Breeding polled cattle in Australia
Economic losses in beef cattle due to bruised meat can be largely attributed to the presence of horns. While dehorning practices can provide some economic improvement, it is more labour intensive and is likely to be subject to renewed animal welfare legislation in the future. Breeding naturally polled animals is the long term alternative to reducing economic loss while maintaining best practice animal welfare. The haplotype Poll test is aimed to estimate the Poll genetics of an animal, given the alleles observed at 10 microsatellites in the vicinity of the Poll locus on chromosome 1. The following provides a summary of the genetics of polled cattle and the test used to estimate Poll probability of beef cattle.
Current status of Australia's diagnostic poll haplotype test
Australia's diagnostic poll haplotype test requires reliable and varied phenotype submissions to estimate polled probabilities of haplotypes, and is dependent on unbiased sampling of the population. This paper provides a review of the effectiveness of the haplotype poll test and shows clear potential for significant ascertainment bias to be affecting the accuracy of the test, resulting from industry submission of mostly unknown or polled phenotypes, with little control over phenotype scoring accuracy. A new project targeting the supplementation with horned animals is underway to address the resulting phenotype proportions, with the aim of greatly increasing the accuracy of the test. Keywords: poll, horn, haplotype, beef.
An efficient method to calculate accuracy of estimated breeding values for individuals without phenotypes
Improved methodology to update the inverse of the coefficient matrix(C)for new individuals without phenotype is described here. Computational performance is significantly improved by re-using parts of the coefficient matrix inverse calculations that do not change from one animal to another, in combination with updated calculations for those that do change. This efficient method delivers more than 500-fold improvement in performance.
Implementation of single-step genomic BREEDPLAN evaluations in Australian beef cattle
Single-step GBLUP (ssGBLUP) procedures have now been implemented into Australia's BREEDPLAN genetic evaluation system for beef cattle. This major remodelling required the development of many new features and modifications to existing procedures. The first requirement was the construction of a flexible but robust set of procedures for handling and processing of raw SNP genotypes to enable the construction of suitable genomic relationship matrices. The analytical processes were modified to replace with and for the explicit fitting of genetic groups. A new accuracy algorithm was developed and the solver was revised. Examples from Australian Angus and Brahman breeds comparing current BLUP evaluation with ssGBLUP are presented to show the resultant changes and effects of implementing the new genomic evaluations.
BESSiE: a software for linear model BLUP and Bayesian MCMC analysis of large-scale genomic data
Background: The advent of genomic marker data has triggered the development of various Bayesian algorithms for estimation of marker effects, but software packages implementing these algorithms are not readily available, or are limited to a single algorithm, uni-variate analysis or a limited number of factors. Moreover, script based environments like R may not be able to handle large-scale genomic data or exploit model properties which save computing time or memory (RAM). Results: BESSiE is a software designed for best linear unbiased prediction (BLUP) and Bayesian Markov chain Monte Carlo analysis of linear mixed models allowing for continuous and/or categorical multivariate, repeated and missing observations, various random and fixed factors and large-scale genomic marker data. BESSiE covers the algorithms genomic BLUP, single nucleotide polymorphism (SNP)-BLUP, BayesA, BayesB, BayesCπ and BayesR for estimating marker effects and/or summarised genomic values. BESSiE is parameter file driven, command line operated and available for Linux environments. BESSiE executable, manual and a collection of examples can be downloaded http:// turing.une.edu.au/~agbu-admin/BESSiE/. Conclusion: BESSiE allows the user to compare several different Bayesian and BLUP algorithms for estimating marker effects from large data sets in complex models with the same software by small alterations in the parameter file. The program has no hard-coded limitations for number of factors, observations or genetic markers.
Increased accuracy of the Poll DNA marker test for Australian beef cattle
The Poll DNA marker test is used to determine the poll genetics of an animal, given the alleles observed within the poll locus. This paper describes improvements made to the commercially available Poll DNA marker test, to capture more variability, enable predictions that are more accurate and clarify uncertainty of polled probabilities.