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Abstract Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set.

Abstract Microarray analyses have become an important tool in animal genomics. While their use is becoming widespread, there is still a lot of ongoing research regarding the analysis of microarray data. In the context of a European Network of Excellence, 31 researchers representing 14 research groups from 10 countries performed and discussed the statistical analyses of real and simulated 2-colour microarray data that were distributed among participants. The real data consisted of 48 microarrays from a disease challenge experiment in dairy cattle, while the simulated data consisted of 10 microarrays from a direct comparison of two treatments (dye-balanced). While there was broader agreement with regards to methods of microarray normalisation and significance testing, there were major differences with regards to quality control. The quality control approaches varied from none, through using statistical weights, to omitting a large number of spots or omitting entire slides. Surprisingly, these very different approaches gave quite similar results when applied to the simulated data, although not all participating groups analysed both real and simulated data. The workshop was very successful in facilitating interaction between scientists with a diverse background but a common interest in microarray analyses.

Background: In recent years, the commercial importance of changes in muscle function of broiler chickens and of the corresponding effects on meat quality has increased. Furthermore, broilers are more sensitive to heat stress during transport and at high ambient temperatures than smaller egg-laying chickens. We hypothesised that heat stress would amplify muscle damage and expression of genes that are involved in such changes and, thus, lead to the identification of pathways and networks associated with broiler muscle and meat quality traits. Broiler and layer chickens were exposed to control or high ambient temperatures to characterise differences in gene expression between the two genotypes and the two environments. Results: Whole-genome expression studies in breast muscles of broiler and layer chickens were conducted before and after heat stress; 2213 differentially-expressed genes were detected based on a significant (P < 0.05) geno-type x treatment interaction. This gene set was analysed with the BioLayout Express 3D and Ingenuity Pathway Analysis software and relevant biological pathways and networks were identified. Genes involved in functions related to inflammatory reactions, cell death, oxidative stress and tissue damage were upregulated in control broilers compared with control and heat-stressed layers. Expression of these genes was further increased in heat-stressed broilers. Conclusions: Differences in gene expression between broiler and layer chickens under control and heat stress conditions suggest that damage of breast muscles in broilers at normal ambient temperatures is similar to that in heat-stressed layers and is amplified when broilers are exposed to heat stress. The patterns of gene expression of the two genotypes under heat stress were almost the polar opposite of each other, which is consistent with the conclusion that broiler chickens were not able to cope with heat stress by dissipating their body heat. The differentially expressed gene networks and pathways were consistent with the pathological changes that are observed in the breast muscle of heat-stressed broilers. [ABSTRACT FROM AUTHOR]

Abstract: A primary infection of Salmonella enteritidis causes a spatial-temporal dependent change in the gene expression patterns in the intestine of chickens (Gallus gallus). This is the result of a dynamic intestinal response to adapt to the altered environment and to optimize its ‘health’ and functionality under the new circumstances. By inferring gene association networks (GANs), the complexities of and changes in biological networks can be uncovered. Within such GANs highly interacting (hub) genes can be identified, which are supposed to be high-level regulators connected to multiple processes. By exploring the intestinal expression of genes differing between control and Salmonella infected chicken in a time-dependent manner differences in GANs were found. In control chickens more developmental processes were observed, whereas in infected chickens relatively more processes were associated to ‘defense/pathogen response’. Moreover the conserved protein domains of the identified hub genes in controls were nuclear-associated, whereas hub genes in infected chickens were involved in ‘cellular communication’. The shift in topology and functionality of the intestinal GANs in control and Salmonella infected animals and the identification of GAN-specific hubs is a first step to understand the complexity of biological networks and processes regulating intestinal health and functionality under normal and disturbed conditions. [Copyright &y& Elsevier]

Background: We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy. Results: A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes precorrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiplemarker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance. Conclusion: The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis. [ABSTRACT FROM AUTHOR]

Understanding how variation is controlled in complex traits is a major challenge. Combining forward and reverse genetics to study the gene expression signatures of quantitative trait loci and candidate genes has enabled the dissection of complex traits. However, this approach cannot be implemented in vivo in many species. The recent development of a cell-line-based connectivity map demonstrates that gene signatures can be conserved across tissues and species, and that in vitro approaches are relevant to the whole organism. Thus, we propose that the in vitro study of gene expression, using both natural variation and perturbations induced, for example, by RNA interference, might provide a rapid and effective method for complex trait dissection in many species, including humans. [Copyright &y& Elsevier]

Genetical genomics has been proposed to map loci controlling gene-expression differences (eQTLs) that might underlie functional trait variation. We briefly review the studies in model species and conclude that, although they successfully demonstrate the utility of genetical genomics, they are too limited to unlock the full potential of this approach and some results should be interpreted with caution. We subsequently elaborate on two recent studies that use this approach in humans. The many differences between these studies complicate meaningful comparisons between them. A joint analysis of the two experiments offers some scope for more powerful genetical genomics. [Copyright &y& Elsevier]

Abstract: Functional genomics has been applied to the genetic dissection of immune response in different ways: (1) experimental crosses between lines that differ in their (non-) specific immune response have been used to detect quantitative trait loci (QTL) underlying these differences. (2) The measurement of gene expression levels for thousands of genes using microarrays or oligonucleotide chips to identify differential expression with regard to antigen challenge: (a) before and after infection, (b) resistant versus susceptible lines, or (c) combinations of both. Interpretation of QTL results is hampered by the fact that confidence regions of the QTL are large and can contain hundreds of potential candidate genes for the QTL. At the same time, the microarray experiments tend to show large numbers of differentially expressed genes without identifying the relationships between these genes. In the recently proposed ‘genetical genomics’ framework, members of a segregating population are characterised for genome-wide molecular markers and for gene expression levels. This facilitates the mapping of expression-QTL (eQTL): loci in the genome that control the expression of genes. Initial applications of this approach are critically reviewed and potential applications of this approach with regard to immune response are presented. [Copyright &y& Elsevier]

Genetical genomics has been suggested as a powerful approach to study the genotype--phenotype gap. However, the relatively low power of these experiments (usually related to the high cost) has hindered fulfillment of its promise, especially for loci (QTL) of moderate effects. One strategy with which to overcome the issue is to use a targeted approach. It has two clear advantages: (i) it reduces the problem to a simple comparison between different genotypic groups at the QTL and (ii) it is a good starting point from which to investigate downstream effects of the QTL. In this study, from 698 F2 birds used for QTL mapping, gene expression profiles of 24 birds with divergent homozygous QTL genotypes were investigated. The targeted QTL was on chromosome 1 and affected initial pH of breast muscle. The biological mechanisms controlling this trait can be similar to those affecting malignant hyperthermia or muscle fatigue in humans. The gene expression study identified 10 strong local signals that were markedly more significant compared to any genes on the rest of the genome. The differentially expressed genes all mapped to a region,1 Mb, suggesting a remarkable reduction of the QTL interval. These results, combined with analysis of downstream effect of the QTL using gene network analysis, suggest that the QTL is controlling pH by governing oxidative stress. The results were reproducible with use of as few as four microarrays on pooled samples (with lower significance level). The results demonstrate that this cost-effective approach is promising for characterization of QTL. [ABSTRACT FROM AUTHOR]

Combining global gene-expression profiling and genetic analysis of natural allelic variation (genetical genomics) has great potential in dissecting the genetic pathways underlying complex phenotypes. Efficient use of microarrays is paramount in experimental design as the cost of conducting this type of study is high. For those organisms where recombinant inbred lines are available for mapping, the "distant pair design" maximizes the number of informative contrasts over all marker loci. Here, we describe an extension of this design, named the "optimal pair design," for use with F2 crosses between outbred lines. The performance of this design is investigated by simulation and compared to several other two-color microarray designs. We show that, for a given number of microarrays, the optimal pair design outperforms all other designs considered for detection of expression quantitative trait loci (eQTL) with additive effects by linkage analysis. We also discuss the suitability of this design for outbred crosses in organisms with large genomes and for detection of dominance. [ABSTRACT FROM AUTHOR]