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Copy number variations (CNVs) critically influence individual genetic diversity and phenotypic traits. In this study, we employed whole-genome resequencing technology to conduct an in-depth analysis of 50 pigs from five local swine populations [Rongchang pig (RC), Wuzhishan pig (WZS), Tibetan pig (T), Yorkshire (YL) and Landrace (LR)], aiming to assess their genetic potential and explore their prospects in the field of animal model applications. We identified a total of 96,466 CNVs, which were subsequently integrated into 7112 non-redundant CNVRs, encompassing 1.3% of the swine genome. Functional enrichment analysis of the genes within these CNVRs revealed significant associations with sensory perception, energy metabolism, and neural-related pathways. Further selective scan analyses of the local pig breeds RC, T, WZS, along with YL and LR, uncovered that for the RC variety, the genes PLA2G10 and ABCA8 were found to be closely related to fat metabolism and cardiovascular health. In the T breed, the genes NCF2 and CSGALNACT1 were associated with immune response and connective tissue characteristics. As for the WZS breed, the genes PLIN4 and CPB2 were primarily linked to fat storage and anti-inflammatory responses. In summary, this research underscores the pivotal role of CNVs in fostering the diversity and adaptive evolution of pig breeds while also offering valuable insights for further exploration of the advantageous genetic traits inherent to China's local pig breeds. This facilitates the creation of experimental animal models tailored to the specific characteristics of these breeds, contributing to the advancement of livestock and biomedical research. [ABSTRACT FROM AUTHOR]

Copy number variations (CNVs) critically influence individual genetic diversity and phenotypic traits. In this study, we employed whole-genome resequencing technology to conduct an in-depth analysis of 50 pigs from five local swine populations [Rongchang pig (RC), Wuzhishan pig (WZS), Tibetan pig (T), Yorkshire (YL) and Landrace (LR)], aiming to assess their genetic potential and explore their prospects in the field of animal model applications. We identified a total of 96,466 CNVs, which were subsequently integrated into 7112 non-redundant CNVRs, encompassing 1.3% of the swine genome. Functional enrichment analysis of the genes within these CNVRs revealed significant associations with sensory perception, energy metabolism, and neural-related pathways. Further selective scan analyses of the local pig breeds RC, T, WZS, along with YL and LR, uncovered that for the RC variety, the genes PLA2G10 and ABCA8 were found to be closely related to fat metabolism and cardiovascular health. In the T breed, the genes NCF2 and CSGALNACT1 were associated with immune response and connective tissue characteristics. As for the WZS breed, the genes PLIN4 and CPB2 were primarily linked to fat storage and anti-inflammatory responses. In summary, this research underscores the pivotal role of CNVs in fostering the diversity and adaptive evolution of pig breeds while also offering valuable insights for further exploration of the advantageous genetic traits inherent to China’s local pig breeds. This facilitates the creation of experimental animal models tailored to the specific characteristics of these breeds, contributing to the advancement of livestock and biomedical research.

Due to the cost and technical skills required for molecular genetics studies, sequence data and genetic diversity information from livestock species are scarce in Nigeria. Our recent study was therefore conducted to identify single nucleotide polymorphisms (SNP) obtainable in a commercial pig population in Sapele, Delta State, Nigeria. We collected liver samples from 14 growing pigs that were randomly selected from a pig farm and stored using RNALater solution (Qiagen) in a -20oc freezer prior to DNA extraction. DNA concentration and purity was checked using a spectrophotometer while PCR product was confirmed with gel electrophoresis. Primer was designed using the Primer 3 design tool as deployed on NCBI (NCBI 2022). The PCR product was cleaned and sequenced using BioEdit software, sequence data was aligned using MUSCLE algorithm on MEGA version X software, SNP calling, and frequency estimation was executed on GenAlEx 6.503 software. Single nucleotide polymorphisms were identified in 216 loci of the growth hormone gene beginning from locus 11 to locus 459. A maximum of three alleles were obtained in all polymorphic loci. The allelic frequencies obtained for locus 11 was C0.14:G0.86, C0.07:G0.79: T0.14 for locus 31 and G0.93:T0.07 for locus 459. Haploid diversity records of 2.15 ± 0.03, 1.30 ± 0.01, 0.37 ± 0.01, 0.21 ± 0.01, and 0.23 ± 0.01 for mean number of alleles, information index, population diversity and unbiased diversity of the population by locus. The SNP identified in this study showed that the diversity of the population studied is not genetically distant among individuals. [ABSTRACT FROM AUTHOR]