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Feet and leg conformation scores are important traits in beef cattle because they encompass a wide range of locomotion disorders that can lead to productive and reproductive losses. Thus, the study of feet and legs in beef cattle is essential for evaluating possible responses to selection focusing on minimizing economic losses caused by the occurrence of feet and leg problems. The aim of this study was to estimate variance components for feet and leg conformation traits in Nelore cattle. The data set contained records of approximately 300,000 animals that were born between 2000 and 2013. These animals belonged to the commercial beef cattle breeding program of the CRV Lagoa (www. crvlagoa.com.br). Feet and legs were evaluated by assigning visual scores at 2 different time points: feet and leg evaluated as a binary trait (FL1), measured at yearling (about 550 d of age) to identify whether (or not) an animal has feet and leg defects, and feet and leg score (FL2), ranging from 1 (less desirable) to 5 (more desirable) was assigned to the top 20% of animals according to the selection index adopted by the beef cattle breeding program, which was measured 2 to 5 mo after the yearling evaluation. The FL1 and FL2 traits were analyzed together with yearling weight (YW). The (co)variance components and breeding values were estimated by Bayesian inference using 2-trait animal models. The posterior means (standard errors) of the heritabilities for FL1, FL2, and YW were 0.18 (0.04), 0.39 (0.07), and 0.47 (0.01), respectively. The results indicate that the incidence of feet and leg problems in this population might be reduced by selection. The genetic correlation between FL1 and FL2 (-0.47) was moderate and negative as expected because the classification score that holds up each trait has opposite numerical values. The genetic trends estimated for FL1 and FL2 (-0.042 and 0.021 genetic standard deviations per year, respectively) were favorable and they indicate that the independent culling strategy for feet and leg problems promotes favorable changes and contributes to the genetic progress of these traits in the population under study. [ABSTRACT FROM AUTHOR]

There is evidence for genetic variability in residual variance of livestock traits, which offers the potential for selection for increased uniformity of production. Different statistical approaches have been employed to study this topic; however, little is known about the concordance between them. The aim of our study was to investigate the genetic heterogeneity of residual variance on yearling weight (YW; 291.15 ± 46.67) in a Nellore beef cattle population; to compare the results of the statistical approaches, the two-step approach and the double hierarchical generalized linear model (DHGLM); and to evaluate the effectiveness of power transformation to accommodate scale differences. The comparison was based on genetic parameters, accuracy of EBV for residual variance, and cross-validation to assess predictive performance of both approaches. A total of 194,628 yearling weight records from 625 sires were used in the analysis. The results supported the hypothesis of genetic heterogeneity of residual variance on YW in Nellore beef cattle and the opportunity of selection, measured through the genetic coefficient of variation of residual variance (0.10 to 0.12 for the two-step approach and 0.17 for DHGLM, using an untransformed data set). However, low estimates of genetic variance associated with positive genetic correlations between mean and residual variance (about 0.20 for two-step and 0.76 for DHGLM for an untransformed data set) limit the genetic response to selection for uniformity of production while simultaneously increasing YW itself. Moreover, large sire families are needed to obtain accurate estimates of genetic merit for residual variance, as indicated by the low heritability estimates (<0.007). Box-Cox transformation was able to decrease the dependence of the variance on the mean and decreased the estimates of genetic parameters for residual variance. The transformation reduced but did not eliminate all the genetic heterogeneity of residual variance, highlighting its presence beyond the scale effect. The DHGLM showed higher predictive ability of EBV for residual variance and therefore should be preferred over the two-step approach. [ABSTRACT FROM AUTHOR]

Genotypes of 25 artificial insemination sires were used to study the extent of linkage disequilibrium (LD) and the correspondence between pedigree and SNP-based estimators of inbreeding in Brazilian Gyr dairy cattle. Overall, 24,020 SNPs had minor allele frequencies (MAF) greater than 5 % and were used to calculate two measures of LD (r² and D') for all pairs of markers in each autosome. LD was also used to estimate the effective population size (Ne) at different prior generations. Individual inbreeding coefficients (F) were estimated using either pedigree information (Fped, pedigree traced back up to 9 generations) or marker information. Marker-based estimates of F were derived based on the excess homozygosity (Fhet) in SNP markers and the estimated proportion of the genome located in runs of homozygosity (Froh). The mean LD between adjacent markers averaged across all autosomes was approximately 0.20 and 0.75, measured using r² and D'. Useful LD was identified between markers separated by up to 100 kb when screening this sample of Gyr dairy cattle. The effective population size showed a consistent trend of decay along time, falling below 56 in the last three generations. Weaker correspondence between individual inbreeding estimates based on runs of homozygosity and pedigree was verified in the present study (estimated correlations between Fped and Froh varied from 0.32 to 0.42). It appears to be feasible to apply genomic selection to Gyr cattle in Brazil, but further studies on the extent of linkage disequilibrium using a larger sample of this population are needed. [ABSTRACT FROM AUTHOR]

Number of calvings at 53 mo (NC53) was proposed as an alternative selection criterion to improve productive longevity of Nellore cows. This study was carried out to estimate variance components for NC53 by assuming different models, so that the potential for using this selection criterion to improve fertility of Nellore cows could be assessed. Genetic correlations between NC53, number of calvings at 89 mo (NC89), and 2 selection indexes used in this breed were also estimated. The NC53 trait is moderately heritable (posterior mean heritability ≈ 0.17) and selecting for this criterion could improve productive longevity of Nellore cows. Greater response to selection is expected by fitting a threshold animal model for this trait, rather than a linear animal model. Greater accuracy of prediction for this criterion could be achieved by fitting a threshold-linear model, considering this trait and a selection index composed by traits evaluated at weaning and long-yearling. [ABSTRACT FROM AUTHOR]

Genomic selection may accelerate genetic progress in breeding programs of indicine breeds when compared with traditional selection methods. We present results of genomic predictions in Gyr (Bos indicus) dairy cattle of Brazil for milk yield (MY), fat yield (FY), protein yield (PY), and age at first calving using information from bulls and cows. Four different single nucleotide polymorphism (SNP) chips were studied. Additionally, the effect of the use of imputed data on genomic prediction accuracy was studied. A total of 474 bulls and 1,688 cows were genotyped with the Illumina BovineHD (HD; San Diego, CA) and BovineSNP50 (50K) chip, respectively. Genotypes of cows were imputed to HD using FImpute v2.2. After quality check of data, 496,606 markers remained. The HD markers present on the GeneSeek SGGP-20Ki (15,727; Lincoln, NE), 50K (22,152), and GeneSeek GGP-75Ki (65,018) were subset and used to assess the effect of lower SNP density on accuracy of prediction. Deregressed breeding values were used as pseudophenotypes for model training. Data were split into reference and validation to mimic a forward prediction scheme. The reference population consisted of animals whose birth year was ≤2004 and consisted of either only bulls (TR1) or a combination of bulls and dams (TR2), whereas the validation set consisted of younger bulls (born after 2004). Genomic BLUP was used to estimate genomic breeding values (GEBV) and reliability of GEBV (R²PEV) was based on the prediction error variance approach. Reliability of GEBV ranged from ~0.46 (FY and PY) to 0.56 (MY) with TR1 and from 0.51 (PY) to 0.65 (My) with TR2. When averaged across all traits, R²PEV were substantially higher (R²PEV of TR1 = 0.50 and TR2 = 0.57) compared with reliabilities of parent averages (0.35) computed from pedigree data and based on diagonals of the coefficient matrix (prediction error variance approach). Reliability was similar for all the 4 marker panels using either TR1 or TR2, except that imputed HD cow data set led to an inflation of reliability. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. A reduced panel of ~15K markers resulted in reliabilities similar to using HD markers. Reliability of GEBV could be increased by enlarging the limited bull reference population with cow information. [ABSTRACT FROM AUTHOR]

Genotype imputation is widely used as a cost-effective strategy in genomic evaluation of cattle. Key determinants of imputation accuracies, such as linkage disequilibrium patterns, marker densities, and ascertainment bias, differ between Bos indicus and Bos taurus breeds. Consequently, there is a need to investigate effectiveness of genotype imputation in indicine breeds. Thus, the objective of the study was to investigate strategies and factors affecting the accuracy of genotype imputation in Gyr (Bos indicus) dairy cattle. Four imputation scenarios were studied using 471 sires and 1,644 dams genotyped on Illumina BovineHD (HD-777K; San Diego, CA) and BovineSNP50 (50K) chips, respectively. Scenarios were based on which reference high-density single nucleotide polymorphism (SNP) panel (HDP) should be adopted [HD-777K, 50K, and GeneSeek GGP-75Ki (Lincoln, NE)]. Depending on the scenario, validation animals had their genotypes masked for one of the lower-density panels: Illumina (3K, 7K, and 50K) and GeneSeek (SGGP-20Ki and GGP-75Ki). We randomly selected 171 sires as reference and 300 as validation for all the scenarios. Additionally, all sires were used as reference and the 1,644 dams were imputed for validation. Genotypes of 98 individuals with 4 and more offspring were completely masked and imputed. Imputation algorithms FImpute and Beagle v3.3 and v4 were used. Imputation accuracies were measured using the correlation and allelic correct rate. FImpute resulted in highest accuracies, whereas Beagle 3.3 gave the least-accurate imputations. Accuracies evaluated as correlation (allelic correct rate) ranged from 0.910 (0.942) to 0.961 (0.974) using 50K as HDP and with 3K (7K) as low-density panels. With GGP-75Ki as HDP, accuracies were moderate for 3K, 7K, and 50K, but high for SGGP-20Ki. The use of HD-777K as HDP resulted in accuracies of 0.888 (3K), 0.941 (7K), 0.980 (SGGP-20Ki), 0.982 (50K), and 0.993 (GGP-75Ki). Ungenotyped individuals were imputed with an average accuracy of 0.970. The average top 5 kinship coefficients between reference and imputed individuals was a strong predictor of imputation accuracy. FImpute was faster and used less memory than Beagle v4. Beagle v4 outperformed Beagle v3.3 in accuracy and speed of computation. A genotyping strategy that uses the HD-777K SNP chip as a reference panel and SGGP-20Ki as the lower-density SNP panel should be adopted as accuracy was high and similar to that of the 50K. However, the effect of using imputed HD-777K genotypes from the SGGP-20Ki on genomic evaluation is yet to be studied. [ABSTRACT FROM AUTHOR]