ctl (version 1.0.0-0)

CTLmapping: CTLmapping - Scan for correlated trait loci (CTL)

Description

Scan for correlated trait loci (CTL)

Usage

CTLmapping(genotypes, phenotypes, phenocol = 1, nperm = 100, 
strategy = c("Exact", "Full", "Pairwise"), qtls=NULL, 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".

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.

qtls

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

verbose

Be verbose.

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

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

# NOT RUN {
  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
# }