CTLscan.cross {ctl} R Documentation

## CTLscan.cross - Scan for Correlated Trait Locus (CTL) (R/qtl cross object)

### Description

Scan for Correlated Trait Locus (CTL) in populations (using an R/qtl cross object)

### Usage

CTLscan.cross(cross, ...)


### Arguments

 cross An object of class cross. See read.cross for details. ... Passed to CTLscan function: phenocol - Which phenotype column should we analyse. method - We provide 3 ways of mapping correlation differences across the genome: Exact: Uses a Correlation to Z score transformation to calculate the likelihood of a difference in correlation: Cor(AA) - Cor(BB) Power: More powerful analysis method using the squared difference in correlation: (Cor(AA) - Cor(BB))^2 Adjacency: Adjacency method which using the squared difference in squared correlation, but keeping the sign of correlation: (sign*Cor(AA)^2 - sign*Cor(BB)^2)^2 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 (Power and Adjacency) perform permutations to assign significance. n.perm - Number of permutations to perform. strategy - The permutation strategy to use, either Full (Compensate for marker and trait correlation structure) or Pairwise (Compensate for marker correlation structure). This parameter is not used when method="Exact". conditions - A vector of experimental conditions applied during the experiment. These conditions will be used as covariates in the QTL modeling step. n.cores - Number of CPU cores to use during the analysis. verbose - Be verbose.

### Details

TODO

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

### Value

CTLscan object, a list with at each index a CTL matrix (Rows: Phenotypes, Columns: Genetic markers) for the phenotype.

TODO

### Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

### References

TODO

• CTLscan - Main function to scan for CTL

• CTLsignificant - Significant interactions from a CTLscan

• CTLnetwork - Create a CTL network from a CTLscan

• image.CTLobject - Heatmap overview of a CTLscan

• plot.CTLscan - Plot the CTL curve for a single trait

### Examples

  library(ctl)
data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)

mtrait <- calc.genoprob(multitrait)          # Calculate genotype probabilities
qtls   <- scanone(mtrait, pheno.col = 1)     # Scan for QTLS using R/qtl

ctls   <- CTLscan.cross(mtrait, phenocol = 1, qtl = FALSE)
ctls[[1]]\$qtl <- qtls[,3]

ctl.lineplot(ctls, qtls[,1:2], significance = 0.05) # Line plot all the phenotypes

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


[Package ctl version 1.0.0-7 Index]