E.mat {sommer} | R Documentation |
Calculates the realized epistatic relationship matrix of second order (additive x additive, additive x dominance, or dominance x dominance).
E.mat(X,min.MAF=NULL,max.missing=NULL,impute.method="mean",tol=0.02, n.core=1,shrink=FALSE,return.imputed=FALSE, type="A#A", ploidy=2)
X |
Matrix (n \times m) of unphased genotypes for n lines and m biallelic markers, coded as {-1,0,1}. Fractional (imputed) and missing values (NA) are allowed. |
min.MAF |
Minimum minor allele frequency. The A matrix is not sensitive to rare alleles, so by default only monomorphic markers are removed. |
max.missing |
Maximum proportion of missing data; default removes completely missing markers. |
impute.method |
There are two options. The default is "mean", which imputes with the mean for each marker. The "EM" option imputes with an EM algorithm (see details). |
tol |
Specifies the convergence criterion for the EM algorithm (see details). |
n.core |
Specifies the number of cores to use for parallel execution of the EM algorithm (use only at UNIX command line). |
shrink |
Set shrink=TRUE to use the shrinkage estimation procedure (see Details). |
return.imputed |
When TRUE, the imputed marker matrix is returned. |
type |
An argument specifying the type of epistatic relationship matrix desired. The default is the second order epistasis (additive x additive) type="A#A". Other options are additive x dominant (type="A#D"), or dominant by dominant (type="D#D"). |
ploidy |
The ploidy of the organism. The default is 2 which means diploid but higher ploidy levels are supported. |
it is computed as the Hadamard product of the epistatic relationship matrix (A); E=A#A.
If return.imputed = FALSE, the n \times n epistatic relationship matrix is returned.
If return.imputed = TRUE, the function returns a list containing
the E matrix
the imputed marker matrix
Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744
Su G, Christensen OF, Ostersen T, Henryon M, Lund MS. 2012. Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers. PLoS ONE 7(9): e45293. doi:10.1371/journal.pone.0045293
Endelman, J.B., and J.-L. Jannink. 2012. Shrinkage estimation of the realized relationship matrix. G3:Genes, Genomes, Genetics. 2:1405-1413. doi: 10.1534/g3.112.004259
Poland, J., J. Endelman et al. 2012. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103-113. doi: 10.3835/plantgenome2012.06.0006
The core functions of the package mmer
####=========================================#### ####random population of 200 lines with 1000 markers ####=========================================#### X <- matrix(rep(0,200*1000),200,1000) for (i in 1:200) { X[i,] <- sample(c(-1,0,0,1), size=1000, replace=TRUE) } E <- E.mat(X, type="A#A") # if heterozygote markers are present can be used "A#D" or "D#D"