ctl.scan.cross: CTLscan.cross - Scan for Correlated Trait Locus (CTL) (R/qtl...

CTLscan.crossR Documentation

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


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


CTLscan.cross(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.



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


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




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



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


  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

ctl documentation built on March 18, 2022, 7:49 p.m.