Reliable Association INference By Optimizing Weights with R

Author : Kosuke Hamazaki (hamazaki@ut-biomet.org)

Date : 2019/03/25 (Last update: 2022/01/05)


The paper for RAINBOWR has been published in PLOS Computational Biology (https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007663). If you use this RAINBOWR in your paper, please cite RAINBOWR as follows:

Hamazaki, K. and Iwata, H. (2020) RAINBOW: Haplotype-based genome-wide association study using a novel SNP-set method. PLOS Computational Biology, 16(2): e1007663.

The stable version for RAINBOWR package is now available at the CRAN (Comprehensive R Archive Network).

Please check the change in RAINBOWR with the version update from NEWS.md.

The older version of RAINBOWR is RAINBOW, which is available at https://github.com/KosukeHamazaki/RAINBOW.

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/package=rainbow) when we submitted our package to CRAN (https://cran.r-project.org/).

In this repository, the R package RAINBOWR is available. Here, we describe how to install and how to use RAINBOWR.


RAINBOWR(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of GWAS as follows.

RAINBOWR also offers some functions to solve the linear mixed effects model.

By utilizing these functions, you can estimate the genomic heritability and perform genomic prediction (GP).

Finally, RAINBOWR offers other useful functions.


The stable version of RAINBOWR is now available at the CRAN (Comprehensive R Archive Network). The latest version of RAINBOWR is also available at the KosukeHamazaki/RAINBOWR repository in the GitHub, so please run the following code in the R console.

#### Stable version of RAINBOWR ####

#### Latest version of RAINBOWR ####
### If you have not installed yet, ...

### Install RAINBOWR from GitHub

If you get some errors via installation, please check if the following packages are correctly installed. (We removed a dependency on rgl package!)

Rcpp,      # also install `Rtools` for Windows user
plotly,    # Suggests
ggplot2,     # Suggests
ggtree,      # Suggests, install from Bioconducter with `BiocManager::install("ggtree")`
scatterpie,  # Suggests
phylobase,   # Suggests
haplotypes,  # Suggests
here,        # Suggests
adegenet,    # Suggests
furrr,       # Suggests
future,      # Suggests
progressr,   # Suggests
foreach,     # Suggests
doParallel   # Suggests

In RAINBOWR, 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 (hamazaki@ut-biomet.org).


First, import RAINBOWR 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 RAINBOWR

### Load example datasets
Rice_geno_score <- Rice_Zhao_etal$genoScore
Rice_geno_map <- Rice_Zhao_etal$genoMap
Rice_pheno <- Rice_Zhao_etal$pheno

### View each dataset

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$x
map <- MAF.cut.res$map

Next, we estimate additive genomic relationship matrix (GRM) by using calcGRM function.

### Estimate genomic relationship matrix (GRM) 
K.A <- calcGRM(genoMat = x)

Next, we modify these data into the GWAS format of RAINBOWR 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 RAINBOWR

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 markers

You 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.


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 RAINBOWR, please see the help for each function by ?function_name. You can also check how to determine each argument by


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.


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