Estimate the acting type of expression QTL
Estimate wether an eQTL is cis- or trans- acting.
classify.qtl(cross, peak, etrait.coord, data.gmap)
An object of class
An object of class
Useful in the case of genome-wide expression QTL mapping. Determines cis-acting and trans-acting eQTL (or cis- and trans- eQTL) and gives a basic overview about the global eQTL network. The (potential) cis-eQTL are those which colocalize with the controlled gene. These could be typically explained by a modification within a gene promoter and therefore actually correspond to a cis-regulation (note that it would remain to be confirmed on a case by case basis: due to the lack of precision in QTLs localization for all analysis methods, a cis-acting is still biologically hypothetical; plus it could also correspond to a trans-acting eQTL localised close to its target gene). eQTLs which contains the regulated gene within their LOD support interval are classified in this category as
cis. The trans-acting eQTLs are defined as those which do not colocalize with the affected gene. These could typically correspond to the mode of action of a transcription factor on the regulation of another gene's expression. eQTL which do not contain the regulated gene within their LOD support interval are classified as
peak object is returned with a component
type added to the components of
names(peak\$trait\$chromosome) for each previously detected QTL:
The QTL support interval locations are defined within a
peak object. This classification (performed by
classify.qtl) depends entirely on the support interval definition computed by the
define.peak function. This function tend to underestimate cis-eQTL number as LOD-drop value are more conservative. This, however, does not replace the scientist's own manual examination of the LOD curve.
Hamid A. Khalili
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data(seed10); # Genotype probabilities ## Not run: seed10 <- calc.genoprob( cross=seed10, step=2, off.end=0, error.prob=0, map.function='kosambi', stepwidth='fixed'); seed10 <- sim.geno( cross=seed10, step=2, off.end=0, error.prob=0, map.function='kosambi', stepwidth='fixed'); ## End(Not run) # Genome scan and QTL detection out.em <- scanone( seed10, pheno.col=1:50, model='normal', method='hk'); out.peak <- define.peak( out.em, 'all'); # Additive effect computing and peaks localization out.peak <- calc.adef(seed10,out.em,out.peak); data(BSpgmap); out.peak <- localize.qtl(seed10,out.peak,BSpgmap); # Estimated actind-type of the expression QTL affecting # the 100th expression trait and localized on chromosome 1 data(ATH.coord) out.peak <- classify.qtl(seed10,out.peak,ATH.coord,BSpgmap); out.peak[]$'4'$type; # idem for the trait 'CATrck' out.peak$CATrck$'4'$type;
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