lmem.qtler (version 0.1.1)

qtl.memq: Performs Multi-Environment (or Multi-Trait) Multi-QTL analysis for balanced populations.

Description

Mixed models have been used in balanced populations to detect QTL-by-environment (QEI) effects while modeling the variance-covariance matrix. This function performs a multi-environment (or multi-trait) multi-QTL biparental analysis modeling the correlations across environments (traits).

Usage

qtl.memq (crossobj = crossobj, P.data = NULL, env.label = NULL, trait, step, method, threshold, distance, cofactors, window.size = 50)

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 data.

P.data

The name of the file containing the phenotypic information in a long format.

env.label

vector with the names of the environment (or traits) to select for the QTL analysis.

trait

name for the phenotypic trait to be analyzed.

step

Maximum distance (in cM) between positions at which the genotype probabilities are calculated, though for step = 0, probabilities are calculated only at the marker locations.

method

'SIM' or 'CIM' for simple interval (SIM) or composite interval mapping (CIM).

threshold

options are: Li&Ji (Li and Ji, 2005), FDR (Benjamini and Hochberg, 1995), and set alpha levels (p.values).

distance

To avoid co-linearity, nearby markers are not allowed in the same model. This is the minimum distance within which two markers are allowed to stay in the model.

cofactors

Vector of genetic predictors to be used as cofactors

window.size

To avoid co-linearity, marker cofactors close to the markers being tested are not allowed in the model. This is the minimum distance to allow a co-factor when testing for a specific marker. Given the resolution of common QTL studies, it is recommended to use a large window.size (i.e. 50 cM). The default is set to 50 cM.

Value

The function returns a data.frame with the final QTL indicating the locus names, chromosome, position, p.values tested and QTL effects that are printed to qtl_memq_reports.

Details

'SIM' or 'CIM' could be perform.

References

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. Malosetti, M., C.G. van der Linden, B. Vosman, and F. a van Eeuwijk. 2007a. A mixed-model approach to association mapping using pedigree information with an illustration of resistance to Phytophthora infestans in potato. Genetics 175(2): 879-89. Malosetti, M., J.M. Ribaut, M. Vargas, J. Crossa, and F. a. Eeuwijk. 2007b. A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.). Euphytica 161(1-2): 241-257.

See Also

qtl.analysis

Examples

 ## Not run: 
# data (SxM_geno)
# data (SxM_map)
# data (SxMxE_pheno)
# 
# P.data <- SxMxE_pheno
# G.data <- SxM_geno
# map.data <- SxM_map
# 
# cross.data <- qtl.cross (P.data, G.data, map.data, cross='dh',
#                          heterozygotes=FALSE)
# 
# summary (cross.data)
# 
# ## Pheno Quality
# pq.diagnostics (crossobj=cross.data, boxplot =FALSE)
# 
# ## Marker Quality
# mq.diagnostics (crossobj=cross.data,I.threshold=0.1,
#              p.val=0.01,na.cutoff=0.1)
# 
# # QTL_SIM
# QTL.result <- qtl.memq (crossobj = cross.data, P.data = P.data,
#                         env.label = c('ID91','ID92','MAN92','MTd91',
#                         'MTd92','MTi91','MTi92','SKs92','WA91','WA92'),
#                         trait = 'yield', step = 10, method = 'SIM',
#                         threshold = 'Li&Ji', distance = 50, cofactors = NULL,
#                         window.size = 50)
# 
# ## QTL_CIM
# QTL.result <- qtl.memq (crossobj = cross.data, P.data = P.data,
#                        env.label = c('ID91','ID92','MAN92','MTd91','MTd92',
#                        'MTi91','MTi92','SKs92','WA91','WA92'),
#                        trait = 'yield', step = 10, method = 'CIM',
#                        threshold = 'Li&Ji', distance = 50,
#                        cofactors = QTL.result$selected$marker, window.size = 50)
# ## End(Not run)