Assessment of inbreeding coefficients and inbreeding depression on complex traits from genomic and pedigree data in Nelore cattle.

BACKGROUND: Nelore cattle play a key role in tropical production systems due to their resilience to harsh conditions, such as heat stress and seasonally poor nutrition. Monitoring their genetic diversity is essential to manage the negative impacts of inbreeding. Traditionally, inbreeding and inbreeding depression are assessed by pedigree-based coefficients (F), but recently, genetic markers have been preferred for their precision in capturing the inbreeding level and identifying animals at risk of reduced productive and reproductive performance. Hence, we compared the inbreeding and inbreeding depression for productive and reproductive performance traits in Nelore cattle using different inbreeding coefficient estimation methods from pedigree information (FPed), the genomic relationship matrix (FGRM), runs of homozygosity (FROH) of different lengths (> 1 Mb (genome), between 1 and 2 Mb - FROH 1-2; 2-4 Mb FROH 2-4 or > 8 Mb FROH >8) and excess homozygosity (FSNP). RESULTS: The correlation between FPed and FROH was lower when the latter was based on shorter segments (r = 0.15 with FROH 1-2, r = 0.20 with FROH 2-4 and r = 0.28 with FROH 4-8). Meanwhile, the FPed had a moderate correlation with FSNP (r = 0.47) and high correlation with FROH >8 (r = 0.58) and FROH-genome (r = 0.60). The FROH-genome was highly correlated with inbreeding based on FROH>8 (r = 0.93) and FSNP (r = 0.88). The FGRM exhibited a high correlation with FROH-genome (r = 0.55) and FROH >8 (r = 0.51) and a lower correlation with other inbreeding estimators varying from 0.30 for FROH 2-4 to 0.37 for FROH 1-2. Increased levels of inbreeding had a negative impact on the productive and reproductive performance of Nelore cattle. The unfavorable inbreeding effect on productive and reproductive traits ranged from 0.12 to 0.51 for FPed, 0.19-0.59 for FGRM, 0.21-0.58 for FROH-genome, and 0.19-0.54 for FSNP per 1% of inbreeding scaled on the percentage of the mean. When scaling the linear regression coefficients on the standard deviation, the unfavorable inbreeding effect varied from 0.43 to 1.56% for FPed, 0.49-1.97% for FGRM, 0.34-2.2% for FROH-genome, and 0.50-1.62% for FSNP per 1% of inbreeding. The impact of the homozygous segments on reproductive and performance traits varied based on the chromosomes. This shows that specific homozygous chromosome segments can be signs of positive selection due to their beneficial effects on the traits. CONCLUSIONS: The low correlation observed between FPed and genomic-based inbreeding estimates suggests that the presence of animals with one unknown parent (sire or dam) in the pedigree does not account for ancient inbreeding. The ROH hotspots surround genes related to reproduction, growth, meat quality, and adaptation to environmental stress. Inbreeding depression has adverse effects on productive and reproductive traits in Nelore cattle, particularly on age at puberty in young bulls and heifer calving at 30 months, as well as on scrotal circumference and body weight when scaled on the standard deviation of the trait.

Genotype-by-environment interactions in beef and dairy cattle populations: A review of methodologies and perspectives on research and applications.

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.

Genetic parameters for visual scores, growth and carcass traits in Nellore Cattle.

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.

Estimation of genetic parameters for maintenance energy requirements and residual feed intake in Nellore cattle.

We estimated heritabilities and genetic and phenotypic correlation estimates for maintenance energy requirements (NEmR), residual feed intake (RFI), growth, carcass and reproductive indicator traits, using data from 41 feed efficiency trials in Brazil, comprising 4381 males and females. Continuous traits were analysed using a linear animal model and threshold traits were analysed using a threshold animal model. The heritability estimates were low for RFI (0.190) and NEmR (0.193); other heritabilities were mainly moderate (growth and carcass traits) or high (sexual precocity traits). The genetic correlation of RFI with NEmR was high (0.701). The genetic correlations of NEmR were low with carcass and reproductive traits, and moderate with growth traits. Thus, selection to improve weaning weight and female sexual precocity indicator traits would not affect maintenance energy requirement. Genetic selection to reduce maintenance energy requirements is feasible and would also reduce DMI and RFI. Selection to improve RFI can be used to identify animals with lower maintenance energy requirements. Long-term selection to reduce RFI and NEmR would have favourable effects on yearling weight, carcass muscle indicator traits and female sexual precocity. Genetic (co)variance component estimates for NEmR, in conjunction with economic values of selection criteria, may be used to develop novel approaches for genetic selection to improve efficiency of beef production.

Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP.

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 A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle.

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle.

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.

Genotype-by-environment interactions for feed efficiency traits in Nellore cattle based on bi-trait reaction norm models.

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.

Assessment of copy number variants in three Brazilian locally adapted cattle breeds using whole-genome re-sequencing data.

Further characterization of genetic structural variations should strongly focus on small and endangered local breeds given their role in unraveling genes and structural variants underlying selective pressures and phenotype variation. A comprehensive genome-wide assessment of copy number variations (CNVs) based on whole-genome re-sequencing data was performed on three Brazilian locally adapted cattle breeds (Caracu Caldeano, Crioulo Lageano, and Pantaneiro) using the ARS-UCD1.2 genome assembly. Data from 36 individuals with an average coverage depth of 14.07× per individual was used. A total of 24 945 CNVs were identified distributed among the breeds (Caracu Caldeano = 7285, Crioulo Lageano = 7297, and Pantaneiro = 10 363). Deletion events were 1.75-2.07-fold higher than duplications, and the total length of CNVs is composed mostly of a high number of segments between 10 and 30 kb. CNV regions (CNVRs) are not uniformly scattered throughout the genomes (n = 463), and 105 CNVRs were found overlapping among the studied breeds. Functional annotation of the CNVRs revealed variants with high consequence on protein sequence harboring relevant genes, in which we highlighted the BOLA-DQB, BOLA-DQA5, CD1A, ß-defensins, PRG3, and ULBP21 genes. Enrichment analysis based on the gene list retrieved from the CNVRs disclosed over-represented terms (p < 0.01) strongly associated with immunity and cattle resilience to harsh environments. Additionally, QTL associated with body conformation and dairy-related traits were also unveiled within the CNVRs. These results provide better understanding of the selective forces shaping the genome of such cattle breeds and identify traces of natural selection pressures by which these populations have been exposed to challenging environmental conditions.

Weighted genomic prediction for growth and carcass-related traits in Nelore cattle.

This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.

Integrating genome-wide association study and pathway analysis reveals physiological aspects affecting heifer early calving defined at different ages in Nelore cattle.

Heifer early calving (HC) plays a key role in beef cattle herds' economic sustainability and profitability by reducing production costs and generation intervals. However, the genetic basis of HC in Nelore heifers at different ages remains to be well understood. In this study, we aimed to perform a multi-trait weighted single-step genome-wide association (MT w-ssGWAS) to uncover the genetic mechanism involved in HC at 24 (HC24), 26 (HC26), 28 (HC28), and 30 (HC30) months of age in Nelore heifers. The MT w-ssGWAS pointed out four shared windows regions for HC24, HC26, HC28, and HC30 on BTA 5, 6, 14, and 16, explaining a larger proportion of genetic variation from 9.2% for HC30 to 10.6% for HC28. The shared regions harbored candidate genes related with the major gatekeeper for early puberty onset by controlling metabolic aspects related to homeostasis, reproductive, and growth (IGF1, PARPBP, PMCH, GNRHR, LYN, TMEM68, PLAG1, CHCHD7, KISS1, GOLT1A, and PPP1R15B). The MT w-ssGWAS and pathway analysis highlighted differences in physiological processes that support complex interactions between the gonadotropic axes, growth aspects, and sexual precocity in Nelore heifers, providing useful information for genetic improvement and management strategies.

Estimates of genetic and phenotypic parameters for feeding behaviour and feed efficiency-related traits in Nelore cattle.

The objective of this study was to obtain (co)variance components, heritability, and genetic and phenotypic correlation estimates for feed efficiency and feed behaviour-related indicator traits. Further, it aimed to predict the direct and correlated responses for feed efficiency traits when selection was applied for feeding behaviour-related traits in Nelore cattle. Phenotypic records (n = 4840) from 125 feed efficiency tests (RFI: Residual feed intake and DMI: Dry matter intake) carried out between 2011 and 2018 were considered in this study. Animals belonged to five farms located in two Brazilian geographical regions (Midwest and Southeast). Animals under similar management and environmental conditions in the feedlot were evaluated when they attained an average of 13.5 ± 4.15 months of age. Feed behaviour-related traits were also obtained, including meal criteria (MC), meal frequency (MF), average meal duration (AMD), meal duration (MD), average consumption per meal (ACM), and consumption rate (CR) through the GrowSafe System® electronic bunk system. The contemporary groups for all traits were composed of farm, management group, feed efficiency test, sex, and birth year. The (co)variance components were estimated using the restricted maximum likelihood method considering a multi-trait (n = 8) animal model. The heritability estimates for RFI (0.23 ± 0.02), DMI (0.31 ± 0.02), MF (0.65 ± 0.02), AMD (0.29 ± 0.02), ACM (0.24 ± 0.02), MD (0.41 ± 0.02), MC (0.48 ± 0.02), and CR (0.42 ± 0.02) were moderate to high. The highest genetic correlation was obtained between CR and MD (-0.91 ± 0.04), MD and AMD (0.73 ± 0.03), CR and AMD (-0.68 ± 0.04), and RFI and DMI (0.81 ± 0.02). The highest phenotypic correlation was between ACM and AMD (0.76 ± 0.02), DMI and MD (0.77 ± 0.02), and DMI and RFI (0.77 ± 0.02). Genetic improvement for feed efficiency and feeding behaviour-related traits is feasible and the results obtained herein provided valuable information regarding the genetic background of Nelore feeding behaviour-related traits. The genetic association between feeding behaviour and feed efficiency-related traits suggested that animals spending less time feeding at a low feeding rate also had lower DMI and higher feed efficiency (RFI), and likely had lower energy maintenance requirements. The relative efficiency of selection showed that feeding behaviour-related traits were not adequate indicator traits to improve RFI and DMI. The DMI might be an effective selection criterion to improve RFI and reduce the herd's maintenance requirements.

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm.

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.

Genomic evaluation of commercial herds with different pedigree structures using the single-step genomic BLUP in Nelore cattle.

The aim of this work was to evaluate the impact of applying genomic information in pedigree uncertainty situations on genetic evaluations for growth- and cow productivity-related traits in Nelore commercial herds. Records for accumulated cow productivity (ACP) and adjusted weight at 450 days of age (W450) were used, as well as genotypes of registered and commercial herd animals, genotyped with the Clarifide Nelore 3.1 panel (~29,000 SNPs). The genetic values for commercial and registered populations were estimated using different approaches that included (ssGBLUP) or did not include genomic information (BLUP), with different pedigree structures. Different scenarios were tested, varying the proportion of young animals with unknown sires (0, 25, 50, 75, and 100%), and unknown maternal grandsires (0, 25, 50, 75, and 100%). The prediction accuracies and abilities were calculated. The estimated breeding value accuracies decreased as the proportion of unknown sires and maternal grandsires increased. The genomic estimated breeding value accuracy using the ssGBLUP was higher in scenarios with a lower proportion of known pedigree when compared to the BLUP methodology. The results obtained with the ssGBLUP showed that it is possible to obtain reliable direct and indirect predictions for young animals from commercial herds without pedigree structure.

Genetic association between feed efficiency, growth, scrotal circumference, and carcass traits in Guzerat cattle.

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.

Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle.

BACKGROUND: Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs. RESULTS: The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10). CONCLUSION: Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes.

Small genetic variation affecting mRNA isoforms associated with marbling and meat color in beef cattle.

The aim of this study was to identify mRNA isoforms and small genetic variants that may be affecting marbling and beef color in Nellore cattle. Longissimus thoracis muscle samples from 20 bulls with different phenotypes (out of 80 bulls set) for marbling (moderate (n = 10) and low (n = 10) groups) and beef color (desirable (n = 10) and undesirable (n = 9) group) traits were used to perform transcriptomic analysis using RNA sequencing. Fourteen and 15 mRNA isoforms were detected as differentially expressed (DE) (P-value ≤ 0.001) between divergent groups for marbling and meat color traits, respectively. Some of those DE mRNA isoforms have shown sites of splicing modified by small structural variants as single nucleotide variant (SNV), insertion, and/or deletion. Enrichment analysis identified metabolic pathways, such as O2/CO2 exchange in erythrocytes, tyrosine biosynthesis, and phenylalanine degradation. The results obtained suggest potential key regulatory genes associated with these economically important traits for the beef industry and for the consumer.

Transcriptomic profile of longissimus thoracis associated with fatty acid content in Nellore beef cattle.

The beef fatty acid (FA) profile has the potential to impact human health, and displays polygenic and complex features. This study aimed to identify the transcriptomic FA profile in the longissimus thoracis muscle in Nellore beef cattle finished in feedlot. Forty-four young bulls were sampled to assess the beef FA profile by considering 14 phenotypes and including differentially expressed genes (DEG), co-expressed (COE), and differentially co-expressed genes (DCO) analyses. All samples (n = 44) were used for COE analysis, whereas 30 samples with extreme phenotypes for the beef FA profile were used for DEG and DCO. A total of 912 DEG were identified, and the polyunsaturated (n = 563) and unsaturated ω-3 (n = 346) FA sums groups were the most frequently observed. The COE analyses identified three modules, of which the blue module (n = 1776) was correlated with eight of 14 FA phenotypes. Also, 759 DCO genes were listed, and the oleic acid (n = 358) and monounsaturated fatty acids sum (n = 120) were the most frequent. Furthermore, 243 and 13, 319 and seven, and 173 and 12 gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were enriched respectively for the DEG, COE, and DCO analyses. Combining the results, we highlight the unexplored GIPC2, ASB5, and PPP5C genes in cattle. Besides LIPE and INSIG2 genes in COE modules, the ACSL3, ECI1, DECR2, FITM1, and SDHB genes were signaled in at least two analyses. These findings contribute to understand the genetic mechanisms underlying the beef FA profile in Nellore beef cattle finished in feedlot.

Integration analyses of structural variations and differential gene expression associated with beef fatty acid profile in Nellore cattle.

This study aimed to integrate analyses of structural variations and differentially expressed genes (DEGs) associated with the beef fatty acid (FA) profile in Nellore cattle. Copy numbers variation (CNV) detection was performed using the penncnv algorithm and CNVRuler software in 3794 genotyped animals through the High-Density Bovine BeadChip. In order to perform the genomic wide association study (GWAS), a total of 963 genotyped animals were selected to obtain the intramuscular lipid concentration and quantify the beef FA profile. A total of 48 animals belonging to the same farm and management lot were extracted from the 963 genotyped and phenotyped animals to carry out the transcriptomic and differentially expressed gene analyses. The GWAS with extreme groups of FA profiles was performed using a logistic model. A total of 43, 42, 66 and 35 significant CNV regions (p < 0.05) for saturated, monounsaturated, polyunsaturated and omega 3 and 6 fatty acids were identified respectively. The paired-end sequencing of 48 samples was performed using the Illumina HiSeq2500 platform. Real-time quantitative PCR was used to validate the DEGs identified by RNA-seq analysis. The results showed several DEGs associated with the FA profile of Longissimus thoracis, such as BSCL2 and SAMD8. Enriched terms as the cellular response to corticosteroid (GO:0071384) and glucocorticoid stimulus (GO:0071385) could be highlighted. The identification of structural variations harboring candidate genes for beef FA must contribute to the elucidation of the genetic basis that determines the beef FA composition of intramuscular fat in Nellore cattle. Our results will contribute to the identification of potential biomarkers for complex phenotypes, such as the FA profile, to improve the reliability of the genomic predictions including pre-selected variants using differentiated weighting in the genomic models.

Early growth, backfat thickness and body condition has major effect on early heifer pregnancy in Nellore cattle.

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.