A number of cattle breeds have become highly specialized for milk or beef production, following strong artificial selection for these traits. In this paper, we compare allele frequencies from 9323 single nucleotide polymorphism (SNP) markers genotyped in dairy and beef cattle breeds averaged in sliding windows across the genome, with the aim of identifying divergently selected regions of the genome between the production types. The value of the method for identifying selection signatures was validated by four sources of evidence. First, differences in allele frequencies between dairy and beef cattle at individual SNPs were correlated with the effects of those SNPs on production traits. Secondly, large differences in allele frequencies generally occurred in the same location for two independent data sets (correlation 0.45) between sliding window averages. Thirdly, the largest differences in sliding window average difference in allele frequencies were found on chromosome 20 in the region of the growth hormone receptor gene, which carries a mutation known to have an effect on milk production traits in a number of dairy populations. Finally, for the chromosome tested, the location of selection signatures between dairy and beef cattle was correlated with the location of selection signatures within dairy cattle.

A key question for the implementation of marker-assisted selection (MAS) using markers in linkage disequilibrium with quantitative trait loci (QTLs) is how many markers surrounding each QTL should be used to ensure the marker or marker haplotypes are in sufficient linkage disequilibrium (LD) with the QTL. In this paper we compare the accuracy of MAS using either single markers or marker haplotypes in an Angus cattle data set consisting of 9323 genome-wide single nucleotide polymorphisms (SNPs) genotyped in 379 Angus cattle. The extent of LD in the data set was such that the average marker-marker r2 was 0.2 at 200 kb. The accuracy of MAS increased as the number of markers in the haplotype surrounding the QTL increased, although only when the number of markers in the haplotype was 4 or greater did the accuracy exceed that achieved when the SNP in the highest LD with the QTL was used. A large number of phenotypic records (>1000) were required to accurately estimate the effects of the haplotypes.

Blue-eye trevalla (Hyperoglyphe antarctica), blue warehou (Seriolella brama) and silver warehou (Seriolella punctata) from the family Centrolophidae are three commercially important species in the Australian fishery. These species are currently managed as single stocks. We tested the hypothesis that patterns of phenotypic structuring in these species reflect underlying genetic stock structure using an analysis of mitochondrial DNA control region sequences. The analysis revealed high levels of haplotype diversity within populations. The most common haplotypes for the species occurred in all geographical locations sampled. For S. brama, although structuring was not significant after Bonferroni correction, differences between two sites were sufficient to warrant caution in the management of fishery zones for this species. There were also some indications of structuring when sites were grouped into common regions. Demographic analysis suggested that S. brama might have had a history of population bottlenecks followed by sudden population expansion, potentially contributing to genetic structuring in the fishery. No structuring was detected for H. antarctica and S. punctata. The present study highlights the need for, and the utility of, multiple sources of information, that is, genetic, phenotypic, behavioural and ecological, when managing marine fisheries and the need to take a cautionary approach to the interpretation of genetic data for fisheries management.