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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.

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Introduction: Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement. Methods: We investigated the genetic basis of MLD resistance in a breeding population of E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS). Results: The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants. Discussion: Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.

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Selection for feed efficiency is the goal for many genetic breeding programs in beef cattle. Residual feed intake has been included in genetic evaluations to reduce feed intake without compromising performance traits as liveweight, body gain or carcass traits. However, measuring feed intake is expensive, and only a small percentage of selection candidates are phenotyped. Genomic selection has become a very important tool to achieve effective genetic progress in these traits. Another effective strategy has been the implementation of multi-trait prediction using easily recordable predictor traits on both reference animals and candidates without phenotypes, and this could be another inexpensive way to increase accuracy. The objective of this work was to analyse and compare the prediction ability of two alternative different approaches to predict GEBVs for RFI. The population of inference was Hereford bulls in Uruguay that were genotyped candidates for to selection. The first model was the conventional univariate model for RFI and the second model was a multi-trait model which included a predictor trait (weaning weight, WW), in addition to the traits used in the first one (dry matter intake, metabolic mid test weight, average daily gain and ultrasound back fat) (DMI, MWT, ADG, UBF, respectively). GEBVs from the multi-trait model were combined using selection index theory to derive RFI values. All analyses were performed using ssGBLUP procedure. The prediction ability of both models was tested using two validation strategies (30 different replicates of random groups of animals and validation across 9 different feed intake tests). The prediction quality was assessed by the following parameters: bias, dispersion, ratio of accuracies and the relative increase in accuracy by adding phenotypic information. All parameters showed that the univariate model outperforms the multi-trait model, regardless of the validation strategy considered. These results indicate that including WW as a proxy trait in a multi-trait analysis does not improve the prediction ability when all animals to be predicted are genotyped.

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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.

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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.

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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.

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The multiple sire system (MSS) is a common mating scheme in extensive beef production systems. However, MSS does not allow paternity identification and lead to inaccurate genetic predictions. The objective of this study was to investigate the implementation of single-step genomic BLUP (ssGBLUP) in different scenarios of uncertain paternity in the evaluation for 450-day adjusted liveweight (W450) and age at first calving (AFC) in a Nellore cattle population. To estimate the variance components using BLUP and ssGBLUP, the relationship matrix (A) with different proportions of animals with missing sires (MS) (scenarios 0, 25, 50, 75, and 100% of MS) was created. The genotyped animals with MS were randomly chosen, and ten replicates were performed for each scenario and trait. Five groups of animals were evaluated in each scenario: PHE, all animals with phenotypic records in the population; SIR, proven sires; GEN, genotyped animals; YNG, young animals without phenotypes and progeny; and YNGEN, young genotyped animals. The additive genetic variance decreased for both traits as the proportion of MS increased in the population when using the regular REML. When using the ssGBLUP, accuracies ranged from 0.13 to 0.47 for W450 and from 0.10 to 0.25 for AFC. For both traits, the prediction ability of the direct genomic value (DGV) decreased as the percentage of MS increased. These results emphasize that indirect prediction via DGV of young animals is more accurate when the SNP effects are derived from ssGBLUP with a reference population with known sires. The ssGBLUP could be applied in situations of uncertain paternity, especially when selecting young animals. This methodology is shown to be accurate, mainly in scenarios with a high percentage of MS.

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Reproductive traits in breeding herds can be improved through crossbreeding, which results in breed differences, heterosis and breed complementarity. The aim of this study was to estimate group additive genetic and dominance effects for three reproductive traits; probability of artificial insemination (AIP); calving success (CS); and days to calving (DC) for Hereford (H), Angus (A), Nellore (N) and Salers (S) breeds under grazing conditions. Data were obtained from an experiment carried out during 1992-2002 by the Faculty of Agronomy, Universidad de la Republica (UdelaR), Uruguay and Caja Notarial de Seguridad Social. The data set contained reproductive information of 1,164 females from 11 different genetic groups (GG) consisting of crosses between H, N, S and A. AIP, CS and DC were examined in first-calf heifers, while CS and DC were examined in second-calf and 3- to 7-year-old cows. Least square means for each GG and group additive genetic and dominance effects were estimated for each trait. F1 crossbreed females performed better for artificial insemination probability than purebred females. Crossbred A/H heifers had the highest AIPs and CS rates, while crossbred N/H 3- to 7-year-old cows recorded the highest averages for CS and DC. Estimates of group additive genetic effects did not differ amongst A, S, N and H; however, dominance increased the AIP and CS of the heifers.

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The aim of this study was to evaluate the accuracy of imputation in a Gyr population using two medium-density panels (Bos taurus - Bos indicus) and to test whether the inclusion of the Nellore breed increases the imputation accuracy in the Gyr population. The database consisted of 289 Gyr females from Brazil genotyped with the GGP Bovine LDv4 chip containing 30 000 SNPs and 158 Gyr females from Colombia genotyped with the GGP indicus chip containing 35 000 SNPs. A customized chip was created that contained the information of 9109 SNPs (9K) to test the imputation accuracy in Gyr populations; 604 Nellore animals with information of LD SNPs tested in the scenarios were included in the reference population. Four scenarios were tested: LD9K_30KGIR, LD9K_35INDGIR, LD9K_30KGIR_NEL, and LD9K_35INDGIR_NEL. Principal component analysis (PCA) was computed for the genomic matrix and sample-specific imputation accuracies were calculated using Pearson's correlation (CS) and the concordance rate (CR) for imputed genotypes. The results of PCA of the Colombian and Brazilian Gyr populations demonstrated the genomic relationship between the two populations. The CS and CR ranged from 0.88 to 0.94 and from 0.93 to 0.96, respectively. Among the scenarios tested, the highest CS (0.94) was observed for the LD9K_30KGIR scenario. The present results highlight the importance of the choice of chip for imputation in the Gyr breed. However, the variation in SNPs may reduce the imputation accuracy even when the chip of the Bos indicus subspecies is used.

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Intense selection for milk yield has increased environmental sensitivity in animals, and currently, heat stress is an expensive problem in dairy farming. The objectives were to identify the best model for characterizing environmental sensitivity in Holstein cattle, using the test-day milk yield (TDMY) combined with the temperature-humidity index (THI), and identify sires genetically superior for heat-stress (HS) tolerance and milk yield, through random regression. The data comprised 94,549 TDMYs of 11,294 first-parity Holstein cows in Brazil, collected from 1997 to 2013. The yield data were fitted to Legendre orthogonal polynomials, linear splines and the Wilmink function. The THI (the average of two days before the dairy control) was used as an environmental gradient. An animal model that fitted production using a Legendre polynomials of quartic order for the days in milk and quadratic equations for the THI presented a better quality of fit (Akaike's information criterion (AIC) and Bayesian information criterion (BIC)). The Spearman correlation coefficient of greatest impact was 0.54, between the top 1% for TDMY and top 1% for HS. Only 9% of the sires showed plasticity and an aptitude for joint selection. Thus, despite the small population fraction allowed for joint selection, sufficient genetic variability for selecting more resilient sires was found, which promoted concomitant genetic gains in milk yield and thermotolerance.

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OBJECTIVE: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. METHODS: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. RESULTS: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. CONCLUSION: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

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Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.

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Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and -0.019 (-0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and -0.022 (-0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

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BACKGROUND: Single-step genomic best linear unbiased prediction (SSGBLUP) is a comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for genome-wide association studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for single-marker GWAS studies within the single-step GWAS (SSGWAS) framework by deriving computational algorithms and procedures, and by applying these to a large beef cattle population. METHODS: P-values were obtained based on the prediction error (co)variances for single nucleotide polymorphisms (SNPs), which were obtained from the prediction error (co)variances of genomic predictions based on the inverse of the coefficient matrix and formulas to estimate SNP effects. RESULTS: Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation of statistics was observed. The SNPs that passed the Bonferroni threshold of 10-5.9 were the same as those that explained the highest proportion of additive genetic variance, but even at the same significance levels and effects, some of them explained less genetic variance due to lower allele frequency. CONCLUSIONS: The use of a p-value for SSGWAS is a very general and efficient strategy to identify quantitative trait loci (QTL). It can be used for complex datasets such as those used in animal breeding, where only a proportion of the pedigreed animals are genotyped.

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KEY MESSAGE: Multi-trait genomic prediction models are useful to allocate available resources in breeding programs by targeted phenotyping of correlated traits when predicting expensive and labor-intensive quality parameters. Multi-trait genomic prediction models can be used to predict labor-intensive or expensive correlated traits where phenotyping depth of correlated traits could be larger than phenotyping depth of targeted traits, reducing resources and improving prediction accuracy. This is particularly important in the context of allocating phenotyping resource in plant breeding programs. The objective of this work was to evaluate multi-trait models predictive ability with different depth of phenotypic information from correlated traits. We evaluated 495 wheat advanced breeding lines for eight baking quality traits which were genotyped with genotyping-by-sequencing. Through different approaches for cross-validation, we evaluated the predictive ability of a single-trait model and a multi-trait model. Moreover, we evaluated different sizes of the training population (from 50 to 396 individuals) for the trait of interest, different depth of phenotypic information for correlated traits (50 and 100%) and the number of correlated traits to be used (one to three). There was no loss in the predictive ability by reducing the training population up to a 30% (149 individuals) when using correlated traits. A multi-trait model with one highly correlated trait phenotyped for both the training and testing sets was the best model considering phenotyping resources and the gain in predictive ability. The inclusion of correlated traits in the training and testing lines is a strategic approach to replace phenotyping of labor-intensive and high cost traits in a breeding program.

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The aim of the present study was to compare the predictive ability of SNP-BLUP model using different pseudo-phenotypes such as phenotype adjusted for fixed effects, estimated breeding value, and genomic estimated breeding value, using simulated and real data for beef FA profile of Nelore cattle finished in feedlot. A pedigree with phenotypes and genotypes of 10,000 animals were simulated, considering 50% of multiple sires in the pedigree. Regarding to phenotypes, two traits were simulated, one with high heritability (0.58), another with low heritability (0.13). Ten replicates were performed for each trait and results were averaged among replicates. A historical population was created from generation zero to 2020, with a constant size of 2000 animals (from generation zero to 1000) to produce different levels of linkage disequilibrium (LD). Therefore, there was a gradual reduction in the number of animals (from 2000 to 600), producing a "bottleneck effect" and consequently, genetic drift and LD starting in the generation 1001 to 2020. A total of 335,000 markers (with MAF greater or equal to 0.02) and 1000 QTL were randomly selected from the last generation of the historical population to generate genotypic data for the test population. The phenotypes were computed as the sum of the QTL effects and an error term sampled from a normal distribution with zero mean and variance equal to 0.88. For simulated data, 4000 animals of the generations 7, 8, and 9 (with genotype and phenotype) were used as training population, and 1000 animals of the last generation (10) were used as validation population. A total of 937 Nelore bulls with phenotype for fatty acid profiles (Sum of saturated, monounsaturated, omega 3, omega 6, ratio of polyunsaturated and saturated and polyunsaturated fatty acid profile) were genotyped using the Illumina BovineHD BeadChip (Illumina, San Diego, CA) with 777,962 SNP. To compare the accuracy and bias of direct genomic value (DGV) for different pseudo-phenotypes, the correlation between true breeding value (TBV) or DGV with pseudo-phenotypes and linear regression coefficient of the pseudo-phenotypes on TBV for simulated data or DGV for real data, respectively. For simulated data, the correlations between DGV and TBV for high heritability traits were higher than obtained with low heritability traits. For simulated and real data, the prediction ability was higher for GEBV than for Yc and EBV. For simulated data, the regression coefficient estimates (b(Yc,DGV)), were on average lower than 1 for high and low heritability traits, being inflated. The results were more biased for Yc and EBV than for GEBV. For real data, the GEBV displayed less biased results compared to Yc and EBV for SFA, MUFA, n-3, n-6, and PUFA/SFA. Despite the less biased results for PUFA using the EBV as pseudo-phenotype, the b(Yi,DGV estimates obtained for the different pseudo-phenotypes (Yc, EBV and GEBV) were very close. Genomic information can assist in improving beef fatty acid profile in Zebu cattle, since the use of genomic information yielded genomic values for fatty acid profile with accuracies ranging from low to moderate. Considering both simulated and real data, the ssGBLUP model is an appropriate alternative to obtain more reliable and less biased GEBVs as pseudo-phenotype in situations of missing pedigree, due to high proportion of multiple sires, being more adequate than EBV and Yc to predict direct genomic value for beef fatty acid profile.

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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.

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The objective of this study was to estimate the genetic-quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal's slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and -0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, -0.06 and -0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and -0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer.

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BACKGROUND: Saturated fatty acids can be detrimental to human health and have received considerable attention in recent years. Several studies using taurine breeds showed the existence of genetic variability and thus the possibility of genetic improvement of the fatty acid profile in beef. This study identified the regions of the genome associated with saturated, mono- and polyunsaturated fatty acids, and n-6 to n-3 ratios in the Longissimus thoracis of Nellore finished in feedlot, using the single-step method. RESULTS: The results showed that 115 windows explain more than 1 % of the additive genetic variance for the 22 studied fatty acids. Thirty-one genomic regions that explain more than 1 % of the additive genetic variance were observed for total saturated fatty acids, C12:0, C14:0, C16:0 and C18:0. Nineteen genomic regions, distributed in sixteen different chromosomes accounted for more than 1 % of the additive genetic variance for the monounsaturated fatty acids, such as the sum of monounsaturated fatty acids, C14:1 cis-9, C18:1 trans-11, C18:1 cis-9, and C18:1 trans-9. Forty genomic regions explained more than 1 % of the additive variance for the polyunsaturated fatty acids group, which are related to the total polyunsaturated fatty acids, C20:4 n-6, C18:2 cis-9 cis12 n-6, C18:3 n-3, C18:3 n-6, C22:6 n-3 and C20:3 n-6 cis-8 cis-11 cis-14. Twenty-one genomic regions accounted for more than 1 % of the genetic variance for the group of omega-3, omega-6 and the n-6:n-3 ratio. CONCLUSIONS: The identification of such regions and the respective candidate genes, such as ELOVL5, ESSRG, PCYT1A and genes of the ABC group (ABC5, ABC6 and ABC10), should contribute to form a genetic basis of the fatty acid profile of Nellore (Bos indicus) beef, contributing to better selection of the traits associated with improving human health.

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BACKGROUND: The cattle tick is a parasite that adversely affects livestock performance in tropical areas. Although countries such as Australia and Brazil have developed genetic evaluations for tick resistance, these evaluations have not considered genotype by environment (G*E) interactions. Genetic gains could be adversely affected, since breedstock comparisons are environmentally dependent on the presence of G*E interactions, particularly if residual variability is also heterogeneous across environments. The objective of this study was to infer upon the existence of G*E interactions for tick resistance of cattle based on various models with different assumptions of genetic and residual variability. METHODS: Data were collected by the Delta G Connection Improvement program and included 10,673 records of tick counts on 4363 animals. Twelve models, including three traditional animal models (AM) and nine different hierarchical Bayesian reaction norm models (HBRNM), were investigated. One-step models that jointly estimate environmental covariates and reaction norms and two-step models based on previously estimated environmental covariates were used to infer upon G*E interactions. Model choice was based on the deviance criterion information. RESULTS: The best-fitting model specified heterogeneous residual variances across 10 subclasses that were bounded by every decile of the contemporary group (CG) estimates of tick count effects. One-step models generally had the highest estimated genetic variances. Heritability estimates were normally higher for HBRNM than for AM. One-step models based on heterogeneous residual variances also usually led to higher heritability estimates. Estimates of repeatability varied along the environmental gradient (ranging from 0.18 to 0.45), which implies that the relative importance of additive and permanent environmental effects for tick resistance is influenced by the environment. Estimated genetic correlations decreased as the tick infestation level increased, with negative correlations between extreme environmental levels, i.e., between more favorable (low infestation) and harsh environments (high infestation). CONCLUSIONS: HBRNM can be used to describe the presence of G*E interactions for tick resistance in Hereford and Braford beef cattle. The preferred model for the genetic evaluation of this population for tick counts in Brazilian climates was a one-step model that considered heteroscedastic residual variance. Reaction norm models are a powerful tool to identify and quantify G*E interactions and represent a promising alternative for genetic evaluation of tick resistance, since they are expected to lead to greater selection efficiency and genetic progress.

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