- pheno
Data frame where the first column is the line name (gid). The remaining columns should be a phenotype to test.
- geno
Data frame with the marker names in the first column. The second and third columns contain the chromosome and map position.
Columns 4 and higher contain the marker scores for each line, coded as [-1, 0, 1] = [aa, Aa, AA].
- ZETA
A list of covariance (relationship) matrix (K: \(m \times m\)) and its design matrix (Z: \(n \times m\)) of random effects.
Please set names of list "Z" and "K"! You can use more than one kernel matrix.
For example,
ZETA = list(A = list(Z = Z.A, K = K.A), D = list(Z = Z.D, K = K.D))
- Z.A, Z.D
Design matrix (\(n \times m\)) for the random effects. So, in many cases, you can use the identity matrix.
- K.A, K.D
Different kernels which express some relationships between lines.
For example, K.A is additive relationship matrix for the covariance between lines, and K.D is dominance relationship matrix.
package.MM
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.
covariate
A \(n \times 1\) vector or a \(n \times p _ 1\) matrix. You can insert continuous values, such as other traits or genotype score for special markers.
This argument is regarded as one of the fixed effects.
covariate.factor
A \(n \times p _ 2\) dataframe. You should assign a factor vector for each column.
Then RGWAS changes this argument into model matrix, and this model matrix will be included in the model as fixed effects.
structure.matrix
You can use structure matrix calculated by structure analysis when there are population structure.
You should not use this argument with n.PC > 0.
n.PC
Number of principal components to include as fixed effects. Default is 0 (equals K model).
min.MAF
Specifies the minimum minor allele frequency (MAF).
If a marker has a MAF less than min.MAF, it is assigned a zero score.
test.method
RGWAS supports two methods to test effects of each SNP-set.
- "LR"
Likelihood-ratio test, relatively slow, but accurate (default).
"score"
Score test, much faster than LR, but sometimes overestimate -log10(p).
n.core
Setting n.core > 1 will enable parallel execution on a machine with multiple cores.
This argument is not valid when `parallel.method = "furrr"`.
parallel.method
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"`.
kernel.method
It determines how to calculate kernel. There are three methods.
- "gaussian"
It is the default method. Gaussian kernel is calculated by distance matrix.
"exponential"
When this method is selected, exponential kernel is calculated by distance matrix.
"linear"
When this method is selected, linear kernel is calculated by NOIA methods for additive GRM.
So local genomic relation matrix is regarded as kernel.
kernel.h
The hyper parameter for gaussian or exponential kernel.
If kernel.h = "tuned", this hyper parameter is calculated as the median of off-diagonals of distance matrix of genotype data.
haplotype
If the number of lines of your data is large (maybe > 100), you should set haplotype = TRUE.
When haplotype = TRUE, haplotype-based kernel will be used for calculating -log10(p).
(So the dimension of this gram matrix will be smaller.)
The result won't be changed, but the time for the calculation will be shorter.
num.hap
When haplotype = TRUE, you can set the number of haplotypes which you expect.
Then similar arrays are considered as the same haplotype, and then make kernel(K.SNP) whose dimension is num.hap x num.hap.
When num.hap = NULL (default), num.hap will be set as the maximum number which reflects the difference between lines.
test.effect
Effect of each marker to test. You can choose "test.effect" from "additive", "dominance" and "additive+dominance".
You also can choose more than one effect, for example, test.effect = c("additive", "aditive+dominance")
window.size.half
This argument decides how many SNPs (around the SNP you want to test) are used to calculated K.SNP.
More precisely, the number of SNPs will be 2 * window.size.half + 1.
window.slide
This argument determines how often you test markers. If window.slide = 1, every marker will be tested.
If you want to perform SNP set by bins, please set window.slide = 2 * window.size.half + 1.
chi0.mixture
RAINBOWR assumes the deviance is considered to follow a x chisq(df = 0) + (1 - a) x chisq(df = r).
where r is the degree of freedom.
The argument chi0.mixture is a (0 <= a < 1), and default is 0.5.
gene.set
If you have information of gene (or haplotype block), you can use it to perform kernel-based GWAS.
You should assign your gene information to gene.set in the form of a "data.frame" (whose dimension is (the number of gene) x 2).
In the first column, you should assign the gene name. And in the second column, you should assign the names of each marker,
which correspond to the marker names of "geno" argument.
map.gene.set
Genotype map for `gene.set` (list of haplotype blocks).
This is a data.frame with the haplotype block (SNP-set, or gene-set) names in the first column.
The second and third columns contain the chromosome and map position for each block.
The forth column contains the cumulative map position for each block, which can be computed by cumsumPos function.
If this argument is NULL, the map will be constructed by genesetmap function after the SNP-set GWAS.
It will take some time, so you can reduce the computational time by assigning this argument beforehand.
weighting.center
In kernel-based GWAS, weights according to the Gaussian distribution (centered on the tested SNP) are taken into account when calculating the kernel if Rainbow = TRUE.
If weighting.center = FALSE, weights are not taken into account.
weighting.other
You can set other weights in addition to weighting.center. The length of this argument should be equal to the number of SNPs.
For example, you can assign SNP effects from the information of gene annotation.
sig.level
Significance level for the threshold. The default is 0.05.
method.thres
Method for detemining threshold of significance. "BH" and "Bonferroni are offered.
plot.qq
If TRUE, draw qq plot.
plot.Manhattan
If TRUE, draw manhattan plot.
plot.method
If this argument = 1, the default manhattan plot will be drawn.
If this argument = 2, the manhattan plot with axis based on Position (bp) will be drawn.
Also, this plot's color is changed by all chromosomes.
plot.col1
This argument determines the color of the manhattan plot.
You should substitute this argument as color vector whose length is 2.
plot.col1[1] for odd chromosomes and plot.col1[2] for even chromosomes
plot.col2
Color of the manhattan plot. color changes with chromosome and it starts from plot.col2 + 1
(so plot.col2 = 1 means color starts from red.)
plot.type
This argument determines the type of the manhattan plot. See the help page of "plot".
plot.pch
This argument determines the shape of the dot of the manhattan plot. See the help page of "plot".
saveName
When drawing any plot, you can save plots in png format. In saveName, you should substitute the name you want to save.
When saveName = NULL, the plot is not saved.
main.qq
The title of qq plot. If this argument is NULL, trait name is set as the title.
main.man
The title of manhattan plot. If this argument is NULL, trait name is set as the title.
plot.add.last
If saveName is not NULL and this argument is TRUE, then you can add lines or dots to manhattan plots.
However, you should also write "dev.off()" after adding something.
return.EMM.res
When return.EMM.res = TRUE, the results of equation of mixed models are included in the result of RGWAS.
optimizer
The function used in the optimization process. We offer "optim", "optimx", and "nlminb" functions.
thres
If thres = TRUE, the threshold of the manhattan plot is included in the result of RGWAS.
When return.EMM.res or thres is TRUE, the results will be "list" class.
skip.check
As default, RAINBOWR checks the type of input data and modifies it into the correct format.
However, it will take some time, so if you prepare the correct format of input data, you can skip this procedure
by setting `skip.check = TRUE`.
verbose
If this argument is TRUE, messages for the current steps will be shown.
verbose2
If this argument is TRUE, welcome message will be shown.
count
When count is TRUE, you can know how far RGWAS has ended with percent display.
time
When time is TRUE, you can know how much time it took to perform RGWAS.