`RAINBOWR`

is `RAINBOW`

, which is available at https://github.com/KosukeHamazaki/RAINBOW.`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`

`RAINBOWR`

(Reliable Association INference By Optimizing Weights with R) is a package to perform several types of `GWAS`

as follows.

- Single-SNP GWAS with
`RGWAS.normal`

function - SNP-set (or gene set) GWAS with
`RGWAS.multisnp`

function (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)

`RAINBOWR`

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

- Solve one-kernel linear mixed effects model with
`EMM.cpp`

function - Solve multi-kernel linear mixed effects model with
`EM3.cpp`

function (for the general kernel, not so fast) - Solve multi-kernel linear mixed effects model with
`EM3.linker.cpp`

function (for the linear kernel, fast)

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

).

Finally, `RAINBOWR`

offers other useful functions.

`qq`

and`manhattan`

function to draw Q-Q plot and Manhattan plot`modify.data`

function to match phenotype and marker genotype data`CalcThreshold`

function to calculate thresholds for GWAS results`See`

function to see a brief view of data (like`head`

function, but more useful)`genetrait`

function to generate pseudo phenotypic values from marker genotype`SS_GWAS`

function to summarize GWAS results (only for simulation study)

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 ####
install.packages("RAINBOWR")
#### Latest version of RAINBOWR ####
### If you have not installed yet, ...
install.packages("devtools")
### Install RAINBOWR from GitHub
devtools::install_github("KosukeHamazaki/RAINBOWR")
```

If you get some errors via installation, please check if the following packages are correctly installed.

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
require(RAINBOWR)
### 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$x
map <- MAF.cut.res$map
```

Next, we estimate additive genomic relationship matrix (GRM) by using `rrBLUP`

package.

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
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 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.

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 `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.

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.