ctl (version 1.0.0-0)

CTLscan.cross: 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.

• qtls - Can be used to provide QTL results, when external QTL results are available.

• n.cores - Number of CPU cores to use during the analysis.

• verbose - Be verbose.

Value

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

Details

TODO

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

References

TODO

See Also

• 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

# NOT RUN {
  library(ctl)
  data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)

  mtrait <- calc.genoprob(multitrait)          # Calculate genotype probabilities
  qtls   <- scanone(mtrait, pheno.col = 1:4)   # Scan for QTLS using R/qtl
  map    <- qtls[,1:2]                         # Get the genetic map
  qtls   <- qtls[,-(1:2)]                      # Get the LOD scores

  ctls   <- CTLscan.cross(mtrait, phenocol = 1:4, qtls = qtls)
  
  ctl.lineplot(ctls, map, significance = 0.05) # Line plot all the phenotypes

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