Genetic improvement of livestock has to consider a number of traits and is often based on an economic index. The economic index is the sum of the estimated breeding values for traits in the breeding objective weighted by their economic values. The economic value of a trait is the change in profit per unit change in the trait. For example, the economic value of growth rate reflects the lower on-farm costs resulting from reaching a target slaughter weight in fewer days. Economic values of traits depend on management and payment systems, and should be derived for the system of interest. The aim of this study was the estimation of economic values for a typical Australian situation.

PIGBLUP is a PC based genetic evaluation system for pigs, which analyses large data sets within minutes. PIGBLUP has a Windows-based interface enabling easy and efficient operation by PIGBLUP users. Breeders do not need to understand the statistical and genetic theory in order to use PIGBLUP.PIGBLUP uses pedigree and performance data available from your herd recording system to derive Estimated Breeding Values (EBVs) for a number of performance and reproductive traits.The program displays genetic and environmental trends to monitor genetic progress and management decisions. It allows optimisation of selection for different markets and products through the $Index which combines EBVs into a single index using economic, production and marketing data.

Deterministic predictions of the accuracy of genomic breeding values in selection candidates with no phenotypes have been derived based on the heritability of the trait, number of phenotyped and genotyped animals in the reference population where the marker effects are estimated, the effective population size and the length of the genome. We assessed the value of these deterministic predictions given the results that have been achieved in Holstein and Jersey dairy cattle. We conclude that the deterministic predictions are useful guide for establishing the size of the reference populations which must be assembled in order to predict genomic breeding values at a desired level of accuracy in selection candidates.

A genome wide selection (GWS) platform was developed for prediction of genetic merit in dairy cattle. The critical components of the GWS platform included a genome wide SNP analysis assay representing 15,036 SNPs, 1546 progeny tested Holstein Friesian sires with EBV (ABV) for 42 lactation performance traits, and a series of complexity reduction methods with internal and external cross validation. Derived Molecular Breeding Values (MBV) using a fraction of the available SNP information, were shown to have high predictive value for genetic merit (r=0.65-0.87 with ABV) in bulls not used in the training data from which the SNP effects were derived. GWS can be used in the absence of SNP location and pedigree to make potentially highly accurate predictions of genetic merit at an early age from DNA analyses.

Diurnal feeding patterns have been described for wild foraging pigs (Signoret et al. 1975) as well as commercial 'ad libitum' fed pigs (Hall, 1997). Restricted feeding is used in some breeding programs for selection of lean meat growth. In this experiment, we investigated whether feeding regime affects the feeding patterns of pigs.

Background: Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle. Methods: Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls. Results: For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy. All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time. Conclusions: The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.

The National Pig Improvement Program (NPIP) is an across-herd genetic evaluation system for pigs in Australia. Michael Macbeth from the Queensland Department of Primary Industries initiated the NPIP in 1995 with technical support from the Animal Genetics and Breeding Unit (AGBU). Since 2001, the NPIP has been the responsibility of AGBU and the analytical system is now based on the PIGBLUP genetic evaluation system. The NPIP web site at http://npip.une.edu.au provides general information about the NPIP along with estimated breeding values (EBVs) for AI boars, genetic trends, distributions of EBVs for young animals and lists of the top young animals. Detailed information about the NPIP was provided at the last workshop (Crump and Hermesch, 2003) and only a brief overview is provided here.

This AGBU pig genetics workshop is our tenth workshop held in Armidale since 1991. We are looking forward to catching up with some of our regular breeders and to meet some new participants. This workshop coincides with the start of a new project in quantative pig genetics funded by ALP. The proposed topics for this new project will be outlined during the workshop. Breeders will have the opportunity to identify research areas they regards as important and express their willingness to participate in R&D projects. The latest developments of PIGBLUP, the National Pig Improvement Program and PBSELECT, the latest addition to our genetic services, will be outlined at the beginning of the workshop.

We genotyped overlapping samples of Australian dairy bulls using three different SNP chips (15k, 25k and 54k). These chips have different but complementary coverage hence increasing the number of animals and the density and coverage of SNPs to 74k in a combined dataset. A combined analysis of the data from these three SNP chips showed a four fold increase in the coverage of the genome by haplotype blocks over bovine hapmap reported previously (Khatkar et al. 2007). An analysis of contiguous runs of homozygosity revealed long stretches (up to 49.39 Mb) of homozygosity on chromosome 1 in many bulls. Distribution of these segments of homozygosity in a sample of bulls is presented. The results for one chromosome are described in detail.

Insulin-like growth factor-I (IGF-I) is a naturally occurring polypeptide produced in the liver, muscle and fat tissues. It is known to be associated with growth and development during the post-natal growth period. Evidence for strong genetic correlations between juvenile IGF-I and performance traits would suggest this physiological measure would be useful as an early selection criterion. This paper reports estimates of genetic parameters from 9 trials where IGF-I was measured in juvenile pigs. All trials involved populations undergoing active selection for improved performance (e.g. efficient lean meat growth). Juvenile IGF-I was moderately heritable (average h2: 0.31) and influenced by common litter effects (average c2: 0.15). Genetic correlations (rg) between juvenile IGF-I and backfat (BF), feed intake (FI) or feed conversion ratio (FCR) traits were generally large and positive: rg averaged 0.57, 0.41 and 0.65, respectively. Phenotypic correlations (rp) between juvenile IGF-I and BF, FI or FCR were much lower (rp averaged 0.21, 0.09, and 0.15, respectively) as residual correlations between IGF-I and these performance traits were low, consistent with being measured at very different times. Correlations (genetic or phenotypic) between juvenile IGF-I and growth traits (e.g. lifetime daily gain or test daily gain) were relatively low, with average values within ± 0.09 of zero. Results from the trials reported here, and several physiological studies, indicate that information on juvenile IGF-I concentration can be used as an early physiological indicator of performance traits traditionally measured later in life. There is a clear role for juvenile IGF-I to facilitate pre-selection and more accurate selection of livestock for hard to measure traits, such as FCR, in pig breeding programmes.