Genome-Wide Association Study with SNP-Set Methods
Function to adjust genomic relationship matrix (GRM) with subpopulatio...
Function to calculate genomic relationship matrix (GRM)
Function to calculate threshold for GWAS
Function to convert haplotype block list from PLINK to RAINBOWR format
Function to calculate cumulative position (beyond chromosome)
Function to generate design matrix (Z)
Equation of mixed model for multi-kernel considering covariance struct...
Equation of mixed model for multi-kernel (slow, general version)
Equation of mixed model for multi-kernel including using other package...
Equation of mixed model for multi-kernel (fast, for limited cases)
Equation of mixed model for multi-kernel using other packages (much fa...
Equation of mixed model for one kernel, a wrapper of two methods
Equation of mixed model for one kernel, GEMMA-based method (inplemente...
Equation of mixed model for one kernel, EMMA-based method (inplemented...
Function to estimate & plot haplotype network
Function to estimate & plot phylogenetic tree
Function to generate map for gene set
Generate pseudo phenotypic values
Function to judge the square matrix whether it is diagonal matrix or n...
Function to remove the minor alleles
Change a matrix to full-rank matrix
Add points of -log10(p) corrected by kernel methods to manhattan plot
Draw manhattan plot
Draw manhattan plot (another method)
Draw the effects of epistasis (3d plot and 2d plot)
Function to modify genotype and phenotype data to match
Function to parallelize computation with various methods
Function to plot haplotype network from the estimated results
Function to plot phylogenetic tree from the estimated results
Draw qq plot
RAINBOWR: Perform Genome-Wide Asscoiation Study (GWAS) By Kernel-Based...
Check epistatic effects by kernel-based GWAS (genome-wide association ...
Print the R code which you should perform for RAINBOWR GWAS
Testing multiple SNPs and their interaction with some kernel simultane...
Testing multiple SNPs simultaneously for GWAS
Perform normal GWAS including interaction (test each single SNP)
Perform normal GWAS (test each single SNP)
Perform normal GWAS (genome-wide association studies) first, then chec...
Perform normal GWAS (genome-wide association studies) first, then perf...
Physical map of rice genome
Marker genotype of rice genome
Physical map of rice genome
Phenotype data of rice field trial
Calculate -log10(p) of epistatic effects by LR test (multi-cores)
Calculate -log10(p) of epistatic effects by LR test
Calculate -log10(p) of epistatic effects with score test (multi-cores)
Calculate -log10(p) of epistatic effects with score test
Calculate -log10(p) for single-SNP GWAS with interaction (multi-cores)
Calculate -log10(p) for single-SNP GWAS with interaction
Calculate -log10(p) of each SNP-set and its interaction with kernels b...
Calculate -log10(p) of each SNP-set and its interaction with kernels b...
Calculate -log10(p) of each SNP-set by the LR test (multi-cores)
Calculate -log10(p) of each SNP-set by the LR test
Calculate -log10(p) for single-SNP GWAS (multi-cores)
Calculate -log10(p) for single-SNP GWAS
Calculate -log10(p) of each SNP-set by the score test (multi-cores)
Calculate -log10(p) of each SNP-set by the score test
Calculte -log10(p) by score test (slow, for general cases)
Calculte -log10(p) by score test (fast, for limited cases)
Function to view the first part of data (like head(), tail())
Perform spectral decomposition (inplemented by Rcpp)
Calculate some summary statistics of GWAS (genome-wide association stu...
Function to greet to users
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.