The past decade has yielded new tools for pig geneticists and breeders thanks to the considerable developments resulting from efforts to map the pig genome. The pig genetic linkage map now has nearly 5000 loci including several hundred genes, microsatellites and amplified fragment length polymorphisms (AFLP) markers. Using tools that include somatic cell hybrid panels and radiation hybrid panels, the physical genetic map is also growing rapidly and has over 4000 genes and markers. Scientists using both exotic and commercial breeds for quantitative trait loci (QTL) scans and candidate gene analyses have identified a number of important chromosomal regions and individual genes associated with growth rate, leanness, feed intake, meat quality, litter size and disease resistance. Using marker-assisted selection (MAS) the commercial pig industry is actively incorporating these gene markers and traditional performance information to improve traits of economic importance in pig production. Researchers now have novel tools including pig gene arrays and advanced bioinformatics that are being exploited to find new candidate genes and to advance the understanding of gene function in the pig. Sequencing of the pig genome has been initiated and further sequencing is now being considered. Advances in pig genomics and directions for future research and the implications to both the pig industry and human health are reviewed.

Repeated efforts to estimate the genomic deleterious mutation rate per generation (U) in Drosophila melanogaster have yielded inconsistent estimates ranging from 0·01 to nearly 1. We carried out a mutation-accumulation experiment with a cryopreserved control population in hopes of resolving some of the uncertainties raised by these estimates. Mutation accumulation (MA) was carried out by brother–sister mating of 150 sublines derived from two inbred lines. Fitness was measured under conditions chosen to mimic the ancestral laboratory environment of these genotypes. We monitored the insertions of a transposable element, copia, that proved to accumulate at the unusually high rate of 0·24 per genome per generation in one of our MA lines. Mutational variance in fitness increased at a rate consistent with previous studies, yielding a mutational coefficient of variation greater than 3%. The performance of the cryopreserved control relative to the MA lines was inconsistent, so estimates of mutation rate by the Bateman–Mukai method are suspect. Taken at face value, these data suggest a modest decline in fitness of about 0·3% per generation. The element number of copia was a significant predictor of fitness within generations; on average, insertions caused a 0·76% loss in fitness, although the confidence limits on this estimate are wide.

Drosophila ananassae is a cosmopolitan species with a geographic range throughout most of the tropical and subtropical regions of the world. Previous studies of DNA sequence polymorphism in three genes has shown evidence of selection affecting broad expanses of the genome in regions with low rates of recombination in geographically local populations in and around India. The studies suggest that extensive physical and genetic maps based on molecular markers, and detailed studies of population structure may provide insight into the degree to which natural selection affects DNA sequence polymorphism across broad regions of chromosomes. We have isolated 85 dinucleotide repeat microsatellite sequences and developed assay conditions for genotyping using PCR. The dinucleotide repeats we isolated are shorter, on average, than those isolated in many other Drosophila species. Levels of genetic variation are high, comparable to Drosophila melanogaster. The levels of variation indicate the effective population size of an Indonesian population of D. ananassae is 58692 (infinite allele model) and 217284 (stepwise mutation model), similar to estimates of effective population size for D. melanogaster calculated using dinucleotide repeat microsatellites. The data also show that the Indonesian population is in a rapid expansion phase. Cross-species amplification of the microsatellites in 11 species from the Ananassae, Elegans, Eugracilis and Ficusphila subgroups indicates that the loci may be useful for studies of the sister species, D. pallidosa, but will have limited use for more distantly related species.

The Hudson–Kreitman–Aguade (HKA) test is based on the prediction from the neutral theory that levels of polymorphism within a species and the divergence between two closely related species should be correlated. Population subdivision has been shown to alter both the amounts of polymorphism segregating within species and the rate of divergence between species, meaning that genomic regions with different population structures also differ in their divergence to polymorphism ratios. Population subdivision may hence hamper the utility of the HKA test for detecting deviations from the standard neutral model, especially for organelle genomes that often have different patterns of population structure compared with nuclear genes. In this paper, I show that population subdivision inflates the number of instances where the HKA test detects deviations from the neutral model. Using coalescent simulations I show that this bias is most apparent when population subdivision is strong and differs substantially between the loci included. However, if divergence time is large and population structure substantial even changes in the levels of polymorphism and divergence associated with differences in the effective population size between two loci is enough to substantially alter the number of significant outcomes of the HKA test. A dataset on cytoplasmic diversity in Silene vulgaris and S. latifolia (Ingvarsson & Taylor, 2002) is also reanalysed. The previous study had shown a marked excess of intraspecific polymorphism in both species. However, when effects of population subdivision were removed, ad hoc, levels of intraspecific polymorphism were no longer significantly different from neutral expectations, suggesting that population subdivision contributed to the observed excess of intraspecific polymorphism seen in both species of Silene.

We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.

This report presents a theoretical formulation for predicting heterozygosity of a putative marker locus linked to two quantitative trait loci (QTL) in a recurrent selection and backcross (RSB) scheme. Since the heterozygosity at any given marker locus maintained in such a breeding programme reflects its map location relative to QTL, the present study develops the theoretical analysis of the QTL mapping method that recently appeared in the literature. The formulae take into account selection, recombination and finite population size during the multiple-generation breeding scheme. The single-marker and two-QTL model was compared numerically with the model involving two linked marker loci and two QTL. Without recombination interference, the two models predict the same expected heterozygosity at the linked marker loci, indicating that the model is valid for predicting marker heterozygosity maintained at any loci in an RSB breeding scheme. The formulation is demonstrated numerically for several RSB schemes and its implications in developing a likelihood-based statistical framework for modeling the RSB experiments are discussed.

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.