Skip to main content
NCBI home page
Poultry Science logoLink to Poultry Science
. 2024 Aug 25;103(12):104266. doi: 10.1016/j.psj.2024.104266

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

Xinfeng Xie *,2, Liguang Shi †,2, Guanyu Hou †, Ziqi Zhong *, Ziyi Wang *, Deyou Pan *, Wei Na *, Qian Xiao *,1
PMCID: PMC11426044  PMID: 39293262

Abstract

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.

Key words: Danzhou chicken, whole-genome sequencing, copy number variation, genome-wide association studie

INTRODUCTION

Copy number variation (CNV) is a specific type of genomic structural variation, ranging in size from approximately 50 bp to several Mb, primarily characterized by deletions and duplications (Mills et al., 2011). In comparison to single nucleotide polymorphisms, CNV are less frequent in terms of absolute numbers; however, they encompass a larger proportion of the genome in terms of nucleotide sequence, resulting in higher mutation rates and more substantial potential impacts (Cooper et al., 2007). CNVs have the potential to modify gene dosage or disrupt coding sequences and regulatory elements, thereby leading to significant effects on phenotype with economic implications (Liu and Bickhart 2012; Wang et al., 2012; Bickhart and Liu 2014). Numerous studies have reported associations between CNV and both phenotype and function in animals (Fernandes et al., 2021; Qiu et al., 2021; Hu et al., 2022a; Sun et al., 2023; Lotfizadeh et al., 2024).

As the largest livestock animal in terms of breeding scale worldwide, domestic chickens (Gallus gallus) hold significant value in the agricultural economy and play a crucial role in genetic studies (Wolpert 2004). The field of chicken genomics has promising applications and benefits across various disciplines including agriculture, comparative genomics, evolutionary biology, systematics, developmental biology, and human disease modeling (Burt 2005). Previous studies have found that the genes associated with CNV in chickens include the late feathering gene located on Gallus gallus autosome (GGA) Z (Elferink et al., 2008), the pea comb gene located on GGA 1 (Wright et al., 2009), the deep brown feather color gene also located on GGA 1 (Gunnarsson et al., 2011), the gene causing excessive skin pigmentation located on GGA 20 (Dorshorst et al., 2010), the gene associated with body weight located on GGA 3 (Fernandes et al., 2021), and the gene associated with egg weight located on GGA 4 (Yang et al., 2024). These research findings suggest that CNV may be one of the main causes of phenotypic changes in chickens. However, there is currently limited knowledge regarding the association between CNV and chicken body size traits.

Danzhou (DZ) chickens, a local breed in Hainan Province, China (a small island off the southeastern coast of China), have undergone long-term geographical isolation and adaptation to the tropical island ecological environment, resulting in a unique, small-bodied tropical chicken variety specific to Hainan Province. In comparison with other original breeds and subspecies, DZ chickens exhibit the smallest body size (the average weight of adult females is 455g, while that of adult males is 755g). The distinctive geographic environment has facilitated the preservation of wild and primitive characteristics, rendering them highly valuable for research purposes. Regrettably, there have been no reports on CNV identification or genome-wide association studies (GWAS) investigating the relationship between CNV and body size traits in DZ chicken populations.

Currently, various methodologies are available for the detection of CNVs in animals and plants, including comparative genomic hybridization arrays (Pinkel et al., 1998) and high-density SNP arrays (Yau and Holmes 2008). These approaches primarily rely on analyzing signal intensity across the genome to identify CNV in humans and other species. However, with advancements in high-throughput sequencing technology and the subsequent reduction in sequencing costs, whole-genome sequencing data have emerged as a prominent resource for CNV detection. In comparison to comparative genomic hybridization and SNP array-based methods, utilizing whole-genome sequencing technology offers superior coverage and more precise results (Escaramís et al., 2015).

The objective of this study was to generate a comprehensive CNV map for DZ chickens by leveraging whole-genome resequencing data from 200 DZ individuals, enabling the identification of CNVs within the population. We aimed to elucidate the genomic characteristics specific to DZ chickens and integrate them with phenotypic traits for genome-wide association analysis. This investigation significantly enhances our understanding of the genomic features unique to DZ chickens, identifies potential candidate CNVs and genes that can be utilized for chicken genetic improvement programs, and provides a solid theoretical foundation for future breeding endeavors targeting DZ chickens.

MATERIALS AND METHODS

Ethical Approval

The study protocol was approved by the Animal Management and Use Committee of Hainan University (approval number: HNUAUCC-2023-00140).

Experimental Group and Phenotype Measurement

We randomly selected 200 DZ chickens hatched in the same batch from the Institute of Local Farms in Hainan. The chickens were reared according to the Agricultural Industry Standard of the People's Republic of China (PRC)《NY/T33-2004》. At 12 wk of age, we measured body size traits according to the PRC's Poultry Production Performance Terminology and Measurement Statistical Methods《NY/T823-2004》. These traits included body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD). BSL was quantified as the distance between shoulder joint and ischial tuberosity, while KeL represented the distance from front-to-end of the keel process. Tibial length was determined as the straight distance between the superior tibial joint and the third/fourth toes, with middle tibial circumference also recorded. Thoracic depth was measured from the first thoracic vertebra to the anterior edge of the keel. These descriptive statistics are presented in Table 1.

Table 1.

The average, minimum, maximum values and standard deviation (SD) of the performance traits evaluated.

Traita Maximum (mm) Minimum (mm) Mean (mm) SD
KeL 88.16 54.53 72.24 5.11
ChD 86.11 50.87 63.89 5.62
ChW 31.78 22.95 28.81 1.41
BSL 122.36 90.39 107.38 5.55
TiC 29.12 19.26 24.03 1.84
TiL 96 69.19 82.10 5.39
Open in a new tab

Abbreviations: BSL, body sope length; KeL, keel length; TiL, tibial length; TiC, tibial circumference; ChW, chest width; ChD, chest depth.

Quality Control and Read Alignment

Genomic DNA was extracted from blood samples collected from the wing veins of individuals using a commercial kit (Tiangen Biotech Co. Ltd.) following the manufacturer's library construction manual. The concentration and purity of DNA samples were determined using UV spectrophotometry, with an A260nm/A280nm ratio ranging from 1.8 to 2.0. Subsequently, high-quality DNA samples were submitted to Hangzhou Lianchuan Biotechnology Co., Ltd., for 10X deep whole-genome resequencing using Illumina HiSeq platform. Quality control of raw sequence data was performed using FASTP (https://github.com/OpenGene/fastp), which filters sequences to remove adaptors, low quality nucleotides, uncharacterized nucleotides, and reads containing more than 10% low quality nucleotides, ensuring high-quality data. Filtered reads were aligned to the chicken reference genome (bGalGal1. Mat. Broiler. GRCg7b) using the Burrows Wheeler Aligner (BWA version 0.7.17) (Li and Durbin 2009). Duplicate reads were removed using Picard Tools (version 1.9) (Liu et al., 2013).

Identification of CNV

We employed CNVpytor (version 1.3.1), a read depth-based method for detecting CNV (Suvakov et al., 2021). The bin size was standardized to 300bp across all individuals based on sequencing depth and genome size. To enhance the precision of CNV detection, the following criteria were applied to filter CNV calls: P-value < 0.001, q0 (zero mapping quality) < 0.5, and size > 1kb. This resulted in the generation of high-quality CNV segments suitable for subsequent analysis.

Determination of CNVR

After applying CNV filtering, the CNV calls that overlap with at least 1 base pair were merged into copy number variant regions (CNVRs) using the populationRanges function from the CNVRanger/Bioconductor packages (da Silva et al., 2020). To avoid false positive regions, low-density genomic regions within the CNVR, which contributed to less than 10% of individual calls within aggregated regions, were trimmed. The resulting CNVRs were then categorized into duplication and deletion regions. Moreover, overlapping 'gain' and 'loss' regions were consolidated into a single region referred to as 'both'. The frequency of each CNVR was estimated based on the number of samples mapped at genome intervals covered by the respective CNVR.

Genome-Wide Association Analyses

Genome-wide association analysis was performed between morphological traits and CNV segments using the CNVRanger, as initially proposed by Silva (2016) (Silva et al., 2016). The CNVRanger package offers functionality for implementing association analysis specifically designed for CNV calling, supporting sequencing data for accurate detection of CNVs. The detected CNV calls are provided in GRangesList format, including information on chromosome numbers, start and end positions, sample names, and integer states representing copy numbers for each sample. This enables the identification of distinct patterns of CNVs. After converting the text file containing the identified CNVs, phenotypic information related to the studied animals was extracted from a separate text file format that included sample names and phenotype performance details. Prior to conducting GWAS analysis, files were organized and configured using the setupCnvGWAS function. Analysis results are obtained through cnvGWAS's dedicated module for performing comprehensive CNV-GWAS analysis. Because of the increased number of detected CNV segments facilitated by whole-genome resequencing data, we applied a False Discovery Rate (FDR) correction to obtain significant P-values (p-value < 0.0006) which were subsequently visualized using Manhattan plots.

Quantitative PCR Validation

We selected real-time quantitative polymerase chain reaction (qPCR) to validate significant CNV fragments associated with body size traits. Eight significant candidate CVN fragments, including 2 deletions and 6 duplication types, were randomly chosen from the results of a genome-wide association analysis. These fragments were validated using qPCR on 5 samples from the DZ chicken population. Primers were designed using Primer 5 software based on the gene sequences within the CNV fragments. All primers were subjected to PCR to ensure no nonspecific amplification products (see Supplementary Table S1 for primer information). The propionyl coenzyme A carboxylase gene (PCCA, GGA 1) was selected as the reference gene (Wang et al., 2010). Each sample was amplified in triplicate, along with a blank control. The number of copies of the target gene was calculated using the normalized ratio method after calculating the average Ct value: 2 × 2 - (ΔΔCt) (Livak and Schmittgen 2001). Values of approximately 2 were considered normal, while values of 3 or greater and values of 1 or less indicated duplication and deletion status, respectively.

Functional Enrichment Analysis of Candidate Genes Overlapping With CNV

The gene content of all significant CNV segments was assessed based on the GRCg7b genome assembly using the Ensembl Release 111 BioMart tool (Kinsella et al., 2011). We examined the genes within the genomic intervals flanking significantly associated CNV segments, which corresponded to 1Mb windows (500kb upstream and downstream). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted for all identified candidate genes using DAVID (Dennis et al., 2003). GO terms and KEGG pathways exhibiting adjusted P-values < 0.05 after multiple testing correction were considered significantly enriched and subjected to further investigation.

CNV Segments Overlapping Known QTL

We retrieved the published chicken QTLs from the Chicken QTL database (Hu et al., 2022b) and utilized available files containing QTL coordinates based on the GRCg7b genome assembly to identify overlaps between CNV segments significantly associated with our study and previously mapped QTLs. All previously mapped QTLs were referenced by their respective QTL ID numbers, which can be obtained from the Chicken QTL database.

RESULTS

Identification of CNV and CNVR

A total of 144,265 CNV events, including 67,818 deletions and 76,447 duplications, averaging 721.3 per animal, were detected in our resequencing data based on 200 DZ chicken populations, Figure 1 shows the distribution of CNV events on autosomes. The length of all CNV events ranged from 1-802.8 KB, with an average of 30.3 KB. All CNVs were distributed across the 39 autosomes, with varying numbers on each autosome. Overlapping CNVs between all samples were combined to obtain 4,824 CNVRs covering 192.222 MB of the chicken genome, corresponding to 20% of the autosomal genome sequence of the 39 autosomes in the GRCg7b assembly. The distribution of CNVRs on autosomes is illustrated in Figure 2, encompassing a total of 3,442 gain, 1,234 loss, and 148 both gains and losses. The relative chromosome coverage of CNVRs ranged from 12.4% in GGA 37 to 61.1% in GGA 36, whereas the absolute genome length of the overlapping CNVRs ranged from 0.3 KB in GGA 2 to 302 KB in GGA 5, with an average of 40 kb. Table 2 reports the number of CNVRs found as well as the status and proportion of chromosomal coverage. The detection of a CNVR in only 1 individual was considered as a single case. Of the CNVRs identified, 1202 cases (25%) were present in a single individual, 457 cases (9%) were present in 100 or more individuals, and 31 cases (0.06%) were present in all individuals.

Figure 1.

Figure 1

Open in a new tab

Numbers of CNV identified across autosomal chromosomes of Danzhou chicken.

Figure 2.

Figure 2

Open in a new tab

CNVR genome map of the Danzhou chicken.

Table 2.

Distribution of CNVR across autosomal chromosomes of DZ chicken genome.

Chromosome Chromosome length (bp) CNVR count Length of CNVR (bp) Coverage (%) Max size (bp) Min size (bp)
1 196,449,156 907 35,234,100 17.90% 225,900 300
2 149,539,284 634 23,752,200 15.90% 217,500 300
3 110,642,502 454 20,118,300 18.20% 251,700 1,200
4 90,861,225 370 15,278,400 16.80% 261,600 300
5 59,506,338 294 13,240,800 22.30% 302,100 600
6 36,220,557 177 8,339,400 23.00% 264,300 2,100
7 36,382,834 176 7,203,900 19.80% 217,200 300
8 29,578,256 141 6,611,700 22.40% 238,500 300
9 23,733,309 133 6,029,700 25.40% 192,600 300
10 20,453,248 108 4,779,000 23.40% 211,800 900
11 19,638,187 83 3,553,500 18.10% 200,700 300
12 20,119,077 101 4,446,000 22.10% 233,400 300
13 17,905,061 98 4,188,300 23.40% 178,500 600
14 15,331,188 83 3,771,000 24.60% 213,600 300
15 12,703,657 84 4,120,200 32.40% 155,700 600
16 2,706,039 48 912,300 33.70% 67,200 1,500
17 11,092,391 67 2,615,400 23.60% 227,400 1,500
18 11,623,896 66 3,330,600 28.70% 186,000 300
19 10,455,293 61 3,045,300 29.10% 183,600 1,500
20 14,265,659 74 2,834,700 19.90% 151,500 900
21 6,970,754 59 2,566,500 36.80% 168,000 1,800
22 4,686,657 39 899,400 19.20% 81,300 4,200
23 6,253,421 47 2,187,000 35.00% 156,300 900
24 6,478,339 31 1,712,700 26.40% 223,500 7,200
25 3,067,737 58 1,032,900 33.70% 71,700 1,800
26 5,349,051 46 1,996,200 37.30% 180,600 3,900
27 5,228,753 72 1,981,200 37.90% 138,300 1,500
28 5,437,364 65 2,237,700 41.20% 170,100 300
29 726,478 10 428,100 58.90% 172,500 900
30 755,666 22 266,100 35.20% 50,700 600
31 2,457,334 37 470,700 19.10% 63,300 300
32 125,424 3 33,300 26.50% 13,800 6,600
33 3,839,931 37 843,300 22.00% 87,600 600
34 3,469,343 67 1,129,500 32.60% 105,000 1,200
35 554,126 21 324,900 58.60% 50,100 3,900
36 358,375 18 219,000 61.10% 23,700 3,000
37 157,853 1 19,500 12.40% 19,500 19,500
38 667,312 24 368,400 55.20% 36,900 2,400
39 177,356 8 100,500 56.70% 22,200 3,300
Overall 945,968,431 4,824 192,221,700 20.30% 6,015,900 78,300
Open in a new tab

Association of CNV Segments With Body Size

We performed CNV-GWAS 6 somatic traits including BSL, KeL, TiL, TiC, ChW and ChD. Figure 3 shows a Manhattan plot of 39 autosomal CNV segments associated with Body size. QQ plots showed that most points were distributed along the diagonal, with only a few points significantly above the diagonal in the tails, and no significant outliers were found. This suggests that the analysis results were generally consistent with expectations (Figure S1). We identified 6 CNV fragments significantly associated with TiL and 7 CNV fragments significantly associated with TiC, 2 of which overlapped, GGA5:22675801-22732500 and GGA7:21740101-21778200, respectively. Eleven CNV fragments were found to be significantly associated with BSL. Fourteen CNV fragments were significantly correlated with KeL trait and 32 CNV fragments were significantly correlated with ChW which were distributed on multiple chromosomes, while GGA2:43023601-43047900 was shared as a significant CNV by KeL and ChW. Twenty-two significant CNV fragments were found to be associated with ChD, among which 10 CNV fragments were distributed in chromosomes 1, 2 and 3, some significant CNV fragments were close to each other, such as GGA1:71367001-71421900, GGA1:75538801-75581100.

Figure 3.

Figure 3

Open in a new tab

Manhattan plots for CNV segments across the 39 autosomal chromosomes associated with body size. The X-axis represents the somatic chromosomes, and Y-axis shows the corresponding -log10 q-value.

Abbreviations: BSL= Body Sope length, KeL= Keel length, TiL= Tibial length, TiC= Tibial circumference, ChW= Chest width, ChD= Chest depth.

Validation of Identified CNV Using qPCR

To validate the significant CNV fragments validated to be associated with body size traits in this study, we randomly selected 8 significant candidate CNV fragments co-localized with GLUD2, FXOM1, IL12RB2, NAA20, EXOSC7, NUP98, ZIC4, and NKX2-2 for qPCR validation. The qPCR results are shown in Figure S2, where 7 out of 8 CNV fragments (87.5%) were validated in all individuals. One of the CNV fragments (GGA7:36284401-36335700, NAA20) did not align with our expectations, potentially attributed to precise genomic coordinate alterations affecting the efficiency of qPCR primer binding to the target sequence, resulting in variations in amplification product quantities. Nevertheless, the qPCR results demonstrate a validation rate of 87.5% for the tests, confirming the presence of significant CNV fragments associated with body size traits. Detailed information regarding all CNV segments and primers can be found in Supplementary Table S1.

Functional Annotation and Gene Enrichment Analysis

Within a 1 Mb window encompassing the significantly CNV-enriched genomic regions associated with BSL, KeL, TiL, TiC, ChW and ChD phenotypes, a total of 1,682 candidate genes were annotated. Notable genes included IHH, WNT6, WNT10A, LRP4, FZD2, WNT7B and GNAS. Supplementary Table S2 provides a comprehensive list of all significant CNV segments along with their corresponding gene details. Gene enrichment analysis using DAVID identified a total of 61 GO pathways and 5 KEGG pathways (P<0.05; Figure 4; Supplementary Table S3). Several GO terms related to skeletal traits, such as “positive regulation of osteoblast differentiation” (GO:0045669) and “embryonic skeletal system morphogenesis” (GO:0048704). To further identify potential candidate genes associated with skeletal traits in animals or humans, we conducted an extensive search in the NCBI database and relevant literature on skeletal growth development and bone cell differentiation. Ultimately, 14 potential candidate genes related to skeletal traits were identified (Table 3), which may play an important role in controlling the growth and development of chicken body size.

Figure 4.

Figure 4

Open in a new tab

Bubble chart illustrating Gene Ontology (GO) enrichment analysis results and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis results of selected candidate genes. (A) The bubble chart of GO Biological Process. (B) The bubble chart of GO Molecular Function. (C) The bubble chart of GO Cellular Component. (D) The bubble chart of KEGG pathway analysis of candidate selection genes.

Table 3.

List of significant candidate genes within a 1 Mb window of significantly associated CNV segments. ‘State’ indicates the position of the candidate gene in relation to the CNV fragment. ‘Near’ indicates that the gene is located within 500 KB of the CNV fragment, while ‘Overlap’ indicates that the gene overlaps with the CNV fragment.

GGA: first-last positiona Associated trait Gene Name Gene length State
5:2,267,5801–22,732,500 FeL,FeC LRP4 5:22825802–22920775 Near
7:21,740,101–21,778,200 FeL,FeC IHH 7:21924895–21933614 Near
WNT6 7:22026370–22035132 Near
WNT10A 7:22009640–22020889 Near
2:43,023,601–43,047,900 KeL,ChW CTNNB1 2:43356658–43378265 Near
27:959,401–977,400 KeL FZD2 27:1134985–1136942 Near
9:11,571,601–11,593,200 BSL ZIC1 9:11601968–11607494 Near
ZIC4 9:11582188–11599990 Overlap
8:24,818,400–24,821,400 BSL TMEM59 8:24816183–24824532 Overlap
6:3,488,401–3,500,700 ChW BMPR1A 6:3204001–3283880 Near
27:3,562,801-3,579,300 ChW SOST 27:3114589-3119139 Near
MEOX1 27:3141121-3146700 Near
1:71,367,001–71,421,900 ChD WNT7B 1:70831585–70926462 Near
20:10,943,701–10,944,900 ChD GNAS 20:11257331–11388078 Near
Open in a new tab

Abbreviations: BSL, body sope length; KeL, keel length; TiL, tibial length; TiC, tibial circumference; ChW, chest width; ChD, chest depth.

CNV Segments Overlapping Known QTL

The significant CNV fragment GGA1:195761701-195812700 associated with KeL length overlapped with 2 previously localized QTL for feed conversion ratio (QTL: 221641 and QTL: 221610). The CNV fragments associated with ChW overlapped with several QTLs, while GGA2:30995701-31058100 overlapped with 2 QTLs affecting abdominal fat weight (QTL: 261827 and QTL: 261828). GGA8:28319101-28360200 overlapped with Thymus weight (QTL: 21807) and Chest width (QTL: 256987) overlapped, GGA5:25014901-25038300 overlapped with Shank diameter (QTL: 212062). Additionally, the CNV fragments associated with BSL and ChD traits overlapped with 5 QTLs respectively. These QTL were correlated with Comb color, Egg number, Eggshell and Testes percentage, as detailed in Table 4.

Table 4.

Related to previously locate QTL region overlapping segments of CNV.

GGA: first-last positiona QTL_ID Associated trait
1:195,761,701–195,812,700 QTL_ID = 221,641 Feed conversion ratio
QTL_ID = 221,610 Feed conversion ratio
2:30,995,701–31,058,100 QTL_ID = 261,827 Abdominal fat weight
QTL_ID = 261,828 Abdominal fat percentage
3:57,295,201–57,317,700 QTL_ID = 137,232 Feather pecking
5:25,014,901–25,038,300 QTL_ID = 212,062 Shank diameter
8:28,319,101–28,360,200 QTL_ID = 21,807 Thymus weight
QTL_ID=256,987 Chest width
10:10,640,101–10,712,700 QTL_ID = 214,346 Eggshell
QTL_ID = 130,270 Testes percentage
20:10,925,401–10,940,400 QTL_ID = 179,979 Comb color
QTL_ID = 180,089 Egg number
Open in a new tab

Abbreviations: BSL, body sope length; KeL, keel length; TiL, tibial length; TiC, tibial circumference; ChW, chest width; ChD, chest depth.

Information on QTL overlapping with candidate CNV segments show in Supplementary Table S4.

DISCUSSION

CNV and CNVR of the DZ Chicken Population

Even at low coverage, NGS-based methods perform much better than array-based methods in detecting CNV (Zhou et al., 2018). In this study, we analyzed CNVs in the genome of the DZ chicken population using whole-genome resequencing data to further reveal structural variation. Overall, we identified 144,265 CNV events (67,818 deletions and 76,447 duplications) with an average of 721.3 per chicken. Similar results have been reported in other animal studies based on resequencing data, including dairy cows (182,823 CNVs) (Hu et al., 2020), yaks (98,441 CNVs) (Zhang et al., 2016), and goats (208,649 CNVs) (Guo et al., 2020). In a previous report, Seol (2019) employed CNVnator (an earlier version of CNVpytor) to identify CNVs based on resequencing data obtained from various breeds of chickens, including Red Original, Cornish, White Leghorn, and Rhode Island Red (Seol et al., 2019). The study revealed breed-specific differences in the number of detected CNVs, with an average of 714 CNVs observed in Red Original chickens compared to averages of 467, 443, and 471 CNVs in White Leghorn, Cornish, and Rhode Island Red breeds respectively. This observation suggests that there is a reduction in the diversity of CNVs in the domesticated species compared to their wild relatives, in line with the commonly observed reduction in mononucleotide diversity in domesticated species compared to their wild relatives (Swanson-Wagner et al., 2010; Muñoz-Amatriaín et al., 2013; Lye and Purugganan 2019). In our study, the DZ chicken population exhibited high CNV diversity with an average presence of 721.3 CNVs per individual, suggesting that DZ chickens are similar to Red Original chickens (714). This is consistent with our speculation that DZ chickens are a weakly-domesticated species that have experienced only low levels of artificial selection. Several other studies have reported a wide range of CNVs ranging from 12 CNVs in chickens (Griffin et al., 2008) to 1,747,604 CNVs in sheep (Yuan et al., 2021). This discrepancy may be due to differences in sample sizes, algorithms used for CNV calling, and sequencing technologies (Locke et al., 2015).

CNVRs are genomic regions that result from the concatenation of overlapping CNVs. CNVs showing at least 1 base pair overlap in samples from this population were pooled across all individuals as CNVRs. 144,265 CNV fragments were screened and combined into 4,824 CNVRs, with 1,234 and 3,442 regions lost and gained copies, respectively. The presence of both types was observed in 148 regions. the size of CNVRs ranged from 0.3 to 302 kb between 0.3 and 302 kb, with an average size of 40 kb, covering 20% of the chicken autosomal genome length.

In other studies on CNVs in chickens, Han (2014) detected CNVs in 5 chicken breeds from 10 individuals using the comparative genomic hybridization array and screened 281 CNVRs, of which 181 were loss types, 91 were gain types, and 9 were mixed types, and the length of the CNVRs accounted for 1.07% of the entire chicken genome (Han et al., 2014). Rao (2016) used PennCNV software to detect CNVs in 554 individuals from F2 generation flocks of White Cryptomeria and Xinhua chickens and reported 357 CNVRs. Among the CNVRs, 213 were loss types, 112 were gain types, and 32 were mixed types (Rao et al., 2016), and the lengths of CNVRs accounted for 3.97% of the entire chicken genome. Chen (2022) combined several CNV detection methods; mrFAST, CNVnator, BreakDancer and Pindel were tested against 51 individuals of 6 breeds, respectively. A total of 11,123 CNVRs were detected, of which 8,834 were lost, 1,911 were gained, and 378 were mixed, accounting for 7% of the entire chicken genome (Chen et al., 2022). Zhang (2014) used PennCNV software to characterize the CNVs of 475 broiler chickens of different strains, respectively. 203 and 272 of these birds were lean and fat strains, respectively, and a total of 271 and 188 CNVs were identified in each genome, which accounted for 3.92% and 2.98% of the whole genome length (Zhang et al., 2014). Fernandes (2021) used high-density SNP microarray data to infer CNVs in the whole genome of 1,500 broilers, and identified 5,042 CNVRs, including 424 losses, 4,105 duplicates, and 513 mixtures, which covered 12.8% of the length of the chicken genome (Fernandes et al., 2021). Thus, significant differences in the number of CNVRs and the percentage of chromosomes covered can be observed in different studies. There are several factors that may affect the number of CNVRs detected, such as differences in detection algorithms, population size, genetic background, quality of applied techniques, and genome size (Redon et al., 2006; Locke et al., 2015).

CNV-GWAS Analysis of Body Size

Skeletal growth and development in chickens are influenced by genetic, nutritional, environmental, and disease factors, with genetics being the most critical factor (Guo et al., 2017). Most of the previous studies on chicken development were mainly based on GWAS analysis of SNP molecular markers. SNPs are the main variants contributing to phenotypic diversity; however, with further study, it has been found that using GWAS based on SNP molecular markers can explain only part of the genetic variance (2-15%) of complex traits (McCarroll 2008), accounting for the remaining heritable variance has been a major problem. It is crucial to consider that CNVs often exhibit limited linkage disequilibrium with SNPs (Lee et al., 2020), resulting in reduced detectability (Schrider and Hahn 2010). Consequently, traditional SNP-based analyses may not fully capture the genetic variation explained by CNVs. Therefore, incorporating CNVs into GWAS studies can provide valuable insights into the genetic regulation of significant economic traits for livestock breeding programs.

In this study, we employed CNV-GWAS to identify 92 CNV fragments that exhibited significant associations with body size traits in DZ chickens. A total of 1,682 candidate genes were identified in these significant CNV fragments. Pathway enrichment analysis of the candidate genes revealed strong associations with terms related to skeletal growth. We analyzed 2 GO terms, “embryonic skeletal system morphogenesis” (GO:0048704) and “positive regulation of osteoblast differentiation” (GO:0045669), where the development of the embryonic skeletal system is a distinctive feature during animal embryonic development. The skeletal system not only possesses mechanical, supportive and protective functions, but also regulates several important dynamic homeostatic processes in adult animals in a systematic manner, in which several signaling pathways such as Wnt and BMP are involved (Yang, 2009). By analyzing the enrichment of this pathway, we can better understand the regulatory mechanism of DZ chicken bone development and provide new ideas and targets for improving chicken growth efficiency and enhancing breeding efficiency.

We identified several candidate genes on significant CNV segments that were associated with skeletal growth and development and have been validated in previous studies. We note that IHH, WNT6, and WNT10A were simultaneously associated with 3 traits: TiL, TiC and KeL. IHH plays a pivotal role in tissue patterning, skeletal development, and cell proliferation as an indispensable paracrine factor (St-Jacques et al., 1999). Chondrocyte-derived IHH is crucial for regulating chondrocyte proliferation, hypertrophy, and cartilage ossification. Moreover, IHH is essential for osteoblast differentiation, mineralization, and embryonic bone formation (Wang et al., 2022). WNT6 and WNT10A belong to the WNT family members, which are endogenous regulators of mesenchymal precursor adipogenesis and osteoblast differentiation, inhibiting adipogenesis and inducing osteoblast genesis through activation of the Wnt/ β-catenin pathway (Cawthorn et al., 2012).

The CTNNB1 gene, located on chromosome 4, is associated with the phenotypes KeL and ChW. It encodes B-linked protein 1, a component of the junction between calcineurin and actin cytoskeleton. This protein facilitates cell adhesion, communication, and signaling by binding to and maintaining the epithelial, calcineurin, and actin cytoskeletons of neighboring cells. Moreover, it plays a pivotal role in the Wnt signaling pathway essential for embryonic development and tissue homeostasis in adult humans (Drenser 2016; Yoshikawa et al., 2016).

ZIC1, ZIC4, and TMEM59 are found in CNV segments that are significantly associated with BSL. The ZIC genes encode a conserved family of zinc finger proteins that are functionally important in neurodevelopmental and axial skeleton patterning in the vertebrate embryo, and mutations in the ZIC genes result in neurological and skeletal defects. ZIC genes are also expressed in the ectodermal structural domain of cutaneous muscle sections and regulate the activation of the muscle master regulator Myf5 during somite formation in mouse and chick embryos (Pan et al., 2011). TMEM59 is a positive regulator of Wnt/β-catenin signaling, which plays an important role in the regulation of cell proliferation, development, and homeostasis in adult tissues (Gerlach et al., 2018).

BMPR1A was found in significant CNV fragments of ChW and ChD. BMPR1A is an important receptor for bone morphogenetic protein signaling and is one of the major receptors of the BMP signaling pathway. The BMP signaling pathway plays an important role in processes such as bone formation, organ development and tissue repair. By binding to BMP proteins, BMPR1A triggers downstream signaling and is involved in the regulation of embryonic development, cell proliferation and differentiation. In cartilage formation, BMPR1A plays a key role in initiating cartilage formation, regulating cartilage lineage differentiation, and promoting endochondral bone formation (Jing et al., 2015). Furthermore, LRP4, FZD2, WNT7B, SOST, MEOX1, and GNAS were also significantly correlated with body size. These genes also play a role in osteoblast growth, maintain bone steady-state, and play an important role in normal growth and development processes (Skuntz et al., 2009; Shen et al., 2015; Chen et al., 2021; Martínez-Gil et al., 2021; Yang et al., 2023; Zhu et al., 2023).

Additionally, we identified several QTLs and significant CNV segments related to growth and development, including feed efficiency, abdominal fat weight, thymus weight, chest width, and shank diameter. Furthermore, we observed that some significant CNV segments overlap with known QTLs associated with other productive traits. This may be because the known QTLs were mostly identified using microsatellite markers and SNP mapping methods which may not capture the same effects as CNVs (Fernandes et al., 2021). Additionally, although some significant CNVs overlap with other trait QTLs, they can still play important roles in the genome by potentially affecting correlated traits or phenotypic variation through different pathways. Therefore, studying CNVs and QTLs contributes to a comprehensive understanding of genomic function and genetic mechanisms. However, further research is needed to explore the potential associations and modes of action between CNVs and QTLs.

In summary, based on our whole genome resequencing data, we identified genome-wide regions of CNVs in the DZ chicken population. GWAS analysis based on CNVs through phenotypic information identified CNV segments associated with body size traits in DZ chickens, and these regions were further identified as potential candidate genes through gene enrichment and literature information. QTLs and genes overlapping with CNV segments associated with body size traits were identified, and they may be important for future research and have important roles in a wide range of biological, cellular, and molecular processes associated with growth and development. Our findings suggest that alterations in copy number within or near these genes may lead to phenotypic variation, thus contributing to a better understanding of the genetic architecture that influences chicken growth and development.

CONCLUSION

In this study, we examined CNVs in the DZ chicken population through whole-genome resequencing and identified a total of 144,265 CNV variants. These variants were combined to form 4,824 CNVRs, covering 20% of the chicken genome. Furthermore, we identified 92 CNV segments associated with body size traits based on CNV-GWAS analysis of 6 body size traits in the DZ chicken population. Through annotation, we obtained some candidate genes, including, including LRP4, WNT6, ZIC4, CTNNB1 and WNT7, which may affect the growth and development of body size in chickens. Our results reveal the potential role of CNVs in influencing growth and development-related traits in chickens, provide additional genetic information for chicken breeding efforts, and provide a theoretical basis for incorporating CNVs into future poultry breeding programs.

DISCLOSURES

No conflict of interest exits in the submission of this manuscript.

ACKNOWLEDGMENTS

This study was supported by the earmarked fund for Hainan Agriculture Research System (HNARS) (No. HNARS-06-G02) and the Chinese Academy of Tropical Agricultural Sciences for Science and Technology Innovation Team of National Tropical Agricultural Science Center (NO. CATASCXTD202407). We are thankful for the support of this work by HNARS and Hainan Local Chicken Agricultural Research System.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.psj.2024.104266.

Appendix. Supplementary materials

Supplementary Figure S1. QQ plot for genome-wide association analysis.

Abbreviations: BSL= Body Sope length, KeL= Keel length, TiL= Tibial length, TiC= Tibial circumference, ChW= Chest width, ChD= Chest depth.

mmc1.jpg (572.2KB, jpg)

Supplementary Figure S2. The qPCR validation results for 8 selected candidate CNV segments.

mmc2.jpg (314.9KB, jpg)
mmc3.xlsx (11KB, xlsx)
mmc4.xlsx (43.5KB, xlsx)
mmc5.xlsx (34.3KB, xlsx)
mmc6.xlsx (10.9KB, xlsx)

REFERENCES

  1. Burt D.W. Chicken genome: current status and future opportunities. Genome Res. 2005;15:1692–1698. doi: 10.1101/gr.4141805. [DOI] [PubMed] [Google Scholar]
  2. Bickhart D.M., Liu G.E. The challenges and importance of structural variation detection in livestock. Front. Genet. 2014;5:37. doi: 10.3389/fgene.2014.00037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cooper G.M., Nickerson D.A., Eichler E.E. Mutational and selective effects on copy-number variants in the human genome. Nat. Genet. 2007;39:S22–S29. doi: 10.1038/ng2054. [DOI] [PubMed] [Google Scholar]
  4. Cawthorn W.P., Bree A.J., Yao Y., Du B., Hemati N., Martinez-Santibañez G., MacDougald O.A. Wnt6, Wnt10a and Wnt10b inhibit adipogenesis and stimulate osteoblastogenesis through a β-catenin-dependent mechanism. Bone. 2012;50:477–489. doi: 10.1016/j.bone.2011.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chen H., Song F., Long F. WNT7B overexpression rescues bone loss caused by glucocorticoids in mice. Faseb J. 2021;35:e21683. doi: 10.1096/fj.202100151RR. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Chen X., Bai X., Liu H., Zhao B., Yan Z., Hou Y., Chu Q. Population genomic sequencing delineates global landscape of copy number variations that drive domestication and breed formation of in chicken. Front. Genet. 2022;13 doi: 10.3389/fgene.2022.830393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Dennis G., Jr., Sherman B.T., Hosack D.A., Yang J., Gao W., Lane H.C., Lempicki R.A. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4:3. [PubMed] [Google Scholar]
  8. Dorshorst B., Okimoto R., Ashwell C. Genomic regions associated with dermal hyperpigmentation, polydactyly and other morphological traits in the Silkie chicken. J. Hered. 2010;101:339–350. doi: 10.1093/jhered/esp120. [DOI] [PubMed] [Google Scholar]
  9. Drenser K.A. Wnt signaling pathway in retinal vascularization. Eye Brain. 2016;8:141–146. doi: 10.2147/EB.S94452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. da Silva V., Ramos M., Groenen M., Crooijmans R., Johansson A., Regitano L., Coutinho L., Zimmer R., Waldron L., Geistlinger L. CNVRanger: association analysis of CNVs with gene expression and quantitative phenotypes. Bioinformatics. 2020;36:972–973. doi: 10.1093/bioinformatics/btz632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Elferink M.G., Vallée A.A., Jungerius A.P., Crooijmans R.P., Groenen M.A. Partial duplication of the PRLR and SPEF2 genes at the late feathering locus in chicken. BMC. Genomics. 2008;9:1471–2164. doi: 10.1186/1471-2164-9-391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Escaramís G., Docampo E., Rabionet R. A decade of structural variants: description, history and methods to detect structural variation. Brief Funct. Genomics. 2015;14:305–314. doi: 10.1093/bfgp/elv014. [DOI] [PubMed] [Google Scholar]
  13. Fernandes A.C., da Silva V.H., Goes C.P., Moreira G.C.M., Godoy T.F., Ibelli A.M.G., Peixoto J.O., Cantão M.E., Ledur M.C., de Rezende F.M., Coutinho L.L. Genome-wide detection of CNVs and their association with performance traits in broilers. BMC. Genomics. 2021;22:021–07676. doi: 10.1186/s12864-021-07676-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gerlach J.P., Jordens I., Tauriello D.V.F., van 't Land-Kuper I., Bugter J.M., Noordstra I., van der Kooij J., Low T.Y., Pimentel-Muiños F.X., Xanthakis D., Fenderico N., Rabouille C., Heck A.J.R., Egan D.A., Maurice M.M. TMEM59 potentiates wnt signaling by promoting signalosome formation. Proc. Natl. Acad. Sci. USA. 2018;115:E3996–E4005. doi: 10.1073/pnas.1721321115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Griffin D.K., Robertson L.B., Tempest H.G., Vignal A., Fillon V., Crooijmans R.P., Groenen M.A., Deryusheva S., Gaginskaya E., Carré W., Waddington D., Talbot R., Völker M., Masabanda J.S., Burt D.W. Whole genome comparative studies between chicken and turkey and their implications for avian genome evolution. BMC. Genomics. 2008;9:1471–2164. doi: 10.1186/1471-2164-9-168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Gunnarsson U., Kerje S., Bed'hom B., Sahlqvist A.S., Ekwall O., Tixier-Boichard M., Kämpe O., Andersson L. The dark brown plumage color in chickens is caused by an 8.3-kb deletion upstream of SOX10. Pigment Cell. Melanom. Res. 2011;24:268–274. doi: 10.1111/j.1755-148X.2011.00825.x. [DOI] [PubMed] [Google Scholar]
  17. Guo J., Sun C., Qu L., Shen M., Dou T., Ma M., Wang K., Yang N. Genetic architecture of bone quality variation in layer chickens revealed by a genome-wide association study. Sci. Rep. 2017;7:45317. doi: 10.1038/srep45317. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Guo J., Zhong J., Liu G.E., Yang L., Li L., Chen G., Song T., Zhang H. Identification and population genetic analyses of copy number variations in six domestic goat breeds and Bezoar ibexes using next-generation sequencing. BMC. Genomics. 2020;21:020–07267. doi: 10.1186/s12864-020-07267-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Han R., Yang P., Tian Y., Wang D., Zhang Z., Wang L., Li Z., Jiang R., Kang X. Identification and functional characterization of copy number variations in diverse chicken breeds. BMC. Genomics. 2014;15:1471–2164. doi: 10.1186/1471-2164-15-934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hu Y., Xia H., Li M., Xu C., Ye X., Su R., Zhang M., Nash O., Sonstegard T.S., Yang L., Liu G.E., Zhou Y. Comparative analyses of copy number variations between bos taurus and bos indicus. BMC. Genomics. 2020;21:020–07097. doi: 10.1186/s12864-020-07097-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hu L., Zhang L., Li Q., Liu H., Xu T., Zhao N., Han X., Xu S., Zhao X., Zhang C. Genome-wide analysis of CNVs in three populations of Tibetan sheep using whole-genome resequencing. Front. Genet. 2022;13 doi: 10.3389/fgene.2022.971464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hu Z.L., Park C.A., Reecy J.M. Bringing the Animal QTLdb and CorrDB into the future: meeting new challenges and providing updated services. Nucl. Acids Res. 2022;50:D956–D961. doi: 10.1093/nar/gkab1116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Jing J., Hinton R.J., Feng J.Q. Bmpr1a signaling in cartilage development and endochondral bone formation. Vitam. Horm. 2015;99:273–291. doi: 10.1016/bs.vh.2015.06.001. [DOI] [PubMed] [Google Scholar]
  24. Kinsella R.J., Kähäri A., Haider S., Zamora J., Proctor G., Spudich G., Almeida-King J., Staines D., Derwent P., Kerhornou A., Kersey P., Flicek P. Ensembl BioMarts: A hub for data retrieval across taxonomic space. Database (Oxford) 2011;23:bar030. doi: 10.1093/database/bar030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262. [DOI] [PubMed] [Google Scholar]
  26. Li H., Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–1760. doi: 10.1093/bioinformatics/btp324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu G.E., Bickhart D.M. Copy number variation in the cattle genome. Funct. Integr. Genomics. 2012;12:609–624. doi: 10.1007/s10142-012-0289-9. [DOI] [PubMed] [Google Scholar]
  28. Liu X., Han S., Wang Z., Gelernter J., Yang B.Z. Variant callers for next-generation sequencing data: A comparison study. PLoS. One. 2013;8:e75619. doi: 10.1371/journal.pone.0075619. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Locke M.E., Milojevic M., Eitutis S.T., Patel N., Wishart A.E., Daley M., Hill K.A. Genomic copy number variation in Mus musculus. BMC. Genomics. 2015;16:015–1713. doi: 10.1186/s12864-015-1713-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lye Z.N., Purugganan M.D. Copy number variation in domestication. Trends Plant Sci. 2019;24:352–365. doi: 10.1016/j.tplants.2019.01.003. [DOI] [PubMed] [Google Scholar]
  31. Lee Y.L., Bosse M., Mullaart E., Groenen M.A.M., Veerkamp R.F., Bouwman A.C. Functional and population genetic features of copy number variations in two dairy cattle populations. BMC. Genomics. 2020;21:020–6496. doi: 10.1186/s12864-020-6496-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lotfizadeh F., Masoudi A.A., Vaez Torshizi R., Emrani H. Genome-wide association study of copy number variations with shank traits in a F2 crossbred chicken population. Anim. Genet. 2024;55:559–574. doi: 10.1111/age.13447. [DOI] [PubMed] [Google Scholar]
  33. McCarroll S.A. Extending genome-wide association studies to copy-number variation. Hum. Mol. Genet. 2008;17:R135–R142. doi: 10.1093/hmg/ddn282. [DOI] [PubMed] [Google Scholar]
  34. Mills R.E., Walter K., Stewart C., Handsaker R.E., Chen K., Alkan C., Abyzov A., Yoon S.C., Ye K., Cheetham R.K., Chinwalla A., Conrad D.F., Fu Y., Grubert F., Hajirasouliha I., Hormozdiari F., Iakoucheva L.M., Iqbal Z., Kang S., Kidd J.M., Konkel M.K., Korn J., Khurana E., Kural D., Lam H.Y., Leng J., Li R., Li Y., Lin C.Y., Luo R., Mu X.J., Nemesh J., Peckham H.E., Rausch T., Scally A., Shi X., Stromberg M.P., Stütz A.M., Urban A.E., Walker J.A., Wu J., Zhang Y., Zhang Z.D., Batzer M.A., Ding L., Marth G.T., McVean G., Sebat J., Snyder M., Wang J., Ye K., Eichler E.E., Gerstein M.B., Hurles M.E., Lee C., McCarroll S.A., Korbel J.O. Mapping copy number variation by population-scale genome sequencing. Nature. 2011;470:59–65. doi: 10.1038/nature09708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Muñoz-Amatriaín M., Eichten S.R., Wicker T., Richmond T.A., Mascher M., Steuernagel B., Scholz U., Ariyadasa R., Spannagl M., Nussbaumer T., Mayer K.F., Taudien S., Platzer M., Jeddeloh J.A., Springer N.M., Muehlbauer G.J., Stein N. Distribution, functional impact, and origin mechanisms of copy number variation in the barley genome. Genome Biol. 2013;14:2013–2014. doi: 10.1186/gb-2013-14-6-r58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Martínez-Gil N., Roca-Ayats N., Cozar M., Garcia-Giralt N., Ovejero D., Nogués X., Grinberg D., Balcells S. Genetics and Genomics of SOST: Functional Analysis of variants and genomic regulation in osteoblasts. Int. J. Mol. Sci. 2021;22:489. doi: 10.3390/ijms22020489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pinkel D., Segraves R., Sudar D., Clark S., Poole I., Kowbel D., Collins C., Kuo W.L., Chen C., Zhai Y., Dairkee S.H., Ljung B.M., Gray J.W., Albertson D.G. High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 1998;20:207–211. doi: 10.1038/2524. [DOI] [PubMed] [Google Scholar]
  38. Pan H., Gustafsson M.K., Aruga J., Tiedken J.J., Chen J.C., Emerson C.P., Jr. A role for Zic1 and Zic2 in Myf5 regulation and somite myogenesis. Dev Biol. 2011;351:120–127. doi: 10.1016/j.ydbio.2010.12.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Qiu Y., Ding R., Zhuang Z., Wu J., Yang M., Zhou S., Ye Y., Geng Q., Xu Z., Huang S., Cai G., Wu Z., Yang J. Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs. BMC Genomics. 2021;22:332. doi: 10.1186/s12864-021-07654-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Redon R., Ishikawa S., Fitch K.R., Feuk L., Perry G.H., Andrews T.D., Fiegler H., Shapero M.H., Carson A.R., Chen W., Cho E.K., Dallaire S., Freeman J.L., González J.R., Gratacòs M., Huang J., Kalaitzopoulos D., Komura D., MacDonald J.R., Marshall C.R., Mei R., Montgomery L., Nishimura K., Okamura K., Shen F., Somerville M.J., Tchinda J., Valsesia A., Woodwark C., Yang F., Zhang J., Zerjal T., Zhang J., Armengol L., Conrad D.F., Estivill X., Tyler-Smith C., Carter N.P., Aburatani H., Lee C., Jones K.W., Scherer S.W., Hurles M.E. Global variation in copy number in the human genome. Nature. 2006;444:444–454. doi: 10.1038/nature05329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rao Y.S., Li J., Zhang R., Lin X.R., Xu J.G., Xie L., Xu Z.Q., Wang L., Gan J.K., Xie X.J., He J., Zhang X.Q. Copy number variation identification and analysis of the chicken genome using a 60K SNP BeadChip. Poult. Sci. 2016;95:1750–1756. doi: 10.3382/ps/pew136. [DOI] [PubMed] [Google Scholar]
  42. St-Jacques B., Hammerschmidt M., McMahon A.P. Indian hedgehog signaling regulates proliferation and differentiation of chondrocytes and is essential for bone formation. Genes Dev. 1999;13:2072–2086. doi: 10.1101/gad.13.16.2072. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Skuntz S., Mankoo B., Nguyen M.T., Hustert E., Nakayama A., Tournier-Lasserve E., Wright C.V., Pachnis V., Bharti K., Arnheiter H. Lack of the mesodermal homeodomain protein MEOX1 disrupts sclerotome polarity and leads to a remodeling of the cranio-cervical joints of the axial skeleton. Dev. Biol. 2009;332:383–395. doi: 10.1016/j.ydbio.2009.06.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Schrider D.R., Hahn M.W. Lower linkage disequilibrium at CNVs is due to both recurrent mutation and transposing duplications. Mol. Biol. Evol. 2010;27:103–111. doi: 10.1093/molbev/msp210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Swanson-Wagner R.A., Eichten S.R., Kumari S., Tiffin P., Stein J.C., Ware D., Springer N.M. Pervasive gene content variation and copy number variation in maize and its undomesticated progenitor. Genome Res. 2010;20:1689–1699. doi: 10.1101/gr.109165.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Shen C., Xiong W.C., Mei L. LRP4 in neuromuscular junction and bone development and diseases. Bone. 2015;80:101–108. doi: 10.1016/j.bone.2015.05.012. [DOI] [PubMed] [Google Scholar]
  47. Silva V.H., Regitano L.C., Geistlinger L., Pértille F., Giachetto P.F., Brassaloti R.A., Morosini N.S., Zimmer R., Coutinho L.L. Genome-wide detection of CNV and their association with meat tenderness in Nelore cattle. PLoS. One. 2016;11 doi: 10.1371/journal.pone.0157711. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Seol D., Ko B.J., Kim B., Chai H.H., 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;9:809. doi: 10.3390/ani9100809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Suvakov M., Panda A., Diesh C., Holmes I., Abyzov A. CNVpytor: a tool for copy number variation detection and analysis from read depth and allele imbalance in whole-genome sequencing. Gigascience. 2021;10:giab074. doi: 10.1093/gigascience/giab074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sun T., Pei S., Liu Y., Hanif Q., Xu H., Chen N., Lei C., Yue X. Whole genome sequencing of simmental cattle for SNP and CNV discovery. BMC. Genomics. 2023;24:179. doi: 10.1186/s12864-023-09248-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wolpert L. Much more from the chicken's egg than breakfast–a wonderful model system. Mech. Dev. 2004;121:1015–1017. doi: 10.1016/j.mod.2004.04.021. [DOI] [PubMed] [Google Scholar]
  52. Wright D., Boije H., Meadows J.R., Bed'hom B., Gourichon D., Vieaud A., Tixier-Boichard M., Rubin C.J., Imsland F., Hallböök F., Andersson L. Copy number variation in intron 1 of SOX5 causes the pea-comb phenotype in chickens. PLoS. Genet. 2009;5:12. doi: 10.1371/journal.pgen.1000512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wang X., Nahashon S., Feaster T.K., Bohannon-Stewart A., Adefope N. An initial map of chromosomal segmental copy number variations in the chicken. BMC. Genomics. 2010;11:1471–2164. doi: 10.1186/1471-2164-11-351. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Wang Y., Gu X., Feng C., Song C., Hu X., Li N. A genome-wide survey of copy number variation regions in various chicken breeds by array comparative genomic hybridization method. Anim. Genet. 2012;43:282–289. doi: 10.1111/j.1365-2052.2011.02308.x. [DOI] [PubMed] [Google Scholar]
  55. Wang Y., Dong Z., Yang R., Zong S., Wei X., Wang C., Guo L., Sun J., Li H., Li P. Inactivation of Ihh in Sp7-expressing cells inhibits osteoblast proliferation, differentiation, and bone formation, resulting in a dwarfism phenotype with severe skeletal dysplasia in mice. Calcif. Tissue Int. 2022;111:519–534. doi: 10.1007/s00223-022-00999-5. [DOI] [PubMed] [Google Scholar]
  56. Yau C., Holmes C.C. CNV discovery using SNP genotyping arrays. Cytogenet Genome Res. 2008;123:307–312. doi: 10.1159/000184722. [DOI] [PubMed] [Google Scholar]
  57. Yang Y. Skeletal morphogenesis during embryonic development. Crit. Rev. Eukaryot. Gene Expr. 2009;19:197–218. doi: 10.1615/critreveukargeneexpr.v19.i3.30. [DOI] [PubMed] [Google Scholar]
  58. Yoshikawa Y., Yamada T., Tai-Nagara I., Okabe K., Kitagawa Y., Ema M., Kubota Y. Developmental regression of hyaloid vasculature is triggered by neurons. J. Exp. Med. 2016;213:1175–1183. doi: 10.1084/jem.20151966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Yuan C., Lu Z., Guo T., Yue Y., Wang X., Wang T., Zhang Y., Hou F., Niu C., Sun X., Zhao H., Zhu S., Liu J., Yang B. A global analysis of CNVs in Chinese indigenous fine-wool sheep populations using whole-genome resequencing. BMC. Genomics. 2021;22:021–07387. doi: 10.1186/s12864-021-07387-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Yang W., Zuo Y., Zhang N., Wang K., Zhang R., Chen Z., He Q. GNAS locus: bone related diseases and mouse models. Front. Endocrinol. 2023;14 doi: 10.3389/fendo.2023.1255864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Yang S., Ning C., Yang C., Li W., Zhang Q., Wang D., Tang H. 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. Vet. Sci. 2024;11:76. doi: 10.3390/vetsci11020076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Zhang H., Du Z.Q., Dong J.Q., Wang H.X., Shi H.Y., Wang N., Wang S.Z., Li H. Detection of genome-wide copy number variations in two chicken lines divergently selected for abdominal fat content. BMC. Genomics. 2014;15:1471–2164. doi: 10.1186/1471-2164-15-517. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Zhang X., Wang K., Wang L., Yang Y., Ni Z., Xie X., Shao X., Han J., Wan D., Qiu Q. Genome-wide patterns of copy number variation in the Chinese yak genome. BMC. Genomics. 2016;17:016–2702. doi: 10.1186/s12864-016-2702-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Zhou B., Ho S.S., Zhang X., Pattni R., Haraksingh R.R., Urban A.E. Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis. J. Med. Genet. 2018;55:735–743. doi: 10.1136/jmedgenet-2018-105272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Zhu X., Xu M., Leu N.A., Morrisey E.E., Millar S.E. FZD2 regulates limb development by mediating β-catenin-dependent and -independent wnt signaling pathways. Dis. Model. Mech. 2023;16:24. doi: 10.1242/dmm.049876. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figure S1. QQ plot for genome-wide association analysis.

Abbreviations: BSL= Body Sope length, KeL= Keel length, TiL= Tibial length, TiC= Tibial circumference, ChW= Chest width, ChD= Chest depth.

mmc1.jpg (572.2KB, jpg)

Supplementary Figure S2. The qPCR validation results for 8 selected candidate CNV segments.

mmc2.jpg (314.9KB, jpg)
mmc3.xlsx (11KB, xlsx)
mmc4.xlsx (43.5KB, xlsx)
mmc5.xlsx (34.3KB, xlsx)
mmc6.xlsx (10.9KB, xlsx)

Articles from Poultry Science are provided here courtesy of Elsevier

RESOURCES