Assessing genomic diversity and selection signatures of Rhipicephalus microplus in Shaanxi, China based on whole-genome sequences

Assessing genomic diversity and selection signatures of Rhipicephalus microplus in Shaanxi, China based on whole-genome sequences | 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 Assessing genomic diversity and selection signatures of Rhipicephalus microplus in Shaanxi, China based on whole-genome sequences Yi-yao Mou, FU-wen Wang, YU-ying Yang, Tian-yuan Liu, Chu-zhao Lei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4519193/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 Background Ticks play a crucial role in transmitting and carrying various disease-causing microorganisms, which poses a significant risk to public health and the growth of the animal farming industry. Research on the whole genome sequence of ticks is consistently progressing due to the ongoing advancement of high-throughput sequencing technologies. Methods This study performed whole-genome resequencing on Rhipicephalus microplus obtained from free-range cattle in Hanzhong City, Shaanxi Province. The newly obtained data was then combined with existing whole genome resequencing data of R. microplus from the NGDC database (project ID: PRJCA002242) for further analysis. The purpose of this analysis was to assess genomic diversity and selection signatures in the Shaanxi group. Results The study identified single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) in the Shaanxi group. The R. microplus from China has been classified into three main branches, and there were variations in nucleotide diversity among populations in different places. All populations exhibited a high level of heterozygosity. Additionally, the value of Tajima's D deviated significantly from zero. Upon examining the mitochondrial genetic diversity of the tick, the study observed subtle variations compared to the phylogenetic tree created using the entire autosomal genome. These differences may arise from variances in population structure and migration patterns between the paternal and maternal tick populations. Genes associated with pesticide resistance, metal ion transportation, and antioxidant activity were identified during the selection study of the Shaanxi group. Conclusions The data acquired from our research holds significance in comprehending the biology of ticks, enhancing our understanding of their disease transmission, and formulating efficient strategies for tick management. Rhipicephalus microplus genetic diversity population structure whole-genome resequencing China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Being the arthropod to be recognized as a carrier of disease-causing agents [ 1 – 3 ], ticks belong to the phylum Arthropoda and the order Parasitiformes within the class Arachnida. Specifically, they are classified under the superorder Ixodoidea [ 4 ]. In addition to being a specialized group of bloodsucking arthropods, ticks are also well known for being ectoparasites and transmitters of several diseases that affect humans and animals. They are regarded as the primary arthropod vectors for human and domestic animal disease infections globally, along with mosquitoes [ 5 ]. Ticks have extremely diverse hosts and are widely distributed throughout the world. The distribution of ticks is influenced by multiple factors, including seasons, climatic conditions, vegetation distribution, and host animals [ 6 – 8 ]. Ticks can transfer a wide range of disease-causing microorganisms, including bacteria, viruses, protozoa, and parasites, to livestock, animals, and humans. Furthermore, there is a continuous rise in the variety of infections they harbour. This has a significant impact on both the economic progress of animal husbandry and the overall public health of humans [ 5 , 9 – 11 ]. China's extensive land area and diverse natural geographical conditions result in a vast range of tick species and notable regional variations in their distribution. In China, there have been documented hard ticks and soft ticks, totaling 9 genera and 124 species. This includes 26 species in the Ixodes family, 10 species in the Amblyoma genus, 42 species in the Haemaphysalis genus, 15 species in the Dermacenter genus, 3 species in the Anomalohimalaya genus, 8 species in the Rhopicephalus genus, 7 species in the Hyalomma genus. Additionally, there are 4 species in the Ornithodoros genus and 9 species in the Argas genus within the family of soft ticks. Rhipicephalus microplus is a member of the hard tick family and the Rhipicephalus genus. It has been documented to harbour a maximum of 31 different types of disease-causing microorganisms [ 12 ], formerly known as Boophilus microplus , and has a single-host life cycle. Because it primarily bites on bovine animals, it is popularly referred to as bovine lice in China. High-throughput sequencing has proven successful in acquiring whole-genome sequences of ticks, representing a significant advancement [ 13 – 17 ]. This study involved the analysis of 138 sets of whole genome resequencing data from ticks (12 R. microplus from Shaanxi, China). The aim was to assess the features of single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) in the entire genome of R. microplus from the Shaanxi region. Additionally, it offers valuable insights for the examination of genetic variation and adaptive evolution in the R. microplus . Gaining knowledge about the fundamental data of tick genomes and genetic diversity will provide new opportunities and establish a theoretical basis for studying tick biology, interactions between vectors and pathogens, disease transmission, and ways for controlling it. Materials and methods Genomic data gathering and adjustment This study utilized a total of 138 whole genome resequencing data of the R. microplus . A total of 126 whole-genome resequencing datasets were acquired from the NGDC database (Project ID: PRJCA002242). Ticks were gathered from free-ranging cattle herds in Hanzhong City, Shaanxi Province, China, between March 2022 and August 2023. Ticks were visually examined and identified using a stereomicroscope based on their physical characteristics. A total of 12 specimens of R. microplus were carefully chosen and dispatched to BGI to construct a library of the organism's whole genome and subsequently conducted sequencing. The Illumina second-generation sequencing method (dual-end sequencing, read length: 150 bp each) was chosen for this purpose. The reference genome and gene annotation file were obtained from NCBI (BIME_Rmic_1.3). Read Mapping and SNP site detection annotation The study employed the Trimmomatic software [ 18 ] to do quality control on 138 whole genome resequencing data. The research utilized the specified parameters to exclude low-quality reads (Leading: 20, Trailing: 20, Sliding Window: 3:15, Average Quality: 20, Minimum Length: 35, TopHred33). BWA-mem [ 19 ] was used to align the quality control resequencing data to the reference genome of R. microplus . The aligned data was then converted into bam files using Samtools software. The bam files were sorted and deduplicated using Picard software. The quality of the mapping rate and sequencing depth of the samples were evaluated using Qualimap software. The Genome Analysis Toolkit (GATK, version 3.8) [ 20 ] (using the HaplotypeCaller, GenotypeGVCFs, and Select Variants modules) was employed to identify single nucleotide sequences. The modules were utilized to find single nucleotide polymorphisms (SNPs) and filtered all SNPs (QD 60.0, MQ < 40.0, MQRankSum < -12.5, ReadPosRankSum < -8.0). This process allowed for the identification of high-quality SNPs. The SNPs discovered during the examination of Shaanxi R. microplus were annotated and categorized using the SnpEff software [ 21 ]. The annotated results encompass introns, untranslated regions (5'UTR or 3'UTR), upstream, downstream, splice variations (including splice acceptor, splice donor, and splice region), and intergenic regions. Furthermore, SnpEFF can anticipate the consequences of mutations occurring in coding regions, including synonymous or non-synonymous amino acid changes, codon gains or losses, as well as stop codon gains and losses. Detection of InDels The study utilized GATK to isolate InDels from the data of Shaanxi R. microplus and subsequently filtered it (QD 200.0, ReadPosRankSum 10.0, InbreedingCoeff <-0.8). The InDels were annotated and classified using the SnpEff software. The functional categories exclusively used for InDel were conservative inframe deletions and insertions, disruptive inframe deletions and insertions, bidirectional gene fusions, and frameshift variants. Analysis of population genetic structure The population genetic structure of R. microplus was accurately identified using a series of software tools. First, the Vcftools software was employed to filter SNPs (− maf 0.05). Then, Plink software was used to perform a neutral LD screening of the SNP data (− indep airwise 50 10 0.2). Finally, Plink software was utilized to analyze the data and construct a genetic distance matrix. To construct a phylogenetic tree using the Neighbor-Joining method, MEGA11 software was employed. Additionally, the online software iTOL ( https://itol.embl.de ) was used to Beautify the Neighbor-joining (NJ) phylogenetic tree. The principal component Analysis (PCA) was conducted using the Smartpca module in the EIGENSOFT software [ 22 ] to analyze unlinked SNPs. The resulting graphs were drawn using the ggplot2 package in the R software. Admixture analysis was conducted using the ADMIXTURE software, followed by graph visualization using TBtools. Analysis of population genetic diversity To examine the genetic variation within the R. microplus population, the study evaluated whole genome SNP filtered data (− maf 0.05) to assess population heterozygosity, Tajima's D, nucleotide diversity, and linkage disequilibrium decay (LD decay). The heterozygosity of various geographic populations of R. microplus was computed using the Plink software (− hardy). Additionally, Tajima's D (− TajimaD 50000) and nucleotide diversity calculations (− window-pi 50000 − window --pi-step 20000) for different geographic populations were conducted separately using the Vcftools software. The outcomes were visualized using the Origin software. The primary function of the PopLDdecay software was to examine the LD decay across several populations. Additionally, the Plot_MultiPop.pl script was utilized to generate a graphical representation of the LD decay curve. Analysis of genetic diversity in the whole mitochondrial genome The mitochondrial genome of each individual was extracted from the 138 whole-genome bam files using the Samtools software. Subsequently, the SamToFastq module in the Picard software was utilized to convert the mitochondrial whole genome bam files into fastq format. Ultimately, the Mapping Iterative Assembler v1.0 (MIA) was employed to align the mitochondrial whole genome fastq files to the reference genome of R. microplus mitochondrial DNA. The study obtained the assembled mitochondrial whole genome sequences and conducted the muscle analysis. A mitochondrial phylogenetic tree was created using the maximum likelihood (ML) method with the IQ-TREE software, utilizing the whole mitochondrial genome of Rhipicephalus sanguineus (AF081829.1) acquired from GenBank as the outgroup. The genetic diversity characteristics of R. microplus in Shaanxi were calculated using the Shaanxi mitochondrial whole genome data in the DnaSP v6.12.03 software [ 23 ]. Identification of positive genes for R. microplus in Shaanxi The identified genetic signals were screened using two methods: nucleotide diversity analysis (π) and integrated haplotype score (IHS). The value of π was computed using the Vcftools software, with a window size of 50 K and a step size of 20 K (− fst window size 50000 − fst window step 20000 − maf 0.05). IHS employed the Beagle and Selscan software for performing computations. The candidate genes were obtained by overlapping the results (top 1%) obtained from two methods. Results Analysis of autosomal whole genome variation (identification of SNPs and InDels) To assess the features of autosomal SNPs and InDels in the complete genome of the R. microplus in Shaanxi, the study conducted a comparison between the whole genome resequencing data of 12 R. microplus samples from Shaanxi and the reference genome of the R. microplus . The average mapping rate was found to be 95.4%, and the average sequencing depth was approximately 10.51× (Table S1). Following variant detection and quality control, a total of 131,950,109 SNPs and 15,289,151 InDels (insertion: 6,932,848; deletion: 8,356,303) were found. Upon further analysis of the SNP and InDel counts on each chromosome, it was observed that chromosome 2 had a relatively low number of detected SNPs and InDels. In contrast, the remaining chromosomes followed a pattern where the number of SNPs and InDels increased with the length of the chromosome (Fig. S1, Fig. S2). Mostly, the size of the InDels ranges from 1 to 4 bp (Fig. 1 ). The SNPs and InDels annotation findings for the Shaanxi group can be found in Table 1 and Table 2 . The total number of annotations was significantly more than the number of SNPs and InDel, which was due to the possibility of multiple annotation results for the same locus [ 24 ]. The study identified a total of 85,779,785 SNPs in intergenic regions, 67,459,126 SNPs in intronic regions, and 67,543,526 SNPs as non-coding transcriptional variants. Additionally, 17,077,474 and 17,063,069 SNPs were located in the upstream and downstream regions. The study identified 426,539 missense variations and 150,890 splice region variants, which were classified as high and moderate effect SNPs, while the study also detected other SNPs such as stop gained (6,200), start lost (1,004), and stop lost (1,086). Table 1 All SNPs annotation data for the Shaanxi group Type Count 3 prime UTR variant 1,186,110 5 prime UTR premature start codon gain variant 29,410 5 prime UTR variant 200,436 Downstream gene variant 17,063,069 Initiator codon variant 155 Intergenic region 85,779,785 Intragenic variant 1,753,502 Intron variant 67,459,126 Missense variant 426,539 Non coding transcript exon variant 220,926 Non coding transcript variant 67,543,526 Splice acceptor variant 1,779 Splice donor variant 2,621 Splice region variant 150,890 Start lost 1,004 Stop gained 6,200 Stop lost 1,086 Stop retained variant 887 Synonymous variant 735,715 Upstream gene variant 17,077,474 Table 2 All InDels annotation data for the Shaanxi group Type Count 3 prime UTR truncation 3 3 prime UTR variant 138,672 5 prime UTR truncation 8 5 prime UTR variant 25,691 Bidirectional gene fusion 103 Conservative inframe deletion 1,906 Conservative inframe insertion 1,883 Disruptive inframe deletion 3,814 Disruptive inframe insertion 2,326 Downstream gene variant 2,150,639 Exon loss variant 18 Feature ablation 1 Frameshift variant 22,585 Gene fusion 119 Intergenic region 9,546,542 Intragenic variant 203,257 Intron variant 8,678,633 Non coding transcript exon variant 19,976 Non coding transcript variant 8,586,268 Splice acceptor variant 1,685 Splice donor variant 1,295 Splice region variant 18,951 Start lost 240 Start retained variant 55 Stop gained 729 Stop lost 285 Stop retained variant 61 Transcript ablation 28 Upstream gene variant 2,184,473 Out of the total number of InDels, 9,546,542 InDels were located in intergenic regions, 8,678,633 InDels were located in intronic regions and 8,586,268 InDels were noncoding transcriptional variations. A total of 6,140 and 3,789 InDels were identified and classified as Disruptive frame InDels and Conservative frame InDels. Furthermore, 22,585 InDels were frameshift variations, and 21,931 InDels were splice sites (including splice region variants, splice donor variants and splice acceptor variants). Whole genome genetic structure of R. microplus The study conducted phylogenetic tree construction, Principal component analysis (PCA), and admixture analysis on the SNP data of 138 samples to investigate the relationship between various regional populations of R. microplus . Applying the NJ approach for constructing a phylogenetic tree revealed that the R. microplus in China could be categorized into three distinct branches (Fig. 2 a). The southern R. microplus population (Zhejiang, Jiangxi, Hainan, Guangxi, Guangdong, Fujian province, China) are grouped in one branch, while the southwestern R. microplus population (Chongqing, Guizhou, Yunnan province, China) are grouped in another branch. Similarly, the central R. microplus population (Hunan, Hubei, Anhui province, China) are also grouped in a separate branch. The classification of R. microplus in Shaanxi placed it in the southwestern population, and it was found to be most similar to R. microplus in Chongqing, which aligned with the geographical proximity between the two regions. The PCA of Chinese R. microplus populations demonstrated that PC1 effectively distinguished the Shaanxi and southwestern populations from the southern and central populations. Additionally, PC2 successfully differentiated the southern population from the other populations (Fig. 2 b). The Shaanxi and southwestern populations were grouped and the study results aligned with the phylogenetic tree. By employing Admixture analysis of 138 samples, it was discovered that the central population exhibited distinct ancestral origins when K = 2 and K = 3. In contrast, the southern and southwestern populations displayed two ancestral origins at K = 2 and three ancestral origins at K = 3, while The Shaanxi group consistently exhibited two ancestral origins. At K = 4, the majority of the central population maintained its distinct lineage, whereas the southern and southwestern populations had four ancestral origins and the Shaanxi group had three ancestral origins. At K = 5, the central population had two ancestor components, while the southern and southwestern populations shared the same ancestral components and the Shaanxi group had three ancestral components (Fig. 2 c). The cv value was the smallest when K = 4 (CV error = 0.3763). Thus, it was postulated that the Chinese R. microplus population originated from four ancestral groups. The whole genome genetic diversity of the R. microplus This study utilized whole-genome SNP data to examine the genetic diversity within R. microplus populations. The analysis focused on heterozygosity, Tajima's D, nucleotide diversity (π) and LD decay in three distinct geographic populations: central, southern, and southwestern. The group of Shaanxi was encompassed into the population of the southwestern region. The findings indicated that the southern population exhibited the lowest levels of both predicted and observed heterozygosity, whereas the central population displayed the highest levels of both expected and observed heterozygosity. The observed heterozygosity findings of the three populations were discovered to be lower than the expected heterozygosity results (Table 3 ). This suggests the presence of selection within the populations, resulting in a deviation in genotype frequency. Table 3 Calculation of heterozygosity in R. microplus populations Population Observed heterozygosity Expected heterozygosity Central 0.2777 0.2902 South 0.2010 0.2619 Southwest 0.2215 0.2699 Tajima's D value serves as an indicator of the presence of natural selection among populations, as long as it deviates from 0 [ 25 ]. The result demonstrated that the Tajima's D values of the three geographic populations all exhibited deviation from 0 (Fig. 3 a), suggesting the existence of natural selection pressure within these populations. Nucleotide diversity is a metric used to quantify the extent of polymorphism within a specific population. A higher value of π corresponded to a greater nucleotide diversity in the population. The Figure demonstrated that the nucleotide diversities of the three populations were nearly the same. The southwestern population had the highest nucleotide diversity (π = 8.01 × 10 3 ), the southern population had the lowest (π = 7.66 × 10 3 ), and the central population fell in between (π = 7.92 × 10 3) (Fig. 3 b). Each of the three regional groups exhibited rapid LD decay, with the southwestern population demonstrating the most rapid decay and the lowest r 2 value, approximately 0.03. The central population's r 2 was approximately 0.04 during the stabilization process. In contrast, the southern population experienced the slowest drop in LD. At stabilization, its r 2 value is roughly 0.05 (Fig. 3 c). The variations in the LD decay patterns also suggested that there were distinctions among the populations. Genetic diversity of mitochondrial whole genome in Shaanxi group The R. microplus ’ mitochondrial DNA genome measured 14936 bp in total length. 6 haplotypes were identified in the 12 whole mitochondrial genome sequences of the Shaanxi R. microplus (Table 4 ). The Shaanxi group exhibited haplotype diversity of 0.758 ± 0.122 and nucleotide diversity of 0.00073 ± 0.00023. Table 4 Genetic diversity of mitochondrial genomes in R. microplus from Shaanxi, China Group Sample size Variable sites Number of haplotypes Haplotype diversity(h) Nucleotide diversity Average number of nucleotide differences(K) Shaanxi 12 38 6 0.758 ± 0.122 0.00073 ± 0.00023 10.576 The ML phylogenetic tree was generated using the whole mitochondrial DNA sequence of R. microplus (Fig. 4 ). R. sanguineus , serving as an outgroup, was positioned at the highest point of the phylogenetic tree. The ML phylogenetic tree, derived from the whole mitochondrial genome, categorized 138 R. microplus specimens into three distinct clades: T1 (from southern China), T2 (from the Yunnan region), and T3 (from other locations). The group of R. microplus in Shaanxi was categorized into the T3 clade, which exhibited the highest similarity to the Chongqing group. The phylogenetic trees derived from the entire genome of autosomes and mitochondria exhibited discrepancies, which were the same as the results of a previous study [ 16 ]. Population selection analysis of R. microplus in Shaanxi This study employed two methods, nucleotide diversity analysis (π) and IHS, to screen the positive genes of R. microplus in Shaanxi. The candidate regions were determined by selecting the top 1% from each of the two selection methods. After annotation, a total of 410 genes were obtained using the π method and 215 genes were obtained using the IHS method (Fig. 5 , Fig. 6 ). By identifying the common genes found in the top 1% of candidates from both techniques, a total of 22 genes were ultimately retrieved. Within this set of genes, we identified LOC119160966, which is associated with defence against pesticides, LOC119167678, which is involved in the transport of metal ions, and LOC119161455, which is related to antioxidant activity. Discussion This paper conducted a comprehensive analysis of the whole genomic sequence of the R. microplus in Shaanxi, China. A total of 131.95 million SNPs and approximately 15.29 million InDels were detected from 11 autosomes. The presence of population SNPs and InDels allows for the investigation of the specific traits of R. microplus in the Shaanxi region. An investigation of the population genetic structure of R. microplus showed that the Chinese population of R. microplus might be essentially categorised into three branches: the central branch, the southern branch, and the southwestern branch. Among them, the Shaanxi group was divided into the southwestern branch, which was found to be the most similar to the population from Chongqing. This similarity aligns with the geographical proximity between the two regions. The findings from PCA and admixture analysis provided additional evidence that Chinese R. microplus can be categorized into three distinct branches. This suggests that R. microplus has experienced a certain level of intraspecific differentiation, and different populations may have adopted diverse strategies for environmental adaptation and evolutionary progression. This implies that it may be necessary to implement varying strategies to manage and eradicate R. microplus in different geographical areas. An investigation of population genetic diversity was performed on various populations of R. microplus . The results revealed that the observed heterozygosity in the central, southern, and southwestern populations was lower than the expected heterozygosity. Tajima's D deviated from 0 in all groups of R. microplus in China, suggesting the presence of natural selection. In addition, the three populations exhibited a consistently high level of heterozygosity (observed heterozygosity: 0.2–0.28). This can reflect the genetic diversity of the populations [ 26 – 27 ]. It is worth noting that higher levels of heterozygosity are associated with greater adaptive capacity to diverse environmental conditions [ 28 ]. A study on the butterfly and the diamondback moth revealed that diamondback moths with heterozygous resistance to Bacillus thuringiensis (Bt) exhibited notable resistance to low dosages of Bt [ 29 ]. The butterfly that was observed in urban environments had reduced heterozygosity [ 27 ]. The greater heterozygosity seen in the R. microplus in this study could potentially be a crucial genetic element enabling their adaptation to diverse habitats in different locales, as well as their resistance to several insecticides. An analysis was conducted on the genetic structure of R. microplus populations in various regions of China using the mitochondrial whole genome. The results showed that the phylogenetic tree constructed from the mitochondrial whole genome divided the tick populations into three groups. However, there were variations in the classifications when compared to the autosomal whole genome, which aligned with previous research findings. The variation in population structure and migration tactics between the paternal and maternal populations could account for this disparity [ 16 ]. The research performed a selection analysis on the SNP data of R. microplus in Shaanxi to see what positive selection signals would present in this group. The study discovered a gene, namely LOC119160966 (nose resistant to fluoxetine protein 6-like). Research has demonstrated that the nose resistant to fluoxetine protein 6 (NRF6) has a role in the absorption and transport of various molecules, and is increased in Harmonia axyridis ’ reaction to sulfamethoxazole pesticides [ 30 ]. NRF6 may be a defensive molecule that H. axyridis commonly depends on when they encounter insecticides. Additionally, the study suggests that NRF6 can possibly function as an action molecule in the resistance of Shaanxi R. microplus to pesticides. Prior research has demonstrated that bloodsucking ticks have undergone significant gene loss in relation to the production and breakdown of heme, rendering exogenous heme essential for bloodsucking ticks. Thus, there is speculation that bloodsucking ticks can obtain and carry external heme and iron for important physiological functions [ 16 ]. Ticks must maintain redox equilibrium to prevent damage to themselves, as free heme and iron facilitate the generation of reactive oxygen species (ROS). LOC119167678 (metal cation symporter ZIP14-like), is a member of the ZIP family. This gene is responsible for facilitating the cellular uptake of essential divalent metals like zinc, iron, and manganese, as well as the hazardous heavy metal cadmium [ 31 ]. In terms of antioxidant activity, the study discovered a gene called LOC119161455 (glucose peroxidation like). Glutathione peroxidase is a prominent antioxidant enzyme responsible for neutralizing reactive oxygen species (ROS) within cells, hence preventing oxidative stress in the body. This protective mechanism safeguards tissues, reduces oxidative damage, and ultimately lowers the mortality rate of organisms [ 32 ]. The study suspects that ZIP14-like and glutathione peroxidase-like play a role in iron transport and acquisition in the Shaanxi R. microplus . Abbreviations R. microplus : Rhipicephalus microplus : R. sanguineus : Rhipicephalus sanguineus ; SNPs: single nucleotide polymorphisms; InDels: insertions/deletions; LD decay： linkage disequilibrium decay; NJ: Neighbor-joining; PCA: Principle component analysis; ML: Maximum likelihood; π: Nucleotide diversity; IHS: Integrated Haplotype Score; Bt: Bacillus thuringiensis; H. axyridis: Harmonia axyridis; NRF6: nose resistant to fluoxetine protein 6. Declarations Acknowledgments We are grateful to the cooperation of the cattle owners during the process of sampling. We thank the High-Performance Computing of Northwest A&F University for providing computing resources. Funding The present study was partly supported by the National Key Research and Development Program of China (grant number No. 2023YFD1801205 to QL) and the China Agriculture Research System of MOF and MARA (grant number No. CARS-37 to CZL). Availability of data and materials The whole-genome re-sequencing data of 126 R. microplus were downloaded from the NGDC database (project ID: PRJCA002242). The reference genome BIME_Rmic_1.3 ( R. microplus ) were downloaded from NCBI (project ID: PRJNA633311). Authors’ contributions QL, CZL and FWW conceived and designed the experiments. YYM, TYL and QL collected the samples. YYM and YYY performed the experiments. YYM performed the sequences analyses. YYM wrote this paper. All authors read and approved the final manuscript. Ethics approval This study was conducted strictly according to the legal requirements of guide for the Care and Use of Laboratory Animals of the Ministry of Health, China and approved by the Research Ethics Committee of Northwest A&F University. Sampling was permitted by cattle owners and no specific authority was needed for sample collection. Consent for publication Not applicable. Competing interests The authors declare that they have no conflict of interests. 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Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989;123:585–95. Boca SM, Huang L, Rosenberg NA. On the heterozygosity of an admixed population. J Math Biol. 2020;81:1217–50. Rochat E, Manel S, Deschamps-Cottin M, Widmer I, Joost S. Persistence of butterfly populations in fragmented habitats along urban density gradients: motility helps. Heredity. 2017;119:328–38. Chapman JR, Nakagawa S, Coltman DW, Slate J, Sheldon BC. A quantitative review of heterozygosity-fitness correlations in animal populations. Mol Ecol. 2009;18:2746–65. Raymond B, Wright DJ, Bonsall MB. Effects of host plant and genetic background on the fitness costs of resistance to Bacillus thuringiensis. Heredity 2011;106:281–8. Nawaz M, Hafeez M, Mabubu JI, Dawar FU, Li X, Khan MM, et al. Transcriptomic analysis of differentially expressed genes and related pathways in Harmonia axyridis after sulfoxaflor exposure. Int J Biol Macromol. 2018;119:157–65. Jenkitkasemwong S, Wang CY, Mackenzie B, Knutson MD. Physiologic implications of metal-ion transport by ZIP14 and ZIP8. Biometals. 2012;25:643–55. Tavares CP, Sabadin GA, Sousa IC, Gomes MN, Soares AMS, Monteiro CMO, et al. Effects of carvacrol and thymol on the antioxidant and detoxifying enzymes of Rhipicephalus microplus (Acari: Ixodidae). Ticks Tick Borne Dis. 2022;13:101929. Additional Declarations No competing interests reported. Supplementary Files Abstractimage.png Size data for InDels in the from Shaanxi. Fig.S1.tiff Population genetic diversity analysis of . (a) Population Tajima's D calculation. (b) Population nucleotide diversity calculation. (c) Population LD decay results. Fig.S2.tiff Manhattan plots of two selection methods for the from Shaanxi. (a) Group π calculation analysis. (b) Group IHS calculation analysis. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4519193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314627973,"identity":"2cdee5e5-6262-4c3f-a827-787351cac59c","order_by":0,"name":"Yi-yao Mou","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Yi-yao","middleName":"","lastName":"Mou","suffix":""},{"id":314627974,"identity":"7bde438d-69fe-42e2-b03d-dc91b9db5882","order_by":1,"name":"FU-wen Wang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"FU-wen","middleName":"","lastName":"Wang","suffix":""},{"id":314627975,"identity":"a0198d60-9608-4024-af20-5ed76d185feb","order_by":2,"name":"YU-ying Yang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"YU-ying","middleName":"","lastName":"Yang","suffix":""},{"id":314627976,"identity":"29248270-1198-430a-bc5d-ef72c560980d","order_by":3,"name":"Tian-yuan Liu","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Tian-yuan","middleName":"","lastName":"Liu","suffix":""},{"id":314627977,"identity":"a1bf0a87-c3eb-41bf-8d85-28f8dcef3184","order_by":4,"name":"Chu-zhao Lei","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Chu-zhao","middleName":"","lastName":"Lei","suffix":""},{"id":314627978,"identity":"77d28d18-0dde-4efd-9f8c-2a48e08d4654","order_by":5,"name":"Qing Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYDACduaGAwwMEkBGY+ODD0RpYWaEauE53Gw4g1gtEIZEeps0BzE6DA4zNh4uKLPIk4982CDNwGAnp9tAWEvD4RnnJIoNbyc2GBcwJBubHSBGC2+bROLG2YkNyTMYDiRuI17LzIMNh3lI0jJfgrGxmSgtkiAtPOckEjfwJDYzzjAgwi98x5sPf+Ypq0uc3378+Y8PFXZyBLUogBWwAV0IZhgQUA4C8g1QLRDGKBgFo2AUjAIsAADwIUfjdU6u6wAAAABJRU5ErkJggg==","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-06-03 04:22:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4519193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4519193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58499499,"identity":"a9d56cda-5227-4f0b-bffc-8ab9714321b4","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":126001,"visible":true,"origin":"","legend":"\u003cp\u003eSize data for InDels in the \u003cem\u003eR. microplus\u003c/em\u003e from Shaanxi.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/7ac9b9362f8878d500fe4d1f.png"},{"id":58499497,"identity":"f7c250f2-7cd5-44af-9ab8-e3e3bf25b71d","added_by":"auto","created_at":"2024-06-17 13:05:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":523886,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure analysis of \u003cem\u003eR. microplus\u003c/em\u003e. (a) NJ tree constructed from whole genome SNPs. (b) Principal component analysis (PCA) shows the clustering of \u003cem\u003eR. microplus\u003c/em\u003e, with the X-axis representing PC1 and the Y-axis representing PC2. (c) Admixture analysis, assuming the proportion of ancestral components when K is 2, 3, 4 and 5. Each colour represents an ancestral group.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/3c74121c2c1a36bf5819279a.png"},{"id":58499498,"identity":"8ae529f7-fb31-4650-af28-42b494a3bc2d","added_by":"auto","created_at":"2024-06-17 13:05:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182278,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation genetic diversity analysis of \u003cem\u003eR. microplus\u003c/em\u003e. (a) Population Tajima's D calculation. (b) Population nucleotide diversity calculation. (c) Population LD decay results.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/fbaf07e97734d2008c73fd1a.png"},{"id":58499500,"identity":"3198f9a2-3167-448a-b3b5-197f7d6f4562","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":542246,"visible":true,"origin":"","legend":"\u003cp\u003eMitochondrial ML phylogenetic tree of \u003cem\u003eR. microplus\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/bb1bffa14ec95124c3213afe.png"},{"id":58499503,"identity":"646b7d48-0aed-413b-9dde-7b6f22ad01b1","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":497341,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plots of two selection methods for the \u003cem\u003eR. microplus\u003c/em\u003e from Shaanxi. (a) Group π calculation analysis. (b) Group IHS calculation analysis.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/546b1b78eba363d0e2bfed26.png"},{"id":58500049,"identity":"f6797ef1-1562-40eb-99ec-d183950405e7","added_by":"auto","created_at":"2024-06-17 13:13:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":316561,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram of two selection methods for \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/2e07236feeda6199c79350db.png"},{"id":69139027,"identity":"ccf61399-bc5d-4da5-88ed-289afd3597b3","added_by":"auto","created_at":"2024-11-16 09:53:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2795346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/56ff242c-e26e-4d98-a777-63b9bb6dbf95.pdf"},{"id":58499501,"identity":"1a6eb520-c058-4f09-a24b-f7ede5257696","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":400785,"visible":true,"origin":"","legend":"Size data for InDels in the from Shaanxi.","description":"","filename":"Abstractimage.png","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/9ded29e4d5f21c4322ef9a96.png"},{"id":58499504,"identity":"b1a5c5db-5217-475f-9063-7cf41f4c5902","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4853160,"visible":true,"origin":"","legend":"Population genetic diversity analysis of . (a) Population Tajima's D calculation. (b) Population nucleotide diversity calculation. (c) Population LD decay results.","description":"","filename":"Fig.S1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/41ab6b24de879731a15f11f1.tiff"},{"id":58499505,"identity":"7c7413e3-6374-4c02-b272-1c593ad06d15","added_by":"auto","created_at":"2024-06-17 13:05:11","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":789976,"visible":true,"origin":"","legend":"Manhattan plots of two selection methods for the from Shaanxi. (a) Group π calculation analysis. (b) Group IHS calculation analysis.","description":"","filename":"Fig.S2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/114a08600d40d3464c92f838.tiff"},{"id":58500050,"identity":"2fbdca80-8695-4630-8044-312a6b12746c","added_by":"auto","created_at":"2024-06-17 13:13:11","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15395,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4519193/v1/cabcc6525f984c68fa5ac90c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing genomic diversity and selection signatures of Rhipicephalus microplus in Shaanxi, China based on whole-genome sequences","fulltext":[{"header":"Background","content":"\u003cp\u003eBeing the arthropod to be recognized as a carrier of disease-causing agents [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], ticks belong to the phylum Arthropoda and the order Parasitiformes within the class Arachnida. Specifically, they are classified under the superorder Ixodoidea [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition to being a specialized group of bloodsucking arthropods, ticks are also well known for being ectoparasites and transmitters of several diseases that affect humans and animals. They are regarded as the primary arthropod vectors for human and domestic animal disease infections globally, along with mosquitoes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Ticks have extremely diverse hosts and are widely distributed throughout the world. The distribution of ticks is influenced by multiple factors, including seasons, climatic conditions, vegetation distribution, and host animals [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ticks can transfer a wide range of disease-causing microorganisms, including bacteria, viruses, protozoa, and parasites, to livestock, animals, and humans. Furthermore, there is a continuous rise in the variety of infections they harbour. This has a significant impact on both the economic progress of animal husbandry and the overall public health of humans [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChina's extensive land area and diverse natural geographical conditions result in a vast range of tick species and notable regional variations in their distribution. In China, there have been documented hard ticks and soft ticks, totaling 9 genera and 124 species. This includes 26 species in the Ixodes family, 10 species in the \u003cem\u003eAmblyoma\u003c/em\u003e genus, 42 species in the \u003cem\u003eHaemaphysalis\u003c/em\u003e genus, 15 species in the \u003cem\u003eDermacenter\u003c/em\u003e genus, 3 species in the \u003cem\u003eAnomalohimalaya\u003c/em\u003e genus, 8 species in the \u003cem\u003eRhopicephalus\u003c/em\u003e genus, 7 species in the \u003cem\u003eHyalomma\u003c/em\u003e genus. Additionally, there are 4 species in the \u003cem\u003eOrnithodoros\u003c/em\u003e genus and 9 species in the \u003cem\u003eArgas\u003c/em\u003e genus within the family of soft ticks. \u003cem\u003eRhipicephalus microplus\u003c/em\u003e is a member of the hard tick family and the Rhipicephalus genus. It has been documented to harbour a maximum of 31 different types of disease-causing microorganisms [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], formerly known as \u003cem\u003eBoophilus microplus\u003c/em\u003e, and has a single-host life cycle. Because it primarily bites on bovine animals, it is popularly referred to as bovine lice in China.\u003c/p\u003e \u003cp\u003eHigh-throughput sequencing has proven successful in acquiring whole-genome sequences of ticks, representing a significant advancement [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study involved the analysis of 138 sets of whole genome resequencing data from ticks (12 \u003cem\u003eR. microplus\u003c/em\u003e from Shaanxi, China). The aim was to assess the features of single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) in the entire genome of \u003cem\u003eR. microplus\u003c/em\u003e from the Shaanxi region. Additionally, it offers valuable insights for the examination of genetic variation and adaptive evolution in the \u003cem\u003eR. microplus\u003c/em\u003e. Gaining knowledge about the fundamental data of tick genomes and genetic diversity will provide new opportunities and establish a theoretical basis for studying tick biology, interactions between vectors and pathogens, disease transmission, and ways for controlling it.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenomic data gathering and adjustment\u003c/h2\u003e \u003cp\u003eThis study utilized a total of 138 whole genome resequencing data of the \u003cem\u003eR. microplus\u003c/em\u003e. A total of 126 whole-genome resequencing datasets were acquired from the NGDC database (Project ID: PRJCA002242). Ticks were gathered from free-ranging cattle herds in Hanzhong City, Shaanxi Province, China, between March 2022 and August 2023. Ticks were visually examined and identified using a stereomicroscope based on their physical characteristics. A total of 12 specimens of \u003cem\u003eR. microplus\u003c/em\u003e were carefully chosen and dispatched to BGI to construct a library of the organism's whole genome and subsequently conducted sequencing. The Illumina second-generation sequencing method (dual-end sequencing, read length: 150 bp each) was chosen for this purpose. The reference genome and gene annotation file were obtained from NCBI (BIME_Rmic_1.3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRead Mapping and SNP site detection annotation\u003c/h2\u003e \u003cp\u003eThe study employed the Trimmomatic software [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to do quality control on 138 whole genome resequencing data. The research utilized the specified parameters to exclude low-quality reads (Leading: 20, Trailing: 20, Sliding Window: 3:15, Average Quality: 20, Minimum Length: 35, TopHred33). BWA-mem [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used to align the quality control resequencing data to the reference genome of \u003cem\u003eR. microplus\u003c/em\u003e. The aligned data was then converted into bam files using Samtools software. The bam files were sorted and deduplicated using Picard software. The quality of the mapping rate and sequencing depth of the samples were evaluated using Qualimap software. The Genome Analysis Toolkit (GATK, version 3.8) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] (using the HaplotypeCaller, GenotypeGVCFs, and Select Variants modules) was employed to identify single nucleotide sequences. The modules were utilized to find single nucleotide polymorphisms (SNPs) and filtered all SNPs (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). This process allowed for the identification of high-quality SNPs. The SNPs discovered during the examination of Shaanxi \u003cem\u003eR. microplus\u003c/em\u003e were annotated and categorized using the SnpEff software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The annotated results encompass introns, untranslated regions (5'UTR or 3'UTR), upstream, downstream, splice variations (including splice acceptor, splice donor, and splice region), and intergenic regions. Furthermore, SnpEFF can anticipate the consequences of mutations occurring in coding regions, including synonymous or non-synonymous amino acid changes, codon gains or losses, as well as stop codon gains and losses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDetection of InDels\u003c/h2\u003e \u003cp\u003eThe study utilized GATK to isolate InDels from the data of Shaanxi \u003cem\u003eR. microplus\u003c/em\u003e and subsequently filtered it (QD\u0026thinsp;\u0026lt;\u0026thinsp;2.0, FS\u0026thinsp;\u0026gt;\u0026thinsp;200.0, ReadPosRankSum \u0026lt;-20.0, SOR\u0026thinsp;\u0026gt;\u0026thinsp;10.0, InbreedingCoeff \u0026lt;-0.8). The InDels were annotated and classified using the SnpEff software. The functional categories exclusively used for InDel were conservative inframe deletions and insertions, disruptive inframe deletions and insertions, bidirectional gene fusions, and frameshift variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of population genetic structure\u003c/h2\u003e \u003cp\u003eThe population genetic structure of \u003cem\u003eR. microplus\u003c/em\u003e was accurately identified using a series of software tools. First, the Vcftools software was employed to filter SNPs (\u0026minus;\u0026thinsp;maf 0.05). Then, Plink software was used to perform a neutral LD screening of the SNP data (\u0026minus;\u0026thinsp;indep airwise 50 10 0.2). Finally, Plink software was utilized to analyze the data and construct a genetic distance matrix. To construct a phylogenetic tree using the Neighbor-Joining method, MEGA11 software was employed. Additionally, the online software iTOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de\u003c/span\u003e\u003cspan address=\"https://itol.embl.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to Beautify the Neighbor-joining (NJ) phylogenetic tree. The principal component Analysis (PCA) was conducted using the Smartpca module in the EIGENSOFT software [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] to analyze unlinked SNPs. The resulting graphs were drawn using the ggplot2 package in the R software. Admixture analysis was conducted using the ADMIXTURE software, followed by graph visualization using TBtools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of population genetic diversity\u003c/h2\u003e \u003cp\u003eTo examine the genetic variation within the \u003cem\u003eR. microplus\u003c/em\u003e population, the study evaluated whole genome SNP filtered data (\u0026minus;\u0026thinsp;maf 0.05) to assess population heterozygosity, Tajima's D, nucleotide diversity, and linkage disequilibrium decay (LD decay). The heterozygosity of various geographic populations of \u003cem\u003eR. microplus\u003c/em\u003e was computed using the Plink software (\u0026minus;\u0026thinsp;hardy). Additionally, Tajima's D (\u0026minus;\u0026thinsp;TajimaD 50000) and nucleotide diversity calculations (\u0026minus;\u0026thinsp;window-pi 50000\u0026thinsp;\u0026minus;\u0026thinsp;window --pi-step 20000) for different geographic populations were conducted separately using the Vcftools software. The outcomes were visualized using the Origin software. The primary function of the PopLDdecay software was to examine the LD decay across several populations. Additionally, the Plot_MultiPop.pl script was utilized to generate a graphical representation of the LD decay curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of genetic diversity in the whole mitochondrial genome\u003c/h2\u003e \u003cp\u003eThe mitochondrial genome of each individual was extracted from the 138 whole-genome bam files using the Samtools software. Subsequently, the SamToFastq module in the Picard software was utilized to convert the mitochondrial whole genome bam files into fastq format. Ultimately, the Mapping Iterative Assembler v1.0 (MIA) was employed to align the mitochondrial whole genome fastq files to the reference genome of \u003cem\u003eR. microplus\u003c/em\u003e mitochondrial DNA. The study obtained the assembled mitochondrial whole genome sequences and conducted the muscle analysis. A mitochondrial phylogenetic tree was created using the maximum likelihood (ML) method with the IQ-TREE software, utilizing the whole mitochondrial genome of \u003cem\u003eRhipicephalus sanguineus\u003c/em\u003e (AF081829.1) acquired from GenBank as the outgroup.\u003c/p\u003e \u003cp\u003eThe genetic diversity characteristics of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi were calculated using the Shaanxi mitochondrial whole genome data in the DnaSP v6.12.03 software [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of positive genes for \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi\u003c/h2\u003e \u003cp\u003eThe identified genetic signals were screened using two methods: nucleotide diversity analysis (π) and integrated haplotype score (IHS). The value of π was computed using the Vcftools software, with a window size of 50 K and a step size of 20 K (\u0026minus;\u0026thinsp;fst window size 50000\u0026thinsp;\u0026minus;\u0026thinsp;fst window step 20000\u0026thinsp;\u0026minus;\u0026thinsp;maf 0.05). IHS employed the Beagle and Selscan software for performing computations. The candidate genes were obtained by overlapping the results (top 1%) obtained from two methods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of autosomal whole genome variation (identification of SNPs and InDels)\u003c/h2\u003e\n \u003cp\u003eTo assess the features of autosomal SNPs and InDels in the complete genome of the \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi, the study conducted a comparison between the whole genome resequencing data of 12 \u003cem\u003eR. microplus\u003c/em\u003e samples from Shaanxi and the reference genome of the \u003cem\u003eR. microplus\u003c/em\u003e. The average mapping rate was found to be 95.4%, and the average sequencing depth was approximately 10.51\u0026times; (Table S1).\u003c/p\u003e\n \u003cp\u003eFollowing variant detection and quality control, a total of 131,950,109 SNPs and 15,289,151 InDels (insertion: 6,932,848; deletion: 8,356,303) were found. Upon further analysis of the SNP and InDel counts on each chromosome, it was observed that chromosome 2 had a relatively low number of detected SNPs and InDels. In contrast, the remaining chromosomes followed a pattern where the number of SNPs and InDels increased with the length of the chromosome (Fig. S1, Fig. S2). Mostly, the size of the InDels ranges from 1 to 4 bp (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe SNPs and InDels annotation findings for the Shaanxi group can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The total number of annotations was significantly more than the number of SNPs and InDel, which was due to the possibility of multiple annotation results for the same locus [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study identified a total of 85,779,785 SNPs in intergenic regions, 67,459,126 SNPs in intronic regions, and 67,543,526 SNPs as non-coding transcriptional variants. Additionally, 17,077,474 and 17,063,069 SNPs were located in the upstream and downstream regions. The study identified 426,539 missense variations and 150,890 splice region variants, which were classified as high and moderate effect SNPs, while the study also detected other SNPs such as stop gained (6,200), start lost (1,004), and stop lost (1,086).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003cbr\u003e\u003cbr\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAll SNPs annotation data for the Shaanxi group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 prime UTR variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,186,110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 prime UTR premature start codon gain variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29,410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 prime UTR variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200,436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,063,069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInitiator codon variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergenic region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85,779,785\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntragenic variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,753,502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntron variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67,459,126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMissense variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e426,539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon coding transcript exon variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220,926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon coding transcript variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67,543,526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice acceptor variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice donor variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice region variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150,890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStart lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop gained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop retained variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e887\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSynonymous variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e735,715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17,077,474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAll InDels annotation data for the Shaanxi group\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 prime UTR truncation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 prime UTR variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138,672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 prime UTR truncation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 prime UTR variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25,691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBidirectional gene fusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConservative inframe deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConservative inframe insertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisruptive inframe deletion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisruptive inframe insertion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDownstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,150,639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExon loss variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeature ablation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrameshift variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22,585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGene fusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergenic region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,546,542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntragenic variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203,257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntron variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,678,633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon coding transcript exon variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19,976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon coding transcript variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,586,268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice acceptor variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice donor variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSplice region variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStart lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStart retained variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop gained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop lost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStop retained variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTranscript ablation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpstream gene variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,184,473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOut of the total number of InDels, 9,546,542 InDels were located in intergenic regions, 8,678,633 InDels were located in intronic regions and 8,586,268 InDels were noncoding transcriptional variations. A total of 6,140 and 3,789 InDels were identified and classified as Disruptive frame InDels and Conservative frame InDels. Furthermore, 22,585 InDels were frameshift variations, and 21,931 InDels were splice sites (including splice region variants, splice donor variants and splice acceptor variants).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eWhole genome genetic structure of \u003cem\u003eR. microplus\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe study conducted phylogenetic tree construction, Principal component analysis (PCA), and admixture analysis on the SNP data of 138 samples to investigate the relationship between various regional populations of \u003cem\u003eR. microplus\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eApplying the NJ approach for constructing a phylogenetic tree revealed that the \u003cem\u003eR. microplus\u003c/em\u003e in China could be categorized into three distinct branches (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The southern \u003cem\u003eR. microplus\u003c/em\u003e population (Zhejiang, Jiangxi, Hainan, Guangxi, Guangdong, Fujian province, China) are grouped in one branch, while the southwestern \u003cem\u003eR. microplus\u003c/em\u003e population (Chongqing, Guizhou, Yunnan province, China) are grouped in another branch. Similarly, the central \u003cem\u003eR. microplus\u003c/em\u003e population (Hunan, Hubei, Anhui province, China) are also grouped in a separate branch. The classification of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi placed it in the southwestern population, and it was found to be most similar to \u003cem\u003eR. microplus\u003c/em\u003e in Chongqing, which aligned with the geographical proximity between the two regions.\u003c/p\u003e\n \u003cp\u003eThe PCA of Chinese \u003cem\u003eR. microplus\u003c/em\u003e populations demonstrated that PC1 effectively distinguished the Shaanxi and southwestern populations from the southern and central populations. Additionally, PC2 successfully differentiated the southern population from the other populations (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). The Shaanxi and southwestern populations were grouped and the study results aligned with the phylogenetic tree.\u003c/p\u003e\n \u003cp\u003eBy employing Admixture analysis of 138 samples, it was discovered that the central population exhibited distinct ancestral origins when K\u0026thinsp;=\u0026thinsp;2 and K\u0026thinsp;=\u0026thinsp;3. In contrast, the southern and southwestern populations displayed two ancestral origins at K\u0026thinsp;=\u0026thinsp;2 and three ancestral origins at K\u0026thinsp;=\u0026thinsp;3, while The Shaanxi group consistently exhibited two ancestral origins. At K\u0026thinsp;=\u0026thinsp;4, the majority of the central population maintained its distinct lineage, whereas the southern and southwestern populations had four ancestral origins and the Shaanxi group had three ancestral origins. At K\u0026thinsp;=\u0026thinsp;5, the central population had two ancestor components, while the southern and southwestern populations shared the same ancestral components and the Shaanxi group had three ancestral components (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). The cv value was the smallest when K\u0026thinsp;=\u0026thinsp;4 (CV error\u0026thinsp;=\u0026thinsp;0.3763). Thus, it was postulated that the Chinese \u003cem\u003eR. microplus\u003c/em\u003e population originated from four ancestral groups.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eThe whole genome genetic diversity of the \u003cem\u003eR. microplus\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThis study utilized whole-genome SNP data to examine the genetic diversity within \u003cem\u003eR. microplus\u003c/em\u003e populations. The analysis focused on heterozygosity, Tajima\u0026apos;s D, nucleotide diversity (\u0026pi;) and LD decay in three distinct geographic populations: central, southern, and southwestern. The group of Shaanxi was encompassed into the population of the southwestern region.\u003c/p\u003e\n \u003cp\u003eThe findings indicated that the southern population exhibited the lowest levels of both predicted and observed heterozygosity, whereas the central population displayed the highest levels of both expected and observed heterozygosity. The observed heterozygosity findings of the three populations were discovered to be lower than the expected heterozygosity results (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests the presence of selection within the populations, resulting in a deviation in genotype frequency.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCalculation of heterozygosity in \u003cem\u003eR. microplus\u003c/em\u003e populations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObserved heterozygosity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpected heterozygosity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCentral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2902\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthwest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTajima\u0026apos;s D value serves as an indicator of the presence of natural selection among populations, as long as it deviates from 0 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The result demonstrated that the Tajima\u0026apos;s D values of the three geographic populations all exhibited deviation from 0 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea), suggesting the existence of natural selection pressure within these populations. Nucleotide diversity is a metric used to quantify the extent of polymorphism within a specific population. A higher value of \u0026pi; corresponded to a greater nucleotide diversity in the population. The Figure demonstrated that the nucleotide diversities of the three populations were nearly the same. The southwestern population had the highest nucleotide diversity (\u0026pi;\u0026thinsp;=\u0026thinsp;8.01 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e), the southern population had the lowest (\u0026pi;\u0026thinsp;=\u0026thinsp;7.66 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e), and the central population fell in between (\u0026pi;\u0026thinsp;=\u0026thinsp;7.92 \u0026times; 10\u003csup\u003e3)\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Each of the three regional groups exhibited rapid LD decay, with the southwestern population demonstrating the most rapid decay and the lowest r\u003csup\u003e2\u003c/sup\u003e value, approximately 0.03. The central population\u0026apos;s r\u003csup\u003e2\u003c/sup\u003e was approximately 0.04 during the stabilization process. In contrast, the southern population experienced the slowest drop in LD. At stabilization, its r\u003csup\u003e2\u003c/sup\u003e value is roughly 0.05 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec). The variations in the LD decay patterns also suggested that there were distinctions among the populations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eGenetic diversity of mitochondrial whole genome in Shaanxi \u003cem\u003egroup\u003c/em\u003e\u003c/h2\u003e\n \u003cp\u003eThe \u003cem\u003eR. microplus\u003c/em\u003e\u0026rsquo; mitochondrial DNA genome measured 14936 bp in total length. 6 haplotypes were identified in the 12 whole mitochondrial genome sequences of the Shaanxi \u003cem\u003eR. microplus\u003c/em\u003e (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The Shaanxi group exhibited haplotype diversity of 0.758\u0026thinsp;\u0026plusmn;\u0026thinsp;0.122 and nucleotide diversity of 0.00073\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00023.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenetic diversity of mitochondrial genomes in \u003cem\u003eR. microplus\u003c/em\u003e from Shaanxi, China\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable sites\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of haplotypes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHaplotype diversity(h)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNucleotide diversity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage number of nucleotide differences(K)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShaanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.758\u0026thinsp;\u0026plusmn;\u0026thinsp;0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00073\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.576\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe ML phylogenetic tree was generated using the whole mitochondrial DNA sequence of \u003cem\u003eR. microplus\u003c/em\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eR. sanguineus\u003c/em\u003e, serving as an outgroup, was positioned at the highest point of the phylogenetic tree. The ML phylogenetic tree, derived from the whole mitochondrial genome, categorized 138 \u003cem\u003eR. microplus\u003c/em\u003e specimens into three distinct clades: T1 (from southern China), T2 (from the Yunnan region), and T3 (from other locations). The group of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi was categorized into the T3 clade, which exhibited the highest similarity to the Chongqing group. The phylogenetic trees derived from the entire genome of autosomes and mitochondria exhibited discrepancies, which were the same as the results of a previous study [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003ePopulation selection analysis of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi\u003c/h2\u003e\n \u003cp\u003eThis study employed two methods, nucleotide diversity analysis (\u0026pi;) and IHS, to screen the positive genes of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi. The candidate regions were determined by selecting the top 1% from each of the two selection methods. After annotation, a total of 410 genes were obtained using the \u0026pi; method and 215 genes were obtained using the IHS method (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). By identifying the common genes found in the top 1% of candidates from both techniques, a total of 22 genes were ultimately retrieved. Within this set of genes, we identified LOC119160966, which is associated with defence against pesticides, LOC119167678, which is involved in the transport of metal ions, and LOC119161455, which is related to antioxidant activity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper conducted a comprehensive analysis of the whole genomic sequence of the \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi, China. A total of 131.95\u0026nbsp;million SNPs and approximately 15.29\u0026nbsp;million InDels were detected from 11 autosomes. The presence of population SNPs and InDels allows for the investigation of the specific traits of \u003cem\u003eR. microplus\u003c/em\u003e in the Shaanxi region.\u003c/p\u003e \u003cp\u003eAn investigation of the population genetic structure of \u003cem\u003eR. microplus\u003c/em\u003e showed that the Chinese population of \u003cem\u003eR. microplus\u003c/em\u003e might be essentially categorised into three branches: the central branch, the southern branch, and the southwestern branch. Among them, the Shaanxi group was divided into the southwestern branch, which was found to be the most similar to the population from Chongqing. This similarity aligns with the geographical proximity between the two regions. The findings from PCA and admixture analysis provided additional evidence that Chinese \u003cem\u003eR. microplus\u003c/em\u003e can be categorized into three distinct branches. This suggests that \u003cem\u003eR. microplus\u003c/em\u003e has experienced a certain level of intraspecific differentiation, and different populations may have adopted diverse strategies for environmental adaptation and evolutionary progression. This implies that it may be necessary to implement varying strategies to manage and eradicate \u003cem\u003eR. microplus\u003c/em\u003e in different geographical areas.\u003c/p\u003e \u003cp\u003eAn investigation of population genetic diversity was performed on various populations of \u003cem\u003eR. microplus\u003c/em\u003e. The results revealed that the observed heterozygosity in the central, southern, and southwestern populations was lower than the expected heterozygosity. Tajima's D deviated from 0 in all groups of \u003cem\u003eR. microplus\u003c/em\u003e in China, suggesting the presence of natural selection. In addition, the three populations exhibited a consistently high level of heterozygosity (observed heterozygosity: 0.2\u0026ndash;0.28). This can reflect the genetic diversity of the populations [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It is worth noting that higher levels of heterozygosity are associated with greater adaptive capacity to diverse environmental conditions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A study on the butterfly and the diamondback moth revealed that diamondback moths with heterozygous resistance to \u003cem\u003eBacillus thuringiensis\u003c/em\u003e (Bt) exhibited notable resistance to low dosages of Bt [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The butterfly that was observed in urban environments had reduced heterozygosity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The greater heterozygosity seen in the \u003cem\u003eR. microplus\u003c/em\u003e in this study could potentially be a crucial genetic element enabling their adaptation to diverse habitats in different locales, as well as their resistance to several insecticides.\u003c/p\u003e \u003cp\u003eAn analysis was conducted on the genetic structure of \u003cem\u003eR. microplus\u003c/em\u003e populations in various regions of China using the mitochondrial whole genome. The results showed that the phylogenetic tree constructed from the mitochondrial whole genome divided the tick populations into three groups. However, there were variations in the classifications when compared to the autosomal whole genome, which aligned with previous research findings. The variation in population structure and migration tactics between the paternal and maternal populations could account for this disparity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe research performed a selection analysis on the SNP data of \u003cem\u003eR. microplus\u003c/em\u003e in Shaanxi to see what positive selection signals would present in this group. The study discovered a gene, namely LOC119160966 (nose resistant to fluoxetine protein 6-like). Research has demonstrated that the nose resistant to fluoxetine protein 6 (NRF6) has a role in the absorption and transport of various molecules, and is increased in \u003cem\u003eHarmonia axyridis\u003c/em\u003e\u0026rsquo; reaction to sulfamethoxazole pesticides [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. NRF6 may be a defensive molecule that \u003cem\u003eH. axyridis\u003c/em\u003e commonly depends on when they encounter insecticides. Additionally, the study suggests that NRF6 can possibly function as an action molecule in the resistance of Shaanxi \u003cem\u003eR. microplus\u003c/em\u003e to pesticides.\u003c/p\u003e \u003cp\u003ePrior research has demonstrated that bloodsucking ticks have undergone significant gene loss in relation to the production and breakdown of heme, rendering exogenous heme essential for bloodsucking ticks. Thus, there is speculation that bloodsucking ticks can obtain and carry external heme and iron for important physiological functions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Ticks must maintain redox equilibrium to prevent damage to themselves, as free heme and iron facilitate the generation of reactive oxygen species (ROS). LOC119167678 (metal cation symporter ZIP14-like), is a member of the ZIP family. This gene is responsible for facilitating the cellular uptake of essential divalent metals like zinc, iron, and manganese, as well as the hazardous heavy metal cadmium [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In terms of antioxidant activity, the study discovered a gene called LOC119161455 (glucose peroxidation like). Glutathione peroxidase is a prominent antioxidant enzyme responsible for neutralizing reactive oxygen species (ROS) within cells, hence preventing oxidative stress in the body. This protective mechanism safeguards tissues, reduces oxidative damage, and ultimately lowers the mortality rate of organisms [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The study suspects that ZIP14-like and glutathione peroxidase-like play a role in iron transport and acquisition in the Shaanxi \u003cem\u003eR. microplus\u003c/em\u003e.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cem\u003eR. microplus\u003c/em\u003e: \u003cem\u003eRhipicephalus microplus\u003c/em\u003e: \u003cem\u003eR.\u003c/em\u003e \u003cem\u003esanguineus\u003c/em\u003e:\u0026nbsp;\u003cem\u003eRhipicephalus sanguineus\u003c/em\u003e;\u0026nbsp;SNPs:\u0026nbsp;single nucleotide polymorphisms;\u0026nbsp;InDels:\u0026nbsp;insertions/deletions;\u0026nbsp;LD decay：\u0026nbsp;linkage disequilibrium decay;\u0026nbsp;NJ:\u0026nbsp;Neighbor-joining;\u0026nbsp;PCA:\u0026nbsp;Principle component analysis; ML:\u0026nbsp;Maximum likelihood;\u0026nbsp;\u0026pi;:\u0026nbsp;Nucleotide diversity;\u0026nbsp;IHS: Integrated Haplotype Score; Bt: \u003cem\u003eBacillus thuringiensis; H. axyridis: Harmonia axyridis;\u0026nbsp;\u003c/em\u003eNRF6: nose resistant to fluoxetine protein 6.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the cooperation of the cattle owners during the process of sampling.\u0026nbsp;We thank the High-Performance Computing of Northwest A\u0026amp;F University for providing computing resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was partly supported by the National Key Research and Development Program of China (grant number No. 2023YFD1801205 to QL) and the China Agriculture Research System of MOF and MARA (grant number No. CARS-37\u0026nbsp;to CZL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome re-sequencing data of 126 \u003cem\u003eR. microplus\u003c/em\u003e were downloaded from the NGDC database (project ID: PRJCA002242). The reference genome BIME_Rmic_1.3 (\u003cem\u003eR. microplus\u003c/em\u003e) were downloaded from NCBI (project ID: PRJNA633311).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQL, CZL and FWW conceived and designed the experiments. YYM, TYL and QL collected the samples. YYM and YYY performed the experiments. YYM performed the sequences analyses. YYM wrote this paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted\u0026nbsp;strictly\u0026nbsp;according to the legal requirements of guide for the Care and Use of Laboratory Animals of the Ministry of Health, China and approved by the Research Ethics Committee of Northwest A\u0026amp;F University.\u0026nbsp;Sampling\u0026nbsp;was\u0026nbsp;permitted by\u0026nbsp;cattle\u0026nbsp;owners\u0026nbsp;and\u0026nbsp;no specific\u0026nbsp;authority\u0026nbsp;was\u0026nbsp;needed for\u0026nbsp;sample collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCholewiński M, Derda M, Hadaś E. 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Ticks Tick Borne Dis. 2022;13:101929.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rhipicephalus microplus, genetic diversity, population structure, whole-genome resequencing, China","lastPublishedDoi":"10.21203/rs.3.rs-4519193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4519193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTicks play a crucial role in transmitting and carrying various disease-causing microorganisms, which poses a significant risk to public health and the growth of the animal farming industry. Research on the whole genome sequence of ticks is consistently progressing due to the ongoing advancement of high-throughput sequencing technologies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study performed whole-genome resequencing on \u003cem\u003eRhipicephalus microplus\u003c/em\u003e obtained from free-range cattle in Hanzhong City, Shaanxi Province. The newly obtained data was then combined with existing whole genome resequencing data of \u003cem\u003eR. microplus\u003c/em\u003e from the NGDC database (project ID: PRJCA002242) for further analysis. The purpose of this analysis was to assess genomic diversity and selection signatures in the Shaanxi group.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study identified single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) in the Shaanxi group. The \u003cem\u003eR. microplus\u003c/em\u003e from China has been classified into three main branches, and there were variations in nucleotide diversity among populations in different places. All populations exhibited a high level of heterozygosity. Additionally, the value of Tajima's D deviated significantly from zero. Upon examining the mitochondrial genetic diversity of the tick, the study observed subtle variations compared to the phylogenetic tree created using the entire autosomal genome. These differences may arise from variances in population structure and migration patterns between the paternal and maternal tick populations. Genes associated with pesticide resistance, metal ion transportation, and antioxidant activity were identified during the selection study of the Shaanxi group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe data acquired from our research holds significance in comprehending the biology of ticks, enhancing our understanding of their disease transmission, and formulating efficient strategies for tick management.\u003c/p\u003e","manuscriptTitle":"Assessing genomic diversity and selection signatures of Rhipicephalus microplus in Shaanxi, China based on whole-genome sequences","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-17 13:05:06","doi":"10.21203/rs.3.rs-4519193/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":"9b2501e1-72a1-4475-96b0-eabb6bb333be","owner":[],"postedDate":"June 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-16T09:53:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-17 13:05:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4519193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4519193","identity":"rs-4519193","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}