qtl.memq: Performs Multi-Environment (or Multi-Trait) Multi-QTL...
In lmem.qtler: Linear Mixed Effects Models for QTL Mapping for Multienvironment and Multitrait Analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

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).

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

`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.

'SIM' or 'CIM' could be perform.

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.

For single trait and single environment see qtl.analysis

Lucia Gutierrez

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.

qtl.analysis

 ## 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)