Chinese Simmental Research Articles

The effect of CPT1B gene on lipid metabolism and its polymorphism analysis in Chinese Simmental cattle

Carnitine palmitoyltransferase 1B (CPT1B) is a candidate gene that regulates livestock animal lipid metabolism and encodes the rate-limiting enzyme in fatty acid β-oxidation. To explore the effect of this gene on lipid metabolism in cattle, this study examined CPT1B gene polymorphism in Chinese Simmental cattle and the effect of CPT1B on lipid metabolism. The results showed that the triglyceride content increased significantly with increasing CPT1B gene expression in bovine fetal fibroblasts (BFFs) (p < 0.05), while CPT1B knockout led to decreased CPT1B expression and a downward trend in triglyceride levels. Correlation analysis showed a significant association between the g.119896238 G > C locus and Chinese Simmental cattle backfat thickness (p < 0.05). Backfat thickness was significantly greater in individuals with the GC genotype (0.93 ± 0.67 cm) than in those with the CC genotype (0.84 ± 0.60 cm). The g.119889302 T > C locus was significantly correlated with arachidonic acid content in Chinese Simmental cattle (p < 0.05). The arachidonic acid content in the longissimus muscle was significantly higher in CC genotype beef cattle (0.054 g/100 g) than in those with the other two genotypes (0.046 g/100 g, 0.049 g/100 g). These molecular markers can be effectively used for marker-assisted selection in cattle breeding.

Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle.

Simple SummaryComplex traits that require observations over multiple time points for the same individual are called longitudinal traits. Understanding the genetic architecture of beef cattle growth cannot be limited simply to a genome-wide association study (GWAS) for body weight at any specific ages, but should be extended to a more general purpose by considering the longitudinal weight–age using a growth curve approach. We compared three nonlinear models that described the body weight data of Chinese Simmental beef cattle. The parameters of the suitable model were treated as phenotypes of single-trait GWAS and multi-trait GWAS. We identified 87 significant single nucleotide polymorphisms (SNPs) associated with body weight. Many candidate genes associated with body weight were identified which may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight–age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters’ mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

Application of ensemble learning to genomic selection in chinese simmental beef cattle.

Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ultimate goal of breeders. With the rapid development of artificial intelligence, machine learning algorithms were applied to estimate the GEBVs increasingly. Although some machine learning methods have better performance in phenotype prediction, there is still considerable room for improvement. In this study, we applied an ensemble-learning algorithm, Adaboost.RT, which integrated support vector regression (SVR), kernel ridge regression (KRR) and random forest (RF), to predict genomic breeding values of three economic traits (carcass weight, live weight, and eye muscle area) in Chinese Simmental beef cattle. Predictive accuracy measured as the Pearson correlation between the corrected phenotypes and predicted GEBVs. Moreover, we compared the reliability of SVR, KRR, RF, Adaboost.RT and GBLUP methods. The result showed that machine learning methods outperformed GBLUP, and the average improvement of four machine learning methods over the GBLUP was 12.8%, 14.9%, 5.4% and 14.4%, respectively. Among the four machine learning methods, the reliability of Adaboost.RT was comparable to KRR with higher stability. We therefore believe that the Adaboost.RT algorithm is a reliable and efficient method for GS.

Genomic sequencing analysis reveals copy number variations and their associations with economically important traits in beef cattle

Copy number variation (CNV) represents a major source of genetic variation, which may have potentially large effects, including alternating gene regulation and dosage, as well as contributing to gene expression and risk for normal phenotypic variability. We carried out a comprehensive analysis of CNV based on whole genome sequencing in Chinese Simmental beef cattle. Totally, we found 9313 deletion and 234 duplication events, covering 147.5 Mb autosomal regions. Within them, 257 deletion events of high frequency overlapped with 193 known RefGenes. Among these genes, we observed several genes were related to economically important traits, like residual feed intake, immune responding, pregnancy rate and muscle differentiation. Using a locus-based analysis, we identified 11 deletions and 1 duplication, which were significantly associated with three traits including carcass weight, tenderloin and longissimus muscle area. Our sequencing-based study provided important insights into investigating the association of CNVs with important traits in beef cattle.

Muscle fiber characteristics and postmortem quality of longissimus thoracis, psoas major and semitendinosus from Chinese Simmental bulls.

Using Chinese Simmental cattle semitendinosus, psoas major, and longissimus thoracis samples, we assessed muscle fiber characteristics and postmortem quality. The type I, IIA, and IIB fiber diameters were greater in semitendinosus and longissimus thoracis relative to psoas major, with psoas major, semitendinosus, and longissimus thoracis having the highest respective percentages of type I, IIB, and IIA fibers. Psoas major had the highest R 248 and R 250 values and lowest R 258 values at 1‐ and 6‐hr postmortem. Psoas major had the lowest Warner–Bratzler shear force (WBSF), hardness, and chewiness values. The trends of WBSF, hardness, and chewiness changes decreased with increasing aging time. Semitendinosus had higher changes in WBSF than psoas major, and the number % type I fibers was correlated negatively with % changes of WBSF. Therefore, muscles with a high proportion of type IIB fibers and a low proportion of type I had lower tenderness and higher tenderization rate. Further research should be done to seek the optimal composition of muscle fiber type in order to improve beef quality, as muscle fiber type has opposite effect of tenderness background and tenderization rate.

Evaluation of single nucleotide polymorphisms identified through the use of SNP assay in Romanian and Chinese Holstein and Simmental cattle breeds.

Simmental and Holstein cattle, being among the most widely distributed breeds worldwide, have been subjected to continuous selection for distinct purposes. In the current study, we evaluated the levels of SNPs identified through the use of SNP assay in Romanian Holstein and Romanian Simmental cattle, which then were compared to the data from Chinese Holstein and Chinese Simmental cattle. In total, 282 animals were genotyped: Romanian Holstein (n=30), Romanian Simmental (n=22), Chinese Holstein (n=96) and Chinese Simmental cattle (n=136), using 39,724 common SNPs to analyze minor allele frequency, genetic variability and level of SNPs. Among studied breeds, the average percentage of polymorphic markers was 90.84%, with the highest value in Chinese Simmental (91.37%) and lowest in Romanian Simmental cattle (90.31%). The average HO ranged from 0.426 in Romanian Holstein to 0.416 in Romanian Simmental, and from 0.425 in Chinese Holstein to 0.422 in Chinese Simmental. The distribution of SNPs was homogenous across the breeds, except the Romanian Simmental which displayed the lowest percentage of polymorphic markers (24,66 and 32,48%) from higher MAF category (0.3 to <0.4 and 0.4 to <0.5) and the highest percentage (3.82 and 12.00%) for SNPs from low and intermediate MAF categories (0.05 to <0.1 and 0.1 to <0.2). In the current study, the SNP assay was successfully used to analyze the level of SNP sites of Romanian cattle breeds, however, a higher number of samples and production data are needed for future applications of the results in genomic selection, genome-wide association studies and genetic diversity analysis.

Fatty Acid Profile of Muscles from Crossbred Angus-Simmental, Wagyu-Simmental, and Chinese Simmental Cattles.

This study assessed breed differences in fatty acid composition and meat quality of Longissimus thoracis et lumborum (LTL) and semitendinosus (SE) of Angus× Chinese Simmental (AS), Wagyu×Chinese Simmental (WS), and Chinese Simmental (CS). CS (n=9), AS (n=9) and WS (n=9) were randomly selected from a herd of 80 bulls which were fed and managed under similar conditions. Fatty acid profile and meat quality parameters were analyzed in duplicate. Significant breed difference was observed in fatty acid and meat quality profiles. AS exhibited significantly (p<0.05) lower C16:0 and higher C18:1n9c compared with CS. AS breed also had a tendency (p<0.10) to lower total saturated fatty acid (SFA), improve C18:3n3 and total unsaturated fatty acid (UFA) compared with CS. Crossbreed of AS and WS had significantly (p<0.05) improved the lightness, redness, and yellowness of muscles, and lowered cooking loss, pressing loss, and shear force compared with CS. These results indicated that fatty acid composition and meat quality generally differed among breeds, although the differences were not always similar in different tissues. Fatty acid composition, meat color, water holding capacity, and tenderness favored AS over CS. Thus, Angus cattle might be used to improve fatty acid and meat quality profiles of CS, and AS might contain better nutritive value, organoleptic properties, and flavor, and could be potentially developed as an ideal commercial crossbreed.

Analysis of the Bovine DLK1 Gene Polymorphism and Its Relation to Lipid Metabolism in Chinese Simmental.

Simple SummaryDelta-like non-canonical Notch ligand 1 (DLK1) is a candidate gene associated with lipid metabolism. In order to verify the function of the DLK1 gene on lipid metabolism in Chinese Simmental cattle, we identified the effect of DLK1 on lipid metabolism in bovine fetal fibroblast cells (BFFs). At the same time, we also detected the relationship between single-nucleotide polymorphism sites (SNPs) of bovine DLK1 with the economic traits and fatty acids composition in Chinese Simmental cattle, such as the carcass fat coverage rate, loin eye muscle area, and fat color score, etc. In the present study, we precisely constructed and transfected the overexpression and interference vectors of the DLK1 gene in BFFs. the results showed that the overexpression of the DLK1 gene could decrease the contents of intracellular triglycerides (TGs) while interference of the DLK1 gene could increase the TGs contents. Gas chromatography analysis of the fatty acid composition showed that the contents of octanoate acid and γ-linolenate acid were regulated as the expression of DLK1 was altered. We found two SNPs and genes associated with these traits in Chinese Simmental by the restriction fragment length polymorphism (RPLF-PCR) detection method. In summary, we verified the effects of bovine DLK1 on fatty acid metabolism from the cellular level to population genetic polymorphism, which also paved the way for further studying of the effects of the DLK1 gene on lipid metabolism in vivo.In this study, we precisely constructed and transfected the overexpression and interference vectors in BFFs to evaluate the role of DLK1 gene on lipid metabolism in vitro. The expression of of DLK1 in the mRNA and protein level tended to reduce, and TGs were significantly increased in the pGPU6-shDLK1 group compared to the control group (p < 0.05). The expression of DLK1 in the mRNA and protein level were increased in the pBI-CMV3-DLK1 group compared to the control group, and the TGs content showed a significant decrease in the pBI-CMV3-DLK1 group (p < 0.05). Meanwhile, we used the restriction fragment length polymorphism (RFLP-PCR) detection method to screen SNPs further to explore and analyze the relationship between the gene and the economic traits of 28-month-old Chinese Simmental and the fatty acids composition of cattle longissimus muscle. The result showed that two SNPs, IVS3 + 478 C > T and IVS3 + 609 T > G, were identified as being significantly associated with carcass and meat quality traits in Chinese Simmental, such as the carcass fat coverage rate, loin eye muscle area, and fat color score. In summary, our results indicated that DLK1 can affect lipid metabolism in bovine and these two SNPs might be applied as genetic markers of meat quality traits for beef cattle breeding.

Incorporating Genome Annotation Into Genomic Prediction for Carcass Traits in Chinese Simmental Beef Cattle.

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.

Weighted Single-Step Genome-Wide Association Study for Growth Traits in Chinese Simmental Beef Cattle.

Improving the genetic process of growth traits is one of the major goals in the beef cattle industry, as it can increase meat production and reduce the cost of raising animals. Although several quantitative trait loci affecting growth traits in beef cattle have been identified, the genetic architecture of these economically important traits remains elusive. This study aims to map single nucleotide polymorphisms (SNPs) and genes associated with birth weight (BW), yearling weight (YW), average daily gain from birth to yearling (BYADG), and body weight at the age of 18 months (18MW) in a Chinese Simmental beef cattle population using a weighted, single-step, genome-wide association study (wssGWAS). Phenotypic and pedigree data from 6022 animals and genotypes from 744 animals (596,297 SNPs) were used for an association analysis. The results showed that 66 genomic windows explained 1.01–20.15% of the genetic variance for the four examined traits, together with the genes near the top SNP within each window. Furthermore, the identified genomic windows (>1%) explained 50.56%, 57.71%, 61.78%, and 37.82% of the genetic variances for BW, YW, BYADG, and 18MW, respectively. Genes with potential functions in muscle development and regulation of cell growth were highlighted as candidates for growth traits in Simmental cattle (SQOR and TBCB for BW, MYH10 for YW, RLF for BYADG, and ARHGAP31 for 18MW). Moreover, we found 40 SNPs that had not previously been identified as being associated with growth traits in cattle. These findings will further advance our understanding of the genetic basis for growth traits and will be useful for the molecular breeding of BW, YW, BYADG, and 18MW in the context of genomic selection in beef cattle.

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle.

Simple SummaryDominance 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.

Genome-wide association studies using binned genotypes.

Linear mixed models (LMM) that tests trait association one marker at a time have been the most popular methods for genome-wide association studies. However, this approach has potential pitfalls: over conservativeness after Bonferroni correction, ignorance of linkage disequilibrium (LD) between neighboring markers, and power reduction due to overfitting SNP effects. So, multiple locus models that can simultaneously estimate and test all markers in the genome are more appropriate. Based on the multiple locus models, we proposed a bin model that combines markers into bins based on their LD relationships. A bin is treated as a new synthetic marker and we detect the associations between bins and traits. Since the number of bins can be substantially smaller than the number of markers, a penalized multiple regression method can be adopted by fitting all bins to a single model. We developed an innovative method to bin the neighboring markers and used the least absolute shrinkage and selection operator (LASSO) method. We compared BIN-Lasso with SNP-Lasso and Q + K-LMM in a simulation experiment, and showed that the new method is more powerful with less Type I error than the other two methods. We also applied the bin model to a Chinese Simmental beef cattle population for bone weight association study. The new method identified more significant associations than the classical LMM. The bin model is a new dimension reduction technique that takes advantage of biological information (i.e., LD). The new method will be a significant breakthrough in associative genomics in the big data era.

Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle.

SummaryGenomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies.

Simple SummaryImproving statistical power and computational efficiency are always the research foci in genome-wide association studies (GWAS). In this study, we proposed a fast empirical Bayes GWAS method, which is based on the linear mixed model framework. The method is called Fast-EB-LMM in short. Results from simulation studies show that the Fast-EB-LMM has the highest power for quantitative trait nucleotides (QTNs) detection, the highest computational efficiency, and the strongest robustness, as compared with the efficient mixed model association (EMMA) and empirical Bayes (EB). Application to beef cattle population also verified the effectiveness of this method. We believe that Fast-EB-LMM is a valuable additional tool for GWAS.Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods. Here, we proposed a fast empirical Bayes method, which is based on linear mixed models. We call it Fast-EB-LMM in short. The novelty of this method is that it uses a modified kinship matrix accounting for individual relatedness to avoid competition between the locus of interest and its counterpart in the polygene. This property has increased statistical power. We adopted two special algorithms to ease the computational burden: Eigenvalue decomposition and Woodbury matrix identity. Simulation studies showed that Fast-EB-LMM has significantly increased statistical power of marker detection and improved computational efficiency compared with two widely used GWAS methods, EMMA and EB. Real data analyses for two carcass traits in a Chinese Simmental beef cattle population showed that the significant single-nucleotide polymorphisms (SNPs) and candidate genes identified by Fast-EB-LMM are highly consistent with results of previous studies. We therefore believe that the Fast-EB-LMM method is a reliable and efficient method for GWAS.

Genome-wide association study identifies the PLAG1-OXR1 region on BTA14 for carcass meat yield in cattle.

Carcass meat yield is an important carcass trait that contributes to the production efficiency and economic benefits in beef cattle. It is therefore critical to identify quantitative trait loci associated with carcass traits to enable selection. Our previous studies have identified several causal variants within the pleomorphic adenoma gene 1 (PLAG1) and coiled-coil-helix-coiled-coil-helix domain-containing 7 (CHCHD7) genes on BTA14 for carcass traits in Chinese Simmental. In the current study, we carried out a genome-wide association study for carcass meat yield in 472 Wagyu cattle with Bovine HD SNP array. Our results showed that 27 single nucleotide polymorphisms (SNPs) were identified for tenderloin weight (TDW), striploin weight (SPW), chuck roll weight (CRW), bicep weight (BPW), knuckle weight (KCW), and flank steak weight (FSW) in Wagyu cattle. Of these SNPs, 10 distinct SNPs were detected within the oxidation resistance 1 (OXR1), fatty acid binding protein 5 (FABP5), TNF receptor superfamily member 11b (TNFRSF11B), and zinc finger CCCH-type containing 3 (ZC3H3) genes on BTA14. Notably, three significant SNPs, BovineHD1400016738, BovineHD1400016743, and BovineHD1400016665 within OXR1, were shown strong linkage disequilibrium (r2 > 0.8) and significantly associated with CRW (P = 1.37 × 10-8 ~ 1.94 × 10-8). Moreover, Ingenuity Pathway Analysis showed that OXR1, FABP5, and CAP1A genes were involved in a single network and FABP5 may regulate the expression of OXR1 gene via node gene, peroxisome proliferator-activated receptor gamma (PPARG). Overall, this study suggests that OXR1 and FABP5 are candidate genes affecting carcass traits in Wagyu and the PLAG1-OXR1 region on BTA14 as a putative susceptibility locus for carcass meat yield for both Chinese Simmental and Wagyu.

Polymorphisms of the &amp;lt;i&amp;gt;ASIP&amp;lt;/i&amp;gt; gene and the haplotype are associated with fat deposition traits and fatty acid composition in Chinese Simmental steers

Unlike specific expression in the skin of wild mice, the agouti signaling protein (ASIP) is expressed widely in the tissue of cattle, including adipose and muscle tissue. Hence, it has been suggested that ASIP plays a role in bovine fat metabolism. An inserted L1-BT element was recently identified upstream of the ASIP locus which led to an ectopic expression of ASIP mRNA in cattle. In this study, we detected the indel of the L1-BT element at g. nt and three SNPs in introns of the ASIP gene (g. A G, g. A T, and g. 4805A T) in a Chinese Simmental steer population. The association analysis between variants of ASIP and economic traits showed that the homozygous genotype of L1-BT element insertion, AA genotype of g. A G, and AT genotype of g. 4805A T were significantly correlated with carcass and fat-related traits, such as live weight and back fat thickness. Moreover, three haplotypes (H1: AT; H2: AA; H3: GT) were identified by linkage disequilibrium analysis and formed six combined genotypes. Results indicated that Chinese Simmental steers with an H1H2 combined genotype had a higher measured value of fat-deposition-related traits (), including thickness of back fat and percentage of carcass fat coverage, but a lower content of linoleic acid and -linolenic acid (). Individuals of an H3H3 combination had a lower marbling score, perirenal fat weight, and carcass weight (). This suggests that these three SNPs and two combined haplotypes might be molecular markers for beef cattle breeding selection.

Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle.

Chinese Simmental beef cattle are the most economically important cattle breed in China. Estimated breeding values for growth, carcass, and meat quality traits are commonly used as selection criteria in animal breeding. The objective of this study was to evaluate the accuracy of alternative statistical methods for the estimation of genomic breeding values. Analyses of the accuracy of genomic best linear unbiased prediction (GBLUP), BayesB, and elastic net (EN) were performed with an Illumina BovineHD BeadChip on 1,217 animals by applying 5-fold cross-validation. Overall, the accuracies ranged from 0.17 to 0.296 for ten traits, and the heritability estimates ranged from 0.36 to 0.63. The EN (alpha = 0.001) model provided the most accurate prediction, which was also slightly higher (0.2–2%) than that of GBLUP for most traits, such as average daily weight gain (ADG) and carcass weight (CW). BayesB was less accurate for each trait than were EN (alpha = 0.001) and GBLUP. These findings indicate the importance of using an appropriate variable selection method for the genomic selection of traits and suggest the influence of the genetic architecture of the traits we analyzed.

Probe-based association analysis identifies several deletions associated with average daily gain in beef cattle

BackgroundAverage daily gain (ADG) is an important trait that contributes to the production efficiency and economic benefits in the beef cattle industry. The molecular mechanisms of ADG have not yet been fully explored because most recent association studies for ADG are based on SNPs or haplotypes. We reported a systematic CNV discovery and association analysis for ADG in Chinese Simmental beef cattle.ResultsOur study identified 4912 nonredundant CNVRs with a total length of ~ 248.7 Mb, corresponding to ~ 8.9% of the cattle genome. Using probe-based CNV association, we identified 24 and 12 significant SNP probes within five deletions and two duplications for ADG, respectively. Among them, we found one common deletion with 89 kb imbedded in LHFPL Tetraspan Subfamily Member 6 (LHFPL6) at 22.9 Mb on BTA12, which has high frequency (12.9%) dispersing across population. CNV selection test using VST statistic suggested this common deletion may be under positive selection in Chinese Simmental cattle. Moreover, this deletion was not overlapped with any candidate SNP for ADG compared with previous SNPs-based association studies, suggesting its important role for ADG. In addition, we identified one rare deletion near gene Growth Factor Receptor-bound Protein 10 (GRB10) at 5.1 Mb on BTA4 for ADG using both probe-based association and region-based approaches.ConclusionsOur results provided some valuable insights to elucidate the genetic basis of ADG in beef cattle, and these findings offer an alternative perspective to understand the genetic mechanism of complex traits in terms of copy number variations in farm animals.

A rapid and efficient linear mixed model approach using the score test and its application to GWAS

Recent studies have demonstrated that Linear mixed model (LMM) successfully correct for population structure and genetic relatedness in genome-wide association studies (GWAS). However, currently available LMM methods usually suffer from computational inefficiency. Here, we propose a novel method, consisting of an LMM using the score test (LMM-Score). Our method takes advantage of score test that does not require estimation of parameters under the alternative hypothesis or the full model. We performed extensive simulation studies to estimate the statistical power of LMM-Score with various assumptions of sample size or marker size under different trait heritabilities. Although the limited power of simulation studies due to small sample size and conservativeness of Bonferroni correction, LMM-Score reduces the computing time greatly and maintained statistical power when compared with routine LMM method. Therefore, we believe that the LMM-Score is a valuable method for identifying the genetic basis of complex traits. We also applied our approach to real beef cattle data and suggested SOX17, RP1, LYN RPS20, snoU54, U1, MOS, PLAG1, CHCHD7, and SDR16C5 on BTA14 as the most promising candidate genes affecting pure meat weight (PMW), foreshank weight (FSW) and silverside weight (SSW) in Chinese Simmental beef cattle.

Effects of polymorphisms in CAPN1 and CAST genes on meat tenderness of Chinese Simmental cattle.

Considerable evidence has demonstrated that the -calpain (CAPN1) gene and its inhibitor calpastatin (CAST) gene are major factors affecting meat quality. Marker-assisted selection (MAS) has been widely used to improve beef quality traits. Therefore, the objective of the present study was to investigate the single nucleotide polymorphisms (SNPs) of bovine CAPN1 and CAST genes using 367 animals representing the four main Chinese cattle breeds and to explore the effects of these SNPs on meat quality traits. Two SNPs within CAPN1 and one SNP in CAST were successfully identified in cattle. Genetic diversity analyses suggested that most SNPs in the four breeds exhibited a moderate genetic diversity. Moreover, associations between individual markers and meat quality traits were analyzed in Chinese Simmental cattle. The CAPN1 4558 A > G locus was found to be significantly associated with shear force value (SFV) and marbling score (BMS), and CAPN1 4684 C > T exerted a significant effect on SFV, while the CAST genotype was not significantly associated with any of the measured traits. SFV, commonly used to measure meat tenderness, represents an important quality trait as it contributes to the flavor of cooked meat. This work confirms the effect of CAPN1 on beef tenderness and lays an important foundation for future cattle breeding.