Genomic Diversity and Selection Signatures for Zaosheng Cattle

Genomic Diversity and Selection Signatures for Zaosheng Cattle | 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 Genomic Diversity and Selection Signatures for Zaosheng Cattle Yanyan Wang, Jianfeng Xu, Fuyue Shi, Hailong Guo, Bo Gao, Junxiang Yang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6059907/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 Zaosheng cattle have a long history, closely related to Qinchuan cattle. This cattle breed is still under developing phase from a drought type to a beef breed. Followed by years of improvement, Zaosheng cattle have shown certain germline characteristics and genetic potential. Here, we used whole genome sequencing data from 19 Zaosheng cattle and 91 published genomes to understand its genetic diversity, population structure, and environmentally adapted performance. We provide a comprehensive overview of the sequence variation in the Zaosheng cattle genome to explore the genetic changes in Zaosheng cattle due to environmental adaptation. The findings of this study demonstrate that the genetic composition of Zaosheng cattle was primarily derived from Chinese and East Asian indicine cattle, where, Zaosheng and Qinchuan cattle were found to be genetically closest. Through ancestral fragment inference and selective sweep, we identified several genes linked to lipid metabolism, immune regulation, fertility and meat quality across the mosaic genome of Zaosheng cattle showing an excess of taurine or indicine ancestry. In summary, this study supplies essential genetic insights into the genome diversity within Zaosheng cattle and provides a foundational framework for comprehending the genetic basis of indigenous cattle breeds. Animal Science whole-genome sequencing genetic diversity selection signatures local ancestry Figures Figure 1 Figure 2 Figure 3 Introduction As the “head of the six animals”, cattle have attracted much attention since ancient times. In ancient China, cattle were used for plowing fields and sacrifices and held a preeminent status among the diverse range of domesticated species. Cattle can be categorized into two subspecies: humpless taurine ( Bos taurus ) and humped indicine ( Bos indicus )(Decker et al., 2014 ). With the application of whole-genome sequencing technology, it has become possible to study the genomic genetic diversity of domestic animals, which is crucial for the genetic improvement of livestock and the conservation of local breeds. Previously, some scholars proposed that domestic cattle around the world can be divided into five core groups: European taurine, Eurasian taurine, East Asian taurine, Chinese indicine and Indian indicine (Chen et al., 2018 ). Native Chinese cattle have been shown to have three types of ancestry: Eurasian taurine and East Asian taurine in northern China and Chinese indicine in southern China. However, few studies have explored the genetic architecture of Zaosheng cattle. Zaosheng cattle are found primarily on the Zaosheng Plateau, Qingyang, Gansu Province.Zaosheng cattle are crossbred. As a region with a predominantly agrarian culture, the local people have a long history of raising cattle due to the need for agricultural cultivation on the Zaosheng Plateau. Historical records show that people in the Zaosheng area have been selectively breeding Qinchuan cattle from the Guanzhong area of Shaanxi Province since 490 AD. This has led to the formation of a larger local population of cattle. Zaosheng cattle have transitioned from draft type to a beef breed, making it highly practical, resistant to rough feeding, adaptable, and renowned for its high-quality meat (Hengwei et al., 2024 ). Recently, whole­genome sequencing technology has been widely applied to explore livestock population structure, genetic diversity and selection signatures, including those of pigs (Gurgul et al., 2018 ), horses (Liu et al., 2022 ), cattle (Chen et al., 2018 ; Jiang et al., 2021 ), sheep (Yang et al., 2016 ) and chickens (Guo et al., 2022 ). However, research on the population genetics and selection footprints of existing Zaosheng cattle is lacking, necessitating a more in-depth analysis of their genomic characteristics. In the current study, whole-genome resequencing data from 119 cattle genomes, including 19 Zaosheng cattle and commercial and native cattle breeds previously collected from across the world were used. We performed admixture analysis, assessed the present genome ancestry of Zaosheng cattle and identified the genomic selection signatures showcasing an excess of taurine or indicine ancestry in Zaosheng cattle. Moreover, the population structure, genetic diversity and selection footprints of Zaosheng cattle were investigated. This investigation will help in understanding genomic characteristics and identifying important candidate regions under selection pressures, simultaneously providing a genome reference for breed improvement of Zaosheng cattle . Results 1. Data collection and Sequencing and identification of Single Nucleotide Polymorphisms (SNPs) Here discuss about the reference used and also about the published data set as mentioned in the comments too Individual genomes of 19 Zaosheng cattle were jointly genotyped with publicly available genomes for genetic background analysis. A total of 91 cattle genomes were added in a control group, including samples of the European taurine, East Asian taurine, Indian indicine,Chinese center cattle and Chinese indicine groups.Therefore,110 samples were used in this study, with an average sequence coverage of ~ 9.1 × and an average mapping rate of 99.69% (Tables S1 and S2). A total of 46,329,833 double allelic autosomal SNPs were detected. The functional annotation of the polymorphic loci revealed that the majority of the SNPs were present in intergenic (38.0%) or intronic regions (58.9%). Exons represented 0.8% of the total SNPs, 93,688 of which were nonsynonymous SNPs and 142,001 of which were synonymous SNPs. Table S3 shows the total number of SNPs detected in each breed, with Chinese indicine cattle having the highest number of SNPs, followed by crossbred Zaosheng and Qinchuan cattle, and taurine cattle having least SNPs (Table S3). Population Structure and Genetic Diversity To explore correlations between Zaosheng cattle and other cattle breeds distributed around the world, we performed admixture, neighbor-joining (NJ) tree and Principle component analysis (PCA) using autosomal genomic SNPs (Fig. 1 ). The analyses revealed clear geographical patterns between the cattle populations. In the admixture analysis, the cattle breeds were divided into taurine or indicine ancestry at K = 2, whereas at K = 3, East Asian taurine cattle were separated from European taurine cattle, whereas, Indian indicine cattle were further separated at K = 4. At K = 5, Zaosheng cattle and Qinchuan cattle presented clear genetic heterogeneity. The PCA revealed a clear genetic structure, with samples from each geographical region clustering together. The first PC explained 6.19% of the total genetic variation and was driven by considerable genetic distance between Bos taurus and Bos indicus . The second PC explained 2.58% of the total variation and geographically separated the different indicine groups, such as Chinese indicine cattle and Indian indicine cattle (Fig. 1 b). NJ tree analysis was used to construct a phylogenetic tree based on genetic distances, both of which corroborated the findings from the ADMIXTURE analysis (Fig. 1 c,e).Here, Qinchuan and Zaosheng cattle clustered together. To explore the affinities between Zaosheng cattle and the rest of the animals, we calculated smartpca-based F ST matrix which depicted decreased F ST values between Zaosheng cattle and Qinchuan cattle revealing their close genetic identity(Fig. 1 d). The closest distance between Zaosheng cattle and Qinchuan cattle was 0.006 while, Indian indicine cattle was the farthest by0.205. Genome-wide selective scanning signals from Zaosheng cattle a. Genetic signature of positive selection in Zaosheng cattle The nucleotide diversity analysis ( θ π) and the composite likelihood ratio (CLR) methods were applied to detect the genomic regions related to selection in the Zaosheng breed. The two methods yielded outlier signals (top 1%) in overlapping regions and were therefore considered candidate selective regions. A total of 1388 ( θ π) and 530 (CLR) genes/regions with selection signatures in Zaosheng cattle were identified (Supplementary Figure. 2a, b; Table S4 and S5),336 of which overlapped (Table S6). Among these overlapping genes, ACSS2 (Z. Huang et al., 2018 ), CLASP1 (Wang et al., 2019 ), NCOA6 (Oh et al., 2022 ) and ASIP (Liu et al., 2019 ) were found to be related to lipids, including lipid metabolism, fatty acid synthesis and lipid deposition. SLAMF1 (Ma et al., 2022 ) and ROMO1 (Tsoneva et al., 2023 ) play important roles in inflammation and the immune response. GOLGA4 (Ortega et al., 2016 ) and TP53INP2 (Sekar & Thirumurugan, 2024 ) for fertility and reproductive traits. DCLK3 (Galvan et al., 2018 ) was found to be involved in heat stress. Furthermore, CYP19A1 (Sahmi et al., 2019 ) is involved in pigmentation. b. Selective signals between zhaosheng and chinese indicine cattle Furthermore, further elucidation of the positive selective sweep regions was acquired through F ST , the θπ ratio, and XPEHH between Zaosheng cattle and Chinese indicine cattle (Fig. 2 a,b,c). The genomic regions identified ( p < 0.005) by at least two methods were considered candidate regions of positive selection (Tables S7, S8 and S9). This multimethod approach increased the robustness of our identification of selection signatures. Among the identified candidate regions,, the genes/regions harboring functional importance in biological areas were focused (Table S10). The function of genes under positive selection included immunity ( HYAL1 , HYAL2 ) (Jin et al., 2019 ), phenotype ( ASIP ), fat metabolism and deposition( TP53INP2 , NCOA6 , ACSS2 ), quality of meat༈ IGF1R , PLAGL2 )(Ardicli et al., 2019 ; Arikawa et al., 2024 ) and stress-related genes ( GSS ) (J. Huang et al., 2018 ), suggesting their strong selection in Zaosheng cattle. HYAL2 and NCOA6 were further confirmed by high F ST values, differential Tajima's D values and a distinctive haplotype pattern in Zaosheng cattle (Fig. 2 d,e). c. Selection signature between Zaosheng cattle and East Asian taurine We also implemented three methods ( F ST , π-ratio, XPEHH) to further elucidate the positive correlation characteristics between Zaosheng cattle and East Asian taurine cattle (Supplementary Figure. 1a, b, c). The genomic regions identified ( p < 0.005) by at least two methods were considered candidate regions of positive selection (Tables S12, S13 and S14). The functionally important genes among the candidate genes were identified (Table S15), including genes related to immunity and growth ( SLAMF1 , CD84 ) (Engel, 2019 ; Marom et al., 2017 ). Genes related to feed efficiency and meat quality traits ( ACSS2 and PPP3R1 ) were also identified as candidate genes under selection(Z. Huang et al., 2018 ; Santos Silva et al., 2020 ). Moreover, the gene related to growth and body size ( HDAC9 )(Tashireva et al., 2020 ); fertility (PROKR1)(Baryla et al., 2023 ); and adaptation (USH2A)(Birch et al., 2022 ) were also found under selection pressure.At the same time, the high signal values of these genes revealed that Zaosheng cattle are potential local breeds and are subject to selection in a variety of areas such as fat deposition, growth and body size. Local ancestry inference of Zaosheng cattle Zaosheng cattle are considered to have a hybrid origin of indicine and taurine cattle. To infer the local taurine and indicine ancestries across the Zaosheng cattle genomes LOTER was employed (Fig. 3 a; Table S16)(Dias-Alves et al., 2018 ). Chinese indicine(n = 20) and East Asian taurine(n = 20) were selected as reference panels. The segments with frequencies of at least 0.85 and lengths of at least 1,000bp were regarded as high-frequency ancestral fragments (P value < 0.01). In due course, 680 Chinese indicine segments and 5,686 East Asian taurine segments were retained. The maximum lengths of the segments in the Chinese indicine and East Asian taurine groups were 112,449,384 bp and 112,447,365 bp, respectively. For excessive East Asian taurine segments in Zaosheng cattle, 442 genes were annotated. These genes were enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (corrected P value < 0.05). The candidate genes enriched various pathways including, Metabolic, Phospholipase D signaling, cGMP − PKG signaling, Vascular smooth muscle contraction, and Retrograde endocannabinoid signaling pathways (Table S17). For excess taurine ancestry, we used genetic signature of positive selection in Zaosheng cattle detect traits from the taurine ancestor(Table S19). Here, we identified a total of 129 genes involved in immunity ( ROMO1 , SLAMF1 , CD48 , CD84 , MYO5A , and MMP28 )(Engel, 2019 ; Jian et al., 2011 ; Marom et al., 2017 ; McArdel et al., 2016 ; Tsoneva et al., 2023 ; Zhou et al., 2024 ), reproduction ( ADAMTSL1 and LEO1 )(Carre et al., 2011 ; Chu et al., 2012 ), muscle production ( TMOD3 , MYLK3 and TMOD2 )(Colpan et al., 2016 ; Hitsumoto et al., 2023 ; Shrestha et al., 2021 ), fat metabolism and deposition ( ANGPT4 , DEDD , UFC1 , CLASP1 , ASIP , TP53INP2 , ACSS2 , and NCOA6 )(Guangjun et al., 2019 ; Z. Huang et al., 2018 ; Liu et al., 2019 ; Oh et al., 2022 ; Sekar & Thirumurugan, 2024 ; Wang et al., 2019 ; Zhang et al., 2023 ) and growth ( NCSTN and GALNTL6 )(Doran et al., 2014 ; Yao et al., 2021 ). In our study, we found that the regions of the TP53INP2 gene in Zaosheng cattle showed a significantly high FST value, low Tajima’s D value, and high taurine ancestry. At the same time, the Zaosheng cattle have the same haplotype as East Asian taurine cattle, but the haplotype is very different from that of Chinese indicine cattle (Fig. 3 c). Moreover,95 genes observed in Chinese indicine segments in Zaosheng cattle, were annotated. Functional enrichment analysis via KEGG pathway was performed against these genes. The only significant KEGG pathway was “cell adhesion molecules (CAMs)” (corrected P value < 0.05) (Fig. 3 b; Table S18). Zaosheng cattle have a hybrid origin involving indicine and taurine cattle, and haplotypes of indicine or taurine ancestry may confer a relative adaptive advantage under selection pressures.For excess indicine ancestry,we used genetic signature of positive selection in Zaosheng cattle detect traits from the indicine ancestor(Table S19). A total of 14 genes were involved in growth ( GBP4 )(Cao et al., 2018 ), reproduction ( TUSC3 )(Yu et al., 2017 ) and adaptation ( USH2A )(Birch et al., 2022 ).We found that PROKR1 gene was subject to selection in Zaosheng cattle,and that a high proportion of indicine segment pedigrees At the same time, we aslo found through haplotype maps that PROKR1 were selected in Zaosheng cattle (Fig. 3 d). Discussion Chinese indigenous cattle are characterized by their rich genetic resources, wide distribution range and diverse ecological types harboring rich genetic. Zaosheng cattle constitute a local breed in Gansu Province. The current study explored the population genetic structure of Zaosheng cattle. As shown by the ADMIXTURE analysis (Fig. 1 c), the ancestral contributions of Zaosheng cattle were Chinese indicine (~ 25.9%), East Asian taurine (~ 72.5%) and European taurine (~ 0.16%).Moreover, Zaosheng cattle are closely related to the Qinchuan breed. In ancient times, the Zaosheng area was remote, and to develop agriculture, Qinchuan cattle were introduced from the Guanzhong area of Shaanxi, where agriculture was well developed and the hybrid breed flourished. Similarly, low inbreeding coefficient revealed that Zaosheng cattle presented high genetic diversity, depicting that the Zaosheng cattle have greater breeding potential than mature commercial cattle. In recent years, with the improvement of the quality of life, quantity and quality of beef has been a leading concern. It's well known that Qinchuan cattle constitute a representative breed of Chinese indigenous cattle, known as ‘the treasure of the country’. However, since the 1990s, the number of Qinchuan cattle has declined sharply. Efforts by the local government has been made to protect the Qinchuan cattle and its related breeds. The present study focusses on the genomic information of Zaosheng cattle. In current analysis, Zaosheng cattle and Qinchuan cattle presented a very close genetic relationship. We believe that cattle breeds with equivalent beef qualities could be used to help develop the market. Utilizing the whole­genome sequence data, more signatures of selection were identified than with SNP array data. The selection signal analysis of Zaosheng cattle in this study provides new insights into the genetic basis of adaptations to the local environment. Here, θπ and CLR methods were employed to detect selected genes in Zaosheng cattle, and some genes were strongly selected. For example, Nuclear receptor coactivator 6 ( NCOA6 ) is a transcriptional coregulator that can interact with multiple nuclear receptors(Tong et al., 2019 ). It affects cell growth, differentiation and proliferation, which in turn plays a role in skeletal muscle development. Reactive oxygen species (ROS) modulator 1 ( ROMO1 ) is a mitochondrial membrane protein that is essential for the regulation of mitochondrial ROS production and redox sensing and has been shown to play an important role in the regulation of mitochondrial ROS production and redox sensing(Chung et al., 2006 ). Oxidative stress can be induced by various environmental factors, such as heat stress, disease, or injury. ROMO1 may help in modulating the response to such stresses, protecting cells from damage and promoting recovery. Recent studies have shown that the overexpression of Romo1 increases anti-inflammatory function and promotes the reprogramming of the cellular metabolism of macrophages (Sun et al., 2020 ). GOLGA4 is a Golgi matrix protein, and recent studies have shown that GOLGA4 is also expressed in mouse testes.(Guo et al., 2020 ; F. Zhang et al., 2019 ) Subsequently a comparative analysis was conducted between Zaosheng cattle and Chinese indicine cattle to identify advantageous genes specific to Zaosheng cattle relative to Chinese indicine cattle. The analysis identified several genes involved in the immune system, particularly the HYAL1 and HYAL2 genes, which overlapped among the three selection methods. Tajima's D also revealed that HYAL2 was indeed selected between Zaosheng cattle and Chinese indicine cattle. In general, Chinese indicine cattle, exemplified by Hainan cattle, have good immune performance, but we identified clusters of immune genes in Zaosheng cattle that presented different haplotypes. These findings demonstrate that Zaosheng cattle and Chinese indicine cattle have different immune mechanisms, presumably due to environmental differences. Hyaluronidase ( HYAL )-2 is a weak, acid-active, hyaluronan-degrading enzyme broadly expressed in somatic tissues (Midgley et al., 2020 ). Some studies have shown that deregulated hyaluronan metabolism in the tumor microenvironment drives cancer inflammation, but HYAL2 has catabolic functions, which can reduce excessive inflammation and contribute to the resolution of the immune response(Donelan et al., 2022 ).Several studies have confirmed that NCOA6 deletion can lead to early embryonic death or slow growth in mice, potentially by disrupting the cell cycle and increasing apoptosis (Mahajan & Samuels, 2008 ). Thus, it is speculated that NCOA6 may be involved in regulating bovine growth and development. In our study, we found that the haplotypes of this region were different in Zaosheng cattle and Chinese indicine cattle, confirming that this region is subjected to selection. Zaosheng cattle, a native Chinese breed, demonstrate remarkable resilience and environmental adaptability. We detected high signal values for genes related to immunity in Zaosheng cattle compared with East Asian taurine cattle, indicating outstanding immunity in Zaosheng cattle. Previously, in Qinchuan cattle, the USH2A gene was found to affect cattle hair color, which may be associated with adaptive traits or survival advantages (Wang et al., 2023 ). Interestingly, all three methods were used to screen for PROKR1 . Prokineticin 1 ( PROK1 ) is also termed endocrine gland-derived vascular endothelial growth factor (endocrine gland-derived VEGF). Studies in pigs have shown that PROK1 , which acts via PROKR1 , promotes the formation of capillary-like structures via endothelial cells isolated from porcine corpus luteum in vitro and stimulates the synthesis of VEGFA and the mRNA expression of angiogenin (another angiogenic factor) in the corpus luteum (Baryla et al., 2024 ). Prokineticin 1 is a novel factor that regulates porcine corpus luteum function (Baryla et al., 2023 ). LIMCH1 , an actin stress fiber-associated protein, is a paralogous protein with C-terminal LIM domains(Y. Zhang et al., 2019 ). A study examining myofibers affected by neurogenic muscular atrophy revealed the overexpression of 55 proteins, with LIMCH1 being one of them, and found that most of them were involved in myofibrillogenesis(Midgley et al., 2020 ). In cattle, a study concluded that the LIMCH1 locus is a putative region underlying greater forehead size in Brahman cattle than in Yunling cattle(Chen et al., 2020 ). Taken together, LIMCH1 may affect skeletal development and, subsequently, body size in cattle. In our study, we applied LOTER combined with selection analysis to infer local ancestry and obtain the ancestry of selection signatures in Zaosheng cattle. These excessive East Asian taurine segment-annotated genes were enriched mainly in growth, metabolism, development, and disease pathways. For a few Chinese indicine segments, the annotated genes were enriched mainly in cell adhesion molecules. Cell adhesion molecules, including receptors of the immunoglobulin superfamily and integrins, particularly integrins, play a vital role in regulating all aspects of immune cell function (Harjunpaa et al., 2019 ). The genes related to fat metabolism and muscle production were more common in East Asian taurine descendants and Zaosheng cattle. The high muscle fat content and better taste of beef quality in East Asian taurine cattle, such as Yanbian cattle(Shen et al., 2020 ), indicate that the target group of Zaosheng cattle has excellent meat quality. The Chinese Indicine descent accounted for fewer genes, and fewer genes were fixed. Interestingly, USH2A was also screened for this gene in an interpopulation selection with East Asian taurine cattle, and we believe that Zaosheng cattle also have strong environmental adaptability. It is worth noting that the PROKR1 gene was selected in both the selected and loter populations. We further analyzed this region and found that this gene was subjected to strong selection in the Zaosheng cattle, and we hypothesized that it originated from the Chinese indicine population, but after long-term evolution, it also had its own unique characteristics.The tumor protein p53-induced nuclear protein 2 ( TP53INP2 ) regulates apoptosis, autophagy, and cell differentiation(Dong et al., 2020 ). In the context of skeletal muscle, TP53INP2 controls muscle mass in adult mice, and its overexpression in muscle leads to increased autophagy and a moderate reduction in muscle fiber size(Sala et al., 2014 ) Some studies have shown that high TP53INP2 protein levels are associated with greater muscle strength, physical performance, and healthy aging in humans(Sala et al., 2014 ). In the haplotype map, Zaosheng cattle shared the same haplotype as East Asian taurine cattle, but differed significantly from that of Chinese indicine cattle. It is presumed that this is partly influenced by East Asian taurine bloodlines. Overall, TP53INP2 is subject to selection and contributes to the meat quality traits of Zaosheng cattle. Materials and methods 1. Sample preparation and DNA sequencing 2. Read mapping and variant calling To study the genetic diversity of Zaosheng cattle in China, we collected 19 samples from the Zaosheng of Gansu Province (Table S1). We adopted a standard phenol–chloroform method to extract genomic DNA from methylated preserved ear tissue. For each individual, paired-end sequencing data were obtained with an average insert size of 500 bp and an average read length of 150 bp via the Illumina NovaSeq system. The sequencing platform was from Novogene Bioinformatics Institute, Beijing, China. To compare the differences, the Illumina whole genomes of 91 cattle individuals from previous studies, including Corssbreed (Qinchuan), European taurine (Angus and Hereford), East Asian taurine (Yanbian and Hanwoo), Chinese indicine (Guangfeng, Ji'an, Wenshan and Wannan) and Indian indicine cattle (Table S2), were used. The clean reads were mapped onto the Bos taurus reference genome assembly ARS-­UCD1.2 via BWA-­MEM (0.7.13-­r1126) with default parameters(Li & Durbin, 2009 ). After mapping, SNPs were detected via SAMtools(Heng et al., 2009 ), Picard tools ( http://broadinstitute.github.io/picard ), and the Genome Analysis Toolkit (GATK, version 3.6-­0-­g89b7209). The raw SNPs were called via the ‘HaplotypeCaller’, ‘GenotypeGVCFs’, and ‘SelectVariants’ of GATK. Moreover, “VariantFiltration” was used to filter the raw SNPs on the basis of the hard filtering parameters “QD 60.0, MQ < 40.0, MQRankSum < -12.5, ReadPosRankSum 3.0” and the mean sequencing depth of variants (all individuals) “ 3 × ”. Afterward, a The transcript FASTA file for the database was built via the retrieve_seq_from_fasta.pl module of ANNOVAR on the basis of the annotation file (GCF_002263795.1_ARS- UCD1.2_genomic.gff) of the B. taurus reference genome. The functional annotation of each SNP was performed via ANNOWAR(Wang et al., 2010 ). Population genetic analysis The autosomal SNPs were pruned at high levels of pairwise linkage disequilibrium (LD) by PLINK(Purcell et al., 2007 ) with the parameters (-­-­indep-­pair-­wise 50 5 0.2) to obtain genetic structure and perform principal component analysis (PCA). The genetic structure was speculated via admixture (Alexander & Lange, 2011 )with a kinship set from 2 to 4. Principal component analysis was conducted via smartPCA of the eigensoft (Patterson et al., 2006 ). A phylogenetic tree was constructed from 110 samples via PLINK via MEGA v7.0(Kumar et al., 2016 ) and visualized using itol(Letunic & Bork, 2019 ). VCFtools (Danecek et al., 2011 ) was used to estimate the nucleotide diversity (θπ) of each breed, keeping a window size of 50 kb and a step size of 20 kb. It was used to calculate the fixation index ( F ST ) between the six cattle breeds. Detection of selection signals In the present study, our aim was to identify regions exhibiting positive selection signatures in Zaosheng cattle. We detected the selection signatures within Zaosheng cattle via two different statistics: the nucleotide diversity ( θπ ) and the composite likelihood ratio (CLR)(Nielsen et al., 2005 ). Nucleotide diversity was estimated via a sliding window approach with windows of 50 kb and a step of 20 kb via VCFtools(Danecek et al., 2011 ). The CLR test was calculated for sites in nonoverlapping 50 kb windows by using SweepFinder2 (DeGiorgio et al., 2016 ). Empirical P values were calculated for the π and CLR windows, and the overlaps of the top 1% windows of each method were considered candidate signatures of selection. Furthermore, the F ST , nucleotide diversity ratio (θπ ratio) and cross-population extended haplotype homozygosity (XPEHH) (Hudson et al., 1992 ; Sabeti et al., 2002 ) were used to compare Zaosheng cattle (as a target population) with East Asian taurine cattle and Chinese indicine cattle (as a reference population) to perform cross-population analysis for a comprehensive investigation of the common selection signals present among these diverse populations. F ST and θπ ratio analyses were performed in 50 kb windows with 20 kb steps via VCFtools v0.1.16(Danecek et al., 2011 ). XPEHH statistics were calculated for each population pair via Selscan v1.1 (Szpiech & Hernandez, 2014 ) We identified putative selective sweeps by selecting the top 1% of the original scores from each method, which represented the most likely candidates for the regions under selection. Tajima's D statistic was computed via VCFtools for several important candidate genes. Local ancestry inference LOTER (Dias-Alves et al., 2018 ) was used to infer taurine and indicine ancestry in the genomes of Zaosheng cattle. We selected the Chinese indicine and East Asian taurine groups as reference panels on the basis of population structure. The length and frequency of ancestral segments in each reference group were subsequently calculated. To detect a high proportion of fragments with ancestry, the ancestry-specific haplotypes for each fragment were compared to the total number of ancestry-specific haplotypes for all fragments, with regions of significance having a P value < 0.01 (Z test). The ideogram package(Hao et al., 2020 ) in R was used to draw chromosome maps to visualize excessive segments of Chinese indicine and East Asian taurine cattle on the basis of the B. taurus reference genome. Functional enrichment analysis was performed on the list of genes within the excessive segments detected by KOBAS v3.0(Bu et al., 2021 ). Conclusions In conclusion, this study comprehensively investigated the genomic variations in Zaosheng cattle by screening whole-genome resequencing data. We explored the population structure of current Zaosheng cattle, elucidated its genetic diversity and conducted selective sweep analysis. Simultaneously a set of potential candidate genes with potential impacts on fat deposition and development, meat quality and the immune response were identified within this breed. These discoveries not only advance our knowledge of the unique characteristics of Zaosheng cattle but also provide a basis for genetic breeding and resource protection in Qinchuan and Zaosheng cattle. Declarations Fund item ：The central government guides local funds for science and technology development，Number: 24ZYQL003 References Alexander, D. H., & Lange, K. (2011). Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. Bmc Bioinformatics , 12 , Article 246. https://doi.org/10.1186/1471-2105-12-246 Ardicli, S., Samli, H., Vatansever, B., Soyudal, B., Dincel, D., & Balci, F. (2019). Comprehensive assessment of candidate genes associated with fattening performance in Holstein-Friesian bulls. Archives Animal Breeding , 62 (1), 9-32. https://doi.org/10.5194/aab-62-9-2019 Arikawa, L. M., Mota, L. F. M., Schmidt, P. I., Frezarim, G. B., Fonseca, L. F. S., Magalhaes, A. F. B., Silva, D. A., Carvalheiro, R., Chardulo, L. A. L., & de Albuquerque, L. G. (2024). Genome-wide scans identify biological and metabolic pathways regulating carcass and meat quality traits in beef cattle. Meat Science , 209 . https://doi.org/ARTN 109402 10.1016/j.meatsci.2023.109402 Baryla, M., Goryszewska-Szczurek, E., Kaczynski, P., Balboni, G., & Waclawik, A. (2023). Prokineticin 1 is a novel factor regulating porcine corpus luteum function. Scientific reports , 13 (1), 5085-5085. https://doi.org/10.1038/s41598-023-32132-3 Baryla, M., Kaczynski, P., Goryszewska-Szczurek, E., & Waclawik, A. (2024). The regulation of the expression of prokineticin 1 and its receptors and its mechanism of action in the porcine corpus luteum. Theriogenology , 226 , 39-48. https://doi.org/10.1016/j.theriogenology.2024.05.044 Birch, D. G., Samarakoon, L., Melia, M., Duncan, J. L., Ayala, A. R., Audo, I., Cheetham, J. K., Durham, T. A., Iannaccone, A., Pennesi, M. E., Stingl, K., for the Foundation Fighting Blindness Consortium Investigator, G., Fdn Fighting Blindness, C., & Foundation Fighting Blindness Consortium Investigator, G. (2022). The RUSH2A Study: Dark-Adapted Visual Fields in Patients With Retinal Degeneration Associated With Biallelic Variants in the USH2A Gene. Investigative ophthalmology & visual science , 63 (3), 17-17. https://doi.org/10.1167/iovs.63.3.17 Bu, D., Luo, H., Huo, P., Wang, Z., Zhang, S., He, Z., Wu, Y., Zhao, L., Liu, J., Guo, J., Fang, S., Cao, W., Yi, L., Zhao, Y., & Kong, L. (2021). KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic acids research , 49 (W1), W317-W325. https://doi.org/10.1093/nar/gkab447 Cao, X.-K., Huang, Y.-Z., Ma, Y.-L., Cheng, J., Qu, Z.-X., Ma, Y., Bai, Y.-Y., Tian, F., Lin, F.-P., Ma, Y.-L., & Chen, H. (2018). Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature. Functional & integrative genomics , 18 (5), 559-567. https://doi.org/10.1007/s10142-018-0613-0 Carre, G.-A., Couty, I., Hennequet-Antier, C., & Govoroun, M. S. (2011). Gene Expression Profiling Reveals New Potential Players of Gonad Differentiation in the Chicken Embryo. PloS one , 6 (9), e23959-e23959. https://doi.org/10.1371/journal.pone.0023959 Chen, N., Cai, Y., Chen, Q., Li, R., Wang, K., Huang, Y., Hu, S., Huang, S., Zhang, H., Zheng, Z., Song, W., Ma, Z., Ma, Y., Dang, R., Zhang, Z., Xu, L., Jia, Y., Liu, S., Yue, X., . . . Lei, C. (2018). Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia. Nat Commun , 9 (1), 2337. https://doi.org/10.1038/s41467-018-04737-0 Chen, Q., Huang, B., Zhan, J., Wang, J., Qu, K., Zhang, F., Shen, J., Jia, P., Ning, Q., Zhang, J., Chen, N., Chen, H., & Lei, C. (2020). Whole-genome analyses identify loci and selective signals associated with body size in cattle. Journal of animal science , 98 (3), 1-8. https://doi.org/10.1093/jas/skaa068 Chu, T., Dufort, I., & Sirard, M. A. (2012). Effect of ovarian stimulation on oocyte gene expression in cattle. Theriogenology , 77 (9), 1928-1938. https://doi.org/10.1016/j.theriogenology.2012.01.015 Chung, Y. M., Kim, J. S., & Yoo, Y. D. (2006). A novel protein, Romo1, induces ROS production in the mitochondria. Biochemical and biophysical research communications , 347 (3), 649-655. https://doi.org/10.1016/j.bbrc.2006.06.140 Colpan, M., Moroz, N. A., Gray, K. T., Cooper, D. A., Diaz, C. A., & Kostyukova, A. S. (2016). Tropomyosin-binding properties modulate competition between tropomodulin isoforms. Archives of biochemistry and biophysics , 600 , 23-32. https://doi.org/10.1016/j.abb.2016.04.006 Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G., Durbin, R., Genomes Project Analysis, G., Genomes Project Anal, G., & Genomes Project Analysis, G. (2011). The variant call format and VCFtools. BIOINFORMATICS , 27 (15), 2156-2158. https://doi.org/10.1093/bioinformatics/btr330 Decker, J. E., McKay, S. D., Rolf, M. M., Kim, J., Molina Alcala, A., Sonstegard, T. S., Hanotte, O., Gotherstrom, A., Seabury, C. M., Praharani, L., Babar, M. E., de Almeida Regitano, L. C., Yildiz, M. A., Heaton, M. P., Liu, W.-S., Lei, C.-Z., Reecy, J. M., Saif-Ur-Rehman, M., Schnabel, R. D., & Taylor, J. F. (2014). Worldwide Patterns of Ancestry, Divergence, and Admixture in Domesticated Cattle. PLoS genetics , 10 (3), e1004254-e1004254. https://doi.org/10.1371/journal.pgen.1004254 DeGiorgio, M., Huber, C. D., Hubisz, M. J., Hellmann, I., & Nielsen, R. (2016). SWEEPFINDER2: increased sensitivity, robustness and flexibility. BIOINFORMATICS , 32 (12), 1895-1897. https://doi.org/10.1093/bioinformatics/btw051 Dias-Alves, T., Mairal, J., & Blum, M. G. B. (2018). Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. Molecular biology and evolution , 35 (9), 2318-2326. https://doi.org/10.1093/molbev/msy126 Donelan, W., Dominguez-Gutierrez, P. R., & Kusmartsev, S. (2022). Deregulated hyaluronan metabolism in the tumor microenvironment drives cancer inflammation and tumor-associated immune suppression. Frontiers in immunology , 13 , 971278-971278. https://doi.org/10.3389/fimmu.2022.971278 Dong, S., Li, J., & Zhang, X. (2020). Tumor protein p53-induced nuclear protein 2 modulates osteogenic differentiation of human adipose derived stem/stromal cells by activating Wnt/beta-catenin signaling. American journal of translational research , 12 (10), 6853-6867. https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NatwwEBZJKCFQQn9pmjao0KvXXsuW7GO6JOQSyGFLoZdFskatoZ bN2tuS58kb9EH6TJ3R2m039FB6tmTJzIc033jmG8ZEOkuie2dCmpkEEmcSURaVESYrBcxlge5HDkqWVJy8uCo_ vpMXN2q5x_KpNCbk8FemnvkvzczXn0OqZddU8ZQ2Ft9cL5TMhUxUvM_2VVpMjH17GkvqLnPEDgVCDbmL2nEk_5YGee_qCdfM5SN2PPqH_ Hy78GO2B_4JO7we_4A_ZXfLTdOueZBXqD3vchEhq0b7WO5Jmlj_fpbyprXUnQt6T qUcLWKlrvjUEmXYGo W3jodGfVzbumt74BZB-RXfRxLPcT-s2wY3RAH-nptbToUQFMb1n_gHP8Q_vkeUU-VxPUoF0VTd_oy9v7xYLq6isd FC1KWZGCKrTOKQSBpttSXBF5lW4HQGQhpSxykdUu4ySwFA5bYQVoBGmqRpwryyiXjOHmpKyPdDKNyzLxh3du4kaAG5BOSQUBZWFpk xTlU6U6I8YW8mQ6wQ0fQV2kO76VekcEcqRBLHqB0LrbqtAseKNLF3nyA2gjb2iIUT9vZPW_6aSC5qgT5aVtA9jQvM_2XYYpRLJ5mA4eV_b-qUHaXE1kMA5xU7GNYbeM0e-G_9RruzEFU4C-D9CR5xBQc Doran, A. G., Berry, D. P., & Creevey, C. J. (2014). Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle. BMC Genomics , 15 (1), 837-837. https://doi.org/10.1186/1471-2164-15-837 Engel, P. (2019). SLAMF receptors and disease. Clinical immunology (Orlando, Fla.) , 204 , 1-2. https://doi.org/10.1016/j.clim.2019.07.008 Galvan, L., Francelle, L., Gaillard, M.-C., de Longprez, L., Carrillo-de Sauvage, M.-A., Liot, G., Cambon, K., Stimmer, L., Luccantoni, S., Flament, J., Valette, J., de Chaldee, M., Auregan, G., Guillermier, M., Josephine, C., Petit, F., Jan, C., Jarrige, M., Dufour, N., . . . Brouillet, E. (2018). The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin. Brain (London, England : 1878) , 141 , 1434-1454. https://doi.org/10.1093/brain/awy057 Guangjun, X. I. A., Luomeng, Z., Hongyan, X. U., Chang, X. U., & Baozhen, Y. I. N. (2019). Yanbian yellow cattle meat quality-related ANGPT4 gene SNP molecular marker, primer pair, kit and application thereof . https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwvV1LT8JAEJ6gEjUxUVGj-Mh66Um0dOnr0BhYQC6WRhsTvZA-thENpYESgr_e2RWEmOjRS9NXmu10Z3amM983AFS7Vis_bIJNo2pk2LqmJoHJ9cSgZhJads2iQWhRSWPAOvZLw2h5pl-AtwU0RtKGTiVXIipYhOqfS_OdL f9pNWWp5fgm7OOp4W3bd5rKPFjGWERTDaXZcFpet9llCmMOcxX3wcFFT69V0XrX12BDsHBJn_OpIUAq2eoK0979z8 HsQdHDu9N8HwofryXYYouubyX YvJ8n23F3rvfjA5g8B6kgJyczkauZkkhSH5MBGnLyBcucVSQqhsek7t55fo3gxOTk0fXIYNGAlwxEKdDoimSipcCIZEEfD977OQnSmKxk0olwSvkw OYTL dstnnQoKq_ctjB5zl69Cj2AnEEX6aS7 BfPExEENPTMotjluKjmJk4Tww QpUnsa0lGDmdQPn3B5b_ungK20LCss7EOIP1fDTh51BMp-NJkFzIT_sJ2I7EQg Guo, S. S., Lv, C. Y., Ouyang, S. J., Wang, X. L., Liao, A. H., & Yuan, S. Q. (2020). GOLGA4, A Golgi matrix protein, is dispensable for spermatogenesis and male fertility in mice. Biochemical and biophysical research communications , 529 (3), 642-646. https://doi.org/10.1016/j.bbrc.2020.05.170 Guo, X., Xing, C.-H., Wei, W., Zhang, X.-F., Wei, Z.-Y., Ren, L.-L., Jiang, J.-J., Li, M., Wang, J.-X., He, X.-X., Wang, M.-S., & Jiang, R.-S. (2022). Genome-wide scan for selection signatures and genes related to heat tolerance in domestic chickens in the tropical and temperate regions in Asia. Poultry science , 101 (7), 101821-101821. https://doi.org/10.1016/j.psj.2022.101821 Gurgul, A., Jasielczuk, I., Ropka-Molik, K., Semik-Gurgul, E., Pawlina-Tyszko, K., Szmatola, T., Szyndler-Nedza, M., Bugno-Poniewierska, M., Blicharski, T., Szulc, K., Skrzypczak, E., & Krupinski, J. (2018). A genome-wide detection of selection signatures in conserved and commercial pig breeds maintained in Poland. BMC genetics , 19 (1), 95-95. https://doi.org/10.1186/s12863-018-0681-0 Hao, Z. D., Lv, D. K., Ge, Y., Shi, J. S., Weijers, D., Yu, G. C., & Chen, J. H. (2020). RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. Peerj Computer Science , Article e251. https://doi.org/10.7717/peerj-cs.251 Harjunpaa, H., Asens, M. L., Guenther, C., & Fagerholm, S. C. (2019). Cell Adhesion Molecules and Their Roles and Regulation in the Immune and Tumor Microenvironment. Frontiers in immunology , 10 , 1078-1078. https://doi.org/10.3389/fimmu.2019.01078 Heng, L., Bob, H., Alec, W., Tim, F., Jue, R., Nils, H., Gabor, M., Goncalo, A., & Richard, D. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics (Oxford, England) , 25 (16), 2078-2079. https://next.cnki.net/middle/abstract?v=AhJL6SqmbxBcRvxvdcIFVN1RrbIqsfXEZ0xUqHGQQ76MUJOSCQkUOOMv7LmvBkjSxgVKJaAr6d83apjv3osxAbpHOQAaMpMaSQRrvpj SC23kZfi6-Hu396UpPdNKI4uQXNFSajwTQv_PmJlvFtpRxM8ck7LFKlTfDh9MbK-CQoFylDyXNrEFu9Brhdoih66A2KbHzDh7d9Y=&uniplatform=NZKPT&language=CHS&scence=null Hengwei, Y., Ke, Z., Gong, C., Chugang, M., Hongbao, W., & Linsen, Z. (2024). Genome-wide analysis reveals genomic diversity and signatures of selection in Qinchuan beef cattle. BMC Genomics , 25 (1), 558-558. https://next.cnki.net/middle/abstract?v=AhJL6SqmbxCSmDBlpbZLAXNPjwGktsN1CsryUsUKn_8Y5Z2pp3IouIdM1HE0HRne5kdMjlbWo-FN9Dt7-i3m8zFaZlWVdsNkiCk6j96QEtlaD8SWiTIxcgISR7kckrsrz2Ygr4f7iI8VlHxGaaYfxYe5etSwtaDSLd0BqaMhrFq-ZXsOkUeb_cmbGXh9KHFkdxMVT4iEXzmEx4Q2PowSL5OfZKeOkanw&uniplatform=NZKPT&language=CHS&scence=null Hitsumoto, T., Tsukamoto, O., Matsuoka, K., Li, J., Liu, L., Kuramoto, Y., Higo, S., Ogawa, S., Fujino, N., Yoshida, S., Kioka, H., Kato, H., Hakui, H., Saito, Y., Okamoto, C., Inoue, H., Hyejin, J., Ueda, K., Segawa, T., . . . Takashima, S. (2023). Restoration of Cardiac Myosin Light Chain Kinase Ameliorates Systolic Dysfunction by Reducing Superrelaxed Myosin. Circulation (New York, N.Y.) , 147 (25), 1902-1918. https://doi.org/10.1161/CIRCULATIONAHA.122.062885 Huang, J., Jia, Y., Li, Q., Son, K., Hamilton, C., Burris, W. R., Bridges, P. J., Stromberg, A. J., & Matthews, J. C. (2018). Glutathione content and expression of proteins involved with glutathione metabolism differs in longissimus dorsi, subcutaneous adipose, and liver tissues of finished vs. growing beef steers. Journal of animal science , 96 (12), 5152-5165. https://doi.org/10.1093/jas/sky362 Huang, Z., Zhang, M., Plec, A. A., Estill, S. J., Cai, L., Repa, J. J., McKnight, S. L., & Tu, B. P. (2018). ACSS2 promotes systemic fat storage and utilization through selective regulation of genes involved in lipid metabolism. Proceedings of the National Academy of Sciences - PNAS , 115 (40), E9499-E9506. https://doi.org/10.1073/pnas.1806635115 Hudson, R. R., Slatkin, M., & Maddison, W. P. (1992). ESTIMATION OF LEVELS OF GENE FLOW FROM DNA-SEQUENCE DATA. Genetics , 132 (2), 583-589. ://WOS:A1992JQ14600025 https://pmc.ncbi.nlm.nih.gov/articles/PMC1205159/pdf/ge1322583.pdf Jian, P., Yanfang, T., Zhuan, Z., Jian, W., Xueming, Z., & Jian, N. (2011). MMP28 (epilysin) as a novel promoter of invasion and metastasis in gastric cancer. BMC cancer , 11 (1), 200-200. https://doi.org/10.1186/1471-2407-11-200 Jiang, L., Kon, T., Chen, C., Ichikawa, R., Zheng, Q., Pei, L., Takemura, I., Nsobi, L. H., Tabata, H., Pan, H., Omori, Y., & Ogura, A. (2021). Whole-genome sequencing of endangered Zhoushan cattle suggests its origin and the association of MC1R with black coat colour. Scientific reports , 11 (1), 17359-17359. https://doi.org/10.1038/s41598-021-96896-2 Jin, Z., Zhang, G., Liu, Y., He, Y., Yang, C., Du, Y., & Gao, F. (2019). The suppressive role of HYAL1 and HYAL2 in the metastasis of colorectal cancer. Journal of gastroenterology and hepatology , 34 (10), 1766-1776. https://doi.org/10.1111/jgh.14660 Kumar, S., Stecher, G., & Tamura, K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Molecular biology and evolution , 33 (7), 1870-1874. https://doi.org/10.1093/molbev/msw054 Letunic, I., & Bork, P. (2019). Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic acids research , 47 (W1), W256-W259. https://doi.org/10.1093/nar/gkz239 Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows–Wheeler transform. BIOINFORMATICS , 25 (14), 1754-1760. https://doi.org/10.1093/bioinformatics/btp324 Liu, X., Zhang, Y., Liu, W., Li, Y., Pan, J., Pu, Y., Han, J., Orlando, L., Ma, Y., & Jiang, L. (2022). A single-nucleotide mutation within the TBX3 enhancer increased body size in Chinese horses. Current biology , 32 (2), 480-487.e486. https://doi.org/10.1016/j.cub.2021.11.052 Liu, Y., Fang, X., Zhao, Z., Li, J., Albrecht, E., Schering, L., Maak, S., & Yang, R. (2019). Polymorphisms of the ASIP gene and the haplotype are associated with fat deposition traits and fatty acid composition in Chinese Simmental steers. Archiv für Tierzucht , 62 (1), 135-142. https://doi.org/10.5194/aab-62-135-2019 Ma, X., Cheng, H., Liu, Y., Sun, L., Chen, N., Jiang, F., You, W., Yang, Z., Zhang, B., Song, E., & Lei, C. (2022). Assessing Genomic Diversity and Selective Pressures in Bohai Black Cattle Using Whole-Genome Sequencing Data. Animals (Basel) , 12 (5), 665. https://doi.org/10.3390/ani12050665 Mahajan, M. A., & Samuels, H. H. (2008). Nuclear receptor coactivator/coregulator NCoA6(NRC) is a pleiotropic coregulator involved in transcription, cell survival, growth and development. Nuclear receptor signaling , 6 (1), e002-e002. https://doi.org/10.1621/nrs.06002 Marom, A., Barak, A. F., Kramer, M. P., Lewinsky, H., Binsky-Ehrenreich, I., Cohen, S., Tsitsou-Kampeli, A., Kalchenko, V., Kuznetsov, Y., Mirkin, V., Dezorella, N., Shapiro, M., Schwartzberg, P. L., Cohen, Y., Shvidel, L., Haran, M., Becker-Herman, S., Herishanu, Y., & Shachar, I. (2017). CD84 mediates CLL-microenvironment interactions. Oncogene , 36 (5), 628-638. https://doi.org/10.1038/onc.2016.238 McArdel, S. L., Terhorst, C., & Sharpe, A. H. (2016). Roles of CD48 in regulating immunity and tolerance. Clinical immunology (Orlando, Fla.) , 164 , 10-20. https://doi.org/10.1016/j.clim.2016.01.008 Midgley, A. C., Woods, E. L., Jenkins, R. H., Brown, C., Khalid, U., Chavez, R., Hascall, V., Steadman, R., Phillips, A. O., & Meran, S. (2020). Hyaluronidase-2 Regulates RhoA Signaling, Myofibroblast Contractility, and Other Key Profibrotic Myofibroblast Functions. The American journal of pathology , 190 (6), 1236-1255. https://doi.org/10.1016/j.ajpath.2020.02.012 Nielsen, R., Williamson, S., Kim, Y., Hubisz, M. J., Clark, A. G., & Bustamante, C. (2005). Genomic scans for selective sweeps using SNP data. Genome research , 15 (11), 1566-1575. https://doi.org/10.1101/gr.4252305 Oh, G.-S., Kim, S.-R., Lee, E.-S., Yoon, J., Shin, M.-K., Ryu, H. K., Kim, D. S., & Kim, S.-W. (2022). Regulation of Hepatic Gluconeogenesis by Nuclear Receptor Coactivator 6. Molecules and cells , 45 (4), 180-192. https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LS8NAEF5EFAQRn_gse9GL RJp3evDQ1tqiaKXtyUtJshsJalKaRsm_dya7TWIRHwcvISwhJPmWb76 ZzIMQXbuoKwuc4NQ9tCyeHnCmauhnuJplM6uuc1MNAgS-3Ws8tqzOgz0qs5zLtX8FHtYAeiyk_QP4xU1hAc5hC8ARNgEcf7UNBmLWvJSFPT7JO7R2MVE94vETMl2YoAa9x8bG7hR1JJ-AIw5MgTUPb-i Un1tV DXsnJupy0d0ZI_-FLu_nQZpulkbKMCxqgOTE5mGoDLJYWsoyB6iDF8-lfs4_IhHgRvYElyEJrRKfxFiAAgZPUCoXPAakqGDfvirpih6ScnMZFQZVxWAnaYxVMSjvc5_sBftVZBXeNG_7-DjgYAON6yZoszNsoP7KQn92yS MlTbDLAag8bAd61ZrzjtHAStKSh0AB2ziRpPgbBQZDlFTKlwNHBtV9WFEio02yIV0I2hRgb5Gl53ibrIqhotkO6ZaQ0zigEnK6ADn1Miohp3PI aQVyau2S0-vOqN1T8ocYRyx5GX_x7voeWX exTCKa5eWUbJ9Qz2K6q3o NYG3PMH3HYdz2fc2xDIuppuMckNr3Nz386YIjsoYrIkp1TJZn05SfkJXoPUndoJZ_9w-lTUUw Ortega, M. S., Denicol, A. C., Cole, J. B., Null, D. J., & Hansen, P. J. (2016). Use of single nucleotide polymorphisms in candidate genes associated with daughter pregnancy rate for prediction of genetic merit for reproduction in Holstein cows. Animal genetics , 47 (3), 288-297. https://doi.org/10.1111/age.12420 Patterson, N., Price, A. L., & Reich, D. (2006). Population structure and eigenanalysis. PLoS genetics , 2 (12), 2074-2093. https://doi.org/10.1371/journal.pgen.0020190 Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., & Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics , 81 (3), 559-575. https://doi.org/10.1086/519795 Sabeti, P. C., Reich, D. E., Higgins, J. M., Levine, H. Z. P., Richter, D. J., Schaffner, S. F., Gabriel, S. B., Platko, J. V., Patterson, N. J., McDonald, G. J., Ackerman, H. C., Campbell, S. J., Altshuler, D., Cooper, R., Kwiatkowski, D., Ward, R., & Lander, E. S. (2002). Detecting recent positive selection in the human genome from haplotype structure. Nature , 419 (6909), 832-837. https://doi.org/10.1038/nature01140 Sahmi, F., Sahmi, M., Gévry, N., Sahadevan, P., Allen, B. G., & Price, C. A. (2019). A putative protein–RNA complex regulates posttranscriptional processing of cytochrome P450 aromatase (CYP19A1) in bovine granulosa cells. Molecular reproduction and development , 86 (12), 1901-1908. https://doi.org/10.1002/mrd.23289 Sala, D., Ivanova, S., Plana, N., Ribas, V., Duran, J., Bach, D., Turkseven, S., Laville, M., Vida, H., Karczewska-Kupczewska, M., Kowalska, I., Straczkowski, M., Testar, X., Palacin, M., Sandri, M., Serrano, A. L., & Zorzano, A. (2014). Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes. The Journal of clinical investigation , 124 (5), 1914-1927. https://doi.org/10.1172/JCI72327 Santos Silva, D. B. d., Fonseca, L. F. S., Magalhães, A. F. B., Muniz, M. M. M., Baldi, F., Ferro, J. A., Chardulo, L. A. L., Pinheiro, D. G., & Albuquerque, L. G. d. (2020). Transcriptome profiling of muscle in Nelore cattle phenotypically divergent for the ribeye muscle area. Genomics (San Diego, Calif.) , 112 (2), 1257-1263. https://doi.org/10.1016/j.ygeno.2019.07.012 Sekar, M., & Thirumurugan, K. (2024). Autophagic Regulation of Adipogenesis Through TP53INP2: Insights from In Silico and In Vitro Analysis. Molecular biotechnology , 66 (5), 1188-1205. https://doi.org/10.1007/s12033-023-01020-6 Shen, J., Hanif, Q., Cao, Y., Yu, Y., Lei, C., Zhang, G., & Zhao, Y. (2020). Whole Genome Scan and Selection Signatures for Climate Adaption in Yanbian Cattle. Frontiers in genetics , 11 , 94-94. https://doi.org/10.3389/fgene.2020.00094 Shrestha, M. M., Lim, C.-Y., Bi, X., Robinson, R. C., & Han, W. (2021). Tmod3 Phosphorylation Mediates AMPK-Dependent GLUT4 Plasma Membrane Insertion in Myoblasts. Frontiers in endocrinology (Lausanne) , 12 , 653557-653557. https://doi.org/10.3389/fendo.2021.653557 Sun, G., Cao, Y., Qian, C., Wan, Z., Zhu, J., Guo, J., & Shi, L. (2020). Romo1 is involved in the immune response of glioblastoma by regulating the function of macrophages. Aging (Albany, NY.) , 12 (2), 1114-1127. https://doi.org/10.18632/aging.102648 Szpiech, Z. A., & Hernandez, R. D. (2014). selscan: An Efficient Multithreaded Program to Perform EHH-Based Scans for Positive Selection. Molecular biology and evolution , 31 (10), 2824-2827. https://doi.org/10.1093/molbev/msu211 Tashireva, L. A., Alifanov, V. V., Simanov, G. S., Gautreau, A. M., Cherdyntseva, N., & Perelmuter, V. M. (2020). LIMCH1: A protein regulating cell migration and proliferation. Žurnal obŝej biologii , 81 (3), 234-240. https://doi.org/10.31857/S0044459620030082 Tong, Z., Liu, Y., Yu, X., Martinez, J. D., & Xu, J. (2019). The transcriptional co-activator NCOA6 promotes estrogen-induced GREB1 transcription by recruiting ERα and enhancing enhancer–promoter interactions. The Journal of biological chemistry , 294 (51), 19667-19682. https://doi.org/10.1074/jbc.RA119.010704 Tsoneva, E., Vasileva-Slaveva, M. B., Kostov, S. G., & Yordanov, A. D. (2023). ROMO1-a potential immunohistochemical prognostic marker for cancer development. Oncologie (Paris, France) , 25 (6), 753-758. https://doi.org/10.1515/oncologie-2023-0345 Wang, K., Li, M., & Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research , 38 (16), e164-e164. https://doi.org/10.1093/nar/gkq603 Wang, S., Raza, S. H. A., Zhang, K., Mei, C., Alamoudi, M. O., Aloufi, B. H., Alshammari, A. M., & Zan, L. (2023). Selection signatures of Qinchuan cattle based on whole-genome sequences. Animal biotechnology , 34 (4), 1483-1491. https://doi.org/10.1080/10495398.2022.2033252 Wang, Z., Zhu, B., Niu, H., Zhang, W., Xu, L., Xu, L., Chen, Y., Zhang, L., Gao, X., Gao, H., Zhang, S., Xu, L., & Li, J. (2019). Genome wide association study identifies SNPs associated with fatty acid composition in Chinese Wagyu cattle. Journal of animal science and biotechnology , 10 (1), 27-27. https://doi.org/10.1186/s40104-019-0322-0 Yang, J., Li, W.-R., Lv, F.-H., He, S.-G., Tian, S.-L., Peng, W.-F., Sun, Y.-W., Zhao, Y.-X., Tu, X.-L., Zhang, M., Xie, X.-L., Wang, Y.-T., Li, J.-Q., Liu, Y.-G., Shen, Z.-Q., Wang, F., Liu, G.-J., Lu, H.-F., Kantanen, J., . . . Liu, M.-J. (2016). Whole-Genome Sequencing of Native Sheep Provides Insights into Rapid Adaptations to Extreme Environments. Molecular biology and evolution , 33 (10), 2576-2592. https://doi.org/10.1093/molbev/msw129 Yao, Y.-F., Lyu, S., Wang, X., Zhang, Z., Qu, K., Xu, J., Cai, C., Li, Z., Xie, J., Ru, B., Xu, Z., Wang, E., Lei, C., Chen, H., Huang, B., & Huang, Y. (2021). The combination between NCSTN gene copy number variation and growth traits in Chinese cattle. Animal biotechnology , 32 (6), 683-687. https://doi.org/10.1080/10495398.2020.1741382 Yu, X., Zhai, C., Fan, Y., Zhang, J., Liang, N., Liu, F., Cao, L., Wang, J., & Du, J. (2017). TUSC3: a novel tumour suppressor gene and its functional implications. Journal of cellular and molecular medicine , 21 (9), 1711-1718. https://doi.org/10.1111/jcmm.13128 Zhang, F., Qu, K., Chen, N., Hanif, Q., Jia, Y., Huang, Y., Dang, R., Zhang, J., Lan, X., Chen, H., Huang, B., & Lei, C. (2019). Genome-Wide SNPs and InDels Characteristics of Three Chinese Cattle Breeds. Animals (Basel) , 9 (9), 596. https://doi.org/10.3390/ani9090596 Zhang, H., Mi, S., Brito, L. F., Hu, L., Wang, L., Ma, L., Xu, Q., Guo, G., Yu, Y., & Wang, Y. (2023). Genomic and transcriptomic analyses enable the identification of important genes associated with subcutaneous fat deposition in Holstein cows. Journal of genetics and genomics , 50 (6), 385-397. https://doi.org/10.1016/j.jgg.2023.01.011 Zhang, Y., Zhang, Y., & Xu, H. (2019). LIMCH1 suppress the growth of lung cancer by interacting with HUWE1 to sustain p53 stability. Gene , 712 , 143963-143963. https://doi.org/10.1016/j.gene.2019.143963 Zhou, R., Pan, J., Zhang, W.-B., & Li, X.-d. (2024). Myosin-5a facilitates stress granule formation by interacting with G3BP1. Cellular and molecular life sciences : CMLS , 81 (1), 430. https://doi.org/10.1007/s00018-024-05468-w Supplementary Figures Supplementary Figures 1 and 2 are not available with this version. Additional Declarations The authors declare no competing interests. Supplementary Files 2.xls 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6059907","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417896358,"identity":"3e4b04d6-72c4-42af-a454-44258b30d6f7","order_by":0,"name":"Yanyan Wang","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Wang","suffix":""},{"id":417896359,"identity":"c884a096-3863-4f14-8cae-9455803f0260","order_by":1,"name":"Jianfeng Xu","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Jianfeng","middleName":"","lastName":"Xu","suffix":""},{"id":417896360,"identity":"e50f3ce4-779c-48e5-9ec7-889fec885228","order_by":2,"name":"Fuyue Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACxvkHGw5I8PznYWNvIFIL8wzmgwcsZJjl+HgOEKmFfQZb8oEKG2ZjOYkEIrXwzu4xOHAjhy2xTfLxxhsMNTbRBLVIzjljcHDGGZ7ENum0YguGY2m5DYS0GDbkGByW7JEAaskxk2BsOExYi/0BoJa//wyADjtDpBbGGWkJwEBOMGaT4CFWS8/hA0AtB+TYeIB+SSDGL4ztjc0fgFp45NsPb7zxocaGsBZkYEB01CBpIVXHKBgFo2AUjAwAANdnQW198rOtAAAAAElFTkSuQmCC","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":true,"prefix":"","firstName":"Fuyue","middleName":"","lastName":"Shi","suffix":""},{"id":417896361,"identity":"95e2fb43-d101-4c02-94ce-c89d3434392b","order_by":3,"name":"Hailong Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYHACgwMJBjZybOzNB4jWYnjgQ0WaMR/PsQSitRgfnHHmcOI8iRwF4tTr9h/ecJi37XB6G0MOA8OPim2EtZjdSCsAaknPbWM4e4Cx58xtYrTwGAC1WOe2MfYlMDO2EaPl/BmQFuZ0NmYeAyK1HMgxAHrfOYGNjWgtQL+AAtmwjYct4SBxfjl/ePMHYFTKy89/fPDBjwoitKCAAySqHwWjYBSMglGACwAAwb5A/TNWB6cAAAAASUVORK5CYII=","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":true,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Guo","suffix":""},{"id":417896362,"identity":"cae7a381-156a-4a35-8abd-9b924edd4daa","order_by":4,"name":"Bo Gao","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Gao","suffix":""},{"id":417896363,"identity":"d552de7f-d992-445c-b883-a2dc2af1d3ad","order_by":5,"name":"Junxiang Yang","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Junxiang","middleName":"","lastName":"Yang","suffix":""},{"id":417896364,"identity":"84b22e24-01ec-45df-81a3-fea183058e35","order_by":6,"name":"Lingrong Gu","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Lingrong","middleName":"","lastName":"Gu","suffix":""},{"id":417896365,"identity":"8fa75e83-5448-44de-a78c-af4aee066335","order_by":7,"name":"Dezhi Yang","email":"","orcid":"","institution":"Animal Husbandry and Aguaculture Technology Promotion Center","correspondingAuthor":false,"prefix":"","firstName":"Dezhi","middleName":"","lastName":"Yang","suffix":""},{"id":417896366,"identity":"7db7416e-7c40-4749-b475-633b86f043c5","order_by":8,"name":"Fengtao Zhang","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Station","correspondingAuthor":false,"prefix":"","firstName":"Fengtao","middleName":"","lastName":"Zhang","suffix":""},{"id":417896367,"identity":"76e214e9-631d-4295-8177-02d5711b37fe","order_by":9,"name":"Dengwei Gao","email":"","orcid":"","institution":"Animal Husbandry Technology Promotion Center","correspondingAuthor":false,"prefix":"","firstName":"Dengwei","middleName":"","lastName":"Gao","suffix":""},{"id":417896368,"identity":"90a07fd2-15f0-4be8-9d67-b702e7a207fd","order_by":10,"name":"Jin Wang","email":"","orcid":"","institution":"Institute of Gansu Pingliang Red Cattle","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Wang","suffix":""},{"id":417896369,"identity":"4a448f1e-0e37-41fd-88df-896a2a0a0f4b","order_by":11,"name":"Shengming Wang","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Shengming","middleName":"","lastName":"Wang","suffix":""},{"id":417896370,"identity":"26a9f223-b7aa-49e9-b03d-fcc71f7c06b6","order_by":12,"name":"Ziyue Gao","email":"","orcid":"","institution":"Animal Husbandry and Veterinary Research Institute of Gansu Province","correspondingAuthor":false,"prefix":"","firstName":"Ziyue","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-02-19 02:44:12","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6059907/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6059907/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77045150,"identity":"08fcc37c-503b-4141-9be5-fbe42452104e","added_by":"auto","created_at":"2025-02-24 14:44:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":752184,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic diversity of Zaosheng cattle. (a)The distribution map of the Zaosheng cattle.(b) Principal component analysis of cattle populations with PC1 against PC2. (c) Neighbor-joining tree of the relationships between Zaosheng cattle and possible ancestors.(d) Genetic distances estimated between each population via the FST. (e) Model-based clustering of cattle breeds via ADMIXTURE. The breeds are colored according to their geographic region and labeled with breed names.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6059907/v1/aeebaf73da989181b5c52b91.jpg"},{"id":77045152,"identity":"185af364-19fa-45fd-971a-4f3634421ea3","added_by":"auto","created_at":"2025-02-24 14:44:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3805566,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic region with strong selective sweep signals in Zaosheng cattle and Chinese indicine.The manhattan plots against selective sweep analysis between Zaosheng cattle and Chinese indicine using (a) \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (b) π-ratio(c) XPEHH. (d) Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST \u003c/sub\u003evalues \u003cem\u003eTajima's D \u003c/em\u003evalue and haplotype pattern heatmap of the\u003cem\u003e HYAL2 \u003c/em\u003egene region in Zaosheng cattle and Chinese indicine\u003cem\u003e. \u003c/em\u003e(e)Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST \u003c/sub\u003evalues \u003cem\u003eTajima's D \u003c/em\u003evalue and haplotype pattern heatmap of the\u003cem\u003e NOCA6 \u003c/em\u003egene region in Zaosheng cattle and Chinese indicine\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6059907/v1/39717ee993833e9733351e15.jpg"},{"id":77044203,"identity":"5e214c50-404b-424c-a84a-d9f056813a35","added_by":"auto","created_at":"2025-02-24 14:36:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4594839,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the local segments in which proportions of a certain ancestry were significantly greater than the proportion in the whole genome in Zaosheng cattle. (a) Distribution of the local segments with proportions of Chinese indicine and East Asian taurine ancestries. (b)\u003cstrong\u003e \u003c/strong\u003eKEGG pathway enrichment analysis of genes associated with excessive Chinese indicine proportions and East Asian taurine proportions.(c)Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e,Tajima's D value,haplotype patterns heatmap andAverage taurine ancestry (%) of \u003cem\u003eTP53INP2\u003c/em\u003e gene region in Zaosheng cattle,East Asian taurine and Chinese indicine.(d)Pairwise \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e,Tajima's D value,haplotype patterns heatmap andAverage taurine ancestry (%) of PROKR1 gene region in Zaosheng cattle,East Asian taurine and Chinese indicine.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6059907/v1/70283a3b162414edb934aed5.jpg"},{"id":77045527,"identity":"a91b9b7a-619c-4092-bc7b-4cfd3acfe8d5","added_by":"auto","created_at":"2025-02-24 14:52:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10052550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6059907/v1/52ae8ca3-7003-41df-b88f-170fec4c402a.pdf"},{"id":77044211,"identity":"d456d4a6-8f03-4619-a7c9-a4b3081922c7","added_by":"auto","created_at":"2025-02-24 14:36:29","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1693184,"visible":true,"origin":"","legend":"","description":"","filename":"2.xls","url":"https://assets-eu.researchsquare.com/files/rs-6059907/v1/085f16f231680174d7c69bcb.xls"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenomic Diversity and Selection Signatures for Zaosheng Cattle\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs the \u0026ldquo;head of the six animals\u0026rdquo;, cattle have attracted much attention since ancient times. In ancient China, cattle were used for plowing fields and sacrifices and held a preeminent status among the diverse range of domesticated species. Cattle can be categorized into two subspecies: humpless taurine (\u003cem\u003eBos taurus\u003c/em\u003e) and humped indicine (\u003cem\u003eBos indicus\u003c/em\u003e)(Decker et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). With the application of whole-genome sequencing technology, it has become possible to study the genomic genetic diversity of domestic animals, which is crucial for the genetic improvement of livestock and the conservation of local breeds. Previously, some scholars proposed that domestic cattle around the world can be divided into five core groups: European taurine, Eurasian taurine, East Asian taurine, Chinese indicine and Indian indicine (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Native Chinese cattle have been shown to have three types of ancestry: Eurasian taurine and East Asian taurine in northern China and Chinese indicine in southern China. However, few studies have explored the genetic architecture of Zaosheng cattle.\u003c/p\u003e \u003cp\u003eZaosheng cattle are found primarily on the Zaosheng Plateau, Qingyang, Gansu Province.Zaosheng cattle are crossbred. As a region with a predominantly agrarian culture, the local people have a long history of raising cattle due to the need for agricultural cultivation on the Zaosheng Plateau. Historical records show that people in the Zaosheng area have been selectively breeding Qinchuan cattle from the Guanzhong area of Shaanxi Province since 490 AD. This has led to the formation of a larger local population of cattle. Zaosheng cattle have transitioned from draft type to a beef breed, making it highly practical, resistant to rough feeding, adaptable, and renowned for its high-quality meat (Hengwei et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recently, whole\u0026shy;genome sequencing technology has been widely applied to explore livestock population structure, genetic diversity and selection signatures, including those of pigs (Gurgul et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), horses (Liu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), cattle (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), sheep (Yang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and chickens (Guo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, research on the population genetics and selection footprints of existing Zaosheng cattle is lacking, necessitating a more in-depth analysis of their genomic characteristics.\u003c/p\u003e \u003cp\u003eIn the current study, whole-genome resequencing data from 119 cattle genomes, including 19 Zaosheng cattle and commercial and native cattle breeds previously collected from across the world were used. We performed admixture analysis, assessed the present genome ancestry of Zaosheng cattle and identified the genomic selection signatures showcasing an excess of taurine or indicine ancestry in Zaosheng cattle. Moreover, the population structure, genetic diversity and selection footprints of Zaosheng cattle were investigated. This investigation will help in understanding genomic characteristics and identifying important candidate regions under selection pressures, simultaneously providing a genome reference for breed improvement of Zaosheng cattle .\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Data collection and Sequencing and identification of Single Nucleotide Polymorphisms (SNPs)\u003c/h2\u003e \u003cp\u003e \u003cb\u003eHere discuss about the reference used and also about the published data set as mentioned in the comments too\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIndividual genomes of 19 Zaosheng cattle were jointly genotyped with publicly available genomes for genetic background analysis. A total of 91 cattle genomes were added in a control group, including samples of the European taurine, East Asian taurine, Indian indicine,Chinese center cattle and Chinese indicine groups.Therefore,110 samples were used in this study, with an average sequence coverage of ~\u0026thinsp;9.1 \u0026times; and an average mapping rate of 99.69% (Tables S1 and S2). A total of 46,329,833 double allelic autosomal SNPs were detected. The functional annotation of the polymorphic loci revealed that the majority of the SNPs were present in intergenic (38.0%) or intronic regions (58.9%). Exons represented 0.8% of the total SNPs, 93,688 of which were nonsynonymous SNPs and 142,001 of which were synonymous SNPs. Table S3 shows the total number of SNPs detected in each breed, with Chinese indicine cattle having the highest number of SNPs, followed by crossbred Zaosheng and Qinchuan cattle, and taurine cattle having least SNPs (Table S3).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation Structure and Genetic Diversity\u003c/h3\u003e\n\u003cp\u003eTo explore correlations between Zaosheng cattle and other cattle breeds distributed around the world, we performed admixture, neighbor-joining (NJ) tree and Principle component analysis (PCA) using autosomal genomic SNPs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The analyses revealed clear geographical patterns between the cattle populations. In the admixture analysis, the cattle breeds were divided into taurine or indicine ancestry at K\u0026thinsp;=\u0026thinsp;2, whereas at K\u0026thinsp;=\u0026thinsp;3, East Asian taurine cattle were separated from European taurine cattle, whereas, Indian indicine cattle were further separated at K\u0026thinsp;=\u0026thinsp;4. At K\u0026thinsp;=\u0026thinsp;5, Zaosheng cattle and Qinchuan cattle presented clear genetic heterogeneity. The PCA revealed a clear genetic structure, with samples from each geographical region clustering together. The first PC explained 6.19% of the total genetic variation and was driven by considerable genetic distance between \u003cem\u003eBos taurus\u003c/em\u003e and \u003cem\u003eBos indicus\u003c/em\u003e. The second PC explained 2.58% of the total variation and geographically separated the different indicine groups, such as Chinese indicine cattle and Indian indicine cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). NJ tree analysis was used to construct a phylogenetic tree based on genetic distances, both of which corroborated the findings from the ADMIXTURE analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec,e).Here, Qinchuan and Zaosheng cattle clustered together. To explore the affinities between Zaosheng cattle and the rest of the animals, we calculated smartpca-based \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e matrix which depicted decreased \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values between Zaosheng cattle and Qinchuan cattle revealing their close genetic identity(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). The closest distance between Zaosheng cattle and Qinchuan cattle was 0.006 while, Indian indicine cattle was the farthest by0.205.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGenome-wide selective scanning signals from Zaosheng cattle\u003c/b\u003e \u003c/p\u003e\n\u003ch3\u003ea. Genetic signature of positive selection in Zaosheng cattle\u003c/h3\u003e\n\u003cp\u003eThe nucleotide diversity analysis (\u003cem\u003eθ\u003c/em\u003eπ) and the composite likelihood ratio (CLR) methods were applied to detect the genomic regions related to selection in the Zaosheng breed. The two methods yielded outlier signals (top 1%) in overlapping regions and were therefore considered candidate selective regions. A total of 1388 (\u003cem\u003eθ\u003c/em\u003eπ) and 530 (CLR) genes/regions with selection signatures in Zaosheng cattle were identified (Supplementary Figure. 2a, b; Table S4 and S5),336 of which overlapped (Table S6). Among these overlapping genes, \u003cem\u003eACSS2\u003c/em\u003e(Z. Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eCLASP1\u003c/em\u003e(Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), \u003cem\u003eNCOA6\u003c/em\u003e(Oh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eASIP\u003c/em\u003e(Liu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) were found to be related to lipids, including lipid metabolism, fatty acid synthesis and lipid deposition. \u003cem\u003eSLAMF1\u003c/em\u003e(Ma et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u003cem\u003eROMO1\u003c/em\u003e(Tsoneva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) play important roles in inflammation and the immune response. \u003cem\u003eGOLGA4\u003c/em\u003e(Ortega et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and \u003cem\u003eTP53INP2\u003c/em\u003e(Sekar \u0026amp; Thirumurugan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for fertility and reproductive traits. \u003cem\u003eDCLK3\u003c/em\u003e(Galvan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was found to be involved in heat stress. Furthermore, \u003cem\u003eCYP19A1\u003c/em\u003e(Sahmi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) is involved in pigmentation.\u003c/p\u003e\n\u003ch3\u003eb. Selective signals between zhaosheng and chinese indicine cattle\u003c/h3\u003e\n\u003cp\u003eFurthermore, further elucidation of the positive selective sweep regions was acquired through \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, the \u003cem\u003eθπ\u003c/em\u003e ratio, and XPEHH between Zaosheng cattle and Chinese indicine cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea,b,c). The genomic regions identified (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005) by at least two methods were considered candidate regions of positive selection (Tables S7, S8 and S9). This multimethod approach increased the robustness of our identification of selection signatures. Among the identified candidate regions,, the genes/regions harboring functional importance in biological areas were focused (Table S10). The function of genes under positive selection included immunity (\u003cem\u003eHYAL1\u003c/em\u003e, \u003cem\u003eHYAL2\u003c/em\u003e) (Jin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), phenotype (\u003cem\u003eASIP\u003c/em\u003e), fat metabolism and deposition(\u003cem\u003eTP53INP2\u003c/em\u003e,\u003cem\u003eNCOA6\u003c/em\u003e,\u003cem\u003eACSS2\u003c/em\u003e), quality of meat༈\u003cem\u003eIGF1R\u003c/em\u003e,\u003cem\u003ePLAGL2\u003c/em\u003e)(Ardicli et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arikawa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and stress-related genes (\u003cem\u003eGSS\u003c/em\u003e) (J. Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting their strong selection in Zaosheng cattle. \u003cem\u003eHYAL2\u003c/em\u003e and \u003cem\u003eNCOA6\u003c/em\u003e were further confirmed by high \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values, differential Tajima's \u003cem\u003eD\u003c/em\u003e values and a distinctive haplotype pattern in Zaosheng cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed,e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ec. Selection signature between Zaosheng cattle and East Asian taurine\u003c/h3\u003e\n\u003cp\u003eWe also implemented three methods (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, π-ratio, XPEHH) to further elucidate the positive correlation characteristics between Zaosheng cattle and East Asian taurine cattle (Supplementary Figure. 1a, b, c). The genomic regions identified (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.005) by at least two methods were considered candidate regions of positive selection (Tables S12, S13 and S14). The functionally important genes among the candidate genes were identified (Table S15), including genes related to immunity and growth (\u003cem\u003eSLAMF1\u003c/em\u003e, \u003cem\u003eCD84\u003c/em\u003e) (Engel, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marom et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Genes related to feed efficiency and meat quality traits (\u003cem\u003eACSS2\u003c/em\u003e and \u003cem\u003ePPP3R1\u003c/em\u003e) were also identified as candidate genes under selection(Z. Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Santos Silva et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the gene related to growth and body size (\u003cem\u003eHDAC9\u003c/em\u003e)(Tashireva et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); fertility (PROKR1)(Baryla et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); and adaptation (USH2A)(Birch et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were also found under selection pressure.At the same time, the high signal values of these genes revealed that Zaosheng cattle are potential local breeds and are subject to selection in a variety of areas such as fat deposition, growth and body size.\u003c/p\u003e \u003cp\u003eLocal ancestry inference of Zaosheng cattle\u003c/p\u003e \u003cp\u003eZaosheng cattle are considered to have a hybrid origin of indicine and taurine cattle. To infer the local taurine and indicine ancestries across the Zaosheng cattle genomes LOTER was employed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Table S16)(Dias-Alves et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Chinese indicine(n\u0026thinsp;=\u0026thinsp;20) and East Asian taurine(n\u0026thinsp;=\u0026thinsp;20) were selected as reference panels. The segments with frequencies of at least 0.85 and lengths of at least 1,000bp were regarded as high-frequency ancestral fragments (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In due course, 680 Chinese indicine segments and 5,686 East Asian taurine segments were retained. The maximum lengths of the segments in the Chinese indicine and East Asian taurine groups were 112,449,384 bp and 112,447,365 bp, respectively. For excessive East Asian taurine segments in Zaosheng cattle, 442 genes were annotated. These genes were enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (corrected \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The candidate genes enriched various pathways including, Metabolic, Phospholipase D signaling, cGMP\u0026thinsp;\u0026minus;\u0026thinsp;PKG signaling, Vascular smooth muscle contraction, and Retrograde endocannabinoid signaling pathways (Table S17).\u003c/p\u003e \u003cp\u003eFor excess taurine ancestry, we used genetic signature of positive selection in Zaosheng cattle detect traits from the taurine ancestor(Table S19). Here, we identified a total of 129 genes involved in immunity (\u003cem\u003eROMO1\u003c/em\u003e, \u003cem\u003eSLAMF1\u003c/em\u003e, \u003cem\u003eCD48\u003c/em\u003e, \u003cem\u003eCD84\u003c/em\u003e, \u003cem\u003eMYO5A\u003c/em\u003e, and \u003cem\u003eMMP28\u003c/em\u003e)(Engel, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jian et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Marom et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McArdel et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tsoneva et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), reproduction (\u003cem\u003eADAMTSL1\u003c/em\u003e and \u003cem\u003eLEO1\u003c/em\u003e)(Carre et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), muscle production (\u003cem\u003eTMOD3\u003c/em\u003e, \u003cem\u003eMYLK3\u003c/em\u003e and \u003cem\u003eTMOD2\u003c/em\u003e)(Colpan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hitsumoto et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shrestha et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), fat metabolism and deposition (\u003cem\u003eANGPT4\u003c/em\u003e, \u003cem\u003eDEDD\u003c/em\u003e, \u003cem\u003eUFC1\u003c/em\u003e, \u003cem\u003eCLASP1\u003c/em\u003e,\u003cem\u003eASIP\u003c/em\u003e,\u003cem\u003eTP53INP2\u003c/em\u003e, \u003cem\u003eACSS2\u003c/em\u003e,\u003cem\u003eand NCOA6\u003c/em\u003e)(Guangjun et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Z. Huang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sekar \u0026amp; Thirumurugan, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and growth (\u003cem\u003eNCSTN\u003c/em\u003e and \u003cem\u003eGALNTL6\u003c/em\u003e)(Doran et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study, we found that the regions of the \u003cem\u003eTP53INP2\u003c/em\u003e gene in Zaosheng cattle showed a significantly high FST value, low Tajima\u0026rsquo;s \u003cem\u003eD\u003c/em\u003e value, and high taurine ancestry. At the same time, the Zaosheng cattle have the same haplotype as East Asian taurine cattle, but the haplotype is very different from that of Chinese indicine cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eMoreover,95 genes observed in Chinese indicine segments in Zaosheng cattle, were annotated. Functional enrichment analysis via KEGG pathway was performed against these genes. The only significant KEGG pathway was \u0026ldquo;cell adhesion molecules (CAMs)\u0026rdquo; (corrected \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; Table S18). Zaosheng cattle have a hybrid origin involving indicine and taurine cattle, and haplotypes of indicine or taurine ancestry may confer a relative adaptive advantage under selection pressures.For excess indicine ancestry,we used genetic signature of positive selection in Zaosheng cattle detect traits from the indicine ancestor(Table S19). A total of 14 genes were involved in growth (\u003cem\u003eGBP4\u003c/em\u003e)(Cao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), reproduction (\u003cem\u003eTUSC3\u003c/em\u003e)(Yu et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and adaptation (\u003cem\u003eUSH2A\u003c/em\u003e)(Birch et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).We found that \u003cem\u003ePROKR1\u003c/em\u003e gene was subject to selection in Zaosheng cattle,and that a high proportion of indicine segment pedigrees At the same time, we aslo found through haplotype maps that \u003cem\u003ePROKR1\u003c/em\u003e were selected in Zaosheng cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChinese indigenous cattle are characterized by their rich genetic resources, wide distribution range and diverse ecological types harboring rich genetic. Zaosheng cattle constitute a local breed in Gansu Province. The current study explored the population genetic structure of Zaosheng cattle. As shown by the ADMIXTURE analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), the ancestral contributions of Zaosheng cattle were Chinese indicine (~\u0026thinsp;25.9%), East Asian taurine (~\u0026thinsp;72.5%) and European taurine (~\u0026thinsp;0.16%).Moreover, Zaosheng cattle are closely related to the Qinchuan breed. In ancient times, the Zaosheng area was remote, and to develop agriculture, Qinchuan cattle were introduced from the Guanzhong area of Shaanxi, where agriculture was well developed and the hybrid breed flourished. Similarly, low inbreeding coefficient revealed that Zaosheng cattle presented high genetic diversity, depicting that the Zaosheng cattle have greater breeding potential than mature commercial cattle. In recent years, with the improvement of the quality of life, quantity and quality of beef has been a leading concern. It's well known that Qinchuan cattle constitute a representative breed of Chinese indigenous cattle, known as \u0026lsquo;the treasure of the country\u0026rsquo;. However, since the 1990s, the number of Qinchuan cattle has declined sharply. Efforts by the local government has been made to protect the Qinchuan cattle and its related breeds. The present study focusses on the genomic information of Zaosheng cattle. In current analysis, Zaosheng cattle and Qinchuan cattle presented a very close genetic relationship. We believe that cattle breeds with equivalent beef qualities could be used to help develop the market.\u003c/p\u003e \u003cp\u003eUtilizing the whole\u0026shy;genome sequence data, more signatures of selection were identified than with SNP array data. The selection signal analysis of Zaosheng cattle in this study provides new insights into the genetic basis of adaptations to the local environment. Here, θπ and CLR methods were employed to detect selected genes in Zaosheng cattle, and some genes were strongly selected. For example, Nuclear receptor coactivator 6 (\u003cem\u003eNCOA6\u003c/em\u003e) is a transcriptional coregulator that can interact with multiple nuclear receptors(Tong et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It affects cell growth, differentiation and proliferation, which in turn plays a role in skeletal muscle development. Reactive oxygen species (ROS) modulator 1 (\u003cem\u003eROMO1\u003c/em\u003e) is a mitochondrial membrane protein that is essential for the regulation of mitochondrial ROS production and redox sensing and has been shown to play an important role in the regulation of mitochondrial ROS production and redox sensing(Chung et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Oxidative stress can be induced by various environmental factors, such as heat stress, disease, or injury. \u003cem\u003eROMO1\u003c/em\u003e may help in modulating the response to such stresses, protecting cells from damage and promoting recovery. Recent studies have shown that the overexpression of Romo1 increases anti-inflammatory function and promotes the reprogramming of the cellular metabolism of macrophages (Sun et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eGOLGA4\u003c/em\u003e is a Golgi matrix protein, and recent studies have shown that \u003cem\u003eGOLGA4\u003c/em\u003e is also expressed in mouse testes.(Guo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; F. Zhang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSubsequently a comparative analysis was conducted between Zaosheng cattle and Chinese indicine cattle to identify advantageous genes specific to Zaosheng cattle relative to Chinese indicine cattle. The analysis identified several genes involved in the immune system, particularly the \u003cem\u003eHYAL1\u003c/em\u003e and \u003cem\u003eHYAL2\u003c/em\u003e genes, which overlapped among the three selection methods. Tajima's D also revealed that \u003cem\u003eHYAL2\u003c/em\u003e was indeed selected between Zaosheng cattle and Chinese indicine cattle. In general, Chinese indicine cattle, exemplified by Hainan cattle, have good immune performance, but we identified clusters of immune genes in Zaosheng cattle that presented different haplotypes. These findings demonstrate that Zaosheng cattle and Chinese indicine cattle have different immune mechanisms, presumably due to environmental differences. Hyaluronidase (\u003cem\u003eHYAL\u003c/em\u003e)-2 is a weak, acid-active, hyaluronan-degrading enzyme broadly expressed in somatic tissues (Midgley et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies have shown that deregulated hyaluronan metabolism in the tumor microenvironment drives cancer inflammation, but \u003cem\u003eHYAL2\u003c/em\u003e has catabolic functions, which can reduce excessive inflammation and contribute to the resolution of the immune response(Donelan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Several studies have confirmed that \u003cem\u003eNCOA6\u003c/em\u003e deletion can lead to early embryonic death or slow growth in mice, potentially by disrupting the cell cycle and increasing apoptosis (Mahajan \u0026amp; Samuels, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, it is speculated that \u003cem\u003eNCOA6\u003c/em\u003e may be involved in regulating bovine growth and development. In our study, we found that the haplotypes of this region were different in Zaosheng cattle and Chinese indicine cattle, confirming that this region is subjected to selection.\u003c/p\u003e \u003cp\u003eZaosheng cattle, a native Chinese breed, demonstrate remarkable resilience and environmental adaptability. We detected high signal values for genes related to immunity in Zaosheng cattle compared with East Asian taurine cattle, indicating outstanding immunity in Zaosheng cattle. Previously, in Qinchuan cattle, the \u003cem\u003eUSH2A\u003c/em\u003e gene was found to affect cattle hair color, which may be associated with adaptive traits or survival advantages (Wang et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Interestingly, all three methods were used to screen for \u003cem\u003ePROKR1\u003c/em\u003e. Prokineticin 1 (\u003cem\u003ePROK1\u003c/em\u003e) is also termed endocrine gland-derived vascular endothelial growth factor (endocrine gland-derived VEGF). Studies in pigs have shown that \u003cem\u003ePROK1\u003c/em\u003e, which acts via \u003cem\u003ePROKR1\u003c/em\u003e, promotes the formation of capillary-like structures via endothelial cells isolated from porcine corpus luteum in vitro and stimulates the synthesis of VEGFA and the mRNA expression of angiogenin (another angiogenic factor) in the corpus luteum (Baryla et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Prokineticin 1 is a novel factor that regulates porcine corpus luteum function (Baryla et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eLIMCH1\u003c/em\u003e, an actin stress fiber-associated protein, is a paralogous protein with C-terminal LIM domains(Y. Zhang et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A study examining myofibers affected by neurogenic muscular atrophy revealed the overexpression of 55 proteins, with \u003cem\u003eLIMCH1\u003c/em\u003e being one of them, and found that most of them were involved in myofibrillogenesis(Midgley et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In cattle, a study concluded that the \u003cem\u003eLIMCH1\u003c/em\u003e locus is a putative region underlying greater forehead size in Brahman cattle than in Yunling cattle(Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Taken together, \u003cem\u003eLIMCH1\u003c/em\u003e may affect skeletal development and, subsequently, body size in cattle.\u003c/p\u003e \u003cp\u003eIn our study, we applied LOTER combined with selection analysis to infer local ancestry and obtain the ancestry of selection signatures in Zaosheng cattle. These excessive East Asian taurine segment-annotated genes were enriched mainly in growth, metabolism, development, and disease pathways. For a few Chinese indicine segments, the annotated genes were enriched mainly in cell adhesion molecules. Cell adhesion molecules, including receptors of the immunoglobulin superfamily and integrins, particularly integrins, play a vital role in regulating all aspects of immune cell function (Harjunpaa et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe genes related to fat metabolism and muscle production were more common in East Asian taurine descendants and Zaosheng cattle. The high muscle fat content and better taste of beef quality in East Asian taurine cattle, such as Yanbian cattle(Shen et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), indicate that the target group of Zaosheng cattle has excellent meat quality. The Chinese Indicine descent accounted for fewer genes, and fewer genes were fixed. Interestingly, \u003cem\u003eUSH2A\u003c/em\u003e was also screened for this gene in an interpopulation selection with East Asian taurine cattle, and we believe that Zaosheng cattle also have strong environmental adaptability. It is worth noting that the \u003cem\u003ePROKR1\u003c/em\u003e gene was selected in both the selected and loter populations. We further analyzed this region and found that this gene was subjected to strong selection in the Zaosheng cattle, and we hypothesized that it originated from the Chinese indicine population, but after long-term evolution, it also had its own unique characteristics.The tumor protein p53-induced nuclear protein 2 (\u003cem\u003eTP53INP2\u003c/em\u003e) regulates apoptosis, autophagy, and cell differentiation(Dong et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of skeletal muscle, \u003cem\u003eTP53INP2\u003c/em\u003e controls muscle mass in adult mice, and its overexpression in muscle leads to increased autophagy and a moderate reduction in muscle fiber size(Sala et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) Some studies have shown that high \u003cem\u003eTP53INP2\u003c/em\u003e protein levels are associated with greater muscle strength, physical performance, and healthy aging in humans(Sala et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the haplotype map, Zaosheng cattle shared the same haplotype as East Asian taurine cattle, but differed significantly from that of Chinese indicine cattle. It is presumed that this is partly influenced by East Asian taurine bloodlines. Overall, \u003cem\u003eTP53INP2\u003c/em\u003e is subject to selection and contributes to the meat quality traits of Zaosheng cattle.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e1. Sample preparation and DNA sequencing\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2. Read mapping and variant calling\u003c/h2\u003e \u003cp\u003eTo study the genetic diversity of Zaosheng cattle in China, we collected 19 samples from the Zaosheng of Gansu Province (Table S1). We adopted a standard phenol\u0026ndash;chloroform method to extract genomic DNA from methylated preserved ear tissue. For each individual, paired-end sequencing data were obtained with an average insert size of 500 bp and an average read length of 150 bp via the Illumina NovaSeq system. The sequencing platform was from Novogene Bioinformatics Institute, Beijing, China. To compare the differences, the Illumina whole genomes of 91 cattle individuals from previous studies, including Corssbreed (Qinchuan), European taurine (Angus and Hereford), East Asian taurine (Yanbian and Hanwoo), Chinese indicine (Guangfeng, Ji'an, Wenshan and Wannan) and Indian indicine cattle (Table S2), were used.\u003c/p\u003e \u003cp\u003eThe clean reads were mapped onto the \u003cem\u003eBos taurus\u003c/em\u003e reference genome assembly ARS-\u0026shy;UCD1.2 via BWA-\u0026shy;MEM (0.7.13-\u0026shy;r1126) with default parameters(Li \u0026amp; Durbin, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). After mapping, SNPs were detected via SAMtools(Heng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Picard tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard\u003c/span\u003e\u003cspan address=\"http://broadinstitute.github.io/picard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the Genome Analysis Toolkit (GATK, version 3.6-\u0026shy;0-\u0026shy;g89b7209). The raw SNPs were called via the \u0026lsquo;HaplotypeCaller\u0026rsquo;, \u0026lsquo;GenotypeGVCFs\u0026rsquo;, and \u0026lsquo;SelectVariants\u0026rsquo; of GATK. Moreover, \u0026ldquo;VariantFiltration\u0026rdquo; was used to filter the raw SNPs on the basis of the hard filtering parameters \u0026ldquo;QD\u0026thinsp;\u0026lt;\u0026thinsp;2.0, FS\u0026thinsp;\u0026gt;\u0026thinsp;60.0, MQ\u0026thinsp;\u0026lt;\u0026thinsp;40.0, MQRankSum \u0026lt; -12.5, ReadPosRankSum \u0026lt; -8.0 and SOR\u0026thinsp;\u0026gt;\u0026thinsp;3.0\u0026rdquo; and the mean sequencing depth of variants (all individuals) \u0026ldquo; \u0026lt; 1/3 \u0026times; and \u0026gt;\u0026thinsp;3 \u0026times; \u0026rdquo;. Afterward, a\u003c/p\u003e \u003cp\u003eThe transcript FASTA file for the database was built via the retrieve_seq_from_fasta.pl module of ANNOVAR on the basis of the annotation file (GCF_002263795.1_ARS- UCD1.2_genomic.gff) of the \u003cem\u003eB. taurus\u003c/em\u003e reference genome. The functional annotation of each SNP was performed via ANNOWAR(Wang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePopulation genetic analysis\u003c/h2\u003e \u003cp\u003eThe autosomal SNPs were pruned at high levels of pairwise linkage disequilibrium (LD) by PLINK(Purcell et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) with the parameters (-\u0026shy;-\u0026shy;indep-\u0026shy;pair-\u0026shy;wise 50 5 0.2) to obtain genetic structure and perform principal component analysis (PCA). The genetic structure was speculated via admixture (Alexander \u0026amp; Lange, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)with a kinship set from 2 to 4. Principal component analysis was conducted via smartPCA of the eigensoft (Patterson et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A phylogenetic tree was constructed from 110 samples via PLINK via MEGA v7.0(Kumar et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and visualized using itol(Letunic \u0026amp; Bork, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVCFtools (Danecek et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to estimate the nucleotide diversity (θπ) of each breed, keeping a window size of 50 kb and a step size of 20 kb. It was used to calculate the fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e) between the six cattle breeds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDetection of selection signals\u003c/h2\u003e \u003cp\u003eIn the present study, our aim was to identify regions exhibiting positive selection signatures in Zaosheng cattle. We detected the selection signatures within Zaosheng cattle via two different statistics: the nucleotide diversity (\u003cem\u003eθπ\u003c/em\u003e) and the composite likelihood ratio (CLR)(Nielsen et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Nucleotide diversity was estimated via a sliding window approach with windows of 50 kb and a step of 20 kb via VCFtools(Danecek et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The CLR test was calculated for sites in nonoverlapping 50 kb windows by using SweepFinder2 (DeGiorgio et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Empirical P values were calculated for the π and CLR windows, and the overlaps of the top 1% windows of each method were considered candidate signatures of selection.\u003c/p\u003e \u003cp\u003eFurthermore, the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, nucleotide diversity ratio (θπ ratio) and cross-population extended haplotype homozygosity (XPEHH) (Hudson et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sabeti et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) were used to compare Zaosheng cattle (as a target population) with East Asian taurine cattle and Chinese indicine cattle (as a reference population) to perform cross-population analysis for a comprehensive investigation of the common selection signals present among these diverse populations. \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e and θπ ratio analyses were performed in 50 kb windows with 20 kb steps via VCFtools v0.1.16(Danecek et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). XPEHH statistics were calculated for each population pair via Selscan v1.1 (Szpiech \u0026amp; Hernandez, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) We identified putative selective sweeps by selecting the top 1% of the original scores from each method, which represented the most likely candidates for the regions under selection. Tajima's D statistic was computed via VCFtools for several important candidate genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLocal ancestry inference\u003c/h2\u003e \u003cp\u003eLOTER (Dias-Alves et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to infer taurine and indicine ancestry in the genomes of Zaosheng cattle. We selected the Chinese indicine and East Asian taurine groups as reference panels on the basis of population structure. The length and frequency of ancestral segments in each reference group were subsequently calculated. To detect a high proportion of fragments with ancestry, the ancestry-specific haplotypes for each fragment were compared to the total number of ancestry-specific haplotypes for all fragments, with regions of significance having a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (Z test). The ideogram package(Hao et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in R was used to draw chromosome maps to visualize excessive segments of Chinese indicine and East Asian taurine cattle on the basis of the \u003cem\u003eB. taurus\u003c/em\u003e reference genome. Functional enrichment analysis was performed on the list of genes within the excessive segments detected by KOBAS v3.0(Bu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study comprehensively investigated the genomic variations in Zaosheng cattle by screening whole-genome resequencing data. We explored the population structure of current Zaosheng cattle, elucidated its genetic diversity and conducted selective sweep analysis. Simultaneously a set of potential candidate genes with potential impacts on fat deposition and development, meat quality and the immune response were identified within this breed. These discoveries not only advance our knowledge of the unique characteristics of Zaosheng cattle but also provide a basis for genetic breeding and resource protection in Qinchuan and Zaosheng cattle.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFund item\u003c/strong\u003e：The central government guides local funds for science and technology development，Number: 24ZYQL003\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlexander, D. H., \u0026amp; Lange, K. (2011). Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. \u003cem\u003eBmc Bioinformatics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e, Article 246. https://doi.org/10.1186/1471-2105-12-246\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArdicli, S., Samli, H., Vatansever, B., Soyudal, B., Dincel, D., \u0026amp; Balci, F. (2019). Comprehensive assessment of candidate genes associated with fattening performance in Holstein-Friesian bulls. \u003cem\u003eArchives Animal Breeding\u003c/em\u003e,\u003cem\u003e\u0026nbsp;62\u003c/em\u003e(1), 9-32. https://doi.org/10.5194/aab-62-9-2019\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArikawa, L. M., Mota, L. F. M., Schmidt, P. I., Frezarim, G. B., Fonseca, L. F. S., Magalhaes, A. F. B., Silva, D. A., Carvalheiro, R., Chardulo, L. A. L., \u0026amp; de Albuquerque, L. G. (2024). Genome-wide scans identify biological and metabolic pathways regulating carcass and meat quality traits in beef cattle. \u003cem\u003eMeat Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;209\u003c/em\u003e. https://doi.org/ARTN 109402\u003c/li\u003e\n \u003cli\u003e10.1016/j.meatsci.2023.109402\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBaryla, M., Goryszewska-Szczurek, E., Kaczynski, P., Balboni, G., \u0026amp; Waclawik, A. (2023). Prokineticin 1 is a novel factor regulating porcine corpus luteum function. \u003cem\u003eScientific reports\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e(1), 5085-5085. https://doi.org/10.1038/s41598-023-32132-3\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBaryla, M., Kaczynski, P., Goryszewska-Szczurek, E., \u0026amp; Waclawik, A. (2024). The regulation of the expression of prokineticin 1 and its receptors and its mechanism of action in the porcine corpus luteum. \u003cem\u003eTheriogenology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;226\u003c/em\u003e, 39-48. https://doi.org/10.1016/j.theriogenology.2024.05.044\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBirch, D. G., Samarakoon, L., Melia, M., Duncan, J. L., Ayala, A. R., Audo, I., Cheetham, J. K., Durham, T. A., Iannaccone, A., Pennesi, M. E., Stingl, K., for the Foundation Fighting Blindness Consortium Investigator, G., Fdn Fighting Blindness, C., \u0026amp; Foundation Fighting Blindness Consortium Investigator, G. (2022). The RUSH2A Study: Dark-Adapted Visual Fields in Patients With Retinal Degeneration Associated With Biallelic Variants in the USH2A Gene. \u003cem\u003eInvestigative ophthalmology \u0026amp; visual science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;63\u003c/em\u003e(3), 17-17. https://doi.org/10.1167/iovs.63.3.17\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBu, D., Luo, H., Huo, P., Wang, Z., Zhang, S., He, Z., Wu, Y., Zhao, L., Liu, J., Guo, J., Fang, S., Cao, W., Yi, L., Zhao, Y., \u0026amp; Kong, L. (2021). KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. \u003cem\u003eNucleic acids research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;49\u003c/em\u003e(W1), W317-W325. https://doi.org/10.1093/nar/gkab447\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCao, X.-K., Huang, Y.-Z., Ma, Y.-L., Cheng, J., Qu, Z.-X., Ma, Y., Bai, Y.-Y., Tian, F., Lin, F.-P., Ma, Y.-L., \u0026amp; Chen, H. (2018). Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature. \u003cem\u003eFunctional \u0026amp; integrative genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;18\u003c/em\u003e(5), 559-567. https://doi.org/10.1007/s10142-018-0613-0\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCarre, G.-A., Couty, I., Hennequet-Antier, C., \u0026amp; Govoroun, M. S. (2011). Gene Expression Profiling Reveals New Potential Players of Gonad Differentiation in the Chicken Embryo. \u003cem\u003ePloS one\u003c/em\u003e,\u003cem\u003e\u0026nbsp;6\u003c/em\u003e(9), e23959-e23959. https://doi.org/10.1371/journal.pone.0023959\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen, N., Cai, Y., Chen, Q., Li, R., Wang, K., Huang, Y., Hu, S., Huang, S., Zhang, H., Zheng, Z., Song, W., Ma, Z., Ma, Y., Dang, R., Zhang, Z., Xu, L., Jia, Y., Liu, S., Yue, X., . . . Lei, C. (2018). Whole-genome resequencing reveals world-wide ancestry and adaptive introgression events of domesticated cattle in East Asia. \u003cem\u003eNat Commun\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e(1), 2337. https://doi.org/10.1038/s41467-018-04737-0\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen, Q., Huang, B., Zhan, J., Wang, J., Qu, K., Zhang, F., Shen, J., Jia, P., Ning, Q., Zhang, J., Chen, N., Chen, H., \u0026amp; Lei, C. (2020). Whole-genome analyses identify loci and selective signals associated with body size in cattle. \u003cem\u003eJournal of animal science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;98\u003c/em\u003e(3), 1-8. https://doi.org/10.1093/jas/skaa068\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChu, T., Dufort, I., \u0026amp; Sirard, M. A. (2012). Effect of ovarian stimulation on oocyte gene expression in cattle. \u003cem\u003eTheriogenology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;77\u003c/em\u003e(9), 1928-1938. https://doi.org/10.1016/j.theriogenology.2012.01.015\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChung, Y. M., Kim, J. S., \u0026amp; Yoo, Y. D. (2006). A novel protein, Romo1, induces ROS production in the mitochondria. \u003cem\u003eBiochemical and biophysical research communications\u003c/em\u003e,\u003cem\u003e\u0026nbsp;347\u003c/em\u003e(3), 649-655. https://doi.org/10.1016/j.bbrc.2006.06.140\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eColpan, M., Moroz, N. A., Gray, K. T., Cooper, D. A., Diaz, C. A., \u0026amp; Kostyukova, A. S. (2016). Tropomyosin-binding properties modulate competition between tropomodulin isoforms. \u003cem\u003eArchives of biochemistry and biophysics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;600\u003c/em\u003e, 23-32. https://doi.org/10.1016/j.abb.2016.04.006\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDanecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., DePristo, M. A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherry, S. T., McVean, G., Durbin, R., Genomes Project Analysis, G., Genomes Project Anal, G., \u0026amp; Genomes Project Analysis, G. (2011). The variant call format and VCFtools. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;27\u003c/em\u003e(15), 2156-2158. https://doi.org/10.1093/bioinformatics/btr330\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDecker, J. E., McKay, S. D., Rolf, M. M., Kim, J., Molina Alcala, A., Sonstegard, T. S., Hanotte, O., Gotherstrom, A., Seabury, C. M., Praharani, L., Babar, M. E., de Almeida Regitano, L. C., Yildiz, M. A., Heaton, M. P., Liu, W.-S., Lei, C.-Z., Reecy, J. M., Saif-Ur-Rehman, M., Schnabel, R. D., \u0026amp; Taylor, J. F. (2014). Worldwide Patterns of Ancestry, Divergence, and Admixture in Domesticated Cattle. \u003cem\u003ePLoS genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;10\u003c/em\u003e(3), e1004254-e1004254. https://doi.org/10.1371/journal.pgen.1004254\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDeGiorgio, M., Huber, C. D., Hubisz, M. J., Hellmann, I., \u0026amp; Nielsen, R. (2016). SWEEPFINDER2: increased sensitivity, robustness and flexibility. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;32\u003c/em\u003e(12), 1895-1897. https://doi.org/10.1093/bioinformatics/btw051\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDias-Alves, T., Mairal, J., \u0026amp; Blum, M. G. B. (2018). Loter: A Software Package to Infer Local Ancestry for a Wide Range of Species. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e,\u003cem\u003e\u0026nbsp;35\u003c/em\u003e(9), 2318-2326. https://doi.org/10.1093/molbev/msy126\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDonelan, W., Dominguez-Gutierrez, P. R., \u0026amp; Kusmartsev, S. (2022). Deregulated hyaluronan metabolism in the tumor microenvironment drives cancer inflammation and tumor-associated immune suppression. \u003cem\u003eFrontiers in immunology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 971278-971278. https://doi.org/10.3389/fimmu.2022.971278\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDong, S., Li, J., \u0026amp; Zhang, X. (2020). Tumor protein p53-induced nuclear protein 2 modulates osteogenic differentiation of human adipose derived stem/stromal cells by activating Wnt/beta-catenin signaling. \u003cem\u003eAmerican journal of translational research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(10), 6853-6867. https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3NatwwEBZJKCFQQn9pmjao0KvXXsuW7GO6JOQSyGFLoZdFskatoZ bN2tuS58kb9EH6TJ3R2m039FB6tmTJzIc033jmG8ZEOkuie2dCmpkEEmcSURaVESYrBcxlge5HDkqWVJy8uCo_ vpMXN2q5x_KpNCbk8FemnvkvzczXn0OqZddU8ZQ2Ft9cL5TMhUxUvM_2VVpMjH17GkvqLnPEDgVCDbmL2nEk_5YGee_qCdfM5SN2PPqH_ Hy78GO2B_4JO7we_4A_ZXfLTdOueZBXqD3vchEhq0b7WO5Jmlj_fpbyprXUnQt6T qUcLWKlrvjUEmXYGo W3jodGfVzbumt74BZB-RXfRxLPcT-s2wY3RAH-nptbToUQFMb1n_gHP8Q_vkeUU-VxPUoF0VTd_oy9v7xYLq6isd FC1KWZGCKrTOKQSBpttSXBF5lW4HQGQhpSxykdUu4ySwFA5bYQVoBGmqRpwryyiXjOHmpKyPdDKNyzLxh3du4kaAG5BOSQUBZWFpk xTlU6U6I8YW8mQ6wQ0fQV2kO76VekcEcqRBLHqB0LrbqtAseKNLF3nyA2gjb2iIUT9vZPW_6aSC5qgT5aVtA9jQvM_2XYYpRLJ5mA4eV_b-qUHaXE1kMA5xU7GNYbeM0e-G_9RruzEFU4C-D9CR5xBQc\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDoran, A. G., Berry, D. P., \u0026amp; Creevey, C. J. (2014). Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle. \u003cem\u003eBMC Genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;15\u003c/em\u003e(1), 837-837. https://doi.org/10.1186/1471-2164-15-837\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eEngel, P. (2019). SLAMF receptors and disease. \u003cem\u003eClinical immunology (Orlando, Fla.)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;204\u003c/em\u003e, 1-2. https://doi.org/10.1016/j.clim.2019.07.008\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGalvan, L., Francelle, L., Gaillard, M.-C., de Longprez, L., Carrillo-de Sauvage, M.-A., Liot, G., Cambon, K., Stimmer, L., Luccantoni, S., Flament, J., Valette, J., de Chaldee, M., Auregan, G., Guillermier, M., Josephine, C., Petit, F., Jan, C., Jarrige, M., Dufour, N., . . . Brouillet, E. (2018). The striatal kinase DCLK3 produces neuroprotection against mutant huntingtin. \u003cem\u003eBrain (London, England : 1878)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;141\u003c/em\u003e, 1434-1454. https://doi.org/10.1093/brain/awy057\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGuangjun, X. I. A., Luomeng, Z., Hongyan, X. U., Chang, X. U., \u0026amp; Baozhen, Y. I. N. (2019). \u003cem\u003eYanbian yellow cattle meat quality-related ANGPT4 gene SNP molecular marker, primer pair, kit and application thereof\u003c/em\u003e. https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwvV1LT8JAEJ6gEjUxUVGj-Mh66Um0dOnr0BhYQC6WRhsTvZA-thENpYESgr_e2RWEmOjRS9NXmu10Z3amM983AFS7Vis_bIJNo2pk2LqmJoHJ9cSgZhJads2iQWhRSWPAOvZLw2h5pl-AtwU0RtKGTiVXIipYhOqfS_OdL f9pNWWp5fgm7OOp4W3bd5rKPFjGWERTDaXZcFpet9llCmMOcxX3wcFFT69V0XrX12BDsHBJn_OpIUAq2eoK0979z8 HsQdHDu9N8HwofryXYYouubyX YvJ8n23F3rvfjA5g8B6kgJyczkauZkkhSH5MBGnLyBcucVSQqhsek7t55fo3gxOTk0fXIYNGAlwxEKdDoimSipcCIZEEfD977OQnSmKxk0olwSvkw OYTL dstnnQoKq_ctjB5zl69Cj2AnEEX6aS7 BfPExEENPTMotjluKjmJk4Tww QpUnsa0lGDmdQPn3B5b_ungK20LCss7EOIP1fDTh51BMp-NJkFzIT_sJ2I7EQg\u003c/li\u003e\n \u003cli\u003eGuo, S. S., Lv, C. Y., Ouyang, S. J., Wang, X. L., Liao, A. H., \u0026amp; Yuan, S. Q. (2020). GOLGA4, A Golgi matrix protein, is dispensable for spermatogenesis and male fertility in mice. \u003cem\u003eBiochemical and biophysical research communications\u003c/em\u003e,\u003cem\u003e\u0026nbsp;529\u003c/em\u003e(3), 642-646. https://doi.org/10.1016/j.bbrc.2020.05.170\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGuo, X., Xing, C.-H., Wei, W., Zhang, X.-F., Wei, Z.-Y., Ren, L.-L., Jiang, J.-J., Li, M., Wang, J.-X., He, X.-X., Wang, M.-S., \u0026amp; Jiang, R.-S. (2022). Genome-wide scan for selection signatures and genes related to heat tolerance in domestic chickens in the tropical and temperate regions in Asia. \u003cem\u003ePoultry science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;101\u003c/em\u003e(7), 101821-101821. https://doi.org/10.1016/j.psj.2022.101821\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGurgul, A., Jasielczuk, I., Ropka-Molik, K., Semik-Gurgul, E., Pawlina-Tyszko, K., Szmatola, T., Szyndler-Nedza, M., Bugno-Poniewierska, M., Blicharski, T., Szulc, K., Skrzypczak, E., \u0026amp; Krupinski, J. (2018). A genome-wide detection of selection signatures in conserved and commercial pig breeds maintained in Poland. \u003cem\u003eBMC genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e(1), 95-95. https://doi.org/10.1186/s12863-018-0681-0\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHao, Z. D., Lv, D. K., Ge, Y., Shi, J. S., Weijers, D., Yu, G. C., \u0026amp; Chen, J. H. (2020). \u0026lt;i\u0026gt;RIdeogram\u0026lt;/i\u0026gt;: drawing SVG graphics to visualize and map genome-wide data on the idiograms. \u003cem\u003ePeerj Computer Science\u003c/em\u003e, Article e251. https://doi.org/10.7717/peerj-cs.251\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHarjunpaa, H., Asens, M. L., Guenther, C., \u0026amp; Fagerholm, S. C. (2019). Cell Adhesion Molecules and Their Roles and Regulation in the Immune and Tumor Microenvironment. \u003cem\u003eFrontiers in immunology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;10\u003c/em\u003e, 1078-1078. https://doi.org/10.3389/fimmu.2019.01078\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHeng, L., Bob, H., Alec, W., Tim, F., Jue, R., Nils, H., Gabor, M., Goncalo, A., \u0026amp; Richard, D. (2009). The Sequence Alignment/Map format and SAMtools. \u003cem\u003eBioinformatics (Oxford, England)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;25\u003c/em\u003e(16), 2078-2079. https://next.cnki.net/middle/abstract?v=AhJL6SqmbxBcRvxvdcIFVN1RrbIqsfXEZ0xUqHGQQ76MUJOSCQkUOOMv7LmvBkjSxgVKJaAr6d83apjv3osxAbpHOQAaMpMaSQRrvpj SC23kZfi6-Hu396UpPdNKI4uQXNFSajwTQv_PmJlvFtpRxM8ck7LFKlTfDh9MbK-CQoFylDyXNrEFu9Brhdoih66A2KbHzDh7d9Y=\u0026amp;uniplatform=NZKPT\u0026amp;language=CHS\u0026amp;scence=null\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHengwei, Y., Ke, Z., Gong, C., Chugang, M., Hongbao, W., \u0026amp; Linsen, Z. (2024). Genome-wide analysis reveals genomic diversity and signatures of selection in Qinchuan beef cattle. \u003cem\u003eBMC Genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;25\u003c/em\u003e(1), 558-558. https://next.cnki.net/middle/abstract?v=AhJL6SqmbxCSmDBlpbZLAXNPjwGktsN1CsryUsUKn_8Y5Z2pp3IouIdM1HE0HRne5kdMjlbWo-FN9Dt7-i3m8zFaZlWVdsNkiCk6j96QEtlaD8SWiTIxcgISR7kckrsrz2Ygr4f7iI8VlHxGaaYfxYe5etSwtaDSLd0BqaMhrFq-ZXsOkUeb_cmbGXh9KHFkdxMVT4iEXzmEx4Q2PowSL5OfZKeOkanw\u0026amp;uniplatform=NZKPT\u0026amp;language=CHS\u0026amp;scence=null\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHitsumoto, T., Tsukamoto, O., Matsuoka, K., Li, J., Liu, L., Kuramoto, Y., Higo, S., Ogawa, S., Fujino, N., Yoshida, S., Kioka, H., Kato, H., Hakui, H., Saito, Y., Okamoto, C., Inoue, H., Hyejin, J., Ueda, K., Segawa, T., . . . Takashima, S. (2023). Restoration of Cardiac Myosin Light Chain Kinase Ameliorates Systolic Dysfunction by Reducing Superrelaxed Myosin. \u003cem\u003eCirculation (New York, N.Y.)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;147\u003c/em\u003e(25), 1902-1918. https://doi.org/10.1161/CIRCULATIONAHA.122.062885\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHuang, J., Jia, Y., Li, Q., Son, K., Hamilton, C., Burris, W. R., Bridges, P. J., Stromberg, A. J., \u0026amp; Matthews, J. C. (2018). Glutathione content and expression of proteins involved with glutathione metabolism differs in longissimus dorsi, subcutaneous adipose, and liver tissues of finished vs. growing beef steers. \u003cem\u003eJournal of animal science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;96\u003c/em\u003e(12), 5152-5165. https://doi.org/10.1093/jas/sky362\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHuang, Z., Zhang, M., Plec, A. A., Estill, S. J., Cai, L., Repa, J. J., McKnight, S. L., \u0026amp; Tu, B. P. (2018). ACSS2 promotes systemic fat storage and utilization through selective regulation of genes involved in lipid metabolism. \u003cem\u003eProceedings of the National Academy of Sciences - PNAS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;115\u003c/em\u003e(40), E9499-E9506. https://doi.org/10.1073/pnas.1806635115\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHudson, R. R., Slatkin, M., \u0026amp; Maddison, W. P. (1992). ESTIMATION OF LEVELS OF GENE FLOW FROM DNA-SEQUENCE DATA. \u003cem\u003eGenetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;132\u003c/em\u003e(2), 583-589. \u0026lt;Go to ISI\u0026gt;://WOS:A1992JQ14600025\u003c/li\u003e\n \u003cli\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC1205159/pdf/ge1322583.pdf\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJian, P., Yanfang, T., Zhuan, Z., Jian, W., Xueming, Z., \u0026amp; Jian, N. (2011). MMP28 (epilysin) as a novel promoter of invasion and metastasis in gastric cancer. \u003cem\u003eBMC cancer\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e(1), 200-200. https://doi.org/10.1186/1471-2407-11-200\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJiang, L., Kon, T., Chen, C., Ichikawa, R., Zheng, Q., Pei, L., Takemura, I., Nsobi, L. H., Tabata, H., Pan, H., Omori, Y., \u0026amp; Ogura, A. (2021). Whole-genome sequencing of endangered Zhoushan cattle suggests its origin and the association of MC1R with black coat colour. \u003cem\u003eScientific reports\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e(1), 17359-17359. https://doi.org/10.1038/s41598-021-96896-2\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJin, Z., Zhang, G., Liu, Y., He, Y., Yang, C., Du, Y., \u0026amp; Gao, F. (2019). The suppressive role of HYAL1 and HYAL2 in the metastasis of colorectal cancer. \u003cem\u003eJournal of gastroenterology and hepatology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;34\u003c/em\u003e(10), 1766-1776. https://doi.org/10.1111/jgh.14660\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKumar, S., Stecher, G., \u0026amp; Tamura, K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e,\u003cem\u003e\u0026nbsp;33\u003c/em\u003e(7), 1870-1874. https://doi.org/10.1093/molbev/msw054\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLetunic, I., \u0026amp; Bork, P. (2019). Interactive Tree Of Life (iTOL) v4: recent updates and new developments. \u003cem\u003eNucleic acids research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;47\u003c/em\u003e(W1), W256-W259. https://doi.org/10.1093/nar/gkz239\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLi, H., \u0026amp; Durbin, R. (2009). Fast and accurate short read alignment with Burrows\u0026ndash;Wheeler transform. \u003cem\u003eBIOINFORMATICS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;25\u003c/em\u003e(14), 1754-1760. https://doi.org/10.1093/bioinformatics/btp324\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu, X., Zhang, Y., Liu, W., Li, Y., Pan, J., Pu, Y., Han, J., Orlando, L., Ma, Y., \u0026amp; Jiang, L. (2022). A single-nucleotide mutation within the TBX3 enhancer increased body size in Chinese horses. \u003cem\u003eCurrent biology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;32\u003c/em\u003e(2), 480-487.e486. https://doi.org/10.1016/j.cub.2021.11.052\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu, Y., Fang, X., Zhao, Z., Li, J., Albrecht, E., Schering, L., Maak, S., \u0026amp; Yang, R. (2019). Polymorphisms of the ASIP gene and the haplotype are associated with fat deposition traits and fatty acid composition in Chinese Simmental steers. \u003cem\u003eArchiv f\u0026uuml;r Tierzucht\u003c/em\u003e,\u003cem\u003e\u0026nbsp;62\u003c/em\u003e(1), 135-142. https://doi.org/10.5194/aab-62-135-2019\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMa, X., Cheng, H., Liu, Y., Sun, L., Chen, N., Jiang, F., You, W., Yang, Z., Zhang, B., Song, E., \u0026amp; Lei, C. (2022). Assessing Genomic Diversity and Selective Pressures in Bohai Black Cattle Using Whole-Genome Sequencing Data. \u003cem\u003eAnimals (Basel)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(5), 665. https://doi.org/10.3390/ani12050665\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMahajan, M. A., \u0026amp; Samuels, H. H. (2008). Nuclear receptor coactivator/coregulator NCoA6(NRC) is a pleiotropic coregulator involved in transcription, cell survival, growth and development. \u003cem\u003eNuclear receptor signaling\u003c/em\u003e,\u003cem\u003e\u0026nbsp;6\u003c/em\u003e(1), e002-e002. https://doi.org/10.1621/nrs.06002\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMarom, A., Barak, A. F., Kramer, M. P., Lewinsky, H., Binsky-Ehrenreich, I., Cohen, S., Tsitsou-Kampeli, A., Kalchenko, V., Kuznetsov, Y., Mirkin, V., Dezorella, N., Shapiro, M., Schwartzberg, P. L., Cohen, Y., Shvidel, L., Haran, M., Becker-Herman, S., Herishanu, Y., \u0026amp; Shachar, I. (2017). CD84 mediates CLL-microenvironment interactions. \u003cem\u003eOncogene\u003c/em\u003e,\u003cem\u003e\u0026nbsp;36\u003c/em\u003e(5), 628-638. https://doi.org/10.1038/onc.2016.238\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMcArdel, S. L., Terhorst, C., \u0026amp; Sharpe, A. H. (2016). Roles of CD48 in regulating immunity and tolerance. \u003cem\u003eClinical immunology (Orlando, Fla.)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;164\u003c/em\u003e, 10-20. https://doi.org/10.1016/j.clim.2016.01.008\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMidgley, A. C., Woods, E. L., Jenkins, R. H., Brown, C., Khalid, U., Chavez, R., Hascall, V., Steadman, R., Phillips, A. O., \u0026amp; Meran, S. (2020). Hyaluronidase-2 Regulates RhoA Signaling, Myofibroblast Contractility, and Other Key Profibrotic Myofibroblast Functions. \u003cem\u003eThe American journal of pathology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;190\u003c/em\u003e(6), 1236-1255. https://doi.org/10.1016/j.ajpath.2020.02.012\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNielsen, R., Williamson, S., Kim, Y., Hubisz, M. J., Clark, A. G., \u0026amp; Bustamante, C. (2005). Genomic scans for selective sweeps using SNP data. \u003cem\u003eGenome research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;15\u003c/em\u003e(11), 1566-1575. https://doi.org/10.1101/gr.4252305\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOh, G.-S., Kim, S.-R., Lee, E.-S., Yoon, J., Shin, M.-K., Ryu, H. K., Kim, D. S., \u0026amp; Kim, S.-W. (2022). Regulation of Hepatic Gluconeogenesis by Nuclear Receptor Coactivator 6. \u003cem\u003eMolecules and cells\u003c/em\u003e,\u003cem\u003e\u0026nbsp;45\u003c/em\u003e(4), 180-192. https://nwsuaf.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LS8NAEF5EFAQRn_gse9GL RJp3evDQ1tqiaKXtyUtJshsJalKaRsm_dya7TWIRHwcvISwhJPmWb76 ZzIMQXbuoKwuc4NQ9tCyeHnCmauhnuJplM6uuc1MNAgS-3Ws8tqzOgz0qs5zLtX8FHtYAeiyk_QP4xU1hAc5hC8ARNgEcf7UNBmLWvJSFPT7JO7R2MVE94vETMl2YoAa9x8bG7hR1JJ-AIw5MgTUPb-i Un1tV DXsnJupy0d0ZI_-FLu_nQZpulkbKMCxqgOTE5mGoDLJYWsoyB6iDF8-lfs4_IhHgRvYElyEJrRKfxFiAAgZPUCoXPAakqGDfvirpih6ScnMZFQZVxWAnaYxVMSjvc5_sBftVZBXeNG_7-DjgYAON6yZoszNsoP7KQn92yS MlTbDLAag8bAd61ZrzjtHAStKSh0AB2ziRpPgbBQZDlFTKlwNHBtV9WFEio02yIV0I2hRgb5Gl53ibrIqhotkO6ZaQ0zigEnK6ADn1Miohp3PI aQVyau2S0-vOqN1T8ocYRyx5GX_x7voeWX exTCKa5eWUbJ9Qz2K6q3o NYG3PMH3HYdz2fc2xDIuppuMckNr3Nz386YIjsoYrIkp1TJZn05SfkJXoPUndoJZ_9w-lTUUw\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOrtega, M. S., Denicol, A. C., Cole, J. B., Null, D. J., \u0026amp; Hansen, P. J. (2016). Use of single nucleotide polymorphisms in candidate genes associated with daughter pregnancy rate for prediction of genetic merit for reproduction in Holstein cows. \u003cem\u003eAnimal genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;47\u003c/em\u003e(3), 288-297. https://doi.org/10.1111/age.12420\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePatterson, N., Price, A. L., \u0026amp; Reich, D. (2006). Population structure and eigenanalysis. \u003cem\u003ePLoS genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;2\u003c/em\u003e(12), 2074-2093. https://doi.org/10.1371/journal.pgen.0020190\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePurcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., \u0026amp; Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. \u003cem\u003eAmerican Journal of Human Genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;81\u003c/em\u003e(3), 559-575. https://doi.org/10.1086/519795\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSabeti, P. C., Reich, D. E., Higgins, J. M., Levine, H. Z. P., Richter, D. J., Schaffner, S. F., Gabriel, S. B., Platko, J. V., Patterson, N. J., McDonald, G. J., Ackerman, H. C., Campbell, S. J., Altshuler, D., Cooper, R., Kwiatkowski, D., Ward, R., \u0026amp; Lander, E. S. (2002). Detecting recent positive selection in the human genome from haplotype structure. \u003cem\u003eNature\u003c/em\u003e,\u003cem\u003e\u0026nbsp;419\u003c/em\u003e(6909), 832-837. https://doi.org/10.1038/nature01140\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSahmi, F., Sahmi, M., G\u0026eacute;vry, N., Sahadevan, P., Allen, B. G., \u0026amp; Price, C. A. (2019). A putative protein\u0026ndash;RNA complex regulates posttranscriptional processing of cytochrome P450 aromatase (CYP19A1) in bovine granulosa cells. \u003cem\u003eMolecular reproduction and development\u003c/em\u003e,\u003cem\u003e\u0026nbsp;86\u003c/em\u003e(12), 1901-1908. https://doi.org/10.1002/mrd.23289\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSala, D., Ivanova, S., Plana, N., Ribas, V., Duran, J., Bach, D., Turkseven, S., Laville, M., Vida, H., Karczewska-Kupczewska, M., Kowalska, I., Straczkowski, M., Testar, X., Palacin, M., Sandri, M., Serrano, A. L., \u0026amp; Zorzano, A. (2014). Autophagy-regulating TP53INP2 mediates muscle wasting and is repressed in diabetes. \u003cem\u003eThe Journal of clinical investigation\u003c/em\u003e,\u003cem\u003e\u0026nbsp;124\u003c/em\u003e(5), 1914-1927. https://doi.org/10.1172/JCI72327\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSantos Silva, D. B. d., Fonseca, L. F. S., Magalh\u0026atilde;es, A. F. B., Muniz, M. M. M., Baldi, F., Ferro, J. A., Chardulo, L. A. L., Pinheiro, D. G., \u0026amp; Albuquerque, L. G. d. (2020). Transcriptome profiling of muscle in Nelore cattle phenotypically divergent for the ribeye muscle area. \u003cem\u003eGenomics (San Diego, Calif.)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;112\u003c/em\u003e(2), 1257-1263. https://doi.org/10.1016/j.ygeno.2019.07.012\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSekar, M., \u0026amp; Thirumurugan, K. (2024). Autophagic Regulation of Adipogenesis Through TP53INP2: Insights from In Silico and In Vitro Analysis. \u003cem\u003eMolecular biotechnology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;66\u003c/em\u003e(5), 1188-1205. https://doi.org/10.1007/s12033-023-01020-6\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShen, J., Hanif, Q., Cao, Y., Yu, Y., Lei, C., Zhang, G., \u0026amp; Zhao, Y. (2020). Whole Genome Scan and Selection Signatures for Climate Adaption in Yanbian Cattle. \u003cem\u003eFrontiers in genetics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;11\u003c/em\u003e, 94-94. https://doi.org/10.3389/fgene.2020.00094\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShrestha, M. M., Lim, C.-Y., Bi, X., Robinson, R. C., \u0026amp; Han, W. (2021). Tmod3 Phosphorylation Mediates AMPK-Dependent GLUT4 Plasma Membrane Insertion in Myoblasts. \u003cem\u003eFrontiers in endocrinology (Lausanne)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e, 653557-653557. https://doi.org/10.3389/fendo.2021.653557\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSun, G., Cao, Y., Qian, C., Wan, Z., Zhu, J., Guo, J., \u0026amp; Shi, L. (2020). Romo1 is involved in the immune response of glioblastoma by regulating the function of macrophages. \u003cem\u003eAging (Albany, NY.)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e(2), 1114-1127. https://doi.org/10.18632/aging.102648\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSzpiech, Z. A., \u0026amp; Hernandez, R. D. (2014). selscan: An Efficient Multithreaded Program to Perform EHH-Based Scans for Positive Selection. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e,\u003cem\u003e\u0026nbsp;31\u003c/em\u003e(10), 2824-2827. https://doi.org/10.1093/molbev/msu211\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTashireva, L. A., Alifanov, V. V., Simanov, G. S., Gautreau, A. M., Cherdyntseva, N., \u0026amp; Perelmuter, V. M. (2020). LIMCH1: A protein regulating cell migration and proliferation. \u003cem\u003eŽurnal obŝej biologii\u003c/em\u003e,\u003cem\u003e\u0026nbsp;81\u003c/em\u003e(3), 234-240. https://doi.org/10.31857/S0044459620030082\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTong, Z., Liu, Y., Yu, X., Martinez, J. D., \u0026amp; Xu, J. (2019). The transcriptional co-activator NCOA6 promotes estrogen-induced GREB1 transcription by recruiting ER\u0026alpha; and enhancing enhancer\u0026ndash;promoter interactions. \u003cem\u003eThe Journal of biological chemistry\u003c/em\u003e,\u003cem\u003e\u0026nbsp;294\u003c/em\u003e(51), 19667-19682. https://doi.org/10.1074/jbc.RA119.010704\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTsoneva, E., Vasileva-Slaveva, M. B., Kostov, S. G., \u0026amp; Yordanov, A. D. (2023). ROMO1-a potential immunohistochemical prognostic marker for cancer development. \u003cem\u003eOncologie (Paris, France)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;25\u003c/em\u003e(6), 753-758. https://doi.org/10.1515/oncologie-2023-0345\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang, K., Li, M., \u0026amp; Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. \u003cem\u003eNucleic acids research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;38\u003c/em\u003e(16), e164-e164. https://doi.org/10.1093/nar/gkq603\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang, S., Raza, S. H. A., Zhang, K., Mei, C., Alamoudi, M. O., Aloufi, B. H., Alshammari, A. M., \u0026amp; Zan, L. (2023). Selection signatures of Qinchuan cattle based on whole-genome sequences. \u003cem\u003eAnimal biotechnology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;34\u003c/em\u003e(4), 1483-1491. https://doi.org/10.1080/10495398.2022.2033252\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang, Z., Zhu, B., Niu, H., Zhang, W., Xu, L., Xu, L., Chen, Y., Zhang, L., Gao, X., Gao, H., Zhang, S., Xu, L., \u0026amp; Li, J. (2019). Genome wide association study identifies SNPs associated with fatty acid composition in Chinese Wagyu cattle. \u003cem\u003eJournal of animal science and biotechnology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;10\u003c/em\u003e(1), 27-27. https://doi.org/10.1186/s40104-019-0322-0\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYang, J., Li, W.-R., Lv, F.-H., He, S.-G., Tian, S.-L., Peng, W.-F., Sun, Y.-W., Zhao, Y.-X., Tu, X.-L., Zhang, M., Xie, X.-L., Wang, Y.-T., Li, J.-Q., Liu, Y.-G., Shen, Z.-Q., Wang, F., Liu, G.-J., Lu, H.-F., Kantanen, J., . . . Liu, M.-J. (2016). Whole-Genome Sequencing of Native Sheep Provides Insights into Rapid Adaptations to Extreme Environments. \u003cem\u003eMolecular biology and evolution\u003c/em\u003e,\u003cem\u003e\u0026nbsp;33\u003c/em\u003e(10), 2576-2592. https://doi.org/10.1093/molbev/msw129\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYao, Y.-F., Lyu, S., Wang, X., Zhang, Z., Qu, K., Xu, J., Cai, C., Li, Z., Xie, J., Ru, B., Xu, Z., Wang, E., Lei, C., Chen, H., Huang, B., \u0026amp; Huang, Y. (2021). The combination between NCSTN gene copy number variation and growth traits in Chinese cattle. \u003cem\u003eAnimal biotechnology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;32\u003c/em\u003e(6), 683-687. https://doi.org/10.1080/10495398.2020.1741382\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYu, X., Zhai, C., Fan, Y., Zhang, J., Liang, N., Liu, F., Cao, L., Wang, J., \u0026amp; Du, J. (2017). TUSC3: a novel tumour suppressor gene and its functional implications. \u003cem\u003eJournal of cellular and molecular medicine\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e(9), 1711-1718. https://doi.org/10.1111/jcmm.13128\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang, F., Qu, K., Chen, N., Hanif, Q., Jia, Y., Huang, Y., Dang, R., Zhang, J., Lan, X., Chen, H., Huang, B., \u0026amp; Lei, C. (2019). Genome-Wide SNPs and InDels Characteristics of Three Chinese Cattle Breeds. \u003cem\u003eAnimals (Basel)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e(9), 596. https://doi.org/10.3390/ani9090596\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang, H., Mi, S., Brito, L. F., Hu, L., Wang, L., Ma, L., Xu, Q., Guo, G., Yu, Y., \u0026amp; Wang, Y. (2023). Genomic and transcriptomic analyses enable the identification of important genes associated with subcutaneous fat deposition in Holstein cows. \u003cem\u003eJournal of genetics and genomics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;50\u003c/em\u003e(6), 385-397. https://doi.org/10.1016/j.jgg.2023.01.011\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhang, Y., Zhang, Y., \u0026amp; Xu, H. (2019). LIMCH1 suppress the growth of lung cancer by interacting with HUWE1 to sustain p53 stability. \u003cem\u003eGene\u003c/em\u003e,\u003cem\u003e\u0026nbsp;712\u003c/em\u003e, 143963-143963. https://doi.org/10.1016/j.gene.2019.143963\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZhou, R., Pan, J., Zhang, W.-B., \u0026amp; Li, X.-d. (2024). Myosin-5a facilitates stress granule formation by interacting with G3BP1. \u003cem\u003eCellular and molecular life sciences : CMLS\u003c/em\u003e,\u003cem\u003e\u0026nbsp;81\u003c/em\u003e(1), 430. https://doi.org/10.1007/s00018-024-05468-w\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Figures","content":"\u003cp\u003eSupplementary Figures 1 and 2 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"whole-genome sequencing, genetic diversity, selection signatures, local ancestry","lastPublishedDoi":"10.21203/rs.3.rs-6059907/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6059907/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eZaosheng cattle have a long history, closely related to Qinchuan cattle. This cattle breed is still under developing phase from a drought type to a beef breed. Followed by years of improvement, Zaosheng cattle have shown certain germline characteristics and genetic potential. Here, we used whole genome sequencing data from 19 Zaosheng cattle and 91 published genomes to understand its genetic diversity, population structure, and environmentally adapted performance. We provide a comprehensive overview of the sequence variation in the Zaosheng cattle genome to explore the genetic changes in Zaosheng cattle due to environmental adaptation. The findings of this study demonstrate that the genetic composition of Zaosheng cattle was primarily derived from Chinese and East Asian indicine cattle, where, Zaosheng and Qinchuan cattle were found to be genetically closest. Through ancestral fragment inference and selective sweep, we identified several genes linked to lipid metabolism, immune regulation, fertility and meat quality across the mosaic genome of Zaosheng cattle showing an excess of taurine or indicine ancestry. In summary, this study supplies essential genetic insights into the genome diversity within Zaosheng cattle and provides a foundational framework for comprehending the genetic basis of indigenous cattle breeds.\u003c/p\u003e","manuscriptTitle":"Genomic Diversity and Selection Signatures for Zaosheng Cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-24 14:36:24","doi":"10.21203/rs.3.rs-6059907/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":"4894583d-fef2-4ebc-8172-1f8716ac7f5d","owner":[],"postedDate":"February 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44554724,"name":"Animal Science"}],"tags":[],"updatedAt":"2025-02-24T14:36:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-24 14:36:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6059907","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6059907","identity":"rs-6059907","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}