Development and Application Validation of a 20K-Liquid Chip for the Qinchuan Cattle

Development and Application Validation of a 20K-Liquid Chip for the Qinchuan Cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Application Validation of a 20K-Liquid Chip for the Qinchuan Cattle Kaiyan Zhang, Jun Ma, Luyang Sun, Dawei Wei, Xue Gao, Weidong Ma, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8742497/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract As a core indigenous beef cattle breed in China, Qinchuan cattle face bottlenecks in genetic improvement due to inadequate adaptability of generic SNP chips and the absence of dedicated molecular detection tools. This study developed and validated a Qinchuan-specific 20K low-density liquid-phase SNP chip based on targeted sequencing genotyping technology. Whole-genome resequencing data from 193 Qinchuan cattle were integrated with data from 274 external reference populations sourced from the European Gene Database. Core loci were selected through genome-wide homozygous fragment analysis and selection signal detection. Following quality control and association studies, 20k core target loci were finalized for chip synthesis. Chip validation demonstrated an average detection rate of 98.48% for core loci, with 99.01% genotypic consistency across technical replicates. Principal component analysis and phylogenetic tree analysis confirmed the chip's ability to accurately distinguish Qinchuan cattle from other breeds. This chip balances low cost and high specificity, providing a dedicated tool for genetic diversity assessment, breed identification, and genomic selection in Qinchuan cattle. It also establishes a technical foundation for innovative utilization of local beef cattle germplasm resources. Qinchuan cattle liquid-phase SNP chip locus screening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Domesticated animals serve as excellent biological models for studying phenotypic development and evolution. Through prolonged human selection and domestication, distinct breeds have developed significant variations in morphological traits, physiological functions, and environmental adaptability [ 1 – 3 ]. Cattle (Bos taurus) represent a vital livestock resource for humanity, exhibiting rich phenotypic diversity. Their complex population evolutionary history and trait inheritance mechanisms have attracted considerable scholarly attention[ 4 ]. Qinchuan cattle, a highly representative local beef breed in China, are renowned for their imposing stature, docile temperament, rapid growth rate, tender meat quality, and strong stress resistance. Their body conformation traits are not only directly linked to economic characteristics such as dressing percentage and meat yield but also embody unique genetic codes shaped by long-term artificial selection and environmental adaptation. Decoding the genetic mechanisms underlying its body conformation holds significant practical value for targeted breeding and germplasm resource innovation. Current research on genetic mechanisms underlying animal body conformation has yielded substantial progress: studies in cattle have identified genes such as IGF1 and GHR as closely linked to body development[ 5 ]; comparative genomics has elucidated key genes regulating equine body conformation[ 6 ]; and population genomics analysis has uncovered novel loci potentially influencing poultry body variation[ 7 ]. The marked differentiation in body size between domesticated animals and their wild ancestors renders them ideal subjects for studying genetic variation in traits. As a core trait in both breeding practice and fundamental research, the genetic mechanisms underlying body size have been extensively explored and characterised[ 8 ]. With the rapid advancement of genomic sequencing technologies, it has become feasible to decipher the gene regulatory networks underlying complex traits such as body size in domesticated animals. For instance current research has successfully elucidated key genetic mechanisms, including cold tolerance adaptationin high-latitude Chinese pigs[ 9 ]and canine adaptation to high-starch diets[ 10 ]. However, compared to these species, genetic research on Qinchuan cattle body conformation remains underdeveloped. Existing universal SNP chips lack coverage of its breed-specific loci, hindering accurate capture of genetic signals for body conformation traits—this limits identification of key functional genes and genomic selection efficiency, constraining breed improvement precision. Single nucleotide polymorphisms (SNPs), as the most prevalent and stable form of genetic variation in genomes, constitute indispensable core molecular markers in genetic research. They exert profound influences on crucial biological processes including organismal growth and development, metabolic regulation, and environmental adaptability[ 11 ]. SNP chips, as the core technology enabling efficient SNP detection, have become indispensable due to their unique advantages of high throughput, high accuracy, and standardisation. They are employed for analysing population structure and admixture proportions, estimating effective population size[ 12 – 13 ], localising quantitative trait loci (QTL)[ 14 – 15 ], conducting genome-wide association studies (GWAS)[ 16 – 17 ], and implementing genomic selection (GS)[ 18 – 20 ]. The advent of targeted sequencing genotyping (GBTS) liquid chips has effectively addressed the limitations of traditional techniques, emerging as a more cost-effective alternative to whole-genome sequencing. Dueto its unique technical design, this approach has now been extensively applied in genetic breeding and germplasm resource innovation studies across various livestock and poultry species, including pigs, cattle, sheep, and chickens[ 21 – 22 ]. The GBTS liquid chip substantially lowers the technical barriers and economic costs of large-scale population genotyping, providinggrassroots breeding units and research institutions with a more accessible technological option[ 23 ]. GenoBaits technology employs liquid-phase probe hybridisation for selective DNA capture, making it more suitable for precise detection at medium-to-high numbers of loci[ 24 – 25 ]. These chip advantages provide efficient and reliable technical tools for genetic breeding in relevant livestock breeds, while also injecting new impetus into the conservation and innovative utilization of germplasm resources from local superior breeds[ 26 – 27 ]. Within Qinchuan cattle research, limited studies have employed generic chips such as the Illumina BovineSNP50 for genetic diversity analysis, however, existing tools exhibit significant limitations.The design for generic chips is based on imported breed genomes, resulting in inadequate coverage of Qinchuan cattle-specific loci and consequently low detection efficiency for loci associated with core economic traits[ 28 ]. Moreover, existing research has not sufficiently elucidated the genetic mechanisms underlying Qinchuan cattle's superior traits, and there is a lack of dedicated molecular detection tools tailored to their unique genetic background. This constrains the precision of genetic diversity assessment and molecular breeding for Qinchuan cattle [ 29 ]. Therefore, this study employed methods including homozygous fragment analysis, selection signal detection, and genome-wide association studies to identify candidate loci. We developed a custom 10K functional locus gene chip. Utilising this chip technology, we conducted efficient detection and analysis of genetic variation in Qinchuan cattle and selected breeding lines. This aims to precisely identify key genes and molecular markers associated with Qinchuan cattle growth, providing scientific tools and valuable data support for elucidating the genetic basis of their superior traits and optimising breeding strategies. 2. Materials and Methods 2.1 Sample Collection and Data Processing In this study, jugular vein blood samples from 95 Qinchuan cattle (Bos taurus) collected by the laboratory in advance at the Shaanxi Qinchuan Cattle Breeding Station were used. Blood DNA was extracted using the standard phenol-chloroform method. Sequencing was performed by BGI using the DNBSEQ-T7 sequencer. Following quality control, gene libraries were constructed with an average fragment size of 300 bp per sample[ 30 ]. Descriptive statistics for nine trait data sets were performed using SPSS. For each trait, sample size, mean, standard deviation, minimum value, maximum value, and coefficient of variation (Coefficient of Variation = Standard Deviation / Mean × 100%). Data normality was verified via Shapiro-Wilk tests (P > 0.05 indicating normal distribution). Outliers (exceeding mean ± 3 standard deviations) were excluded, ultimately retaining complete phenotypic data from 189 Qinchuan cattle for subsequent analysis. To conduct an in-depth analysis of Qinchuan cattle genetic information, we expanded our sample collection scope. We obtained 374 samples from the European genetic database for further analysis, which included Qinchuan cattle that exhibited larger body size and superior traits following selective breeding[ 31 ].Detailed data and grouping are presented in the supplementary tabl e1 . The raw sequencing data (FASTQ format) underwent quality control using Trimmomatic (version 0.38) with parameters set as LEADING:20, TRAILING:20, SLIDINGWINDOW:3:15, AVGQUAL:20, MINLEN:35 to filter low-quality bases (Q < 20) and adapter contamination sequences [ 32 ]. First, raw sequencing data underwent quality control using Trimmomatic (version 0.38). Parameters LEADING:20, TRAILING:20, SLIDINGWINDOW:3:15, AVGQUAL:20, and MINLEN:35 were used to filter out low-quality sequences and adapter contamination[ 33 ]. Subsequently, the processed sequences were aligned to the reference genome using BWA (Burrows-Wheeler Aligner). The aligned files were converted to BAM format via Samtools and sorted according to chromosomal coordinates. Picard Tools were then employed for deduplication, with parameters REMOVE_DUPLICATES=true, CREATE_INDEX=true, and VALIDATION_STRINGENCY=LENIENT set to remove PCR duplicates and generate an index file, preparing for subsequent variant detection analysis. During the variant detection phase, GATK (version 3.8) modules—Haplotype Caller, Genotype GVCFs, and Select Variants—were employed to detect SNPs. Subsequently, the Variant Filtration module was used to filter SNPs based on the following criteria: DP 5272 || QD 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum 3.0 [ 34 ]. Finally, additional quality control of SNPs was performed using BCFtool software[ 35 ], with quality control criteria including genotyping dropout rate 0.05, ultimately retaining 21,183,737 SNPs. 2.2 Core Region Screening PLINK 1.9 software was employed to conduct ROH analysis on sequenced populations of Qinchuan cattle and other subpopulations. Quality control was first performed, excluding SNPs with detection rates < 95%, samples with individual detection rates < 95%, and SNPs with minor allele frequencies < 0.05 or Hardy - Weinberg equilibrium test P-value < 10⁻⁶, and excluded non-chromosomal and duplicate SNPs. ROH detection followed the methodology described in previous studies[ 36 ], employing a sliding window threshold of 0.05, window size of 50 SNPs, permitting 2 missing SNPs and 1 heterozygous SNP per window, with a maximum SNP spacing of 100 kb and minimum density of 1 SNP per 50 kb. The genomic inbreeding coefficient (FROH) was calculated as FROH = ∑LROH / Lgenome, where ∑LROH denotes the sum of lengths of all ROH segments on autosomes, and Lgenome represents the total physical length of autosomes[ 37 ]. Finally, the --homozyg group parameter was employed to merge adjacent ROH segments into larger ROH islands based on predefined ROH parameters. The study employed VCFtools to calculate genetic differentiation indices (Fst) and nucleotide diversity (π) values between Qinchuan cattle populations and other sequenced breeds, retaining only loci unique to Qinchuan cattle. Fst analysis was conducted by calculating weighted FST values across all SNPs within the step size, followed by arithmetic averaging to obtain the mean FST value for all SNPs within the window. π analysis calculated nucleotide diversity values within the window [ 38 ]. Combining Fst and π analysis provides a more comprehensive reflection of population genetic structure and the influence of natural selection. In this study, the top 5% of values were used as a threshold to identify regions under selection, which were subsequently annotated and visualised. To systematically analyse the biological functions of the identified candidate genes, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the online platform DAVID 6.8, with a threshold of p < 0.05. Based on a genome-wide SNP dataset from 193 Qinchuan cattle individuals with somatic trait data, GWAS analyses was conducted for nine quantitative traits. The primary software employed for GWAS was GEMMA [ 39 ], utilising a mixed linear model for analysis. The first principal component and sex matrix were employed as covariates, with significance and suggestive site thresholds set at 5.5 ×10⁻⁸(1/17,937,451) respectively. Manhattan and QQ plots were generated using the CMplot package within the R software[ 40 ]. To further analyse SNP functionality, we employed a Perl script to identify significant SNPs within 100 kb upstream and downstream regions as candidate gene-targeting areas based on Ensembl database screening. Deduplication of previously screened sites was performed, excluding duplicate sites, low-polymorphism sites (MAF 0.01); sites were integrated according to "functional priority" and "genomic uniformity" principles: prioritise retaining SNPs within exonic regions and known functional genes, while ensuring uniform SNP distribution across the genome. This process ultimately identifies core SNPs for chip construction. 2.3 Microarray Synthesis Based on the reference genome Bos taurus (ARS-UCD1.2_Btau5.0.1Y), the genome was partitioned into 26,204 sites using 95,000 bp windows. A sliding window scoring strategy was employed to select key candidate target sites, with specific DNA probes designed for each qualified site. Prioritising core functional sites identified through multidimensional screening, probes were designed with dual-probe coverage for critical sites to enhance capture stability. Probes measured 100–120 bp with 35–65% GC content, optimised via kmer alignment and deep learning models to ensure fewer than five homologous regions (≥ 95% sequence similarity). Optimised probes underwent in vitro transcription with biotin labelling. Labelled probes are mixed at equimolar concentrations to form a homogenised probe pool, supplemented with three exogenous quality control reference probes. The pool concentration is adjusted to 100 ng/µL and stored sealed at -20°C. The chip assembly employed a liquid-phase capture scheme utilising targeted sequencing genotyping (GBTS) technology. The core components comprised: biotin-labelled probe pool, PCR amplification primers, hybridisation buffer, and magnetic bead enrichment reagent. All components were encapsulated according to standardised procedures. Each batch of chips included one positive control (Qinchuan cattle DNA sample with known genotype) and one negative control (no template DNA) for experimental quality control. 2.4 Chip Validation To comprehensively evaluate chip performance, genotyping was performed on 135 samples (see supplementary table for details): Genomic DNA libraries were constructed using the GenoBaits DNA-seq library preparation kit. The library was hybridised with chip probes at 65°C for 16 hours; Dynabeads MyOne Streptavidin C1 were added to capture hybridised fragments. Following PCR amplification and magnetic bead purification, PE150 sequencing was performed on the MGISEQ-2000 platform. Sequencing data quality control was conducted using FastQC, with reference genome alignment via BWA and SNP genotyping performed using GATK. The SNP detection rate for each sample was calculated for each sample (number of detected SNPs / total core SNPs × 100%). For Qinchuan cattle samples undergoing whole-genome resequencing, compare chip-detected SNP genotypes with resequencing data genotypes to calculate concordance rate (number of identical genotype sites / total detected sites × 100%). Statistically analyse the MAF distribution and proportion of polymorphic sites among core SNPs in the sample (number of SNPs with MAF > 0.01 / total core SNPs × 100%), investigating the proportion of target polymorphic sites. Principal component analysis was performed using PLINK software with the --pca 10 parameter to extract the top 10 principal components. The calculation was based on allele frequencies at core target loci, generating principal component score files and variance explained by principal components files. The ggplot2 package in R software was employed to read the principal component score files and plot scatter plots for the top principal components[ 41 ]. Additionally, VCF2Dis was employed to compute genetic differences between samples, generating a distance matrix for constructing a neighbour-neighbour tree. SNP data were processed to account for minor missing sites, preventing sample exclusion due to localised data gaps. The ITOLsoftware package was used to read the phylogenetic tree file and refine the tree[ 42 ]. 3. Results 3.1 Core Site Screening Results In the statistics of ROH segment across populations(Fig. 1 A and 1 B), distinct characteristics emerged in the proportion and quantity of short The proportion of short ROH segments (0.5–1 Mb) was 76.61% (44,475 segments) in the sampled Qinchuan cattle, 79.40% ( 18,116 segments), and 14.97% (6,832 segments) in the medium-length ROH segments (1–2 Mb) in the selected Qinchuan cattle, significantly the proportion of 1-2Mb medium-length ROH segments was 14.97% (6,832), significantly higher than the 12.02% (2,178) observed in the selected Qinchuan cattle population. Figure 1 C indicates more recent inbreeding events in the sampled Qinchuan cattle population, medium, and long segments among the Qinchuan cattle, the Qinchuan cattle population, and the selected Qinchuan cattle population. higher than the 1202% (18,116 segments) observed in the selected Qinchuan cattle (Fig. 1 D). In terms of proportion, both populations were primarily composed of short ROHs of 0.5–1 Mb. The sampled Qinchuan cattle exhibited an average inbreeding coefficient of 0.055957, slightly higher than that of the selected Qinchuan cattle. This indicates that the overall inbreeding level of the sampled Qinchuan cattlemarginally higher than that of the selected Qinchuan cattle. Subsequently, the study analysed the distribution of regions of shared haplotypes (ROH) within the population and visualised them based on SNP positions on chromosomes (Fig. 1 E). The majority of the 95 loci annotated within ROH islands were located within growth-related genes, providing a prioritisation basis for functional locus screening on the chip. To identify candidate genomic regions under selection during growth and development in Qinchuan cattle compared to other populations, this study employed the Fst-π ratio method to detect selection signals, using the top 5% threshold as the screening criterion. Regions significantly detected by both methods were deemed genuinely selected. Figure 2 displays Manhattan plots of different Qinchuan cattle populations generated using the Fst-π ratio method with a 5% threshold. The results reveal distinct selection regions across populations identified by the Fst-π ratio approach,with a total of 291 overlapping selected sites detected. Functional enrichment analysis of these overlapping genes was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO). The most significantly enriched KEGG pathway was Metabolic pathways. GO terms with significant association (P-value < 0.05) among these overlapping genes included: nucleoplasm, protein binding, and cytoplasm. Genome-wide association studies were conducted on eight body-size traits inQinchuan cattle(Fig. 3 ). The QQ plot indicated satisfactory model fit. A total of 137 significant SNPs (P < 5×10 − 8 were identified in 193 individuals) identified, with 14 genes located within exonic regions across all loci. PubMed literature review revealed these genes are associated with signal transduction, key pathways, and cell proliferation, and are closely linked to growth and development in Qinchuan cattle. DHRS3, as a member of the short-chain dehydrogenase/reductase family, catalyses the reduction of all-trans retinaldehyde to retinol, maintaining its dynamic equilibrium and promoting skeletal muscle regeneration during myoblast differentiation[ 43 ]. CENPS functions primarily in chromosome segregation and the maintenance of genomic stability. CENPS maintains centromere integrity during the G1 phase via homologous recombination repair mechanisms, preventing chromosomal translocations[ 44 ]. SIK2 AMP-activated protein, a member of thekinase family, functions as a serine/threonine kinase. It primarily participates in multiple biological processes—including energy metabolism regulation and cell cycle progression—by phosphorylating downstream target proteins [ 45 ]. ANKRD11 is a multifunctional protein containing ankyrin repeat domains, playing a pivotal role in development and disease through involvement in transcriptional regulation, chromatin remodelling, and cellular signalling pathways[ 46 ]. UNC5B, a member of the UNC5 family, initiates pro-apoptotic signals in the absence of key factors. By activating downstream pathways, it induces apoptosis, participating in the clearance of surplus cells during tissue development to maintain cellular homeostasis. UNC5B regulates endothelial cell migration, proliferation, and vascular branching, thereby sustaining the normal structure of vascular networks[ 47 ]. The reference genome (ARS-UCD1.2_Btau5.0.1Y) was divided into 26,204 sites using 95,000-base window lengths across autosomes, yielding 23,183 successfully designed probes. Combining with the previously mentioned loci and excluding loci with a missing rate exceeding 50% in the test population, 20,566 loci were ultimately retained. 3.2 Microarray Synthesis Results The study retained 20,566 valid probes, corresponding to 20,566 core target sites. The core target sites were evenly distributed across 29 autosomes, with no obvious clusters or gaps. Sites on chromosomes 1–6 exhibited higher density (8–10 sites per Mb on average), while chromosomes 25–29 showed relatively lower density (3–5 sites per Mb on average). The overall distribution met the uniformity requirements for chip design (Fig. 4 A). Functional sites (trait-associated + selected genes + immune-related) accounted for 62.3%, while Qinchuan cattle-specific sites constituted 28.7%, ensuring sites balanced functional targeting with genomic coverage. Exon regions contained 1,913 sites (9.3%), of which 832 (43.5%) were missense mutation sites; 11,973 sites (58.7%) were located in intron regions, including 621 sites associated with selected genes; 1,234 functional sites (6.0%) were in non-coding regions, comprising 412 sites in UTR regions and 387 sites associated with lncRNAs, ensuring coverage of key gene expression regulatory regions (Fig. 4 B). 3.3 Chip Result Detection The core SNP detection rate for test samples demonstrated excellent performance, with a population-average detection rate exceeding 98.48%. This indicates the chip's high efficacy in SNP genotyping for Qinchuan cattle samples, enabling stable capture of genotype information for the vast majority of core target loci. This meets the data integrity requirements for subsequent genomic analysis. The genotypic concordance rate for duplicate sample pairs reached 99.01%, demonstrating the chip's excellent repeatability and reliability in Qinchuan cattle SNP genotyping. Genotyping results exhibit minimal susceptibility to experimental variation, with data credibility meeting genomic research standards. A total of 14,320 polymorphic sites (69.66%) exhibited MAF > 0.1, with 7,842 (54.7%) located within functional genes. This indicates that the chip sites balance genetic polymorphism with functional relevance (Fig. 4 C). The proportion of polymorphic sites remained consistently high, indicating that the core SNPs selected for the chip effectively cover the genetic polymorphism of the Qinchuan cattle population and meet the requirements for polymorphism sites in genetic diversity assessment studies. The NJ tree construction results in Fig. 4 D show that all Qinchuan cattle samples clustered into a single branch with bootstrap values ≥ 92 (high node reliability), clearly distinguished from branches of reference breeds such as Angus and Jinnan cattle, with no cross-breed clustering observed. Consistent results from both analyses indicate that the core target sites selected via chip screening effectively capture breed-specific genetic characteristics of Qinchuan cattle, enabling precise analysis of their population genetic structure and breed identification ; Whether based on the 20K chip loci (Fig. 5 A)or the whole-genome loci (Fig. 5 B), Qinchuan cattle and non-Qinchuan control samples formed distinct population boundaries in the PC1-PC2 scatter plot. Qinchuan cattle samples clustered into an independent group, significantly separated from other breeds. PC1 explained 35.17% of genetic variation and PC2 explained 12.36%. This indicates that the 20K chip loci and genome-wide loci exhibithigh consistency in reflecting population genetic structure, demonstrating that these chip loci can effectively capture genetic differentiation information between populations and serve as a reliable alternative for genome-wide analysis. 4. Discussion The size of domesticated animals is not only a commercial standard in livestock production but also a central focus in advancing research on poultry evolution and development [ 48 – 49 ]. As a prime example of a trait influenced by numerous complex factors, body size is affected by a large number of genes, which are often distributed across biologically similar loci[ 50 ]. The Research findings indicate genetic divergence between wild-type Qinchuan cattle and selectively bred Qinchuan cattle. The selectively bred population exhibits lower inbreeding levels due to more extensive hybrid gene exchange, consistent with their distinct breeding histories. This insight aids understanding of their genetic differentiation and evolutionary adaptation. This study, targeting the Qinchuan cattle's breed characteristics of rapid growth, tender meat quality, and strong adaptability successfully customised a 20K functional locus gene chip targeting. This addresses the current lack of dedicated tools in Qinchuan cattle genetic research and the low efficiency of screening markers for core economic traits. Selection signal analysis (Fst versus π ratio method) identified 291 overlapping regions of recent common ancestry (ROH), which encompass multiple genes associated with bovine growth. Notably, a ROH island overlapping with selection signals on chromosome 4 contained 24 genes involved in metabolic regulation,which collectively support cellular proliferation and growth development. GWAS analysis screened 137 significant SNPs, with genes in three overlapping regions exhibiting critical functions. MAN1A2, as a member of the mannosidase family, regulates glycoprotein modification by catalysing N-glycan trimming in the Golgi apparatus. This process directly influencing protein folding, transport, and stability while extensively participating in organ development. Duringoocyte development, its loss-of-function leads to ovarian dysfunction in cattle, disrupting oocyte maturation and subsequent embryonic development capacity. Additionally, animal models reveal its regulation of ciliary function impacts the transport of oocytes by fallopian tube cilia[ 51 ]. FAM83F promotes cell progression from G1 to S phase by interacting with cyclin or CDK4/6, accelerating the cell cycle and activating proliferation-related signalling pathways, thereby enhancing the expression of bovine proliferation-associated genes[ 52 ]. KIFC2 participates in intracellular material transport and division-related processes, and stabilizes regulatory factors governing the G1-to-S phase transition. The stability accelerates cell cycle progression and promotes cell proliferation. Concurrently, KIFC2 amplification correlates with pyrimidine metabolism activation. Pyrimidine metabolism supplies nucleotides as raw materials for DNA synthesis, providing the material foundation for cellular growth and division, thereby further driving bovine cell proliferation[ 53 ]. The core value of gene chips lies in their high compatibility with the genetic background of target breeds, with detection rates and genotypic concordance serving as key indicators of suitability. In this study, the average detection rate for core SNPs in samples exceeded 98.48%, while genotypic concordance in technical replicates reached 99.01%. Both metrics significantly surpassed conventional standards for genomic chip applications and outperformed most universal beef cattle chips when applied to local breeds[ 54 ]. By employing methods such as selection signals and genome-wide association studies to identify Qinchuan cattle-specific loci, thisstudy effectively circumvented the inadequate genomic matching issues inherent in generic chips when applied to local breeds. The high detection rate indicates the chip's stable capture of core genetic information in Qinchuan cattle, minimising interference from data loss in subsequent analyses. The high concordance demonstrates reliable genotyping results, providing high-quality data support for genetic diversity assessment and molecular marker screening, which fully reflects the chip's excellent compatibility with the Qinchuan cattle genetic background[ 55 ]. As a locally adapted breed with a long history of selective breeding, the genetic diversity and distribution of polymorphic sites in Qinchuan cattle directly constitute the genetic basis of breed characteristics. In the samples of this study polymorphic sites with MAF > 0.1 accounted for 69.66% of the total, which was significantly higher thanthe conventional level of low-polymorphism chips. This indicates that the core SNPs selected for the chip effectively cover key genetic variations within the Qinchuan cattle population. Regarding breed characteristics, Qinchuan cattle exhibit polygenic regulation across economic traits including growth development, meat quality, and stress resistance. The functional loci integrated into the chip precisely encompass regions associated with these traits[ 56 ]. This research provides a basis for constructing germplasm resource fingerprinting and breed identification, while also laying the groundwork for subsequent genome-wide association studies. The functional loci on this chip facilitate the localisation of key genes associated with target traits, accelerating the elucidation of the genetic mechanisms underlying Qinchuan cattle's economic traits. Compared to traditional methods, the chip exhibitsthree core advantages: strong breed specificity, precise functional orientation, and cost-effectiveness compatible withindustrial applications. The customised Qinchuan cattle 10K functional locus gene chip developed in this study, with its high detection rate, high consistency, and strong specificity, provides an efficient tool for genetic analysis and molecular breeding of local cattle breeds. This chip not only supports fundamental research such as genetic diversity assessment and germplasm resource conservation in Qinchuan cattle but also directly serves industrial demands for breed improvement, facilitating the transition of Qinchuan cattle breeding from "traditional phenotypic selection" to "molecular design breeding". 5. Conclusions To address the conservation and molecular breeding requirements of Qinchuan cattle, this study employed targeted sequencing-based genotyping technology. Through a multidimensional screening strategy incorporating GWAS, ROH analysis, selection signal analysis, and supplementary literature databases, a synthetic chip comprising 20,566 core target loci was finalised. Validation results demonstrated the chip's outstanding core performance. Functional validation further confirmed the chip's capability for Qinchuan cattle breed identification, providing a reliable tool for genetic analysis of key traits such as body measurements and body weight. Compared to generic commercial chips, the Qinchuan cattle chip offers significant advantages including high specificity and low cost. The successful development of this chip not only fills the gap in dedicated Qinchuan cattle chips but also provides efficient technical support for the precise conservation of Qinchuan cattle genetic resources, the identification of core breeding value genes, and the establishment of a genomic selection breeding system. Abbreviations SNP Single Nucleotide Polymorphisms GBTS Genotyping by Targeted Sequencing QTL Quantitative Trait Loci GWAS Genome-Wide Association Studies GS Genomic Selection ROH Runs of Homozygosity Fst Fixation Index π Nucleotide Diversity GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes MAF Minor Allele Frequency PCA Principal Component Analysis NJ Tree Neighbor-Joining Tree HPC High-Performance Computing BWA Burrows-Wheeler Aligner GATK Genome Analysis Toolkit VCF Variant Call Format BCFtools Binary Call Format tools PLINK Whole-Genome Association Analysis Toolset Picard Tools Picard Sequence Processing Tools Trimmomatic Illumina Sequence Data Trimmer FASTQ Fast Quality Format BAM Binary Alignment Map DP Depth of Coverage QD Quality by Depth FS Fisher Strand MQ Mapping Quality MQRankSum Mapping Quality Rank Sum Test ReadPosRankSum Read Position Rank Sum Test SOR Strand Odds Ratio FROH Genomic Inbreeding Coefficient Based on ROH UTR Untranslated Region lncRNA Long Non-Coding RNA DAVID Database for Annotation, Visualization and Integrated Discovery GEMMA Genome-wide Efficient Mixed-Model Association CMplot Circular Manhattan Plot ggplot2 Grammar of Graphics Plot 2 VCF2Dis VCF to Genetic Distance ITOL Interactive Tree Of Life MGISEQ-2000 MGI Sequencing Platform 2000 DNBSEQ-T7 DNA Nanoball Sequencing Platform T7 Dynabeads MyOne Streptavidin C1 Dynabeads MyOne Streptavidin C1 Magnetic Beads PCR Polymerase Chain Reaction PE150 Paired-End 150bp ARS-UCD1.2_Btau5.0.1Y Bos taurus Reference Genome Version ARS-UCD1.2_Btau5.0.1Y Declarations Ethics declarations All procedures were conducted in accordance with the Chinese laws on animal experimentation and were approved by the Northwest A&F University’s Experimental Animal Management Committee (Approval No. DK2022065), and conducted under the authority of the Project License. Animal use and care were in accordance with the ARRIVE guidelines ( https://arriveguidelines.org/ ). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interest. Funding This project was supported by Key R&D Program of Ningxia (2024BBF01007); National Natural Science Foundation of China (32573186); STI2030-Major Projects (2023ZD040480206); Natural Science Foundation of Henan (252300420687); China Agriculture Research System of MOF and MARA (CARS-37). Author Contribution K.Z., J.M., and L.S.were responsible for participating in sample collection, data collation, and experimental operations related to chip validation.D.W.² provided relevant research data support.X.G., W.M., N.C., X.X., Y.M., C.L., and Y.J.guided the experimental design and data analysis ideas, offered key technical support, and participated in the formulation of research schemes and discussion of results.Y.H.* (corresponding author) oversaw the entire research project, being responsible for experimental design, result interpretation, and manuscript writing and revision.All authors have read and approved the final manuscript. Acknowledgements Not applicable. Data Availability The datasets generated and/or analyzed during the current study are available in the China National Center for Bioinformation (CNCB) repository, with the accession number PRJCA054984. References Darwin C. The Variation of Animals and Plants under Domestication. London: John Murray; 1868. Sutter NB, Bustamante CD, Chase K, et al. A single IGF1 allele is a major determinant of small size in dogs. Science. 2007;316:112–5. 10.1126/science.1137045 . Gou X, Wang Z, Li N, et al. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia. Genome Res. 2014;24:1308–15. 10.1101/gr.171876.113 . Makvandi-Nejad S, Hoffman GE, Allen JJ, et al. Four loci explain 83% of size variation in the horse. PLoS ONE. 2012;7:e39929. 10.1371/journal.pone.0039929 . Maj A, Snochowski M, Siadkowska E, et al. Polymorphism in genes of growth hormone receptor (GHR) and insulin-like growth factor-1 (IGF1) and its association with both the IGF1 expression in liver and its level in blood in Polish Holstein-Friesian cattle. Neuro Endocrinol Lett. 2008;29(6):981–9. Asadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, et al. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony. BMC Genomics. 2020;21:496. 10.1186/s12864-020-06887-2 . Wang MS, Huo YX, Li Y, et al. Comparative population genomics reveals genetic basis underlying body size of domestic chickens. J Mol Cell Biol. 2016;8:542–52. 10.1093/jmcb/mjw044 . Wang K, Hu H, Tian Y, et al. The chicken pan-genome reveals gene content variation and a promoter region deletion in IGF2BP1 affecting body size. Mol Biol Evol. 2021;38:5066–81. 10.1093/molbev/msab231 . Miao YW, Peng MS, Wu GS, et al. Chicken domestication: an updated perspective based on mitochondrial genomes. Heredity. 2013;110:277–82. 10.1038/hdy.2012.83 . Ai H, Fang X, Yang B, et al. Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet. 2015;47:217–25. 10.1038/ng.3199 . Axelsson E, Ratnakumar A, Arendt ML, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013;495:360–4. 10.1038/nature11837 . Fan H, Wang T, Li Y, et al. Development and validation of a 1 K sika deer (Cervus nippon) SNP chip. BMC Genom Data. 2021;22(1):35. 10.1186/s12863-021-00994-z . Sudrajad P, Kusminanto RY, Volkandari SD, et al. Genomic structure of Bali cattle based on linkage disequilibrium and effective population size analyses using 50K single nucleotide polymorphisms data. Vet World. 2022;15(2):449–54. 10.14202/vetworld.2022.449-454 . Tolone M, Sardina MT, Criscione A, et al. High-density single nucleotide polymorphism markers reveal the population structure of 2 local chicken genetic resources. Poult Sci. 2023;102(7):102692. 10.1016/j.psj.2023.102692 . Palti Y, Vallejo RL, Purcell MK, et al. Genome-wide association analysis of resistance to infectious haematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate-effect quantitative trait loci. Front Genet. 2024;15:1394656doi. 10.3389/fgene.2024.1394656 . Ma C, Liu L, Liu T, et al. QTL mapping for important agronomic traits using a Wheat55K SNP array-based genetic map in tetraploid wheat. Plants (Basel). 2023;12(4). 10.3390/plants12040847 . More M, Veli E, Cruz A, et al. Genome-Wide Association Study of Fibre Diameter in Alpacas. Anim (Basel). 2023;13(21). 10.3390/ani13213316 . Heidaritabar M, Bink MCAM, Dervishi E, et al. Genome-wide association studies for additive and dominance effects for body composition traits in commercial crossbred Piétrain pigs. J Anim Breed Genet. 2023;140(4):413–30. 10.1111/jbg.12768 . Garcia A, Tsuruta S, Gao G, et al. Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing. Genet Sel Evol. 2023;55(1):11doi. 10.1186/s12711-023-00782-6 . Mastrangelo S, Ben-Jemaa S, Perini F, et al. Genome-wide mapping of selection signatures using a high-density array identified candidate genes for growth traits and local adaptation in chickens. Genet Sel Evol. 2023;55(1):20. 10.1186/s12711-023-00790-6 . Guo X, Puritz JB, Wang Z, et al. Development and evaluation of high-density SNP arrays for the Eastern Oyster Crassostrea virginica. Mar Biotechnol (NY). 2023;25(1):174–91. 10.1007/s10126-022-10191-3 . Neumann GB, Korkuc P, Arends D, et al. Design and performance of a bovine 200 k SNP chip developed for endangered German Black Pied cattle (DSN). BMC Genomics. 2021;22(1):905. 10.1186/s12864-021-08237-2 . Suratannon N, van Wijck RTA, Broer L, et al. Rapid low-cost microarray-based genotyping for genetic screening in primary immunodeficiency. Front Immunol. 2020;11:614. 10.3389/fimmu.2020.00614 . Balog K, Mizeranschi AE, Wanjala G, et al. Application potential of chicken DNA chip in domestic pigeon species – preliminary results. Saudi J Biol Sci. 2023;30(3):103594doi. 10.1016/j.sjbs.2023.103594 . Samorodnitsky E, Datta J, Jewell BM, et al. Comparison of custom capture for targeted next-generation DNA sequencing. J Mol Diagn. 2015;17(1):64–75. 10.1016/j.jmoldx.2014.09.009 . Guo Z, Wang H, Tao J, et al. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Mol Breed. 2019;39(3):37. .doi.org/10.1007/s11032-019-0940-4 . Guan S, Li W, Jin H, et al. Development and validation of a 54K genome-wide liquid SNP Chip Panel by target sequencing for dairy goat. Genes (Basel). 2023;14(5). 10.3390/genes14051122 . Zhang W, Gao X, Zhang Y, et al. Genome-wide assessment of genetic diversity and population structure insights into admixture and introgression in Chinese indigenous cattle. BMC Genet. 2018;19(1):114. 10.1186/s12863-018-0705-9 . Wang H, Wu H, Zhang W, et al. Development and validation of a 5K low-density SNP chip for Hainan cattle. BMC Genomics. 2024;25(1):873. 10.1186/s12864-024-10753-w . Raza SHA, Khan R, Abdelnour SA, et al. Advances of Molecular Markers and Their Application for Body Variables and Carcass Traits in Qinchuan Cattle. Genes (Basel). 2019;10(9):717. 10.3390/genes10090717 . Yu H, Yu S, Guo J, et al. Genome-Wide Association Study Reveals Novel Loci Associated with Body Conformation Traits in Qinchuan Cattle. Anim (Basel). 2023;13(23):3628. 10.3390/ani13233628 . Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20. 10.1093/bioinformatics/btu170 . Van der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinf. 2013;43(1110):11. 10.1002/0471250953.bi1110s43 . Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2). 10.1093/gigascience/giab008 . Howrigan DP, Simonson MA, Keller MC. Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics. 2011;12:460. 10.1186/1471-2164-12-460 . Peripolli E, Metzger J, de Lemos MVA, et al. Autozygosity islands and ROH patterns in Nellore lineages: evidence of selection for functionally important traits. BMC Genomics. 2018;19(1):680. 10.1186/s12864-018-5060-8 . Danecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156–8. 10.1093/bioinformatics/btr330 . Huang DA, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. 10.1038/nprot.2008.211 . Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821–4. 10.1038/ng.2310 . Yin L, Zhang H, Tang Z, et al. rMVP: A Memory-efficient, Visualisation-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study. Genomics Proteom Bioinf. 2021;19:619–28. 10.1016/j.gpb.2020.10.007 . Elhaik E. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci Rep. 2022;12(1):14683. 10.1038/s41598-022-14395-4 . Xu L, He W, Tai S, et al. VCF2Dis: an ultra-fast and efficient tool to calculate pairwise genetic distance and construct population phylogeny from VCF files. Gigascience. 2025;14:giaf032. 10.1093/gigascience/giaf032 . Rubin CJ, Megens HJ, Barrio AM, et al. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci U S A. 2012;109:19529–36. 10.1073/pnas.1217149109 . Li J, Ruan Y, Jiang C, et al. Tissue-Specific Expression of the Porcine DHRS3 Gene and Its Impact on the Proliferation and Differentiation of Myogenic Cells. Anim (Basel). 2025;15(8):1101. 10.3390/ani15081101 . Yi Z, Jia Y, Lu R, et al. E2F1-driven CENPM expression promotes glycolytic reprogramming and tumourigenicity in glioblastoma. Cell Biol Toxicol. 2024;41(1):4. 10.1007/s10565-024-09945-7 . Wang J, Yu X, Wu S, et al. Identification of candidate SNPs and genes associated with resistance to nervous necrosis virus in leopard coral grouper (Plectropomus leopardus) using GWAS. Fish Shellfish Immunol. 2024;144:109295. 10.1016/j.fsi.2023.109295 . Han J, Shao H, Sun M, et al. Genomic insights into the genetic diversity and genetic basis of body height in endangered Chinese Ningqiang ponies. BMC Genomics. 2025;26(1):292. 10.1186/s12864-025-11484-2 . Zhang C, Xie Z, Wang N. Single-cell RNA sequencing of adult primate neocortex reveals the regulatory dynamics of neural plasticity. Am J Transl Res. 2025;17(4):2562–76. 10.62347/ZEOR5569 . Rimbault M, Beale HC, Schoenebeck JJ, et al. Derived variants at six genes explain nearly half of size reduction in dog breeds. Genome Res. 2013;23:1985–95. 10.1101/gr.157339.113 . Devlin RH, Sakhrani D, Tymchuk WE, et al. Domestication and growth hormone transgenesis cause similar changes in gene expression in coho salmon (Oncorhynchus kisutch). Proc Natl Acad Sci U S A. 2009;106:3047–52. 10.1073/pnas.0809798106 . Wang JH, Zhao QY, Zhou YL, et al. Application and prospect of gene chips in genetic breeding of livestock and poultry. Yi Chuan. 2023;45(12):1114–27. 10.16288/j.yczz.23-233 . Cuellar CJ, Zayas GA, Amaral TF, et al. Ovarian hyperplasia linked to a mutation in MAN1A2 in a cow with excessive follicular growth and functional oocytes. Vet Res Commun. 2024;48(5):3239–43. 10.1007/s11259-024-10435-8 . Jones RA, Cooper F, Kelly G, et al. Zebrafish reveal new roles for Fam83f in hatching and the DNA damage-mediated autophagic response. Open Biol. 2024;14(10):240194. 10.1098/rsob.240194 . Yang SY, Jin ML, Andriani L, et al. Kinesin-like protein KIFC2 stabilises CDK4 to accelerate growth and confer resistance in HR+/HER2- breast cancer. J Clin Invest. 2025;135(12):e183531. 10.1172/JCI183531 . Rafter P, Gormley IC, Parnell AC, et al. Concordance rate between copy number variants detected using either high- or medium-density single nucleotide polymorphism genotype panels and the potential of imputing copy number variants from flanking high density single nucleotide polymorphism haplotypes in cattle. BMC Genomics. 2020;21(1):205. 10.1186/s12864-020-6627-8 . Lu X, Yang Y, Zhang Y, et al. The relationship between myofibre characteristics and meat quality of Chinese Qinchuan and Luxi cattle. Anim Biosci. 2021;34(4):743–50. 10.5713/ajas.20.0066 . Additional Declarations No competing interests reported. Supplementary Files file.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 07 Feb, 2026 First submitted to journal 07 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8742497","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606805867,"identity":"1ae479c0-095a-484b-9c58-82d2331a3d68","order_by":0,"name":"Kaiyan Zhang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Kaiyan","middleName":"","lastName":"Zhang","suffix":""},{"id":606805868,"identity":"108da2a9-c867-45b5-a311-29d15e8f89ab","order_by":1,"name":"Jun Ma","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ma","suffix":""},{"id":606805869,"identity":"bb57a50e-d832-4d8e-97ed-e72b9bf1ad6b","order_by":2,"name":"Luyang Sun","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Luyang","middleName":"","lastName":"Sun","suffix":""},{"id":606805870,"identity":"f7db6356-af7e-466c-b55e-eb37e222c5c5","order_by":3,"name":"Dawei Wei","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Wei","suffix":""},{"id":606805871,"identity":"3b259d77-d99d-4f07-87e0-c3e125f00a80","order_by":4,"name":"Xue Gao","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Gao","suffix":""},{"id":606805872,"identity":"5c7d8455-1c95-40b1-ac64-b530a18e4ae8","order_by":5,"name":"Weidong Ma","email":"","orcid":"","institution":"Shaanxi Agricultural and Animal Husbandry Seed Farm, Shaanxi Fufeng","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Ma","suffix":""},{"id":606805873,"identity":"dc6526bd-89b0-4f39-8ea8-86829fe99367","order_by":6,"name":"Ningbo Chen","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Ningbo","middleName":"","lastName":"Chen","suffix":""},{"id":606805874,"identity":"9f1dff9e-eb53-4b8c-a6cc-66d41ad38d89","order_by":7,"name":"Xiaoting Xia","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoting","middleName":"","lastName":"Xia","suffix":""},{"id":606805875,"identity":"5ae1fd98-35b4-4989-80d6-24b3b371de9f","order_by":8,"name":"Yun Ma","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Ma","suffix":""},{"id":606805876,"identity":"57fbdedc-4ca2-4c20-b6c6-c4ac8daf36f5","order_by":9,"name":"Chuzhao Lei","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Chuzhao","middleName":"","lastName":"Lei","suffix":""},{"id":606805877,"identity":"8c59a346-b1cf-4d79-a117-68f1227933b8","order_by":10,"name":"Yu Jiang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Jiang","suffix":""},{"id":606805878,"identity":"d36bb7b3-961b-48aa-ae88-bb6162213448","order_by":11,"name":"Yongzhen Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACAwhpAyQYHwAJZqK1pIFUG5CiheEwCVrMJdIff/hRcD7P4PhhNgmGCuvEBvazB/BqsZyRkCbZY3C72OBMMlDLmfTEBp68BPwOu5FwjJnB4HbitgP5xyQY2w4nNkjwGBDQktj8mcHgXOK284/ZJBj/EaUlmUGaweBA4rYbQIcxNhCj5cwzNqBfkhP333jMbJFwLN24jSeHgJbjoBD7Y5c4sz+Z8caHGmvZfvYz+LUwCCQgcUBsNvzqgYD/AEElo2AUjIJRMNIBAPsJRjmm0pCMAAAAAElFTkSuQmCC","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":true,"prefix":"","firstName":"Yongzhen","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2026-01-30 15:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8742497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8742497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105007119,"identity":"4d7bec4b-3ebb-45fd-a5f5-cf50028cbdc7","added_by":"auto","created_at":"2026-03-19 18:43:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256209,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterisation and distribution of runs of homozygosity (ROH). \u003c/strong\u003e(A) Genome coverage proportion by ROH length categories per group. Different colors represent distinct ROH length ranges: 0.5–1 Mb, 1–2 Mb, 2–4 Mb, 4–8 Mb, 8–16 Mb, and \u0026gt;16 Mb. The y-axis indicates the proportion of genome covered by ROHs of each length category. (B) ROH frequency versus length distribution. The x-axis represents ROH length, and the y-axis represents the number of ROH segments (frequency) corresponding to each length. (C) Scatter plots of ROH segment count versus largest segment size. Each point denotes an individual, with the x-axis showing the total number of ROH segments per individual and the y-axis showing the length of the largest ROH segment in that individual. (D) Correlation between total ROH length and segment count. The x-axis represents the total number of ROH segments, and the y-axis represents the total length of all ROH segments per individual. (E) Distribution of the linkage disequilibrium coefficient of ROH across the whole genome. The x-axis lists autosomes (Chr1–Chr29), and the y-axis represents the linkage disequilibrium coefficient of ROH regions on each chromosome.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/3e07f1ef38c928a7348f7e53.jpg"},{"id":105035422,"identity":"5d803cc3-d348-4f1d-be70-f41454823ebf","added_by":"auto","created_at":"2026-03-20 07:26:04","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSelection signature results of Qinchuan cattle. \u003c/strong\u003e(A) SvsA Fst Plot (50 kb windows, top 5% Fst). The x-axis shows chromosomes (Chr1–Chr29), and colored bars represent Fst values (genetic differentiation coefficient) of each 50 kb window. Bars exceeding the top 5% threshold indicate genomic regions with high genetic differentiation between populations. (B) SvsA Pi Plot (50 kb windows, top 5% Pi). The x-axis lists chromosomes, and colored bars represent Pi values (nucleotide diversity) of each 50 kb window. Bars exceeding the top 5% threshold highlight regions with high nucleotide diversity. (C) Relationships between genetic statistics (Fst and Pi) and their frequency distributions. The x-axis represents the value range of genetic statistics, and the y-axis represents the frequency of each value.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/830d1192f7c0048738b82158.jpg"},{"id":105007116,"identity":"8bd3e809-9854-409a-91f8-b99b30ed01f6","added_by":"auto","created_at":"2026-03-19 18:43:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide association study (GWAS) results for Qinchuan cattle. \u003c/strong\u003eManhattan plots (left) and QQ plots (right) for eight body-size traits: (A) ischial end width, (B) body height, (C) cross-shaped part height, (D) body length, (E) chest girth, (F) abdominal girth, (G) cannon circumference, and (H) chest width. For Manhattan plots: The x-axis shows chromosomes (Chr1–Chr29), and the y-axis represents -log₁₀(P-values) of Single Nucleotide Polymorphisms (SNPs). Higher bars indicate stronger associations between the corresponding SNP and the trait. The horizontal dashed line denotes the significance threshold (P \u0026lt; 5×10⁻⁸). For QQ plots: The x-axis shows expected -log₁₀(P-values) under the null hypothesis, and the y-axis shows observed -log₁₀(P-values). Points close to the diagonal indicate a good fit of the GWAS model.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/837fac160ba7013dd6e7476a.jpg"},{"id":105007120,"identity":"c1108a46-5844-4517-b95e-3089f5236c97","added_by":"auto","created_at":"2026-03-19 18:43:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":287862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChip SNP Locus Distribution and Characteristics Analysis Chart.\u003c/strong\u003e(A) Heatmap of SNP distribution across chromosomes in 1 Mb windows. The x-axis lists autosomes (Chr1–Chr29) and their physical positions (in Mb), and the y-axis represents the number of SNPs per 1 Mb window (range: 0–14). Color intensity corresponds to SNP density, with darker colors indicating higher density. (B) Bar chart of SNP quantity statistics by functional type. The x-axis lists SNP functional categories: exon regions (including missense mutation sites), intron regions (including selected gene-associated sites), non-coding regions (including UTR regions and lncRNA-associated sites), and other regions. The y-axis represents the number of SNPs in each category. Functional sites (trait-associated + selected genes + immune-related) and Qinchuan cattle-specific sites are indicated in the chart annotation.(C) Minor Allele Frequency (MAF) distribution map of core loci in samples. The x-axis shows MAF ranges (0–0.5), and the y-axis represents the number of core SNPs corresponding to each MAF range. A total of 14,320 polymorphic sites (69.66%) had MAF \u0026gt; 0.1, with 7,842 (54.7%) located within functional genes. (D) Circular phylogenetic tree of Qinchuan cattle and reference breeds. Branches are labeled with breed names (e.g., Qinchuan, Angus, Jinnan, Hereford), and bootstrap values (≥92) are marked at key nodes, indicating high reliability of the clustering result. All Qinchuan cattle samples cluster into a single independent branch, clearly distinguished from other breeds.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/227b87f2e742a25a8377e26b.jpg"},{"id":105035468,"identity":"a03639a4-deb3-4c81-a2eb-dcf9df794c02","added_by":"auto","created_at":"2026-03-20 07:26:08","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84400,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal Component Analysis (PCA) results. \u003c/strong\u003e(A) PCA plot of cattle population genetic structure based on 20K chip-derived SNPs. The x-axis (PC1) explains 30.78% of genetic variation, and the y-axis (PC2) explains 12.36%. Different symbols and colors represent different breeds (e.g., Qinchuan [QIC], Angus [Aan], Charolais [Chals], Jinnan [Jinj]). Qinchuan cattle samples form an independent cluster, significantly separated from other breeds. (B) PCA plot of cattle population genetic structure based on whole-genome SNPs. The x-axis (PC1) explains 53.3% of genetic variation, and the y-axis (PC2) explains the remaining major variation. Symbols and colors correspond to those in (A). Qinchuan cattle and non-Qinchuan control samples form distinct population boundaries, consistent with the clustering result in (A), indicating high consistency between 20K chip loci and whole-genome loci in reflecting population genetic structure.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/6e37d0901ceb1189af57bc5f.jpg"},{"id":105036771,"identity":"7328a45c-ed5e-46f6-b8c8-1621ebdd1596","added_by":"auto","created_at":"2026-03-20 07:35:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1796502,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/4b90437c-894e-4f6e-a8fe-2410bf084728.pdf"},{"id":105007122,"identity":"52b779b2-e848-48d4-8b4a-8dc1b4cf4b1f","added_by":"auto","created_at":"2026-03-19 18:44:00","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26107,"visible":true,"origin":"","legend":"","description":"","filename":"file.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8742497/v1/44aecf1f30020390f8b43c59.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Application Validation of a 20K-Liquid Chip for the Qinchuan Cattle","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDomesticated animals serve as excellent biological models for studying phenotypic development and evolution. Through prolonged human selection and domestication, distinct breeds have developed significant variations in morphological traits, physiological functions, and environmental adaptability [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Cattle (Bos taurus) represent a vital livestock resource for humanity, exhibiting rich phenotypic diversity. Their complex population evolutionary history and trait inheritance mechanisms have attracted considerable scholarly attention[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Qinchuan cattle, a highly representative local beef breed in China, are renowned for their imposing stature, docile temperament, rapid growth rate, tender meat quality, and strong stress resistance. Their body conformation traits are not only directly linked to economic characteristics such as dressing percentage and meat yield but also embody unique genetic codes shaped by long-term artificial selection and environmental adaptation. Decoding the genetic mechanisms underlying its body conformation holds significant practical value for targeted breeding and germplasm resource innovation. Current research on genetic mechanisms underlying animal body conformation has yielded substantial progress: studies in cattle have identified genes such as IGF1 and GHR as closely linked to body development[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; comparative genomics has elucidated key genes regulating equine body conformation[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; and population genomics analysis has uncovered novel loci potentially influencing poultry body variation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The marked differentiation in body size between domesticated animals and their wild ancestors renders them ideal subjects for studying genetic variation in traits. As a core trait in both breeding practice and fundamental research, the genetic mechanisms underlying body size have been extensively explored and characterised[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. With the rapid advancement of genomic sequencing technologies, it has become feasible to decipher the gene regulatory networks underlying complex traits such as body size in domesticated animals. For instance current research has successfully elucidated key genetic mechanisms, including cold tolerance adaptationin high-latitude Chinese pigs[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]and canine adaptation to high-starch diets[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, compared to these species, genetic research on Qinchuan cattle body conformation remains underdeveloped. Existing universal SNP chips lack coverage of its breed-specific loci, hindering accurate capture of genetic signals for body conformation traits\u0026mdash;this limits identification of key functional genes and genomic selection efficiency, constraining breed improvement precision.\u003c/p\u003e \u003cp\u003eSingle nucleotide polymorphisms (SNPs), as the most prevalent and stable form of genetic variation in genomes, constitute indispensable core molecular markers in genetic research. They exert profound influences on crucial biological processes including organismal growth and development, metabolic regulation, and environmental adaptability[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. SNP chips, as the core technology enabling efficient SNP detection, have become indispensable due to their unique advantages of high throughput, high accuracy, and standardisation. They are employed for analysing population structure and admixture proportions, estimating effective population size[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], localising quantitative trait loci (QTL)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], conducting genome-wide association studies (GWAS)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and implementing genomic selection (GS)[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The advent of targeted sequencing genotyping (GBTS) liquid chips has effectively addressed the limitations of traditional techniques, emerging as a more cost-effective alternative to whole-genome sequencing. Dueto its unique technical design, this approach has now been extensively applied in genetic breeding and germplasm resource innovation studies across various livestock and poultry species, including pigs, cattle, sheep, and chickens[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The GBTS liquid chip substantially lowers the technical barriers and economic costs of large-scale population genotyping, providinggrassroots breeding units and research institutions with a more accessible technological option[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. GenoBaits technology employs liquid-phase probe hybridisation for selective DNA capture, making it more suitable for precise detection at medium-to-high numbers of loci[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These chip advantages provide efficient and reliable technical tools for genetic breeding in relevant livestock breeds, while also injecting new impetus into the conservation and innovative utilization of germplasm resources from local superior breeds[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin Qinchuan cattle research, limited studies have employed generic chips such as the Illumina BovineSNP50 for genetic diversity analysis, however, existing tools exhibit significant limitations.The design for generic chips is based on imported breed genomes, resulting in inadequate coverage of Qinchuan cattle-specific loci and consequently low detection efficiency for loci associated with core economic traits[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, existing research has not sufficiently elucidated the genetic mechanisms underlying Qinchuan cattle's superior traits, and there is a lack of dedicated molecular detection tools tailored to their unique genetic background. This constrains the precision of genetic diversity assessment and molecular breeding for Qinchuan cattle [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, this study employed methods including homozygous fragment analysis, selection signal detection, and genome-wide association studies to identify candidate loci.\u003c/p\u003e \u003cp\u003eWe developed a custom 10K functional locus gene chip. Utilising this chip technology, we conducted efficient detection and analysis of genetic variation in Qinchuan cattle and selected breeding lines. This aims to precisely identify key genes and molecular markers associated with Qinchuan cattle growth, providing scientific tools and valuable data support for elucidating the genetic basis of their superior traits and optimising breeding strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection and Data Processing\u003c/h2\u003e \u003cp\u003eIn this study, jugular vein blood samples from 95 Qinchuan cattle (Bos taurus) collected by the laboratory in advance at the Shaanxi Qinchuan Cattle Breeding Station were used. Blood DNA was extracted using the standard phenol-chloroform method. Sequencing was performed by BGI using the DNBSEQ-T7 sequencer. Following quality control, gene libraries were constructed with an average fragment size of 300 bp per sample[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Descriptive statistics for nine trait data sets were performed using SPSS. For each trait, sample size, mean, standard deviation, minimum value, maximum value, and coefficient of variation (Coefficient of Variation\u0026thinsp;=\u0026thinsp;Standard Deviation / Mean \u0026times; 100%). Data normality was verified via Shapiro-Wilk tests (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating normal distribution). Outliers (exceeding mean\u0026thinsp;\u0026plusmn;\u0026thinsp;3 standard deviations) were excluded, ultimately retaining complete phenotypic data from 189 Qinchuan cattle for subsequent analysis.\u003c/p\u003e \u003cp\u003eTo conduct an in-depth analysis of Qinchuan cattle genetic information, we expanded our sample collection scope. We obtained 374 samples from the European genetic database for further analysis, which included Qinchuan cattle that exhibited larger body size and superior traits following selective breeding[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].Detailed data and grouping are presented in the supplementary tabl\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003ee1\u003c/span\u003e. The raw sequencing data (FASTQ format) underwent quality control using Trimmomatic (version 0.38) with parameters set as LEADING:20, TRAILING:20, SLIDINGWINDOW:3:15, AVGQUAL:20, MINLEN:35 to filter low-quality bases (Q\u0026thinsp;\u0026lt;\u0026thinsp;20) and adapter contamination sequences [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst, raw sequencing data underwent quality control using Trimmomatic (version 0.38). Parameters LEADING:20, TRAILING:20, SLIDINGWINDOW:3:15, AVGQUAL:20, and MINLEN:35 were used to filter out low-quality sequences and adapter contamination[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Subsequently, the processed sequences were aligned to the reference genome using BWA (Burrows-Wheeler Aligner). The aligned files were converted to BAM format via Samtools and sorted according to chromosomal coordinates. Picard Tools were then employed for deduplication, with parameters REMOVE_DUPLICATES=true, CREATE_INDEX=true, and VALIDATION_STRINGENCY=LENIENT set to remove PCR duplicates and generate an index file, preparing for subsequent variant detection analysis. During the variant detection phase, GATK (version 3.8) modules\u0026mdash;Haplotype Caller, Genotype GVCFs, and Select Variants\u0026mdash;were employed to detect SNPs. Subsequently, the Variant Filtration module was used to filter SNPs based on the following criteria: DP\u0026thinsp;\u0026lt;\u0026thinsp;585 || DP\u0026thinsp;\u0026gt;\u0026thinsp;5272 || 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 || SOR\u0026thinsp;\u0026gt;\u0026thinsp;3.0 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Finally, additional quality control of SNPs was performed using BCFtool software[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], with quality control criteria including genotyping dropout rate\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.05, ultimately retaining 21,183,737 SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Core Region Screening\u003c/h2\u003e \u003cp\u003ePLINK 1.9 software was employed to conduct ROH analysis on sequenced populations of Qinchuan cattle and other subpopulations. Quality control was first performed, excluding SNPs with detection rates\u0026thinsp;\u0026lt;\u0026thinsp;95%, samples with individual detection rates\u0026thinsp;\u0026lt;\u0026thinsp;95%, and SNPs with minor allele frequencies\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or Hardy - Weinberg equilibrium test P-value\u0026thinsp;\u0026lt;\u0026thinsp;10⁻⁶, and excluded non-chromosomal and duplicate SNPs. ROH detection followed the methodology described in previous studies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], employing a sliding window threshold of 0.05, window size of 50 SNPs, permitting 2 missing SNPs and 1 heterozygous SNP per window, with a maximum SNP spacing of 100 kb and minimum density of 1 SNP per 50 kb. The genomic inbreeding coefficient (FROH) was calculated as FROH = \u0026sum;LROH / Lgenome, where \u0026sum;LROH denotes the sum of lengths of all ROH segments on autosomes, and Lgenome represents the total physical length of autosomes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Finally, the --homozyg group parameter was employed to merge adjacent ROH segments into larger ROH islands based on predefined ROH parameters.\u003c/p\u003e \u003cp\u003eThe study employed VCFtools to calculate genetic differentiation indices (Fst) and nucleotide diversity (π) values between Qinchuan cattle populations and other sequenced breeds, retaining only loci unique to Qinchuan cattle. Fst analysis was conducted by calculating weighted FST values across all SNPs within the step size, followed by arithmetic averaging to obtain the mean FST value for all SNPs within the window. π analysis calculated nucleotide diversity values within the window [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Combining Fst and π analysis provides a more comprehensive reflection of population genetic structure and the influence of natural selection. In this study, the top 5% of values were used as a threshold to identify regions under selection, which were subsequently annotated and visualised. To systematically analyse the biological functions of the identified candidate genes, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the online platform DAVID 6.8, with a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eBased on a genome-wide SNP dataset from 193 Qinchuan cattle individuals with somatic trait data, GWAS analyses was conducted for nine quantitative traits. The primary software employed for GWAS was GEMMA [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], utilising a mixed linear model for analysis. The first principal component and sex matrix were employed as covariates, with significance and suggestive site thresholds set at 5.5 \u0026times;10⁻⁸(1/17,937,451) respectively. Manhattan and QQ plots were generated using the CMplot package within the R software[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To further analyse SNP functionality, we employed a Perl script to identify significant SNPs within 100 kb upstream and downstream regions as candidate gene-targeting areas based on Ensembl database screening.\u003c/p\u003e \u003cp\u003eDeduplication of previously screened sites was performed, excluding duplicate sites, low-polymorphism sites (MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and sites with high dropout rates (dropout rate\u0026thinsp;\u0026gt;\u0026thinsp;0.01); sites were integrated according to \"functional priority\" and \"genomic uniformity\" principles: prioritise retaining SNPs within exonic regions and known functional genes, while ensuring uniform SNP distribution across the genome. This process ultimately identifies core SNPs for chip construction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Microarray Synthesis\u003c/h2\u003e \u003cp\u003eBased on the reference genome Bos taurus (ARS-UCD1.2_Btau5.0.1Y), the genome was partitioned into 26,204 sites using 95,000 bp windows. A sliding window scoring strategy was employed to select key candidate target sites, with specific DNA probes designed for each qualified site. Prioritising core functional sites identified through multidimensional screening, probes were designed with dual-probe coverage for critical sites to enhance capture stability. Probes measured 100\u0026ndash;120 bp with 35\u0026ndash;65% GC content, optimised via kmer alignment and deep learning models to ensure fewer than five homologous regions (\u0026ge;\u0026thinsp;95% sequence similarity). Optimised probes underwent in vitro transcription with biotin labelling. Labelled probes are mixed at equimolar concentrations to form a homogenised probe pool, supplemented with three exogenous quality control reference probes. The pool concentration is adjusted to 100 ng/\u0026micro;L and stored sealed at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe chip assembly employed a liquid-phase capture scheme utilising targeted sequencing genotyping (GBTS) technology. The core components comprised: biotin-labelled probe pool, PCR amplification primers, hybridisation buffer, and magnetic bead enrichment reagent. All components were encapsulated according to standardised procedures. Each batch of chips included one positive control (Qinchuan cattle DNA sample with known genotype) and one negative control (no template DNA) for experimental quality control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Chip Validation\u003c/h2\u003e \u003cp\u003eTo comprehensively evaluate chip performance, genotyping was performed on 135 samples (see supplementary table for details): Genomic DNA libraries were constructed using the GenoBaits DNA-seq library preparation kit. The library was hybridised with chip probes at 65\u0026deg;C for 16 hours; Dynabeads MyOne Streptavidin C1 were added to capture hybridised fragments. Following PCR amplification and magnetic bead purification, PE150 sequencing was performed on the MGISEQ-2000 platform. Sequencing data quality control was conducted using FastQC, with reference genome alignment via BWA and SNP genotyping performed using GATK. The SNP detection rate for each sample was calculated for each sample (number of detected SNPs / total core SNPs \u0026times; 100%). For Qinchuan cattle samples undergoing whole-genome resequencing, compare chip-detected SNP genotypes with resequencing data genotypes to calculate concordance rate (number of identical genotype sites / total detected sites \u0026times; 100%). Statistically analyse the MAF distribution and proportion of polymorphic sites among core SNPs in the sample (number of SNPs with MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01 / total core SNPs \u0026times; 100%), investigating the proportion of target polymorphic sites.\u003c/p\u003e \u003cp\u003ePrincipal component analysis was performed using PLINK software with the --pca 10 parameter to extract the top 10 principal components. The calculation was based on allele frequencies at core target loci, generating principal component score files and variance explained by principal components files. The ggplot2 package in R software was employed to read the principal component score files and plot scatter plots for the top principal components[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, VCF2Dis was employed to compute genetic differences between samples, generating a distance matrix for constructing a neighbour-neighbour tree. SNP data were processed to account for minor missing sites, preventing sample exclusion due to localised data gaps. The ITOLsoftware package was used to read the phylogenetic tree file and refine the tree[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Core Site Screening Results\u003c/h2\u003e \u003cp\u003eIn the statistics of ROH segment across populations(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), distinct characteristics emerged in the proportion and quantity of short The proportion of short ROH segments (0.5\u0026ndash;1 Mb) was 76.61% (44,475 segments) in the sampled Qinchuan cattle, 79.40% ( 18,116 segments), and 14.97% (6,832 segments) in the medium-length ROH segments (1\u0026ndash;2 Mb) in the selected Qinchuan cattle, significantly the proportion of 1-2Mb medium-length ROH segments was 14.97% (6,832), significantly higher than the 12.02% (2,178) observed in the selected Qinchuan cattle population. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC indicates more recent inbreeding events in the sampled Qinchuan cattle population, medium, and long segments among the Qinchuan cattle, the Qinchuan cattle population, and the selected Qinchuan cattle population. higher than the 1202% (18,116 segments) observed in the selected Qinchuan cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In terms of proportion, both populations were primarily composed of short ROHs of 0.5\u0026ndash;1 Mb. The sampled Qinchuan cattle exhibited an average inbreeding coefficient of 0.055957, slightly higher than that of the selected Qinchuan cattle. This indicates that the overall inbreeding level of the sampled Qinchuan cattlemarginally higher than that of the selected Qinchuan cattle. Subsequently, the study analysed the distribution of regions of shared haplotypes (ROH) within the population and visualised them based on SNP positions on chromosomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). The majority of the 95 loci annotated within ROH islands were located within growth-related genes, providing a prioritisation basis for functional locus screening on the chip.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify candidate genomic regions under selection during growth and development in Qinchuan cattle compared to other populations, this study employed the Fst-π ratio method to detect selection signals, using the top 5% threshold as the screening criterion. Regions significantly detected by both methods were deemed genuinely selected. Figure\u0026nbsp;2 displays Manhattan plots of different Qinchuan cattle populations generated using the Fst-π ratio method with a 5% threshold. The results reveal distinct selection regions across populations identified by the Fst-π ratio approach,with a total of 291 overlapping selected sites detected. Functional enrichment analysis of these overlapping genes was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO). The most significantly enriched KEGG pathway was Metabolic pathways. GO terms with significant association (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) among these overlapping genes included: nucleoplasm, protein binding, and cytoplasm.\u003c/p\u003e \u003cp\u003eGenome-wide association studies were conducted on eight body-size traits inQinchuan cattle(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The QQ plot indicated satisfactory model fit. A total of 137 significant SNPs (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;8 were identified in 193 individuals) identified, with 14 genes located within exonic regions across all loci. PubMed literature review revealed these genes are associated with signal transduction, key pathways, and cell proliferation, and are closely linked to growth and development in Qinchuan cattle. DHRS3, as a member of the short-chain dehydrogenase/reductase family, catalyses the reduction of all-trans retinaldehyde to retinol, maintaining its dynamic equilibrium and promoting skeletal muscle regeneration during myoblast differentiation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. CENPS functions primarily in chromosome segregation and the maintenance of genomic stability. CENPS maintains centromere integrity during the G1 phase via homologous recombination repair mechanisms, preventing chromosomal translocations[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. SIK2 AMP-activated protein, a member of thekinase family, functions as a serine/threonine kinase. It primarily participates in multiple biological processes\u0026mdash;including energy metabolism regulation and cell cycle progression\u0026mdash;by phosphorylating downstream target proteins [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. ANKRD11 is a multifunctional protein containing ankyrin repeat domains, playing a pivotal role in development and disease through involvement in transcriptional regulation, chromatin remodelling, and cellular signalling pathways[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. UNC5B, a member of the UNC5 family, initiates pro-apoptotic signals in the absence of key factors. By activating downstream pathways, it induces apoptosis, participating in the clearance of surplus cells during tissue development to maintain cellular homeostasis. UNC5B regulates endothelial cell migration, proliferation, and vascular branching, thereby sustaining the normal structure of vascular networks[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reference genome (ARS-UCD1.2_Btau5.0.1Y) was divided into 26,204 sites using 95,000-base window lengths across autosomes, yielding 23,183 successfully designed probes. Combining with the previously mentioned loci and excluding loci with a missing rate exceeding 50% in the test population, 20,566 loci were ultimately retained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Microarray Synthesis Results\u003c/h2\u003e \u003cp\u003eThe study retained 20,566 valid probes, corresponding to 20,566 core target sites. The core target sites were evenly distributed across 29 autosomes, with no obvious clusters or gaps. Sites on chromosomes 1\u0026ndash;6 exhibited higher density (8\u0026ndash;10 sites per Mb on average), while chromosomes 25\u0026ndash;29 showed relatively lower density (3\u0026ndash;5 sites per Mb on average). The overall distribution met the uniformity requirements for chip design (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctional sites (trait-associated\u0026thinsp;+\u0026thinsp;selected genes\u0026thinsp;+\u0026thinsp;immune-related) accounted for 62.3%, while Qinchuan cattle-specific sites constituted 28.7%, ensuring sites balanced functional targeting with genomic coverage. Exon regions contained 1,913 sites (9.3%), of which 832 (43.5%) were missense mutation sites; 11,973 sites (58.7%) were located in intron regions, including 621 sites associated with selected genes; 1,234 functional sites (6.0%) were in non-coding regions, comprising 412 sites in UTR regions and 387 sites associated with lncRNAs, ensuring coverage of key gene expression regulatory regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Chip Result Detection\u003c/h2\u003e \u003cp\u003eThe core SNP detection rate for test samples demonstrated excellent performance, with a population-average detection rate exceeding 98.48%. This indicates the chip's high efficacy in SNP genotyping for Qinchuan cattle samples, enabling stable capture of genotype information for the vast majority of core target loci. This meets the data integrity requirements for subsequent genomic analysis. The genotypic concordance rate for duplicate sample pairs reached 99.01%, demonstrating the chip's excellent repeatability and reliability in Qinchuan cattle SNP genotyping. Genotyping results exhibit minimal susceptibility to experimental variation, with data credibility meeting genomic research standards. A total of 14,320 polymorphic sites (69.66%) exhibited MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.1, with 7,842 (54.7%) located within functional genes. This indicates that the chip sites balance genetic polymorphism with functional relevance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The proportion of polymorphic sites remained consistently high, indicating that the core SNPs selected for the chip effectively cover the genetic polymorphism of the Qinchuan cattle population and meet the requirements for polymorphism sites in genetic diversity assessment studies.\u003c/p\u003e \u003cp\u003eThe NJ tree construction results in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD show that all Qinchuan cattle samples clustered into a single branch with bootstrap values\u0026thinsp;\u0026ge;\u0026thinsp;92 (high node reliability), clearly distinguished from branches of reference breeds such as Angus and Jinnan cattle, with no cross-breed clustering observed. Consistent results from both analyses indicate that the core target sites selected via chip screening effectively capture breed-specific genetic characteristics of Qinchuan cattle, enabling precise analysis of their population genetic structure and breed identification ; Whether based on the 20K chip loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA)or the whole-genome loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), Qinchuan cattle and non-Qinchuan control samples formed distinct population boundaries in the PC1-PC2 scatter plot. Qinchuan cattle samples clustered into an independent group, significantly separated from other breeds. PC1 explained 35.17% of genetic variation and PC2 explained 12.36%. This indicates that the 20K chip loci and genome-wide loci exhibithigh consistency in reflecting population genetic structure, demonstrating that these chip loci can effectively capture genetic differentiation information between populations and serve as a reliable alternative for genome-wide analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe size of domesticated animals is not only a commercial standard in livestock production but also a central focus in advancing research on poultry evolution and development [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. As a prime example of a trait influenced by numerous complex factors, body size is affected by a large number of genes, which are often distributed across biologically similar loci[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The Research findings indicate genetic divergence between wild-type Qinchuan cattle and selectively bred Qinchuan cattle. The selectively bred population exhibits lower inbreeding levels due to more extensive hybrid gene exchange, consistent with their distinct breeding histories. This insight aids understanding of their genetic differentiation and evolutionary adaptation. This study, targeting the Qinchuan cattle's breed characteristics of rapid growth, tender meat quality, and strong adaptability successfully customised a 20K functional locus gene chip targeting. This addresses the current lack of dedicated tools in Qinchuan cattle genetic research and the low efficiency of screening markers for core economic traits.\u003c/p\u003e \u003cp\u003eSelection signal analysis (Fst versus π ratio method) identified 291 overlapping regions of recent common ancestry (ROH), which encompass multiple genes associated with bovine growth. Notably, a ROH island overlapping with selection signals on chromosome 4 contained 24 genes involved in metabolic regulation,which collectively support cellular proliferation and growth development. GWAS analysis screened 137 significant SNPs, with genes in three overlapping regions exhibiting critical functions. MAN1A2, as a member of the mannosidase family, regulates glycoprotein modification by catalysing N-glycan trimming in the Golgi apparatus. This process directly influencing protein folding, transport, and stability while extensively participating in organ development. Duringoocyte development, its loss-of-function leads to ovarian dysfunction in cattle, disrupting oocyte maturation and subsequent embryonic development capacity. Additionally, animal models reveal its regulation of ciliary function impacts the transport of oocytes by fallopian tube cilia[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. FAM83F promotes cell progression from G1 to S phase by interacting with cyclin or CDK4/6, accelerating the cell cycle and activating proliferation-related signalling pathways, thereby enhancing the expression of bovine proliferation-associated genes[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. KIFC2 participates in intracellular material transport and division-related processes, and stabilizes regulatory factors governing the G1-to-S phase transition. The stability accelerates cell cycle progression and promotes cell proliferation. Concurrently, KIFC2 amplification correlates with pyrimidine metabolism activation. Pyrimidine metabolism supplies nucleotides as raw materials for DNA synthesis, providing the material foundation for cellular growth and division, thereby further driving bovine cell proliferation[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe core value of gene chips lies in their high compatibility with the genetic background of target breeds, with detection rates and genotypic concordance serving as key indicators of suitability. In this study, the average detection rate for core SNPs in samples exceeded 98.48%, while genotypic concordance in technical replicates reached 99.01%. Both metrics significantly surpassed conventional standards for genomic chip applications and outperformed most universal beef cattle chips when applied to local breeds[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. By employing methods such as selection signals and genome-wide association studies to identify Qinchuan cattle-specific loci, thisstudy effectively circumvented the inadequate genomic matching issues inherent in generic chips when applied to local breeds. The high detection rate indicates the chip's stable capture of core genetic information in Qinchuan cattle, minimising interference from data loss in subsequent analyses. The high concordance demonstrates reliable genotyping results, providing high-quality data support for genetic diversity assessment and molecular marker screening, which fully reflects the chip's excellent compatibility with the Qinchuan cattle genetic background[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e As a locally adapted breed with a long history of selective breeding, the genetic diversity and distribution of polymorphic sites in Qinchuan cattle directly constitute the genetic basis of breed characteristics. In the samples of this study polymorphic sites with MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.1 accounted for 69.66% of the total, which was significantly higher thanthe conventional level of low-polymorphism chips. This indicates that the core SNPs selected for the chip effectively cover key genetic variations within the Qinchuan cattle population. Regarding breed characteristics, Qinchuan cattle exhibit polygenic regulation across economic traits including growth development, meat quality, and stress resistance. The functional loci integrated into the chip precisely encompass regions associated with these traits[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This research provides a basis for constructing germplasm resource fingerprinting and breed identification, while also laying the groundwork for subsequent genome-wide association studies. The functional loci on this chip facilitate the localisation of key genes associated with target traits, accelerating the elucidation of the genetic mechanisms underlying Qinchuan cattle's economic traits.\u003c/p\u003e \u003cp\u003eCompared to traditional methods, the chip exhibitsthree core advantages: strong breed specificity, precise functional orientation, and cost-effectiveness compatible withindustrial applications. The customised Qinchuan cattle 10K functional locus gene chip developed in this study, with its high detection rate, high consistency, and strong specificity, provides an efficient tool for genetic analysis and molecular breeding of local cattle breeds. This chip not only supports fundamental research such as genetic diversity assessment and germplasm resource conservation in Qinchuan cattle but also directly serves industrial demands for breed improvement, facilitating the transition of Qinchuan cattle breeding from \"traditional phenotypic selection\" to \"molecular design breeding\".\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eTo address the conservation and molecular breeding requirements of Qinchuan cattle, this study employed targeted sequencing-based genotyping technology. Through a multidimensional screening strategy incorporating GWAS, ROH analysis, selection signal analysis, and supplementary literature databases, a synthetic chip comprising 20,566 core target loci was finalised. Validation results demonstrated the chip's outstanding core performance. Functional validation further confirmed the chip's capability for Qinchuan cattle breed identification, providing a reliable tool for genetic analysis of key traits such as body measurements and body weight. Compared to generic commercial chips, the Qinchuan cattle chip offers significant advantages including high specificity and low cost. The successful development of this chip not only fills the gap in dedicated Qinchuan cattle chips but also provides efficient technical support for the precise conservation of Qinchuan cattle genetic resources, the identification of core breeding value genes, and the establishment of a genomic selection breeding system.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBTS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenotyping by Targeted Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQTL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative Trait Loci\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-Wide Association Studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomic Selection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRuns of Homozygosity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFst\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFixation Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eπ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNucleotide Diversity\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinor Allele Frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNJ Tree\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeighbor-Joining Tree\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHPC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Performance Computing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBWA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBurrows-Wheeler Aligner\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGATK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome Analysis Toolkit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVCF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariant Call Format\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBCFtools\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBinary Call Format tools\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLINK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole-Genome Association Analysis Toolset\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePicard Tools\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePicard Sequence Processing Tools\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTrimmomatic\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIllumina Sequence Data Trimmer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFASTQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFast Quality Format\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBinary Alignment Map\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDepth of Coverage\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality by Depth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFisher Strand\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMapping Quality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMQRankSum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMapping Quality Rank Sum Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eReadPosRankSum\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRead Position Rank Sum Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrand Odds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFROH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenomic Inbreeding Coefficient Based on ROH\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUntranslated Region\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003elncRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLong Non-Coding RNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDatabase for Annotation, Visualization and Integrated Discovery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEMMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide Efficient Mixed-Model Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMplot\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCircular Manhattan Plot\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eggplot2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrammar of Graphics Plot 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVCF2Dis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVCF to Genetic Distance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITOL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInteractive Tree Of Life\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMGISEQ-2000\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMGI Sequencing Platform 2000\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNBSEQ-T7\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDNA Nanoball Sequencing Platform T7\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDynabeads MyOne Streptavidin C1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDynabeads MyOne Streptavidin C1 Magnetic Beads\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolymerase Chain Reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePE150\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePaired-End 150bp\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARS-UCD1.2_Btau5.0.1Y\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBos taurus Reference Genome Version ARS-UCD1.2_Btau5.0.1Y\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003eAll procedures were conducted in accordance with the Chinese laws on animal experimentation and were approved by the Northwest A\u0026amp;F University\u0026rsquo;s Experimental Animal Management Committee (Approval No. DK2022065), and conducted under the authority of the Project License. Animal use and care were in accordance with the ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org/\u003c/span\u003e\u003cspan address=\"https://arriveguidelines.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was supported by Key R\u0026amp;D Program of Ningxia (2024BBF01007); National Natural Science Foundation of China (32573186); STI2030-Major Projects (2023ZD040480206); Natural Science Foundation of Henan (252300420687); China Agriculture Research System of MOF and MARA (CARS-37).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.Z., J.M., and L.S.were responsible for participating in sample collection, data collation, and experimental operations related to chip validation.D.W.\u0026sup2; provided relevant research data support.X.G., W.M., N.C., X.X., Y.M., C.L., and Y.J.guided the experimental design and data analysis ideas, offered key technical support, and participated in the formulation of research schemes and discussion of results.Y.H.* (corresponding author) oversaw the entire research project, being responsible for experimental design, result interpretation, and manuscript writing and revision.All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the China National Center for Bioinformation (CNCB) repository, with the accession number PRJCA054984.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDarwin C. The Variation of Animals and Plants under Domestication. London: John Murray; 1868.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSutter NB, Bustamante CD, Chase K, et al. A single IGF1 allele is a major determinant of small size in dogs. Science. 2007;316:112\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1137045\u003c/span\u003e\u003cspan address=\"10.1126/science.1137045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGou X, Wang Z, Li N, et al. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia. Genome Res. 2014;24:1308\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.171876.113\u003c/span\u003e\u003cspan address=\"10.1101/gr.171876.113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMakvandi-Nejad S, Hoffman GE, Allen JJ, et al. Four loci explain 83% of size variation in the horse. PLoS ONE. 2012;7:e39929. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0039929\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0039929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaj A, Snochowski M, Siadkowska E, et al. Polymorphism in genes of growth hormone receptor (GHR) and insulin-like growth factor-1 (IGF1) and its association with both the IGF1 expression in liver and its level in blood in Polish Holstein-Friesian cattle. Neuro Endocrinol Lett. 2008;29(6):981\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadollahpour Nanaei H, Esmailizadeh A, Ayatollahi Mehrgardi A, et al. Comparative population genomic analysis uncovers novel genomic footprints and genes associated with small body size in Chinese pony. BMC Genomics. 2020;21:496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-020-06887-2\u003c/span\u003e\u003cspan address=\"10.1186/s12864-020-06887-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang MS, Huo YX, Li Y, et al. Comparative population genomics reveals genetic basis underlying body size of domestic chickens. J Mol Cell Biol. 2016;8:542\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jmcb/mjw044\u003c/span\u003e\u003cspan address=\"10.1093/jmcb/mjw044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang K, Hu H, Tian Y, et al. The chicken pan-genome reveals gene content variation and a promoter region deletion in IGF2BP1 affecting body size. Mol Biol Evol. 2021;38:5066\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/molbev/msab231\u003c/span\u003e\u003cspan address=\"10.1093/molbev/msab231\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiao YW, Peng MS, Wu GS, et al. Chicken domestication: an updated perspective based on mitochondrial genomes. Heredity. 2013;110:277\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/hdy.2012.83\u003c/span\u003e\u003cspan address=\"10.1038/hdy.2012.83\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAi H, Fang X, Yang B, et al. Adaptation and possible ancient interspecies introgression in pigs identified by whole-genome sequencing. Nat Genet. 2015;47:217\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3199\u003c/span\u003e\u003cspan address=\"10.1038/ng.3199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAxelsson E, Ratnakumar A, Arendt ML, et al. The genomic signature of dog domestication reveals adaptation to a starch-rich diet. Nature. 2013;495:360\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature11837\u003c/span\u003e\u003cspan address=\"10.1038/nature11837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan H, Wang T, Li Y, et al. Development and validation of a 1 K sika deer (Cervus nippon) SNP chip. BMC Genom Data. 2021;22(1):35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12863-021-00994-z\u003c/span\u003e\u003cspan address=\"10.1186/s12863-021-00994-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSudrajad P, Kusminanto RY, Volkandari SD, et al. Genomic structure of Bali cattle based on linkage disequilibrium and effective population size analyses using 50K single nucleotide polymorphisms data. Vet World. 2022;15(2):449\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14202/vetworld.2022.449-454\u003c/span\u003e\u003cspan address=\"10.14202/vetworld.2022.449-454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTolone M, Sardina MT, Criscione A, et al. High-density single nucleotide polymorphism markers reveal the population structure of 2 local chicken genetic resources. Poult Sci. 2023;102(7):102692. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psj.2023.102692\u003c/span\u003e\u003cspan address=\"10.1016/j.psj.2023.102692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalti Y, Vallejo RL, Purcell MK, et al. Genome-wide association analysis of resistance to infectious haematopoietic necrosis virus in two rainbow trout aquaculture lines confirms oligogenic architecture with several moderate-effect quantitative trait loci. Front Genet. 2024;15:1394656doi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2024.1394656\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2024.1394656\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa C, Liu L, Liu T, et al. QTL mapping for important agronomic traits using a Wheat55K SNP array-based genetic map in tetraploid wheat. Plants (Basel). 2023;12(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/plants12040847\u003c/span\u003e\u003cspan address=\"10.3390/plants12040847\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMore M, Veli E, Cruz A, et al. Genome-Wide Association Study of Fibre Diameter in Alpacas. Anim (Basel). 2023;13(21). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ani13213316\u003c/span\u003e\u003cspan address=\"10.3390/ani13213316\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeidaritabar M, Bink MCAM, Dervishi E, et al. Genome-wide association studies for additive and dominance effects for body composition traits in commercial crossbred Pi\u0026eacute;train pigs. J Anim Breed Genet. 2023;140(4):413\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jbg.12768\u003c/span\u003e\u003cspan address=\"10.1111/jbg.12768\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia A, Tsuruta S, Gao G, et al. Genomic selection models substantially improve the accuracy of genetic merit predictions for fillet yield and body weight in rainbow trout using a multi-trait model and multi-generation progeny testing. Genet Sel Evol. 2023;55(1):11doi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12711-023-00782-6\u003c/span\u003e\u003cspan address=\"10.1186/s12711-023-00782-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMastrangelo S, Ben-Jemaa S, Perini F, et al. Genome-wide mapping of selection signatures using a high-density array identified candidate genes for growth traits and local adaptation in chickens. Genet Sel Evol. 2023;55(1):20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12711-023-00790-6\u003c/span\u003e\u003cspan address=\"10.1186/s12711-023-00790-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo X, Puritz JB, Wang Z, et al. Development and evaluation of high-density SNP arrays for the Eastern Oyster Crassostrea virginica. Mar Biotechnol (NY). 2023;25(1):174\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10126-022-10191-3\u003c/span\u003e\u003cspan address=\"10.1007/s10126-022-10191-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeumann GB, Korkuc P, Arends D, et al. Design and performance of a bovine 200 k SNP chip developed for endangered German Black Pied cattle (DSN). BMC Genomics. 2021;22(1):905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-021-08237-2\u003c/span\u003e\u003cspan address=\"10.1186/s12864-021-08237-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuratannon N, van Wijck RTA, Broer L, et al. Rapid low-cost microarray-based genotyping for genetic screening in primary immunodeficiency. Front Immunol. 2020;11:614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2020.00614\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2020.00614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalog K, Mizeranschi AE, Wanjala G, et al. Application potential of chicken DNA chip in domestic pigeon species \u0026ndash; preliminary results. Saudi J Biol Sci. 2023;30(3):103594doi. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.sjbs.2023.103594\u003c/span\u003e\u003cspan address=\"10.1016/j.sjbs.2023.103594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamorodnitsky E, Datta J, Jewell BM, et al. Comparison of custom capture for targeted next-generation DNA sequencing. J Mol Diagn. 2015;17(1):64\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmoldx.2014.09.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jmoldx.2014.09.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Z, Wang H, Tao J, et al. Development of multiple SNP marker panels affordable to breeders through genotyping by target sequencing (GBTS) in maize. Mol Breed. 2019;39(3):37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.doi.org/10.1007/s11032-019-0940-4\u003c/span\u003e\u003cspan address=\".10.1007/s11032-019-0940-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan S, Li W, Jin H, et al. Development and validation of a 54K genome-wide liquid SNP Chip Panel by target sequencing for dairy goat. Genes (Basel). 2023;14(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes14051122\u003c/span\u003e\u003cspan address=\"10.3390/genes14051122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Gao X, Zhang Y, et al. Genome-wide assessment of genetic diversity and population structure insights into admixture and introgression in Chinese indigenous cattle. BMC Genet. 2018;19(1):114. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12863-018-0705-9\u003c/span\u003e\u003cspan address=\"10.1186/s12863-018-0705-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Wu H, Zhang W, et al. Development and validation of a 5K low-density SNP chip for Hainan cattle. BMC Genomics. 2024;25(1):873. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-024-10753-w\u003c/span\u003e\u003cspan address=\"10.1186/s12864-024-10753-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaza SHA, Khan R, Abdelnour SA, et al. Advances of Molecular Markers and Their Application for Body Variables and Carcass Traits in Qinchuan Cattle. Genes (Basel). 2019;10(9):717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/genes10090717\u003c/span\u003e\u003cspan address=\"10.3390/genes10090717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu H, Yu S, Guo J, et al. Genome-Wide Association Study Reveals Novel Loci Associated with Body Conformation Traits in Qinchuan Cattle. Anim (Basel). 2023;13(23):3628. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ani13233628\u003c/span\u003e\u003cspan address=\"10.3390/ani13233628\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btu170\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btu170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinf. 2013;43(1110):11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/0471250953.bi1110s43\u003c/span\u003e\u003cspan address=\"10.1002/0471250953.bi1110s43\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gigascience/giab008\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giab008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowrigan DP, Simonson MA, Keller MC. Detecting autozygosity through runs of homozygosity: a comparison of three autozygosity detection algorithms. BMC Genomics. 2011;12:460. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1471-2164-12-460\u003c/span\u003e\u003cspan address=\"10.1186/1471-2164-12-460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeripolli E, Metzger J, de Lemos MVA, et al. Autozygosity islands and ROH patterns in Nellore lineages: evidence of selection for functionally important traits. BMC Genomics. 2018;19(1):680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-018-5060-8\u003c/span\u003e\u003cspan address=\"10.1186/s12864-018-5060-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. Bioinformatics. 2011;27:2156\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btr330\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btr330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang DA, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nprot.2008.211\u003c/span\u003e\u003cspan address=\"10.1038/nprot.2008.211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 2012;44(7):821\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.2310\u003c/span\u003e\u003cspan address=\"10.1038/ng.2310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin L, Zhang H, Tang Z, et al. rMVP: A Memory-efficient, Visualisation-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study. Genomics Proteom Bioinf. 2021;19:619\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gpb.2020.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gpb.2020.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElhaik E. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Sci Rep. 2022;12(1):14683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-14395-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-14395-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, He W, Tai S, et al. VCF2Dis: an ultra-fast and efficient tool to calculate pairwise genetic distance and construct population phylogeny from VCF files. Gigascience. 2025;14:giaf032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gigascience/giaf032\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giaf032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin CJ, Megens HJ, Barrio AM, et al. Strong signatures of selection in the domestic pig genome. Proc Natl Acad Sci U S A. 2012;109:19529\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1217149109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1217149109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Ruan Y, Jiang C, et al. Tissue-Specific Expression of the Porcine DHRS3 Gene and Its Impact on the Proliferation and Differentiation of Myogenic Cells. Anim (Basel). 2025;15(8):1101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ani15081101\u003c/span\u003e\u003cspan address=\"10.3390/ani15081101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi Z, Jia Y, Lu R, et al. E2F1-driven CENPM expression promotes glycolytic reprogramming and tumourigenicity in glioblastoma. Cell Biol Toxicol. 2024;41(1):4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10565-024-09945-7\u003c/span\u003e\u003cspan address=\"10.1007/s10565-024-09945-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Yu X, Wu S, et al. Identification of candidate SNPs and genes associated with resistance to nervous necrosis virus in leopard coral grouper (Plectropomus leopardus) using GWAS. Fish Shellfish Immunol. 2024;144:109295. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.fsi.2023.109295\u003c/span\u003e\u003cspan address=\"10.1016/j.fsi.2023.109295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J, Shao H, Sun M, et al. Genomic insights into the genetic diversity and genetic basis of body height in endangered Chinese Ningqiang ponies. BMC Genomics. 2025;26(1):292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-025-11484-2\u003c/span\u003e\u003cspan address=\"10.1186/s12864-025-11484-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang C, Xie Z, Wang N. Single-cell RNA sequencing of adult primate neocortex reveals the regulatory dynamics of neural plasticity. Am J Transl Res. 2025;17(4):2562\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.62347/ZEOR5569\u003c/span\u003e\u003cspan address=\"10.62347/ZEOR5569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRimbault M, Beale HC, Schoenebeck JJ, et al. Derived variants at six genes explain nearly half of size reduction in dog breeds. Genome Res. 2013;23:1985\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gr.157339.113\u003c/span\u003e\u003cspan address=\"10.1101/gr.157339.113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevlin RH, Sakhrani D, Tymchuk WE, et al. Domestication and growth hormone transgenesis cause similar changes in gene expression in coho salmon (Oncorhynchus kisutch). Proc Natl Acad Sci U S A. 2009;106:3047\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0809798106\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0809798106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang JH, Zhao QY, Zhou YL, et al. Application and prospect of gene chips in genetic breeding of livestock and poultry. Yi Chuan. 2023;45(12):1114\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.16288/j.yczz.23-233\u003c/span\u003e\u003cspan address=\"10.16288/j.yczz.23-233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuellar CJ, Zayas GA, Amaral TF, et al. Ovarian hyperplasia linked to a mutation in MAN1A2 in a cow with excessive follicular growth and functional oocytes. Vet Res Commun. 2024;48(5):3239\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11259-024-10435-8\u003c/span\u003e\u003cspan address=\"10.1007/s11259-024-10435-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones RA, Cooper F, Kelly G, et al. Zebrafish reveal new roles for Fam83f in hatching and the DNA damage-mediated autophagic response. Open Biol. 2024;14(10):240194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1098/rsob.240194\u003c/span\u003e\u003cspan address=\"10.1098/rsob.240194\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang SY, Jin ML, Andriani L, et al. Kinesin-like protein KIFC2 stabilises CDK4 to accelerate growth and confer resistance in HR+/HER2- breast cancer. J Clin Invest. 2025;135(12):e183531. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1172/JCI183531\u003c/span\u003e\u003cspan address=\"10.1172/JCI183531\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafter P, Gormley IC, Parnell AC, et al. Concordance rate between copy number variants detected using either high- or medium-density single nucleotide polymorphism genotype panels and the potential of imputing copy number variants from flanking high density single nucleotide polymorphism haplotypes in cattle. BMC Genomics. 2020;21(1):205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12864-020-6627-8\u003c/span\u003e\u003cspan address=\"10.1186/s12864-020-6627-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu X, Yang Y, Zhang Y, et al. The relationship between myofibre characteristics and meat quality of Chinese Qinchuan and Luxi cattle. Anim Biosci. 2021;34(4):743\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5713/ajas.20.0066\u003c/span\u003e\u003cspan address=\"10.5713/ajas.20.0066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Qinchuan cattle, liquid-phase SNP chip, locus screening","lastPublishedDoi":"10.21203/rs.3.rs-8742497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8742497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a core indigenous beef cattle breed in China, Qinchuan cattle face bottlenecks in genetic improvement due to inadequate adaptability of generic SNP chips and the absence of dedicated molecular detection tools. This study developed and validated a Qinchuan-specific 20K low-density liquid-phase SNP chip based on targeted sequencing genotyping technology. Whole-genome resequencing data from 193 Qinchuan cattle were integrated with data from 274 external reference populations sourced from the European Gene Database. Core loci were selected through genome-wide homozygous fragment analysis and selection signal detection. Following quality control and association studies, 20k core target loci were finalized for chip synthesis. Chip validation demonstrated an average detection rate of 98.48% for core loci, with 99.01% genotypic consistency across technical replicates. Principal component analysis and phylogenetic tree analysis confirmed the chip's ability to accurately distinguish Qinchuan cattle from other breeds. This chip balances low cost and high specificity, providing a dedicated tool for genetic diversity assessment, breed identification, and genomic selection in Qinchuan cattle. It also establishes a technical foundation for innovative utilization of local beef cattle germplasm resources.\u003c/p\u003e","manuscriptTitle":"Development and Application Validation of a 20K-Liquid Chip for the Qinchuan Cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 18:43:54","doi":"10.21203/rs.3.rs-8742497/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-16T10:15:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T18:57:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-08T02:17:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2026-02-08T02:12:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcdaf964-4aaa-4038-8301-b4e66d82eb88","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T18:43:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 18:43:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8742497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8742497","identity":"rs-8742497","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}