In KosukeHamazaki/RAINBOW: Genome-Wide Association Study with SNP-Set Methods

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

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What is 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.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)

RAINBOW 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, RAINBOW 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)

Installation

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
• RcppEigen
• rrBLUP
• rgl
• tcltk
• Matrix
• cluster
• MASS

In 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.

In the near future, we will try to publish RAINBOW on CRAN.

If you have some questions about installation, please contact us by e-mail ([e-mail]).

Usage

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/).

``` {r, include=TRUE}

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.

``` {r, include=TRUE}
### 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.

``` {r, include=TRUE}

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.

``` {r, include=TRUE}
### 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.

``` {r, include=TRUE}

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.

``` {r, include=TRUE}
### Perform single-SNP GWAS
normal.res <- RGWAS.normal(pheno = pheno.GWAS, geno = geno.GWAS,
                           plot.qq = FALSE, plot.Manhattan = FALSE,
                           ZETA = ZETA, n.PC = 4, P3D = TRUE, count = FALSE)
See(normal.res$D)  ### Column 4 contains -log10(p) values for markers

``` {r, echo=FALSE} qq(normal.res$D[, 4]) manhattan(normal.res$D)

Automatically draw Q-Q plot and Manhattan if you set plot.qq = TRUE and plot.Manhattan = TRUE.

Next, we perform SNP-set GWAS by `RGWAS.multisnp` function.

``` {r, include=TRUE, message=FALSE}
### Perform SNP-set GWAS (by regarding 11 SNPs as one SNP-set, first 300 SNPs)
SNP_set.res <- RGWAS.multisnp(pheno = pheno.GWAS, geno = geno.GWAS[1:300, ], ZETA = ZETA,
                              plot.qq = FALSE, plot.Manhattan = FALSE, count = FALSE,
                              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

``` {r, echo=FALSE} qq(SNP_set.res$D[, 4]) manhattan(SNP_set.res$D)

Automatically draw Q-Q plot and Manhattan if you set plot.qq = TRUE and plot.Manhattan = TRUE.

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 | 
| ...    | ... | 


### Help
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

``` {r, include=TRUE, eval=FALSE}
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.

References

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.

KosukeHamazaki/RAINBOW documentation built on Dec. 12, 2020, 8:35 p.m.