check_cofactors: Build a multi-QTL model using step-wise procedure of checking...

Description Usage Arguments Value Examples

View source: R/polyqtlR_functions.R

Description

The function check_cofactors initially fits all significant QTL positions as co-factors, both individually and in combination. Significance thresholds are re-estimated each time, yielding threshold-corrected LOD scores. If this leads to a change in the estimated position of QTL, or detection of subsequent peaks, a second round of co-factor inclusion is performed for all new QTL or novel QTL combinations. Finally, the multi-QTL model that maximises the individual significance of each QTL is returned as a data.frame. This can be directly passed to the function PVE to estimate the percentage variance explained by the full multi-QTL model and all possible sub-models. Note: this function estimates the most likely QTL positions by maximising the threshold-corrected LOD at QTL peaks. Non-additive interactions between QTL may be missed as a result. It is recommended to run a manual co-factor analysis as well, as described in the package vignette.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
check_cofactors(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  LOD_data,
  min_res = 20,
  ncores = 1,
  verbose = TRUE
)

Arguments

IBD_list

List of IBD_probabilities as estimated using one of the various methods available (e.g. estimate_IBD).

Phenotype.df

A data.frame containing phenotypic values

genotype.ID

The colname of Phenotype.df that contains the population identifiers (F1 names) (must be a colname of Phenotype.df)

trait.ID

The colname of Phenotype.df that contains the response variable to use in the model (must be a colname of Phenotype.df)

LOD_data

Output of QTLscan function.

min_res

The minimum genetic distance (resolution) assumed possible to consider 2 linked QTL (on the same linkage group) as independent. By default a value of 20 cM is used. This is not to suggest that 20 cM is a realistic resolution in a practical mapping study, but it provides the function with a criterion to consider 2 significant QTL within this distance as one and the same. For this purpose, 20 cM seems a reasonable value to use. In practice, closely linked QTL will generally "explain" all the variation at nearby positions, making it unlikely to be able to disentangle their effects. QTL positions will vary slightly when co-factors are introduced, but again this variation is presumed not to exceed 20 cM either side.

ncores

How many CPU cores should be used in the evaluation? By default 1 core is used.

verbose

Logical, by default TRUE - should progress messages be printed to the console?

Value

Data frame with the following columns:

Examples

1
2
3
data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
check_cofactors(IBD_list=IBD_4x,Phenotype.df=BLUEs.pheno,
genotype.ID="Geno",trait.ID="BLUE",LOD_data=qtl_LODs.4x)

polyqtlR documentation built on Feb. 2, 2022, 5:09 p.m.