions (CNVRs). Earlier, researchers extensively utilized SNPs as the main source of genetic variation. But now, the focus is on identification of CNVs associated with complex traits. With the recent advances and reduction in the cost of sequencing, arrays are developed for genotyping which cover the maximum number of SNPs at a time that can be used for detection of CNVRs and underlying quantitative trait loci (QTL) for the complex traits to accelerate genetic improvement. CNV studies are also being carried out to understand the evolutionary mechanism in the domestication of livestock and their adaptation to the different environmental conditions. The main aim of the study is to review the available data on CNV and its role in genetic variation among the livestock.

Detection of breed-specific copy number variations in domestic chicken genome

Copy number variations (CNVs) are important large-scale variants. They are widespread in the genome and may contribute to phenotypic variation. Detection and characterization of CNVs can provide new insights into the genetic basis of important traits. Here, we perform whole-genome short read sequence analysis to identify CNVs in two indigenous and commercial chicken breeds to evaluate the impact of the identified CNVs on breed-specific traits. After filtration, a total of 12 955 CNVs spanning (on average) about 9.42% of the chicken genome were found that made up 5467 CNV regions (CNVRs). Chicken quantitative trait loci (QTL) datasets and Ensembl gene annotations were used as resources for the estimation of potential phenotypic effects of our CNVRs on breed-specific traits. In total, 34% of our detected CNVRs were also detected in earlier CNV studies. These CNVRs partly overlap several previously reported QTL and gene ontology terms associated with some important traits, including shank length QTL in Creeper-specific CNVRs and body weight and egg production characteristics, as well as muscle and body organ growth, in the Arian commercial breed. Our findings provide new insights into the genomic structure of the chicken genome for an improved understanding of the potential roles of CNVRs in differentiating between breeds or lines.

Genomic variability in Mexican chicken population using copy number variants

BACKGROUND

Copy number variations are genome polymorphism that influence phenotypic variation and are an important source of genetic variation in populations. The aim of this study was to investigate genetic variability in the Mexican Creole chicken population using CNVs.

RESULTS

The Hidden Markov Model of the PennCNV software detected a total of 1924 CNVs in the genome of the 256 samples processed with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix). The mapped CNVs comprised 1538 gains and 386 losses, resulting at population level in 1216 CNV regions (CNVRs), of which 959 gains, 226 losses and 31 complex (i.e. containing both losses and gains). The CNVRs covered a total of 47 Mb of the whole genome sequence length, corresponding to 5.12% of the chicken galGal4 autosome assembly.

CONCLUSIONS

This study allowed a deep insight into the structural variation in the genome of unselected Mexican chicken population, which up to now has not been genetically characterized. The genomic study disclosed that the population, even if presenting extreme morphological variation, cannot be organized in differentiated genetic subpopulations. Finally this study provides a chicken CNV map based on the 600 K SNP chip array jointly with a genome-wide gene copy number estimates in a native unselected for more than 500 years chicken population.

Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers

Genomic and genetic variation among six Italian chicken native breeds (Livornese, Mericanel della Brianza, Milanino, Bionda Piemontese, Bianca di Saluzzo and Siciliana) were studied using single nucleotide polymorphism (SNP) and copy number variants (CNV) as markers. A total of 94 DNA samples genotyped with Axiom® Genome-Wide Chicken Genotyping Array (Affymetrix) were used in the analyses. The results showed the genetic and genomic variability occurring among the six Italian chicken breeds. The genetic relationship among animals was established with a principal component analysis. The genetic diversity within breeds was calculated using heterozygosity values (expected and observed) and with Wright's F-statistics. The individual-based CNV calling, based on log R ratio and B-allele frequency values, was done by the Hidden-Markov Model (HMM) of PennCNV software on autosomes. A hierarchical agglomerative clustering was applied in each population according to the absence or presence of definite CNV regions (CNV were grouped by overlapping of at least 1 bp). The CNV map was built on a total of 1003 CNV found in individual samples, after grouping by overlaps, resulting in 564 unique CNV regions (344 gains, 213 losses and 7 complex), for a total of 9.43 Mb of sequence and 1.03% of the chicken assembly autosome. All the approaches using SNP data showed that the Siciliana breed clearly differentiate from other populations, the Livornese breed separates into two distinct groups according to the feather colour (i.e. white and black) and the Bionda Piemontese and Bianca di Saluzzo breeds are closely related. The genetic variability found using SNP is comparable with that found by other authors in the same breeds using microsatellite markers. The CNV markers analysis clearly confirmed the SNP results.

Copy number variation identification and analysis of the chicken genome using a 60K SNP BeadChip

Copy number variation (CNV) is an important source of genetic variation in organisms and a main factor that affects phenotypic variation. A comprehensive study of chicken CNV can provide valuable information on genetic diversity and facilitate future analyses of associations between CNV and economically important traits in chickens. In the present study, an F2 full-sib chicken population (554 individuals), established from a cross between Xinghua and White Recessive Rock chickens, was used to explore CNV in the chicken genome. Genotyping was performed using a chicken 60K SNP BeadChip. A total of 1,875 CNV were detected with the PennCNV algorithm, and the average number of CNV was 3.42 per individual. The CNV were distributed across 383 independent CNV regions (CNVR) and covered 41 megabases (3.97%) of the chicken genome. Seven CNVR in 108 individuals were validated by quantitative real-time PCR, and 81 of these individuals (75%) also were detected with the PennCNV algorithm. In total, 274 CNVR (71.54%) identified in the current study were previously reported. Of these, 147 (38.38%) were reported in at least 2 studies. Additionally, 109 of the CNVR (28.46%) discovered here are novel. A total of 709 genes within or overlapping with the CNVR was retrieved. Out of the 2,742 quantitative trait loci (QTL) collected in the chicken QTL database, 43 QTL had confidence intervals overlapping with the CNVR, and 32 CNVR encompassed one or more functional genes. The functional genes located in the CNVR are likely to be the QTG that are associated with underlying economic traits. This study considerably expands our insight into the structural variation in the genome of chickens and provides an important resource for genomic variation, especially for genomic structural variation related to economic traits in chickens.

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

Background

Copy number variation (CNV) is a major source of genome polymorphism that directly contributes to phenotypic variation such as resistance to infectious diseases. Lines 6₃ and 7₂ are two highly inbred experimental chicken lines that differ greatly in susceptibility to Marek’s disease (MD), and have been used extensively in efforts to identify the genetic and molecular basis for genetic resistance to MD. Using next generation sequencing, we present a genome-wide assessment of CNVs that are potentially associated with genetic resistance to MD.

Methods

Three chickens randomly selected from each line were sequenced to an average depth of 20×. Two popular software, CNVnator and Pindel, were used to call genomic CNVs separately. The results were combined to obtain a union set of genomic CNVs in the two chicken lines.

Results

A total of 5,680 CNV regions (CNVRs) were identified after merging the two datasets, of which 1,546 and 1,866 were specific to the MD resistant or susceptible line, respectively. Over half of the line-specific CNVRs were shared by 2 or more chickens, reflecting the reduced diversity in both inbred lines. The CNVRs fixed in the susceptible lines were significantly enriched in genes involved in MAPK signaling pathway. We also found 67 CNVRs overlapping with 62 genes previously shown to be strong candidates of the underlying genes responsible for the susceptibility to MD.

Conclusions

Our findings provide new insights into the genetic architecture of the two chicken lines and additional evidence that MAPK signaling pathway may play an important role in host response to MD virus infection. The rich source of line-specific CNVs is valuable for future disease-related association studies in the two chicken lines.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2080-5) contains supplementary material, which is available to authorized users.

Prospects and challenges for the conservation of farm animal genomic resources, 2015-2025

Livestock conservation practice is changing rapidly in light of policy developments, climate change and diversifying market demands. The last decade has seen a step change in technology and analytical approaches available to define, manage and conserve Farm Animal Genomic Resources (FAnGR). However, these rapid changes pose challenges for FAnGR conservation in terms of technological continuity, analytical capacity and integrative methodologies needed to fully exploit new, multidimensional data. The final conference of the ESF Genomic Resources program aimed to address these interdisciplinary problems in an attempt to contribute to the agenda for research and policy development directions during the coming decade. By 2020, according to the Convention on Biodiversity's Aichi Target 13, signatories should ensure that “…the genetic diversity of …farmed and domesticated animals and of wild relatives …is maintained, and strategies have been developed and implemented for minimizing genetic erosion and safeguarding their genetic diversity.” However, the real extent of genetic erosion is very difficult to measure using current data. Therefore, this challenging target demands better coverage, understanding and utilization of genomic and environmental data, the development of optimized ways to integrate these data with social and other sciences and policy analysis to enable more flexible, evidence-based models to underpin FAnGR conservation. At the conference, we attempted to identify the most important problems for effective livestock genomic resource conservation during the next decade. Twenty priority questions were identified that could be broadly categorized into challenges related to methodology, analytical approaches, data management and conservation. It should be acknowledged here that while the focus of our meeting was predominantly around genetics, genomics and animal science, many of the practical challenges facing conservation of genomic resources are societal in origin and are predicated on the value (e.g., socio-economic and cultural) of these resources to farmers, rural communities and society as a whole. The overall conclusion is that despite the fact that the livestock sector has been relatively well-organized in the application of genetic methodologies to date, there is still a large gap between the current state-of-the-art in the use of tools to characterize genomic resources and its application to many non-commercial and local breeds, hampering the consistent utilization of genetic and genomic data as indicators of genetic erosion and diversity. The livestock genomic sector therefore needs to make a concerted effort in the coming decade to enable to the democratization of the powerful tools that are now at its disposal, and to ensure that they are applied in the context of breed conservation as well as development.