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Fatty acid composition (FA) is an important indicator of meat quality in beef cattle. We investigated potential functional candidate genes for FA in beef cattle by integrating genomic and transcriptomic dataset through multiple strategies. In this study, we observed 65 SNPs overlapping with five candidate genes (CCDC57, FASN, HDAC11, ALG14, and ZMAT4) using two steps association based on the imputed sequencing variants. Using multiple traits GWAS, we further identified three significant SNPs located in the upstream of FASN and one SNP (chr19:50779529) was embedded in FASN. Of those, two SNPs were further identified as the cis-eQTL based on transcriptomic analysis of muscle tissues. Moreover, the knockdown of FASN yielded a significant reduction in intracellular triglyceride content in preadipocytes and impeded lipid droplet accumulation in adipocytes. RNA-seq analysis of preadipocytes with FASN interference revealed that the differentially expressed genes were enriched in cell differentiation and lipid metabolic pathway. Our study underscored the indispensable role of FASN in orchestrating adipocyte differentiation, and fatty acid metabolism. The integrative analysis with multiple strategies may contribute to the understanding of the genetic architecture of FA in farm animals. Multiple strategies association revealed candidate genes for fatty acid composition in cattle and function validation of FASN. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Angus cattle are highly valued by breeders due to their outstanding adaptability, superior meat quality, and excellent reproductive performance. In recent years, the Ningxia region has imported Angus cattle and frozen semen from Australia, the United States, and New Zealand. To better understand the genetic improvement in the selection history of the Ningxia Angus population, we performed whole-genome resequencing of 282 Angus cattle from the Ningxia region to identify selection signatures associated with improved productivity and adaptability in this study. These findings may provide valuable insights for future livestock breeding programs. The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein (PAG) family genes and immune-related genes such as UL16-binding protein 21 (ULBP21), CD1b molecule (CD1B), and tumor necrosis factor ligand superfamily member 11 (TNFSF11). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies. [ABSTRACT FROM AUTHOR] more...

Background: Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging. Methods: We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis. Results: We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database. Conclusions: Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle. [ABSTRACT FROM AUTHOR] more...

Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process. [ABSTRACT FROM AUTHOR] more...

Background: Genomic mating (GM) can effectively control the growth rate of inbreeding in population and achieve long-term sustainable genetic progress. However, the design of GM method and assessment of its effects during long-term selection have not been fully explored in beef cattle breeding. Results: In this study, we constructed a simulated population based on the real genotypes of Huaxi cattle, where five generations of simulated breeding were carried out using the genomic optimal contribution selection (GOCS), genetic algorithms strategy and three traditional mating strategies. During the breeding process, genetic parameters including average genomic estimated breeding value (GEBV), genetic gain values ( Δ G ), the rate of inbreeding values ( Δ F ) were calculated and compared across generations. Our results showed that the GM method could significantly improve the genetic gain while effectively controlling the inbreeding accumulation within the population. When using the GM method, there was an increase in genetic gain for Huaxi cattle ranging from 1.1% to 25.6% compared to traditional mating strategy, with inbreeding decreasing in the range of 5.8% to 36.2%. Validation using the real dataset from Huaxi cattle further confirmed our findings from the simulated study, offspring populations using the GM strategy exhibited a 7.3% increase in genetic gain compared to positive assortative mating. Conclusions: These findings suggest that the GM method shows potential for achieving sustainable genetic gain and could be utilized during long-term selection in beef cattle breeding. [ABSTRACT FROM AUTHOR] more...

Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program. [ABSTRACT FROM AUTHOR] more...

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP. [ABSTRACT FROM AUTHOR] more...

The Pingliang red cattle, an outstanding indigenous resource in China, possesses an exceptional breeding value attributed to its tender meat and superior marbling quality. Currently, research efforts have predominantly concentrated on exploring its maternal origin and conducting conventional phenotypic studies. However, there remains a lack of comprehensive understanding regarding its genetic basis. To address this gap, we conducted a thorough whole-genome analysis to investigate the population structure, phylogenetic relationships, and gene flows of this breed using genomic SNP chip data from 17 bovine breeds. The results demonstrate that Pingliang red cattle have evolved distinct genetic characteristics unique to this breed, clearly distinguishing it from other breeds. Based on the analysis of the population structure and phylogenetic tree, it can be classified as a hybrid lineage between Bos taurus and Bos indicus. Furthermore, Pingliang red cattle display a more prominent B. taurus pedigree in comparison with Jinnan, Qinchuan, Zaosheng, Nanyang, and Luxi cattle. Moreover, this study also revealed closer genetic proximity within the Chinese indigenous cattle breed, particularly Qinchuan cattle, which shares the longest identical by descent (IBD) fragment with Pingliang red cattle. Gene introgression analysis shows that Pingliang red cattle have undergone gene exchange with South Devon and Red Angus cattle from Europe. Admixture analysis revealed that the proportions of East Asian taurine and Chinese indicine in the ancestry of Pingliang red cattle are approximately 52.44% and 21.00%, respectively, while Eurasian taurine, European taurine, and Indian indicine account for approximately 17.55%, 7.27%, and 1.74%. Our findings unveil distinct genetic characteristics in Pingliang red cattle and attribute their origin to B. taurus and B. indicus ancestry, as well as contributions from Qinchuan cattle, South Devon, and Red Angus. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Cartilage development plays a crucial role in human health and animal agriculture, and fetal development takes on particular importance in this process. In this study, we identified open chromatin regions in bovine fetal chondrocytes and peaks enriched in the promoter region. By integrating chromatin accessibility with transcriptome data and Genome-Wide Association Studies results of bovine stature traits, we further described the regulatory functions of these regulatory elements defined by open chromatin accessibility. Overall, these findings provide valuable information for understanding the regulatory mechanisms in cartilage development and conducting beef cattle genetic improvement programs. Despite significant advances of the bovine epigenome investigation, new evidence for the epigenetic basis of fetal cartilage development remains lacking. In this study, the chondrocytes were isolated from long bone tissues of bovine fetuses at 90 days. The Assay for Transposase-Accessible Chromatin with high throughput sequencing (ATAC-seq) and transcriptome sequencing (RNA-seq) were used to characterize gene expression and chromatin accessibility profile in bovine chondrocytes. A total of 9686 open chromatin regions in bovine fetal chondrocytes were identified and 45% of the peaks were enriched in the promoter regions. Then, all peaks were annotated to the nearest gene for Gene Ontology (GO) and Kyoto Encylopaedia of Genes and Genomes (KEGG) analysis. Growth and development-related processes such as amide biosynthesis process (GO: 0043604) and translation regulation (GO: 006417) were enriched in the GO analysis. The KEGG analysis enriched endoplasmic reticulum protein processing signal pathway, TGF-β signaling pathway and cell cycle pathway, which are closely related to protein synthesis and processing during cell proliferation. Active transcription factors (TFs) were enriched by ATAC-seq, and were fully verified with gene expression levels obtained by RNA-seq. Among the top50 TFs from footprint analysis, known or potential cartilage development-related transcription factors FOS, FOSL2 and NFY were found. Overall, our data provide a theoretical basis for further determining the regulatory mechanism of cartilage development in bovine. [ABSTRACT FROM AUTHOR] more...

Background: A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle. Results: We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits. Conclusions: The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle. [ABSTRACT FROM AUTHOR] more...

Genomic prediction (GP) based on haplotype alleles can capture quantitative trait loci (QTL) effects and increase predictive ability because the haplotypes are expected to be in linkage disequilibrium (LD) with QTL. In this study, we constructed haploblocks using LD‐based and the fixed number of single nucleotide polymorphisms (fixed‐SNP) methods with Illumina BovineHD chip in beef cattle. To evaluate the performance of different haplotype block partitioning methods, we constructed haploblocks based on LD thresholds (from r2 > 0.2 to r2 > 0.8) and the number of fixed‐SNPs (5, 10, 20). The performance of predictive methods for three carcass traits including liveweight (LW), dressing percentage (DP), and longissimus dorsi muscle weight (LDMW) was evaluated using three approaches (GBLUP and BayesB model based on the SNP, GHBLUP, and BayesBH models based on the haploblock, and GHBLUP+GBLUP and BayesBH+BayesB models based on the combined haploblock and the nonblocked SNPs, which were located between blocks). In this study, we found the accuracies of LD‐based and fixed‐SNP haplotype Bayesian methods outperformed the Bayesian models (up to 8.54 ± 7.44% and 5.74 ± 2.95%, respectively). GHBLUP showed a high improvement (up to 11.29 ± 9.87%) compared with GBLUP. The Bayesian models have higher accuracies than BLUP models in most scenarios. The average computing time of the BayesBH+BayesB model can reduce by 29.3% compared with the BayesB model. The prediction accuracies using the LD‐based haplotype method showed higher improvements than the fixed‐SNP haplotype method. In addition, to avoid the influence of rare haplotypes generated from haplotype construction, we compared the performance of GP by filtering four types of minor haplotype allele frequency (MHAF) (0.01, 0.025, 0.05, and 0.1) under different conditions (LD levels were set at r2 > 0.3, and the fixed number of SNPs was 5). We found the optimal MHAF threshold for LW was 0.01, and the optimal MHAF threshold for DP and LDMW was 0.025. [ABSTRACT FROM AUTHOR] more...

Genomic selection (GS) has been widely used in livestock breeding. However, the long-term impact of GS on genetic gain, as well as inbreeding levels, has not been fully explored in beef cattle. In this study, we carried out simulation analysis using different approaches involving two types of SNP density (54 K and 100 K) and three levels of heritability traits (h2 = 0.1, 0.3, and 0.5) to explore the long-term effects of selection strategies on genetic gain and average kinship coefficients. Our results showed that GS can improve the genetic gain across generations, and the GBLUP strategy showed slightly better performance than the BayesA model. Higher trait heritability can generate higher genetic gain in all scenarios. Moreover, simulation results using GBLUP and BayesA strategies showed higher average kinship coefficients compared with other strategies. Our study suggested that it is important to design GS strategies by considering the SNP density and trait heritability to achieve long-term and sustainable genetic gain and to effectively control inbreeding levels. [ABSTRACT FROM AUTHOR] more...

Objectives: Although major advances have been made in bovine epigenome study, the epigenetic basis for fetal skeletal muscle development still remains poorly understood. The aim is to recapitulated the time course of fetal skeletal muscle development in vitro, and explore the dynamic changes of chromatin accessibility and gene expression during bovine myoblasts proliferation and differentiation. Methods: PDGFR‐ cells were isolated from bovine fetal skeletal muscle, then cultured and induced myogenic differentiation in vitro in a time‐course study (P, D0, D2,and D4). The assay for transposase‐accessible chromatin sequencing (ATAC‐seq) and RNA sequencing (RNA‐seq) were performed. Results: Among the enriched transcriptional factors with high variability, we determined the effects of MAFF, ZNF384, and KLF6 in myogenesis using RNA interference (RNAi). In addition, we identified both stage‐specific genes and chromatin accessibility regions to reveal the sequential order of gene expression, transcriptional regulatory, and signal pathways involved in bovine skeletal muscle development. Further investigation integrating chromatin accessibility and transcriptome data was conducted to explore cis‐regulatory regions in line with gene expression. Moreover, we combined bovine GWAS results of growth traits with regulatory regions defined by chromatin accessibility, providing a suggestive means for a more precise annotation of genetic variants of bovine growth traits. Conclusion: Overall, these findings provide valuable information for understanding the stepwise regulatory mechanisms in skeletal muscle development and conducting beef cattle genetic improvement programs. [ABSTRACT FROM AUTHOR] more...

Rumen development is a crucial physiological challenge for ruminants. However, the molecular mechanism regulating rumen development has not been clearly elucidated. In this study, we investigated genes involved in rumen development in 13 rumen tissues from three developmental stages (birth, youth, and adult) using RNA sequencing. We identified that 6,048 genes were differentially expressed among three developmental stages. Using weighted correlation network analysis, we found that 12 modules were significantly associated with developmental stages. Functional annotation and protein–protein interaction (PPI) network analysis revealed that CCNB1 , CCNB2 , IGF1 , IGF2 , HMGCL , BDH1 , ACAT1 , HMGCS2 , and CREBBP involved in rumen development. Integrated transcriptome with GWAS information of carcass weight (CW), stomach weight (SW), marbling score (MS), backfat thickness (BFT), ribeye area (REA), and lean meat weight (LMW), we found that upregulated DEGs (fold change 0∼1) in birth–youth comparison were significantly enriched with GWAS signals of MS, downregulated DEGs (fold change >3) were significantly enriched with GWAS signals of SW, and fold change 0∼1 up/downregulated DEGs in birth–adult comparison were significantly enriched with GWAS signals of CW, LMW, REA, and BFT. Furthermore, we found that GWAS signals for CW, LMW, and REA were enriched in turquoise module, and GWAS signals for CW was enriched in lightgreen module. Our study provides novel insights into the molecular mechanism underlying rumen development in cattle and highlights an integrative analysis for illustrating the genetic architecture of beef complex traits. [ABSTRACT FROM AUTHOR] more...

Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3 , MED28 , NCAPG , LCORL , SLIT2 , and IBSP , which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle. [ABSTRACT FROM AUTHOR] more...

Background: Genomic regions with a high frequency of runs of homozygosity (ROH) are related to important traits in farm animals. We carried out a comprehensive analysis of ROH and evaluated their association with production traits using the BovineHD (770 K) SNP array in Chinese Simmental beef cattle. Results: We detected a total of 116,953 homozygous segments with 2.47Gb across the genome in the studied population. The average number of ROH per individual was 99.03 and the average length was 117.29 Mb. Notably, we detected 42 regions with a frequency of more than 0.2. We obtained 17 candidate genes related to body size, meat quality, and reproductive traits. Furthermore, using Fisher's exact test, we found 101 regions were associated with production traits by comparing high groups with low groups in terms of production traits. Of those, we identified several significant regions for production traits (P < 0.05) by association analysis, within which candidate genes including ECT2, GABRA4, and GABRB1 have been previously reported for those traits in beef cattle. Conclusions: Our study explored ROH patterns and their potential associations with production traits in beef cattle. These results may help to better understand the association between production traits and genome homozygosity and offer valuable insights into managing inbreeding by designing reasonable breeding programs in farm animals. [ABSTRACT FROM AUTHOR] more...

A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling's test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for GHBLUP and GHBLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (r 2 ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle. [ABSTRACT FROM AUTHOR] more...

Various methods have been proposed for genomic prediction (GP) in livestock. These methods have mainly focused on statistical considerations and did not include genome annotation information. In this study, to improve the predictive performance of carcass traits in Chinese Simmental beef cattle, we incorporated the genome annotation information into GP. Single nucleotide polymorphisms (SNPs) were annotated to five genomic classes: intergenic, gene, exon, protein coding sequences, and 3′/5′ untranslated region. Haploblocks were constructed for all markers and these five genomic classes by defining a biologically functional unit, and haplotype effects were modeled in both numerical dosage and categorical coding strategies. The first-order epistatic effects among SNPs and haplotypes were modeled using a categorical epistasis model. For all makers, the extension from the SNP-based model to a haplotype-based model improved the accuracy by 5.4–9.8% for carcass weight (CW), live weight (LW), and striploin (SI). For the five genomic classes using the haplotype-based prediction model, the incorporation of gene class information into the model improved the accuracies by an average of 1.4, 2.1, and 1.3% for CW, LW, and SI, respectively, compared with their corresponding results for all markers. Including the first-order epistatic effects into the prediction models improved the accuracies in some traits and genomic classes. Therefore, for traits with moderate-to-high heritability, incorporating genome annotation information of gene class into haplotype-based prediction models could be considered as a promising tool for GP in Chinese Simmental beef cattle, and modeling epistasis in prediction can further increase the accuracy to some degree. [ABSTRACT FROM AUTHOR] more...

Genome-wide association studies (GWAS) have commonly been used to identify candidate genes that control economically important traits in livestock. Our objective was to detect potential candidate genes associated mainly with muscle development traits related to dimension of hindquarter in cattle. A next generation sequencing (NGS) dataset to imputed to 12 million single nucleotide polymorphisms (SNPs) (from 1252 Simmental beef cattle) were used to search for genes affecting hindquarter traits using a linear, mixed model approach. We also used haplotype and linkage disequilibrium blocks to further support our identifications. We identified 202 significant SNPs in the bovine BTA4 chromosome region associated with width of hind leg, based on a stringent statistical threshold (p = 0.05/ effective number of SNPs identified). After exploring the region around these SNPs, we found candidate genes that were potentially related to the associated markers. More importantly, we identified a region of approximately 280 Kb on the BTA4 chromosome that harbored several muscle specific candidate genes, genes to be in a potential region for muscle development. However, we also found candidate gene SLC13A1 on BTA4, which seems to be associated with bone disorders (such as chondrodysplasia) in Simmental beef cattle. [ABSTRACT FROM AUTHOR] more...

Background: Running multiple-chain Markov Chain Monte Carlo (MCMC) provides an efficient parallel computing method for complex Bayesian models, although the efficiency of the approach critically depends on the length of the non-parallelizable burn-in period, for which all simulated data are discarded. In practice, this burn-in period is set arbitrarily and often leads to the performance of far more iterations than required. In addition, the accuracy of genomic predictions does not improve after the MCMC reaches equilibrium. Results: Automatic tuning of the burn-in length for running multiple-chain MCMC was proposed in the context of genomic predictions using BayesA and BayesCπ models. The performance of parallel computing versus sequential computing and tunable burn-in MCMC versus fixed burn-in MCMC was assessed using simulation data sets as well by applying these methods to genomic predictions of a Chinese Simmental beef cattle population. The results showed that tunable burn-in parallel MCMC had greater speedups than fixed burn-in parallel MCMC and both had greater speedups relative to sequential (single-chain) MCMC. Nevertheless, genomic estimated breeding values (GEBVs) and genomic prediction accuracies were highly comparable between the various computing approaches. When applied to the genomic predictions of four quantitative traits in a Chinese Simmental population of 1217 beef cattle genotyped by an Illumina Bovine 770 K SNP BeadChip, tunable burn-in multiple-chain BayesCπ (TBM-BayesCπ) outperformed tunable burn-in multiple-chain BayesCπ (TBM-BayesA) and Genomic Best Linear Unbiased Prediction (GBLUP) in terms of the prediction accuracy, although the differences were not necessarily caused by computational factors and could have been intrinsic to the statistical models per se. Conclusions: Automatically tunable burn-in multiple-chain MCMC provides an accurate and cost-effective tool for high-performance computing of Bayesian genomic prediction models and this algorithm is generally applicable to high-performance computing of any complex Bayesian statistical model. [ABSTRACT FROM AUTHOR] more...

Genomic selection has been widely used for complex quantitative trait in farm animals. Estimations of breeding values for slaughter traits are most important to beef cattle industry, and it is worthwhile to investigate prediction accuracies of genomic selection for these traits. In this study, we assessed genomic predictive abilities for average daily gain weight (ADG), live weight (LW), carcass weight (CW), dressing percentage (DP), lean meat percentage (LMP) and retail meat weight (RMW) using Illumina Bovine 770K SNP Beadchip in Chinese Simmental cattle. To evaluate the abilities of prediction, marker effects were estimated using genomic BLUP (GBLUP) and three parallel Bayesian models, including multiple chains parallel BayesA, BayesB and BayesCπ (PBayesA, PBayesB and PBayesCπ). Training set and validation set were divided by random allocation, and the predictive accuracies were evaluated using 5-fold cross validations. We found the accuracies of genomic predictions ranged from 0.195±0.084 (GBLUP for LMP) to 0.424±0.147 (PBayesB for CW). The average accuracies across traits were 0.327±0.085 (GBLUP), 0.335±0.063 (PBayesA), 0.347±0.093 (PBayesB) and 0.334±0.077 (PBayesCπ), respectively. Notably, parallel Bayesian models were more accurate than GBLUP across six traits. Our study suggested that genomic selections with multiple chains parallel Bayesian models are feasible for slaughter traits in Chinese Simmental cattle. The estimations of direct genomic breeding values using parallel Bayesian methods can offer important insights into improving prediction accuracy at young ages and may also help to identify superior candidates in breeding programs. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Evaluating population genetic background and genomic selection signatures can provide important insights into the uniqueness and the genetic relationship among breeds. In this study, we analyzed the genetic diversity, population structure, and phylogenetic relationships between Huaxi cattle and its ancestors. The results showed that Huaxi cattle has formed its own unique genetic structure features, which could be clearly distinguished from its ancestors and similar populations. Moreover, some genes associated with important economic traits, including growth and development, reproduction, milk quality, and immune response, were identified by scanning potentially selected genomic regions, which may play an essential role in the excellent environmental adaptability and growth performance of Huaxi cattle. Our study will contribute to the genetic evaluation and rational breeding strategy, and give an extensive reference for understanding the genetic basis of economically important traits in Huaxi cattle. Huaxi cattle, a specialized beef cattle breed in China, has the characteristics of fast growth, high slaughter rate, and net meat rate, good reproductive performance, strong stress resistance, and wide adaptability. In this study, we evaluated the genetic diversity, population structure, and genetic relationships of Huaxi cattle and its ancestor populations at the genome-wide level, as well as detecting the selection signatures of Huaxi cattle. Principal component analysis (PCA) and phylogenetic analysis revealed that Huaxi cattle were obviously separated from other cattle populations. The admixture analysis showed that Huaxi cattle has distinct genetic structures among all populations at K = 4. It can be concluded that Huaxi cattle has formed its own unique genetic features. Using integrated haplotype score (iHS) and composite likelihood ratio (CLR) methods, we identified 143 and 199 potentially selected genes in Huaxi cattle, respectively, among which nine selected genes (KCNK1, PDLIM5, CPXM2, CAPN14, MIR2285D, MYOF, PKDCC, FOXN3, and EHD3) related to ion binding, muscle growth and differentiation, and immunity were detected by both methods. Our study sheds light on the unique genetic feature and phylogenetic relationship of Huaxi cattle, provides a basis for the genetic mechanism analysis of important economic traits, and guides further intensive breeding improvement of Huaxi cattle. [ABSTRACT FROM AUTHOR] more...

Simple Summary: To reduce the breeding costs and promote the application of genomic selection (GS) in Chinese Simmental beef cattle, we developed a customized low-density single-nucleotide polymorphism (SNP) panel consisting of 30,684 SNPs. When comparing the predictive performance of the low-density SNP panel to that of the BovineHD Beadchip for 13 traits, we found that this ~30 K panel achieved moderate to high prediction accuracies for most traits, while reducing the prediction accuracies of six traits by 0.04–0.09 and decreasing the prediction accuracy of one trait by 0.2. For the remaining six traits, the usage of the low-density SNP panel was associated with a slight increase in prediction accuracy. Our studies suggested that the low-density SNP panel (~30 K) is a feasible and promising tool for cost-effective genomic prediction in Chinese Simmental beef cattle, which may provide breeding organizations with a cheaper option and greater returns on investment. Chinese Simmental beef cattle play a key role in the Chinese beef industry due to their great adaptability and marketability. To achieve efficient genetic gain at a low breeding cost, it is crucial to develop a customized cost-effective low-density SNP panel for this cattle population. Thirteen growth, carcass, and meat quality traits and a BovineHD Beadchip genotyping of 1346 individuals were used to select trait-associated variants and variants contributing to great genetic variance. In addition, highly informative SNPs with high MAF in each 500 kb sliding window and in each genic region were also included separately. A low-density SNP panel consisting of 30,684 SNPs was developed, with an imputation accuracy of 97.4% when imputed to the 770 K level. Among 13 traits, the average prediction accuracy levels evaluated by genomic best linear unbiased prediction (GBLUP) and BayesA/B/Cπ were 0.22–0.47 and 0.18–0.60 for the ~30 K array and BovineHD Beadchip, respectively. Generally, the predictive performance of the ~30 K array was trait-dependent, with reduced prediction accuracies for seven traits. While differences in terms of prediction accuracy were observed among the 13 traits, the low-density SNP panel achieved moderate to high accuracies for most of the traits and even improved the accuracies for some traits. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Runs of homozygosity (ROH) are continuous homozygous regions that generally exist in the DNA sequence of diploid organisms. Identifications of ROH leading to reduction in performance can provide valuable insight into understanding the genetic architecture of complex traits. We assessed genome-wide homozygosity patterns and inbreeding levels in Chinese Wagyu beef cattle. We identified several high frequency regions overlapped with QTLs which are associated with body weight, calving ease, and stillbirth. Based on these regions, we carried out a region-based association for eight economically important traits. Of these, nine regions were significantly associated with six traits including body height, chest circumference, fat coverage, backfat thickness, ribeye area, and carcass length. Our study identified that many candidate regions and genes overlapped with ROH for several important traits, which could be unitized to assist the design of a selection mating strategy in beef cattle. Runs of homozygosity (ROH) are continuous homozygous regions that generally exist in the DNA sequence of diploid organisms. Identifications of ROH leading to reduction in performance can provide valuable insight into the genetic architecture of complex traits. Here, we evaluated genome-wide patterns of homozygosity and their association with important traits in Chinese Wagyu beef cattle. We identified a total of 29,271 ROH segments from 462 animals. Within each animal, an average number of ROH was 63.36 while an average length was 62.19 Mb. To evaluate the enrichment of ROH across genomes, we initially identified 280 ROH regions by merging ROH events across all individuals. Of these, nine regions containing 154 candidate genes, were significantly associated with six traits (body height, chest circumference, fat coverage, backfat thickness, ribeye area, and carcass length; p < 0.01). Moreover, we found 26 consensus ROH regions with frequencies exceeding 10%, and several regions overlapped with QTLs, which are associated with body weight, calving ease, and stillbirth. Among them, we observed 41 candidate genes, including BCKDHB, MAB21L1, SLC2A13, FGFR3, FGFRL1, CPLX1, CTNNA1, CORT, CTNNBIP1, and NMNAT1, which have been previously reported to be related to body conformation, meat quality, susceptibility, and reproductive traits. In summary, we assessed genome-wide autozygosity patterns and inbreeding levels in Chinese Wagyu beef cattle. Our study identified many candidate regions and genes overlapped with ROH for several important traits, which could be unitized to assist the design of a selection mating strategy in beef cattle. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Dominance effects play important roles in determining genetic changes with regard to complex traits. We conducted genomic predictions and genome-wide association studies in order to investigate the effects of dominance on carcass weight, dressing percentage, meat percentage, average daily gain, and chuck roll in 1233 Simmental beef cattle. Using dominance models, we improved the predictive abilities and found several candidate single-nucleotide polymorphisms (SNPs) and genes associated with these traits. Our studies helped us to understand causal mutation mapping and genomic selection models with dominance effects in Chinese Simmental beef cattle. Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle. [ABSTRACT FROM AUTHOR] more...

Simple Summary: In order to evaluate the potential application of genomic selection (GS) for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. We found the predictive accuracies to be low when the reference and validation populations were sampled from different breeds. When using multiple breeds for the reference population, the predictive accuracies were higher if the reference was comprised of breeds with close relationships. In addition, the accuracy increased in all scenarios when the heritability increased, and the genetic architecture of the QTL can affect genomic prediction. Our study suggested that the application of meta-populations can increase accuracy in scenarios with a reduced size of reference populations. Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships. [ABSTRACT FROM AUTHOR] more...

Background: Understanding the population structure and genetic bases of well-adapted cattle breeds to local environments is one of the most essential tasks to develop appropriate genetic improvement programs. Results: We performed a comprehensive study to investigate the population structure, divergence and selection signatures at genome-wide level in diverse Chinese local cattle using Bovine HD SNPs array, including two breeds from North China, one breed from Northwest China, three breeds from Southwest China and two breeds from South China. Population genetic analyses revealed the genetic structures of these populations were mostly related to the geographic locations. Notably, we detected 294 and 1263 candidate regions under selection using the di and iHS approaches, respectively. A series of group-specific and breed-specific candidate genes were identified, which are involved in immune response, sexual maturation, stature related, birth and bone weight, embryonic development, coat colors and adaptation. Furthermore, haplotype diversity and network pattern for candidate genes, including LPGAT1, LCORL, PPP1R8, RXFP2 and FANCA, suggest that these genes have been under differential selection pressures in various environmental conditions. Conclusions: Our results shed insights into diverse selection during breed formation in Chinese local cattle. These findings may promote the application of genome-assisted breeding for well-adapted local breeds with economic and ecological importance. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Analysis of the genomic regions under consideration can provide important insights into the genetic basis of complex traits. In this study, we utilized a high-density SNP (Single nucleotide polymorphism) array for analysis of genome selection signatures in Chinese Wagyu cattle. In total, 239 selected regions responding to 162 candidate genes were identified, which related to economic traits, including fatty acid, meat quality, growth and developmental traits. Moreover, we found that these selected genes were enriched for the four Gene Ontology (GO) terms of biological regulation and metabolic process. These results also contribute to the understanding of genetic basis of these traits during the formation of this population. Selective breeding can lead to genetic diversity and diverse phenotypes in farm animals. Analysis of the genomic regions under selection can provide important insights into the genetic basis of complex traits. In this study, a high-density SNP array was used for analysis of genome selection signatures in Chinese Wagyu cattle. In total, we obtained 478,903 SNPs and 24,820 no-overlap regions for |iHS| (integrated haplotype score) estimations. Under the threshold of the top 1%, 239 regions were finally identified as candidate selected regions and 162 candidate genes were found based on the UMD3.1 genome assembly. These genes were reported to be associated with fatty acids, such as Bos taurus nitric oxide synthase 1 adaptor protein (NOS1AP), Bos taurus hydroxysteroid 17-beta dehydrogenase 7 (HSD17B7), Bos taurus WD repeat domain 7 (WDR7), Bos taurus ELOVL fatty acid elongase 2 (ELOVL2), Bos taurus calpain 1 (CAPN1), Bos taurus parkin RBR E3 ubiquitin protein ligase (PRKN, also known as PARK2), Bos taurus mitogen-activated protein kinase kinase 6 (MAP2K6), meat quality, including Bos taurus ADAM metallopeptidase domain 12 (ADAM12), Bos taurus 5′-aminolevulinate synthase 1 (ALAS1), Bos taurus small integral membrane protein 13 (SMIM13) and Bos taurus potassium two pore domain channel subfamily K member 2 (KCNK2), growth, and developmental traits, such as Bos taurus insulin like growth factor 2 receptor (IGF2R), Bos taurus RAR related orphan receptor A (RORA), Bos taurus fibroblast growth factor 14 (FGF14), Bos taurus paired box 6 (PAX6) and Bos taurus LIM homeobox 6 (LHX6). In addition, we identified several genes that are associated with body size and weight, including Bos taurus sorting nexin 29 (SNX29), Bos taurus zinc finger imprinted 2 (ZIM2), Bos taurus family with sequence similarity 110 member A (FAM110A), immune system, including Bos taurus toll like receptor 9 (TLR9), Bos taurus TAFA chemokine like family member 1 (TAFA1), Bos taurus glutathione peroxidase 8 (putative) (GPX8), Bos taurus interleukin 5 (IL5), Bos taurus PR domain containing 9 (PRDM9), Bos taurus glutamate ionotropic receptor kainate type subunit 2 (GRIK2) and feed intake efficiency, Bos taurus sodium voltage-gated channel alpha subunit 9 (SCN9A), Bos taurus relaxin family peptide/INSL5 receptor 4 (RXFP4), Bos taurus RNA polymerase II associated protein 3 (RPAP3). Moreover, four GO terms of biological regulation (GO:0009987, GO:0008152) and metabolic process (GO:0003824, GO:0005488) were found based on these genes. In addition, we found that 232 candidate regions (~18 Mb) overlapped with the Quantitative trait loci (QTL)regions extracted from cattle QTLdb. Our findings imply that many genes were selected for important traits in Chinese Wagyu cattle. Moreover, these results can contribute to the understanding of the genetic basis of the studied traits during the formation of this population. [ABSTRACT FROM AUTHOR] more...

Background: Fatty acids are important traits that affect meat quality and nutritive values in beef cattle. Detection of genetic variants for fatty acid composition can help to elucidate the genetic mechanism underpinning these traits and promote the improvement of fatty acid profiles. In this study, we performed a genome-wide association study (GWAS) on fatty acid composition using high-density single nucleotide polymorphism (SNP) arrays in Chinese Wagyu cattle. Results: In total, we detected 15 and 8 significant genome-wide SNPs for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, respectively. Also, we identified nine candidate genes based on 100 kb regions around associated SNPs. Four SNPs significantly associated with C14:1 cis-9 were embedded with stearoyl-CoA desaturase (SCD), while three SNPs in total were identified for C22:6 n-3 within Phospholipid scramblase family member 5 (PLSCR5), Cytoplasmic linker associated protein 1 (CLASP1), and Chymosin (CYM). Notably, we found the top candidate SNP within SCD can explain ~ 7.37% of phenotypic variance for C14:1 cis-9. Moreover, we detected several blocks with high LD in the 100 kb region around SCD. In addition, we found three significant SNPs within a 100 kb region showing pleiotropic effects related to multiple FA groups (PUFA, n-6, and PUFA/SFA), which contains BAI1 associated protein 2 like 2 (BAIAP2L2), MAF bZIP transcription factor F (MAFF), and transmembrane protein 184B (TMEM184B). Conclusions: Our study identified several significant SNPs and candidate genes for individual fatty acids and fatty acid groups in Chinese Wagyu cattle, and these findings will further assist the design of breeding programs for meat quality in cattle. [ABSTRACT FROM AUTHOR] more...

Simple Summary: Evaluation of the population structure and linkage disequilibrium can offer important insights to fully understand the genetic diversity and population history of cattle, which can enable us to appropriately design and implement GWAS and GS in cattle. In this study, we characterized the extent of genome-wide LD and the haplotype block structure, and estimated the persistence of phase of Chinese indigenous cattle with Illumina BovineHD BeadChip. According to our study, 58K, 87K, 95K, 52K, and 52K markers would be necessary for SCHC, NCC, SWC, SIM, and WAG, respectively, in the implementation of GWAS and GS and combining a multipopulation with high persistence of phase is feasible for the implication of genomic selection for Chinese beef cattle. Understanding the linkage disequilibrium (LD) across the genome, haplotype structure, and persistence of phase between breeds can enable us to appropriately design and implement the genome-wide association (GWAS) and genomic selection (GS) in beef cattle. We estimated the extent of genome-wide LD, haplotype block structure, and the persistence of phase in 10 Chinese cattle population using high density BovinHD BeadChip. The overall LD measured by r2 between adjacent SNPs were 0.60, 0.67, 0.58, 0.73, and 0.71 for South Chinese cattle (SCHC), North Chinese cattle (NCC), Southwest Chinese cattle (SWC), Simmental (SIM), and Wagyu (WAG). The highest correlation (0.53) for persistence of phase across groups was observed for SCHC vs. SWC at distances of 0–50 kb, while the lowest correlation was 0.13 for SIM vs. SCHC at the same distances. In addition, the estimated current effective population sizes were 27, 14, 31, 34, and 43 for SCHC, NCC, SWC, SIM, and WAG, respectively. Our result showed that 58K, 87K, 95K, 52K, and 52K markers were required for implementation of GWAS and GS in SCHC, NCC, SWC, SIM, and WAG, respectively. Also, our findings suggested that the implication of genomic selection for multipopulation with high persistence of phase is feasible for Chinese cattle. [ABSTRACT FROM AUTHOR] more...

Simple Summary: In biological processes, it is common that a single gene controls two or more traits, leading to a high genetically correlation between many traits in human beings and livestock. Genome-wide association study (GWAS) is a popular method for mapping causal genes or regions related to studied traits. Taking the advantage of genetically correlation among traits, a combined analysis of two or more traits can improve the power of detection in GWAS analysis. In this study, we prove the improvement of multiple-traits GWAS through theoretical derivation, simulated dataset and real dataset, respectively. In addition, using this approach, we successfully identified a candidate gene for presoma muscle development in cattle that were not be found in the average association analysis. In summary, we conclude that multiple-trait GWAS is an effective method to explore genetic factors of traits, which have high correlations. Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits. [ABSTRACT FROM AUTHOR] more...