# Genome-Wide Association Study with SNP-Set Methods

## Documentation for package ‘RAINBOWR’ version 0.1.14

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

RAINBOWR-package | RAINBOWR: Perform Genome-Wide Asscoiation Study (GWAS) By Kernel-Based Methods
CalcThreshold | Function to calculate threshold for GWAS
cumsumPos | Function to calculate cumulative position (beyond chromosome)
design.Z | Function to generate design matrix (Z)
EM3.cpp | Equation of mixed model for multi-kernel (slow, general version)
EM3.linker.cpp | Equation of mixed model for multi-kernel (fast, for limited cases)
EMM.cpp | Equation of mixed model for one kernel, a wrapper of two methods
EMM1.cpp | Equation of mixed model for one kernel, GEMMA-based method (inplemented by Rcpp)
EMM2.cpp | Equation of mixed model for one kernel, EMMA-based method (inplemented by Rcpp)
genesetmap | Function to generate map for gene set
genetrait | Generate pseudo phenotypic values
MAF.cut | Function to remove the minor alleles
make.full | Change a matrix to full-rank matrix
manhattan | Draw manhattan plot
manhattan.plus | Add points of -log10(p) corrected by kernel methods to manhattan plot
manhattan2 | Draw manhattan plot (another method)
manhattan3 | Draw the effects of epistasis (3d plot and 2d plot)
modify.data | Function to modify genotype and phenotype data to match
qq | Draw qq plot
RAINBOWR | RAINBOWR: Perform Genome-Wide Asscoiation Study (GWAS) By Kernel-Based Methods
RGWAS.epistasis | Check epistatic effects by kernel-based GWAS (genome-wide association studies)
RGWAS.menu | Print the R code which you should perform for RAINBOWR GWAS
RGWAS.multisnp | Testing multiple SNPs simultaneously for GWAS
RGWAS.normal | Perform normal GWAS (test each single SNP)
RGWAS.twostep | Perform normal GWAS (genome-wide association studies) first, then perform SNP-set GWAS for relatively significant markers
RGWAS.twostep.epi | Perform normal GWAS (genome-wide association studies) first, then check epistatic effects for relatively significant markers
Rice_geno_map | Physical map of rice genome
Rice_geno_score | Marker genotype of rice genome
Rice_pheno | Phenotype data of rice field trial
Rice_Zhao_etal | Rice_Zhao_etal:
score.calc | Calculate -log10(p) for single-SNP GWAS
score.calc.epistasis.LR | Calculate -log10(p) of epistatic effects by LR test
score.calc.epistasis.score | Calculate -log10(p) of epistatic effects with score test
score.calc.LR | Calculate -log10(p) of each SNP-set by the LR test
score.calc.LR.MC | Calculate -log10(p) of each SNP-set by the LR test (multi-cores)
score.calc.MC | Calculate -log10(p) for single-SNP GWAS (multi-cores)
score.calc.score | Calculate -log10(p) of each SNP-set by the score test
score.calc.score.MC | Calculate -log10(p) of each SNP-set by the score test (multi-cores)
score.cpp | Calculte -log10(p) by score test (slow, for general cases)
score.linker.cpp | Calculte -log10(p) by score test (fast, for limited cases)
See | Function to view the first part of data (like head(), tail())
spectralG.cpp | Perform spectral decomposition (inplemented by Rcpp)
SS_gwas | Calculate some summary statistics of GWAS (genome-wide association studies) for simulation study
welcome_to_RGWAS | Function to greet to users