lmem.qtler (version 0.1.1)

mq.diagnostics: Performs molecular markers quality diagnostics.

Description

Performs molecular markers quality diagnostic of an object of class cross created by the qtl.cross function, including summary description for marker distribution and coverage, evaluating the map quality, the presence of identical individuals, visualizing marker alleles and missing marker scores for all individuals across the genome, the pairwise number of alleles shared by each pair of individuals, the pairwise recombination fraction among each pair of markers, and a test for segregation distortion for each marker in linkage analysis.

Usage

mq.diagnostics(crossobj, I.threshold = 0.1, estmarker = FALSE, I.quant = FALSE, p.val = 0.01, na.cutoff = 0.1)

Arguments

crossobj

An object of class = cross obtained from the qtl.cross function from this package, or the read.cross function from r/qtl package (Broman and Sen, 2009). This file contains phenotypic means, genotypic marker score, and genetic map.

I.threshold

Threshold for proportion of allelic differences below which individuals are marked as too similar, pairs that differ more than (1-threshold) are marked as exceptioally different. Default is set to 10 per cent (I.threshold = 0.1).

estmarker

Logical value indicating whether a new marker map should be estimated and plotted. If estmarker=TRUE, this is passed onto r/qtl (Broman and Sen, 2009) and performs est.map function. This uses the Lander-Green algorithm (i.e., the hidden Markov model technology) to re-estimate the genetic map for an experimental cross. Default is set to FALSE.

I.quant

Threshold indicating the quantile to identify the most similar individuas. Default is set to FALSE.

p.val

Significance level for the chi-square test for segregation distortion. The default is set to p

na.cutoff

Proportion of missing data above which individuals and markers are reported . Default is set to 10 per cent (na.cutoff = 0.1).

Value

The following reports are written to mq_reports:1) mq_summary_markers, reports on missing data and segregation distortion.2) mq_problems_markers, reports on duplicate or outlier genotypes.Additionally, several diagnostic plots are performed:1) mq_markermap_plot, this figure shows the position of all markers across the genome (equivalent R/qtl: plot.map) (Broman and Sen 2009).2) mq_genotype_plot, this figure shows marker alleles for all individuals across thegenome (equivalent to r/qtl: geno.image) (Broman and Sen 2009).3) mq_missinggenotype_plot, this figure highlights missing marker scores for all individuals across the genome (equivalent to r/qtl: plot.missing) (Broman and Sen 2009).4) mq_comparegenotypes_plot, this figure represents the pairwise number of alleles shared by each pair of individuals (equivalent to r/qtl: comparegeno) (Broman and Sen 2009).5) mq_cf_plot, this figure represents the pairwise recombination fraction among each pair of markers (equivalent to r/qtl: plot.rf). (Broman and Sen 2009).6) mq_genotypic_distortion_plot, this figure represents the -log(p-values) of the test for segregation distortion for each marker represented by its chromosome and position.7) mq_identical_genotypes_plot, this figure is the histogram of the proportion of shared alleles among each pair of individuals.8) mq_estmarkermap_plot, this figure is a comparison between the map provided by the user and the map estimated with the est.map function. Will print only if estmarker=TRUE (equivalent to r/qtl: est.map,plot.map) (Broman and Sen 2009)

Details

Performs plots in the work directory.

References

Broman KW, Sen S (2009) A Guide to QTL Mapping with R/qtl. Springer, NewYork Hayes PM, Liu BH, Knapp SJ, Chen F, Jones B, Blake T, Franckowiak JD, Rasmusson DC, Sorrells M, Ullrich SE, Wesenberg DM, Kleinhofs A (1993) Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theor Appl Genet 87:392-401

See Also

qtl.cross

Examples

## Not run: 
# data (SxM_geno)
# data (SxM_map)
# data (SxM_pheno)
# 
# P.data <- SxM_pheno
# G.data <- SxM_geno
# map.data <- SxM_map
# 
# cross.data  <- qtl.cross (P.data, G.data, map.data,
# cross='gwas', heterozygotes=FALSE, env=NUL)
# summary (cross.data)
# 
# jittermap (cross.data)
# 
# Marker Quality
# mq.diagnostics (crossobj=cross.data,I.threshold=0.1,
#              p.val=0.01,na.cutoff=0.1)
# ## End(Not run)