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Modern livestock production systems are characterized by a greater focus on intensification, involving managing larger numbers of animals to achieve higher productive efficiency and animal health and welfare within herds. Therefore, animal breeding programs need to be strategically designed to select animals that can effectively enhance production performance and animal welfare across a range of environmental conditions. Thus, this review summarizes the main methodologies used for assessing the levels of genotype-by-environment interaction (G × E) in cattle populations. In addition, we explored the importance of integrating genomic and phenotypic information to quantify and account for G × E in breeding programs. An overview of the structure of cattle breeding programs is provided to give insights into the potential outcomes and challenges faced when considering G × E to optimize genetic gains in breeding programs. The role of nutrigenomics and its impact on gene expression related to metabolism in cattle are also discussed, along with an examination of current research findings and their potential implications for future research and practical applications. Out of the 116 studies examined, 60 and 56 focused on beef and dairy cattle, respectively. A total of 83.62% of these studies reported genetic correlations across environmental gradients below 0.80, indicating the presence of G × E. For beef cattle, 69.33%, 24%, 2.67%, 2.67%, and 1.33% of the studies evaluated growth, reproduction, carcass and meat quality, survival, and feed efficiency traits, respectively. By contrast, G × E research in dairy cattle populations predominantly focused on milk yield and milk composition (79.36% of the studies), followed by reproduction and fertility (19.05%), and survival (1.59%) traits. The importance of G × E becomes particularly evident when considering complex traits such as heat tolerance, disease resistance, reproductive performance, and feed efficiency, as highlighted in this review. Genomic models provide a valuable avenue for studying these traits in greater depth, allowing for the identification of candidate genes and metabolic pathways associated with animal fitness, adaptation, and environmental efficiency. Nutrigenetics and nutrigenomics are emerging fields that require extensive investigation to maximize our understanding of gene-nutrient interactions. By studying various transcription factors, we can potentially improve animal metabolism, improving performance, health, and quality of products such as meat and milk., (© 2024 The Author(s). Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.)

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A 22 . By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations., (© 2024 The Author(s). Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.)

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits., (© 2024 Wiley‐VCH GmbH. Published by John Wiley & Sons Ltd.)

Growth and carcass traits are essential selection criteria for beef cattle breeding programs. However, it is necessary to combine these measurements with body composition traits to meet the demand of the consumer market. This study aimed to estimate the genetic parameters for visual scores, growth (pre and post-weaning weights), and carcass (rib eye area (REA), back and rump fat thickness) traits in Nellore cattle using Bayesian inference. Data from 12,060 animals belonging to the HoRa Hofig Ramos herd were used. Morphological traits were evaluated by the MERCOS methodology. The heritability estimates obtained ranged from low to high magnitude, from 0.15 to 0.28 for visual scores, 0.13 to 0.44 for growth, and from 0.42 to 0.46 for carcass traits. Genetic correlations between visual scores and growth traits were generally of moderate to high magnitudes, however, visual scores showed low correlations with carcass traits, except between sacral bone and structure and REA. Selection for visual score traits can lead to favorable responses in body weight and vice versa, but the same is not true for carcass traits. Morphological categorical traits can be used as complementary tools that add value to selection.

Background: Selecting animals for feed efficiency directly impacts the profitability of the beef cattle industry, which contributes to minimizing the environmental footprint of beef production. Genetic and environmental factors influence animal feed efficiency, leading to phenotypic variability when exposed to different environmental conditions (i.e., temperature and nutritional level). Thus, our aim was to assess potential genotype-by-environment (G × E) interactions for dry matter intake (DMI) and residual feed intake (RFI) in Nellore cattle (Bos taurus indicus) based on bi-trait reaction norm models (RN) and evaluate the genetic association between RFI and DMI across different environmental gradient (EG) levels. For this, we used phenotypic information on 12,958 animals (young bulls and heifers) for DMI and RFI recorded during 158 feed efficiency trials., Results: The heritability estimates for DMI and RFI across EG ranged from 0.26 to 0.54 and from 0.07 to 0.41, respectively. The average genetic correlations (± standard deviation) across EG for DMI and RFI were 0.83 ± 0.19 and 0.81 ± 0.21, respectively, with the lowest genetic correlation estimates observed between extreme EG levels (low vs. high) i.e. 0.22 for RFI and 0.26 for DMI, indicating the presence of G × E interactions. The genetic correlation between RFI and DMI across EG levels decreased as the EG became more favorable and ranged from 0.79 (lowest EG) to 0.52 (highest EG). Based on the estimated breeding values from extreme EG levels (low vs. high), we observed a moderate Spearman correlation of 0.61 (RFI) and 0.55 (DMI) and a selection coincidence of 53.3% and 40.0% for RFI and DMI, respectively., Conclusions: Our results show evidence of G × E interactions on feed efficiency traits in Nellore cattle, especially in feeding trials with an average daily gain (ADG) that is far from the expected of 1 kg/day, thus increasing reranking of animals., (© 2023. The Author(s).)

In vitro embryos production from prepubertal heifers can help contribute to breeding programs; however, strategies are necessary to increase their embryo production. The aim of this study was to investigate the effects of two nutritional plans on oocyte recovery, embryo production and growth performance of prepubertal Nelore heifers. Thirty-four Nelore heifers with age of 6.5 months were divided into two feeding treatments (NP1 and NP2). The NP1 diets served as the control and NP2 diets were formulated to contain an average of 1.22-fold more energy than NP1. After 3 months of supplementation, the animals underwent follicular aspiration (ovum pick-up, OPU) every 21 d for 3 months and embryos were produced in vitro . Wither height, chest depth, body weight and subcutaneous fat of animals were measured. The number of retrieved and viable oocytes per OPU were 1.49-fold and 1.42-fold greater in NP2 heifers ( p =  0.018 and p =  0.049, respectively) than those in NP1 heifers. Heifers administered NP2 produced 29.7% blastocysts, a percentage higher than NP1 animals that produced 24.40% embryos ( p <  0.05). Consequently, females in the NP2 treatment showed improved body development. These results indicate a positive effect of a higher energy diet on assisted reproduction and body development in prepubertal heifers.

The objective of this study was to estimate the genetic parameters for feed efficiency-related traits and their genetic correlations with growth, male fertility, and carcass traits using multi-trait analysis in Guzerat cattle. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for growth, male fertility, and carcass traits. The evaluated traits were adjusted weight at 120 (W120), 210 (W210), 365 (W365), and 450 days of age (W450), adjusted scrotal circumference at 365 days of age (SC365) and at 450 days of age (SC450), scrotal circumference, ribeye area (REA), backfat thickness (BFT), rump fat thickness (RFT), residual feed intake (RFI), and dry matter intake (DMI). The genetic parameters were obtained by the restricted maximum likelihood method (REML), using an animal model in multi-trait analyses. The heritability estimates for W120, W210, W365, W450, SC365, and SC450 varied from low to high (0.17 to 0.39). The carcass traits, REA, BFT, and RFT, displayed low to moderate heritability estimates, 0.27, 0.10, and 0.31, respectively. The heritability estimates for RFI (0.15) and DMI (0.23) were low and moderate, respectively. The RFI showed low genetic correlations with growth traits, ranging from - 0.07 to 0.22, from 0.03 to 0.05 for scrotal circumference, and from - 0.35 to 0.16 for carcass, except for DMI, which ranged from 0.42 to 0.46. The RFI and DMI presented enough additive genetic variability to be used as selection criteria in Guzerat breed genetic improvement program. Additionally, the response to selection for RFI would be higher when selection is performed directly for this trait. The selection for residual feed intake would not promote unfavorable correlated responses for scrotal circumference, carcass (yield and finish), and growth traits. Therefore, the selection for more efficient animals would not compromise the productive, reproductive, and carcass performance, contributing to reduce the production costs, increasing the profitability and sustainability of beef cattle production in tropical areas., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

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., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

This study aimed to estimate prediction ability and genetic parameters for residual feed intake (RFI) calculated using a regression equation for each test (RFI test ) and for the whole population (RFI pop ) in Nellore beef cattle. It also aimed to evaluate the correlations between RFI pop and RFI test with growth, reproductive, and carcass traits. Genotypic and phenotypic records from 8354 animals were used. An analysis of variance (ANOVA) was performed to verify the adequacy of the regression equations applied to estimate the RFI test and RFI pop . The (co)variance components were obtained using the single-step genomic best linear unbiased prediction under single and two-trait animal model analyses. The genetic and phenotypic correlations between RFI test and RFI pop with dry matter intake, frame, growth, reproduction, and carcass-related traits were evaluated. The prediction ability and bias were estimated to compare the RFI test and RFI pop genomic breeding values (GEBV). The RFI pop ANOVA showed a higher significance level (p < 0.0001) than did the RFI test for the fixed effects. The RFI pop displayed higher additive genetic variance estimated than the RFI test , although the RFI pop and RFI test displayed similar heritabilities. Overall, the RFI test showed higher residual correlations with growth, reproductive, and carcass traits, while the RFI pop displayed higher genetic correlations with such traits. The GEBV for the RFI test was slightly biased than GEBV RFI pop . The approach to calculate the RFI influenced the decomposition and estimation of variance components and genomic prediction for RFI. The application of RFI pop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias for RFI., (© 2022. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale., (© 2022 John Wiley & Sons Ltd.)

In Nellore beef cattle, studies addressing genetic correlations between ultrasound marbling content and other economically important traits are still incipient. Therefore, this work aimed to estimate heritability and genetic correlations between ultrasound marbling content in the longissimus dorsi muscle (MARB) and growth, reproductive, feed efficiency, and carcass-related traits in a Nellore beef cattle population from Brazil. Phenotypic records of 614,395 Nellore animals were used and included adjusted weight at 210 (W210) and 450 (W450) days of age, adult cow weight (AW), early heifer pregnancy (EH), stayability (STAY), adjusted scrotal circumference at 365 days of age (SC365), ribeye area (REA), subcutaneous backfat thickness (BF), rump fat thickness (RF), and marbling (MARB). The genetic parameters for all traits but EH and STAY were estimated considering a linear animal model, whereas for those two nonlinear traits, a threshold animal model was used. The direct and correlated response to selection for MARB versus the other traits, and the relative efficiency of selection, were also calculated. The heritability estimate for MARB was 0.31 and for the other conventional evaluated traits was low to moderate, with values ranging from 0.14 to 0.41. The genetic correlations between MARB and growth, reproductive, feed efficiency, and carcass-related trait were very low, with values close to zero, with similar correlated responses. The MARB displayed adequate genetic variability to respond to selection and crossbreeding programs looking forward to higher meat quality and differential market standards for the Nellore beef. The selection for growth, reproductive, feed efficiency, and carcass-related traits would not affect MARB in Nellore beef cattle and vice versa. Therefore, this trait should be included as a selection criterion in the Nellore breeding program., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

A bioeconomic model was developed to calculate the economic value (ev) of reproductive and growth performance, feed efficiency and carcass traits of a seedstock Nellore herd. Data from a full-cycle cattle operation (1,436 dams) located in the Brazilian Cerrado were assessed. The ev was calculated by the difference in profit before and after one-unit improvement in the trait, with others remaining unchanged. The ev was standardized by the phenotypic standard deviation of each trait. Preweaning average daily gain (ADG) was the most economically important trait evaluated (R$ 58.04/animal/year), followed by age at first calving (R$ 44.35), postweaning ADG (R$ 31.43), weight at 450 days (R$ 25.36), accumulated productivity (R$ 21.43), ribeye area (R$ 21.35), calving interval (R$ 19.97), feed efficiency (R$ 15.24), carcass dressing per cent (R$ 8.27), weight at 120 days (R$ 6.22), weight at 365 days (R$ 6.06), weight at weaning (210 days, R$ 5.82), stayability (R$ 5.70) and the probability of early calving (R$ 0.32). The effects of all traits on profits are evidence that their selection may result in the economic and genetic progress of the herd if there is genetic variability., (© 2021 Wiley-VCH GmbH.)

The aim was to evaluate the association between growth, carcass and visual scores traits with precocious calving in Nellore cattle. Birth weight (BW), weight at 120, 210, 365 and 450 days of age, pre and post-weaning average daily gain, rib eye area, backfat thickness (BF), rump fat thickness and visual scores obtained at 18 months were used for the analysis. Records from 700 females born between 2009 and 2015, exposed to mating starting at 11 months of age were analyzed. Discriminant analyzes were performed with the software Statistica. BW and BF showed the highest (P>0.01) discrimination value for early heifer pregnancy (EP). Extreme intrauterine growth retardation can result in slower growth, which reflects in the worst reproductive performance, confirmed by the variation in BW between precocious and conventional heifers. The results also demonstrate that the level of body fat affects begin of puberty. Bone structure, musculature, depth, tail insertion and rump scores presented the highest discrimination value for EP. These traits can be used as selection tools to improve sexual precocity in female Nellore cattle. The results obtained in this study would support farmers to guide the heifer management and decisions in order to enhance the EP.

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., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

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, cm 2 ), 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., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

The aim was to conduct a weighted single-step genome-wide association study to detect genomic regions and putative candidate genes related to residual feed intake, dry matter intake, feed efficiency (FE), feed conversion ratio, residual body weight gain, residual intake and weight gain in Nellore cattle. Several protein-coding genes were identified within the genomic regions that explain more than 0.5% of the additive genetic variance for these traits. These genes were associated with insulin, leptin, glucose, protein and lipid metabolisms; energy balance; heat and oxidative stress; bile secretion; satiety; feed behaviour; salivation; digestion; and nutrient absorption. Enrichment analysis revealed functional pathways (p-value < .05) such as neuropeptide signalling (GO:0007218), negative regulation of canonical Wingless/Int-1 (Wnt) signalling (GO:0090090), bitter taste receptor activity (GO:0033038), neuropeptide hormone activity (GO:0005184), bile secretion (bta04976), taste transduction (bta0742) and glucagon signalling pathway (bta04922). The identification of these genes, pathways and their respective functions should contribute to a better understanding of the genetic and physiological mechanisms regulating Nellore FE-related traits., (© 2020 Blackwell Verlag GmbH.)

The goal of this study was to compare the predictive performance of artificial neural networks (ANNs) with Bayesian ridge regression, Bayesian Lasso, Bayes A, Bayes B and Bayes Cπ in estimating genomic breeding values for meat tenderness in Nellore cattle. The animals were genotyped with the Illumina Bovine HD Bead Chip (HD, 777K from 90 samples) and the GeneSeek Genomic Profiler (GGP Indicus HD, 77K from 485 samples). The quality control for the genotypes was applied on each Chip and comprised removal of SNPs located on non-autosomal chromosomes, with minor allele frequency <5%, deviation from HWE (p < 10 -6 ), and with linkage disequilibrium >0.8. The FImpute program was used for genotype imputation. Pedigree-based analyses indicated that meat tenderness is moderately heritable (0.35), indicating that it can be improved by direct selection. Prediction accuracies were very similar across the Bayesian regression models, ranging from 0.20 (Bayes A) to 0.22 (Bayes B) and 0.14 (Bayes Cπ) to 0.19 (Bayes A) for the additive and dominance effects, respectively. ANN achieved the highest accuracy (0.33) of genomic prediction of genetic merit. Even though deep neural networks are recognized to deliver more accurate predictions, in our study ANN with one single hidden layer, 105 neurons and rectified linear unit (ReLU) activation function was sufficient to increase the prediction of genetic merit for meat tenderness. These results indicate that an ANN with relatively simple architecture can provide superior genomic predictions for meat tenderness in Nellore cattle., (© 2020 Blackwell Verlag GmbH.)

Background: The aim of this study was to assess genome-wide autozygosity in a Nellore cattle population and to characterize ROH patterns and autozygosity islands that may have occurred due to selection within its lineages. It attempts also to compare estimates of inbreeding calculated from ROH (F ROH ), genomic relationship matrix (F GRM ), and pedigree-based coefficient (F PED )., Results: The average number of ROH per animal was 55.15 ± 13.01 with an average size of 3.24 Mb. The Nellore genome is composed mostly by a high number of shorter segments accounting for 78% of all ROH, although the proportion of the genome covered by them was relatively small. The genome autozygosity proportion indicates moderate to high inbreeding levels for classical standards, with an average value of 7.15% (178.70 Mb). The average of F PED and F ROH , and their correlations (- 0.05 to 0.26) were low. Estimates of correlation between F GRM -F PED was zero, while the correlation (- 0.01 to - 0.07) between F GRM -F ROH decreased as a function of ROH length, except for F ROH > 8Mb (- 0.03). Overall, inbreeding coefficients were not high for the genotyped animals. Autozygosity islands were evident across the genome (n = 62) and their genomic location did not largely differ within lineages. Enriched terms (p < 0.01) associated with defense response to bacteria (GO:0042742), immune complex reaction (GO:0045647), pregnancy-associated glycoproteins genes (GO:0030163), and organism growth (GO:0040014) were described within the autozygotic islands., Conclusions: Low F PED- F ROH correlation estimates indicate that F PED is not the most suitable method for capturing ancient inbreeding when the pedigree does not extend back many generations and F ROH should be used instead. Enriched terms (p < 0.01) suggest a strong selection for immune response. Non-overlapping islands within the lineages greatly explain the mechanism underlying selection for functionally important traits in Nellore cattle.

The causal mutation for polledness in Nelore (Bos taurus indicus) breed seems to have appeared first in Brazil in 1957. The expression of the polled trait is known to be ruled by a few groups of alleles in taurine breeds; however, the genetic basis of this trait in indicine cattle is still unclear. The aim of this study was to identify genomic regions associated with the hornless trait in a commercial Nelore population. A total of 107,294 animals had phenotypes recorded and 2,238 were genotyped/imputed for 777k SNP. The weighted single-step approach for genome-wide association study (WssGWAS) was used to estimate the SNP effects and variances accounted for by 1 Mb sliding SNP windows. A centromeric region of chromosome 1 with 3.11 Mb size (BTA1: 878,631-3,987,104 bp) was found to be associated with hornless in the studied population. A total of 28 protein-coding genes are mapped in this region, including the taurine Polled locus and the IFNAR1, IFNAR2, IFNGR2, KRTAP11-1, MIS18A, OLIG1, OLIG2, and SOD1 genes, which expression can be related to the horn formation as described in literature. The functional enrichment analysis by DAVID tool revealed cytokine-cytokine receptor interaction, JAK-STAT signaling, natural killer cell mediated cytotoxicity, and osteoclast differentiation pathways as significant (P < 0.05). In addition, a runs of homozygosity (ROH) analysis identified a ROH island in polled animals with 2.47 Mb inside the region identified by WssGWAS. Polledness in Nelore cattle is associated with one region in the genome with 3.1 Mb size in chromosome 1. Several genes are harbored in this region, and they may act together in the determination of the polled/horned phenotype. Fine mapping the locus responsible for polled trait in Nelore breed and the identification of the molecular mechanisms regulating the horn growth deserve further investigation., Competing Interests: Zoetis provided support in the form of salaries for authors FADC and JBO. The commercial affiliation of authors FADC and JBO does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors declare that they have no competing interests.

The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty.

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.

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.

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.

Components of (co)variance and genetic parameters were estimated for adjusted weights at ages 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of Polled Nellore cattle raised on pasture and born between 1987 and 2010. Analyses were performed using an animal model, considering fixed effects: herd-year-season of birth and calf sex as contemporary groups and the age of cow as a covariate. Gibbs Samplers were used to estimate (co)variance components, genetic parameters and additive genetic effects, which accounted for great proportion of total variation in these traits. High direct heritability estimates for the growth traits were revealed and presented mean 0.43, 0.61, 0.72 and 0.67 for W120, W240, W365 and W450, respectively. Maternal heritabilities were 0.07 and 0.08 for W120 and W240, respectively. Direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were strong and positive. These estimates ranged from 0.68 to 0.98. Direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from -0.31 to -0.54. Estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240. This study showed that genetic progress is possible for the growth traits we studied, which is a novel and favorable indicator for an upcoming and promising Polled Zebu breed in Tropical regions. Maternal effects influenced the performance of weight at 120 and 240 days old. These effects should be taken into account in genetic analyses of growth traits by fitting them as a genetic or a permanent environmental effect, or even both. In general, due to a medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised at pasture in tropical regions of Brazil needs improvement and growth performance can be enhanced.

This study used multivariate statistics to identify clusters of animals with similar expected progeny difference (EPD) and also identify leading traits that discriminate between bulls. Various linear selection indices based on specific selection criteria were proposed. Records were collected from 880 young Nelore bulls submitted to performance testing in central Brazil between 2001 and 2012. Pre-weaning average daily gain and weights at 210 days with direct and maternal effects were used in the analysis, in addition to post-weaning weight, scrotal circumference at 365 and 450 days, carcass finish and rib eye area. EPDs were classified into three groups, and the EPD means of two of these groups stood out and were considered important based on principal component analysis that associated higher values of direct EPD of weights, average daily weight gain and scrotal circumference. The EPDs for weight at 210, 365 and 450 days, pre- and post-weaning daily gain and scrotal circumference at 365 and 450 days were major causes of variation. Each linear selection index proposed (SI₁, SI₂, SI₃, SI₄ and SI₅) defined a specific approach meaning that a different selection index should be used depending on breeding goals and selection criteria.

This study was carried out to estimate (co)variance components and genetic parameters for live weight of Nellore cattle from Performance Test of Young Bulls using random regression models. Data of weights and ages of 925 weaned males was used. The animal model included the fixed effect of contemporary group, age of the animal at weighing as a covariate and as random effects it was considered the effect of additive genetic and permanent environment of the animal. The residue was modeled considering four classes of variances. The models were compared based on the Bayesian information criteria of Akaike and Schwartz. The model polynomial of fourth and sixth order for the direct additive genetic effects and permanent environment of the animal, respectively was the most appropriate to describe the changes in the variances of the weights during the period in which the animals participating in the performance test young bulls. Heritability estimates showed moderate magnitudes and indicated that direct selection will promote improvement of selection criteria adopted. Furthermore, due to high positive correlation between the estimated weights, it was suggested selecting the best animals before at 365 days of age, because it is the period in which the animals have a higher growth rate and thus you can select animals heavier and less delayed.

In this study, Bayesian analysis under a threshold animal model was used to estimate genetic correlations between morphological traits (body structure, finishing precocity and muscling) in Nelore cattle evaluated at weaning and yearling. Visual scores obtained from 7651 Nelore cattle at weaning and from 4155 animals at yearling, belonging to the Brazilian Nelore Program, were used. Genetic parameters for the morphological traits were estimated by two-trait Bayesian analysis under a threshold animal model. The genetic correlations between the morphological traits evaluated at two ages of the animal (weaning and yearling) were positive and high for body structure (0.91), finishing precocity (0.96) and muscling (0.94). These results indicate that the traits are mainly determined by the same set of genes of additive action and that direct selection at weaning will also result in genetic progress for the same traits at yearling. Thus, selection of the best genotypes during only one phase of life of the animal is suggested. However, genetic differences between morphological traits were better detected during the growth phase to yearling. Direct selection for body structure, finishing precocity and muscling at only one age, preferentially at yearling, is recommended as genetic differences between traits can be detected at this age., (Copyright 2010 Blackwell Verlag GmbH.)

The aim of this study was to estimate the components of variance and genetic parameters for the visual scores which constitute the Morphological Evaluation System (MES), such as body structure (S), precocity (P) and musculature (M) in Nellore beef-cattle at the weaning and yearling stages, by using threshold Bayesian models. The information used for this was gleaned from visual scores of 5,407 animals evaluated at the weaning and 2,649 at the yearling stages. The genetic parameters for visual score traits were estimated through two-trait analysis, using the threshold animal model, with Bayesian statistics methodology and MTGSAM (Multiple Trait Gibbs Sampler for Animal Models) threshold software. Heritability estimates for S, P and M were 0.68, 0.65 and 0.62 (at weaning) and 0.44, 0.38 and 0.32 (at the yearling stage), respectively. Heritability estimates for S, P and M were found to be high, and so it is expected that these traits should respond favorably to direct selection. The visual scores evaluated at the weaning and yearling stages might be used in the composition of new selection indexes, as they presented sufficient genetic variability to promote genetic progress in such morphological traits.