ctl (version 1.0.0-10)

CTLmapping: 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)

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

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.

Author

Danny Arends
Maintainer: Danny Arends

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

• 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