Skip to main content
Download PDF

Genome-wide detection of CNV regions between Anqing six-end-white and Duroc pigs

Molecular Cytogenetics volume 16, Article number: 12 (2023) Cite this article

Abstract

Background

Anqing six-end-white pig is a native breed in Anhui Province. The pigs have the disadvantages of a slow growth rate, low proportion of lean meat, and thick back fat, but feature the advantages of strong stress resistance and excellent meat quality. Duroc pig is an introduced pig breed with a fast growth rate and high proportion of lean meat. With the latter breed featuring superior growth characteristics but inferior meat quality traits, the underlying molecular mechanism that causes these phenotypic differences between Chinese and foreign pigs is still unclear.

Results

In this study, copy number variation (CNV) detection was performed using the re-sequencing data of Anqing Six-end-white pigs and Duroc pigs, A total of 65,701 CNVs were obtained. After merging the CNVs with overlapping genomic positions, 881 CNV regions (CNVRs) were obtained. Based on the obtained CNVR information combined with their positions on the 18 chromosomes, a whole-genome map of the pig CNVs was drawn. GO analysis of the genes in the CNVRs showed that they were primarily involved in the cellular processes of proliferation, differentiation, and adhesion, and primarily involved in the biological processes of fat metabolism, reproductive traits, and immune processes.

Conclusion

The difference analysis of the CNVs between the Chinese and foreign pig breeds showed that the CNV of the Anqing six-end-white pig genome was higher than that of the introduced pig breed Duroc. Six genes related to fat metabolism, reproductive performance, and stress resistance were found in genome-wide CNVRs (DPF3, LEPR, MAP2K6, PPARA, TRAF6, NLRP4).

Background

The study of genetic variation is an important prerequisite and basis for exploring the characteristics of pig breeds and implementing breeding programs based on molecular technologies. Anqing six-end-white (AQSW) pig is a protected national livestock breed that features the advantages of early sexual maturity, strong disease resistance, high fertility, and excellent meat quality, among other desirable characteristics. Duroc is a major commercial pig breed known for its fast growth rate and high proportion of lean meat [1]. There are substantial differences in the growth, meat quality, and disease resistance between these two pig breeds; the former being Chinese in origin and the latter being foreign in origin [2]. However, little is known about the genetic basis responsible for the phenotypic differences between such representative Chinese and foreign pig breeds. Copy number variation (CNV) is an important source of genetic variation that can be used to identify genetic variation among breeds. CNV mainly affects gene expression and function, and it does so through multiple mechanisms, such as altering gene dosage, disrupting coding sequences, and perturbing the long-range regulation of genes. CNVs are considered to be another important contributor to genetic variation other than SNPs [3].

CNV represents an important component of structural variation within the genome. Genome structural variation is closely related to phenotypic diversity and human disease, but the mechanisms explaining its functional impact have remained elusive. CNV is one aspect of genomic variation that is present in a variety of organisms and acts on different molecular and cellular processes, thereby resulting in a variety of changes in traits such as disease susceptibility, phenotypic diversity, and evolutionary differences. Some CNVRs that affect important economic traits in pigs have been successfully identified. For example, Wang [4] conducted a genome-wide association analysis between CNVs and meat quality traits in pigs and identified 8 CNVRs that were significantly associated with at least one meat quality trait. A genome-wide association analysis of porcine CNVRs based on next-generation sequencing data revealed that CNVRs were associated with genetic variation in fatty acid composition and growth traits [5]. A genome-wide comparison of CNVs in Tongcheng pigs and large white pigs using genome sequencing data showed that two genes, DCUN1D2 and SPARCL1, were associated with disease resistance in pigs, while PLEKHA2 and SLCO1A2 were found to be related to lipid metabolism [6]. The CNVs of Meishan pigs and Duroc pigs were studied using next-generation sequencing technology to discover that the copy number of AHR had a positive effect on pig litter size (P < 0.05) [7]. Qiu et al. [8] found that four CNVRs were significantly correlated with both average daily weight gain and net weight gain, thereby suggesting that these CNVRs play an important role in regulating growth and fat deposition in pigs.

In past studies, it has been recognized that a major driver of variation among individuals is variation in the copy number of genomic segments and the genes contained within those segments, and such CNVs have evolved in species other than primates [9,10,11,12]. AQSW pig is a local pig in Anhui Province,which has the disadvantages of a slow growth rate, low proportion of lean meat.

Results

Genome-wide detection of CNVs

We found a total of 25,463 CNVs in the 20 LB samples, including 13,413 deletions and 12,050 insertions, with an average of 1273 CNVs per pig, and the average size of each CNV was 88,303 bp. Detailed information is shown in Tables 1 and 2.

Table 1 Summary of copy number variants (CNVs) of the 20 analysed AQSW pigs
Full size table
Table 2 Summary of copy number variants (CNVs) of the 20 analysed Duroc pigs
Full size table

Differences between CNVRs in Chinese and foreign pig breeds

As determined by the statistics conducted on the CNVRs detected in the LB pig and Duroc pig breeds, it was found that the number of CNVRs detected in LB pigs (an Anqing local pig in Anhui Province) was the largest (461), while the number of CNVRs detected in the introduced Duroc pig breed was lower (421). A possible reason for this result is that introduced pig breeds may have been subjected to strong artificial selection during the breeding process, thereby resulting in such breeds becoming more homozygous and their genetic variation becoming gradually reduced, while local pig breeds in China have been relatively less artificially selected such that their degree of genetic variation is higher than that of introduced pig breeds.

Comparative analysis of CNVRs in AQSW

We used BEDTools to merge all CNVs within the AQSW breed to identify 461 CNVRs. As shown in Tables 3 and 4, there were 59 CNVRs on chr2, representing the largest number of CNVRs, and only 4 CNVRs on chr18, representing the least, which were the most common CNVRs across all chromosomes. The length distribution map of the CNVRs is shown in Fig. 1.

Table 3 Summary of CNV regions (CNVRs) of AQSW pig breed stratified by chromosome
Full size table
Table 4 Summary of CNV regions (CNVRs) of Duroc pig breed stratified by chromosome
Full size table
Fig. 1
figure 1

Histogram for the chromosome-wise distribution of 461 CNVRs across the AQSW pig genome

Full size image

Functional annotation of genes

We detected a total of 15,469 genes with variation between the two pig breeds, among which, 1081 genes were identified in AQSW pigs while 242 genes were identified in Duroc pigs, comprising a total of 5756 genes. These genetic variations may be related to differences in the native environment of the breeds. Adaptability, immunity, and disease resistance are interrelated biological traits that provide a valuable basis for research on the growth and disease susceptibility of pigs. The gene distribution diagram is shown in Fig. 2.

Fig. 2
figure 2

A Venn diagram of respective_genes and common_genes

Full size image

Enrichment analysis

To further understand the impact of CNV on various aspects of pig growth, this study used the GO database of the DAVID website to perform Gene Ontology enrichment analysis and pathway analysis of the genes in the above 461 CNVRs with Ensemble ID. We first submitted the Ensemble numbers of 461 genes to the Biomart site of the Ensemble website to search for human homologous genes and conducted one-to-one screening to ensure the uniqueness of the homologous genes obtained. We then submitted the obtained homologous gene information to the DAVID website, and GO analysis was performed, as shown in Fig. 3. In Fig. 4, functional clustering included three aspects: molecular biological function (Molecular Function, MF), cell component (Cell Component, CC), and biological process (Biological Process, BP). Using P ≤ 0.05 as the threshold to screen each cluster item, genes within CNVRs were enriched in a total of 52 different functional clusters, including 24 different biological processes (BP) functional clusters, 12 cellular component (CC) functional clusters, and 16 molecular biology functional (MF) clusters. The functional clustering of these genes was determined to involve many biological processes, primarily involving cell adhesion, embryonic development, and cell differentiation. These clustering results indicate that the genes in CNVRs may be more involved in behavioral adaptation during the process of growth, development, immunity, and domestication, and those that are beneficial to the organism may be preserved by the evolutionary process.

Fig. 3
figure 3

Gene Ontology (GO) terms of the selected genes, red referring to biological process, green referring to cellular component and blue referring to molecular function. Note: The horizontal axis represents the enriched items, while the vertical axis represents number of genes

Full size image
Fig. 4
figure 4

Enrichment pathways, green is Human Disease, orange is organismal systems, pink is cellular processes, blue is environmental information processing, purple is genetic information processing, yellow is metabolism

Full size image

Discussion

In this study, the BioMart tool was used to identify genes situated within CNVRs, and DAVID was then used to search the GO terms of genes contained in CNVRs. Combined with the annotation information of the KEGG-GENES database, the purpose of obtaining these results was to speculate on the function of these genes and the possible effects of CNVRs on the biological traits of commercial Chinese native and foreign pig breeds.

The analysis of the gene distribution in CNVRs showed that CNVRs were more concentrated in chromosome segments with lower gene densities. The reason for this may be that CNVs in the genome originate from segmental duplication regions, which often contain redundant sequences with less gene distribution. It is also possible that CNVs in gene-enriched regions are easier to order and create functional genes, and cause damage, which is detrimental to the survival of the organism, so they are eliminated under the action of natural selection.

CNVs can lead to phenotypic variation related to single gene diseases, complex diseases or quantitative traits through molecular mechanisms such as gene structure change, gene fusion and dose effect [13]. For example, a CNV-based whole genome-associated analysis found functional genes related to feed conversion rate and growth and development [14]. The explicit white hair color of the pig is related to the repetition of the 450 kb fragment containing the Kit gene [15]. Mei et al. [16] conducted a genome-wide CNV study on six native cattle breeds in China, and found that a large number of adaptive (BOLA-DQB and EGLN2) and hair color (KIT and MITF) copy number variation information.

Existing studies have found that genes in CNVRs are mainly involved in immunity, the sensory perception of the external environment (involving smell, vision, and taste), the response to stimuli, and neurodevelopment, while they are relatively less involved in nucleic acid binding, metabolism, and cell proliferation [17]. This may be because the occurrence of gene CNVs with important functions is the result of strong purification selection. When a CNV occurs in the coding region, if the affected gene has an important role in growth, the occurrence of the variation is highly likely to be harmful to the organism and is therefore quickly eliminated. This is consistent with our analysis results. A total of 798 genes were obtained from 348 CNVRs. GO analysis found that these genes were mainly involved in cell adhesion, phosphorylation, and learning, while a small number of genes were involved in embryonic development and cell differentiation.

Copy numbers have varieties in the animal group. Some unique CNVs may be selected during the domestication, resulting in the species specificity. The independent CNV between different varieties may cause variety differences to differences in variety differences [18]. There are differences in lipid metabolism, fat deposition, and disease resistance between Chinese local pigs and Western commercial pigs. The main reason may be because Chinese pig local groups and European pig groups have different domestication or variety formation processes [19]. Genetic background information [20]. In this study, the resequencing data of AQSW pigs and Duroc pigs were used for CNV detection, and the genome structure of these Chinese and foreign pig populations was studied. AQSW pigs were found to have advantages in stress resistance, fat metabolism, and cell biology, and this finding provides useful information for the development of breeding programs and the understanding of disease resistance for this population.

Through GO annotation and KEGG enrichment analysis, six genes related to fat metabolism, reproductive performance, and stress resistance were found in the CNVRs of the whole genome; namely DPF3, LEPR, MAP2K6, PPARA, TRAF6, and NLRP4. Among them, the DPF3 gene located on chromosome 7 is related to the number of nipples in pigs. The number of nipples of a sow is an important indicator of its ability to raise a large number of piglets. In sows, the combination of having a large number of nipples and a large litter size is the physiological basis for achieving high reproductive performance. A lack of teats leads to insufficient colostrum intake in piglets, which negatively affects piglet body weight and immunity, and may also lead to piglet death due to starvation and crushing [21]. It has been reported that sow teat number is highly important for piglet mortality, body weight gain, and uniform growth [22]. Related studies have also found that the number of nipples in pigs varies among breeds [23, 24], and has a strong correlation with reproductive performance.

The LEPR gene plays an important role in the decomposition of fatty acids and the maintenance of energy balance in the body. LEPR mRNA is expressed in sheep adipocytes as well as in human adipocytes [25]. Numerous studies have shown that LEPR plays an important role in regulating body fat deposition [26,27,28]. The pig MAP2K6 gene is located on chromosome 12 and is mainly involved in the regulation of reproductive traits [29, 30]. The PPARA gene is an important isoform of the PPARs family and was the first identified member. As a major transcriptional regulator of fatty acid oxidase, it regulates the normal metabolism of lipids in the body [31].

Both the NLRP4 and TRAF6 genes are related to the immune inflammatory response [32]. The TRAF6 gene is a key linker molecule in the regulatory pathways of the tumor necrosis factor (TNF) superfamily, toll-like receptor (TLR) family, and interleukin-1 receptor (TIR) superfamily, as well as an important linker molecule in the innate immune signaling pathway [33, 34].

The above findings indicate that the occurrence of CNVs may have an impact on the immune response, fat metabolism, and reproduction of LB pigs.

Conclusion

In this study, the resequencing data of AQSW pigs and Duroc pigs were used for CNV detection, and a total of 881 CNVRs were detected in these two populations. The results show that the AQSW and Duroc pig populations have significant differences in the genomic localization of genetic variation. We also identified candidate genes related to lipid metabolism, reproductive traits, and stress resistance; namely DPF3, LEPR, MAP2K6, PPARA, TRAF6, and NLRP4. The results of this study lay a foundation for subsequent studies on potential mutations associated with the observed phenotypic variation.

Materials and methods

Quality control

FASTP software version 0.18.0 [35] was used to filter the raw data of the Illumina platform to obtain relatively high-quality sequencing data for clean reads assembly analysis. Our filtering criteria were as follows: (1) removal of reads containing unknown nucleotides (N) ≥ 10%; (2) removal of reads with a Phred quality score ≤ 20 bases ≥ 50%; (3) deletion of reads containing linkers. According to the above criteria, some low-quality reads; such as those containing primers or adapters, those containing non-ATCG bases, and those with an error rate of more than 30%; were removed.

Comparative analysis

The pig reference genome Sus scrofa 11.1 can be obtained from the Ensemble database, and be used to detection of copy number variation. We used the alignment software BWA [36] with the BWA-MEM algorithm to align the filtered reads to the reference genome, and the alignment parameter was -k 32-M; (Dior Biotechnology Co., Ltd. 1.129). Picard was used to mark the repeated reads and count the depth and coverage of the marked reads. Coverage statistics were performed using BEDTools [37].

Detection of CNVs and CNVRs

This study used CNVnator [38] to detect the CNVs. We refer to the threshold recommended in the CNVnator methodology article to filter reliable CNVs to facilitate subsequent analysis of gene copy numbers. The employed criteria were as follows: (1) e-value < 0.01; (2) RD < 0.7 in the deletion region; RD > 1.3, CNV interval size > 1 kb. The genome-wide detection of copy number variants in AQ and Duroc pig breeds by whole-genome sequencing of DNA was performed to identify breed copy numbers.

The CNVs in the two populations of pigs were integrated using BEDTools to determine the CNVRs of each population. Using Ensemble (the website), the reference genome of pigs (Sus scrofa 11.1) was used to find genes that overlapped with CNVRs. The screened genes were then further analyzed for functional enrichment by PANTHER [39].

Gene detection and enrichment analysis

Using the BioMart [40] gene database based on the pig reference genome sequence in Ensemble, the genes located in CNVRs were searched on the basis of having at least 1 bp overlapping, and the functional annotation of the genes in CNVRs was performed.

Gene Ontology terms (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID Bioinformatic Database [41].

Availability of data and materials

This study collected data from 40 pigs, including 20 healthy LB pigs and 20 Duroc pigs. We obtained the re-sequencing data from 20 Duroc pigs using NCBI [7]. We sampled 20 unrelated LB pigs from the LB conservation farm (Anqing, China; longitude, 116° 330 E; latitude, 30° 190 N). Genomic DNA was extracted from the ear samples of pigs using the standard phenol–chloroform method [42], stored at 4 °C to avoid freeze-thawing, and tested for its nucleic acid concentration (as ng/ml) using a Nanodrop instrument. The DNA was subsequently fragmented and treated following the Illumina DNA sample preparation protocol. The protocol employed a process of end-repaired, A-tailed, ligated to paired-end adaptors and PCR amplification with 350-bp inserts. The constructed libraries were then sequenced by the Illumina HiSeq X Ten platform (Illumina, San Diego, CA) for 150-bp paired-end reads at Novogene (Beijing, China).

References

  1. Yu J, Zhao P, Zheng X, Zhou L, Wang C, Liu JF. Genome-wide detection of selection signatures in Duroc revealed candidate genes relating to growth and meat quality. G3 (Bethesda). 2020;10(10):3765–73.

    Article  CAS  PubMed  Google Scholar 

  2. Zhou X, Wang P, Michal JJ, Wang Y, Zhao J, Jiang Z, Liu B. Molecular characterization of the porcine S100A6 gene and analysis of its expression in pigs infected with highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV). J Appl Genet. 2015;56:355–63.

    Article  CAS  PubMed  Google Scholar 

  3. Marklund S, Kijas J, Rodriguez-Martinez H, et al. Molecular basis for the dominant white phenotype in the domestic pig. Genome Res. 1998;8(8):826–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Wang Z, Guo Y, Liu S, Meng Q. Genome-wide assessment characteristics of genes overlapping copy number variation regions in Duroc purebred population. Front Genet. 2021;12: 753748.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Revilla M, Puig-Oliveras A, Crespo-Piazuelo D, Criado-Mesas L, Castello A, Fernandez AI, Ballester M, Folch JM. Expression analysis of candidate genes for fatty acid composition in adipose tissue and identification of regulatory regions. Sci Rep. 2018;8:2045.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Wu Q, Zhou Y, Wang Y, et al. Whole-genome sequencing reveals breed-differential CNVs between Tongcheng and large white pigs. Anim Genet. 2020;51(6):940–4.

    Article  CAS  PubMed  Google Scholar 

  7. Zheng X, Zhao P, Yang K, Ning C, Wang H, Zhou L, Liu J. CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits. J Anim Sci Biotechnol. 2020;11:42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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(1):332.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bailey JA, Eichler EE. Primate segmental duplications: crucibles of evolution, diversity and disease. Nat Rev Genet. 2006;7(7):552.

    Article  CAS  PubMed  Google Scholar 

  10. Dumas L, Kim YH, Karimpour-Fard A, Cox M, Hopkins J, Pollack JR, Sikela JM. Gene copy number variation spanning 60 million years of human and primate evolution. Genome Res. 2007;17(9):1266–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nahon JL. Birth of ‘human-specific’ genes during primate evolution. Genetica. 2003;118(2–3):193–208.

    Article  CAS  PubMed  Google Scholar 

  12. Stankiewicz P. Serial segmental duplications during primate evolution result in complex human genome architecture. Genome Res. 2004;14(11):2209–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lupski JR, Stankiewicz P. Genomic disorders: molecular mechanisms for rearrangements and conveyed phenotypes. PLoS Genet. 2005;1(6): e49.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhou Y, Utsunomiya YT, Xu L, el Hay HA, Bickhart DM, Alexandre PA, Rosen BD, Schroeder SG, Carvalheiro R, de Rezende Neves HH, Sonstegard TS, Van Tassell CP, Ferraz JB, Fukumasu H, Garcia JF, Liu GE. Genome-wide CNV analysis reveals variants associated with growth traits in Bos indicus. BMC Genomics. 2016;17:419.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Seo BY, Park EW, Ahn SJ, Lee SH, Kim JH, Im HT, Lee JH, Cho IC, Kong IK, Jeon JT. An accurate method for quantifying and analyzing copy number variation in porcine KIT by an oligonucleotide ligation assay. BMC Genet. 2007;23(8):81.

    Article  Google Scholar 

  16. Mei C, Junjvlieke Z, Raza SHA, Wang H, Cheng G, Zhao C, Zhu W, Zan L. Copy number variation detection in Chinese indigenous cattle by whole genome sequencing. Genomics. 2020;112(1):831–6.

    Article  CAS  PubMed  Google Scholar 

  17. De Smith AJ, Walters RG, Froguel P, Blakemore AI. Human genes involved in copy number variation: mechanisms of origin, functional effects and implications for disease. Cytogenet Genome Res. 2008;123:17–26.

    Article  PubMed  Google Scholar 

  18. Paudel Y, Madsen O, Megens HJ, Frantz LA, Bosse M, Bastiaansen JW, Crooijmans RP, Groenen MA. Evolutionary dynamics of copy number variation in pig genomes in the context of adaptation and domestication. BMC Genomics. 2013;14:449.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Megens HJ, Crooijmans RP, San Cristobal M, Hui X, Li N, Groenen MA. Biodiversity of pig breeds from China and Europe estimated from pooled DNA samples: differences in microsatellite variation between two areas of domestication. Genet Sel Evol. 2008;40(1):103–28.

    PubMed  PubMed Central  Google Scholar 

  20. Fang M, Andersson L. Mitochondrial diversity in European and Chinese pigs is consistent with population expansions that occurred prior to domestication. Proc Biol Sci. 2006;273(1595):1803–10.

    PubMed  PubMed Central  Google Scholar 

  21. Alexopoulos JG, Lines DS, Hallett S, et al. A review of success factors for piglet fostering in lactation. Animals (Basel). 2018;8(3):38.

    Article  PubMed  Google Scholar 

  22. Chalkias H, Rydhmer L, Lundeheim NJLS. Genetic analysis of functional and non-functional teats in a population of Yorkshire pigs. Livest Sci. 2013;152(2–3):127–34.

    Article  Google Scholar 

  23. Arakawa A, Okumura N, Taniguchi M, et al. Genome-wide association QTL mapping for teat number in a purebred population of Duroc pigs. Anim Genet. 2015;46(5):571–5.

    Article  CAS  PubMed  Google Scholar 

  24. Wang L, Zhang Y, Zhang T, et al. Genotyping by sequencing reveals a new locus for pig teat number. Anim Genet. 2017;48(4):470–2.

    Article  CAS  PubMed  Google Scholar 

  25. Haldar A, French MC, Brauning R, et al. Single-nucleotide polymorphisms in the LEPR gene are associated with divergent phenotypes for age at onset of puberty in davisdale ewes. Biol Reprod. 2014;90(2):33–33.

    Article  PubMed  Google Scholar 

  26. Huvenne H, Le Beyec J, Pépin D, et al. Seven novel deleterious LEPR mutations found in early-onset obesity: aΔExon6–8 shared by subjects from Reunion Island, France, suggests a founder effect. J Clin Endocrinol Metab. 2015;100(5):757–66.

    Article  Google Scholar 

  27. Mackowski M, Szymoniak K, Szydlowski M, et al. Missense mutations in exon 4 of the porcine LEPR gene encoding extracellular domain and their association with fatness traits. Anim Genet. 2005;36(2):135–7.

    Article  CAS  PubMed  Google Scholar 

  28. Saeed S, Bonnefond A, Manzoor J, et al. Novel LEPR mutations in obese Pakistani children identified by PCR based enrichment and next generation sequencing. Obesity. 2014;22(4):1112–7.

    Article  CAS  PubMed  Google Scholar 

  29. Ryu J, Kim Y, Kim C, et al. Association of bovine carcass phenotypes with genes in an adaptive thermogenesis pathway. Mol Biol Rep. 2012;39(2):1441–5.

    Article  CAS  PubMed  Google Scholar 

  30. Zhang Z, Chen Z, Ye SP. Genome-wide association study for reproductive traits in a Duroc pig population. Animals. 2019;9(10):732.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Nagasawa M, Akasaka Y, Ide T, et al. Highly sensitive upregulation of apolipoprotein A-IV by peroxisome proliferator-activated receptor alpha (PPARalpha) agonist in human hepatoma cells. Biochem Pharmacol. 2007;74(12):1738–46.

    Article  CAS  PubMed  Google Scholar 

  32. Bouchier-Hayes L, Conroy H, Egan H, et al. CARDINAL, a novel caspase recruitment domain protein, is an inhibitor of multiple NF-kappa B activation pathways. J Biol Chem. 2001;276(47):44069–77.

    Article  CAS  PubMed  Google Scholar 

  33. Chung JY, Park YC, Ye H, et al. All TRAFs are not created equal: common and distinct molecular mechanisms of TRAF-mediated signal transduction. J Cell Sci. 2002;115(4):679–88.

    Article  CAS  PubMed  Google Scholar 

  34. Kobayashi T, Walsh PT, Walsh MC, et al. TRAF6 is a critical factor for dendritic cell maturation and development. Immunity. 2003;19(3):353–63.

    Article  CAS  PubMed  Google Scholar 

  35. Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):884–90.

    Article  Google Scholar 

  36. Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Abyzov A, Urban AE, Snyder M, et al. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011;21(6):974–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Stafuzza NB, Silva RMO, Fragomeni BO, Masuda Y, Huang Y, Gray K, Lourenco DAL. A genome-wide single nucleotide polymorphism and copy number variation analysis for number of piglets born alive. BMC Genomics. 2019;20(1):321.

    Article  PubMed Central  Google Scholar 

  40. Steffen D, Yves M, Arek K, et al. BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005;16:3439–40.

    Google Scholar 

  41. Dennis G, Sherman BT, Hosack DA, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol. 2003;4(5):3.

    Article  Google Scholar 

  42. Sambrook J, Russell DW. Molecular Cloning: a Laboratory Manual, 3rd Edn, New York, NY: Cold Spring Harbor Laboratory Press, 2001.

Download references

Acknowledgements

This work was supported by the Institute of Agricultural Economics and Information in the Anhui Academy of Agricultural Sciences, Anhui Provincial Financial Agricultural Germplasm Resources Protection and Utilization Fund Project, Anhui Academy of Agricultural Sciences Key Laboratory Project (No. 2021YL023), Anhui Province Academic and Technical Leader Candidate Project (No. 2022H300), Anhui Province Natural Science Foundation Youth Fund Project (2108085QC135), the Special Fund for Anhui Agricultural Research System (AHCYJSTX-05-12, AHCYJSTX-05-23).

Funding

This research was funded by the Anhui Academy of Agricultural Sciences (No. 2023YL014), and the Lu’An major project of industry-university-research cooperation (2020), Anhui Academy of Agricultural Sciences Key Laboratory Project (No. 2021YL023), Anhui Province Academic and Technical Leader Candidate Project (No. 2022H300), Anhui Province Natural Science Foundation Youth Fund Project (No. 2108085QC135), the Special Fund for Anhui Agricultural Research System (No. AHCYJSTX-05-12, No. AHCYJSTX-05-23).

Author information

Author notes

  1. Rong Qian and Fei Xie have contributed equally to this work.

Authors and Affiliations

  1. Institue of Agricultural Economics and Information, Anhui Academy of Agricultural Sciences, Hefei, 230031, Anhui, China

    Rong Qian & JuanJuan Kong

  2. Institue of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Sciences, Hefei, 230031, Anhui, China

    Wei Zhang, Xueli Zhou & Chonglong Wang

  3. College of Animal Science, Anhui Science and Technology University, Fengyang County, 233100, Anhui Province, China

    Fei Xie & Xiaojin Li

Authors
  1. Rong Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. JuanJuan Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xueli Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chonglong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaojin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RQ finished experimentation and paper writing, FX assisted in experiment. WZ processed experiment data. JK collection data samples. XZ finished verifying the data. CW and XL directed and supervised paper writing and experimentation.

Corresponding authors

Correspondence to Chonglong Wang or Xiaojin Li.

Ethics declarations

Ethics approval and consent to participate

The animal study reviewed and approved in this study was carried out in accordance with the recommendations of the Animal Care Committee of Anhui Academy of Agricultural Sciences (Hefei, China). The protocol was approved by the Animal Care Committee of Anhui Academy of Agricultural Sciences (No. AAAS2020-04).

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qian, R., Xie, F., Zhang, W. et al. Genome-wide detection of CNV regions between Anqing six-end-white and Duroc pigs. Mol Cytogenet 16, 12 (2023). https://doi.org/10.1186/s13039-023-00646-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13039-023-00646-0

Keywords

Molecular Cytogenetics

ISSN: 1755-8166