Genome-wide analysis reveals genetic characteristics and selection signatures in Yantai Black Pig of Shandong, China

Genome-wide analysis reveals genetic characteristics and selection signatures in Yantai Black Pig of Shandong, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide analysis reveals genetic characteristics and selection signatures in Yantai Black Pig of Shandong, China Cai Ma, Shaoyang Mou, Yuxin Zhang, Zengguang Wang, Guodong Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9121170/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Yantai Black pig (YT), renowned for its disease resistance and superior meat quality, is a Chinese indigenous pig breed that has developed through natural and artificial selection over an extended period. In recent years, the YT population has dwindled due to the introduction of cosmopolitan pig breeds and the outbreak of African swine fever, putting them at risk of extinction. Meanwhile, there is still a lack of research on its genome. We conducted a genomic comprehensive analysis by high-density SNP chip on 102 YT and comparing them with resequencing genomic data from 20 YT and 16 wild boar (WB). Results The effective population size (Ne), polymorphic marker ratio (P N ), expected heterozygosity (He), and observed heterozygosity (H O ) of this population were 5.0, 0.917, 0.374, and 0.361, respectively, with an average inbreeding coefficient of 0.151 within the population. Based on genomic information, this population was classified into eight different families with boars. It was found that YT was population independent of WB, exhibiting genetic differentiation within the population. Moreover, a total of 125 selected candidate genes were identified by using three methods: F ST , π ratio, and Tajima's D. Functional enrichment analysis identified several annotated genes that might affect growth and development ( DCC , NFKBIZ , TNR , LRRC4C , ERBB4 , TMEM182 , SPRY1 , and MYC), reproduction ( INHBA , COL12A1 , ADRA2A , and DROSHA ), meat quality ( NRG1 , GRM8 , GRIK2 , EFNA5 , COL9A1 , and CHL1 ), and immune response ( SEMA3E , RUNX2 , GRIN3A , PKN2 , SEMA3C , PTPN6 , and SOCS6 ). Conclusion The findings indicated that YT exhibited a decrease in the level of genetic diversity and was a relatively independent indigenous pig breed. It should be protected scientifically and effectively as a valuable germplasm resource. Selection signatures in genomic regions linked to important economic traits in YT. Our results will provide a valuable basis for the future effective protection, breeding, and utilization of YT. Yantai Black pig Single nucleotide polymorphism Genetic diversity Population structure Selection signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background As is well known, the pig is one of the earliest domesticated animals. During the domestication process, rich phenotypic diversity has emerged among different pig breeds, due to regional variations in preferences for pig breed characteristics. After a prolonged period of natural selection and strong artificial selection, China has cultivated an abundance of genetic resources for pig breeds and hosts nearly one-third of all pig breeds in the world [ 1 ]. However, the genetic diversity and population size of Chinese indigenous pig breeds have been declining, owing to the continuous and large-scale introduction of Western pig breeds and crossbreeding for commercial interests [ 2 ]. Consequently, it has become necessary to evaluate the genetic diversity and population structure of Chinese indigenous pig breeds to develop more effective conservation strategies and prevent further genetic loss in these breeds. With the rapid development of sequencing technology and decreasing sequencing costs, single nucleotide polymorphism (SNP) chips and whole-genome resequencing have increasingly been used to study the genetic diversity, population structure, and selection signatures of pigs [ 3 , 4 ]. A large number of candidate genes and genetic markers associated with adaptive, phenotypic, and economically important traits have been identified through genome-level studies. These findings not only contribute to a deeper understanding of the origin, domestication mechanisms, and selection processes but also provide an important basis for the genetic improvement of pigs. YT is a valuable Chinese indigenous pig breed primarily distributed in Yantai City, Shandong Province. YT exhibits distinctive characteristics, such as coarse-feeding tolerance, earlier sexual maturation, and superior meat quality. Additionally, YT is well known for its higher proportion of intramuscular fat (IMF) and better antioxidative ability of pork [ 5 ]. However, YT also shares some notable shortcomings common to many Chinese indigenous pig breeds, such as slower growth rates and lower carcass lean percentages [ 6 ]. In recent years, the main producing area of YT has been gradually shrinking, and the population has been decreasing annually due to the promotion of hybrid utilization of pig breeds. Therefore, it is crucial to enhance the protection and utilization of YT. So far, research on YT remains scarce. In particular, there has been no investigation into the genome-wide genetic characterization of YT. The genetic diversity at the genomic level and the molecular genetic basis of the important economic traits of YT remain unclear. Comparing indigenous pig breeds with WB can help identify selected genes or genomic regions during domestication and selection. To promote the efficient conservation and sustainable development of YT, we detected the genotypes of YT using the Illumina Porcine 50K SNP BeadChip and integrated the resequencing genomic data of YT and WB to investigate the genetic diversity, family information, population structure, and selection signatures within YT. SNPs were identified to analyze genetic diversity and population structure, including runs of homozygosity-based inbreeding coefficients ( F ROH) , neighbor-joining (NJ) tree construction, principal component analysis (PCA), and ADMIXTURE analysis. Moreover, the F ST , π ratio, and Tajima's D methods were employed to filter selection signatures and annotate candidate genes associated with important economic traits. Methods Animals In line with the principle that the sampled individuals must represent all genetic lineages within the population, researchers selected a total of 102 unrelated pigs for ear sampling from the YT Conservation Farm and Breeding Farm located in Yantai, Shandong Province, comprising 47 boars and 55 sows. Every effort was made to minimize stress and discomfort during the procedure. The pigs were treated humanely during sampling and were released afterwards, with no animals sacrificed in this study. Ear tissues were immediately placed in a centrifuge tube containing anhydrous ethanol and stored in a refrigerator at -80°C. Genotyping and quality control Genomic DNA (gDNA) was extracted using TIANamp gDNA kits (Tiangen Biotech, Beijing, China). The concentration and purity of gDNA were assessed using a NanoDrop ND-2000 (Thermo Fisher, Waltham, USA), with all DNA samples having a light absorption ratio (A260/A280) between 1.8 and 2.0 and a concentration of ≥ 50 ng/µL deemed eligible for genotyping. Individual genotyping was conducted using the “Zhongxin-Ⅰ” Porcine Chip (Beijing Compass Agritechnology Co., Ltd., Beijing, China), which contains 51315 SNPs across 18 autosomes and 2 sex chromosomes. PLINK software (v1.90) was used for quality control. For comparison, data sets of 8 Asian wild boar (WA) and 8 European wild boar (WE) were downloaded from the DRYAD website (http://datadryad.org/dataset/doi: 10.5061/dryad.r6t26 ) [ 7 ], while 20 YT resequenced data sets were downloaded from the National Center for Biotechnology Information (NCBI) website [ 8 ]. Genetic diversity analysisa Ne, P N , He, H O , and polymorphism information content (PIC) are widely evaluated parameters in the analysis of the genetic diversity using PLINK (v1.90) [ 9 – 11 ]. Genetic relationships and population structure analysis We employed GCTA (v1.94) to calculate kinship values among individuals, and heat maps were used to visualize the results of kinship. PLINK (v1.90) was utilized to construct an identity-by-sate (IBS) distance matrix. Genetic Distance (D) refers to the probability that two individuals are non homomorphic at the genomic level and is therefore defined as the genetic distance between them: D = \(\:{\text{D}}_{\text{S}\text{T}}\) . D ST denotes the probability of two individuals exhibiting homomorphism at the genomic level. The calculation formula is as follows: \(\:{\text{D}}_{\text{S}\text{T}}\) = \(\:\frac{0.5\ast\:\text{I}\text{B}\text{S}1+\text{I}\text{B}\text{S}2}{\text{N}}\) . IBS1 represents the number of loci with the same observation value, IBS2 denotes the number of loci with the same observation value, and N signifies the total number of marker loci. Based on the IBS distance matrix, the family was clustered using the NJ method and visualized using MEGA (v5.0) software. Additionally, we quantified the distribution of male and female pigs across various familial groups. Inbreeding coefficient analysis The length of ROH for each sample was calculated using Plink (v1.90). Subsequently, we categorized the ROHs into five types: 1 ~ 5, 5 ~ 10, 10 ~ 15, 15 ~ 20, and > 20 Mb. F ROH was determined by calculating the ratio of the total length of ROH fragments in an individual to the overall length of the autosomal genome [ 12 ]. Selection signatures analysis The raw data obtained from the YT using the Zhongxin-Ⅰ” Porcine Chip and whole-genome resequencing were converted to Sscrofa 11.1 genome assembly using LiftOver software. We utilized VCFtools (v0.1.15) to merge and filter the dataset in conjunction with the downloaded WB re-sequencing data, following specific criteria: Hardy-Weinberg equilibrium (HWE) greater than 0.000001, focusing on chromosomes 1 through 18. Ultimately, a total of 31002 SNPs were identified for subsequent analyses. The NJ tree was constructed with PLINK (v1.90) using the matrix of pairwise genetic distances and visualized with MEGA (v5.0). PCA was conducted using PLINK (v1.90) through a dimensionality reduction clustering approach. Additionally, ADMIXTURE (v1.30) was utilized to analyze the population structure, estimating the proportion of variation in each individual's genome that originates from K ancestral populations based on the assumed number of ancestral populations. We calculated the population structure of 118 pigs from the two groups when K was set between 2 to 4. The F ST , π ratio, and Tajima's D were calculated using sliding windows with a window size of 100 kb and a step size of 10kb. Regions under selection were selected based on their belonging to the top 5% of windows. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene ontology (GO) terms were conducted based on the candidate genes via F ST , π ratio Tajima's D methods using KOBAS-intelligence ( http://bioinfo.org/kobas ) to investigate the biological enrichment of genes under selective pressure. The GO terms and KEGG pathways were considered significantly enriched only when the P ≤ 0.05. Results Genotype quality control We excluded 702 SNPs with call rates less than 90%, 1738 SNPs with MAF less than 0.01, 460 SNPs with HWE P -value less than 1 × 10 − 6 , and 6562 SNPs located on sex chromosomes. Finally, a total of 102 individuals and 47998 high-quality SNPs were identified for subsequent analysis (Table 1 ). Chromosome 1 had the highest number of SNPs, totaling 5944, while chromosome 18 had the lowest, with 1193 (Fig. 1 ). Table 1 SNP quality control statistics Quality control standard Number of SNPs Total number of SNPs 57466 SNP with MAF < 0.01 1738 SNP notin Hardy-Weinberg equilibrium ( P < 10 − 6 ) 460 SNP with callrate < 0.90 702 SNPs on chromosome X 4252 SNPs on chromosome 0 2310 Insertion/deletion 6 SNPs used after quality control 47998 Genetic diversity of YT population The analysis of genetic diversity in the YT population was presented in Table 2 . The Ne, and P N were determined to be 5.0, and 0.917, respectively. Here, H O (0.361) was lower than He (0.374), indicating that natural selection or inbreeding have occurred in the YT population. The PIC of SNPs was 0.286, with 60.98% of SNPs classified as moderately polymorphic (PIC > 0.3). Specific distribution ranges can be observed in Fig. 2 A. The MAF ranged from 0 to 0.5, with an average of 0.279. The smallest proportion feel within the range of 0-0.1, accounting for 14.85% (Fig. 2 B). Table 2 Results of YT population genetic diversity analysis Items Data Ne 5.000 P N 0.917 He 0.374 H O 0.361 PIC 0.286 MAF 0.279 Kinship of YT population The distribution range of IBS genetic distance within the YT population spans from 0.106 to 0.3864, with an average IBS genetic distance of 0.3027 (Table S1 ). The majority of individuals exhibit relatively long IBS genetic distances, indicating a moderate level of genetic relationship (depicted in purple); however, some individuals display close IBS genetic distances (depicted in yellow), suggesting a high risk of inbreeding within this subgroup (Fig. 3 A). Subsequently, the genetic relatedness of the YT population was further confirmed through the genomic relationship G matrix established by the SNP loci (Fig. 3 B). The findings regarding genetic relationship derived from the G matrix align with those obtained from the IBS genetic distance matrix, suggesting that certain individuals within the YT population have closer genetic relations (depicted in the purple section). Inbreeding coefficient of YT population A total of 4344 ROHs were identified in the YT population, with an average of 42.59 ± 0.8 ROHs per individual (Table S2). The results revealed that the fewest ROHs, constituting 4.44%, were observed in the 15–20 Mb range, while the highest number, accounting for 54.63%, were found in the 1–5 Mb range (Fig. 4 A). Chromosome 13 exhibited the highest number of ROHs, totaling 500, whereas chromosome 18 displayed the lowest count at 96. The distribution of ROHs across the other chromosomes was relatively consistent (Fig. 4 B). The total length of ROH in each YT individual ranged from 100.46 to 1009.45 Mb, with an average total length of 36.82 Mb. Notably, the majority of individuals had ROH lengths between 200–300 Mb, representing 31.37% of the population (Fig. 4 C). The range of inbreeding coefficients, spanning from 0.042 to 0.422, with a mean value of 0.151, further substantiates this perspective (Fig. 4 D). Family structure of YT population The findings showed that 47 boars were grouped into eight families sharing common genetic ancestors (Fig. 5 A). Furthermore, taking into account the genomic relationships among sows and boars from various familial lineages, the sows were assigned to eight distinct boar families (Fig. 5 B). Aside from the eight families with boars, the YT population also comprised two sows that were genetically unrelated to all the boars; consequently, they were classified in the "other" category. The quantity of boars and sows in each family was detailed in Table 3 . In addition, sows from certain families were interbred and distributed across several distinct families. For example, sow YTM 46 was concurrently assigned to families 1, 2, and 6. Table 3 Consanguinity family construction in YT population Family Gender Quantity 1 Boar 15 Sow 24 2 Boar 6 Sow 23 3 Boar 5 Sow 17 4 Boar 9 Sow 24 5 Boar 6 Sow 16 6 Boar 10 Sow 28 7 Boar 5 Sow 20 8 Boar 17 Sow 0 Other Sow 2 Population structure and relationships From the NJ tree (Fig. 6 A), the two WB populations formed their own separate cluster; however, all individuals of YT were divided into two different branches, which corresponded to the geographic distribution information of this population. The result of the PCA (Fig. 6 B) showed that YT and WB were effectively separated, with 57.4%, 23.9%, and 18.8% of the total genetic variation explained by the first, second and third principal components (PC1, PC2 and PC3). In YT, the phylogenetic relationship between the two groups was relatively distant. Additionally, the ADMIXTURE analysis (Fig. 6 C) at K = 3 indicated that YT and WB were differentiated, with a certain proportion of EWB were still evidenced in YT. Functional enrichment analysis of Candidate genes under selection signatures The selection signatures in YT were detected across the autosomes using F ST , π ratio, and Tajima's D methods by comparing to those in WB. The Manhattan plot of the distribution of F ST , π ratio and Tajima's D values is shown in Fig. 7 . A total of 7787, 7532, and 757 windows with the top 5% of F ST value ( F ST ≥0.3064), π ratio (π ratio ≥ 1.5145), and Tajima's D value (Tajima's D ≥ 2.3880) were identified, respectively (Table S). Combined F ST , π ratio and Tajima's D value approaches, a total of 125 selected genes were identified (Fig. 8 ). Furthermore, we applied three analytical methods ( F ST , π ratio and Tajima's D) to characterize the candidate genes, revealing a strong positive selection signal in YT (Fig. 9 ). GO analysis of the candidate genes showed that 33 genes were significantly enriched ( P -value < 0.05) in 177 terms (Table S3), including 17 molecular functions (MFs), 6 cellular components, and 154 biological processes (BPs). And 12 genes were enriched in the top 10 GO terms with the smallest P -value (Table 4 ), including growth (GO:0040007), negative regulation of neuron apoptotic process (GO:0043524), developmental growth (GO:0048589), developmental growth involved in morphogenesis (GO:0060560), regulation of neuron apoptotic process (GO:0043523), negative regulation of cell adhesion (GO:0007162), regulation of multicellular organismal development (GO:2000026), neuron apoptotic process (GO:0051402), regulation of cell differentiation (GO:0045595), and regulation of cell development (GO:0060284). KEGG enrichment analysis showed that 24 genes were significantly enriched ( P -value < 0.05) in eight pathways, including protein digestion and absorption (ssc04974), axon guidance (ssc04360), proteoglycans in cancer (ssc05205), ErbB signaling pathway (ssc04012), PI3K-Akt signaling pathway (ssc04151), transcriptional misregulation in cancer (ssc05202), glutamatergic synapse (ssc04724), and neuroactive ligand signaling (ssc04082). Among the 32 genes enriched in the ten most important GO terms and eight KEGG pathways, 26 genes under selection might be associated with pigmentation ( MME ), growth and development ( DCC , NFKBIZ , TNR , LRRC4C , ERBB4 , PRKN , TMEM182 , SPRY1 , and MYC ), reproduction ( INHBA , COL12A1 , ADRA2A , and DROSHA ), meat quality ( NRG1 , GRM8 , GRIK2 , EFNA5 , COL9A1 , and CHL1 ), and immune response ( SEMA3E , RUNX2 , GRIN3A , PKN2 , SEMA3C , PTPN6 , and SOCS6 ). Table 4 The top 10 GO terms and eight KEGG pathways with the smallest P -value Terms/Pathways P -value Genes GO:0040007 ~ growth 1.1342×10 − 5 SEMA3E , SOCS6 , TNR , INHBA , TMEM182 , PRKN , SPRY1 , XRCC2 GO:0043524 ~ negative regulation of neuron apoptotic process 8.8589×10 − 5 SEMA3E , PRKN , XRCC2 , CHL1 GO:0048589 ~ developmental growth 1.3098×10 − 4 SEMA3E , TNR , TMEM182 , PRKN , SPRY1 , XRCC2 GO:0060560 ~ developmental growth involved in morphogenesis 2.1913×10 − 4 SEMA3E , TNR , PRKN , SPRY1 GO:0043523 ~ regulation of neuron apoptotic process 2.6242×10 − 4 SEMA3E , PRKN , XRCC2 , CHL1 GO:0007162 ~ negative regulation of cell adhesion 4.9841×10 − 4 SEMA3E , SOCS6 , TNR , PTPN6 GO:2000026 ~ regulation of multicellular organismal development 5.2365×10 − 4 SEMA3E , CBLN2 , TNR , INHBA , MYC , SPRY1 , XRCC2 , PTPN6 GO:0051402 ~ neuron apoptotic process 5.7538×10 − 4 SEMA3E , PRKN , XRCC2 , CHL1 GO:0045595 ~ regulation of cell differentiation 6.7880×10 − 4 SEMA3E , TNR , INHBA , TMEM182 , MYC , SPRY1 , XRCC2 , PTPN6 GO:0060284 ~ regulation of cell development 7.7923×10 − 4 SEMA3E , TNR , INHBA , MYC , XRCC2 , PTPN6 ssc04974 ~ Protein digestion and absorption 4.4585×10 − 3 MME , COL25A1 , COL9A1 , COL12A1 ssc04360 ~ Axon guidance 6.2275×10 − 3 SEMA3E , LRRC4C , DCC , EFNA5 , SEMA3C ssc05205 ~ Proteoglycans in cancer 9.9561×10 − 3 FZD8 , ERBB4 , MYC , DROSHA , PTPN6 ssc04012 ~ ErbB signaling pathway 1.6823×10 − 2 ERBB4 , MYC , NRG1 ssc04151 ~ PI3K-Akt signaling pathway 2.5036×10 − 2 ERBB4 , TNR , MYC , EFNA5 , PKN2 , COL9A1 ssc05202 ~ Transcriptional misregulation in cancer 3.1197×10 − 2 RUNX2 , MYC , ERG , NFKBIZ ssc04724 ~ Glutamatergic synapse 3.7913×10 − 2 GRIK2 , GRM8 , GRIN3A ssc04082 ~ Neuroactive ligand signaling 3.8588×10 − 2 GRIK2 , ADRA2A , GRM8 , GRIN3A Discussion The YT is a popular Chinese indigenous pig breed, but its historical pedigree records are inaccurate. The lack of knowledge regarding genetic relationships may lead to inadvertent inbreeding, consequently diminishing the genetic diversity within the conservation population. Molecular genetic analysis based on high-throughput sequencing data can improve or support efforts to maintain genetic diversity. SNP chip technology allows for the simultaneous detection and analysis of thousands to millions of SNP loci, revealing genotype variations among distinct individuals. In the present study, the “Zhongxin-Ⅰ” Porcine Breeding Chip was employed for individual genotyping, as it is suitable not only for commercial pig breeds but also for the genome analysis of Chinese indigenous pig breeds, offering high-efficiency and accuracy advantages [ 12 , 13 ]. The application of this chip will provide an effective approach to comprehensively understanding the genetic attributes and evolutionary history of the YT population. Using genotype data to assess the Ne of the current population is a significant research focus in conservation genetics. The Ne of the YT population was 5.0, which is notably lower than that of other Chinese indigenous pig breeds [ 14 , 15 ], yet only slight higher than Rongchang pig [ 12 ] and Hechuan black pig [ 16 ]. A reduced Ne indicates diminished genetic diversity, which may lead to the accumulation of genetic defects, an increased risk of extinction, and compromised genetic health. We propose that the reasons for this result may include limited population size, a high degree of inbreeding, and a closed nucleus breeding scheme, all of which have contributed to a decreased level of genetic diversity in this population. Therefore, in conserving the YT population, it is essential to develop mating schemes to prevent the loss of independent familial lineages and to increase genetic exchange by introducing new individuals from other YT conservation farms. The PIC in this population was 0.286, which is also lower than that of other indigenous pig breeds, such as Laiwu, Jiaxing, and Shazi Ling pigs [ 17 ]. This variation may be attributed to differences between the sample and the statistical methods used. Heterozygosity is considered a useful parameter for elucidating the genetic structure and evolutionary trends of populations. In this study, He was slightly higher than H O , which is inconsistent with findings from various Chinese indigenous pig breeds [ 18 ], indicating a certain degree of kinship and inbreeding within the YT population. Compared to previous study [ 19 ], the heterozygosity (He and H O ) in the YT population in this study was higher, which may be due to optimized breeding schemes or an increase in families with higher productivity. Additionally, parameters such as P N , PIC, and MAF were examined to assess gene polymorphism from various perspectives. When compared to other Chinese indigenous pig breeds, such as the Licha black pig [ 20 ] and Fengjing pig [ 21 ], the indicators for the YT population were found to be at moderate to high levels, suggesting relatively high genetic diversity and polymorphism information. To maintain the genetic health and diversity of the population while ensuring effective conservation efforts, it is recommended to prioritize expanding the conservation population, enhancing gene flow to increase Ne, facilitating inter-population communication, judiciously introducing new bloodline genes to enhance genetic diversity, implementing appropriate reproductive management to mitigate inbreeding effects, and reinforcing genetic monitoring and management practices to sustain the genetic integrity of the population. The accuracy and completeness of pedigrees play key roles in the breeding process. However, unsystematic data recording errors are inevitable at local pig conservation farms, and the estimate of pedigree error rate can reach 10%. A genomic relationship matrix can reflect the kinship between individuals and can be used to help correct pedigree errors, thereby effectively protecting the YT population. Through cluster analysis and family construction, 47 boars were categorized into eight families. Furthermore, based on the genetic relationships between the sows and boars from different families, two sows were identified within the YT population that had distant blood relationships with these known families. These findings emphasize the necessity of thoroughly observing the genomic relationships and familial structures within the YT population. To safeguard the genetic health and diversity of the YT population, it is essential to maintain a balanced population comprising individuals from various lineages. The differing lengths of ROH are associated with the genetic backgrounds of individuals and their common ancestors. As genetic relatedness rises, the length of ROHs also expands. ROH contains a wealth of genetic information that can be utilized to assess inbreeding levels and provide a more accurate description of relationship than pedigrees, as well as to identify and screen functional genes associated with economically important traits. Bosse et al. found that ROH lengths greater than 5 Mb were as accurate as genome sequencing [ 22 ]. In this study, 45.37% of the ROHs were greater than 5 Mb, which may be due to SNP chip data not covering all loci on the genome, resulting in ascertainment bias regarding genetic diversity. Thus, resequencing data will be generated and analyzed for this breed in the future, particularly concerning the genetic mechanism underlying the multi-vertebral trait. In addition, the length of ROHs can reflect the time at which inbreeding occurred, and the proportion of ROHs longer than 10 Mb among all ROHs can reach 21.02%. These long ROHs are indicative of recent inbreeding. Based on genomic information, F ROH might be a more accurate alternative for estimating animal relatedness and inbreeding levels in theory. We calculated the F ROH of each individual, and the average inbreeding coefficient of the entire conserved population was 0.151, indicating the presence of some degree of inbreeding among individuals in the population. For highly inbred individuals, breeders need to focus on the potential risk of inbreeding depression. Compared with other Chinese indigenous pig breeds, the inbreeding coefficient of the YT population was higher than that of the Licha [ 20 ] and Tongcheng [ 4 ] pig populations, but lower than that of the Fengjing [ 21 ], and Erhualian [ 18 ] pig populations. Both the limited population size and the relatively closed operation system may have affected the level of inbreeding. Thus, to reduce the potential for inbreeding depression, special preservation programs should be implemented, such as maintaining an equivalent number of boars and sows in each family and selecting individuals with a kinship coefficient of less than 0.1 for mating. Additionally, given the relatively high overall level of inbreeding within the population, it is advisable to expand the search area to identify new individuals of YT or similar breeds. This approach aims to enhance genetic diversity within the population and mitigate the escalation of inbreeding. Furthermore, conducting annual monitoring of the population’s genetic diversity and inbreeding status is recommended to clarity conservation efforts and further reduce inbreeding rates. Finally, prioritizing the establishment of frozen semen banks for various families is essential to safeguard genetic resources against loss from mortality caused by diseases or other unexpected events. Investigating the population structure of indigenous pig breeds is crucial for understanding, evaluating, protecting, and utilizing these resources. In this study, we integrated genotyping results of “Zhongxin-Ⅰ” Porcine Chip, whole-genome resequencing data from 20 YT and 16 WB. The NJ tree and PCA distinctly separated YT from WB, indicating that YT possesses genetic characteristics resulting from long-term domestication and selection. However, 102 YT individuals clustered into two branches, reflecting significant genetic differences among the two population of YT [ 19 ]. Analyzing selection genomic signatures can provide insights into the genetic mechanisms underlying pig adaptive phenotypes and identify important candidate genes related to desirable economic traits. YT exhibits characteristics unique body shape and appearance, young age of sexual maturity, coarse-feeding tolerance, delicious meat quality, and adaptability due to years of domestication and selection. Therefore, it is expected that certain selection signatures exist in the YT genome. A total of 125 commonly selected genes were identified by comparing YT with WB, focusing on widow regions that ranked in the top 5% for both F ST , π ratio, and Tajima's D. Functional enrichment analyses revealed that several candidate genes may play significantly roles in pigmentation, growth and development, reproduction, meat quality, and immune response. Among the candidate genes under selection, DCC , NFKBIZ , TNR , LRRC4C , ERBB4 , TMEM182 , SPRY1 , and MYC were potentially undergoing selection and were functionally associated with growth and development. GWAS and multi-omics analysis indicate that the expression levels of NFKBIZ and LRRC4C can influence the feed conversion rate of pigs and chickens [ 23 , 24 ]. TNR has been identified as a candidate gene associated with the domestic genetic imprint of indigenous pigs, involved in processes such as synaptic transmission and brain development [ 25 ]. ERBB4 , as a ligand of Neuregulin-2, is implicated in physiological functions including individual growth and development, and cell differentiation [ 26 ]. SPRY1 , a modulator of fibroblast growth factor, epidermal growth factor signaling and receptor tyrosine kinase signaling pathways, plays a critical role in tissue development [ 27 ]. Research showed that MYC regulates the expression of key metabolic enzymes during the four-cell stage, influencing porcine early embryonic metabolism and epigenetic reprogramming [ 28 ]. Thus, our findings may offer insight into the role of candidate genes in the growth and development of YT. Four selected genes associated with reproduction were identified, including INHBA , COL12A1 , ADRA2A , and DROSHA . INHBA gene was considered associated with porcine litter (TNB and NBA) and carcass traits[ 29 , 30 ]. ADRA2A gene was linked to sperm motility in cattle and pigs, associated with sperm-specific functions such as capacitation, acrosome reaction, and motility [ 31 ]. Downregulation of DROSHA gene expression may correlate with reduced production of progesterone by CL in response to luteolytic signal from endometrial PGF2α [ 32 ]. We hypothesized that these genes may also be associated with the characteristics of earlier age of sex maturity and larger litter size in YT, but the exact effects required further investigation. Six genes associated with meat quality, NRG1 , GRM8 , GRIK2 , EFNA5 , COL9A1 , and CHL1 , were identified in this study. Based on functional and transcription factor-gene networks, NRG1 merges as a promising candidate genes for pork pH [ 33 ]. A study found that GRM8 was associated with the relative area of longissimus dorsi muscle fiber type I, thereby considered a plausible candidate gene for this trait [ 34 ]. The genome-wide association study for visible intermuscular fat in the Italian Large White breed identified the most significant SNP located within the largest QTL region in the GRIK2 gene [ 35 ]. Previous research indicated that EFNA5 overlapped between the candidate gene from GWAS and transcriptome analysis, situated in a new QTL significantly related to meat color [ 36 ]. COL9A1 was found to be significantly expressed in the skin tissue of Kele pigs, relating to collagen traits [ 37 ]. CHL1 is regarded as a strong candidate gene for drip loss and was linked to insulin secretion and glucose metabolism [ 38 ]. Notably, YT exhibit excellent meat quality with an IMF content of longissimus dorsi exceeding 4.5% [ 5 ], potentially attributable to these selected genes during domestication and breeding. Moreover, several genes related to immune response, including SEMA3E , RUNX2 , GRIN3A , PKN2 , SEMA3C , PTPN6 , and SOCS6 , were also selected in YT. SEMA3C gene might contributed to the development of porcine stromal inflammation or rejection [ 39 ]. SEMA3E is essential for dampening the early inflammatory response to LPS by regulating macrophage function, indicating its pivotal role in macrophage inflammatory response [ 40 ]. RUNX2 , as a transcription factor, was involved in humoral immune response [ 41 ]. Prior research have identified PTPN6 gene as being related to the regulation of type Ⅰ interferon-mediated signaling pathway [ 42 ]. The Suppressor of Cytokine Signaling (SOCS) proteins are crucial regulators of the immune system, with SOCS6 being highly expressed in the large and small intestine tissues of pigs [ 43 ]. It was well-known that YT had been domesticated and raised in complex environments and crude feeding conditions for extended periods, leading to strong adaptability and stress resistance. Therefore, it is worthwhile to further investigate whether these candidate genes are associated with the remarkable adaptability of YT by regulating relevant immune processes. Conclusion This study represents the first genetic survey of the genetic diversity and family structure of the YT population. Five statistics (Ne, P N , H O , He, and F ROH ) collectively indicate the necessity to increase the level of genetic diversity within the current population and to mitigate the potential risk of inbreeding depression. The obtained genomic family information can better illustrate the kinship among individuals and provide a theoretical foundation for developing mating plans. Furthermore, YT exhibited a unique population structure and is genetically differentiated into two subgroups within the population. The genomic candidate genes influenced by natural or artificial selection are associated with traits related to growth and development, reproduction, meat quality, and immune response. Our findings may strengthen the conservation and sustainable development of YT and promote the understanding of the formation mechanisms of various economic traits in YT. Declarations Ethics approval and consent to participate All animal care and treatment procedures were approved by the Animal Ethics Committee of Shandong Agricultural University, China, and were conducted in accordance with the committee’s guidelines and regulations (Approval No.: 2004006). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding The authors are grateful for the support by the Fundamental Research Projects of Science&Technology Innovation and development Plan in Yantai City (No. 2024JCYJ103) and the Yantai Seed Industry Revitalization Project. Author Contribution M.Q., R.L.L., S.Y.M. and C.M. conceived and planned the research. M.Q. and R.L.L. supervised the research. Y.X.Z., Z.G.W., G.D.L., M.Z.L., J.Y.W., P.J., Y.F.S. and Z.P.T. performed the experiments. C.M. and S.YM. Conducted data analysis and wrote the manuscript. M.Q., S.Y.M. and C.M. edited the manuscript. All authors have read and agreed to the published version of the manuscript. Acknowledgement We thank Laizhou Yantai Black Pig Breeding Farm, Jinlai Yantai Black Pig Breeding Farm, Tushan Town, Laizhou City, and Yantai Tuomu Black Pig Breeding Farm for providing the Yantai Black pig samples. Data Availability The raw sequence data reported in this paper have been deposited in the Sequence Read Archive (SRA) under accession number PRJNA1258694. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request. References Yang SL, Wang ZG, Liu B, Zhang GX, Zhao SH, Yu M, et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9121170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621939577,"identity":"661182d8-9618-410d-abeb-e9317426cf06","order_by":0,"name":"Cai Ma","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Cai","middleName":"","lastName":"Ma","suffix":""},{"id":621939578,"identity":"9c34d3f5-9933-4540-a00d-82c4264d4f9e","order_by":1,"name":"Shaoyang Mou","email":"","orcid":"","institution":"Shandong Yisheng Livestock and Poultry Breeding Co., 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2","display":"","copyAsset":false,"role":"figure","size":795605,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of genetic diversity of YT population. (\u003cstrong\u003eA\u003c/strong\u003e) Content distribution of polymorphic information. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution of minimum allele frequency.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/c3c90219981b3c2a3fff0002.png"},{"id":106961092,"identity":"84282f50-e551-4f07-abf3-c80f9c0a506b","added_by":"auto","created_at":"2026-04-15 09:24:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3012865,"visible":true,"origin":"","legend":"\u003cp\u003eIdentity by state (IBS) distance matrix (\u003cstrong\u003eA\u003c/strong\u003e, the smaller the value is, the closer it is to green, that is, the smaller the genetic distance between two individuals is, that is, the two individuals are similar) and \u003cem\u003eG\u003c/em\u003e matrix (\u003cstrong\u003eB\u003c/strong\u003e, the larger the value is, the closer it is to purple, that is, the closer the relationship between two individuals is) of YT population. Each small square represents the value of the relationship between two pairs from the first sample to the last sample.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/99fe58ed042ae0005bf4f5b1.png"},{"id":106908811,"identity":"07abefc7-a4f8-4757-9e4f-06f8f3c605be","added_by":"auto","created_at":"2026-04-14 16:06:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10557941,"visible":true,"origin":"","legend":"\u003cp\u003eEstimation of inbreeding degree of YT population using ROH. (\u003cstrong\u003eA\u003c/strong\u003e) Distribution of ROH lengths in YT population. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution of ROH numbers on the chromosomes in YT population. (\u003cstrong\u003eC\u003c/strong\u003e) Sample number distribution of individual ROH lengths. (\u003cstrong\u003eD\u003c/strong\u003e) Distribution of the inbreeding coefficient based on runs of homozygosity in YT population.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/bb56e07537c8c76c1cdd8fe5.png"},{"id":106960817,"identity":"67ff6b82-9776-42a9-aa19-8bc278c1e01d","added_by":"auto","created_at":"2026-04-15 09:23:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14321583,"visible":true,"origin":"","legend":"\u003cp\u003eFamily construction of the YT population. (\u003cstrong\u003eA\u003c/strong\u003e) Phylogenetic tree of YT boars. Samples labeled with the same color are evaluated as the same family. (\u003cstrong\u003eB\u003c/strong\u003e) Phylogenetic tree of all individuals in YT population, in which all boars are marked by color, with different colors representing different families.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/c6f491c1f917bd732c068788.png"},{"id":106908814,"identity":"841f3551-d3ca-40a6-b31a-509707971351","added_by":"auto","created_at":"2026-04-14 16:06:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4045307,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic relationship and population structure of YT, WA, and WE. (\u003cstrong\u003eA\u003c/strong\u003e) NJ tree constructed from SNP data among two populations. (\u003cstrong\u003eB\u003c/strong\u003e) PCA plots for the first two three PCs among 118 pigs. (\u003cstrong\u003eC\u003c/strong\u003e) Structure analysis with K=2 to 5.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/ddcecbd675867b7a75dab819.png"},{"id":106961150,"identity":"57474dfe-48c7-493d-8b98-2ee7f6e4ad99","added_by":"auto","created_at":"2026-04-15 09:24:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2357821,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of genomic regions with selection in YT compared to WB. (\u003cstrong\u003eA\u003c/strong\u003e) Distribution of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values among autosomal chromosomes. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution of p ratio among autosomal chromosomes. (\u003cstrong\u003eC\u003c/strong\u003e) Distribution of Tajima's D among autosomal chromosomes. The black dotted line represents the threshold value of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e/p ratio/Tajima's D.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/0fbf3ff538194ebf106c8804.png"},{"id":106960598,"identity":"b7aafa4d-1a29-4acf-ae55-438ee81ca789","added_by":"auto","created_at":"2026-04-15 09:21:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1731633,"visible":true,"origin":"","legend":"\u003cp\u003eThe Venn diagram shows the overlap in the number of candidate genes identified by \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, p ratio, and Tajima's D methods.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/b473c4655100e9db0d94181d.png"},{"id":106908816,"identity":"a5f83086-713f-47bb-ae03-a33e4c10f1c0","added_by":"auto","created_at":"2026-04-14 16:06:50","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":576186,"visible":true,"origin":"","legend":"\u003cp\u003eExample of candidate genes with strong selective sweep signal in YT. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST, \u003c/sub\u003ep ratio, and Tajima's D values are plotted using a 10kb sliding window.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/4d59203223179bda95360d86.png"},{"id":109252252,"identity":"be4027f4-ca87-47ab-9e79-770647af6364","added_by":"auto","created_at":"2026-05-14 09:24:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":42986106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/b06a4986-6403-4be3-b6a3-58e8c6c64ee1.pdf"},{"id":106961787,"identity":"43e2a7da-be28-44b0-acd0-cf6cd3fdfd59","added_by":"auto","created_at":"2026-04-15 09:27:02","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":190010,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9121170/v1/f11a4b9433697b1df9628433.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide analysis reveals genetic characteristics and selection signatures in Yantai Black Pig of Shandong, China","fulltext":[{"header":"Background","content":"\u003cp\u003eAs is well known, the pig is one of the earliest domesticated animals. During the domestication process, rich phenotypic diversity has emerged among different pig breeds, due to regional variations in preferences for pig breed characteristics. After a prolonged period of natural selection and strong artificial selection, China has cultivated an abundance of genetic resources for pig breeds and hosts nearly one-third of all pig breeds in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the genetic diversity and population size of Chinese indigenous pig breeds have been declining, owing to the continuous and large-scale introduction of Western pig breeds and crossbreeding for commercial interests [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, it has become necessary to evaluate the genetic diversity and population structure of Chinese indigenous pig breeds to develop more effective conservation strategies and prevent further genetic loss in these breeds.\u003c/p\u003e \u003cp\u003eWith the rapid development of sequencing technology and decreasing sequencing costs, single nucleotide polymorphism (SNP) chips and whole-genome resequencing have increasingly been used to study the genetic diversity, population structure, and selection signatures of pigs [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A large number of candidate genes and genetic markers associated with adaptive, phenotypic, and economically important traits have been identified through genome-level studies. These findings not only contribute to a deeper understanding of the origin, domestication mechanisms, and selection processes but also provide an important basis for the genetic improvement of pigs.\u003c/p\u003e \u003cp\u003eYT is a valuable Chinese indigenous pig breed primarily distributed in Yantai City, Shandong Province. YT exhibits distinctive characteristics, such as coarse-feeding tolerance, earlier sexual maturation, and superior meat quality. Additionally, YT is well known for its higher proportion of intramuscular fat (IMF) and better antioxidative ability of pork [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, YT also shares some notable shortcomings common to many Chinese indigenous pig breeds, such as slower growth rates and lower carcass lean percentages [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent years, the main producing area of YT has been gradually shrinking, and the population has been decreasing annually due to the promotion of hybrid utilization of pig breeds. Therefore, it is crucial to enhance the protection and utilization of YT. So far, research on YT remains scarce. In particular, there has been no investigation into the genome-wide genetic characterization of YT. The genetic diversity at the genomic level and the molecular genetic basis of the important economic traits of YT remain unclear.\u003c/p\u003e \u003cp\u003eComparing indigenous pig breeds with WB can help identify selected genes or genomic regions during domestication and selection. To promote the efficient conservation and sustainable development of YT, we detected the genotypes of YT using the Illumina Porcine 50K SNP BeadChip and integrated the resequencing genomic data of YT and WB to investigate the genetic diversity, family information, population structure, and selection signatures within YT. SNPs were identified to analyze genetic diversity and population structure, including runs of homozygosity-based inbreeding coefficients (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH)\u003c/sub\u003e, neighbor-joining (NJ) tree construction, principal component analysis (PCA), and ADMIXTURE analysis. Moreover, the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio, and Tajima's D methods were employed to filter selection signatures and annotate candidate genes associated with important economic traits.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eIn line with the principle that the sampled individuals must represent all genetic lineages within the population, researchers selected a total of 102 unrelated pigs for ear sampling from the YT Conservation Farm and Breeding Farm located in Yantai, Shandong Province, comprising 47 boars and 55 sows. Every effort was made to minimize stress and discomfort during the procedure. The pigs were treated humanely during sampling and were released afterwards, with no animals sacrificed in this study. Ear tissues were immediately placed in a centrifuge tube containing anhydrous ethanol and stored in a refrigerator at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenotyping and quality control\u003c/h3\u003e\n\u003cp\u003eGenomic DNA (gDNA) was extracted using TIANamp gDNA kits (Tiangen Biotech, Beijing, China). The concentration and purity of gDNA were assessed using a NanoDrop ND-2000 (Thermo Fisher, Waltham, USA), with all DNA samples having a light absorption ratio (A260/A280) between 1.8 and 2.0 and a concentration of \u0026ge;\u0026thinsp;50 ng/\u0026micro;L deemed eligible for genotyping. Individual genotyping was conducted using the \u0026ldquo;Zhongxin-Ⅰ\u0026rdquo; Porcine Chip (Beijing Compass Agritechnology Co., Ltd., Beijing, China), which contains 51315 SNPs across 18 autosomes and 2 sex chromosomes. PLINK software (v1.90) was used for quality control. For comparison, data sets of 8 Asian wild boar (WA) and 8 European wild boar (WE) were downloaded from the DRYAD website (http://datadryad.org/dataset/doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5061/dryad.r6t26\u003c/span\u003e\u003cspan address=\"10.5061/dryad.r6t26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while 20 YT resequenced data sets were downloaded from the National Center for Biotechnology Information (NCBI) website [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGenetic diversity analysisa\u003c/h3\u003e\n\u003cp\u003eNe, P\u003csub\u003eN\u003c/sub\u003e, He, H\u003csub\u003eO\u003c/sub\u003e, and polymorphism information content (PIC) are widely evaluated parameters in the analysis of the genetic diversity using PLINK (v1.90) [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGenetic relationships and population structure analysis\u003c/h3\u003e\n\u003cp\u003eWe employed GCTA (v1.94) to calculate kinship values among individuals, and heat maps were used to visualize the results of kinship. PLINK (v1.90) was utilized to construct an identity-by-sate (IBS) distance matrix. Genetic Distance (D) refers to the probability that two individuals are non homomorphic at the genomic level and is therefore defined as the genetic distance between them: \u003cem\u003eD\u003c/em\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}}_{\\text{S}\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e. D\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e denotes the probability of two individuals exhibiting homomorphism at the genomic level. The calculation formula is as follows: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}}_{\\text{S}\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{0.5\\ast\\:\\text{I}\\text{B}\\text{S}1+\\text{I}\\text{B}\\text{S}2}{\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e. IBS1 represents the number of loci with the same observation value, IBS2 denotes the number of loci with the same observation value, and N signifies the total number of marker loci. Based on the IBS distance matrix, the family was clustered using the NJ method and visualized using MEGA (v5.0) software. Additionally, we quantified the distribution of male and female pigs across various familial groups.\u003c/p\u003e\n\u003ch3\u003eInbreeding coefficient analysis\u003c/h3\u003e\n\u003cp\u003eThe length of ROH for each sample was calculated using Plink (v1.90). Subsequently, we categorized the ROHs into five types: 1\u0026thinsp;~\u0026thinsp;5, 5\u0026thinsp;~\u0026thinsp;10, 10\u0026thinsp;~\u0026thinsp;15, 15\u0026thinsp;~\u0026thinsp;20, and \u0026gt;\u0026thinsp;20 Mb. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e was determined by calculating the ratio of the total length of ROH fragments in an individual to the overall length of the autosomal genome [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection signatures analysis\u003c/h2\u003e \u003cp\u003eThe raw data obtained from the YT using the Zhongxin-Ⅰ\u0026rdquo; Porcine Chip and whole-genome resequencing were converted to Sscrofa 11.1 genome assembly using LiftOver software. We utilized VCFtools (v0.1.15) to merge and filter the dataset in conjunction with the downloaded WB re-sequencing data, following specific criteria: Hardy-Weinberg equilibrium (HWE) greater than 0.000001, focusing on chromosomes 1 through 18. Ultimately, a total of 31002 SNPs were identified for subsequent analyses. The NJ tree was constructed with PLINK (v1.90) using the matrix of pairwise genetic distances and visualized with MEGA (v5.0). PCA was conducted using PLINK (v1.90) through a dimensionality reduction clustering approach. Additionally, ADMIXTURE (v1.30) was utilized to analyze the population structure, estimating the proportion of variation in each individual's genome that originates from K ancestral populations based on the assumed number of ancestral populations. We calculated the population structure of 118 pigs from the two groups when K was set between 2 to 4.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio, and Tajima's D were calculated using sliding windows with a window size of 100 kb and a step size of 10kb. Regions under selection were selected based on their belonging to the top 5% of windows. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene ontology (GO) terms were conducted based on the candidate genes via \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio Tajima's D methods using KOBAS-intelligence (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.org/kobas\u003c/span\u003e\u003cspan address=\"http://bioinfo.org/kobas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate the biological enrichment of genes under selective pressure. The GO terms and KEGG pathways were considered significantly enriched only when the \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenotype quality control\u003c/h2\u003e \u003cp\u003eWe excluded 702 SNPs with call rates less than 90%, 1738 SNPs with MAF less than 0.01, 460 SNPs with HWE \u003cem\u003eP\u003c/em\u003e-value less than 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, and 6562 SNPs located on sex chromosomes. Finally, a total of 102 individuals and 47998 high-quality SNPs were identified for subsequent analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chromosome 1 had the highest number of SNPs, totaling 5944, while chromosome 18 had the lowest, with 1193 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSNP quality control statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality control standard\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of SNPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP with MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP notin Hardy-Weinberg equilibrium (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP with callrate\u0026thinsp;\u0026lt;\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs on chromosome X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs on chromosome 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsertion/deletion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNPs used after quality control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity of YT population\u003c/h2\u003e \u003cp\u003eThe analysis of genetic diversity in the YT population was presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The Ne, and P\u003csub\u003eN\u003c/sub\u003e were determined to be 5.0, and 0.917, respectively. Here, H\u003csub\u003eO\u003c/sub\u003e (0.361) was lower than He (0.374), indicating that natural selection or inbreeding have occurred in the YT population. The PIC of SNPs was 0.286, with 60.98% of SNPs classified as moderately polymorphic (PIC\u0026thinsp;\u0026gt;\u0026thinsp;0.3). Specific distribution ranges can be observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The MAF ranged from 0 to 0.5, with an average of 0.279. The smallest proportion feel within the range of 0-0.1, accounting for 14.85% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of YT population genetic diversity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003csub\u003eN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKinship of YT population\u003c/h2\u003e \u003cp\u003eThe distribution range of IBS genetic distance within the YT population spans from 0.106 to 0.3864, with an average IBS genetic distance of 0.3027 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The majority of individuals exhibit relatively long IBS genetic distances, indicating a moderate level of genetic relationship (depicted in purple); however, some individuals display close IBS genetic distances (depicted in yellow), suggesting a high risk of inbreeding within this subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Subsequently, the genetic relatedness of the YT population was further confirmed through the genomic relationship G matrix established by the SNP loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The findings regarding genetic relationship derived from the G matrix align with those obtained from the IBS genetic distance matrix, suggesting that certain individuals within the YT population have closer genetic relations (depicted in the purple section).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInbreeding coefficient of YT population\u003c/h2\u003e \u003cp\u003eA total of 4344 ROHs were identified in the YT population, with an average of 42.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 ROHs per individual (Table S2). The results revealed that the fewest ROHs, constituting 4.44%, were observed in the 15\u0026ndash;20 Mb range, while the highest number, accounting for 54.63%, were found in the 1\u0026ndash;5 Mb range (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Chromosome 13 exhibited the highest number of ROHs, totaling 500, whereas chromosome 18 displayed the lowest count at 96. The distribution of ROHs across the other chromosomes was relatively consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The total length of ROH in each YT individual ranged from 100.46 to 1009.45 Mb, with an average total length of 36.82 Mb. Notably, the majority of individuals had ROH lengths between 200\u0026ndash;300 Mb, representing 31.37% of the population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The range of inbreeding coefficients, spanning from 0.042 to 0.422, with a mean value of 0.151, further substantiates this perspective (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFamily structure of YT population\u003c/h2\u003e \u003cp\u003eThe findings showed that 47 boars were grouped into eight families sharing common genetic ancestors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Furthermore, taking into account the genomic relationships among sows and boars from various familial lineages, the sows were assigned to eight distinct boar families (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Aside from the eight families with boars, the YT population also comprised two sows that were genetically unrelated to all the boars; consequently, they were classified in the \"other\" category. The quantity of boars and sows in each family was detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In addition, sows from certain families were interbred and distributed across several distinct families. For example, sow YTM 46 was concurrently assigned to families 1, 2, and 6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConsanguinity family construction in YT population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure and relationships\u003c/h2\u003e \u003cp\u003eFrom the NJ tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), the two WB populations formed their own separate cluster; however, all individuals of YT were divided into two different branches, which corresponded to the geographic distribution information of this population. The result of the PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB) showed that YT and WB were effectively separated, with 57.4%, 23.9%, and 18.8% of the total genetic variation explained by the first, second and third principal components (PC1, PC2 and PC3). In YT, the phylogenetic relationship between the two groups was relatively distant. Additionally, the ADMIXTURE analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) at K\u0026thinsp;=\u0026thinsp;3 indicated that YT and WB were differentiated, with a certain proportion of EWB were still evidenced in YT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of Candidate genes under selection signatures\u003c/h2\u003e \u003cp\u003eThe selection signatures in YT were detected across the autosomes using \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio, and Tajima's D methods by comparing to those in WB. The Manhattan plot of the distribution of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio and Tajima's D values is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. A total of 7787, 7532, and 757 windows with the top 5% of \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e value (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u0026ge;0.3064), π ratio (π ratio\u0026thinsp;\u0026ge;\u0026thinsp;1.5145), and Tajima's D value (Tajima's D\u0026thinsp;\u0026ge;\u0026thinsp;2.3880) were identified, respectively (Table S). Combined \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio and Tajima's D value approaches, a total of 125 selected genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Furthermore, we applied three analytical methods (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio and Tajima's D) to characterize the candidate genes, revealing a strong positive selection signal in YT (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGO analysis of the candidate genes showed that 33 genes were significantly enriched (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in 177 terms (Table S3), including 17 molecular functions (MFs), 6 cellular components, and 154 biological processes (BPs). And 12 genes were enriched in the top 10 GO terms with the smallest \u003cem\u003eP\u003c/em\u003e-value (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), including growth (GO:0040007), negative regulation of neuron apoptotic process (GO:0043524), developmental growth (GO:0048589), developmental growth involved in morphogenesis (GO:0060560), regulation of neuron apoptotic process (GO:0043523), negative regulation of cell adhesion (GO:0007162), regulation of multicellular organismal development (GO:2000026), neuron apoptotic process (GO:0051402), regulation of cell differentiation (GO:0045595), and regulation of cell development (GO:0060284). KEGG enrichment analysis showed that 24 genes were significantly enriched (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in eight pathways, including protein digestion and absorption (ssc04974), axon guidance (ssc04360), proteoglycans in cancer (ssc05205), ErbB signaling pathway (ssc04012), PI3K-Akt signaling pathway (ssc04151), transcriptional misregulation in cancer (ssc05202), glutamatergic synapse (ssc04724), and neuroactive ligand signaling (ssc04082).\u003c/p\u003e \u003cp\u003eAmong the 32 genes enriched in the ten most important GO terms and eight KEGG pathways, 26 genes under selection might be associated with pigmentation (\u003cem\u003eMME\u003c/em\u003e), growth and development (\u003cem\u003eDCC\u003c/em\u003e, \u003cem\u003eNFKBIZ\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eLRRC4C\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e), reproduction (\u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eCOL12A1\u003c/em\u003e, \u003cem\u003eADRA2A\u003c/em\u003e, and \u003cem\u003eDROSHA\u003c/em\u003e), meat quality (\u003cem\u003eNRG1\u003c/em\u003e, \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eGRIK2\u003c/em\u003e, \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003eCOL9A1\u003c/em\u003e, and \u003cem\u003eCHL1\u003c/em\u003e), and immune response (\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eRUNX2\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003ePKN2\u003c/em\u003e, \u003cem\u003eSEMA3C\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, and \u003cem\u003eSOCS6\u003c/em\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe top 10 GO terms and eight KEGG pathways with the smallest \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerms/Pathways\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0040007\u0026thinsp;~\u0026thinsp;growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.1342\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eSOCS6\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0043524\u0026thinsp;~\u0026thinsp;negative regulation of neuron apoptotic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e8.8589\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003eCHL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0048589\u0026thinsp;~\u0026thinsp;developmental growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.3098\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0060560\u0026thinsp;~\u0026thinsp;developmental growth involved in morphogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e2.1913\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0043523\u0026thinsp;~\u0026thinsp;regulation of neuron apoptotic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e2.6242\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003eCHL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0007162\u0026thinsp;~\u0026thinsp;negative regulation of cell adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e4.9841\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eSOCS6\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:2000026\u0026thinsp;~\u0026thinsp;regulation of multicellular organismal development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e5.2365\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eCBLN2\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0051402\u0026thinsp;~\u0026thinsp;neuron apoptotic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e5.7538\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003ePRKN\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003eCHL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0045595\u0026thinsp;~\u0026thinsp;regulation of cell differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e6.7880\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0060284\u0026thinsp;~\u0026thinsp;regulation of cell development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e7.7923\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eXRCC2\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04974\u0026thinsp;~\u0026thinsp;Protein digestion and absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e4.4585\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMME\u003c/em\u003e, \u003cem\u003eCOL25A1\u003c/em\u003e, \u003cem\u003eCOL9A1\u003c/em\u003e, \u003cem\u003eCOL12A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04360\u0026thinsp;~\u0026thinsp;Axon guidance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e6.2275\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eLRRC4C\u003c/em\u003e, \u003cem\u003eDCC\u003c/em\u003e, \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003eSEMA3C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc05205\u0026thinsp;~\u0026thinsp;Proteoglycans in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e9.9561\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFZD8\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eDROSHA\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04012\u0026thinsp;~\u0026thinsp;ErbB signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e1.6823\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eNRG1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04151\u0026thinsp;~\u0026thinsp;PI3K-Akt signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e2.5036\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003ePKN2\u003c/em\u003e, \u003cem\u003eCOL9A1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc05202\u0026thinsp;~\u0026thinsp;Transcriptional misregulation in cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e3.1197\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRUNX2\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eERG\u003c/em\u003e, \u003cem\u003eNFKBIZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04724\u0026thinsp;~\u0026thinsp;Glutamatergic synapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e3.7913\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGRIK2\u003c/em\u003e, \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003essc04082\u0026thinsp;~\u0026thinsp;Neuroactive ligand signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c2\"\u003e \u003cp\u003e3.8588\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGRIK2\u003c/em\u003e, \u003cem\u003eADRA2A\u003c/em\u003e, \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe YT is a popular Chinese indigenous pig breed, but its historical pedigree records are inaccurate. The lack of knowledge regarding genetic relationships may lead to inadvertent inbreeding, consequently diminishing the genetic diversity within the conservation population. Molecular genetic analysis based on high-throughput sequencing data can improve or support efforts to maintain genetic diversity. SNP chip technology allows for the simultaneous detection and analysis of thousands to millions of SNP loci, revealing genotype variations among distinct individuals. In the present study, the \u0026ldquo;Zhongxin-Ⅰ\u0026rdquo; Porcine Breeding Chip was employed for individual genotyping, as it is suitable not only for commercial pig breeds but also for the genome analysis of Chinese indigenous pig breeds, offering high-efficiency and accuracy advantages [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The application of this chip will provide an effective approach to comprehensively understanding the genetic attributes and evolutionary history of the YT population.\u003c/p\u003e \u003cp\u003eUsing genotype data to assess the Ne of the current population is a significant research focus in conservation genetics. The Ne of the YT population was 5.0, which is notably lower than that of other Chinese indigenous pig breeds [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], yet only slight higher than Rongchang pig [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and Hechuan black pig [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A reduced Ne indicates diminished genetic diversity, which may lead to the accumulation of genetic defects, an increased risk of extinction, and compromised genetic health. We propose that the reasons for this result may include limited population size, a high degree of inbreeding, and a closed nucleus breeding scheme, all of which have contributed to a decreased level of genetic diversity in this population. Therefore, in conserving the YT population, it is essential to develop mating schemes to prevent the loss of independent familial lineages and to increase genetic exchange by introducing new individuals from other YT conservation farms. The PIC in this population was 0.286, which is also lower than that of other indigenous pig breeds, such as Laiwu, Jiaxing, and Shazi Ling pigs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This variation may be attributed to differences between the sample and the statistical methods used. Heterozygosity is considered a useful parameter for elucidating the genetic structure and evolutionary trends of populations. In this study, He was slightly higher than H\u003csub\u003eO\u003c/sub\u003e, which is inconsistent with findings from various Chinese indigenous pig breeds [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], indicating a certain degree of kinship and inbreeding within the YT population. Compared to previous study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the heterozygosity (He and H\u003csub\u003eO\u003c/sub\u003e) in the YT population in this study was higher, which may be due to optimized breeding schemes or an increase in families with higher productivity. Additionally, parameters such as P\u003csub\u003eN\u003c/sub\u003e, PIC, and MAF were examined to assess gene polymorphism from various perspectives. When compared to other Chinese indigenous pig breeds, such as the Licha black pig [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Fengjing pig [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the indicators for the YT population were found to be at moderate to high levels, suggesting relatively high genetic diversity and polymorphism information. To maintain the genetic health and diversity of the population while ensuring effective conservation efforts, it is recommended to prioritize expanding the conservation population, enhancing gene flow to increase Ne, facilitating inter-population communication, judiciously introducing new bloodline genes to enhance genetic diversity, implementing appropriate reproductive management to mitigate inbreeding effects, and reinforcing genetic monitoring and management practices to sustain the genetic integrity of the population.\u003c/p\u003e \u003cp\u003eThe accuracy and completeness of pedigrees play key roles in the breeding process. However, unsystematic data recording errors are inevitable at local pig conservation farms, and the estimate of pedigree error rate can reach 10%. A genomic relationship matrix can reflect the kinship between individuals and can be used to help correct pedigree errors, thereby effectively protecting the YT population. Through cluster analysis and family construction, 47 boars were categorized into eight families. Furthermore, based on the genetic relationships between the sows and boars from different families, two sows were identified within the YT population that had distant blood relationships with these known families. These findings emphasize the necessity of thoroughly observing the genomic relationships and familial structures within the YT population. To safeguard the genetic health and diversity of the YT population, it is essential to maintain a balanced population comprising individuals from various lineages.\u003c/p\u003e \u003cp\u003eThe differing lengths of ROH are associated with the genetic backgrounds of individuals and their common ancestors. As genetic relatedness rises, the length of ROHs also expands. ROH contains a wealth of genetic information that can be utilized to assess inbreeding levels and provide a more accurate description of relationship than pedigrees, as well as to identify and screen functional genes associated with economically important traits. Bosse et al. found that ROH lengths greater than 5 Mb were as accurate as genome sequencing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this study, 45.37% of the ROHs were greater than 5 Mb, which may be due to SNP chip data not covering all loci on the genome, resulting in ascertainment bias regarding genetic diversity. Thus, resequencing data will be generated and analyzed for this breed in the future, particularly concerning the genetic mechanism underlying the multi-vertebral trait. In addition, the length of ROHs can reflect the time at which inbreeding occurred, and the proportion of ROHs longer than 10 Mb among all ROHs can reach 21.02%. These long ROHs are indicative of recent inbreeding. Based on genomic information, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e might be a more accurate alternative for estimating animal relatedness and inbreeding levels in theory. We calculated the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e of each individual, and the average inbreeding coefficient of the entire conserved population was 0.151, indicating the presence of some degree of inbreeding among individuals in the population. For highly inbred individuals, breeders need to focus on the potential risk of inbreeding depression. Compared with other Chinese indigenous pig breeds, the inbreeding coefficient of the YT population was higher than that of the Licha [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and Tongcheng [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] pig populations, but lower than that of the Fengjing [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and Erhualian [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] pig populations. Both the limited population size and the relatively closed operation system may have affected the level of inbreeding. Thus, to reduce the potential for inbreeding depression, special preservation programs should be implemented, such as maintaining an equivalent number of boars and sows in each family and selecting individuals with a kinship coefficient of less than 0.1 for mating. Additionally, given the relatively high overall level of inbreeding within the population, it is advisable to expand the search area to identify new individuals of YT or similar breeds. This approach aims to enhance genetic diversity within the population and mitigate the escalation of inbreeding. Furthermore, conducting annual monitoring of the population\u0026rsquo;s genetic diversity and inbreeding status is recommended to clarity conservation efforts and further reduce inbreeding rates. Finally, prioritizing the establishment of frozen semen banks for various families is essential to safeguard genetic resources against loss from mortality caused by diseases or other unexpected events.\u003c/p\u003e \u003cp\u003eInvestigating the population structure of indigenous pig breeds is crucial for understanding, evaluating, protecting, and utilizing these resources. In this study, we integrated genotyping results of \u0026ldquo;Zhongxin-Ⅰ\u0026rdquo; Porcine Chip, whole-genome resequencing data from 20 YT and 16 WB. The NJ tree and PCA distinctly separated YT from WB, indicating that YT possesses genetic characteristics resulting from long-term domestication and selection. However, 102 YT individuals clustered into two branches, reflecting significant genetic differences among the two population of YT [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Analyzing selection genomic signatures can provide insights into the genetic mechanisms underlying pig adaptive phenotypes and identify important candidate genes related to desirable economic traits. YT exhibits characteristics unique body shape and appearance, young age of sexual maturity, coarse-feeding tolerance, delicious meat quality, and adaptability due to years of domestication and selection. Therefore, it is expected that certain selection signatures exist in the YT genome. A total of 125 commonly selected genes were identified by comparing YT with WB, focusing on widow regions that ranked in the top 5% for both \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio, and Tajima's D. Functional enrichment analyses revealed that several candidate genes may play significantly roles in pigmentation, growth and development, reproduction, meat quality, and immune response.\u003c/p\u003e \u003cp\u003eAmong the candidate genes under selection, \u003cem\u003eDCC\u003c/em\u003e, \u003cem\u003eNFKBIZ\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eLRRC4C\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e were potentially undergoing selection and were functionally associated with growth and development. GWAS and multi-omics analysis indicate that the expression levels of \u003cem\u003eNFKBIZ\u003c/em\u003e and \u003cem\u003eLRRC4C\u003c/em\u003e can influence the feed conversion rate of pigs and chickens [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. \u003cem\u003eTNR\u003c/em\u003e has been identified as a candidate gene associated with the domestic genetic imprint of indigenous pigs, involved in processes such as synaptic transmission and brain development [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cem\u003eERBB4\u003c/em\u003e, as a ligand of Neuregulin-2, is implicated in physiological functions including individual growth and development, and cell differentiation [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eSPRY1\u003c/em\u003e, a modulator of fibroblast growth factor, epidermal growth factor signaling and receptor tyrosine kinase signaling pathways, plays a critical role in tissue development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Research showed that \u003cem\u003eMYC\u003c/em\u003e regulates the expression of key metabolic enzymes during the four-cell stage, influencing porcine early embryonic metabolism and epigenetic reprogramming [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, our findings may offer insight into the role of candidate genes in the growth and development of YT.\u003c/p\u003e \u003cp\u003eFour selected genes associated with reproduction were identified, including \u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eCOL12A1\u003c/em\u003e, \u003cem\u003eADRA2A\u003c/em\u003e, and \u003cem\u003eDROSHA\u003c/em\u003e. \u003cem\u003eINHBA\u003c/em\u003e gene was considered associated with porcine litter (TNB and NBA) and carcass traits[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. \u003cem\u003eADRA2A\u003c/em\u003e gene was linked to sperm motility in cattle and pigs, associated with sperm-specific functions such as capacitation, acrosome reaction, and motility [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Downregulation of \u003cem\u003eDROSHA\u003c/em\u003e gene expression may correlate with reduced production of progesterone by CL in response to luteolytic signal from endometrial PGF2α [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We hypothesized that these genes may also be associated with the characteristics of earlier age of sex maturity and larger litter size in YT, but the exact effects required further investigation.\u003c/p\u003e \u003cp\u003eSix genes associated with meat quality, \u003cem\u003eNRG1\u003c/em\u003e, \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eGRIK2\u003c/em\u003e, \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003eCOL9A1\u003c/em\u003e, and \u003cem\u003eCHL1\u003c/em\u003e, were identified in this study. Based on functional and transcription factor-gene networks, \u003cem\u003eNRG1\u003c/em\u003e merges as a promising candidate genes for pork pH [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A study found that \u003cem\u003eGRM8\u003c/em\u003e was associated with the relative area of longissimus dorsi muscle fiber type I, thereby considered a plausible candidate gene for this trait [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The genome-wide association study for visible intermuscular fat in the Italian Large White breed identified the most significant SNP located within the largest QTL region in the \u003cem\u003eGRIK2\u003c/em\u003e gene [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Previous research indicated that \u003cem\u003eEFNA5\u003c/em\u003e overlapped between the candidate gene from GWAS and transcriptome analysis, situated in a new QTL significantly related to meat color [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. \u003cem\u003eCOL9A1\u003c/em\u003e was found to be significantly expressed in the skin tissue of Kele pigs, relating to collagen traits [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. \u003cem\u003eCHL1\u003c/em\u003e is regarded as a strong candidate gene for drip loss and was linked to insulin secretion and glucose metabolism [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Notably, YT exhibit excellent meat quality with an IMF content of longissimus dorsi exceeding 4.5% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], potentially attributable to these selected genes during domestication and breeding.\u003c/p\u003e \u003cp\u003eMoreover, several genes related to immune response, including \u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eRUNX2\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003ePKN2\u003c/em\u003e, \u003cem\u003eSEMA3C\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, and \u003cem\u003eSOCS6\u003c/em\u003e, were also selected in YT. \u003cem\u003eSEMA3C\u003c/em\u003e gene might contributed to the development of porcine stromal inflammation or rejection [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. \u003cem\u003eSEMA3E\u003c/em\u003e is essential for dampening the early inflammatory response to LPS by regulating macrophage function, indicating its pivotal role in macrophage inflammatory response [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. \u003cem\u003eRUNX2\u003c/em\u003e, as a transcription factor, was involved in humoral immune response [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Prior research have identified \u003cem\u003ePTPN6\u003c/em\u003e gene as being related to the regulation of type Ⅰ interferon-mediated signaling pathway [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The Suppressor of Cytokine Signaling (SOCS) proteins are crucial regulators of the immune system, with \u003cem\u003eSOCS6\u003c/em\u003e being highly expressed in the large and small intestine tissues of pigs [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It was well-known that YT had been domesticated and raised in complex environments and crude feeding conditions for extended periods, leading to strong adaptability and stress resistance. Therefore, it is worthwhile to further investigate whether these candidate genes are associated with the remarkable adaptability of YT by regulating relevant immune processes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first genetic survey of the genetic diversity and family structure of the YT population. Five statistics (Ne, P\u003csub\u003eN\u003c/sub\u003e, H\u003csub\u003eO\u003c/sub\u003e, He, and \u003cem\u003eF\u003c/em\u003e\u003csub\u003eROH\u003c/sub\u003e) collectively indicate the necessity to increase the level of genetic diversity within the current population and to mitigate the potential risk of inbreeding depression. The obtained genomic family information can better illustrate the kinship among individuals and provide a theoretical foundation for developing mating plans. Furthermore, YT exhibited a unique population structure and is genetically differentiated into two subgroups within the population. The genomic candidate genes influenced by natural or artificial selection are associated with traits related to growth and development, reproduction, meat quality, and immune response. Our findings may strengthen the conservation and sustainable development of YT and promote the understanding of the formation mechanisms of various economic traits in YT.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e All animal care and treatment procedures were approved by the Animal Ethics Committee of Shandong Agricultural University, China, and were conducted in accordance with the committee\u0026rsquo;s guidelines and regulations (Approval No.: 2004006).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors are grateful for the support by the Fundamental Research Projects of Science\u0026amp;Technology Innovation and development Plan in Yantai City (No. 2024JCYJ103) and the Yantai Seed Industry Revitalization Project.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.Q., R.L.L., S.Y.M. and C.M. conceived and planned the research. M.Q. and R.L.L. supervised the research. Y.X.Z., Z.G.W., G.D.L., M.Z.L., J.Y.W., P.J., Y.F.S. and Z.P.T. performed the experiments. C.M. and S.YM. Conducted data analysis and wrote the manuscript. M.Q., S.Y.M. and C.M. edited the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Laizhou Yantai Black Pig Breeding Farm, Jinlai Yantai Black Pig Breeding Farm, Tushan Town, Laizhou City, and Yantai Tuomu Black Pig Breeding Farm for providing the Yantai Black pig samples.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Sequence Read Archive (SRA) under accession number PRJNA1258694. 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Vet Immunol Immunopathol. 2011;144(3\u0026ndash;4):493\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.vetimm.2011.08.002\u003c/span\u003e\u003cspan address=\"10.1016/j.vetimm.2011.08.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Yantai Black pig, Single nucleotide polymorphism, Genetic diversity, Population structure, Selection signature","lastPublishedDoi":"10.21203/rs.3.rs-9121170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9121170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eYantai Black pig (YT), renowned for its disease resistance and superior meat quality, is a Chinese indigenous pig breed that has developed through natural and artificial selection over an extended period. In recent years, the YT population has dwindled due to the introduction of cosmopolitan pig breeds and the outbreak of African swine fever, putting them at risk of extinction. Meanwhile, there is still a lack of research on its genome. We conducted a genomic comprehensive analysis by high-density SNP chip on 102 YT and comparing them with resequencing genomic data from 20 YT and 16 wild boar (WB).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe effective population size (Ne), polymorphic marker ratio (P\u003csub\u003eN\u003c/sub\u003e), expected heterozygosity (He), and observed heterozygosity (H\u003csub\u003eO\u003c/sub\u003e) of this population were 5.0, 0.917, 0.374, and 0.361, respectively, with an average inbreeding coefficient of 0.151 within the population. Based on genomic information, this population was classified into eight different families with boars. It was found that YT was population independent of WB, exhibiting genetic differentiation within the population. Moreover, a total of 125 selected candidate genes were identified by using three methods: \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π ratio, and Tajima's D. Functional enrichment analysis identified several annotated genes that might affect growth and development (\u003cem\u003eDCC\u003c/em\u003e, \u003cem\u003eNFKBIZ\u003c/em\u003e, \u003cem\u003eTNR\u003c/em\u003e, \u003cem\u003eLRRC4C\u003c/em\u003e, \u003cem\u003eERBB4\u003c/em\u003e, \u003cem\u003eTMEM182\u003c/em\u003e, \u003cem\u003eSPRY1\u003c/em\u003e, and MYC), reproduction (\u003cem\u003eINHBA\u003c/em\u003e, \u003cem\u003eCOL12A1\u003c/em\u003e, \u003cem\u003eADRA2A\u003c/em\u003e, and \u003cem\u003eDROSHA\u003c/em\u003e), meat quality (\u003cem\u003eNRG1\u003c/em\u003e, \u003cem\u003eGRM8\u003c/em\u003e, \u003cem\u003eGRIK2\u003c/em\u003e, \u003cem\u003eEFNA5\u003c/em\u003e, \u003cem\u003eCOL9A1\u003c/em\u003e, and \u003cem\u003eCHL1\u003c/em\u003e), and immune response (\u003cem\u003eSEMA3E\u003c/em\u003e, \u003cem\u003eRUNX2\u003c/em\u003e, \u003cem\u003eGRIN3A\u003c/em\u003e, \u003cem\u003ePKN2\u003c/em\u003e, \u003cem\u003eSEMA3C\u003c/em\u003e, \u003cem\u003ePTPN6\u003c/em\u003e, and \u003cem\u003eSOCS6\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings indicated that YT exhibited a decrease in the level of genetic diversity and was a relatively independent indigenous pig breed. It should be protected scientifically and effectively as a valuable germplasm resource. Selection signatures in genomic regions linked to important economic traits in YT. Our results will provide a valuable basis for the future effective protection, breeding, and utilization of YT.\u003c/p\u003e","manuscriptTitle":"Genome-wide analysis reveals genetic characteristics and selection signatures in Yantai Black Pig of Shandong, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 16:06:44","doi":"10.21203/rs.3.rs-9121170/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-07T16:05:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-05T06:43:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T17:28:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T07:12:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-03-27T07:06:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c5fbb6a1-670f-4caf-98e7-61e019d59609","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T16:06:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 16:06:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9121170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9121170","identity":"rs-9121170","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}