Description Usage Arguments Details Value Note Author(s) References See Also Examples
Performs a balanced population QTL mapping analysis through marker-regression (Haley and Knott 1992; Martinez and Curnow 1992). This function could use any of the following populations: double haploid, F2, recombinant inbred lines, back-cross, and 4-way crosses. Performs a Single Marker Analysis, a Single Interval Mapping, or a Composite Interval Mapping analysis, and then constructs a final model with of relevant QTL. This function is for single environment single trait QTL mapping.
1 2 | qtl.analysis(crossobj = crossobj, trait = "pred", step, method,
threshold,distance,cofactors, window.size = 50)
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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. |
trait |
Column 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 |
Threshold cut-of for multi-comparison correction. Value could be either a set threshold or "Li&Ji". If a fixed threshold is desired, a numerical value representing the alpha level should be indicated. If the threshold is set to "Li&Ji", the threshold is estimated through a bonferroni correction based on the effective number of markers (Li and Ji, 2005). The effective number of markers is calculated based on a singular value decomposition of the molecular marker matrix and the Tracy-Widom statistic (Li and Ji, 2005). |
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.
A list of two elements: all, a data-frame containing the markers, map positions, and p-values from the marker-trait test for association for all markers in the data-set; and selected, a data-frame containing selected markers (i.e. putative QTL, selected based on their p-value), their map position, and the p-values from the marker-trait test for association. This is also written as a report to qtl_reports. A profile-plot is created showing the -log(p-value) against the map position.
For multi-trait or multi-environment see qtl.memq
Lucia Gutierrez
Broman KW, Sen S (2009) A Guide to QTL Mapping with R/qtl. Springer, New York Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity, 69: 315-324 Li J, Ji L (2005) Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity, 95: 221-227. Martinez O, Curnow RN (1992) Estimating the locations and the sizes of the effects of quantitative trait loci using flanking markers. Theoretical and Applied Genetics 85(4): 480-488
qtl.cross mq.diagnostics and pq.diagnostics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | data (DHpop_pheno)
data (DHpop_geno)
data (DHpop_map)
G.data <- DHpop_geno
map.data <- DHpop_map
P.data <- DHpop_pheno
cross.data <- qtl.cross (P.data, G.data, map.data, cross='dh',
heterozygotes=FALSE)
summary (cross.data)
## Not run:
QTL_SMA
QTL.result <- qtl.analysis (crossobj=cross.data,step=0,
method='SIM', trait="height", threshold="Li&Ji", distance=30, cofactors=NULL,
window.size=30)
## End(Not run)
# QTL_SIM
QTL.result <- qtl.analysis ( crossobj=cross.data, step=5,
method='SIM',trait="height", threshold="Li&Ji",
distance=30,cofactors=NULL,window.size=30)
# QTL CIM
cofactors <- as.vector (QTL.result$selected$marker)
QTL.result <- qtl.analysis ( crossobj=cross.data, step=5,
method='CIM', trait="height", threshold="Li&Ji", distance=30,
cofactors=cofactors, window.size=30)
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