QTL experiments in pigs are often analysed separately, although similar or same founder breeds are frequently used to establish the experimental design. The aim of the present study was to jointly analyse three porcine F 2-crosses for six growth and four muscling traits. The crosses were a Meishan × Pietrain cross, a Wild Boar × Pietrain cross, and a Wild Boar × Meishan cross. In some cases, same founder animals were used to establish the crosses. 966 F 2-individuals were genotyped for 242 genetic markers (mostly microsatellites) and phenotyped for birth weight, 21 and 35 day weight, slaughter weight, carcass length, food conversion ratio, ham meat weight, shoulder meat weight, loin and neck meat weight, and meat area. A multi-allele multi-QTL model was applied that estimated an additive QTL effect for each founder breed and parental origin (either paternally or maternally derived), and a dominant QTL effect for each cross. This model was previously introduced in plant breeding. Numerous QTL were mapped on the autosomes. Most QTL were localised on SSC1, 2, 3, 4, 6 and 8, and no QTL were on SSC9, 11, 13, 15, 17 and 18. The confidence intervals were short in many cases. QTL with an exceptionally high test statistic were found for carcass length on SSC1, 4, 7 and 17. The coefficient of variation was remarkably small for this trait, which suggests that carcass length is affected by only a few genes with large effects. Positional and functional candidates underlying promising QTL are suggested for further study.

(http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Breast cancer-model expression

Comparison of mammary tumor gene-expression profiles from thirteen murine models using microarrays and with that of human breast tumors showed that many of the defining characteristics of human subtypes were conserved among mouse models.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for largescale analysis of gene expression. Microarrays can be used to measure the relative quantities of speci c mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.

Here we provide the first genome-wide, high-resolution map of the phylogenetic origin of the genome of most extant laboratory mouse inbred strains. Our analysis is based on the genotypes of wild caught mice from three subspecies of Mus musculus. We demonstrate that classical laboratory strains are derived from a few fancy mice with limited haplotype diversity. Their genomes are overwhelmingly M. m. domesticus in origin and the remainder is mostly of Japanese origin. We generated genome-wide haplotype maps based on identity by descent from fancy mice and demonstrate that classical inbred strains have limited and non-randomly distributed genetic diversity. In contrast, wild-derived laboratory strains represent a broad sampling of diversity within M. musculus. Intersubspecific introgression is pervasive in these strains and contamination by laboratory stocks has played role in this process. The subspecific origin, haplotype diversity and identity by descent maps can be visualized and searched online.

Microarray technology is now widely available and is being applied to address increasingly complex scientific questions. Consequently, there is a greater demand for statistical assessment of the conclusions drawn from microarray experiments. This review discusses fundamental issues of how to design an experiment to ensure that the resulting data are amenable to statistical analysis. The discussion focuses on two-color spotted cDNA microarrays, but many of the same issues apply to single-color gene-expression assays as well.