CTLmapping {ctl} R Documentation

CTLmapping - Scan for correlated trait loci (CTL)

Description

Scan for correlated trait loci (CTL)

Usage

CTLmapping(genotypes, phenotypes, phenocol = 1, nperm = 100, nthreads = 1,
strategy = c("Exact", "Full", "Pairwise"), adjust = TRUE, qtl = TRUE, verbose = FALSE)


Arguments

 genotypes Matrix of genotypes. (individuals x markers) phenotypes Matrix of phenotypes. (individuals x phenotypes) phenocol Which phenotype column(s) should we analyse. Default: Analyse a single phenotype. nperm Number of permutations to perform. This parameter is not used when method="Exact". nthreads Number of CPU cores to use during the analysis. strategy The permutation strategy to use, either Exact: Uses exact calculations to calculate the likelihood of a difference in correlation: Cor(AA) - Cor(BB). Using a Bonferroni correction. Full: Most powerful analysis method - Compensate for marker and trait correlation structure (Breitling et al.). Pairwise: Suitable when we have a lot of markers and only a few traits (< 50) (human GWAS)- Compensates only for marker correlation structure. Note: Exact is the default and fastest option it uses a normal distribution for estimating p-values and uses bonferoni correction. It has however the least power to detect CTLs, the two other methods (Full and Pairwise) perform permutations to assign significance. adjust Adjust p-values for multiple testing (only used when strategy = Exact). qtl Use the internal slow QTL mapping method to map QTLs. verbose Be verbose.

Details

TODO

• NOTE: Main bottleneck of the algorithm is the RAM available to the system

Value

CTLscan, a list of:

• $dcor - Matrix of differential correlation scores for each trait at each marker •$perms - Vector of maximums per marker obtained during permutations

• $ctls - Matrix of LOD scores for CTL likelihood Note TODO Author(s) Danny Arends Danny.Arends@gmail.com Maintainer: Danny Arends Danny.Arends@gmail.com References TODO See Also • CTLscan - Main function to scan for CTL • CTLscan.cross - Use an R/qtl cross object with CTLscan • CTLsignificant - Significant interactions from a CTLscan • plot.CTLscan - Plot the CTL curve for a single trait Examples  library(ctl) data(ath.metabolites) # Arabidopsis Thaliana dataset singlescan <- CTLmapping(ath.metab$genotypes, ath.metab\$phenotypes, phenocol = 23)

plot(singlescan)      # Plot the results of the CTL scan for the phenotype

summary <- CTLsignificant(singlescan)
summary               # Get a list of significant CTLs


[Package ctl version 1.0.0-7 Index]