Function to adjust genomic relationship matrix (GRM) with subpopulations
adjustGRM(
y,
X = NULL,
ZETA,
subpopInfo = NULL,
nSubpop = 5,
nPcsFindCluster = 10,
include.epistasis = FALSE,
package.MM = "gaston"
)Adjusted ZETA including only one kernel.
A vector of `subpopInfo` used in this function.
A matrix of covariates used in the mixed effects model.
#'
Results of mixed-effects model for multiple kernels.
`nSubpop` used in this function.
`include.epistasis` used in this function.
A \(n \times 1\) vector. A vector of phenotypic values should be used. NA is allowed.
A \(n \times p\) matrix. You should assign mean vector (rep(1, n)) and covariates. NA is not allowed.
A list of variance matrices and its design matrices of random effects. You can use only one kernel matrix for this function. For example, ZETA = list(A = list(Z = Z.A, K = K.A)) (A for additive) Please set names of lists "Z" and "K"!
The information on group memberships (e.g., subgroups for the population) will be required. You can set a vector of group names (or clustering ids) for each genotype as this argument. This vector should be factor.
When `subpopInfo = NULL`, `subpopInfo` will be automatically determined by using find.clusters function.
You should specify the number of groups by this argument to decide `subpopInfo`.
Number of principal components to be used for `adegenet::find.clusters`. This argument is used inly when `subpopInfo` is `NULL`.
Whether or not including the genome-wide epistastic effects into the model to adjust ZETA.
The package name to be used when solving mixed-effects model. We only offer the following three packages:
"RAINBOWR", "MM4LMM" and "gaston". Default package is `gaston`.
See more details at EM3.general.
Rio S, Mary-Huard T, Moreau L, Bauland C, Palaffre C, et al. (2020) Disentangling group specific QTL allele effects from genetic background epistasis using admixed individuals in GWAS: An application to maize flowering. PLOS Genetics 16(3): e1008241.