Merino sheep representing a range of bloodlines in resource flocks located across Australia were tested for resistance to gastro-intestinal nematodes. These flocks included the JB Pye Flock (Camden, NSW), Katanning Base Flock (Katanning, WA), Turretfield Merino Resource Flock (Rosedale, SA), CSIRO Finewool Flock (Armidale, NSW), and the Trangie D Flock (Trangie, NSW). Faecal egg count (FEC) was used to measure relative resistance of sheep to nematode parasites after either natural or artificial infection with Haemonchus contortus and Trichostrongylus colubriformis. Differences in FEC 0' 33 between strains and between and within bloodlines were examined and the heritability of this trait was estimated. A low proportion of the total variation in parasite resistance could be attributed to strain and bloodline effects (1 and 3.5%, respectively) after either natural or artificial infection. The major source of genetic variation was found within bloodlines (22.2% of total variation), with individual sires showing a wide range in parasite resistance. Paternal half-sib heritability estimates for FEC 0' 33 were significant (P < 0.05) in 9 of the 11 analyses and ranged from 0.07 to 0.42, with a weighted average of 0.22. The influence of the environmental effects of sex, age of dam, birth-rearing rank, and day of birth were also investigated, and were found to be only occasionally significant, accounting for a small proportion (0.3-2.2%) of variation. Management group effects both prior to and at the time of measurement were often significant, and accounted for 2.2-19.4% of variation in FEC. Correction of FEC for effects other than management group would seem to add little to precision of selection. These results have demonstrated that significant genetic variation for nematode parasite resistance exists within a wide range of Merino bloodlines, and within-flock selection of resistant sires appears to be an effective method of improving this trait in Merino sheep.
An in vitro bovine mammosphere model was characterized for use in lactational biology studies using a functional genomics approach. Primary bovine mammary epithelial cells cultured on a basement membrane, Matrigel, formed three-dimensional alveoli-like structures or mammospheres. Gene expression profiling during mammosphere formation by high-density microarray analysis indicated that mammospheres underwent similar molecular and cellular processes to developing alveoli in the mammary gland. Gene expression profiles indicated that genes involved in milk protein and fat biosynthesis were expressed, however, lactose biosynthesis may have been compromised. Investigation of factors influencing mammosphere formation revealed that extracellular matrix (ECM) was responsible for the initiation of this process and that prolactin (Prl) was necessary for high levels of milk protein expression. CSN3 (encoding kappa-casein) was the most highly expressed casein gene, followed by CSN1S1 (encoding alphaS1-casein) and CSN2 (encoding beta-casein). Eighteen Prl-responsive genes were identified, including CSN1S1, SOCS2 and CSN2, however, expression of CSN3 was not significantly increased by Prl and CSN1S2 was not expressed at detectable levels in mammospheres. A number of novel Prl responsive genes were identified, including ECM components and genes involved in differentiation and apoptosis. This mammosphere model is a useful model system for functional genomics studies of certain aspects of dairy cattle lactation.
Genotypic errors, conflict between recorded genotype and the true genotype, can lead to false or biased population genetic parameters. Here, the effect of genotypic errors on accuracy of genomic predictions and genomic relationship matrix are investigated using a simulation study based on population and genomic structure comparable to black tiger prawn, Penaeus monodon. Fifty full-sib families across five generations with phenotypic and genotypic information on 53 K SNPs were simulated. Ten replicates of different scenarios with three heritability estimates, equal and unequal family contributions were generated. Within each scenario, four SNP densities and three genotypic error rates in each SNP density were implemented. Results showed that family contribution did not have a substantial impact on accuracy of predictions across different datasets. In the absence of genotypic errors, 3 K SNP density was found to be efficient in estimating the accuracy, whilst increasing the SNP density from 3 to 20 K resulted in a marginal increase in accuracy of genomic predictions using the current population and genomic parameters. In addition, results showed that the presence of even 10% errors in a 10 and 20 K SNP panel might not have a severe impact on accuracy of predictions. However, below 10 K marker density, even a 5% error can result in lower accuracy of predictions.
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