| LOD.QTLdetect {QTLEMM} | R Documentation |
QTL Detect by LOD
Description
Detect QTL by the likelihood of odds (LOD) matrix.
Usage
LOD.QTLdetect(LOD, bin, thre = 3, QTLdist = 20, console = TRUE)
Arguments
LOD |
matrix. The LOD matrix, which is a t*p matrix, where t is the number of traits and p is the number of bins on the chromosomes. Missing values should be denoted as NA in the matrix. |
bin |
matrix. An n*2 matrix that represents the number of bins on each chromosome, where n is the number of chromosomes. The first column denotes the chromosome number, and the second column denotes how many bins are on that chromosome. It's important to ensure that chromosomes are divided in order. |
thre |
numeric. The LOD threshold. Any LOD score under this threshold will be calculated as 0. |
QTLdist |
numeric. The minimum distance (in bins) among different linked significant QTL. |
console |
logical. Determines whether the process of the algorithm will be displayed in the R console or not. |
Value
detect.QTL.number |
The number of detected QTL in each trait. |
QTL.matrix |
The QTL position matrix. Where the elements 1 donates the position of QTL; elements 0 donate the bins whose LOD score is under the LOD threshold; other positions are shown as NA. |
EQF.matrix |
The matrix denotes the EQF value of each bin. |
linkage.QTL.number |
The linkage QTL number of all detected QTL. In other words, it is the table that denote how many QTL are on one chromosome. |
LOD.threshold |
The LOD threshold used in this analysis. |
bin |
The bin information matrix used in this analysis. |
References
Wu, P.-Y., M.-.H. Yang, and C.-H. KAO 2021 A Statistical Framework for QTL Hotspot Detection. G3: Genes, Genomes, Genetics: jkab056. <doi: 10.1093/g3journal/jkab056>
See Also
Examples
# load the example data
load(system.file("extdata", "LODexample.RDATA", package = "QTLEMM"))
dim(LODexample) # 100 traits, 633 bins on chromosome
# run and result
result <- LOD.QTLdetect(LODexample, bin, thre = 3, QTLdist = 10)
result$detect.QTL.number