Journal of Animal Science and Biotechnology
Research

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

1 ,1,2 ,1

Abstract

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.

Background

Copy number variants (CNV) are structural genomic alterations distributed across the entire genome in all species, with a mean size of at least 50 bp [1, 2], and they are caused by insertions, deletions, duplications, and translocations of DNA fragments [2, 3]. The opportunity to sequence whole genomes has facilitated the use of molecular markers to characterize the breeds; indeed, structural variations of the genome have an important role in gene expression and genome evolution within populations. As with microsatellites and single nucleotide polymorphisms (SNP), CNV can be used to investigate genetic variation and diversity [4,5,6]. In all species, CNV can intersect genes, altering their structure and expression, and causing phenotypic variability and disease susceptibility in humans [7, 8] and animals [6, 9, 10]. The CNV can explain a large portion of the loss of heritability in genome-wide studies for some traits [11, 12]. Although CNV are less prevalent in the genome than other molecular markers, they cover a larger portion of the genome and thus can have powerful effects on phenotypic variability [13, 14]. In contrast to SNP, CNV can span larger genomic regions and have greater mutation rate, potentially exerting substantially more influence on gene structure, regulation, and expression [15].

Several studies in chickens have pinpointed quantitative trait loci (QTL) and positional candidate genes marked by significantly associated SNP for economically important traits, including growth performance, carcass characteristics, and abdominal fat deposition [15]. It is unsurprising that the number of studies dealing with CNV has also increased within chicken populations [15]. Notably, CNV linked to traits such as late feathering, the pea-comb phenotype, dermal hyperpigmentation, dark brown plumage color, and resistance/susceptibility to Marek's disease have been documented [15].

The CNV has a pivotal role in the evolutionary adaptation of organisms and influence their fitness and reproductive capabilities under both natural and artificial selection pressures. This underscores their significance as a substantial source of adaptive potential. For instance, the copy number of AMY1 demonstrates a robust correlation with dietary evolution, with individuals historically consuming high-starch diets typically having more AMY1 copies than those consuming low-starch diets [16]. Furthermore, Minias et al. [17] have reported that the evolution of major histocompatibility complex (MHC) copy number in birds is driven by selective pressures, including those arising from intra- and extra-cellular pathogens and parasites. Copy number variants have also been implicated in the phenotypic variability of traits crucial to domestication and breed development in livestock species. For instance, the duplication of KIT gene is significantly associated with white coat color in both pigs and cattle and the chicken pea-comb phenotype is attributed to the duplication of SOX5 within intron 1 [16].

Copy number variants in the poultry genome have often been mapped using low-density chips [18, 19] or a limited sample size [6]. In recent years, the use of 600 K density chips in the chicken has allowed researchers to obtain a significant number of outputs useful for more accurate detection of CNV [6]. Although whole-genome sequence data can improve the identification of smaller CNV (unlike the SNP-array-based approach), this is economically disadvantageous to be performed at population level [20, 21].

The aims of this study were to investigate i) the type and amount of CNV and CNV regions (CNVR), and ii) the genes that undergo the effect of their presence with an unprecedented resolution using a high-density SNP chip in a large sample of local Italian chickens. Finally, the genetic variability and CNV, CNVR, and genes in common amongst breeds were characterized.

Methods

Sampling and genotyping

The DNA from 508 individuals from 23 local Italian chicken breeds (approximately 22 individuals per breed; Table 1) were genotyped using the Affymetrix Axiom 600 K Chicken Genotyping Array (for full details see Cendron et al. [5]). Local breeds were Ancona (ANC), Bianca di Saluzzo (BSA), Bionda Piemontese (BPT), Cornuta di Caltanissetta (COR), Livorno Bianca (PLB), Livorno Nera (PLN), Mericanel della Brianza (MER), Modenese (MOD), Mugellese (MUG), Ermellinata di Rovigo (PER), Millefiori di Lonigo (PML), Padovana Argentata (PPA), Polverara Bianca (PPB), Padovana Camosciata (PPC), Padovana Dorata (PPD), Polverara Nera (PPN), Pepoi (PPP), Robusta Lionata (PRL), Robusta Maculata (PRM), Romagnola (ROM), Siciliana (SIC), Valdarnese (VLD), and Valplatani (VLP).

Table 1 Descriptive statistics of copy number variants identified in the Italian local chicken breeds
Full size table

Before running the CNV calling, the raw genotype dataset underwent a quality check using the Axiom Analysis Suite Software (Affymetrix) to remove the SNP with call rate < 97% and Dish Quality Control < 82%. The final dataset contained 508 animals and 472,821 SNP targets.

Identification of CNV

The Axiom CNV summary software tool was used to create input files for CNV calling in PennCNV software, which utilizes the Log R ratio (LRR) and B allele frequency (BAF) [22,23,24]. Prior to PennCNV calling, the raw CNV were visualized using the Axiom CNV Viewer software. The individual-based CNV calling was then carried out using the default parameters of the Hidden Markov Model, i.e., a standard deviation of LRR < 0.30, BAF drift set to 0.01, waviness factor at 0.05, and minimum of 3 SNP to define a CNV. The distribution of CNV per individual spanned from 0 to > 100 [6]. To avoid the detection of false positive and/or negative CNV and outliers, different “.hmm” files (agre.hmm, affygw6.hmm, hh550.hmm) were used to run PennCNV as described by Strillacci et al. [2], Gorla et al. [6], and Fernandes et al. [15]. Indeed, the “.hmm” file may substantially affect the false positive and the false negative rate. The PennCNV manual (https://penncnv.openbioinformatics.org/en/latest/) declares that the agre.hmm file returns more false positive calls than the affygw6.hmm file, which produces a lower number of CNV calls. The analysis was conducted also using the hh550.hmm file as the default method for the calling [2]. To obtain a valid CNV, the common calls from all the three hidden Markov models were considered [6, 15]. This solved the critical choice of which.hmm output file is more appropriate to map CNV to control false positive and negative calls (Additional file 1). Genomic waves were adjusted using the chicken GC model file, which was generated by calculating the GC content of 1-Mb genomic regions surrounding each marker (500 kb on each side), after the program argument ‘gcmodel’ was used to adjust the results [25]. In addition, to validate the outputs from PennCNV, the optimal segmenting module of SVS 8.7.0 (Golden Helix Inc., Bozeman, MT, USA) was used to identify CNV through the univariate approach that segments each sample independently. Quality assurance of the LRR data and filtering of outlier samples were performed using SVS software following the approach of Pinto et al. [26]. Individuals were screened for their GC content, which is correlated to long-range waviness of LRR. Outlying samples were detected by the SVS 8.7.0 for waviness and those identified were deleted [26]. The CNV identified through the two algorithms were merged and consensus among the outputs were used to identify the final CNV for further analysis (Additional file 1).

Summary of CNV and definition of CNVR

The R package HandyCNV [27] was used on PennCNV output files to summarize CNV and define CNVR. The following package commands were imputed to the analysis: i) cnv_clean () function to convert CNV results into a standard format and make basic summary (the CNV larger than 5 Mb were discarded) [28]; ii) cnv_summarise_plot () to create the CNV distribution, frequency, and length group plot; iii) call_cnvr () to define the CNVR and their frequency (merging CNV that overlapped by at least 1 bp) [28]. In the CNVR map and definition, “gain” indicates the regions that contain more than two copies of CNV, “loss” indicates the regions that contain deleted CNV, and “mixed” the regions that contain at least one duplicated and one deleted CNV. Consensus CNVR were generated with the call_cnvr () command by combining the identified CNVR and the overlapping regions in the final CNVR distribution map as described in Zhou et al. [28].

Chromosomal distribution and annotation of CNVR

The CNVR distribution map was created through the command cnvr_plot () in HandyCNV R package [28]. Both CNV and CNVR were annotated using get_refgene () and call_gene () functions, to obtain reference genome and genes, respectively, based on formatted reference gene list of Gallus gallus 6.0 chicken assembly (UCSC Genome Browser GRCg6a—https://tinyurl.com/2unb8sf3).

The gene frequency was estimated during the annotation process through of counting the total number of CNV that were annotated to intersect the gene. At the end, the genes under CNV presence were only considered if they were observed in more than five individuals between the breeds [29]. The Gene Ontology (GO) and QTL were identified using Panther algorithm (http://www.pantherdb.org/) and Animal QTLdb database (https://www.animalgenome.org/cgi-bin/QTLdb/index), respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8; https://david.ncifcrf.gov/) was used to perform the GO enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The visual plots and figures were obtained through ggplot2 and tidyverse R package [30,31,32].

Clustering analysis using CNVR

A clustering analysis using the detected CNVR was performed using the method described in Gorla et al. [6]. The scoring matrix of the CNVR was constructed, giving the “0” or “1” values to identify the presence or absence of CNV, respectively, within a specific CNVR. A hierarchical agglomerative clustering was applied to the scoring matrix using pvclust R package [33] and multiscale bootstrap re-sampling (100,000 bootstraps) was used to obtain the approximately unbiased P-value (AU-P) and estimate a bootstrap probability P-value (BP-P) to determinate the branches’ robustness. The unweighted pair group method with arithmetic mean (UPGMA) was chosen as agglomerative method.

Results

Identification of CNV and CNVR

A total of 4,262 common CNV remained after merging results from PennCNV and SVS softwares (Table 1, Additional file 1): 3,990 CNV were deletions (i.e., loss state) and 272 duplications (i.e., gain state).

The ANC, VLP, PPP, and BSA local breeds had the greatest number of CNV, namely 480, 468, 435, and 417, respectively. It is worth reporting that the COR breed had the greatest number of duplicated CNV (43) and ANC the greatest number of CNV losses (451). Total genome coverage by entire CNV presence was greatest in BSA (3.94%), PPP (6.66%), and VLP (6.98%) when considering the 28 autosomes in the GRCg6a. Total genome coverage by entire CNV detected was 32%, with average length of 1,463,770 kb.

A summary of the detected CNV is depicted in Fig. 1. Results are presented as distribution of the number of CNV per sample (Fig. 1a), average length of each type of CNV (Fig. 1b), and location on chromosomes (Fig. 1c). The majority of CNV were deletions (0 or 1) with a mean length between 0.05 and 0.1 Mb (Fig. 1a and b). As expected, a high number of CNV were located on the first six autosomes, as they are the largest of the entire genome; noteworthy, no duplicated CNV were detected on chromosomes 10, 15, 16, 17, 24, 25, 26, or 28 (Fig. 1c).

Fig. 1
figure 1

CNV summary plot. a Representative result of number of CNV per sample. b Frequency of copies of CNV in different length groups. c Total CNV state and distributions on chromosomes. The lines indicate the number of CNV and bar plot the length distribution

The number of identified CNVR by breed is presented in Table 2, along with their state and some quantitative characteristics. These summarized CNV enabled the detection of 1,172 CNVR across all the breeds. These CNVR are comprised of 1,082 losses, 36 gains, and 54 mixed effects. This resulted in 482 common interbreed CNVR and 690 CNVR that were defined as unique to particular breeds (Fig. 2). A high number of loss type CNVR were detected in ANC (345), COR (202), PML (217), and VLP (213), and a high number of gain type CNVR were identified in ANC (7), COR (6), MUG (6), and PPD (7). The CNVR of mixed type were identified in all breeds with greater number in BSA (40), COR (37), and PPP (36). The longest portion of genome covered by a single CNVR was observed in PPP (50,708,742 kb, 4.44% genome coverage), followed by VLP (2.71%), and ANC (2.06%; Table 2). Total genome coverage of identified CNVR was 13.64%, considering 28 autosomes in the GRCg6a.

Table 2 Descriptive statistics of copy number variation regions identified in the Italian chicken breeds
Full size table
Fig. 2
figure 2

Graphical representation of copy number variation regions among breeds, divided in class of state (purple = gain, green = loss, blue = mixed). Copy number variation regions in progressive number from 1 to 1,172 are represented by lines

Due to the high level of CNVR associated to the loss type, overlapping regions between breeds were observed. Likewise, in the cluster of the mixed type, some overlapping regions were detected. In terms of density, the ANC, PML, PPP, and VLP exhibited the greatest number of loss type CNVR (Fig. 2). Unique CNVR were classified as singleton if detected in only one individual (Table 3). Among the identified CNVR, 690 (58.9%) were singleton, 235 (20.1%) were identified in two individuals, 72 (6.1%) in three individuals, 56 (4.8%) in four individuals, 32 (2.7%) in five individuals, and 87 (7.4%) in six or more individuals.

Table 3 Genome covered (%) by copy number variation regions for each chromosome (Chr)
Full size table

Figure 3 depicts the CNVR map according to each type (gain, loss, and mixed) on each chromosome. A detailed overview of the distribution of CNVR on chromosomes of the studied breeds is reported in the Additional file 2. The greatest number of CNVR was identified on chromosome 1 and the breeds ANC, PML, PPP, and VLP had the greatest representation. On second, third, and fourth chromosomes, the aforementioned breeds had the greatest number of CNVR, however, the presence of CNVR decreased drastically on the other chromosomes and ANC and COR had the greatest number of CNVR, with ANC being the only breed with a CNVR on chromosome 16 (Additional file 2).

Fig. 3
figure 3

Physical distribution of copy number variation regions on chromosomes, according to state (gain, loss, and mixed)

The number of CNVR detected together with the state and the proportion of coverage by chromosomes is reported in Table 3. The proportion of coverage ranged from 5% to 36.5%, with the highest value observed on chromosome 16, which was expected since it is the shortest chromosome of the chicken genome.

The CNVR were grouped according to their length into 4 classes: 1 to 10 kb (n = 83), 11 to 100 kb (n = 760), 101 to 200 kb (n = 155), and > 200 kb (n = 174). The greatest number of loss type CNVR were those with length from 11 to 100 kb, and the greatest number of gains and mixed type CNVR were those with length from 11 to 100 kb and > 200 kb, respectively (Additional file 3).

The comparison amongst the CNVR observed in the present study and those of other studies [6, 15, 16, 34,35,36,37] is summarized in Table 4. Out of 1,172 CNVR, from 149 to 1,160 overlapped with CNVR identified in previous studies on chicken, meaning that a decent proportion of them were detected regardless of the method used and the studied population.

Table 4 Number of common copy number variation regions between the current study and the literature
Full size table

Gene annotation inside CNVR

The dataset of the CNVR was intersected with the chicken gene database (UCSC Genome Browser losses GRCg6a). Out of the 1,172 CNVR identified in the present study, 676 (57.7%) did not incorporate any genes and 496 (42.3%) encompassed one or more genes. In detail, there were 968 genes within the genomic regions covered by the identified CNVR; 854 (88.2%) were protein-coding genes, 84 (8.7%) were miRNAs, and 30 (3.1%) were genes of as yet unknown function (LOC; Additional file 4). For the following analysis, the genes present in at least five individuals within a breed were considered, in order to evaluate those with higher incidence in the whole population. A total of 135 genes were identified and carried out to GO analyses and QTL association (Fig. 4 and Additional file 5). The Panther dataset provided the annotation information according to GO terms on the 135 selected genes. Of these genes, 75 are involved in cellular processes, 49 in biological regulations, 45 in metabolic processes, 24 in localization, 20 in multicellular organismal processes, 16 in the developmental system, 24 in signaling processes, 22 in response to stimulus, 10 in locomotion system activities, and 4 in growth processes. It is worth noting that most candidate genes were associated with production focused QTL such as body weight, breast muscle weight, fat deposition, and egg weight and quality (Additional file 5). Moreover, several CNVR were found to be conserved between breeds, both as loss and gain, acting on same genes (Fig. 4). Some of these genes that were targeted by CNVR among breeds were: CDH19 (cadherin 19), DACH1 (Dachshund family transcription factor 1), IMMP2L (inner mitochondrial membrane peptidase subunit 2), DMD (dystrophin), DNPEP (aspartyl aminopeptidase), TMEM123 (transmembrane protein 123), BORA (Bora, aurora kinase A activator), DDX1 (DEAD-box helicase 1), IFT140 (intra-flagellar transport 140), ARL8A (ADP ribosylation factor like GTPase 8A), and CCKAR (cholecystokinin A receptor). Interestingly, the gene coding for the miRNA 6683 (MIR6683) was spread across the majority of the breeds (Fig. 4 and Additional file 5). All the aforementioned genes are associated with several QTL such as carcass and body weight, carcass ash and dry mater content, feed intake, skeletal development, egg quality, and average daily gain.

Fig. 4
figure 4

Genes distribution among the animals belonging to the investigated chicken breeds. Reported genes are the most significant as present in at least 5 animals across breeds. The colors indicate the status of copy number variation regions in which the genes were annotated (red = gain, green = loss, blue = mixed)

The GO enrichment analysis and KEGG pathways analysis invoked in DAVID yielded 126 significant enriched functional terms (48 of biological processes, 77 of cellular components, and 60 of molecular functions). In addition, 49 significant enriched pathways, including metabolic pathway, biosynthesis of aminoacyl, and the MAPK signaling pathway were detected. More details are available in the Additional file 6.

Clustering analysis through CNVR

Figure 5 depicts the cluster-tree performed for the local Italian chicken breeds based on CNVR similarities. In the dendrogram, the branch length is not directly proportional to the genetic distance estimated among the breeds [5]. The AU-P and BP-P indicate how strongly the cluster is supported by the data and, these are reported for each node and the edge number. Here, three clusters were detected, the first composed of PPA, ROM, SIC, PLB, and PLN, the second of ANC and COR, and the third of MUG, BPT, MER, VLP, BSA, and PPP.

Fig. 5
figure 5

Cluster dendrogram with AU-P (approximately unbiased P-value) and BP-P values (bootstrap probability P-value) (%) among breeds. AU-P value in dark grey color, BP-P value in grey color, edge in light grey color. Breeds with high copy number variant similarities are in the red box

Discussion

CNV and CNVR

High-throughput, high-density genotyping technologies such as Affymetrix Axiom and/or Illumina BeadArrays, are employed in genome-wide association studies to facilitate the detection of CNV [33, 38]. For each SNP, an array platform consists of two types of hybridization probes, each specific to one of the two known alleles. The determination of the SNP genotype can be achieved by analyzing the ratios of hybridization intensities for the A and B probes (one for each allele). The CNV, such as duplications and deletions, result in an increase or decrease of the overall intensities. Additionally, in the case of large CNV spanning multiple SNP, the intensity ratios exhibit distinct patterns compared to normal disomic genomic regions. Thus, the in-silico approaches such as those described in the present study, acquire higher importance and credibility in the CNV calling [39,40,41,42].

In the present work, given the large sample size and the density of the chip used, a very large number of CNV has been reported (Table 1) compared to previous studies [6, 39, 41]. Our results are in accordance with the existing literature, which indicates that the distribution of CNV is more prominent in the first six autosomes and linked with the types 0 and 1 (Fig. 2) [39, 43, 44]. It is worth noting that most of the CNV are associated with the loss type. Additionally, the total number of animals in this population, based on the FAO DAD-ID census, is very low [36]. This is particularly noteworthy given that, overall, the inbreeding rate is high in the investigated breeds, especially for PPP [5]. However, this consideration does not strictly apply to all breeds; indeed, the SIC had a low number of CNV, despite a very high level of inbreeding [5].

The minimum length of CNV identified in COR was significantly greater than that reported in previous studies [2, 6, 36] (Table 1). The minimum length of CNV, i.e., 857 bp using the criterion of a minimum of five SNP for CNV mapping, does not coincide with the minimum length mapped by Gorla et al. [6] using the same approach.

In general, the literature reports that most CNV are of types 0 and 1 [36,37,38, 40], in both chicken and other species, which agrees with the present work. However, in the species Gallus gallus, the number of CNV is very heterogeneous between breeds [6, 36, 42], likely due to the method of analysis. In the current study, the method described by Gorla et al. [6] was used; however, those authors reported higher number of gain type CNV than loss type CNV in local Mexican chicken populations, whereas we observed more loss type CNV than gain type CNV in local Italian chicken breeds.

Strillacci et al. [36] investigated CNV and CNVR in some of the chicken breeds included in the present study, namely BPT, BSA, MER, PLB, PLN, and SIC. The number of CNV and CNVR among chromosomes for PLB and PLN in Strillacci et al. [36] was in line with those for PLB and PLN of the current study. As regards BPT, the total number of CNV and CNVR in Strillacci et al. [36] was greater than that of the current study, whereas in BSA and MER the total number of CNV and CNVR was lower. The animals used in our study were more recent and it is likely they have undergone new recombination events since the earlier studies. There are multiple genomic phenomena that can cause changes in the distribution of CNV within the genome over time.

Approximately 40% of the CNVR (480 out of 1,172) detected in our study are conserved and the remaining 692 are new and represent single regions. As reported in Table 3, the proportion of CNVR in common between the present work and past studies is moderate to high. Moreover, regardless of the breeds included in other studies, the CNVR detection is mainly influenced by sample size and the algorithm, and the technology used to map CNV (i.e., high density SNP array, low density SNP array or whole genome sequencing). However, a strength of the present study is the use of intensity signals from genotyping that allowed strong validation of the results [45, 46].

As expected from previous studies, chromosome 16 stood out with a high proportion of its length covered by CNVR (36.5%; Fig. 3 and Table 3), which can be attributed to two main factors. Firstly, chromosome 16 is the shortest autosome of the genome of Gallus gallus. Secondly, it harbors the MHC, which is known to be subject to genomic CNV [43]. This complex contains a cluster of genes responsible for the encoding of the proteins present on cell surfaces, aiding the immune system in identifying exogenous substances. Many of these genes contribute to immune responses with specific alleles at some loci that potentially have a major role in the genetic mechanisms of resistance to infectious diseases [43, 46]. The presence of CNV on chromosome 16 could be crucial in conferring or not a particular resistance to diseases; on the other hand, the presence of CNV as deletion could contribute to susceptibility to the diseases. In the present study, only the ANC breed had several CNV on chromosome 16. However, the chromosome coverage percentage identified was very low in this study when compared with previous studies [20, 44]. Chromosome 16 is very interesting as it contains the key genes for resistance to infectious diseases and therefore it is subject to various natural genetic modifications due to the presence of a large number of polymorphic sites (i.e., avian influenza, Rous sarcoma disease, avian leucosis, Escherichia coli, Salmonella enteritidis) [20, 44].

The CNV identified in the VLD and PPP breeds are primarily present on the first seven autosomes, while the remaining breeds showed a more evenly pattern of CNV coverage throughout the genome. The distribution on the first seven autosomes was expected as they are significantly larger than the others. Notably, the ANC and COR breeds exhibited a well-balanced distribution of CNV across all chromosomes (Additional file 2). In addition, the physical distribution of CNVR on chromosomes is in accordance with Gorla et al. [6].

Out of the identified CNVR, 58.9% were observed in only one individual, 20.1% in two individuals, 6.1% in three individuals, 4.8% in four individuals, and 10.1% in more than five individuals. The high proportion of the singleton has been previously reported by Yi et al. [20] (68.8%), Strillacci et al. [36] (75%), and Han et al. [47] (76.5%). This finding confirms the existence of segregating CNV among individuals, as highlighted by the large proportion of singleton CNVR.

CNVR annotation and QTL

Changes in the CNV can cause the deletion or duplication of genes, and these changes can alter gene expression [48]. Therefore, identifying these affected genes is an important part of studies on CNV and CNVR. Some of the annotated genes within the CNVR have been already associated to functional traits in chickens (Additional file 5). The CNVR located on chromosome 1, shared amongst different breeds, were used to annotate several genes, the most noteworthy being CACNA2D1 (calcium voltage-gated channel auxiliary subunit alpha 2 delta 1) which is related to muscle contraction [49]; DMD (dystrophin), one of the most important factors for muscle development and structural stability of the tissue [50]; and DACH1, involved in skeletal development and inhibitor of growth factor beta [51, 52]. The CNVR that contains the latter gene was identified in the PPP breed, which is a small size breed [53]. Some other genes were BORA, related to cell growth and divisions and consequently influences on whole growth traits [54]; IMMP2L, involved in the reproduction traits and fertility [55]; and TMEM123, associated with adipogenic differentiation of chicken preadipocytes [56].

The CNVR on chromosome 3 harbor several genes, however, the most relevant is DDX1 that strengthens the immunity response and therefore it may have played a role in the acquired resistance of local breeds to environmental stimuli [57]. On chromosome 4, CCKAR is important for body weight and its variants have a central role in the diversification of gene expression [58]. The genes IFT140 and ARL8A were identified on chromosomes 14 and 26, respectively; these genes are associated with eggs and fertility. In detail, IFT140 is involved in the maturation and efficiency of seminal cells and ARL8A in both egg production and brown pigmentation [59,60,61]. These findings are important due to the low efficiency of these local breeds in terms of fertility and egg production [62].

Several genes are of particular interest due to their presence across breeds and they include SLC4A2 (solute carrier family 4 member 2) on chromosome 2, CCNB3 (cyclin B3) on chromosome 4, and DNPEP (aspartyl aminopeptidase) on chromosome 7. These genes have been linked to muscle development and tissue-specific biological processes in muscle [63]. Noteworthy, the gene encoding the miRNA MIR6683 is present in CNVR 621 which has been identified in the BSA, MER, MUG, PPA, SIC and VLP breeds, and is associated with sex determination (Additional file 5) [64].

Another useful information obtained in this study through KEGG analysis is the MAPK signaling pathway, which plays an important role in complex cellular programs like proliferation, differentiation, development, transformation, and apoptosis [65]. These cellular events are critical to immune development and some other processes. Importantly, mutations that constitutively activate or fail to regulate the MAPK signaling properly cause inflammatory disease, including several chicken diseases [66, 67].

Additionally, QTL from the chicken QTLdb were downloaded to verify the overlap with the identified CNVR. Since the confidence interval of some QTL was very wide, only those shorter than 5 Mb and involving loci with high production impact were considered. Out of the 135 gene-containing regions, 39 overlapped with 1,475 QTL, mainly including QTL for body weight, body size, carcass characteristics, egg production, characteristics of egg yolk and albumen, fat deposition, and feed intake (Additional file 5).

Clustering analysis

Some breeds created precise clusters based on the characterization of the CNVR and this contributed to the definition of the classes of the breeds under investigation. Several papers reported procedures to define classes on the basis of CNVR [36, 41]; however, as the length of the arms within the dendrogram are not directly proportional to the estimated genetic distance between the samples, this classification is difficult. Based only on AU-P, the cluster (edge) with AU-P > 95% is the most plausible method [32]. Edge is the order in which the clusters are built: more closely related samples have smaller edge number, whereas higher edge number reflects clusters formed later in the evolutionary process of the breed [36]. The dendrogram in Fig. 5 was carried out by breed and not by animal due to the complexity of the interpretation. Our results do not properly represent the distribution of the breeds and the separation among breeds, due to the limit that the CNV locus must contain at least 5 SNP probes in the statistical analysis. The exclusion of some breed-specific CNV (with less than 5 SNP) could have affected the clustering [36].

Conclusion

Genetic variability and diversity within and between 23 local Italian chicken breeds using CNV markers were assessed. The CNV analysis has not effectively distinguished breeds based on their breeding history and genetic identity. The findings lay the groundwork for acknowledging the local Italian chicken population as a vital repository of genetic diversity, using high-density SNP genotypes. The study permitted the development of a CNV map in local populations well adapted to harsh environments. Interestingly, some of the CNV are located in the chromosomal regions where crucial functional genes have been annotated, such as the MHC region on chromosome 16. In conclusion, this study confirmed the presence of genetic and genomic variability in local Italian chicken breeds and supports the opportunity to utilize them for conservation purposes.

Data Availability

The datasets generated and/or analyzed during the current study are not publicly available. They can be made available from the corresponding author upon reasonable request.

Abbreviations

  • ANC:: Ancona
  • ARL8A:: ADP ribosylation factor like GTPase 8A
  • BAF:: B allele frequency
  • BORA:: Bora, aurora kinase A activator
  • BPT:: Bionda Piemontese
  • BSA:: Bianca di Saluzzo
  • CACNA2D1:: Calcium voltage-gated channel auxiliary subunit alpha 2 delta 1
  • CCKAR:: Cholecystokinin A receptor
  • CCNB3:: Cyclin B3
  • CDH19:: Cadherin 19
  • CNV:: Copy number variant
  • CNVR:: Copy number variation region
  • COR:: Cornuta di Caltanissetta
  • DACH1:: Dachshund family transcription factor 1
  • DAVID:: The database for annotation visualization and integrated discovery
  • DDX1:: DEAD-box helicase 1
  • DMD:: Dystrophin
  • DNPEP:: Aspartyl aminopeptidase
  • GO:: Gene Onthology
  • IFT140:: Intra-flagellar transport 140
  • IMMP2L:: Inner mitochondrial membrane peptidase subunit 2
  • KEGG:: Kyoto Encyclopaedia of Genes and Genomes
  • LRR:: Log R ratio
  • MER:: Mericanel della Brianza
  • MOD:: Modenese
  • MUG:: Mugellese
  • PER:: Ermellinata di Rovigo
  • PLB:: Livorno Bianca
  • PLN:: Livorno Nera
  • PML:: Millefiori di Lonigo
  • PPA:: Padovana Argentata
  • PPB:: Polverara Bianca
  • PPC:: Padovana Camosciata
  • PPD:: Padovana Dorata
  • PPN:: Polverara Nera
  • PPP:: Pepoi
  • PRL:: Robusta Lionata
  • PRM:: Robusta Maculata
  • ROM:: Romagnola
  • QTL:: Quantitative trait loci
  • SIC:: Siciliana
  • SLC4A2:: Solute carrier family 4 member 2
  • SNP:: Single nucleotide polymorphisms
  • TMEM123:: Transmembrane protein 123
  • VLD:: Valdarnese
  • VLP:: Valplatani

References

  1. 1.Mills RE, Walter K, Stewart C, Handsaker RE, Chen K, Alkan C, et al. Mapping copy number variation by population-scale genome sequencing. Nature. 2011;470.(2011)org/10.1038/nature09708.: 59.
    10.1038/nature09708
    Article|CAS|PubMed|Google Scholar
  2. 2.Strillacci MG, Marelli SP, Milanesi R, Zaniboni L, Punturiero C, Cerolini S. Copy number variants in four Italian turkey breeds. Animals. 2021;11.(2021)org/10.3390/ani11020391.: 391.
    10.3390/ani11020391
    Article|CAS|PubMed|Google Scholar
  3. 3.Lye ZN, Purugganan MD. Copy number variation in domestication. Trends Plant Sci. 2019;24.(2019)352–65. https://doi. org/10. 1016/j.tplants.: 352.
    10.1016/j.tplants.2019.01.003
    Article|CAS|PubMed|Google Scholar
  4. 4.Ceccobelli S, Di Lorenzo P, Lancioni H, Ibánez LM, Tejedor MT, Castellini C, et al. Genetic diversity and phylogeographic structure of sixteen Mediterranean chicken breeds assessed with microsatellites and mitochondrial DNA. Lives Sci. 2015;175.(2015)27–36. https://doi. org/10. 1016/j.livsci.: 27.
    10.1016/j.livsci.2015.03.003
    Article|CAS|PubMed|Google Scholar
  5. 5.Cendron F, Perini F, Mastrangelo S, Tolone M, Criscione A, Bordonaro S, et al. Genome-wide SNP analysis reveals the population structure and the conservation status of 23 Italian chicken breeds. Animals. 2020;10.(2020)org/10.3390/ani10081441.: 1441.
    10.3390/ani10081441
    Article|CAS|PubMed|Google Scholar
  6. 6.Gorla E, Cozzi MC, Román-Ponce SI, Ruiz López FJ, Vega-Murillo VE, Cerolini S, et al. Genomic variability in Mexican chicken population using copy number variants. BMC Genet. 2017;18.(2017)org/10.1186/s12863-017-0524-4.: 61.
    10.1186/s12863-017-0524-4
    Article|CAS|PubMed|Google Scholar
  7. 7.Strillacci MG, Gorla E, Ríos-Utrera A, Vega-Murillo VE, Montaño-Bermudez M, Garcia-Ruiz A, et al. Copy number variation mapping and genomic variation of autochthonous and commercial turkey populations. Front Genet. 2019;10.(2019)982. https://doi. org/10.3389/fgene.: 982.
    10.3389/fgene.2019.00982
    Article|CAS|PubMed|Google Scholar
  8. 8.Sismani C, Koufaris C, Voskarides K. Copy number variation in human health, disease and evolution. In.(2015)Felekkis K, Voskarides K, editors. Genomic elements in health, disease and evolution.New York: Springer;.
    10.1007/978-1-4939-3070-8_6
    Article|CAS|PubMed|Google Scholar
  9. 9.Paudel Y, Madsen O, Megens HJ, Frantz LA, Bosse M, Bastiaansen JW, et al. Evolutionary dynamics of copy number variation in pig genomes in the context of adaptation and domestication. BMC Genom. 2013;14.(2013)org/10.1186/1471-2164-14-449.: 449.
    10.1186/1471-2164-14-449
    Article|CAS|PubMed|Google Scholar
  10. 10.Arendt M, Fall T, Lindblad-Toh K, Axelsson E. Amylase activity is associated with AMY 2B copy numbers in dog.(2014)Implications for dog domestication, diet and diabetes.Anim Genet.: 716.
    10.1111/age.12179
    Article|CAS|PubMed|Google Scholar
  11. 11.Génin E. Missing heritability of complex diseases: case solved? Hum Genet. 2020;139:103–13.https://doi.org/10.1007/s00439-019-02034-4.
    Article|CAS|PubMed|Google Scholar
  12. 12.Hay EHA, Utsunomiya YT, Xu L, Zhou Y, Neves HH, Carvalheiro R, et al. Genomic predictions combining SNP markers and copy number variations in Nellore cattle. BMC Genom. 2018;19.(2018)org/10.1186/s12864-018-4787-6.: 441.
    10.1186/s12864-018-4787-6
    Article|CAS|PubMed|Google Scholar
  13. 13.Yang Z, Zhuan B, Yan Y, Jiang S, Wang T. Integrated analyses of copy number variations and gene differential expression in lung squamous-cell carcinoma. Biol Res. 2015;48.(2015)org/10.1186/s40659-015-0038-3.: 47.
    10.1186/s40659-015-0038-3
    Article|CAS|PubMed|Google Scholar
  14. 14.Escaramís G, Docampo E, Rabionet R. A decade of structural variants.(2015)description, history and methods to detect structural variation.Brief Funct Genomics.: 305.
    10.1093/bfgp/elv014
    Article|CAS|PubMed|Google Scholar
  15. 15.Fernandes AC, da Silva VH, Goes CP, Moreira GCM, Godoy TF, Ibelli AMG, et al. Genome-wide detection of CNVs and their association with performance traits in broilers. BMC Genom. 2021;22.(2021)org/10.1186/s12864-021-07676-1.: 354.
    10.1186/s12864-021-07676-1
    Article|CAS|PubMed|Google Scholar
  16. 16.Chen X, Bai X, Liu H, Zhao B, Yan Z, Hou Y, et al. Population genomic sequencing delineates global landscape of copy number variations that drive domestication and breed formation of in chicken. Front Genet. 2022;13.(2022)830393. https://doi. org/10.3389/fgene.: 830393.
    10.3389/fgene.2022.830393
    Article|CAS|PubMed|Google Scholar
  17. 17.Minias P, Pikus E, Whittingham LA, Dunn PO. Evolution of copy number at the MHC varies across the avian tree of life. Genome Biol Evol. 2019;11.(2019)org/10.1093/gbe/evy253.: 17.
    10.1093/gbe/evy253
    Article|CAS|PubMed|Google Scholar
  18. 18.Jia X, Chen S, Zhou H, Li D, Liu W, Yang N. Copy number variations identified in the chicken using a 60K SNP BeadChip. Anim Genet. 2013;44.(2013)1111/age.12009.: 276.
    10.1111/age.12009
    Article|CAS|PubMed|Google Scholar
  19. 19.Zhang H, Du ZQ, Dong JQ, Wang HX, Shi HY, Wang N, et al. Detection of genome-wide copy number variations in two chicken lines divergently selected for abdominal fat content. BMC Genom. 2014;15.(2014)org/10.1186/1471-2164-15-517.: 517.
    10.1186/1471-2164-15-517
    Article|CAS|PubMed|Google Scholar
  20. 20.Yi G, Qu L, Liu J, Yan Y, Xu G, Yang N. Genome-wide patterns of copy number variation in the diversified chicken genomes using next-generation sequencing. BMC Genom. 2014;15.(2014)org/10.1186/1471-2164-15-962.: 962.
    10.1186/1471-2164-15-962
    Article|CAS|PubMed|Google Scholar
  21. 21.Yan Y, Yang N, Cheng HH, Song J, Qu L. Genome-wide identification of copy number variations between two chicken lines that differ in genetic resistance to Marek’s disease. BMC Genom. 2015;16.(2015)org/10.1186/s12864-015-2080-5.: 843.
    10.1186/s12864-015-2080-5
    Article|CAS|PubMed|Google Scholar
  22. 22.Fang L, Wang K. Identification of copy number variants from SNP arrays using PennCNV. In.(2018)Bickhart D, editor. Copy number variants. Methods in Molecular Biology, vol 1833.New York: Humana Press;.
    Article|CAS|PubMed|Google Scholar
  23. 23.Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF, et al. PennCNV.(2007)an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data.Genome Res.: 1665.
    10.1101/gr.6861907
    Article|CAS|PubMed|Google Scholar
  24. 24.Kumar H, Panigrahi M, Saravanan KA, Rajawat D, Parida S, Bhushan B, et al. Genome-wide detection of copy number variations in Tharparkar cattle. Anim Biotechnol. 2023;34.(2023)2021.1942027.: 448.
    10.1080/10495398.2021.1942027
    Article|CAS|PubMed|Google Scholar
  25. 25.Durán Aguilar M, Román Ponce SI, Ruiz Lopez FJ, González Padilla E, Vásquez Peláez CG, Bagnato A, et al. Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers. J Anim Breed Genet. 2017;134.(2017)1111/jbg.12238.: 49.
    10.1111/jbg.12238
    Article|CAS|PubMed|Google Scholar
  26. 26.Pinto D, Darvishi K, Shi X, Rajan D, Rigler D, Fitzgerald T, et al. Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants. Nat Biotechnol. 2011;8.(2011)1038/nbt.1852.: 512.
    10.1038/nbt.1852
    Article|CAS|PubMed|Google Scholar
  27. 27.Zhou J, Liu L, Lopdell TJ, Garrick DJ, Shi Y. HandyCNV.(2021)standardized summary, annotation, comparison, and visualization of copy number variant, copy number variation region, and runs of homozygosity.Front Genet.: 731355.
    Article|CAS|PubMed|Google Scholar
  28. 28.Zhou J, Liu L, Reynolds E, Huang X, Garrick D, Shi Y. Discovering copy number variation in dual-purpose xinjiang brown cattle. Front Genet. 2022;12.(2022)2021.747431.: 747431.
    Article|CAS|PubMed|Google Scholar
  29. 29.Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, et al. Global variation in copy number in the human genome. Nature. 2006;444.(2006)org/10.1038/nature05329.: 444.
    10.1038/nature05329
    Article|CAS|PubMed|Google Scholar
  30. 30.Wickham H, Chang W, Wickham MH. Package ‘ggplot2’. Create elegant data visualizations using the grammar of graphics. Versio. 2016;2.(2016)1–189.: 1.
    Article|CAS|PubMed|Google Scholar
  31. 31.Wickham H. Tidyverse.(2017)easily install and load the 'Tidyverse'. R package v. 1. 2.1.
    Article|CAS|PubMed|Google Scholar
  32. 32.Suzuki R, Shimodaira H. Pvclust.(2016)an R package for assessing the uncertainty in hierarchical clustering.Bioinform.: 1540.
    10.1093/bioinformatics/btl117
    Article|CAS|PubMed|Google Scholar
  33. 33.Kumar S, Kumar M, Mir RR, Kumar R, Kumar S. Advances in molecular markers and their use in genetic improvement of wheat. In.(2021)Wani SH, Mohan A, Singh GP, editors. Physiologica, molecular, and genetic perspectives of wheat improvement.Cham: Springer;.
    10.1007/978-3-030-59577-7_8
    Article|CAS|PubMed|Google Scholar
  34. 34.Rao YS, Li J, Zhang R, Lin XR, Xu JG, Xie L, et al. Copy number variation identification and analysis of the chicken genome using a 60K SNP BeadChip. Poult Sci. 2016;95.(2016)org/10.3382/ps/pew136.: 1750.
    10.3382/ps/pew136
    Article|CAS|PubMed|Google Scholar
  35. 35.Sohrabi SS, Mohammadabadi M, Wu DD, Esmailizadeh A. Detection of breed-specific copy number variations in domestic chicken genome. Genome. 2018;61.(2018)org/10.1139/gen-2017-0016.: 7.
    10.1139/gen-2017-0016
    Article|CAS|PubMed|Google Scholar
  36. 36.Strillacci MG, Cozzi MC, Gorla E, Mosca F, Schiavini F, Román-Ponce SI, et al. Genomic and genetic variability of six chicken populations using single nucleotide polymorphism and copy number variants as markers. Animal. 2017;11.(2017)org/10.1017/S1751731116002135.: 737.
    10.1017/S1751731116002135
    Article|CAS|PubMed|Google Scholar
  37. 37.Seol D, Ko BJ, Kim B, Chai HH, Lim D, Kim H. Identification of copy number variation in domestic chicken using whole-genome sequencing reveals evidence of selection in the genome. Animals. 2019;9.(2019)org/10.3390/ani9100809.: 809.
    10.3390/ani9100809
    Article|CAS|PubMed|Google Scholar
  38. 38.Gheyas AA, Burt DW. Microarray resources for genetic and genomic studies in chicken.(2013)a review.Genesis.: 337.
    10.1002/dvg.22387
    Article|CAS|PubMed|Google Scholar
  39. 39.Tian M, Wang Y, Gu X, Feng C, Fang S, Hu X, et al. Copy number variants in locally raised Chinese chicken genomes determined using array comparative genomic hybridization. BMC Genom. 2013;14.(2013)org/10.1186/1471-2164-14-262.: 262.
    10.1186/1471-2164-14-262
    Article|CAS|PubMed|Google Scholar
  40. 40.Lin CF, Naj AC, Wang LS. Analyzing copy number variation using SNP array data.(2013)protocols for calling CNV and association tests.Curr Protoc.: 1.
    10.1002/0471142905.hg0127s79
    Article|CAS|PubMed|Google Scholar
  41. 41.Ma Q, Liu X, Pan J, Ma L, Ma Y, He X, et al. Genome-wide detection of copy number variation in Chinese indigenous sheep using an ovine high-density 600 K SNP array. Sci Rep. 2017;7.(2017)org/10.1038/s41598-017-00847-9.: 912.
    10.1038/s41598-017-00847-9
    Article|CAS|PubMed|Google Scholar
  42. 42.Bovo S, Ribani A, Munoz M, Alves E, Araujo JP, Bozzi R, et al. Genome-wide detection of copy number variants in European autochthonous and commercial pig breeds by whole-genome sequencing of DNA pools identified breed-characterising copy number states. Anim Genet. 2020;51.(2020)1111/age.12954.: 541.
    10.1111/age.12954
    Article|CAS|PubMed|Google Scholar
  43. 43.Fulton JE, McCarron AM, Lund AR, Pinegar KN, Wolc A, Chazara O, et al. A high-density SNP panel reveals extensive diversity, frequent recombination and multiple recombination hotspots within the chicken major histocompatibility complex B region between BG2 and CD1A1. Genet Sel Evol. 2016;48.(2016)org/10.1186/s12711-015-0181-x.: 1.
    10.1186/s12711-015-0181-x
    Article|CAS|PubMed|Google Scholar
  44. 44.Garcia-Camacho L, Schat KA, Brooks R Jr, Bounous DI. Early cell-mediated immune responses to Marek’s disease virus in two chicken lines with defined major histocompatibility complex antigens. Vet Immunol Immunopathol. 2003;95.(2003)org/10.1016/s0165-2427(03)00140-5.: 145.
    10.1016/s0165-2427(03)00140-5
    Article|CAS|PubMed|Google Scholar
  45. 45.Lavrichenko K, Johansson S, Jonassen I. Comprehensive characterization of copy number variation (CNV) called from array, long-and short-read data. BMC Genom. 2021;22.(2021)org/10.1186/s12864-021-08082-3.: 826.
    10.1186/s12864-021-08082-3
    Article|CAS|PubMed|Google Scholar
  46. 46.Di Gerlando R, Mastrangelo S, Tolone M, Rizzuto I, Sutera AM, Moscarelli A, et al. Identification of copy number variations and genetic diversity in Italian insular sheep breeds. Animals. 2022;12.(2022)org/10.3390/ani12020217.: 217.
    10.3390/ani12020217
    Article|CAS|PubMed|Google Scholar
  47. 47.Han R, Yang P, Tian Y, Wang D, Zhang Z, Wang L, et al. Identification and functional characterization of copy number variations in diverse chicken breeds. BMC Genom. 2014;15.(2014)org/10.1186/1471-2164-15-934.: 934.
    10.1186/1471-2164-15-934
    Article|CAS|PubMed|Google Scholar
  48. 48.Fukunaga K, Yamashita Y, Yagisawa T. Copy number variations in BOLA-DQA2, BOLA-DQB, and BOLA-DQA5 show the genomic architecture and haplotype frequency of major histocompatibility complex class II genes in Holstein cows. HLA. 2020;96.(2020)1111/tan.14086.: 601.
    10.1111/tan.14086
    Article|CAS|PubMed|Google Scholar
  49. 49.Zhang J, Cao J, Geng A, Wang H, Chu Q, Yang L, et al. Comprehensive proteomic characterization of the pectoralis major at three chronological ages in Beijing-You chicken. Front Physiol. 2021;12.(2021)658711. https://doi. org/10.3389/fphys.: 658711.
    10.3389/fphys.2021.658711
    Article|CAS|PubMed|Google Scholar
  50. 50.Hoffman EP, Hudecki MS, Rosenberg PA, Pollina CM, Kunkel LM. Cell and fiber type distribution of dystrophin. Neuron. 1988;1.(1988)org/10.1016/0896-6273(88)90191-2.: 411.
    10.1016/0896-6273(88)90191-2
    Article|CAS|PubMed|Google Scholar
  51. 51.Horner A, Shum L, Ayres JA, Nonaka K, Nuckolls GH. Fibroblast growth factor signaling regulates Dach1 expression during skeletal development. Dev Dyn. 2002;225.(2002)1002/dvdy.10132.: 35.
    10.1002/dvdy.10132
    Article|CAS|PubMed|Google Scholar
  52. 52.Wu K, Yang Y, Wang C, Davoli MA, D’Amico M, Li A, et al. DACH1 inhibits transforming growth factor-β signaling through binding Smad4. Biol Chem. 2003;278.(2003)1074/jbc.M310021200.: 51673.
    10.1074/jbc.M310021200
    Article|CAS|PubMed|Google Scholar
  53. 53.Perini F, Cendron F, Wu Z, Sevane N, Li Z, Huang C, et al. Genomics of Dwarfism in Italian local chicken breeds. Genes. 2023;14.(2023)org/10.3390/genes14030633.: 633.
    10.3390/genes14030633
    Article|CAS|PubMed|Google Scholar
  54. 54.Emrani H, Torshizi RV, Masoudi AA, Ehsani A. Identification of new loci for body weight traits in F2 chicken population using genome-wide association study. Livest Sci. 2017;206.(2017)1111/age.13038.: 125.
    10.1111/age.13038
    Article|CAS|PubMed|Google Scholar
  55. 55.Khalkhali-Evrigh R, Hedayat N, Ming L, Jirimutu. Identification of selection signatures in Iranian dromedary and Bactrian camels using whole genome sequencing data. Sci Rep. 2022;12.(2022)org/10.1038/s41598-022-14376-7.: 9653.
    10.1038/s41598-022-14376-7
    Article|CAS|PubMed|Google Scholar
  56. 56.Chen L, Zhang T, Zhang S, Huang J, Zhang G, Xie K, et al. Identification of long non-coding RNA-associated competing endogenous RNA network in the differentiation of chicken preadipocytes. Genes. 2019;10.(2019)org/10.3390/genes10100795.: 795.
    10.3390/genes10100795
    Article|CAS|PubMed|Google Scholar
  57. 57.Lin Z, Wang J, Zhu W, Yu X, Wang Z, Ma J, et al. Chicken DDX1 acts as an RNA sensor to mediate IFN-β signaling pathway activation in antiviral innate immunity. Front Immunol. 2021;12.(2021)742074. https://doi. org/10.3389/fimmu.: 742074.
    10.3389/fimmu.2021.742074
    Article|CAS|PubMed|Google Scholar
  58. 58.Wang Z, Zhou W. Research note.(2021)fine mapping of sequence variants associated with body weight of Lueyang black-boned chicken in the CCKAR gene.Poult Sci.: 101448.
    10.1016/j.psj.2021.101448
    Article|CAS|PubMed|Google Scholar
  59. 59.Liao R, Zhang X, Chen Q, Wang Z, Wang Q, Yang C, et al. Genome-wide association study reveals novel variants for growth and egg traits in Dongxiang blue-shelled and White Leghorn chickens. Anim Gen. 2016;47.(2016)1111/age.12456.: 588.
    10.1111/age.12456
    Article|CAS|PubMed|Google Scholar
  60. 60.Tarsani E, Kranis A, Maniatis G, Avendano S, Hager-Theodorides AL, Kominakis A. Deciphering the mode of action and position of genetic variants impacting on egg number in broiler breeders. BMC Genom. 2020;21.(2020)org/10.1186/s12864-020-06915-1.: 512.
    10.1186/s12864-020-06915-1
    Article|CAS|PubMed|Google Scholar
  61. 61.Absalon S, Blisnick T, Kohl L, Toutirais G, Doré G, Julkowska D, et al. Intraflagellar transport and functional analysis of genes required for flagellum formation in trypanosomes. Mol Biol Cell. 2008;19.(2008)1091/mbc.e07-08-0749.: 929.
    10.1091/mbc.e07-08-0749
    Article|CAS|PubMed|Google Scholar
  62. 62.Rizzi C, Cendron F, Penasa M, Cassandro M. Egg quality of Italian local chicken breeds.(2022)I. yield performance physical characteristics.Animals.: 148.
    10.3390/ani13010148
    Article|CAS|PubMed|Google Scholar
  63. 63.Yuhara H, Ohtani A, Matano M, Kashiwagi Y, Maehashi K. Molecular characterization of a novel aspartyl aminopeptidase that contributes to the increase in glutamic acid content in chicken meat during cooking. Food Chem. 2021;2.(2021)100015. https://doi. org/10. 1016/j.fochms.: 100015.
    10.1016/j.fochms.2021.100015
    Article|CAS|PubMed|Google Scholar
  64. 64.Prastowo S, Ratriyanto A. miRNA target prediction of avian Z-linked DMRT1 gene during sex determination in chicken (G. Gallus). IOP Conf Ser: Earth Environ Sci. 2021;905:012148.
    Article|CAS|PubMed|Google Scholar
  65. 65.Zhang W, Liu HT. MAPK signal pathways in the regulation of cell proliferation in mammalian cells. Cell Res. 2002;12.(2002)cr.7290105.: 9.
    10.1038/sj.cr.7290105
    Article|CAS|PubMed|Google Scholar
  66. 66.Yan J, Gan L, Chen D, Sun C. Adiponectin impairs chicken preadipocytes differentiation through p38 MAPK/ATF-2 and TOR/p70 S6 kinase pathways. PLoS ONE. 2013;8.(2013)pone.0077716.
    Article|CAS|PubMed|Google Scholar
  67. 67.Zhu Y, Wang H, Wang J, Han S, Zhang Y, Ma M, et al. Zearalenone induces apoptosis and cytoprotective autophagy in chicken granulosa cells by PI3K-AKT-mTOR and MAPK signaling pathways. Toxins. 2021;13.(2021)org/10.3390/toxins13030199.: 199.
    10.3390/toxins13030199
    Article|CAS|PubMed|Google Scholar

Acknowledgements

We acknowledge the conservation centers—I.I.S. “Duca degli Abruzzi” Padova (Italy), I.S.I.S.S. “D. Sartor” Castelfranco Veneto (Treviso, Italy), I.I.S. “A. Della Lucia” Feltre (Belluno, Italy), the experimental farm “Sasse Rami” Ceregnano (Rovigo, Italy), University of Pisa—UNIPI (Pisa), STUARD farm (Parma, Italy), University of Bologna—UNIBO (Bologna), University of Catania—UNICT (Catania), “Il Gallolarino” farm by Dalia Roberto (Monte S. Giovanni Campano, Frosinone, Italy), Acquaroli Gabriele farm (Monteprandone, Ascoli Piceno, Italy), association “Tutela Razze Siciliane” (San Cataldo, Caltanissetta, Italy) for the supply of blood samples of poultry breeds. Also, authors are grateful to Dr. Nicholas Sneddon (Massey University, Palmerston North, New Zealand) for the thorough language revision.

Funding

Open access funding provided by Università degli Studi di Padova. This work was supported by the project “Protection of biodiversity of Italian poultry breeds—TuBAvI”, funded in the framework of the PSRN 2014–2020, sub-measure 10.2 “Support for sustainable conservation, use and development of genetic resources in agriculture”.

Ethics Declaration

Ethics approval and consent to participate

Ethics approval was not required for the current study. Blood samples were collected in compliance with the European rules [Council Regulation (EC) No. 1/2005 and Council Regulation (EC) No. 1099/2009] during routine health controls by the public veterinary service.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Rights and Permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Reprints and permissions