D.mat: Dominance relationship matrix

In sommer: Solving Mixed Model Equations in R

Dominance relationship matrix

Description

C++ implementation of the dominance matrix. Calculates the realized dominance relationship matrix. Can help to increase the prediction accuracy when 2 conditions are met; 1) The trait has intermediate to high heritability, 2) The population contains a big number of individuals that are half or full sibs (HS & FS).

Usage

D.mat(X,nishio=TRUE,min.MAF=0,return.imputed=FALSE)

Arguments

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.
nishio	If TRUE Nishio ans Satoh. (2014), otherwise Su et al. (2012). See references.
min.MAF	Minimum minor allele frequency. The D matrix is not sensitive to rare alleles, so by default only monomorphic markers are removed.
return.imputed	When TRUE, the imputed marker matrix is returned.

Details

The additive marker coefficients will be used to compute dominance coefficients as: Xd = 1-abs(X) for diploids.

For nishio method: the marker matrix is centered by subtracting column means M= Xd - ms where ms is the column means. Then A=M M'/c, where c = 2 \sum_k {p_k (1-p_k)}.

For su method: the marker matrix is normalized by subtracting row means M= Xd - 2pq where 2pq is the product of allele frequencies times 2. Then A=M M'/c, where c = 2 \sum_k {2pq_k (1-2pq_k)}.

Value

If return.imputed = FALSE, the n \times n additive relationship matrix is returned.

If return.imputed = TRUE, the function returns a list containing

$D

the D matrix

$imputed

the imputed marker matrix

References

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

Nishio M and Satoh M. 2014. Including Dominance Effects in the Genomic BLUP Method for Genomic Evaluation. Plos One 9(1), doi:10.1371/journal.pone.0085792

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

See Also

The core functions of the package mmer

Examples

####=========================================####
#### EXAMPLE 1
####=========================================####
####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)
}

D <- D.mat(X)

sommer documentation built on Nov. 13, 2023, 9:05 a.m.