Analysis of microarrays incorporating adjustments for spatial effects

Various models were used to extract spatial effects from microarray data. Large discrepancies between the rankings of genes for the different methods were found, due to the roughness of the signal. Models assuming separability and autocorrelation did not perform as well as wavelets because the data were fractal in dimension, so assumptions underlying those models were violated

A Simulation Study of Spatial Effects in Microarray Slides

There are many sources of variation in microarray studies that need to be accounted for so that gene expression estimates are accurate. One such source of variation is spatial trends that can accumulate on the microarray slides. This spatial variation may arise from printing blocks, pin effects and uneven washing of the solution over the slide. These trends can significantly change the biological conclusions of an experiment depending on how they are modeled. Hence, it is important to test various methods of removing spatial trends in microarray slides so that a method can be recommended for a particular slide depending on its properties. Complexities in the procedures and high costs of microarrays mean that performing real microarray experiments to test methods of removing bias is not always viable. Simulation is a practical way to test potential strategies in processing microarray experiments. Previous simulation studies consider sources of variation such as background noise, expression signals, spot location, spot shape and irregularities on the slide (Wierling et al. 2002; Y. Balagurunathan and Trent 2004). Simulation of spatial trends in the single channel background adjusted signal will be considered here. In this paper, results from a previous murine study (Woolaston et al. 2005) are used to generate data to assess the effectiveness of three methods of modeling spatial noise in microarray slides. Spatial effects of varying roughness or fractal dimension are also simulated

Genome wide selection: issues and implications

Single nucleotide polymorphic (SNP) chips enable us to account for variation within genomes very well. It is possible to associate this variation with variation in phenotypes and so predict genetic merit of young individuals better than ancestral indexes. This has significant implications for livestock industries as to accuracy and timing of selection decisions. and how resources are allocated to maximize the returns from investments in genotypic and phenotypic data collection. High density genotyping platforms will exacerbate the problem of the joint analysis of individuals with heterogeneous amounts of genotypic information

Genome-wide selection in dairy cattle: Use of genetic algorithms in the estimation of molecular breeding values

A procedure has been developed for the prediction of genetic merit and the simultaneous assessment of multiple genotypes for subsequent use in gene detection. The system utilises a large volume of genotype information but ignores pedigree. With a simple additive model of inheritance, high correlations between estimates of molecular breeding value and highly reliable progeny test estimated breeding values were observed (0.70–0.77)