Author : Kosuke Hamazaki ([email protected])
The newest version of RAINBOW is RAINBOWR, which is available at https://github.com/KosukeHamazaki/RAINBOWR.
We changed the package name from RAINBOW to RAINBOWR because the original package name RAINBOW conflicted with the package rainbow (https://cran.r-project.org/web/packages/rainbow/index.html) when we submitted our package to CRAN (https://cran.r-project.org/).
We will continue to maintain this repository, but we strongly recommend the installation of RAINBOWR from https://github.com/KosukeHamazaki/RAINBOWR.
In this repository, the R package RAINBOW is available.
Here, we describe how to install and how to use RAINBOW.
RAINBOW(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of GWAS as follows.
- Single-SNP GWAS with
RGWAS.normalfunction - SNP-set (or gene set) GWAS with
RGWAS.multisnpfunction (which tests multiple SNPs at the same time) - Check epistatic (SNP-set x SNP-set interaction) effects with
RGWAS.epistasis(very slow and less reliable)
RAINBOW also offers some functions to solve the linear mixed effects model.
- Solve one-kernel linear mixed effects model with
EMM.cppfunction - Solve multi-kernel linear mixed effects model with
EM3.cppfunction (for the general kernel, not so fast) - Solve multi-kernel linear mixed effects model with
EM3.linker.cppfunction (for the linear kernel, fast)
By utilizing these functions, you can estimate the genomic heritability and perform genomic prediction (GP).
Finally, RAINBOW offers other useful functions.
qqandmanhattanfunction to draw Q-Q plot and Manhattan plotmodify.datafunction to match phenotype and marker genotype dataCalcThresholdfunction to calculate thresholds for GWAS resultsSeefunction to see a brief view of data (likeheadfunction, but more useful)genetraitfunction to generate pseudo phenotypic values from marker genotypeSS_GWASfunction to summarize GWAS results (only for simulation study)
The stable version of RAINBOW is now available at the CRAN (Comprehensive R Archive Network). The latest version of RAINBOW is also available at the KosukeHamazaki/RAINBOW repository in the GitHub, so please run the following code in the R console.
#### Stable version of RAINBOW ####
install.packages("RAINBOW")
#### Latest version of RAINBOW ####
### If you have not installed yet, ...
install.packages("devtools")
### Install RAINBOW from GitHub
devtools::install_github("KosukeHamazaki/RAINBOW")If you get some errors via installation, please check if the following packages are correctly installed.
Rcpp,
rgl,
tcltk,
Matrix,
cluster,
MASS,
pbmcapply,
optimx,
methods,
ape,
stringr,
pegas,
ggplot2,
ggtree,
scatterpie,
phylobase,
haplotypes,
ggimageIn RAINBOW, since part of the code is written in Rcpp (C++ in R), please check if you can use C++ in R.
For Windows users, you should install Rtools.
If you have some questions about installation, please contact us by e-mail ([email protected]).
First, import RAINBOW package and load example datasets. These example datasets consist of marker genotype (scored with {-1, 0, 1}, 1,536 SNP chip (Zhao et al., 2010; PLoS One 5(5): e10780)), map with physical position, and phenotypic data (Zhao et al., 2011; Nature Communications 2:467). Both datasets can be downloaded from Rice Diversity homepage (http://www.ricediversity.org/data/).
### Import RAINBOW
require(RAINBOW)
### Load example datasets
data("Rice_Zhao_etal")
Rice_geno_score <- Rice_Zhao_etal$genoScore
Rice_geno_map <- Rice_Zhao_etal$genoMap
Rice_pheno <- Rice_Zhao_etal$pheno
### View each dataset
See(Rice_geno_score)
See(Rice_geno_map)
See(Rice_pheno)You can check the original data format by See function.
Then, select one trait (here, Flowering.time.at.Arkansas) for example.
### Select one trait for example
trait.name <- "Flowering.time.at.Arkansas"
y <- Rice_pheno[, trait.name, drop = FALSE]For GWAS, first you can remove SNPs whose MAF <= 0.05 by MAF.cut function.
### Remove SNPs whose MAF <= 0.05
x.0 <- t(Rice_geno_score)
MAF.cut.res <- MAF.cut(x.0 = x.0, map.0 = Rice_geno_map)
x <- MAF.cut.res$x
map <- MAF.cut.res$mapNext, we estimate additive genomic relationship matrix (GRM) by using rrBLUP package.
### Estimate genomic relationship matrix (GRM)
K.A <- calcGRM(genoMat = x)Next, we modify these data into the GWAS format of RAINBOW by modify.data function.
### Modify data
modify.data.res <- modify.data(pheno.mat = y, geno.mat = x, map = map,
return.ZETA = TRUE, return.GWAS.format = TRUE)
pheno.GWAS <- modify.data.res$pheno.GWAS
geno.GWAS <- modify.data.res$geno.GWAS
ZETA <- modify.data.res$ZETA
### View each data for RAINBOW
See(pheno.GWAS)
See(geno.GWAS)
str(ZETA)ZETA is a list of genomic relationship matrix (GRM) and its design matrix.
Finally, we can perform GWAS using these data.
First, we perform single-SNP GWAS by RGWAS.normal function as follows.
### Perform single-SNP GWAS
normal.res <- RGWAS.normal(pheno = pheno.GWAS, geno = geno.GWAS,
ZETA = ZETA, n.PC = 4, P3D = TRUE)
See(normal.res$D) ### Column 4 contains -log10(p) values for markers
### Automatically draw Q-Q plot and Manhattan by default.Next, we perform SNP-set GWAS by RGWAS.multisnp function.
### Perform SNP-set GWAS (by regarding 11 SNPs as one SNP-set)
SNP_set.res <- RGWAS.multisnp(pheno = pheno.GWAS, geno = geno.GWAS, ZETA = ZETA,
n.PC = 4, test.method = "LR", kernel.method = "linear", gene.set = NULL,
test.effect = "additive", window.size.half = 5, window.slide = 11)
See(SNP_set.res$D) ### Column 4 contains -log10(p) values for markersYou can perform SNP-set GWAS with sliding window by setting window.slide = 1.
And you can also perform gene-set (or haplotype-based) GWAS by assigning the following data set to gene.set argument.
ex.)
| gene (or haplotype block) | marker |
|---|---|
| gene_1 | id1000556 |
| gene_1 | id1000673 |
| gene_2 | id1000830 |
| gene_2 | id1000955 |
| gene_2 | id1001516 |
| ... | ... |
If you have some help before performing GWAS with RAINBOW, please see the help for each function by ?function_name.
You can also check how to determine each argument by
RGWAS.menu()RGWAS.menu function asks some questions, and by answering these question, the function tells you how to determine which function use and how to set arguments.
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.
Storey, J.D. and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci. 100(16): 9440-9445.
Yu, J. et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet. 38(2): 203-208.
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.
Endelman, J.B. (2011) Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP. Plant Genome J. 4(3): 250.
Endelman, J.B. and Jannink, J.L. (2012) Shrinkage Estimation of the Realized Relationship Matrix. G3 Genes, Genomes, Genet. 2(11): 1405-1413.
Su, G. et al. (2012) Estimating Additive and Non-Additive Genetic Variances and Predicting Genetic Merits Using Genome-Wide Dense Single Nucleotide Polymorphism Markers. PLoS One. 7(9): 1-7.
Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet. 44(7): 821-824.
Listgarten, J. et al. (2013) A powerful and efficient set test for genetic markers that handles confounders. Bioinformatics. 29(12): 1526-1533.
Lippert, C. et al. (2014) Greater power and computational efficiency for kernel-based association testing of sets of genetic variants. Bioinformatics. 30(22): 3206-3214.
Jiang, Y. and Reif, J.C. (2015) Modeling epistasis in genomic selection. Genetics. 201(2): 759-768.