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    • "Alberts et al. (2005) and Williams et al. (2006) identified that artefacts due to batch variation between groups of microarrays could result in spurious master-regulator eQTLs, which are eQTLs that are apparently linked/associated with large numbers of genes (see below for more extensive discussion). Williams et al. (2006) also showed that there could be poor concordance between eQTLs linked to a gene following normalization by several different, but widely used procedures for raw microarray data processing. Finally, Manly et al. (2005) showed that some arrays contain multiple probes that, in principle, represent the same gene, but these different probes identified different eQTLs. "
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    ABSTRACT: The view that changes to the control of gene expression rather than alterations to protein sequence are central to the evolution of organisms has become something of a truism in molecular biology. In reality, the direct evidence for this is limited, and only recently have we had the ability to look more globally at how genetic variation influences gene expression, focusing upon inter-individual variation in gene expression and using microarrays to test for differences in mRNA levels. Here, we review the scope of these experimental analyses, what they are designed to tell us about genetic variation, and what are their limitations from both a technical and a conceptual viewpoint. We conclude that while we are starting to understand the impact of this class of genetic variation upon steady-state mRNA levels, we are still far from identifying the potential phenotypic and evolutionary outcomes.
    Preview · Article · Jan 2008 · Genome Research
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    • "As a consequence, the results are a collection of successive quantitative trait loci analysis (eQTL in the terminology introduced by Schadt et al. [1,2]), where each gene expression level is analysed independently. It is surprising that much effort has been dedicated to issues like data normalization [3] or computing efficiency [4] whereas modelling the trait itself (i.e., the expression level) has been severely neglected. "
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    ABSTRACT: Genetical genomics is a very powerful tool to elucidate the basis of complex traits and disease susceptibility. Despite its relevance, however, statistical modeling of expression quantitative trait loci (eQTL) has not received the attention it deserves. Based on two reasonable assertions (i) a good model should consider all available variables as potential effects, and (ii) gene expressions are highly interconnected, we suggest that an eQTL model should consider the rest of expression levels as potential regressors, in addition to the markers. It is shown that power can be increased with this strategy. We also show, using classical statistical and support vector machines techniques in a reanalysis of public data, that the external transcripts, i.e., transcripts other than the one being analysed, explain on average much more variability than the markers themselves. The presence of eQTL hotspots is reassessed in the light of these results. Model choice is a critical yet neglected issue in genetical genomics studies. Although we are far from having a general strategy for model choice in this area, we can at least propose that any transcript level is scanned not only for the markers genotyped but also for the rest of gene expression levels. Some sort of stepwise regression strategy can be used to select the final model.
    Full-text · Article · Feb 2007 · BMC Genomics
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    • "There is great interest in understanding genetic factors related to variable expression of genes. Recently, several studies have shown the first evidence of heritability of mRNA between individuals [1-6]. By treating the expression phenotypes for each transcript (or probe) as a quantitative phenotype, a variance-components linkage analysis could be used [1-6]. "
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    ABSTRACT: The goal of this paper is to investigate the effect of using principal components as a data reduction method for expression data in linkage analysis. We used 45 probes normalized using the Affymetrix Global Scaling that had evidence of high heritability to estimate the first 10 principal components (PC). A genome-wide linkage scan was performed on the 45 expression values and the 10 PCs using 2272 single-nucleotide polymorphisms. Our conclusions were: 1) PC analyses under-performed the single-probe analysis for known signals; 2) the PC that best reproduced the single-probe analysis was primarily composed of that probe; 3) no new signals were detected in the PC analysis; 4) no new pleiotropic effects were detected in the PC analysis.
    Full-text · Article · Feb 2007 · BMC proceedings
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