ALBERT

Notes: Two channel microarray data often contain systematic variations that can be minimized by data transformation prior to further analysis. The most commonly observed effects are revealed by viewing scatter plots of the logarithm of the ratio by the average logarithmic intensity of the two color channels (RI plots). In this paper we present a general model for signal intensity data with multiple error sources. We demonstrate how these sources of error influence the shape of an RI plot. We then compare some currently available transformation strategies in terms of their mechanism and performance on both simulated and real microarray data. A linlog transformation is proposed to stabilize the variance of the log ratios. We also propose a regional smoothing method to remove variation in log ratios due to spatial heterogeneity on the microarray surface. The discussed transformations represent an important initial step in microarray data analysis for both ratio-based and ANOVA methods.

Notes: The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by systematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan based on single locus or multi-locus models is used to identify the putative loci. Since the number of quantitative trait loci (QTLs) is very likely to be small relative to the number of markers genotyped, a one-stage selective genotyping approach is commonly used to reduce the genotyping burden, whereby markers are genotyped solely on individuals with extreme trait values. This approach is powerful in the presence of a single quantitative trait locus (QTL) but may result in substantial loss of information in the presence of multiple QTLs. Here we investigate the efficiency of sequential two stage designs to identify QTLs in experimental populations. Our investigations for backcross and F2 crosses suggest that genotyping all the markers on 60% of the subjects in Stage 1 and genotyping the chromosomes significant at 20% level using additional subjects in Stage 2 and testing using all the subjects provides an efficient approach to identify the QTLs and utilizes only 70% of the genotyping burden relative to a one stage design, regardless of the heritability and genotyping density. Complex traits are a consequence of multiple QTLs conferring main effects as well as epistatic interactions. We propose a two-stage analytic approach where a single-locus genome scan is conducted in Stage 1 to identify promising chromosomes, and interactions are examined using the loci on these chromosomes in Stage 2. We examine settings under which the two-stage analytic approach provides sufficient power to detect the putative QTLs.