This study aimed to estimate (co)variance components and genetic parameters for accumulated profitability (AFP) and profit per kilogram of liveweight gain (PFT), and their relationships with weight at 450 days of age (W450), scrotal circumference at 365 days of age (SC365), age at first calving (AFC), probability of precocious calving at 30 months of age (PPC30), age at puberty in males (APM), stayability (STAY), accumulated cow productivity (ACP), rib eye area (REA), rump fat thickness (RFT), residual feed intake (RFI), dry-matter intake (DMI), residual live weight gain (RG), and frame score (FRAME). Data of profitability from 3614 Nelore (Bos indicus) animals were used. The (co)variance components and genetic parameters were estimated using Bayesian inference in a multi-trait animal model. The heritability estimates for AFP and PFT were 0.18 and 0.02, respectively. AFP and PFT exhibited moderate to high genetic correlations with growth traits (0.64 to 0.65), carcass (0.43 to 0.44), feed efficiency (0.72 to 0.88), and frame (0.44 to 0.77), except for the correlation with RFT (-0.10 to -0.68), RFI (-0.23 to 0.28), and between PFT and DMI (0.26). Low to high genetic correlations (-0.47 to 0.76) with female reproductive traits and low correlations (-0.03 to -0.21) with male reproductive traits were observed for both traits. These results provide important information for improving economic performance by including complementary AFP and PFT tools in the selection criteria. Such traits can be strategic tools for producers when identifying animals with greater genetic potential for profitability, supporting decision-making in genetic planning and herd management.

This study aimed to conduct a genome-wide association study to identify genomic regions associated with profitability traits in Nelore beef cattle. The dataset included 3614 phenotypic records of accumulated feedlot profitability (AFP) and profit per 15 kg of liveweight gain (PFT) from animals born between 2020 and 2022, participating in the Nelore Brazilian breeding program from the National Association of Breeders and Researchers. From this total, 2127 animals were genotyped with the Clarifide® Nelore 3.0 SNP panel. After quality control, 2127 genotyped animals and 35 658 SNPs remained in the dataset for analysis. The weighted single-step approach for genome-wide association methodology was used to identify genomic regions associated with AFP and PFT. A single-trait animal model was applied to predict genetic values, and SNP effect solutions were obtained from these values. Genomic windows of 10-SNP sliding windows that explained >0.5% of the additive genetic variance of each trait were selected to investigate potential candidate genes. A total of 83 genes within 21 windows and 268 genes within 52 windows associated with the AFP and PFT were identified, respectively. Several genes related to fertility, feed efficiency, carcass traits, muscle and adipose tissue development and metabolism, and lipid and carbohydrate metabolism were identified, along with genes associated with animal behavior. The results revealed that identifying genomic regions and their respective candidate genes contributes substantially to a better understanding of the genetic mechanisms regarding these novel profitability-related phenotypes in Nelore beef cattle.

The aim of this study was to assess the accuracy, bias and dispersion of genomic predictions for accumulated profitability (APF) and profit per kilogram of liveweight gain (PFT) in Nelore cattle using different prediction approaches. The dataset consisted of 3969 phenotypic records for each trait. The pedigree harboured information from 38,930 animals born between 1998 and 2016, including 2691 sires and 19,884 dams. A total of 2449 animals were genotyped using the Clarifide Nelore 3.0 SNP panel. Nine models for genomic prediction were evaluated: a linear animal model was applied to estimate genetic parameters and perform the genomic single-trait best linear unbiased prediction (ST_ss-default). Additionally, a two-trait (ssGBLUP TT_W450 and TT_DMI), three-trait (TTT_CAR) and multi-trait ssGBLUP (MT_ss) were tested. Finally, two models employing the weighted linear (ST_sswl1 and ST_sswl2) and non-linear (ST_sswnl1 and ST_sswnl2) single-step genomic approach (WssGBLUP) were used to predict genomic breeding values (GEBV). The ability to predict future performance was assessed by calculating the correlation between GEBV and adjusted phenotypes. The average prediction accuracy of the GEBV models ranged from 0.345 to 0.665 for PFT and from 0.425 to 0.603 for APF. The predictive capability of the MT_ss model (0.665) was significantly higher than that of the other models for PFT, except for the TTT_CAR model (0.604), which also showed an improvement in predictive performance. For APF, the MT_ss (0.561) and TT_W450 (0.556) models demonstrated improved genomic prediction accuracy compared to the other models. In general, the single trait ssGBLUP (ST_ss-default) models and the non-linear weighting approach did not enhance prediction accuracy for either trait. For the phenotypic prediction ability of PFT, the linear WssGBLUP models ST_sswl1 (0.65) and ST_sswl2 (0.70), TT_W450 (0.64) and ssGBLUP-M (0.66) demonstrated the highest prediction accuracies. Similar results were observed for the phenotypic prediction ability of APF for both models. However, the linear WssGBLUP models ST_sswl1 (0.84) and ST_sswl2 (0.94) provided higher prediction performance compared to the two-, three- and multi-trait models. The results indicate that the multi-trait model achieved better predictive ability for the novel traits PFT and APF. Multi-trait genomic selection may yield greater genetic gains than other models for these forthcoming economically important traits in breeding programmes.

This study aimed to assess the influence of differential weighting in genomic regions harboring candidate causal loci on the prediction accuracy and inflation for early heifer pregnancy (heifers that calved up to 30 months of age) in Nellore (Bos indicus) heifers using the single step genomic BLUP model (ssGBLUP). Phenotypic records of 102,294 Nellore heifers born between 2010 and 2017 were used in this study. The pedigree dataset harbored information from 176,107 animals born between 1998 and 2017, including 5,145 sires and 35,705 dams. Seven different models for genomic prediction were defined by combining the SNP weights obtained in the iterations (1st and 2nd) of the weighted single step GWAS (ssw1GBLUP and ssw2GBLUP) or candidate QTLs reported in the literature. Hence, the lambda (7) values estimated in the WssGWAS were used to weight the SNPs adjacent to the candidate regions or QTL previously reported in the literature. To estimate the genetic parameters and perform the WssGWAS and WssGBLUP for early heifer pregnancy, a single-trait Bayesian analysis considering a threshold animal model was used. Accuracy, bias, and inflation parameters were evaluated in the validation subset based on the linear regression (LR) method. Genomic windows of ten consecutive SNPs that explained >0.5 % of the additive genetic variance were selected to explore and determine possible candidate genes. Among the identified genes, we can highlight the PGRMC2, TENM3, GRIP1, TMEM45A, and KLF3, given their roles in endocrine fertility, expression of contractile proteins, average daily gain, dry matter intake, and fat deposition. Several genomic regions associated with QTL related to early heifer pregnancy were identified. The identification of such regions and the respective candidate genes associated with sexual precocity and fertility would contribute to improve the genetic knowledge regarding early sexual precocity of Nellore cattle. The prediction accuracy increased roughly 25.6 % using the ssGBLUP compared to BLUP models. The prediction accuracy with the WssGBLUP when incorporating weighted SNPs with the 7 values obtained in the 1st (ssw1GBLUP) and 2nd iteration (ssw2GBLUP) of the WssGWAS was higher (similar to 18 %) than that described for the ssGBLUP model. The inflation also increased the weighting of the most relevant SNPs obtained with the GWAS, most likely overestimating the GEBV. The models that weighted SNPs close to QTLs reported in the literature yielded to less biased and deflated predictions compared to ssw1GBLUP and ssw2GBLUP models. Genomic selection is a feasible alternative for genomic evaluation of early heifer pregnancy in Nellore beef cattle by increasing the prediction accuracy of young animals. In addition, the use of information obtained from the WssGWAS is an alternative to increase reliability and reduce genomic prediction bias. Therefore, the results obtained herein indicate that it is possible to improve the prediction accuracy and reduce the bias of genomic prediction by using genomic information and differentially weighted genomic regions harboring candidate QTLs previously reported in the literature for early heifer pregnancy.

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.

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

Abstract 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.

Context In beef cattle populations, there is little evidence regarding the minimum number of genetic markers needed to obtain reliable genomic prediction and imputed genotypes. Aims This study aimed to evaluate the impact of single nucleotide polymorphism (SNP) marker density and minor allele frequency (MAF), on genomic predictions and imputation performance for high and low heritability traits using the single-step genomic Best Linear Unbiased Prediction methodology (ssGBLUP) in a simulated beef cattle population. Methods The simulated genomic and phenotypic data were obtained through QMsim software. 735 293 SNPs markers and 7000 quantitative trait loci (QTL) were randomly simulated. The mutation rate (10−5), QTL effects distribution (gamma distribution with shape parameter = 0.4) and minor allele frequency (MAF ≥ 0.02) of markers were used for quality control. A total of 335k SNPs (high density, HD) and 1000 QTLs were finally considered. Densities of 33 500 (35k), 16 750 (16k), 4186 (4k) and 2093 (2k) SNPs were customised through windows of 10, 20, 80 and 160 SNPs by chromosome, respectively. Three marker selection criteria were used within windows: (1) informative markers with MAF values close to 0.5 (HI); (2) less informative markers with the lowest MAF values (LI); (3) markers evenly distributed (ED). We evaluated the prediction of the high-density array and of 12 scenarios of customised SNP arrays, further the imputation performance of them. The genomic predictions and imputed genotypes were obtained with Blupf90 and FImpute software, respectively, and statistics parameters were applied to evaluate the accuracy of genotypes imputed. The Pearson’s correlation, the coefficient of regression, and the difference between genomic predictions and true breeding values were used to evaluate the prediction ability (PA), inflation (b), and bias (d), respectively. Key results Densities above 16k SNPs using HI and ED criteria displayed lower b, higher PA and higher imputation accuracy. Consequently, similar values of PA, b and d were observed with the use of imputed genotypes. The LI criterion with densities higher than 35k SNPs, showed higher PA and similar predictions using imputed genotypes, however lower b and quality of imputed genotypes were observed. Conclusion The results obtained showed that at least 5% of HI or ED SNPs available in the HD array are necessary to obtain reliable genomic predictions and imputed genotypes. Implications The development of low-density customised arrays based on criteria of MAF and even distribution of SNPs, might be a cost-effective and feasible approach to implement genomic selection in beef cattle.

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, β-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.

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.

Context A frame score prediction equation developed specifically for Nellore cattle could be an auxiliary tool to improve mating decisions on the basis of feed resources and production-system objectives. Aims Estimate genetic parameters for frame by using a prediction equation developed for Nellore cattle and genetic associations between frame score (FRAME) and growth-, reproductive-, carcass- and feed efficiency-related traits, and five bioeconomic indexes. Methods Birth weight (BW), adjusted weight at 120 (W120), 210 (W210) and 450 (W450) days of age, adult weight (AW), age at first calving (AFC), probability of precocious calving (PPC30), stayability (STAY), accumulated cow productivity (ACP), adjusted scrotal circumference at 365 (SC365) and 450 (SC450) days of age, rib eye area (REA), subcutaneous backfat thickness (BFT), rump fat thickness (RFT), intramuscular fat percentage (IMF), residual feed intake (RFI) and dry-matter intake (DMI) were included in the analyses. Frame score was calculated using the multiple linear regression (MLR) prediction method. The estimation of genetic parameters was performed using a linear animal model, except for PPC30 and STAY, which were estimated through a threshold animal model. The correlated response in FRAME considering selection for growth-, reproductive-, carcass- and feed efficiency-indicator traits were obtained in the context of single-trait selection and a multiple-trait context. Key results Heritability estimated for FRAME was moderate (0.30 ± 0.09). Frame score showed moderate genetic correlations with growth traits, BW (0.51 ± 0.08), W120 (0.41 ± 0.07), W210 (0.35 ± 0.07) and W450 (0.29 ± 0.08). The genetic correlation estimates between FRAME and RFT was high (−0.84 ± 0.02), but low with ACP (0.25 ± 0.08) and RFI (0.10 ± 0.13). In the single-trait and multi-trait contexts, there was a lower correlated gain for FRAME when the selection was applied for traits commonly measured in beef cattle breeding programs. Conclusion Selection to increase growth traits would lead to an increase in frame size and herd nutritional requirements, and it would reduce the carcass fatness level and early heifer sexual precocity. FRAME could be an alternative trait to monitor calf birth weight. Implications Selection for FRAME is feasible, and the most suitable frame score value depends on the production system objectives and feed resources.

Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

This study aimed to estimate prediction ability and genetic parameters for residual feed intake (RFI) calculated using a regression equation for each test (RFItest) and for the whole population (RFIpop) in Nellore beef cattle. It also aimed to evaluate the correlations between RFIpop and RFItest 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 RFItest and RFIpop. 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 RFItest and RFIpop with dry matter intake, frame, growth, reproduction, and carcass-related traits were evaluated. The prediction ability and bias were estimated to compare the RFItest and RFIpop genomic breeding values (GEBV). The RFIpop ANOVA showed a higher significance level (p < 0.0001) than did the RFItest for the fixed effects. The RFIpop displayed higher additive genetic variance estimated than the RFItest, although the RFIpop and RFItest displayed similar heritabilities. Overall, the RFItest showed higher residual correlations with growth, reproductive, and carcass traits, while the RFIpop displayed higher genetic correlations with such traits. The GEBV for the RFItest was slightly biased than GEBV RFIpop. The approach to calculate the RFI influenced the decomposition and estimation of variance components and genomic prediction for RFI. The application of RFIpop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias for RFI.

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. 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). 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.

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.

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.

Abstract This study aimed to estimate genetic parameters and the prediction ability and bias of the genomic values for residual feed intake (RFI) using genotypic and phenotypic records from 8,354 Nellore animals. The RFI was calculated using a regression equation for each test (RFItest) and for the whole population (RFIpop). An analysis of variance (ANOVA) using the same dataset and number of records was performed to verify the adequacy of the regression equations applied to estimate the RFItest and RFIpop. The (co)variance components were obtained using the single-step genomic best linear unbiased prediction (ssGBLUP) under single and two-trait animal model analyses. The ssGBLUP method was used for the genomic prediction and the cross-validation method as a validation strategy. The prediction ability and bias were estimated to compare the RFItest and RFIpop genomic breeding values (GEBVs). The RFIpop ANOVA showed a higher significance level (p < 0.0001) than did the RFItest for the fixed effects. The RFIpop displayed higher additive genetic variance estimated than the RFItest, although the RFIpop and RFItest displayed similar heritabilities. Overall, the RFItest showed higher residual correlations with growth, reproductive and carcass traits, while the RFIpop displayed higher genetic correlations with such traits. The GEBV for the RFItest was slightly biased than that obtained for the RFIpop. The method of calculating the RFI influences the decomposition and estimation of variance components, and genomic prediction. The genetic correlations between RFIpop and RFItest were high, implying that changes in sires ranking and genetic response are expected using one or the other. The application of RFIpop would be more appropriate for genetic evaluation purpose to adjust or correct for non-genetic effects and to decrease the prediction bias.

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.

Bos indicus cattle acquired very particular traits to thrive in distinct environments based on the evolutionary adaptation process they have undergone, becoming particularly relevant for tropical production systems. In this review we present and discuss the current status of the application of genomic selection (GS) for Bos indicus (Nellore) breed, also quoting GS applications in other Bos indicus and influenced Bos indicus beef breeds raised in pasture-based tropical conditions. Traditional Bos indicus beef breeding programs have achieved expressive annual genetic gains for growth performance-related traits. Lately, higher genetic gains have been achieved through GS compared to traditional pedigree-based evaluations. With genomics, Bos indicus breeding programs have intensified the use of young bulls. Moreover, GS boosted the genetic progress for traits such as those that are measured late in animal life and are non-routinely recorded due to elevated expenses (i.e., feed efficiency, carcass and meat quality, tick resistance, male puberty-related traits). Screening for young non-proven sires to be used in commercial herds and selecting genetically superior young females through a combined adoption of genomic selection and embryo transfer are other important genomic applications. Genotyping strategy (i.e., animals and markers that should be genotyped) highly impacts the cost-effectiveness of genomic selection, and alternatives currently implemented in Bos indicus are presented. The use of whole genome sequence, multi-breed models and selection accounting for gametic variance are possibilities to enhance genomic impact. With genomics, besides growth and reproduction, traits related to quality, efficiency and sustainability of production have been included as selection criteria for Bos indicus beef cattle.

This study aimed to investigate the prediction ability for growth and maternal traits using different low-density customized SNP arrays selected by informativeness and distribution of markers across the genome employing single-step genomic BLUP (ssGBLUP). Phenotypic records for adjusted weight at 210 and 450 days of age were utilized. A total of 945 animals were genotyped with high-density chip, and 267 individuals born after 2008 were selected as validation population. We evaluated 11 scenarios using five customized density arrays (40 k, 20 k, 10 k, 5 k and 2 k) and the HD array was used as desirable scenario. The GEBV predictions and BIF (Beef Improvement Federation) accuracy were obtained with BLUPF90 family programs. Linear regression was used to evaluate the prediction ability, inflation, and bias of GEBV of each customized array. An overestimation of partial GEBVs in contrast with complete GEBVs and increase of BIF accuracy with the density arrays diminished were observed. For all traits, the prediction ability was higher as the array density increased and it was similar with customized arrays higher than 10 k SNPs. Level of inflation was lower as the density array increased of and was higher for MW210 effect. The bias was susceptible to overestimation of GEBVs when the density customized arrays decreased. These results revealed that the BIF accuracy is sensible to overestimation using low-density customized arrays while the prediction ability with least 10,000 informative SNPs obtained from the Illumina BovineHD BeadChip shows accurate and less biased predictions. Low-density customized arrays under ssGBLUP method could be feasible and cost-effective in genomic selection.