Analyzing the genetic diversity of Gayal (Bos frontalis) based on whole genome resequencing

Analyzing the genetic diversity of Gayal (Bos frontalis) based on whole genome resequencing | 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 Analyzing the genetic diversity of Gayal (Bos frontalis) based on whole genome resequencing Ruiyang LI, Xiaodong Wang, Yuan Zhang, Maosheng Cao, wei Guo, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6263934/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Gayal ( Bos frontalis ) is a rare and endangeredsemi-domesticated cattle species with unique genetic background and physiological characteristics. These physiological traits are like those of yaks lived on the Qinghai-Tibet Plateau, such as thicker myocardial connective tissue and abundant vascular distribution. The unique genes carried in its genome, including candidate genes related to immunity, meat quality, and reproduction, further highlight its biological value. However, due to its small population size, severe inbreeding, and the impact of human activities, the genetic diversity of Gayal is relatively low, placing it at risk of endangerment. Therefore, protecting Gayal is not only a rescue effort for this rare species but also a significant contribution to biodiversity and genetic resources. Through scientific research and effective conservation measures, the unique genetic resources of Gayal hold promise for providing valuable references for future livestock breeding and biomedical research. Genomic studies have revealed significant differences between Gayal and other cattle species, suggesting its potential as a genetic resource for hybrid improvement, which is of great importance for the development of China's livestock industry. To evaluate the population structure and genetic diversity of Gayal, this study used a 55K genotyping array to perform whole genome resequencing on 30 Gayal samples and downloaded 69 samples from 18 cattle breeds from the NCBI database (National Center for Biotechnology Information) for joint analysis. Using population genetic structure analysis, evaluate genetic diversity parameters (heterozygosity, proportion of polymorphic markers, and nucleotide diversity), population phylogenetic tree analysis, linkage disequilibrium (LD), population structure, and genetic differentiation (FST and genetic distance). The genetic diversity results indicate that the genetic diversity of Gayal is relatively low compared to other ruminant species. The LD pattern indicates that the genetic recombination rate of Gayal is high. Principal component analysis showed that the Gayal, as an independent species, exhibited significant genetic differentiation from the Mithun. We also identified a series of candidate genes, including TRIM77, RASGRP1, API5, CLDN18, NAALAD2, DZIP1L, RAB3C, PDE4D , which may be related to the meat quality, immunity, and reproduction of this breed. This study provides valuable genomic resources and theoretical basis for the protection, development, and utilization of Gayal in the future. Gayal whole genome resequencing SNPs genomic diversity population structure conservation. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Systematically evaluating the genetic diversity and population structure of Gayal provides a theoretical foundation for their conservation and utilization. Several unique genes of Gayal have been discovered. These include immunity related genes including TRIM77, RASGRP1, and API5; Genes related to meat quality, such as CLDN18, NAALAD2, DZIP1L, and RAB3C, as well as genes associated with reproduction, such as PDE4D. 1. Introduction Genetic diversity refers to the sum of genetic information of all living organisms on Earth [1].Generally speaking, the higher the genetic diversity of a population, the stronger its ability to adapt to the environment[2].Through genetic diversity analysis, we can understand the historical origin, evolutionary process, and germplasm characteristics of a variety[3, 4],which is of great significance for variety improvement and protection. The whole genome sequencing technology has become an popular tool for analyzing the genetic diversity of biological population. The application of this technology has greatly improved our understanding of the genetic diversity of different populations. By sequencing and analyzing the whole genome of specific animal populations, important information such as genetic variation levels, population genetic structure, and population health status can be revealed within the population. The whole genome sequencing technology not only helps to understand the richness of genetic diversity in endangered animals but also provides a basis for developing scientific conservation and management measures. Through these measures, valuable genetic resources can be effectively protected and utilized, avoiding population decline or disappearance caused by human activities. The Bos frontalis, as one of the rare and endangered species in China that is semi wild and semi domesticated, has been included in the national list of livestock and poultry genetic resources protection. Worldwide, Gayal are mainly distributed in India, Bhutan, Myanmar, Malaysia and other places. In China, Gayal are mainly distributed in the Dulong River and Nujiang River basins of Gaoligong Mountain in the west of Yunnan Province (located between Gaoligong Mountain and Dandalika Mountain in the northwest of Gongshan County, bordering Myanmar, covering an area of 1994km ², with an altitude of 1170~4964m), and scattered in Xizang Menyu, Luoyang and Chongqing. Unlike ordinary cattle and tumor cattle, Gayal is one of the seven existing cattle breeds in the world. The most prominent feature of Gayal is that its muscle fibers are finer, denser, and more tender than ordinary domestic cattle. Especially, its intramuscular fat content is extremely low (0.36%) and evenly distributed. Its meat is characterized by genetic resource value and economic utilization value [5]. The population of Gayal is relatively small, with only about 3000 in China currently[6].Rich cattle breed resources are a necessary condition for the diversified development of China's animal husbandry industry and an important component of the agricultural economy. Protecting the genetic resources of Gayal not only involves natural resource management and genetic resource conservation but also relates to the inheritance of local culture [7]. The task of preventing it from mixing and extinction is necessary. This study performed whole genome resequencing using blood samples (n=30) of Gayal, and downloaded data from Yunling cattle, Simmenta, Angus, Hanwoo, Guangfeng cattle, Wannan cattle, Leiqiong cattle, WensHanwoo, Sahiwal, Nelore, Tharparkar, Hariana, Brahman, Gir, Mithun, Banteng, and Yak (n=69). The genetic status was evaluated by calculating nucleotide diversity and heterozygosity indicators, and the population structure of Gayal was evaluated by combining whole genome level analysis. Analyze the inbreeding status of the Gayal population to gain a clearer understanding of its evolutionary history and the genetic mechanisms underlying complex traits. Analyze its genomic characteristics in terms of adaptability, meat quality, immunity, and reproductive capacity. This study is beneficial to understand the population structure and genetic characteristics of the Gayal, clarify the origin, evolutionary relationship, and genetic diversity of Gayal. In addition, selection signal analysis was conducted on Gayal to analyze their genomic characteristics in adaptability, meat quality, immunity, and reproductive capacity. This study will reveal the genetic diversity of Gayal from a whole genome perspective, and elucidate their unique genetic variations and germplasm characteristics, providing a scientific basis for the evaluation, improvement, and protection of existing genetic resources of Gayal. 2. Materials and Methods 2.1 Sample Collection and Whole-Genome Sequencing Blood samples were collected from 30 Gayal in Yunnan Province and sent to Novogene Co., Ltd. in Beijing for whole-genome resequencing. Libraries with an insert size of 500 bp were constructed for each individual and sequenced. Additionally, publicly available whole-genome data of cattle were collected and divided into nine populations based on geographical location and breed for analysis: Gayal (n = 30); Mithun (n = 9); banteng (n = 9); Yak (n = 3); Yunling cattle (n = 9); Chinese zebu (including Guangfeng cattle (n = 4), Wannan cattle (n = 2), Leiqiong cattle (n = 1), and Wenshan cattle (n = 5)); South Asian cattle (including Brahman (n = 3), Gir (n = 2), Nelore (n = 1), Sahiwal (n = 3), Hariana (n = 1), and Tharparkar (n = 3)); European cattle (including Angus (n = 3) and Simmental (n = 6)); and Northeast Asian cattle (Hanwoo (n = 5)). The whole-genome resequencing data of these populations were jointly analyzed with the Gayal data. (Detailed information is provided in Table S1 ). 2.2Reads Mapping and SNP Calling The clean reads of each individual sample were mapped to the cattle reference genome (GCF_002263795.3_ARS-UCD2.0_genomic.fna). The high-quality sequencing data were aligned to the reference genome using BWA-mem2 software [ 8 ] with the parameters: mem -t 16 -k 32 -M. The alignment results were sorted and deduplicated using samtools v0.1.19 [ 9 ] with the parameters: sort and rmdup. Population-level SNP and InDel detection was performed using SAMTOOLS software. Variant sites detected by SAMTOOLS v1.3.1 were filtered and screened to obtain high-quality sites based on the following criteria: (1) Q20 quality control (filtering out SNPs with a sequencing error rate greater than 1%); (2) The number of supporting reads (coverage depth) for the variant site is greater than 4; (3) The proportion of missing genotypes for the variant site in the resequenced samples is less than 10%; (4) The minimum allele frequency (MAF) of the variant site is greater than 0.05. SNPs and InDels were annotated using ANNOVAR software [ 10 ]. The statistical results of variant sites are shown in Table 1 . After filtering, a total of 1,301,177 sites were retained for subsequent analysis. Table 1 Statistics of Variable Outliers reference genome Number of samples Variation type Filter conditions Total number of loci Total number of filtered sites ARS-UCD2.0 99 SNP dp4-miss0.1-maf0.05 86431492 1301177 ARS-UCD2.0 99 InDel dp4-miss0.1-maf0.05 7678052 105233 2.3 Select signal analysis Based on the SNP statistical results, positive selection signals were detected. FST and θπ have been proven to be effective methods for detecting selective sweep regions, particularly in identifying functional regions closely related to survival environments, where strong selection signals are often observed. Therefore, both methods were used jointly to screen for strong selection signals, facilitating the identification of target genes. Using vcftools v0.1.14, Fst and θπ values were calculated in 50k windows with a 10k step size across the genome. Perl v5.18.2 was used to plot the Fst values, while R v2.15.3 was used to plot the θπ values. A Perl script was employed to jointly screen the Fst and θπ values, with the top 5% of windows selected as candidate windows to identify potential regional differences between Gayal and Javan banteng. The joint screening results were visualized using R v2.15.3. Gene functional annotation was obtained by comparing sequences with the SwissProt database using the blastx function of ncbi-blast v2.2.28, with results having a P-value < 10^-5 considered reliable. Gene Ontology (GO) enrichment analysis was performed using the GOseq R package in R v2.15.3, and the calculated p-values were corrected using the Bonferroni method. GO terms with corrected p-values less than 0.05 were considered significantly enriched. KOBAS v3.0 was used to statistically test the enrichment of genes in KEGG pathways ( http://www.genome.jp/kegg/ ), with pathways having p-values less than 0.05 considered significantly enriched. 2.4 Population Structure and Genetic Diversity The genetic diversity of the Gayal genome was revealed by calculating parameters such as polymorphic information content (PIC), expected heterozygosity (He), observed heterozygosity (Ho), and nucleotide diversity (Pi) using Plink (V1.90) software. Principal component analysis (PCA) was performed using GCTA v1.24.2 ( http://cnsgenomics.com/software/gcta/pca.html ) to compute eigenvectors and eigenvalues, and the PCA distribution plot was generated using R v2.15.3[ 11 ]. Based on this, a distance matrix was calculated using TreeBest v1.9.2 ( http://treesoft.sourceforge.net/treebest.shtml ), and a phylogenetic tree was constructed using the neighbor-joining method[ 12 ]. Bootstrap values were obtained from 1000 replicates. The tree was visualized using iTOL v7. Population structure analysis was conducted using ADMIXTURE v1.23 ( http://dalexander.github.io/admixture/ ). First, the input file for Plink v1.07 ( http://pngu.mgh.harvard.edu/~purcell/plink/ ), the Ped file, was created. Then, ADMIXTURE was used to infer population genetic structure and lineage information, with the number of ancestral populations (K) set from 2 to 8[ 13 ]. The population structure plot was generated using R v2.15.3. FST was used to measure the degree of differentiation between populations[ 14 ]. The FST value ranges from 0 to 1. A value of 0 indicates random mating between two populations with identical genotypes, while a value of 1 indicates complete genetic differentiation. For the LD decay analysis based on the physical distance between SNPs, PopLDdecay v1.24.2 [ 15 ] was used for calculation, and the results were visualized using Perl v5.18.2. Based on the number of autosomal SNPs, Plink v1.07 was used to statistically analyze runs of homozygosity (ROH) with the following parameters: an ROH length threshold of 100 kb and a minimum of 5 SNPs per ROH. The number and length of ROHs in the population were statistically analyzed. The inbreeding coefficient refers to the probability that two gametes forming a zygote originate from the same common ancestor. A higher inbreeding coefficient indicates a higher probability that the two gametes of an individual come from the same ancestor. The inbreeding coefficient for each sample was calculated using vcftools v0.1.15 and visualized using R v2.15.3. 2.5 Demographic analysis In this study, the PSMC (Pairwise Sequentially Markovian Coalescent) method was used to analyze the effective population size at different historical periods. To ensure the quality of the consensus sequences, samples with higher sequencing depth were selected for analysis. During the analysis, bases with low sequencing depth (less than one-third of the minimum sequencing depth, set as 3 in this study) or high sequencing depth (more than three times the maximum sequencing depth, set as 500 in this study) were excluded. The analysis was performed using psmc v0.6.4-r49 software. The parameters in this study were referenced from Wu et al.'s research on cattle population history [ 16 ]and set as follows: -N30 -t15 -r5 -p "4 + 25*2 + 4 + 6". The generation time (g) was set to 6 years per generation, and the mutation rate (u) was set to 2.2e-9.Using Treemix v1.12 [ 17 ], a maximum likelihood tree of nine populations was inferred to deduce population admixture events and analyze gene flow. The R package OptM was employed to analyze the Treemix results to determine the optimal m value. The optimal m value is identified when Δm reaches its maximum. In this project, the optimal m value was found to be 3. 3. Results 3.1 Analysis of Population Genetic Diversity 18 cattle breeds were divided into nine populations according to their geographical locations and breed relationships, and their genetic diversity indices are shown in Table 2 (please refer to materials and methods section). Heterozygosity reflects the degree of genetic variation within a population, with higher heterozygosity indicating greater genetic diversity within the population. Among the 18 breeds, Yak had the lowest values for Pi (0.029), Pic (0.019), Ho (0.035), and He (0.024). In contrast, Chinese zebu breeds had the highest values for Pi, Pic, Ho, and He, which were 0.237353, 0.185202, 0.246926, and 0.227229, respectively. Except for DLN, the Ho values of other cattle breeds were higher than their He values. The Pi value of DLN was higher than that of YDDEN, BAN, AGS, and XMTER, and was similar to that of European commercial cattle and HN. The PIC value of DLN ranked fourth among these cattle populations, where PIC > 0.5 indicates high polymorphism, 0.25 < PIC < 0.5 indicates moderate polymorphism, and PIC < 0.25 indicates low polymorphism. The linkage disequilibrium (LD) decay pattern can provide detailed information on population evolution. After excluding samples from populations with only one individual, the LD decay plot (Fig. 1) shows that when the physical distance of SNPs is less than 10 Kb, AGS had the highest average LD (r²), followed by Indian zebu, YAK, Chinese zebu, XMTER, YDDEN, YLN, and DLN. When the physical distance of SNPs exceeds 100 Kb, DLN had a lower average LD compared to other cattle breeds, indicating that DLN has the lowest LD level among these cattle breeds. Table 2 Genetic Diversity Parameters of Cattle Breeds from Different Regions #Species Pi PIC Ho He DLN 0.098991 0.082191 0.092598 0.097263 YDDEN 0.079541 0.058128 0.094405 0.072401 BAN 0.082396 0.063128 0.091031 0.077004 YAK 0.029951 0.019639 0.03513 0.024789 China 0.237353 0.185202 0.246926 0.227229 South Asian 0.216214 0.166757 0.217947 0.207728 YLN 0.199052 0.149335 0.217665 0.183811 Europe 0.095828 0.07223 0.099973 0.09046 East Asian 0.100545 0.072207 0.11095 0.090299 3.2 Analysis of Population Genetic Structure Principal Component Analysis (PCA), phylogenetic tree analysis, and population structure analysis were employed to elucidate the population structure of these cattle. The PCA results indicated that the first two principal components accounted for 23.1% (PC1) and 15.88% (PC2) of the total variance, respectively (Fig. 2). The analysis divided the cattle into seven clusters. Among these, three independent populations were identified as YLN, BAN, and YAK. DLN and YDDEN were grouped into one cluster, while XMTER, AGS, and HN formed another cluster. Additionally, WN, WSN, and GFN were grouped together, and THA, SHA, NEL, PLM, GIR, and HAR were classified into another cluster. YLN and BAN exhibited independent clustering but were relatively dispersed. The phylogenetic tree analysis results, as shown in Fig. 3 , divided the 18 breeds into eight lineages. These branches are as follows: DLN was classified into one branch, and YDDEN was classified into another. HN, AGS, and XMTER formed one branch, while WSN, WN, LQ, and GFN constituted another. Similarly, BAN and YAK were grouped into one branch. YLN was classified as an independent branch, and THA, PLM, SHA, NEL, HAR, and GIR were grouped into another branch, with one THA individual clustering within HAR. The clustering of these cattle breeds was generally consistent with their geographical distribution and PCA results. 3.3 Kinship Analysis The ADMIX analysis results for 18 local breeds with K values ranging from 2 to 8 are shown in Fig. 4. The cross-validation error was minimized at K = 5. The ADMIX analysis results for K values from 2 to 8 are illustrated in Fig. 4. At K = 2, DLN, YDDEN, YAK, and BAN were distinguished from other domesticated cattle breeds, although it is noteworthy that some genetic background admixture was observed among DLN, YAK, and Chinese zebu breeds. At K = 3, BAN was separated, and the genetic background of YAK showed admixture. The optimal K value, determined by the smallest cross-validation error, was K = 5. At this point, YAK and BAN were distinctly separated, while Chinese zebu breeds and YLN exhibited a clearly multicolored distribution, indicating mixed genetic backgrounds. The multicolored distribution was less pronounced in YDDEN, and some DLN samples also displayed a multicolored genetic background. To assess the genomic inbreeding level of DLN, this study calculated the number of runs of homozygosity (ROH) for each cattle population. Compared to the other eight cattle populations, DLN had the longest average ROH length, with moderate total ROH length and count, which were higher than those of Chinese and South Asian zebu breeds. Additionally, the pi value of DLN (0.099) was comparable to that of European and Northeast Asian commercial cattle but lower than that of Chinese and South Asian zebu breeds. The average ROH inbreeding coefficient (FROH) of DLN was approximately 0.55–0.65, second only to YAK, indicating a high level of inbreeding in this breed. Pairwise genetic differentiation indices (FST) among the breeds are presented in Table 3 . The FST values ranged from 0.0235 (between European and East Asian breeds) to 0.2948 (between BAN and South Asian breeds). DLN showed moderate to high differentiation from other breeds, with an average FST value of 0.12. Compared to other local breeds, BAN had the highest FST values, with an average FST of 0.26, indicating significant differentiation from other breeds. The levels of genetic differentiation, as indicated by FST values (ranging from 0 to 1), were categorized as follows: FST > 0.25 (very great differentiation), 0.15 to 0.25 (great differentiation), 0.05 to 0.15 (moderate differentiation), and FST < 0.05 (negligible differentiation) [ 18 ]. Table 3 FST statistics between populations DLN YDDEN BAN YAK China South Asian YLN Europe YDDEN 0.0316 BAN 0.227 0.2343 YAK 0.0969 0.131 0.2056 China 0.1418 0.1456 0.2518 0.1279 South Asian 0.1555 0.167 0.2948 0.1458 0.0525 YLN 0.1158 0.1585 0.2845 0.1565 0.053 0.0406 Europe 0.1054 0.1487 0.2829 0.1591 0.1168 0.1201 0.0777 East Asian 0.0968 0.1543 0.2831 0.172 0.0958 0.1005 0.0719 0.0235 3.4 Demographic analysis By applying the PSMC model to infer the population history of DLN, the study revealed the unique evolutionary trajectory of this species (Fig. 7 A). Genomic analysis indicated that variation in the effective population size of DLN can be traced back to approximately 1 million years ago, with its population dynamics showing significant differences from those of Chinese and South Asian zebu populations. Notably, DLN and YDDEN exhibited similar population fluctuation patterns: both experienced two significant population expansion periods (occurring around 500,000 years ago and 50,000 years ago, respectively) and two distinct population bottlenecks (around 400,000 years ago and 100,000 years ago). Compared to the rapid and large-scale expansion characteristics commonly observed in other cattle populations, the evolutionary process of DLN demonstrated distinct uniqueness, with its expansion process characterized by a larger scale but slower rate. 3.5 Genome-wide Selective Sweep and Gene Enrichment Analysis By comparing the genomes of the Gayal (DLN) and the Javan banteng (YDDEN), we employed the Fst and θπ methods to identify potential selective regions in the DLN genome (Fig. 10). A total of 15 candidate genes, 13 KEGG-enriched pathways, and 318 GO pathways were screened for further analysis (Supplementary Tables S2, S3, S4). Among these, immune-related genes included TRIM77, RASGRP1 , and API5 [ 18 – 20 ], meat quality-related genes included CLDN18, NAALAD2, DZIP1L , and RAB3C [ 21 – 23 ], and reproduction-related genes included PDE4D [ 24 ]. KEGG pathways identified included "Leukocyte transendothelial migration (bta04670)", "Tight junction pathway (bta04530)", "Hepatitis C (bta05160)", and "Cell adhesion molecules (bta04514)". GO enrichment analysis revealed that several pathways involved in important biological processes, such as "carboxypeptidase activity (GO:0004180)", "exopeptidase activity (GO:0008238)" ( NAALAD2 ), "microtubule basal body (GO:0005932)", "cilium basal body (GO:0036064)" ( DZIP1L ), "apical junction assembly (GO:0043297)", "tight junction assembly (GO:0070830)" ( CLDN18 ), and "viral process (GO:0016032)" ( LOC132343336 ).Notably, both KEGG and GO functional enrichment analyses identified pathways such as "Tight junction (bta04530)" and "tight junction assembly (GO:0070830)", which exclusively involve the CLDN18 gene, indicating strong selection for this gene in DLN. Discussion This study employed whole-genome data from 30 Gayal (DLN) and 99 individuals from 17 other cattle populations to investigate the genomic diversity of DLN from a comprehensive perspective. By comparing expected heterozygosity (He) and observed heterozygosity (Ho), the study aimed to reveal characteristics of population genetic structure, such as the effects of natural selection and inbreeding on population genetic diversity. When He is higher than Ho, it may indicate that the population has been influenced by selection or inbreeding. Conversely, when He is lower than Ho, it may suggest that the population has high genetic diversity or has experienced gene flow from external breeds. Additionally, higher Ho values indicate richer genetic diversity within the population [ 25 ]. Our results showed that, except for the DLN population, the Ho values of the other eight cattle populations were higher than their He values. This may be due to insufficient development and utilization of the DLN breed, a small population size, and increased inbreeding, leading to inbreeding depression [ 26 ]. This study demonstrates that DLN has low genetic diversity, consistent with previous research findings [ 27 – 29 ]. Individuals within the breed exhibit close kinship, necessitating corresponding conservation measures, such as controlling inbreeding or hybridization, to protect this valuable cattle breed. According to population genetic structure analysis, when K = 2, the cattle population can be clearly divided into two clusters: domestic cattle and wild cattle. When K = 3, the Javan wild cattle exhibit unique genetic characteristics, showing significant differentiation from other cattle breeds. Notably, the Gayal displays genetic admixture across different K values. In the best-fit model (K = 5), some Gayal show genetic mixing with Chinese indicine cattle, Angus cattle, Simmental cattle, and Hanwoo cattle. Gene flow analysis further confirms that European cattle breeds and Hanwoo introduced new genetic material into the Gayal population before their divergence, explaining the reason for their mixed ancestry. Therefore, it is necessary to conduct purity identification of Gayal through molecular genetic methods to prevent further genetic admixture[ 30 ].In contrast, while Indian bison and yak exhibit genetic admixture, this phenomenon is not observed in Gayal, which may be related to their geographic distribution differences [ 31 ]. When K = 7, Gayal and Indian bison are clearly distinguished, indicating that geographic isolation has led to different genetic variations between the two. Based on the PCA and phylogenetic tree analysis results, the Gayal are most closely related to the Indian gaur and the Javan banteng, while showing a more distant relationship with the native cattle of Yunnan. This finding aligns with the semi-wild, semi-domesticated characteristics of the Gayal, suggesting that they are more likely to have originated from the Indian gaur rather than the zebu. This view is also supported by studies from Mei and Dorji et al. [ 32 , 33 ]. However, the Javan banteng exhibits a closer genetic relationship with the Indian gaur than the Gayal. Prabhu et al. [ 34 ] further confirmed the close evolutionary relationship between the Indian gaur and the Indian gaur using mtDNA sequence analysis. Additionally, in clustering analysis, the Indian gaur tends to group with the wild gaur, indicating that the Indian gaur may be a descendant of the wild gaur. Linkage disequilibrium (LD) analysis reflects the selection intensity, breeding systems, and genetic diversity of different populations [ 35 ]. The rate of LD decay infers the degree of selection pressure in each population. Generally, a slower LD decay rate indicates a higher degree of selection. According to the LD decay pattern, the Gayal exhibit the lowest LD decay rate, suggesting a stronger ability to maintain linkage between genetic loci during meiosis. In addition, the nucleotide diversity of Gayal is slightly higher than that of European commercial cattle and wild cattle breeds, indicating lower selection pressure and relatively rich genetic diversity in Gayal. Linderholm et al. [ 36 ] noted that wild cattle populations, which are larger in size and have not undergone artificial selection, retain greater genetic diversity. The Gayal exhibits low genetic diversity and a high level of inbreeding (FROH), which is reflected by a significant portion of their genome consisting of runs of homozygosity (ROH) of varying lengths. Generally, in larger populations, ROH segments tend to be shorter and less frequent compared to isolated small populations. Hybrid populations show the smallest amount of ROH, while inbred populations display very long continuous homozygous segments [ 37 ]. A high number of ROHs often indicates low genetic diversity within the population, suggesting a history of genetic bottlenecks or inbreeding[ 38 ]. Among the Gayal, the average ROH segment length is the longest of all populations. Reduced genetic diversity may lead to the accumulation of deleterious variants, negatively impacting the health and adaptability of the population [ 39 , 40 ]. The low genetic diversity of Gayal is not only due to their small population size but also a result of the rapid decline in purebred Gayal over the past two decades, driven by agricultural modernization and the indiscriminate introduction of non-native species by humans. Additionally, the widespread use of artificial insemination in breeding practices has limited the number of bulls used, significantly increasing inbreeding. This current situation provides important insights into future breeding strategies for Gayal. A Population history analysis of cattle reveals that approximately 3 million years ago, the population sizes of taurine cattle, zebu, and wild cattle reached their first peak. This phenomenon may be linked to climate changes occurring between 4 million and 1 million years ago, during which the expansion of grassland ecosystems provided large mammals, such as cattle, with broader habitats and abundant food resources [ 41 ], thereby promoting population expansion. During this period, cattle populations increased significantly, and extensive genetic exchange occurred both within and between species. The Gayal may have originated from hybridization between male wild cattle and female zebu or taurine cattle during this time [ 28 ]. The physiological characteristics of Gayal are similar to those of yaks lived on the Qinghai-Tibet Plateau. Compared to other local cattle in Yunnan, Gayal have smaller muscle fiber diameters, higher density, and a greater number of muscle fibers. Additionally, the connective tissue in the myocardium of Gayal is thicker and surrounded by abundant blood vessels and capillaries, which is a notable difference [ 42 ]. Zhao et al. found that the methylation level of the NAALAD2 gene is associated with beef quality [ 21 ]. NAALAD2 negatively associated between expression and methylation, and this gene can serve as a DNA methylation biomarker for regulating beef tenderness. The DZIP1L gene has also been studied in cattle, sheep, and other animals. Sallam et al. found that DZIP1L is involved in the regulation of cilia assembly and microtubule cytoskeleton organization in goats [ 43 ]. Cilia are conserved organelles that play an important role in controlling cell polarity, differentiation, and proliferation. Zhu et al. discovered that higher expression levels of DZIP1L promote fatty acid synthesis and metabolism in cattle, leading to changes in the content and composition of fatty acids in beef, showing a positive correlation [ 44 ]. Jin et al. demonstrated that the loss of the PDE4D gene results in reduced fertility and fewer offspring in mice[ 24 ]. These genes may be related to muscle development, fat deposition, and reproduction in Gayal. However, this is only speculative and requires further theoretical and experimental support. KEGG enrichment analysis revealed several pathways involved in important biological processes. The "Leukocyte transendothelial migration (bta04670)" pathway may be related to dairy cattle physiology and immune regulation [ 45 ]. The "Tight junction (bta04530)" pathway plays a crucial regulatory role in various tissues and physiological processes in cattle, including skeletal muscle development, mammary gland development and function, uterine and placental development, and tumorigenesis [ 46 , 47 ]. These processes are closely related to milk quality and yield. The "Cell adhesion molecules (bta04514)" pathway is closely associated with the interaction between muscle and adipose tissue metabolism, affecting beef quality [ 48 ], and is also related to parasite resistance traits in cattle breeds [ 49 ]. The terms "apical junction assembly (GO:0043297)", "tight junction assembly (GO:0070830)", and "structural molecule activity (GO:0005198)" all correspond to the CLDN18 gene. This gene has been found to be under selection in the genomes of rabbits [ 50 ] and goats [ 43 ], suggesting its potential role in the adaptability and immune response of these animals. In studies on pigs, Li et al. found that the expression of CLDN18 is associated with meat quality traits and immune regulation [ 22 ]. In cattle, Niu et al. discovered that CLDN18 is related to reproductive traits, such as morphogenesis and reproductive organ development [ 51 ]. The CLDN18 gene has been shown to be associated with multiple important production traits, including immunity, meat quality, and reproduction, in animal models. Therefore, we speculate that *CLDN18* may be related to the meat quality, reproductive capacity, and adaptability of Gayal, but its specific functions require further exploration. Conclusion This study conducted a comprehensive analysis of the genomic diversity and selection pressures in Gayal. Population structure analysis revealed that Gayal, as an independent species, are more closely related to wild cattle than to native Yunnan cattle. The study highlighted that Gayal exhibits low genomic diversity due to severe inbreeding, necessitating enhanced conservation efforts to prevent hybridization and further inbreeding. Strong selection signals were identified in the Gayal genome, and through the screening of target genes, several unique genes were discovered. These include immune-related genes such as TRIM77, RASGRP1 , and API5 ; meat quality-related genes such as CLDN18, NAALAD2, DZIP1L and RAB3C , and reproduction-related genes such as PDE4D . These genes may serve as candidates associated with the unique reproductive performance, meat quality, and immune responses of Gayal. The findings provide a theoretical foundation for the conservation, development, and utilization of Gayal. Declarations Author Contributions: L.R. wrote the main manuscript text and designed the research framework; W.X. and G.W. participated in revising the manuscript and polishing the language to ensure logical clarity and accurate expression; Z.Y. and W.X. performed the relevant data analysis; C.M. contributed to literature research and data visualization; L.J. and J.Q. participated in the discussion of results; J.Y. was responsible for the collection of experimental samples; W.Q. participated in revising the manuscript and polishing the language to ensure logical clarity and accurate expression; W.Y. assisted in data analysis and participated in partial results analysis; C.X. was responsible for the overall project design, research guidance, and the final review and approval of the manuscript. All authors reviewed and approved the final version of the manuscript. Funding: This research was funded by the National Key Research and Development Program of China (Grant No. 2021YFD1200403). Institutional Review Board Statement: The animal care and treatment protocols adhered to the guidelines outlined in the Chinese Animal Welfare Regulations and were approved by the Animal Protection and Utilization CommiTee at Guizhou University, Guiyang, China (Approval number: EAE-GZU-2022-E068). Data Availability Statement: The authors will provide the raw data that underpins the findings presented in this article, without any unnecessary restrictions. Conflicts of Interest: The authors have no competing interests to declare. Data Availability The datasets generated during the current study are not yet publicly available but will be deposited in the NCBI repository upon manuscript acceptance, and a persistent link will be provided at that time. Until then, the data are available from the corresponding author upon reasonable request. References Ellegren H, Galtier N: Determinants of genetic diversity . NAT REV GENET 2016, 17 (7):422-433. DeWoody JA, Harder AM, Mathur S, Willoughby JR: The long-standing significance of genetic diversity in conservation . MOL ECOL 2021, 30 (17):4147-4154. Li S, Zhang X, Dong X, Guo R, Nan J, Yuan J, Schlebusch CM, Sheng Z: Genetic structure and characteristics of Tibetan chickens . POULTRY SCI 2023, 102 (8):102767. Wang MS, Li Y, Peng MS, Zhong L, Wang ZJ, Li QY, Tu XL, Dong Y, Zhu CL, Wang L et al : Genomic Analyses Reveal Potential Independent Adaptation to High Altitude in Tibetan Chickens . MOL BIOL EVOL 2015, 32 (7):1880-1889. Felius M, Beerling M, Buchanan D, Theunissen B, Koolmees P, Lenstra J: On the History of Cattle Genetic Resources . Diversity 2014, 6 (4):705-750. Porter, Valerie, Alderson, Lawrence, Hall, Stephen JG, Sponenberg, Phillip D: The Encyclopedia of Livestock Breeds and Breeding, Types and Varieties . 2016. Dorji T, Wangdi J, Shaoliang Y, Chettri N, Wangchuk K: Mithun (Bos frontalis): the neglected cattle species and their significance to ethnic communities in the Eastern Himalaya - A review . ANIM BIOSCI 2021, 34 (11):1727-1738. Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform . BIOINFORMATICS 2010, 26 (5):589-595. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: The Sequence Alignment/Map format and SAMtools . BIOINFORMATICS 2009, 25 (16):2078-2079. Wang K, Li M, Hakonarson H: ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data . NUCLEIC ACIDS RES 2010, 38 (16):e164. Yang J, Lee SH, Goddard ME, Visscher PM: GCTA: a tool for genome-wide complex trait analysis . AM J HUM GENET 2011, 88 (1):76-82. Kumar S, Stecher G, Li M, Knyaz C, Tamura K: MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms . MOL BIOL EVOL 2018, 35 (6):1547-1549. Alexander DH, Novembre J, Lange K: Fast model-based estimation of ancestry in unrelated individuals . GENOME RES 2009, 19 (9):1655-1664. Weir BS, Cockerham CC: ESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE . EVOLUTION 1984, 38 (6):1358-1370. Zhang C, Dong SS, Xu JY, He WM, Yang TL: PopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files . BIOINFORMATICS 2019, 35 (10):1786-1788. Wu DD, Ding XD, Wang S, Wojcik JM, Zhang Y, Tokarska M, Li Y, Wang MS, Faruque O, Nielsen R et al : Pervasive introgression facilitated domestication and adaptation in the Bos species complex . NAT ECOL EVOL 2018, 2 (7):1139-1145. Pickrell JK, Pritchard JK: Inference of population splits and mixtures from genome-wide allele frequency data . PLOS GENET 2012, 8 (11):e1002967. Salzer E, Cagdas D, Hons M, Mace EM, Garncarz W, Petronczki OY, Platzer R, Pfajfer L, Bilic I, Ban SA et al : RASGRP1 deficiency causes immunodeficiency with impaired cytoskeletal dynamics . NAT IMMUNOL 2016, 17 (12):1352-1360. Chen R, Tie Y, Lu J, Li L, Zeng Z, Chen M, Zhang S: Tripartite motif family proteins in inflammatory bowel disease: Mechanisms and potential for interventions . CELL PROLIFERAT 2022, 55 (5):e13222. Matsuzawa-Ishimoto Y, Yao X, Koide A, Ueberheide BM, Axelrad JE, Reis BS, Parsa R, Neil JA, Devlin JC, Rudensky E et al : The gammadelta IEL effector API5 masks genetic susceptibility to Paneth cell death . NATURE 2022, 610 (7932):547-554. Zhao C, Ji G, Carrillo JA, Li Y, Tian F, Baldwin RL, Zan L, Song J: The Profiling of DNA Methylation and Its Regulation on Divergent Tenderness in Angus Beef Cattle . FRONT GENET 2020, 11 :939. Li B, Yang J, He J, Peng X, Zeng Q, Song Y, Xu K, Ma H: Characterization of the whole transcriptome of spleens from Chinese indigenous breed Ningxiang pig reveals diverse coding and non-coding RNAs for immunity regulation . GENOMICS 2021, 113 (4):2468-2482. Casey TM, Gouveia KM, Beckett LM, Markworth JF, Boerman JP: Molecular signatures of longissimus dorsi differ between dairy cattle based on prepartum muscle reserves and branched-chain volatile fatty acid supplementation . PHYSIOL GENOMICS 2024, 56 (9):597-608. Jin SL, Richard FJ, Kuo WP, D'Ercole AJ, Conti M: Impaired growth and fertility of cAMP-specific phosphodiesterase PDE4D-deficient mice . P NATL ACAD SCI USA 1999, 96 (21):11998-12003. Hao S, Zhen W, Zhe Z, Qian X, Zhong XU, Xiang-Zhe Z, Hong-Jie Y, Man-Xing Z, Ming X, Xiao-Hui L: Exploring the Current Situation of Conservation of Meishan Pigs Based on Genome Sequencing Data . Journal of Shanghai Jiaotong University(Agricultural Science) 2017. Villanueva B, Fernandez A, Saura M, Caballero A, Fernandez J, Morales-Gonzalez E, Toro MA, Pong-Wong R: The value of genomic relationship matrices to estimate levels of inbreeding . GENET SEL EVOL 2021, 53 (1):42. Qu K, Nguyen SN, He Z, Huang B, Yuan X, Zhang Y, Zan L: Genetic diversity and bottleneck analysis of Yunnan mithun (Bos frontalis) using microsatellite loci . African journal of biotechnology 2012, 11 (12):2912-2919. Gou X, Wang Y, Yang S, Deng W, Mao H: Genetic diversity and origin of Gayal and cattle in Yunnan revealed by mtDNA control region and SRY gene sequence variation . J ANIM BREED GENET 2010, 127 (2):154-160. Xi D, Liu Q, Guo J, Yu H, Yang Y, He Y, Mao H, Gou X, Deng W: Genetic variability of the coding region for the prion protein gene (PRNP) in gayal (Bos frontalis) . MOL BIOL REP 2012, 39 (2):2011-2020. Ahrestani FS: Bos frontalis and Bos gaurus (Artiodactyla: Bovidae) . Mammalian species 2018, 50 (959):1-17. Payne WJA, Hodges J: Tropical cattle: origins, breeds and breeding policies. Blackwell Science 1997. Mei C, Wang H, Zhu W, Wang H, Cheng G, Qu K, Guang X, Li A, Zhao C, Yang W et al : Whole-genome sequencing of the endangered bovine species Gayal (Bos frontalis) provides new insights into its genetic features . SCI REP-UK 2016, 6 :19787. Dorji T, Mannen H, Namikawa T, Inamura T, Kawamoto Y: Diversity and phylogeny of mitochondrial DNA isolated from mithun Bos frontalis located in Bhutan . ANIM GENET 2010, 41 (5):554-556. Prabhu VR, Arjun MS, Bhavana K, Kamalakkannan R, Nagarajan M: Complete mitochondrial genome of Indian mithun, Bos frontalis and its phylogenetic implications . MOL BIOL REP 2019, 46 (2):2561-2566. Waples RK, Larson WA, Waples RS: Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci . HEREDITY 2016. Linderholm A: The genetic legacy of the wild ancestors of modern cattle . NATURE 2024, 635 (8037):43-45. Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF: Runs of homozygosity: windows into population history and trait architecture . NAT REV GENET 2018, 19 (4):220-234. Marras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, Williams JL, Macciotta NPP: Analysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy . ANIM GENET 2015, 46 (2):110-121. Szpiech ZA, Xu J, Pemberton TJ, Peng W, Zollner S, Rosenberg NA, Li JZ: Long runs of homozygosity are enriched for deleterious variation . AM J HUM GENET 2013, 93 (1):90-102. Bortoluzzi C, Bosse M, Derks M, Crooijmans R, Groenen M, Megens HJ: The type of bottleneck matters: Insights into the deleterious variation landscape of small managed populations . EVOL APPL 2020, 13 (2):330-341. Bobe R, Behrensmeyer AK: The expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo . PALAEOGEOGR PALAEOCL 2004, 207 (3-4):399-420. Chen Y, Zhang T, Xian M, Zhang R, Yang W, Su B, Yang G, Sun L, Xu W, Xu S et al : A draft genome of Drung cattle reveals clues to its chromosomal fusion and environmental adaptation . COMMUN BIOL 2022, 5 (1):353. Sallam AM: Genomic regions underlying variation in wattles, horns, and supernumerary teats phenotypes in Egyptian goats . CAN J ANIM SCI 2024, 104 (4):549-557. Zhu B, Niu H, Zhang W, Wang Z, Liang Y, Guan L, Guo P, Chen Y, Zhang L, Guo Y et al : Genome wide association study and genomic prediction for fatty acid composition in Chinese Simmental beef cattle using high density SNP array . BMC GENOMICS 2017, 18 (1):464. Dadousis C, Pegolo S, Rosa G, Gianola D, Bittante G, Cecchinato A: Pathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle . J DAIRY SCI 2017, 100 (2):1223-1231. Maiorano AM, Lourenco DL, Tsuruta S, Ospina A, Stafuzza NB, Masuda Y, Filho A, Cyrillo J, Curi RA, Silva J: Assessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information . PLOS ONE 2018, 13 (8):e200694. Hao D, Wang X, Yang Y, Thomsen B, Holm LE, Qu K, Huang B, Chen H: Integrated Analysis of mRNA and MicroRNA Co-expressed Network for the Differentiation of Bovine Skeletal Muscle Cells After Polyphenol Resveratrol Treatment . FRONT VET SCI 2021, 8 :777477. Lee HJ, Park HS, Kim W, Yoon D, Seo S: Comparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach . INT J GENOMICS 2014, 2014 :679437. May K, Scheper C, Brügemann K, Yin T, Strube C, Korkuć P, Brockmann GA, König S: Genome-wide associations and functional gene analyses for endoparasite resistance in an endangered population of native German Black Pied cattle . BMC GENOMICS 2019, 20 (1). Xie K, Ning C, Yang A, Zhang Q, Wang D, Fan X: Resequencing Analyses Revealed Genetic Diversity and Selection Signatures during Rabbit Breeding and Improvement . GENES-BASEL 2024, 15 (4). Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, Zhang L, Gao H, Song J, Li J et al : Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle . GENOMICS 2021, 113 (5):3325-3336. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-6263934","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441603324,"identity":"4537e5b4-39ce-4eae-a268-e94e12a3723d","order_by":0,"name":"Ruiyang LI","email":"","orcid":"","institution":"College of Animal Science, Guizhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruiyang","middleName":"","lastName":"LI","suffix":""},{"id":441603325,"identity":"4144dcec-9c88-464b-91d1-c84056a86836","order_by":1,"name":"Xiaodong Wang","email":"","orcid":"","institution":"College of Animal Science, Guizhou 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07:32:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69046,"visible":true,"origin":"","legend":"\u003cp\u003eGenome-wide Average LD Decay Estimated for Each Breed\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/e938342d8139aa73d867ccc4.jpg"},{"id":81267166,"identity":"140887b3-b459-4694-a0be-4177f5b1f662","added_by":"auto","created_at":"2025-04-24 07:40:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":103035,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Clustering (principal component analysis of different cattle species\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/05cfbcec6c9a8224b65c3271.jpg"},{"id":81266024,"identity":"00ba1d96-c0f2-41ba-ad52-1de940c26187","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56770,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic Tree of Genetic Relationships Among 99 Cattle Samples\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/cf775a6e446bbe0a16e1bf94.jpg"},{"id":81266028,"identity":"8816a233-e326-495f-9bf7-85d51c4dd6d5","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":303072,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 5 illustrates the ancestral lineage composition patterns of 18 cattle breeds. Different numbers of hypothetical ancestors (ranging from K=2 to K=8) were assumed for these breeds. The optimal number of hypothetical ancestors was determined to be 5 (K=5), at which point the cross-validation error reached its minimum level.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/673a5368adcf60d327dd6a37.jpg"},{"id":81266026,"identity":"55a69da3-682e-4a3f-af35-7076d5221650","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":77079,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 6 Analysis of ROH Distribution Patterns and Inbreeding Coefficients\u003c/p\u003e\n\u003cp\u003e(a) Average ROH Length (b) Number of ROH (c) Total ROH Length (d) Inbreeding Coefficient\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/2472725db1f72eda70aac3ab.jpg"},{"id":81266040,"identity":"ad4e0bb2-1f5d-4afe-9c49-7d9c8730c519","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":63902,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 7 Analysis of Nine Populations Divided by Geographic Location\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/06b3c884debba52f7fce5187.jpg"},{"id":81266042,"identity":"bd7d960e-3acf-4d50-925a-904fae613366","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":170743,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 10 Selective Signals and Functional Enrichment Analysis of Gayal\u003c/p\u003e\n\u003cp\u003e(a) θπ Distribution Plot. (b) Fst Distribution Plot.\u003c/p\u003e\n\u003cp\u003e(c) Fst \u0026amp; θπ Selective Sweep Results. (d) KEGG Pathway Enrichment Results.\u003c/p\u003e\n\u003cp\u003e(e) GO Functional Annotation Results.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/f0397f1d3b884fa74a33f568.jpg"},{"id":92391999,"identity":"70bc5dee-6be8-48ce-abb2-81ef715a894c","added_by":"auto","created_at":"2025-09-29 08:48:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3745927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/f0f515ba-d02f-412a-a929-b69d598b04a5.pdf"},{"id":81266022,"identity":"90b46e26-2d21-4cd6-abe3-19961fd1213f","added_by":"auto","created_at":"2025-04-24 07:32:49","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":48894,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6263934/v1/d97bd39df529dd0b9bcf1cd6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing the genetic diversity of Gayal (Bos frontalis) based on whole genome resequencing","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eSystematically evaluating the genetic diversity and population structure of Gayal provides a theoretical foundation for their conservation and utilization.\u003c/li\u003e\n \u003cli\u003eSeveral unique genes of Gayal have been discovered. These include immunity related genes including TRIM77, RASGRP1, and API5; Genes related to meat quality, such as CLDN18, NAALAD2, DZIP1L, and RAB3C, as well as genes associated with reproduction, such as PDE4D.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eGenetic diversity refers to the sum of genetic information of all living organisms on Earth\u0026nbsp;[1].Generally speaking, the higher the genetic diversity of a population, the stronger its ability to adapt to the environment[2].Through genetic diversity analysis, we can understand the historical origin, evolutionary process, and germplasm characteristics of a variety[3, 4],which is of great significance for variety improvement and protection. The whole genome sequencing technology has become an popular tool for analyzing the genetic diversity of biological population. The application of this technology has greatly improved our understanding of the genetic diversity of different populations. By sequencing and analyzing the whole genome of specific animal populations, important information such as genetic variation levels, population genetic structure, and population health status can be revealed within the population. The whole genome sequencing technology not only helps to understand the richness of genetic diversity in endangered animals but also provides a basis for developing scientific conservation and management measures. Through these measures, valuable genetic resources can be effectively protected and utilized, avoiding population decline or disappearance caused by human activities.\u003c/p\u003e\n\u003cp\u003eThe Bos frontalis, as one of the rare and endangered species in China that is semi wild and semi domesticated, has been included in the national list of livestock and poultry genetic resources protection. Worldwide, Gayal are mainly distributed in India, Bhutan, Myanmar, Malaysia and other places. In China, Gayal are mainly distributed in the Dulong River and Nujiang River basins of Gaoligong Mountain in the west of Yunnan Province (located between Gaoligong Mountain and Dandalika Mountain in the northwest of Gongshan County, bordering Myanmar, covering an area of 1994km ², with an altitude of 1170~4964m), and scattered in Xizang Menyu, Luoyang and Chongqing. Unlike ordinary cattle and tumor cattle, Gayal is one of the seven existing cattle breeds in the world. The most prominent feature of Gayal is that its muscle fibers are finer, denser, and more tender than ordinary domestic cattle. Especially, its intramuscular fat content is extremely low (0.36%) and evenly distributed. Its meat is characterized by genetic resource value and economic utilization value\u0026nbsp;[5]. The population of Gayal is relatively small, with only about 3000 in China currently[6].Rich cattle breed resources are a necessary condition for the diversified development of China's animal husbandry industry and an important component of the agricultural economy. Protecting the genetic resources of Gayal not only involves natural resource management and genetic resource conservation but also relates to the inheritance of local culture\u0026nbsp;[7]. The task of preventing it from mixing and extinction is necessary.\u003c/p\u003e\n\u003cp\u003eThis study performed whole genome resequencing using blood samples (n=30) of Gayal, and downloaded data from Yunling cattle, Simmenta, Angus, Hanwoo, Guangfeng cattle, Wannan cattle, Leiqiong cattle, WensHanwoo, Sahiwal, Nelore, Tharparkar, Hariana, Brahman, Gir, Mithun, Banteng, and Yak (n=69). The genetic status was evaluated by calculating nucleotide diversity and heterozygosity indicators, and the population structure of Gayal was evaluated by combining whole genome level analysis. Analyze the inbreeding status of the Gayal population to gain a clearer understanding of its evolutionary history and the genetic mechanisms underlying complex traits. Analyze its genomic characteristics in terms of adaptability, meat quality, immunity, and reproductive capacity. This study is beneficial to understand the population structure and genetic characteristics of the Gayal, clarify the origin, evolutionary relationship, and genetic diversity of Gayal. In addition, selection signal analysis was conducted on Gayal to analyze their genomic characteristics in adaptability, meat quality, immunity, and reproductive capacity. This study will reveal the genetic diversity of Gayal from a whole genome perspective, and elucidate their unique genetic variations and germplasm characteristics, providing a scientific basis for the evaluation, improvement, and protection of existing genetic resources of Gayal.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection and Whole-Genome Sequencing\u003c/h2\u003e \u003cp\u003eBlood samples were collected from 30 Gayal in Yunnan Province and sent to Novogene Co., Ltd. in Beijing for whole-genome resequencing. Libraries with an insert size of 500 bp were constructed for each individual and sequenced. Additionally, publicly available whole-genome data of cattle were collected and divided into nine populations based on geographical location and breed for analysis: Gayal (n\u0026thinsp;=\u0026thinsp;30); Mithun (n\u0026thinsp;=\u0026thinsp;9); banteng (n\u0026thinsp;=\u0026thinsp;9); Yak (n\u0026thinsp;=\u0026thinsp;3); Yunling cattle (n\u0026thinsp;=\u0026thinsp;9); Chinese zebu (including Guangfeng cattle (n\u0026thinsp;=\u0026thinsp;4), Wannan cattle (n\u0026thinsp;=\u0026thinsp;2), Leiqiong cattle (n\u0026thinsp;=\u0026thinsp;1), and Wenshan cattle (n\u0026thinsp;=\u0026thinsp;5)); South Asian cattle (including Brahman (n\u0026thinsp;=\u0026thinsp;3), Gir (n\u0026thinsp;=\u0026thinsp;2), Nelore (n\u0026thinsp;=\u0026thinsp;1), Sahiwal (n\u0026thinsp;=\u0026thinsp;3), Hariana (n\u0026thinsp;=\u0026thinsp;1), and Tharparkar (n\u0026thinsp;=\u0026thinsp;3)); European cattle (including Angus (n\u0026thinsp;=\u0026thinsp;3) and Simmental (n\u0026thinsp;=\u0026thinsp;6)); and Northeast Asian cattle (Hanwoo (n\u0026thinsp;=\u0026thinsp;5)). The whole-genome resequencing data of these populations were jointly analyzed with the Gayal data. (Detailed information is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e2.2Reads Mapping and SNP Calling\u003c/p\u003e \u003cp\u003eThe clean reads of each individual sample were mapped to the cattle reference genome (GCF_002263795.3_ARS-UCD2.0_genomic.fna). The high-quality sequencing data were aligned to the reference genome using BWA-mem2 software [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] with the parameters: mem -t 16 -k 32 -M. The alignment results were sorted and deduplicated using samtools v0.1.19 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] with the parameters: sort and rmdup. Population-level SNP and InDel detection was performed using SAMTOOLS software. Variant sites detected by SAMTOOLS v1.3.1 were filtered and screened to obtain high-quality sites based on the following criteria:\u003c/p\u003e \u003cp\u003e(1) Q20 quality control (filtering out SNPs with a sequencing error rate greater than 1%);\u003c/p\u003e \u003cp\u003e(2) The number of supporting reads (coverage depth) for the variant site is greater than 4;\u003c/p\u003e \u003cp\u003e(3) The proportion of missing genotypes for the variant site in the resequenced samples is less than 10%;\u003c/p\u003e \u003cp\u003e(4) The minimum allele frequency (MAF) of the variant site is greater than 0.05.\u003c/p\u003e \u003cp\u003eSNPs and InDels were annotated using ANNOVAR software [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The statistical results of variant sites are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After filtering, a total of 1,301,177 sites were retained for subsequent analysis.\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\u003eStatistics of Variable Outliers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereference genome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNumber of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariation type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFilter conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal number of loci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTotal number of filtered sites\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARS-UCD2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edp4-miss0.1-maf0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86431492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1301177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARS-UCD2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eInDel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edp4-miss0.1-maf0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7678052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105233\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\n\u003ch3\u003e2.3 Select signal analysis\u003c/h3\u003e\n\u003cp\u003eBased on the SNP statistical results, positive selection signals were detected. FST and θπ have been proven to be effective methods for detecting selective sweep regions, particularly in identifying functional regions closely related to survival environments, where strong selection signals are often observed. Therefore, both methods were used jointly to screen for strong selection signals, facilitating the identification of target genes. Using vcftools v0.1.14, Fst and θπ values were calculated in 50k windows with a 10k step size across the genome. Perl v5.18.2 was used to plot the Fst values, while R v2.15.3 was used to plot the θπ values. A Perl script was employed to jointly screen the Fst and θπ values, with the top 5% of windows selected as candidate windows to identify potential regional differences between Gayal and Javan banteng. The joint screening results were visualized using R v2.15.3. Gene functional annotation was obtained by comparing sequences with the SwissProt database using the blastx function of ncbi-blast v2.2.28, with results having a P-value\u0026thinsp;\u0026lt;\u0026thinsp;10^-5 considered reliable. Gene Ontology (GO) enrichment analysis was performed using the GOseq R package in R v2.15.3, and the calculated p-values were corrected using the Bonferroni method. GO terms with corrected p-values less than 0.05 were considered significantly enriched. KOBAS v3.0 was used to statistically test the enrichment of genes in KEGG pathways (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with pathways having p-values less than 0.05 considered significantly enriched.\u003c/p\u003e\n\u003ch3\u003e2.4 Population Structure and Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eThe genetic diversity of the Gayal genome was revealed by calculating parameters such as polymorphic information content (PIC), expected heterozygosity (He), observed heterozygosity (Ho), and nucleotide diversity (Pi) using Plink (V1.90) software. Principal component analysis (PCA) was performed using GCTA v1.24.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cnsgenomics.com/software/gcta/pca.html\u003c/span\u003e\u003cspan address=\"http://cnsgenomics.com/software/gcta/pca.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to compute eigenvectors and eigenvalues, and the PCA distribution plot was generated using R v2.15.3[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Based on this, a distance matrix was calculated using TreeBest v1.9.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://treesoft.sourceforge.net/treebest.shtml\u003c/span\u003e\u003cspan address=\"http://treesoft.sourceforge.net/treebest.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a phylogenetic tree was constructed using the neighbor-joining method[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Bootstrap values were obtained from 1000 replicates. The tree was visualized using iTOL v7. Population structure analysis was conducted using ADMIXTURE v1.23 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dalexander.github.io/admixture/\u003c/span\u003e\u003cspan address=\"http://dalexander.github.io/admixture/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). First, the input file for Plink v1.07 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pngu.mgh.harvard.edu/~purcell/plink/\u003c/span\u003e\u003cspan address=\"http://pngu.mgh.harvard.edu/~purcell/plink/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the Ped file, was created. Then, ADMIXTURE was used to infer population genetic structure and lineage information, with the number of ancestral populations (K) set from 2 to 8[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The population structure plot was generated using R v2.15.3. FST was used to measure the degree of differentiation between populations[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The FST value ranges from 0 to 1. A value of 0 indicates random mating between two populations with identical genotypes, while a value of 1 indicates complete genetic differentiation. For the LD decay analysis based on the physical distance between SNPs, PopLDdecay v1.24.2 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] was used for calculation, and the results were visualized using Perl v5.18.2. Based on the number of autosomal SNPs, Plink v1.07 was used to statistically analyze runs of homozygosity (ROH) with the following parameters: an ROH length threshold of 100 kb and a minimum of 5 SNPs per ROH. The number and length of ROHs in the population were statistically analyzed. The inbreeding coefficient refers to the probability that two gametes forming a zygote originate from the same common ancestor. A higher inbreeding coefficient indicates a higher probability that the two gametes of an individual come from the same ancestor. The inbreeding coefficient for each sample was calculated using vcftools v0.1.15 and visualized using R v2.15.3.\u003c/p\u003e\n\u003ch3\u003e2.5 Demographic analysis\u003c/h3\u003e\n\u003cp\u003eIn this study, the PSMC (Pairwise Sequentially Markovian Coalescent) method was used to analyze the effective population size at different historical periods. To ensure the quality of the consensus sequences, samples with higher sequencing depth were selected for analysis. During the analysis, bases with low sequencing depth (less than one-third of the minimum sequencing depth, set as 3 in this study) or high sequencing depth (more than three times the maximum sequencing depth, set as 500 in this study) were excluded. The analysis was performed using psmc v0.6.4-r49 software. The parameters in this study were referenced from Wu et al.'s research on cattle population history [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]and set as follows: -N30 -t15 -r5 -p \"4\u0026thinsp;+\u0026thinsp;25*2\u0026thinsp;+\u0026thinsp;4\u0026thinsp;+\u0026thinsp;6\". The generation time (g) was set to 6 years per generation, and the mutation rate (u) was set to 2.2e-9.Using Treemix v1.12 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], a maximum likelihood tree of nine populations was inferred to deduce population admixture events and analyze gene flow. The R package OptM was employed to analyze the Treemix results to determine the optimal m value. The optimal m value is identified when Δm reaches its maximum. In this project, the optimal m value was found to be 3.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Analysis of Population Genetic Diversity\u003c/h2\u003e\n \u003cp\u003e18 cattle breeds were divided into nine populations according to their geographical locations and breed relationships, and their genetic diversity indices are shown in Table\u0026nbsp;2 (please refer to materials and methods section). Heterozygosity reflects the degree of genetic variation within a population, with higher heterozygosity indicating greater genetic diversity within the population. Among the 18 breeds, Yak had the lowest values for Pi (0.029), Pic (0.019), Ho (0.035), and He (0.024). In contrast, Chinese zebu breeds had the highest values for Pi, Pic, Ho, and He, which were 0.237353, 0.185202, 0.246926, and 0.227229, respectively. Except for DLN, the Ho values of other cattle breeds were higher than their He values. The Pi value of DLN was higher than that of YDDEN, BAN, AGS, and XMTER, and was similar to that of European commercial cattle and HN. The PIC value of DLN ranked fourth among these cattle populations, where PIC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 indicates high polymorphism, 0.25\u0026thinsp;\u0026lt;\u0026thinsp;PIC\u0026thinsp;\u0026lt;\u0026thinsp;0.5 indicates moderate polymorphism, and PIC\u0026thinsp;\u0026lt;\u0026thinsp;0.25 indicates low polymorphism. The linkage disequilibrium (LD) decay pattern can provide detailed information on population evolution. After excluding samples from populations with only one individual, the LD decay plot (Fig.\u0026nbsp;1) shows that when the physical distance of SNPs is less than 10 Kb, AGS had the highest average LD (r\u0026sup2;), followed by Indian zebu, YAK, Chinese zebu, XMTER, YDDEN, YLN, and DLN. When the physical distance of SNPs exceeds 100 Kb, DLN had a lower average LD compared to other cattle breeds, indicating that DLN has the lowest LD level among these cattle breeds.\u003c/p\u003e\n \u003cp\u003eTable 2 Genetic Diversity Parameters of Cattle Breeds from Different Regions\u003c/p\u003e\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e#Species\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePi\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePIC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHo\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHe\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYDDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.077004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYAK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.237353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.166757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.207728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.217665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.183811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.099973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.090299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e3.2 Analysis of Population Genetic Structure\u003c/h3\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA), phylogenetic tree analysis, and population structure analysis were employed to elucidate the population structure of these cattle. The PCA results indicated that the first two principal components accounted for 23.1% (PC1) and 15.88% (PC2) of the total variance, respectively (Fig. 2). The analysis divided the cattle into seven clusters. Among these, three independent populations were identified as YLN, BAN, and YAK. DLN and YDDEN were grouped into one cluster, while XMTER, AGS, and HN formed another cluster. Additionally, WN, WSN, and GFN were grouped together, and THA, SHA, NEL, PLM, GIR, and HAR were classified into another cluster. YLN and BAN exhibited independent clustering but were relatively dispersed. The phylogenetic tree analysis results, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, divided the 18 breeds into eight lineages. These branches are as follows: DLN was classified into one branch, and YDDEN was classified into another. HN, AGS, and XMTER formed one branch, while WSN, WN, LQ, and GFN constituted another. Similarly, BAN and YAK were grouped into one branch. YLN was classified as an independent branch, and THA, PLM, SHA, NEL, HAR, and GIR were grouped into another branch, with one THA individual clustering within HAR. The clustering of these cattle breeds was generally consistent with their geographical distribution and PCA results.\u003c/p\u003e\n\u003ch3\u003e3.3 Kinship Analysis\u003c/h3\u003e\n\u003cp\u003eThe ADMIX analysis results for 18 local breeds with K values ranging from 2 to 8 are shown in Fig.\u0026nbsp;4. The cross-validation error was minimized at K\u0026thinsp;=\u0026thinsp;5. The ADMIX analysis results for K values from 2 to 8 are illustrated in Fig.\u0026nbsp;4. At K\u0026thinsp;=\u0026thinsp;2, DLN, YDDEN, YAK, and BAN were distinguished from other domesticated cattle breeds, although it is noteworthy that some genetic background admixture was observed among DLN, YAK, and Chinese zebu breeds. At K\u0026thinsp;=\u0026thinsp;3, BAN was separated, and the genetic background of YAK showed admixture. The optimal K value, determined by the smallest cross-validation error, was K\u0026thinsp;=\u0026thinsp;5. At this point, YAK and BAN were distinctly separated, while Chinese zebu breeds and YLN exhibited a clearly multicolored distribution, indicating mixed genetic backgrounds. The multicolored distribution was less pronounced in YDDEN, and some DLN samples also displayed a multicolored genetic background. To assess the genomic inbreeding level of DLN, this study calculated the number of runs of homozygosity (ROH) for each cattle population. Compared to the other eight cattle populations, DLN had the longest average ROH length, with moderate total ROH length and count, which were higher than those of Chinese and South Asian zebu breeds. Additionally, the pi value of DLN (0.099) was comparable to that of European and Northeast Asian commercial cattle but lower than that of Chinese and South Asian zebu breeds. The average ROH inbreeding coefficient (FROH) of DLN was approximately 0.55\u0026ndash;0.65, second only to YAK, indicating a high level of inbreeding in this breed.\u003c/p\u003e\n\u003cp\u003ePairwise genetic differentiation indices (FST) among the breeds are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The FST values ranged from 0.0235 (between European and East Asian breeds) to 0.2948 (between BAN and South Asian breeds). DLN showed moderate to high differentiation from other breeds, with an average FST value of 0.12. Compared to other local breeds, BAN had the highest FST values, with an average FST of 0.26, indicating significant differentiation from other breeds. The levels of genetic differentiation, as indicated by FST values (ranging from 0 to 1), were categorized as follows: FST\u0026thinsp;\u0026gt;\u0026thinsp;0.25 (very great differentiation), 0.15 to 0.25 (great differentiation), 0.05 to 0.15 (moderate differentiation), and FST\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (negligible differentiation) [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFST statistics between populations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDLN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYDDEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBAN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYAK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSouth Asian\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYLN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYDDEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYAK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Demographic analysis\u003c/h2\u003e\n \u003cp\u003eBy applying the PSMC model to infer the population history of DLN, the study revealed the unique evolutionary trajectory of this species (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). Genomic analysis indicated that variation in the effective population size of DLN can be traced back to approximately 1 million years ago, with its population dynamics showing significant differences from those of Chinese and South Asian zebu populations. Notably, DLN and YDDEN exhibited similar population fluctuation patterns: both experienced two significant population expansion periods (occurring around 500,000 years ago and 50,000 years ago, respectively) and two distinct population bottlenecks (around 400,000 years ago and 100,000 years ago). Compared to the rapid and large-scale expansion characteristics commonly observed in other cattle populations, the evolutionary process of DLN demonstrated distinct uniqueness, with its expansion process characterized by a larger scale but slower rate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Genome-wide Selective Sweep and Gene Enrichment Analysis\u003c/h2\u003e\n \u003cp\u003eBy comparing the genomes of the Gayal (DLN) and the Javan banteng (YDDEN), we employed the Fst and \u0026theta;\u0026pi; methods to identify potential selective regions in the DLN genome (Fig. 10). A total of 15 candidate genes, 13 KEGG-enriched pathways, and 318 GO pathways were screened for further analysis (Supplementary Tables S2, S3, S4). Among these, immune-related genes included \u003cem\u003eTRIM77, RASGRP1\u003c/em\u003e, and \u003cem\u003eAPI5\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e], meat quality-related genes included \u003cem\u003eCLDN18, NAALAD2, DZIP1L\u003c/em\u003e, and \u003cem\u003eRAB3C\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], and reproduction-related genes included \u003cem\u003ePDE4D\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. KEGG pathways identified included \u0026quot;Leukocyte transendothelial migration (bta04670)\u0026quot;, \u0026quot;Tight junction pathway (bta04530)\u0026quot;, \u0026quot;Hepatitis C (bta05160)\u0026quot;, and \u0026quot;Cell adhesion molecules (bta04514)\u0026quot;. GO enrichment analysis revealed that several pathways involved in important biological processes, such as \u0026quot;carboxypeptidase activity (GO:0004180)\u0026quot;, \u0026quot;exopeptidase activity (GO:0008238)\u0026quot; (\u003cem\u003eNAALAD2\u003c/em\u003e), \u0026quot;microtubule basal body (GO:0005932)\u0026quot;, \u0026quot;cilium basal body (GO:0036064)\u0026quot; (\u003cem\u003eDZIP1L\u003c/em\u003e), \u0026quot;apical junction assembly (GO:0043297)\u0026quot;, \u0026quot;tight junction assembly (GO:0070830)\u0026quot; (\u003cem\u003eCLDN18\u003c/em\u003e), and \u0026quot;viral process (GO:0016032)\u0026quot; (\u003cem\u003eLOC132343336\u003c/em\u003e).Notably, both KEGG and GO functional enrichment analyses identified pathways such as \u0026quot;Tight junction (bta04530)\u0026quot; and \u0026quot;tight junction assembly (GO:0070830)\u0026quot;, which exclusively involve the \u003cem\u003eCLDN18\u003c/em\u003e gene, indicating strong selection for this gene in DLN.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed whole-genome data from 30 Gayal (DLN) and 99 individuals from 17 other cattle populations to investigate the genomic diversity of DLN from a comprehensive perspective. By comparing expected heterozygosity (He) and observed heterozygosity (Ho), the study aimed to reveal characteristics of population genetic structure, such as the effects of natural selection and inbreeding on population genetic diversity. When He is higher than Ho, it may indicate that the population has been influenced by selection or inbreeding. Conversely, when He is lower than Ho, it may suggest that the population has high genetic diversity or has experienced gene flow from external breeds. Additionally, higher Ho values indicate richer genetic diversity within the population [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our results showed that, except for the DLN population, the Ho values of the other eight cattle populations were higher than their He values. This may be due to insufficient development and utilization of the DLN breed, a small population size, and increased inbreeding, leading to inbreeding depression [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This study demonstrates that DLN has low genetic diversity, consistent with previous research findings [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Individuals within the breed exhibit close kinship, necessitating corresponding conservation measures, such as controlling inbreeding or hybridization, to protect this valuable cattle breed.\u003c/p\u003e \u003cp\u003e According to population genetic structure analysis, when K\u0026thinsp;=\u0026thinsp;2, the cattle population can be clearly divided into two clusters: domestic cattle and wild cattle. When K\u0026thinsp;=\u0026thinsp;3, the Javan wild cattle exhibit unique genetic characteristics, showing significant differentiation from other cattle breeds. Notably, the Gayal displays genetic admixture across different K values. In the best-fit model (K\u0026thinsp;=\u0026thinsp;5), some Gayal show genetic mixing with Chinese indicine cattle, Angus cattle, Simmental cattle, and Hanwoo cattle. Gene flow analysis further confirms that European cattle breeds and Hanwoo introduced new genetic material into the Gayal population before their divergence, explaining the reason for their mixed ancestry. Therefore, it is necessary to conduct purity identification of Gayal through molecular genetic methods to prevent further genetic admixture[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].In contrast, while Indian bison and yak exhibit genetic admixture, this phenomenon is not observed in Gayal, which may be related to their geographic distribution differences [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. When K\u0026thinsp;=\u0026thinsp;7, Gayal and Indian bison are clearly distinguished, indicating that geographic isolation has led to different genetic variations between the two.\u003c/p\u003e \u003cp\u003eBased on the PCA and phylogenetic tree analysis results, the Gayal are most closely related to the Indian gaur and the Javan banteng, while showing a more distant relationship with the native cattle of Yunnan. This finding aligns with the semi-wild, semi-domesticated characteristics of the Gayal, suggesting that they are more likely to have originated from the Indian gaur rather than the zebu. This view is also supported by studies from Mei and Dorji et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. However, the Javan banteng exhibits a closer genetic relationship with the Indian gaur than the Gayal. Prabhu et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] further confirmed the close evolutionary relationship between the Indian gaur and the Indian gaur using mtDNA sequence analysis. Additionally, in clustering analysis, the Indian gaur tends to group with the wild gaur, indicating that the Indian gaur may be a descendant of the wild gaur.\u003c/p\u003e \u003cp\u003eLinkage disequilibrium (LD) analysis reflects the selection intensity, breeding systems, and genetic diversity of different populations [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The rate of LD decay infers the degree of selection pressure in each population. Generally, a slower LD decay rate indicates a higher degree of selection. According to the LD decay pattern, the Gayal exhibit the lowest LD decay rate, suggesting a stronger ability to maintain linkage between genetic loci during meiosis. In addition, the nucleotide diversity of Gayal is slightly higher than that of European commercial cattle and wild cattle breeds, indicating lower selection pressure and relatively rich genetic diversity in Gayal. Linderholm et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] noted that wild cattle populations, which are larger in size and have not undergone artificial selection, retain greater genetic diversity.\u003c/p\u003e \u003cp\u003eThe Gayal exhibits low genetic diversity and a high level of inbreeding (FROH), which is reflected by a significant portion of their genome consisting of runs of homozygosity (ROH) of varying lengths. Generally, in larger populations, ROH segments tend to be shorter and less frequent compared to isolated small populations. Hybrid populations show the smallest amount of ROH, while inbred populations display very long continuous homozygous segments [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A high number of ROHs often indicates low genetic diversity within the population, suggesting a history of genetic bottlenecks or inbreeding[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Among the Gayal, the average ROH segment length is the longest of all populations. Reduced genetic diversity may lead to the accumulation of deleterious variants, negatively impacting the health and adaptability of the population [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The low genetic diversity of Gayal is not only due to their small population size but also a result of the rapid decline in purebred Gayal over the past two decades, driven by agricultural modernization and the indiscriminate introduction of non-native species by humans. Additionally, the widespread use of artificial insemination in breeding practices has limited the number of bulls used, significantly increasing inbreeding. This current situation provides important insights into future breeding strategies for Gayal.\u003c/p\u003e \u003cp\u003eA Population history analysis of cattle reveals that approximately 3\u0026nbsp;million years ago, the population sizes of taurine cattle, zebu, and wild cattle reached their first peak. This phenomenon may be linked to climate changes occurring between 4\u0026nbsp;million and 1\u0026nbsp;million years ago, during which the expansion of grassland ecosystems provided large mammals, such as cattle, with broader habitats and abundant food resources [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], thereby promoting population expansion. During this period, cattle populations increased significantly, and extensive genetic exchange occurred both within and between species. The Gayal may have originated from hybridization between male wild cattle and female zebu or taurine cattle during this time [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe physiological characteristics of Gayal are similar to those of yaks lived on the Qinghai-Tibet Plateau. Compared to other local cattle in Yunnan, Gayal have smaller muscle fiber diameters, higher density, and a greater number of muscle fibers. Additionally, the connective tissue in the myocardium of Gayal is thicker and surrounded by abundant blood vessels and capillaries, which is a notable difference [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Zhao et al. found that the methylation level of the \u003cem\u003eNAALAD2\u003c/em\u003e gene is associated with beef quality [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cem\u003eNAALAD2\u003c/em\u003enegatively associated between expression and methylation, and this gene can serve as a DNA methylation biomarker for regulating beef tenderness. The \u003cem\u003eDZIP1L\u003c/em\u003e gene has also been studied in cattle, sheep, and other animals. Sallam et al. found that \u003cem\u003eDZIP1L\u003c/em\u003e is involved in the regulation of cilia assembly and microtubule cytoskeleton organization in goats [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Cilia are conserved organelles that play an important role in controlling cell polarity, differentiation, and proliferation. Zhu et al. discovered that higher expression levels of \u003cem\u003eDZIP1L\u003c/em\u003e promote fatty acid synthesis and metabolism in cattle, leading to changes in the content and composition of fatty acids in beef, showing a positive correlation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Jin et al. demonstrated that the loss of the \u003cem\u003ePDE4D\u003c/em\u003e gene results in reduced fertility and fewer offspring in mice[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These genes may be related to muscle development, fat deposition, and reproduction in Gayal. However, this is only speculative and requires further theoretical and experimental support.\u003c/p\u003e \u003cp\u003eKEGG enrichment analysis revealed several pathways involved in important biological processes. The \"Leukocyte transendothelial migration (bta04670)\" pathway may be related to dairy cattle physiology and immune regulation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The \"Tight junction (bta04530)\" pathway plays a crucial regulatory role in various tissues and physiological processes in cattle, including skeletal muscle development, mammary gland development and function, uterine and placental development, and tumorigenesis [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. These processes are closely related to milk quality and yield. The \"Cell adhesion molecules (bta04514)\" pathway is closely associated with the interaction between muscle and adipose tissue metabolism, affecting beef quality [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and is also related to parasite resistance traits in cattle breeds [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The terms \"apical junction assembly (GO:0043297)\", \"tight junction assembly (GO:0070830)\", and \"structural molecule activity (GO:0005198)\" all correspond to the \u003cem\u003eCLDN18\u003c/em\u003e gene. This gene has been found to be under selection in the genomes of rabbits [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and goats [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], suggesting its potential role in the adaptability and immune response of these animals. In studies on pigs, Li et al. found that the expression of \u003cem\u003eCLDN18\u003c/em\u003e is associated with meat quality traits and immune regulation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In cattle, Niu et al. discovered that \u003cem\u003eCLDN18\u003c/em\u003e is related to reproductive traits, such as morphogenesis and reproductive organ development [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The \u003cem\u003eCLDN18\u003c/em\u003e gene has been shown to be associated with multiple important production traits, including immunity, meat quality, and reproduction, in animal models. Therefore, we speculate that *CLDN18* may be related to the meat quality, reproductive capacity, and adaptability of Gayal, but its specific functions require further exploration.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study conducted a comprehensive analysis of the genomic diversity and selection pressures in Gayal. Population structure analysis revealed that Gayal, as an independent species, are more closely related to wild cattle than to native Yunnan cattle. The study highlighted that Gayal exhibits low genomic diversity due to severe inbreeding, necessitating enhanced conservation efforts to prevent hybridization and further inbreeding. Strong selection signals were identified in the Gayal genome, and through the screening of target genes, several unique genes were discovered. These include immune-related genes such as \u003cem\u003eTRIM77, RASGRP1\u003c/em\u003e, and \u003cem\u003eAPI5\u003c/em\u003e; meat quality-related genes such as \u003cem\u003eCLDN18, NAALAD2, DZIP1L\u003c/em\u003e and \u003cem\u003eRAB3C\u003c/em\u003e, and reproduction-related genes such as \u003cem\u003ePDE4D\u003c/em\u003e. These genes may serve as candidates associated with the unique reproductive performance, meat quality, and immune responses of Gayal. The findings provide a theoretical foundation for the conservation, development, and utilization of Gayal.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e L.R. wrote the main manuscript text and designed the research framework; W.X. and G.W. participated in revising the manuscript and polishing the language to ensure logical clarity and accurate expression; Z.Y. and W.X. performed the relevant data analysis; C.M. contributed to literature research and data visualization; L.J. and J.Q. participated in the discussion of results; J.Y. was responsible for the collection of experimental samples; W.Q. participated in revising the manuscript and polishing the language to ensure logical clarity and accurate expression; W.Y. assisted in data analysis and participated in partial results analysis; C.X. was responsible for the overall project design, research guidance, and the final review and approval of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the National Key Research and Development Program of China (Grant No. 2021YFD1200403).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe animal care and treatment protocols adhered to the guidelines outlined in the Chinese Animal Welfare Regulations and were approved by the Animal Protection and Utilization CommiTee at Guizhou University, Guiyang, China (Approval number: EAE-GZU-2022-E068).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The authors will provide the raw data that underpins the findings presented in this article, without any unnecessary restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors have no competing interests to declare.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during the current study are not yet publicly available but will be deposited in the NCBI repository upon manuscript acceptance, and a persistent link will be provided at that time. Until then, the data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEllegren H, Galtier N: \u003cstrong\u003eDeterminants of genetic diversity\u003c/strong\u003e. \u003cem\u003eNAT REV GENET\u003c/em\u003e 2016, \u003cstrong\u003e17\u003c/strong\u003e(7):422-433.\u003c/li\u003e\n\u003cli\u003eDeWoody JA, Harder AM, Mathur S, Willoughby JR: \u003cstrong\u003eThe long-standing significance of genetic diversity in conservation\u003c/strong\u003e. \u003cem\u003eMOL ECOL\u003c/em\u003e 2021, \u003cstrong\u003e30\u003c/strong\u003e(17):4147-4154.\u003c/li\u003e\n\u003cli\u003eLi S, Zhang X, Dong X, Guo R, Nan J, Yuan J, Schlebusch CM, Sheng Z: \u003cstrong\u003eGenetic structure and characteristics of Tibetan chickens\u003c/strong\u003e. \u003cem\u003ePOULTRY SCI\u003c/em\u003e 2023, \u003cstrong\u003e102\u003c/strong\u003e(8):102767.\u003c/li\u003e\n\u003cli\u003eWang MS, Li Y, Peng MS, Zhong L, Wang ZJ, Li QY, Tu XL, Dong Y, Zhu CL, Wang L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenomic Analyses Reveal Potential Independent Adaptation to High Altitude in Tibetan Chickens\u003c/strong\u003e. \u003cem\u003eMOL BIOL EVOL\u003c/em\u003e 2015, \u003cstrong\u003e32\u003c/strong\u003e(7):1880-1889.\u003c/li\u003e\n\u003cli\u003eFelius M, Beerling M, Buchanan D, Theunissen B, Koolmees P, Lenstra J: \u003cstrong\u003eOn the History of Cattle Genetic Resources\u003c/strong\u003e. \u003cem\u003eDiversity\u003c/em\u003e 2014, \u003cstrong\u003e6\u003c/strong\u003e(4):705-750.\u003c/li\u003e\n\u003cli\u003ePorter, Valerie, Alderson, Lawrence, Hall, Stephen JG, Sponenberg, Phillip D: \u003cstrong\u003eThe Encyclopedia of Livestock Breeds and Breeding, Types and Varieties\u003c/strong\u003e. 2016.\u003c/li\u003e\n\u003cli\u003eDorji T, Wangdi J, Shaoliang Y, Chettri N, Wangchuk K: \u003cstrong\u003eMithun (Bos frontalis): the neglected cattle species and their significance to ethnic communities in the Eastern Himalaya - A review\u003c/strong\u003e. \u003cem\u003eANIM BIOSCI\u003c/em\u003e 2021, \u003cstrong\u003e34\u003c/strong\u003e(11):1727-1738.\u003c/li\u003e\n\u003cli\u003eLi H, Durbin R: \u003cstrong\u003eFast and accurate long-read alignment with Burrows-Wheeler transform\u003c/strong\u003e. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e 2010, \u003cstrong\u003e26\u003c/strong\u003e(5):589-595.\u003c/li\u003e\n\u003cli\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: \u003cstrong\u003eThe Sequence Alignment/Map format and SAMtools\u003c/strong\u003e. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e 2009, \u003cstrong\u003e25\u003c/strong\u003e(16):2078-2079.\u003c/li\u003e\n\u003cli\u003eWang K, Li M, Hakonarson H: \u003cstrong\u003eANNOVAR: functional annotation of genetic variants from high-throughput sequencing data\u003c/strong\u003e. \u003cem\u003eNUCLEIC ACIDS RES\u003c/em\u003e 2010, \u003cstrong\u003e38\u003c/strong\u003e(16):e164.\u003c/li\u003e\n\u003cli\u003eYang J, Lee SH, Goddard ME, Visscher PM: \u003cstrong\u003eGCTA: a tool for genome-wide complex trait analysis\u003c/strong\u003e. \u003cem\u003eAM J HUM GENET\u003c/em\u003e 2011, \u003cstrong\u003e88\u003c/strong\u003e(1):76-82.\u003c/li\u003e\n\u003cli\u003eKumar S, Stecher G, Li M, Knyaz C, Tamura K: \u003cstrong\u003eMEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms\u003c/strong\u003e. \u003cem\u003eMOL BIOL EVOL\u003c/em\u003e 2018, \u003cstrong\u003e35\u003c/strong\u003e(6):1547-1549.\u003c/li\u003e\n\u003cli\u003eAlexander DH, Novembre J, Lange K: \u003cstrong\u003eFast model-based estimation of ancestry in unrelated individuals\u003c/strong\u003e. \u003cem\u003eGENOME RES\u003c/em\u003e 2009, \u003cstrong\u003e19\u003c/strong\u003e(9):1655-1664.\u003c/li\u003e\n\u003cli\u003eWeir BS, Cockerham CC: \u003cstrong\u003eESTIMATING F-STATISTICS FOR THE ANALYSIS OF POPULATION STRUCTURE\u003c/strong\u003e. \u003cem\u003eEVOLUTION\u003c/em\u003e 1984, \u003cstrong\u003e38\u003c/strong\u003e(6):1358-1370.\u003c/li\u003e\n\u003cli\u003eZhang C, Dong SS, Xu JY, He WM, Yang TL: \u003cstrong\u003ePopLDdecay: a fast and effective tool for linkage disequilibrium decay analysis based on variant call format files\u003c/strong\u003e. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e 2019, \u003cstrong\u003e35\u003c/strong\u003e(10):1786-1788.\u003c/li\u003e\n\u003cli\u003eWu DD, Ding XD, Wang S, Wojcik JM, Zhang Y, Tokarska M, Li Y, Wang MS, Faruque O, Nielsen R\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePervasive introgression facilitated domestication and adaptation in the Bos species complex\u003c/strong\u003e. \u003cem\u003eNAT ECOL EVOL\u003c/em\u003e 2018, \u003cstrong\u003e2\u003c/strong\u003e(7):1139-1145.\u003c/li\u003e\n\u003cli\u003ePickrell JK, Pritchard JK: \u003cstrong\u003eInference of population splits and mixtures from genome-wide allele frequency data\u003c/strong\u003e. \u003cem\u003ePLOS GENET\u003c/em\u003e 2012, \u003cstrong\u003e8\u003c/strong\u003e(11):e1002967.\u003c/li\u003e\n\u003cli\u003eSalzer E, Cagdas D, Hons M, Mace EM, Garncarz W, Petronczki OY, Platzer R, Pfajfer L, Bilic I, Ban SA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eRASGRP1 deficiency causes immunodeficiency with impaired cytoskeletal dynamics\u003c/strong\u003e. \u003cem\u003eNAT IMMUNOL\u003c/em\u003e 2016, \u003cstrong\u003e17\u003c/strong\u003e(12):1352-1360.\u003c/li\u003e\n\u003cli\u003eChen R, Tie Y, Lu J, Li L, Zeng Z, Chen M, Zhang S: \u003cstrong\u003eTripartite motif family proteins in inflammatory bowel disease: Mechanisms and potential for interventions\u003c/strong\u003e. \u003cem\u003eCELL PROLIFERAT\u003c/em\u003e 2022, \u003cstrong\u003e55\u003c/strong\u003e(5):e13222.\u003c/li\u003e\n\u003cli\u003eMatsuzawa-Ishimoto Y, Yao X, Koide A, Ueberheide BM, Axelrad JE, Reis BS, Parsa R, Neil JA, Devlin JC, Rudensky E\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe gammadelta IEL effector API5 masks genetic susceptibility to Paneth cell death\u003c/strong\u003e. \u003cem\u003eNATURE\u003c/em\u003e 2022, \u003cstrong\u003e610\u003c/strong\u003e(7932):547-554.\u003c/li\u003e\n\u003cli\u003eZhao C, Ji G, Carrillo JA, Li Y, Tian F, Baldwin RL, Zan L, Song J: \u003cstrong\u003eThe Profiling of DNA Methylation and Its Regulation on Divergent Tenderness in Angus Beef Cattle\u003c/strong\u003e. \u003cem\u003eFRONT GENET\u003c/em\u003e 2020, \u003cstrong\u003e11\u003c/strong\u003e:939.\u003c/li\u003e\n\u003cli\u003eLi B, Yang J, He J, Peng X, Zeng Q, Song Y, Xu K, Ma H: \u003cstrong\u003eCharacterization of the whole transcriptome of spleens from Chinese indigenous breed Ningxiang pig reveals diverse coding and non-coding RNAs for immunity regulation\u003c/strong\u003e. \u003cem\u003eGENOMICS\u003c/em\u003e 2021, \u003cstrong\u003e113\u003c/strong\u003e(4):2468-2482.\u003c/li\u003e\n\u003cli\u003eCasey TM, Gouveia KM, Beckett LM, Markworth JF, Boerman JP: \u003cstrong\u003eMolecular signatures of longissimus dorsi differ between dairy cattle based on prepartum muscle reserves and branched-chain volatile fatty acid supplementation\u003c/strong\u003e. \u003cem\u003ePHYSIOL GENOMICS\u003c/em\u003e 2024, \u003cstrong\u003e56\u003c/strong\u003e(9):597-608.\u003c/li\u003e\n\u003cli\u003eJin SL, Richard FJ, Kuo WP, D\u0026apos;Ercole AJ, Conti M: \u003cstrong\u003eImpaired growth and fertility of cAMP-specific phosphodiesterase PDE4D-deficient mice\u003c/strong\u003e. \u003cem\u003eP NATL ACAD SCI USA\u003c/em\u003e 1999, \u003cstrong\u003e96\u003c/strong\u003e(21):11998-12003.\u003c/li\u003e\n\u003cli\u003eHao S, Zhen W, Zhe Z, Qian X, Zhong XU, Xiang-Zhe Z, Hong-Jie Y, Man-Xing Z, Ming X, Xiao-Hui L: \u003cstrong\u003eExploring the Current Situation of Conservation of Meishan Pigs Based on Genome Sequencing Data\u003c/strong\u003e. \u003cem\u003eJournal of Shanghai Jiaotong University(Agricultural Science)\u003c/em\u003e 2017.\u003c/li\u003e\n\u003cli\u003eVillanueva B, Fernandez A, Saura M, Caballero A, Fernandez J, Morales-Gonzalez E, Toro MA, Pong-Wong R: \u003cstrong\u003eThe value of genomic relationship matrices to estimate levels of inbreeding\u003c/strong\u003e. \u003cem\u003eGENET SEL EVOL\u003c/em\u003e 2021, \u003cstrong\u003e53\u003c/strong\u003e(1):42.\u003c/li\u003e\n\u003cli\u003eQu K, Nguyen SN, He Z, Huang B, Yuan X, Zhang Y, Zan L: \u003cstrong\u003eGenetic diversity and bottleneck analysis of Yunnan mithun (Bos frontalis) using microsatellite loci\u003c/strong\u003e. \u003cem\u003eAfrican journal of biotechnology\u003c/em\u003e 2012, \u003cstrong\u003e11\u003c/strong\u003e(12):2912-2919.\u003c/li\u003e\n\u003cli\u003eGou X, Wang Y, Yang S, Deng W, Mao H: \u003cstrong\u003eGenetic diversity and origin of Gayal and cattle in Yunnan revealed by mtDNA control region and SRY gene sequence variation\u003c/strong\u003e. \u003cem\u003eJ ANIM BREED GENET\u003c/em\u003e 2010, \u003cstrong\u003e127\u003c/strong\u003e(2):154-160.\u003c/li\u003e\n\u003cli\u003eXi D, Liu Q, Guo J, Yu H, Yang Y, He Y, Mao H, Gou X, Deng W: \u003cstrong\u003eGenetic variability of the coding region for the prion protein gene (PRNP) in gayal (Bos frontalis)\u003c/strong\u003e. \u003cem\u003eMOL BIOL REP\u003c/em\u003e 2012, \u003cstrong\u003e39\u003c/strong\u003e(2):2011-2020.\u003c/li\u003e\n\u003cli\u003eAhrestani FS: \u003cstrong\u003eBos frontalis and Bos gaurus (Artiodactyla: Bovidae)\u003c/strong\u003e. \u003cem\u003eMammalian species\u003c/em\u003e 2018, \u003cstrong\u003e50\u003c/strong\u003e(959):1-17.\u003c/li\u003e\n\u003cli\u003ePayne WJA, Hodges J: \u003cstrong\u003eTropical cattle: origins, breeds and breeding policies.\u003c/strong\u003e \u003cem\u003eBlackwell Science\u003c/em\u003e 1997.\u003c/li\u003e\n\u003cli\u003eMei C, Wang H, Zhu W, Wang H, Cheng G, Qu K, Guang X, Li A, Zhao C, Yang W\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eWhole-genome sequencing of the endangered bovine species Gayal (Bos frontalis) provides new insights into its genetic features\u003c/strong\u003e. \u003cem\u003eSCI REP-UK\u003c/em\u003e 2016, \u003cstrong\u003e6\u003c/strong\u003e:19787.\u003c/li\u003e\n\u003cli\u003eDorji T, Mannen H, Namikawa T, Inamura T, Kawamoto Y: \u003cstrong\u003eDiversity and phylogeny of mitochondrial DNA isolated from mithun Bos frontalis located in Bhutan\u003c/strong\u003e. \u003cem\u003eANIM GENET\u003c/em\u003e 2010, \u003cstrong\u003e41\u003c/strong\u003e(5):554-556.\u003c/li\u003e\n\u003cli\u003ePrabhu VR, Arjun MS, Bhavana K, Kamalakkannan R, Nagarajan M: \u003cstrong\u003eComplete mitochondrial genome of Indian mithun, Bos frontalis and its phylogenetic implications\u003c/strong\u003e. \u003cem\u003eMOL BIOL REP\u003c/em\u003e 2019, \u003cstrong\u003e46\u003c/strong\u003e(2):2561-2566.\u003c/li\u003e\n\u003cli\u003eWaples RK, Larson WA, Waples RS: \u003cstrong\u003eEstimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci\u003c/strong\u003e. \u003cem\u003eHEREDITY\u003c/em\u003e 2016.\u003c/li\u003e\n\u003cli\u003eLinderholm A: \u003cstrong\u003eThe genetic legacy of the wild ancestors of modern cattle\u003c/strong\u003e. \u003cem\u003eNATURE\u003c/em\u003e 2024, \u003cstrong\u003e635\u003c/strong\u003e(8037):43-45.\u003c/li\u003e\n\u003cli\u003eCeballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF: \u003cstrong\u003eRuns of homozygosity: windows into population history and trait architecture\u003c/strong\u003e. \u003cem\u003eNAT REV GENET\u003c/em\u003e 2018, \u003cstrong\u003e19\u003c/strong\u003e(4):220-234.\u003c/li\u003e\n\u003cli\u003eMarras G, Gaspa G, Sorbolini S, Dimauro C, Ajmone-Marsan P, Valentini A, Williams JL, Macciotta NPP: \u003cstrong\u003eAnalysis of runs of homozygosity and their relationship with inbreeding in five cattle breeds farmed in Italy\u003c/strong\u003e. \u003cem\u003eANIM GENET\u003c/em\u003e 2015, \u003cstrong\u003e46\u003c/strong\u003e(2):110-121.\u003c/li\u003e\n\u003cli\u003eSzpiech ZA, Xu J, Pemberton TJ, Peng W, Zollner S, Rosenberg NA, Li JZ: \u003cstrong\u003eLong runs of homozygosity are enriched for deleterious variation\u003c/strong\u003e. \u003cem\u003eAM J HUM GENET\u003c/em\u003e 2013, \u003cstrong\u003e93\u003c/strong\u003e(1):90-102.\u003c/li\u003e\n\u003cli\u003eBortoluzzi C, Bosse M, Derks M, Crooijmans R, Groenen M, Megens HJ: \u003cstrong\u003eThe type of bottleneck matters: Insights into the deleterious variation landscape of small managed populations\u003c/strong\u003e. \u003cem\u003eEVOL APPL\u003c/em\u003e 2020, \u003cstrong\u003e13\u003c/strong\u003e(2):330-341.\u003c/li\u003e\n\u003cli\u003eBobe R, Behrensmeyer AK: \u003cstrong\u003eThe expansion of grassland ecosystems in Africa in relation to mammalian evolution and the origin of the genus Homo\u003c/strong\u003e. \u003cem\u003ePALAEOGEOGR PALAEOCL\u003c/em\u003e 2004, \u003cstrong\u003e207\u003c/strong\u003e(3-4):399-420.\u003c/li\u003e\n\u003cli\u003eChen Y, Zhang T, Xian M, Zhang R, Yang W, Su B, Yang G, Sun L, Xu W, Xu S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eA draft genome of Drung cattle reveals clues to its chromosomal fusion and environmental adaptation\u003c/strong\u003e. \u003cem\u003eCOMMUN BIOL\u003c/em\u003e 2022, \u003cstrong\u003e5\u003c/strong\u003e(1):353.\u003c/li\u003e\n\u003cli\u003eSallam AM: \u003cstrong\u003eGenomic regions underlying variation in wattles, horns, and supernumerary teats phenotypes in Egyptian goats\u003c/strong\u003e. \u003cem\u003eCAN J ANIM SCI\u003c/em\u003e 2024, \u003cstrong\u003e104\u003c/strong\u003e(4):549-557.\u003c/li\u003e\n\u003cli\u003eZhu B, Niu H, Zhang W, Wang Z, Liang Y, Guan L, Guo P, Chen Y, Zhang L, Guo Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenome wide association study and genomic prediction for fatty acid composition in Chinese Simmental beef cattle using high density SNP array\u003c/strong\u003e. \u003cem\u003eBMC GENOMICS\u003c/em\u003e 2017, \u003cstrong\u003e18\u003c/strong\u003e(1):464.\u003c/li\u003e\n\u003cli\u003eDadousis C, Pegolo S, Rosa G, Gianola D, Bittante G, Cecchinato A: \u003cstrong\u003ePathway-based genome-wide association analysis of milk coagulation properties, curd firmness, cheese yield, and curd nutrient recovery in dairy cattle\u003c/strong\u003e. \u003cem\u003eJ DAIRY SCI\u003c/em\u003e 2017, \u003cstrong\u003e100\u003c/strong\u003e(2):1223-1231.\u003c/li\u003e\n\u003cli\u003eMaiorano AM, Lourenco DL, Tsuruta S, Ospina A, Stafuzza NB, Masuda Y, Filho A, Cyrillo J, Curi RA, Silva J: \u003cstrong\u003eAssessing genetic architecture and signatures of selection of dual purpose Gir cattle populations using genomic information\u003c/strong\u003e. \u003cem\u003ePLOS ONE\u003c/em\u003e 2018, \u003cstrong\u003e13\u003c/strong\u003e(8):e200694.\u003c/li\u003e\n\u003cli\u003eHao D, Wang X, Yang Y, Thomsen B, Holm LE, Qu K, Huang B, Chen H: \u003cstrong\u003eIntegrated Analysis of mRNA and MicroRNA Co-expressed Network for the Differentiation of Bovine Skeletal Muscle Cells After Polyphenol Resveratrol Treatment\u003c/strong\u003e. \u003cem\u003eFRONT VET SCI\u003c/em\u003e 2021, \u003cstrong\u003e8\u003c/strong\u003e:777477.\u003c/li\u003e\n\u003cli\u003eLee HJ, Park HS, Kim W, Yoon D, Seo S: \u003cstrong\u003eComparison of Metabolic Network between Muscle and Intramuscular Adipose Tissues in Hanwoo Beef Cattle Using a Systems Biology Approach\u003c/strong\u003e. \u003cem\u003eINT J GENOMICS\u003c/em\u003e 2014, \u003cstrong\u003e2014\u003c/strong\u003e:679437.\u003c/li\u003e\n\u003cli\u003eMay K, Scheper C, Br\u0026uuml;gemann K, Yin T, Strube C, Korkuć P, Brockmann GA, K\u0026ouml;nig S: \u003cstrong\u003eGenome-wide associations and functional gene analyses for endoparasite resistance in an endangered population of native German Black Pied cattle\u003c/strong\u003e. \u003cem\u003eBMC GENOMICS\u003c/em\u003e 2019, \u003cstrong\u003e20\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eXie K, Ning C, Yang A, Zhang Q, Wang D, Fan X: \u003cstrong\u003eResequencing Analyses Revealed Genetic Diversity and Selection Signatures during Rabbit Breeding and Improvement\u003c/strong\u003e. \u003cem\u003eGENES-BASEL\u003c/em\u003e 2024, \u003cstrong\u003e15\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eNiu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, Zhang L, Gao H, Song J, Li J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIntegration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle\u003c/strong\u003e. \u003cem\u003eGENOMICS\u003c/em\u003e 2021, \u003cstrong\u003e113\u003c/strong\u003e(5):3325-3336.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gayal, whole genome resequencing, SNPs, genomic diversity, population structure, conservation.","lastPublishedDoi":"10.21203/rs.3.rs-6263934/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6263934/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Gayal (\u003cem\u003eBos frontalis\u003c/em\u003e) is a rare and endangeredsemi-domesticated cattle species with unique genetic background and physiological characteristics. These physiological traits are like those of yaks lived on the Qinghai-Tibet Plateau, such as thicker myocardial connective tissue and abundant vascular distribution. The unique genes carried in its genome, including candidate genes related to immunity, meat quality, and reproduction, further highlight its biological value. However, due to its small population size, severe inbreeding, and the impact of human activities, the genetic diversity of Gayal is relatively low, placing it at risk of endangerment. Therefore, protecting Gayal is not only a rescue effort for this rare species but also a significant contribution to biodiversity and genetic resources. Through scientific research and effective conservation measures, the unique genetic resources of Gayal hold promise for providing valuable references for future livestock breeding and biomedical research. Genomic studies have revealed significant differences between Gayal and other cattle species, suggesting its potential as a genetic resource for hybrid improvement, which is of great importance for the development of China's livestock industry. To evaluate the population structure and genetic diversity of Gayal, this study used a 55K genotyping array to perform whole genome resequencing on 30 Gayal samples and downloaded 69 samples from 18 cattle breeds from the NCBI database (National Center for Biotechnology Information) for joint analysis. Using population genetic structure analysis, evaluate genetic diversity parameters (heterozygosity, proportion of polymorphic markers, and nucleotide diversity), population phylogenetic tree analysis, linkage disequilibrium (LD), population structure, and genetic differentiation (FST and genetic distance). The genetic diversity results indicate that the genetic diversity of Gayal is relatively low compared to other ruminant species. The LD pattern indicates that the genetic recombination rate of Gayal is high. Principal component analysis showed that the Gayal, as an independent species, exhibited significant genetic differentiation from the Mithun. We also identified a series of candidate genes, including \u003cem\u003eTRIM77, RASGRP1, API5, CLDN18, NAALAD2, DZIP1L, RAB3C, PDE4D\u003c/em\u003e, which may be related to the meat quality, immunity, and reproduction of this breed. This study provides valuable genomic resources and theoretical basis for the protection, development, and utilization of Gayal in the future.\u003c/p\u003e","manuscriptTitle":"Analyzing the genetic diversity of Gayal (Bos frontalis) based on whole genome resequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 07:32:44","doi":"10.21203/rs.3.rs-6263934/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dad0b5d4-5fd2-42a6-aaf1-ae0f47a81c22","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T08:41:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 07:32:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6263934","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6263934","identity":"rs-6263934","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}