Estimating genetic parameters of reproductive, carcass, and meat quality traits in Polled Nellore cattle.

Considering the economic and commercial efficiency of the beef production chain, the yield and quality of the meat produced must also be included in breeding programs. For the Nellore breed, including the polled herd, these aspects have not been much studied. The aim of this study was to estimate genetic parameters for scrotal circumference adjusted to 365 (SC365) and 450 (SC450) days of age, age at first calving (AFC), accumulated productivity (AP), stayability (STAY), longissimus muscle area (LMA), thickness of subcutaneous fat over the 12th-13th ribs (BF), thickness of subcutaneous fat over the rump (RF), and shear force measured by Warner-Bratzler shear force (WBSF) of polled Nellore cattle. Bayesian analyses were performed by adopting a linear animal model, whereas STAY analyses used the linear threshold model. Heritability estimates were 0.31 (SC365), 0.37 (SC450), 0.16 (AFC), 0.25 (AP), 0.16 (STAY), 0.30 (LMA), 0.13 (BF), 0.24 (RF), and 0.15 (WBSF), indicating moderate response to selection. Genetic and residual correlations between SC365 and SC450 were high (0.91 and 0.74, respectively), as well as the genetic correlations of AP with SC365, SC450, AFC, and STAY (0.61, 0.62, - 0.69, and 0.83, respectively). Genetic and residual correlations of WBSF with reproductive and carcass characteristics exhibited high standard deviations, however favorable. Based on the results, it is expected that in the medium term, animals with greater sexual precocity will also have greater accumulated productivity and longer permanence of females in the herd, along with superior carcass traits. However, due to the low heritabilities and small genetic associations with reproductive traits, fat thickness characteristics (BF and RF) will still require direct selection.

Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle.

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.

Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle.

Several methods have been used for genome-enabled prediction (or genomic selection) of complex traits, for example, multiple regression models describing a target trait with a linear function of a set of genetic markers. Genomic selection studies have been focused mostly on single-trait analyses. However, most profitability traits are genetically correlated, and an increase in prediction accuracy of genomic breeding values for genetically correlated traits is expected when using multiple-trait models. Thus, this study was carried out to assess the accuracy of genomic prediction for carcass and meat quality traits in Nelore cattle, using single- and multiple-trait approaches. The study considered 15 780, 15 784, 15 742 and 526 records of rib eye area (REA, cm2), back fat thickness (BF, mm), rump fat (RF, mm) and Warner-Bratzler shear force (WBSF, kg), respectively, in Nelore cattle, from the Nelore Brazil Breeding Program. Animals were genotyped with a low-density single nucleotide polymorphism (SNP) panel and subsequently imputed to arrays with 54 and 777 k SNPs. Four Bayesian specifications of genomic regression models, namely, Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression; blending methods, BLUP; and single-step genomic best linear unbiased prediction (ssGBLUP) methods were compared in terms of prediction accuracy using a fivefold cross-validation. Estimates of heritability ranged from 0.20 to 0.35 and from 0.21 to 0.46 for RF and WBSF on single- and multiple-trait analyses, respectively. Prediction accuracies for REA, BF, RF and WBSF were all similar using the different specifications of regression models. In addition, this study has shown the impact of genomic information upon genetic evaluations in beef cattle using the multiple-trait model, which was also advantageous compared to the single-trait model because it accounted for the selection process using multiple traits at the same time. The advantage of multi-trait analyses is attributed to the consideration of correlations and genetic influences between the traits, in addition to the non-random association of alleles.

Responses of Spodoptera frugiperda and Trichogramma pretiosum to Rice Plants Exposed to Herbivory and Phytohormones.

Damage caused by herbivorous insects and application of phytohormones can activate signaling pathways, which result in greater production of secondary metabolites, increasing plant defenses. This study aimed to evaluate the induced direct resistance (local and systemic) of rice plants caused by herbivorous insects and exogenous application of methyl jasmonate (MJ) and salicylic acid (SA) in the development of fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae). Moreover, indirect defenses through chemotactic responses of Trichogramma pretiosum (Riley) (Hymenoptera: Trichogrammatidae) submitted to the same treatments were investigated. Direct defense was evaluated by measuring weight gain of fall armyworm larvae fed with leaves of plants previously exposed to herbivores or treated with MJ (2 and 5 mM), SA (8 and 16 mM), or control. Indirect defense was verified by chemotactic behavior of wasps in two-choice olfactometer tests to plants exposed to herbivores and evaluated after 24, 48, and 72 h in comparison with undamaged ones, as well as plants treated with the same phytohormones contrasted with the control. The gain of weight was reduced in immature developmental stage of S. frugiperda fed in leaves previously damaged by fall armyworm and in newly formed leaves after damage to the plants, comparing with control. Leaves treated with MJ (2 mM and 5 mM) and SA (8 mM) were less eaten than those not treated. Parasitoids triggered a positive chemotactic behavior in rice plants that had been sprayed with same concentrations. This study showed that rice plants can activate direct and indirect defenses through an exogenous application of phytohormones.

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low-density SNP genotypes.

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP-LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP-LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de-regressed EBV was slightly small (i.e. 0.87%-18.75%). The present study also compared the performance of five genomic prediction models and two cross-validation methods. The five genomic models predicted EBV and de-regressed EBV of the ten traits similarly well. Of the two cross-validation methods, leave-one-out cross-validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle.

Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture.

This study was carried out to investigate (co)variance components and genetic parameters for growth traits in beef cattle using a multi-trait model by Bayesian methods. Genetic and residual (co)variances and parameters were estimated for weights at standard ages of 120 (W120), 210 (W210), 365 (W365), and 450 days (W450), and for pre- and post-weaning daily weight gain (preWWG and postWWG) in Nellore cattle. Data were collected over 16 years (1993-2009), and all animals were raised on pasture in eight farms in the North of Brazil that participate in the National Association of Breeders and Researchers. Analyses were run by the Bayesian approach using Gibbs sampler. Additive direct heritabilities for W120, W210, W365, and W450 and for preWWG and postWWG were 0.28 ± 0.013, 0.32 ± 0.002, 0.31 ± 0.002, 0.50 ± 0.026, 0.61 ± 0.047, and 0.79 ± 0.055, respectively. The estimates of maternal heritability were 0.32 ± 0.012, 0.29 ± 0.004, 0.30 ± 0.005, 0.25 ± 0.015, 0.23 ± 0.017, and 0.22 ± 0.016, respectively, for W120, W210, W365, and W450 and for preWWG and postWWG. The estimates of genetic direct additive correlation among all traits were positive and ranged from 0.25 ± 0.03 (preWWG and postWWG) to 0.99 ± 0.00 (W210 and preWWG). The moderate to high estimates of heritability and genetic correlation for weights and daily weight gains at different ages is suggestive of genetic improvement in these traits by selection at an appropriate age. Maternal genetic effects seemed to be significant across the traits. When the focus is on direct and maternal effects, W210 seems to be a good criterium for the selection of Nellore cattle considering the importance of this breed as a major breed of beef cattle not only in Northern Brazil but all regions covered by tropical pastures. As in this study the genetic correlations among all traits were high, the selection based on weaning weight might be a good choice because at this age there are two important effects (maternal and direct genetic effects). In contrast, W120 should be preferred when the objective is improving the maternal ability of the dams. Furthermore, selection for postWWG can be used if the animals show both heavier weaning weights and high growth rate after weaning because it is possible to shorten the time between weaning and slaughter based on weaning weight, postWWG, and desired weight at the time of slaughter.

Genomewide association mapping and pathway analysis of meat tenderness in Polled Nellore cattle.

Brazil is one of the world's largest beef exporters, although the product has a low price due to quality issues. The meat exported by Brazil is considered medium and low quality by international buyers, mainly due to lack of tenderness. The predominant Zebu breeds (80% Nellore) are known for producing tougher beef than taurine breeds. Nonetheless, some studies have shown that there is substantial genetic variability for tenderness within the Nellore breed, although it is a difficult trait to improve by conventional selection methods. Therefore, the aim of this study was to perform a genomewide association study (GWAS) and a gene set enrichment analysis to identify genomic regions and biologically relevant pathways associated with meat tenderness in Polled Nellore cattle. Data consisted of Warner-Bratzler shear force values of LM from 427 Polled Nellore animals divided into 3 experimental slaughters (years 2005, 2008, and 2010). The animals were genotyped with either the Illumina BovineHD BeadChip (777k, on 61 samples) or the GGP Indicus HD chip (77k, on 366 samples). Single nucleotide polymorphisms were excluded when the call rate was <90%, the Hardy-Weinberg proportions -value was <1% (Fisher exact test, Bonferroni adjusted), and the minor allele frequency was <1%. Imputation from the GGP Indicus HD chip to the Illumina BovineHD BeadChip was performed using the FImput program. Genomewide association analysis was performed using the Efficient Mixed Model Association eXpedited (EMMAx) and the population parameters previously determined (P3D) methods. The GWAS was complemented with a gene set enrichment analysis performed using the FatiGO procedure. Significant markers ( < 0.0001) explaining a larger proportion of variation than other significant SNPs were located on chromosomes 3, 13, 17, 20, 21, and 25, indicating QTL associated with meat tenderness throughout the genome. Additionally, gene set analysis identified 22 Gene Ontology functional terms and 2 InterPro entries that showed significant enrichment of genes associated with tenderness. The functional categories included protein tyrosine and serine/threonine kinase activity, calcium ion binding, lipid metabolic process, and growth factors, among others. These results help to elucidate the genetic architecture and metabolic pathways underlying this trait, which is of extreme economic and social importance to Brazil, because Nellore is the dominant beef cattle breed in the country.

Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle.

Zebu () cattle, mostly of the Nellore breed, comprise more than 80% of the beef cattle in Brazil, given their tolerance of the tropical climate and high resistance to ectoparasites. Despite their advantages for production in tropical environments, zebu cattle tend to produce tougher meat than Bos taurus breeds. Traditional genetic selection to improve meat tenderness is constrained by the difficulty and cost of phenotypic evaluation for meat quality. Therefore, genomic selection may be the best strategy to improve meat quality traits. This study was performed to compare the accuracies of different Bayesian regression models in predicting molecular breeding values for meat tenderness in Polled Nellore cattle. The data set was composed of Warner-Bratzler shear force (WBSF) of longissimus muscle from 205, 141, and 81 animals slaughtered in 2005, 2010, and 2012, respectively, which were selected and mated so as to create extreme segregation for WBSF. The animals were genotyped with either the Illumina BovineHD (HD; 777,000 from 90 samples) chip or the GeneSeek Genomic Profiler (GGP Indicus HD; 77,000 from 337 samples). The quality controls of SNP were Hard-Weinberg Proportion -value ≥ 0.1%, minor allele frequency > 1%, and call rate > 90%. The FImpute program was used for imputation from the GGP Indicus HD chip to the HD chip. The effect of each SNP was estimated using ridge regression, least absolute shrinkage and selection operator (LASSO), Bayes A, Bayes B, and Bayes Cπ methods. Different numbers of SNP were used, with 1, 2, 3, 4, 5, 7, 10, 20, 40, 60, 80, or 100% of the markers preselected based on their significance test (-value from genomewide association studies [GWAS]) or randomly sampled. The prediction accuracy was assessed by the correlation between genomic breeding value and the observed WBSF phenotype, using a leave-one-out cross-validation methodology. The prediction accuracies using all markers were all very similar for all models, ranging from 0.22 (Bayes Cπ) to 0.25 (Bayes B). When preselecting SNP based on GWAS results, the highest correlation (0.27) between WBSF and the genomic breeding value was achieved using the Bayesian LASSO model with 15,030 (3%) markers. Although this study used relatively few animals, the design of the segregating population ensured wide genetic variability for meat tenderness, which was important to achieve acceptable accuracy of genomic prediction. Although all models showed similar levels of prediction accuracy, some small advantages were observed with the Bayes B approach when higher numbers of markers were preselected based on their -values resulting from a GWAS analysis.