Genotype-environment interaction for milk production of Gyr cattle in Brazil and Colombia.

BACKGROUND: Genotype by environment interactions (G × E) can play an important role in cattle populations and should be included in breeding programs in order to select the best animals for different environments. OBJECTIVE: The aim of this study was to investigate the G × E for milk production of Gyr cattle in Brazil and Colombia by applying a reaction norm model used genomics information, and to identify genomic regions associated with milk production in the two countries. METHODS: The Brazilian and Colombian database included 464 animals (273 cows and 33 sires from Brazil and 158 cows from Colombia) and 27,505 SNPs. A two-trait animal model was used for milk yield adjusted to 305 days in Brazil and Colombia as a function of country of origin, which included genomic information obtained with a single-step genomic reaction norm model. The GIBBS3F90 and POSTGSf90 programs were used. RESULTS: The results obtained indicate G × E based on the reranking of bulls between Brazil and Colombia, demonstrating environmental differences between the two countries. The findings highlight the importance of considering the environment when choosing breeding animals in order to ensure the adequate performance of their progeny. Within this context, the reranking of bulls and the different SNPs associated with milk production in the two countries suggest that G × E is an important effect that should be included in the genetic evaluation of Dairy Gyr cattle in Brazil and Colombia. CONCLUSION: The Gyr breeding program can be optimized by choosing a selection environment that will allow maximum genetic progress in milk production in different environments within and between countries.

Assessing the accuracy of imputation in the Gyr breed using different SNP panels.

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.

Genetic principal components for reproductive and productive traits in Holstein cows reared under tropical conditions.

The objective of this study was to compare the standard multi-trait model and five reduced-rank models fitted to the first principal components and genetic parameter estimates in order to determine the most appropriate method to model the covariance structure of reproductive and productive traits in Brazilian Holstein cows. Individual records of the following traits from 5217 cows were analyzed: 305-day milk yield (MY305), peak yield, milk yield per day of calving interval, days from calving to first estrus, days from calving to last service (CLS), calving interval (CI), and gestation length. Schwarz's Bayesian information criterion was used to compare the different models. The results indicated that four principal components were necessary to model the genetic (co)variance structure, reducing the number of parameters to be estimated. Analysis of genetic and phenotypic correlations showed that milk production-related traits were strongly correlated with each other (ranging from 0.74 to 0.99), while the correlation of these traits with the reproductive traits was weak (ranging from - 0.14 to 0.27). Heritability estimates for the traits ranged from 0.03 to 0.18. The reproductive traits CLS and CI and the production trait MY305 should be included as selection criteria in dairy cattle breeding programs because they are correlated with the first two principal components, retaining 91% of the genetic variability of the data.

Genome-wide identification of runs of homozygosity islands in the Gyr breed (Bos indicus).

Runs of homozygosity (ROH) are contiguous homozygous regions of the genome. These regions can be used to identify genes associated with traits of economic interest, as well as inbreeding levels. The aim of the present study was to analyse the length and distribution of ROH islands in Gyr cattle and to identify genes within these regions. A population of 173 animals selected for beef production and a population of 291 animals selected for dairy production were used. Differences in the number of short ROH (ROH1-2 Mb ) were observed between the two populations, while the number of long ROH (ROH>16 Mb ) was similar. ROH islands with the highest incidences (>0.50) overlapped in several segments of the genome in the two populations. The genes identified were associated with milk production, growth, reproduction, immune response and resistance traits. Our results contribute to the understanding of how selection can shape the distribution of ROH and ROH islands within the same breed when animals are selected for different purposes such as dairy or beef production.