Calculate -log10(p) of each SNP by the Wald test for the model inluding interaction term.
score.calc.int.MC(
M.now,
ZETA.now,
y,
X.now,
package.MM = "gaston",
interaction.with.SNPs.now,
test.method.interaction = "simultaneous",
include.SNP.effect = TRUE,
Hinv,
n.core = 2,
parallel.method = "mclapply",
P3D = TRUE,
eigen.G = NULL,
optimizer = "nlminb",
min.MAF = 0.02,
count = TRUE
)-log10(p) for each marker
A \(n \times m\) genotype matrix where \(n\) is sample size and \(m\) is the number of markers.
A list of variance (relationship) matrix (K; \(m \times m\)) and its design matrix (Z; \(n \times m\)) of random effects. You can use only one kernel matrix. For example, ZETA = list(A = list(Z = Z, K = K)) Please set names of list "Z" and "K"!
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.
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.
A \(m \times q\) matrix. Interaction between each SNP and this matrix will also be tested. For example, principal components of genomic relationship matrix can be used as this matrix to test the interaction between SNPs and the genetic background.
Method for how to test SNPs and the interactions between SNPs and the genetic background. We offer three methods as follows:
"simultaneous": All effects (including SNP efects) are tested simultanously.
"snpSeparate": SNP effects are tested as one effect, and the other interaction effects are simulateneously.
"oneByOne": All efects are tested separately, one by one.
Whether or not including SNP effects into the tested effects.
The inverse of \(H = ZKZ' + \lambda I\) where \(\lambda = \sigma^2_e / \sigma^2_u\).
Setting n.core > 1 will enable parallel execution on a machine with multiple cores. This argument is not valid when `parallel.method = "furrr"`.
Method for parallel computation. We offer three methods, "mclapply", "furrr", and "foreach".
When `parallel.method = "mclapply"`, we utilize pbmclapply function in the `pbmcapply` package
with `count = TRUE` and mclapply function in the `parallel` package with `count = FALSE`.
When `parallel.method = "furrr"`, we utilize future_map function in the `furrr` package.
With `count = TRUE`, we also utilize progressor function in the `progressr` package to show the progress bar,
so please install the `progressr` package from github (https://github.com/HenrikBengtsson/progressr).
For `parallel.method = "furrr"`, you can perform multi-thread parallelization by
sharing memories, which results in saving your memory, but quite slower compared to `parallel.method = "mclapply"`.
When `parallel.method = "foreach"`, we utilize foreach function in the `foreach` package
with the utilization of makeCluster function in `parallel` package,
and registerDoParallel function in `doParallel` package.
With `count = TRUE`, we also utilize setTxtProgressBar and
txtProgressBar functions in the `utils` package to show the progress bar.
We recommend that you use the option `parallel.method = "mclapply"`, but for Windows users, this parallelization method is not supported. So, if you are Windows user, we recommend that you use the option `parallel.method = "foreach"`.
When P3D = TRUE, variance components are estimated by REML only once, without any markers in the model. When P3D = FALSE, variance components are estimated by REML for each marker separately.
A list with
Eigen values
Eigen vectors
The result of the eigen decompsition of \(G = ZKZ'\). You can use "spectralG.cpp" function in RAINBOWR. If this argument is NULL, the eigen decomposition will be performed in this function. We recommend you assign the result of the eigen decomposition beforehand for time saving.
The function used in the optimization process. We offer "optim", "optimx", and "nlminb" functions. This argument is only valid when `package.MM = 'RAINBOWR'`.
Specifies the minimum minor allele frequency (MAF). If a marker has a MAF less than min.MAF, it is assigned a zero score.
When count is TRUE, you can know how far RGWAS has ended with percent display.
Kennedy, B.W., Quinton, M. and van Arendonk, J.A. (1992) Estimation of effects of single genes on quantitative traits. J Anim Sci. 70(7): 2000-2012.
Kang, H.M. et al. (2008) Efficient Control of Population Structure in Model Organism Association Mapping. Genetics. 178(3): 1709-1723.
Kang, H.M. et al. (2010) Variance component model to account for sample structure in genome-wide association studies. Nat Genet. 42(4): 348-354.
Zhang, Z. et al. (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet. 42(4): 355-360.