eQTL analyses results:
QTL analysis attempts to dissect continuous trait variation into its component loci (quantitative trait loci or QTLs) and to map each one to a particular chromosomal region. When the trait subjected to QTL analysis is transcript (mRNA) abundance for a particular gene, the QTL is referred to as an eQTL (expression QTL). An eQTL is a site on a chromosome containing a gene (or genes) that controls a proportion of expression (mRNA) variation for a particular gene. An eQTL can map to its gene, suggesting apparent cis regulation, or it can map elsewhere from the gene in the case of trans regulation. The former may reflect the effects of the promoter or other regulatory elements in or near the gene, while the latter may involve the influence of transcription factors or the effects of other genes playing an important regulatory role in a given pathway. There may be two or more eQTLs for any given gene, suggesting polygenic inheritance.
The results of these QTL analyses are currently available in two forms; the complete B6D2F2 data set and a much smaller summary version. Both represent the results of a QTL analysis of all transcripts (probe sets) on the Affymetrix 430A and 430B chips based on 56 B6D2F2 mice, with the individual mouse as the unit of analysis (no pooling). Variation in transcript abundance for each probe set represented on both chips was treated as a phenotype (trait) for QTL analysis using R/qtl running in the R statistical programming environment.
The complete data set contains LOD scores for about 200 markers and intervals between markers genome-wide for each of the 45,000 transcripts on the 430A and B chips. The summary version is filtered to include only those transcripts with a peak LOD of 3.7 or greater (FDR<0.01 or p<.0002) for at least one eQTL, which comprised approximately 10% of all transcripts, or 4,500 transcripts. For each transcript it lists the peak LOD and the associated marker and chromosomal location.Right-click to download full eQTL data set (48 Mb): f2_430a&b_eqtl_bld33.csv .
Summary version w/ significant loci: f2_summary.csv.
The complete data set can also be used to generate graphical QTL maps for any transcript. The fourth row (labelled XaxisPlot) converts each marker location into a relative location in the genome. Use these values as the x-axis, and the LOD scores for a particular transcript as the y-axis.
Coexpressed genes:With the normalized expression data it is possible to search for sets of genes whose expression levels are correlated across subjects.
MS Excel file (55 Mb)
by Mark Rutledge-Gorman
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