Two approaches to account for genotype-by-environment interactions for production traits and age at first calving in South African Holstein cattle.

BACKGROUND: If not accounted for, genotype x environment (G×E) interactions can decrease the accuracy of genetic evaluations and the efficiency of breeding schemes. These interactions are reflected by genetic correlations between countries lower than 1. In countries that are characterized by a heterogeneity of production systems, they are also likely to exist within country, especially when production systems are diverse, as is the case in South Africa. We illustrate several alternative approaches to assess the existence of G×E interactions for production traits and age at first calving in Holsteins in South Africa. Data from 257,836 first lactation cows were used. First, phenotypes that were collected in different regions were considered as separate traits and various multivariate animal models were fitted to calculate the estimates of heritability for each region and the genetic correlations between them. Second, a random regression approach using long-term averages of climatic variables at the herd level in a reaction norm model, was used as an alternative way to account for G×E interactions. Genetic parameter estimates and goodness-of-fit measures were compared. RESULTS: Genetic correlations between regions as low as 0.80 or even lower were found for production traits, which reflect strong G×E interactions within South Africa that can be linked to the production systems (pasture vs total mixed ration). A random regression model including average rainfall during several decades in the herd surroundings gave the best goodness-of-fit for production traits. This can be related to a preference for total mixed ration on farms with limited rainfall. For age at first calving, the best model was based on a random regression on maximum relative humidity and maximum temperature in summer. CONCLUSIONS: Our results indicate that G×E interactions can be accounted for when genetic evaluations of production traits are performed in South Africa, by either considering production records in different regions as different correlated traits or using a reaction norm model based on herd management characteristics. From a statistical point of view, climatic variables such as average rainfall over a long period can be included in a random regression model as proxies of herd production systems and climate.

Genetic variability and relationships in nine South African cattle breeds using microsatellite markers.

Genetic variability within and between breeds allows adaptation to a changing environment and consequently prepares producers for the future. Eleven bovine-specific microsatellite markers were used to genotype animals from each of nine South African cattle breeds: Afrikaner (N = 550), Angus (N = 550), Bonsmara (N = 550), Boran (N = 321), Brahman (N = 550), Drakensberger (N = 550), Nguni (N = 550), Simmental (N = 550), and Tuli (N = 311). These breeds were drawn from Bos taurus africanus, Bos taurus, and Bos indicus. Genetic variability estimates included unbiased heterozygosity, effective number of alleles, and inbreeding. Ranges of these parameters were 0.569-0.741, 8.818-11.455, and - 0.001-0.050, respectively. Breed private allele and breed pairwise comparison was also used to characterize the breeds. The analysis of population structure with K = 2 revealed clusters comprised of Sanga-indicine and taurine, while K = 3 included separate clusters of Sanga, indicine, and taurine, and with K = 9 showed the breeds arising from unique progenitor populations. This study broke new ground in molecular cattle genetic diversity by genotyping a large sample size per breed and using a larger number of breeds compared with similar studies that have been conducted in the recent past which have either used a smaller number of breeds or smaller sample sizes but with a larger number of marker loci. Thus, opportunities that arise to explore genetic diversity and relationships in both the livestock and wildlife industries in Southern Africa may capitalize on microsatellite marker databases which remain cost-effective and accessible due to their extensive use for parentage verification.