Package: RAINBOWR 0.1.42

RAINBOWR: Genome-Wide Association Study with SNP-Set Methods

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>.

Authors:Kosuke Hamazaki [aut, cre], Hiroyoshi Iwata [aut, ctb]

RAINBOWR_0.1.42.tar.gz
RAINBOWR_0.1.42.zip(r-4.7-x86_64)RAINBOWR_0.1.42.zip(r-4.7-arm64)RAINBOWR_0.1.42.zip(r-4.6-x86_64)RAINBOWR_0.1.42.zip(r-4.6-arm64)RAINBOWR_0.1.42.zip(r-4.5-x86_64)
RAINBOWR_0.1.42.tgz(r-4.6-x86_64)RAINBOWR_0.1.42.tgz(r-4.6-arm64)RAINBOWR_0.1.42.tgz(r-4.5-x86_64)RAINBOWR_0.1.42.tgz(r-4.5-arm64)
RAINBOWR_0.1.42.tar.gz(r-4.7-arm64)RAINBOWR_0.1.42.tar.gz(r-4.7-x86_64)RAINBOWR_0.1.42.tar.gz(r-4.6-arm64)RAINBOWR_0.1.42.tar.gz(r-4.6-x86_64)
RAINBOWR_0.1.42.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RAINBOWR/json (API)

# Install 'RAINBOWR' in R:

install.packages('RAINBOWR', repos = c('https://kosukehamazaki.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kosukehamazaki/rainbowr/issues

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

Rice_Zhao_etal - Rice_Zhao_etal:

On CRAN:

cpp

6.70 score 24 stars 30 scripts 526 downloads 2 mentions 58 exports 68 dependencies

Last updated from:3fed0ec272. Checks:13 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	198
linux-devel-x86_64	WARNING	195
source / vignettes	OK	331
linux-release-arm64	WARNING	197
linux-release-x86_64	WARNING	228
macos-release-arm64	WARNING	180
macos-release-x86_64	WARNING	593
macos-oldrel-arm64	WARNING	244
macos-oldrel-x86_64	WARNING	604
windows-devel-arm64	WARNING	424
windows-devel-x86_64	WARNING	277
windows-release-arm64	WARNING	434
windows-release-x86_64	WARNING	237
windows-oldrel-x86_64	WARNING	204
wasm-release	OK	151

Exports:adjustGRM calcGRM calcParamSE calcParamSE.cov CalcThreshold convertBlockList cumsumPos design.Z EM3.cov EM3.cpp EM3.general EM3.linker.cpp EMM.cpp EMM1.cpp EMM2.cpp estNetwork estPhylo genesetmap genetrait is.diag MAF.cut make.full manhattan manhattan.plus manhattan2 manhattan3 modify.data parallel.compute plotHaploNetwork plotPhyloTree qq RGWAS.epistasis RGWAS.multisnp RGWAS.multisnp.interaction RGWAS.normal RGWAS.normal.interaction RGWAS.twostep RGWAS.twostep.epi score.calc score.calc.epistasis.LR score.calc.epistasis.LR.MC score.calc.epistasis.score score.calc.epistasis.score.MC score.calc.int score.calc.int.MC score.calc.LR score.calc.LR.int score.calc.LR.int.MC score.calc.LR.MC score.calc.MC score.calc.score score.calc.score.MC score.cpp score.linker.cpp See spectralG.cpp SS_gwas welcome_to_RGWAS

Dependencies:ape base64enc bslib cachem cli cluster corpcor digest dplyr evaluate expm fastmap fontawesome fs gaston generics glue here highr htmltools htmlwidgets jquerylib jsonlite knitr lattice lifecycle magrittr MASS Matrix memoise mime MM4LMM nlme nloptr numDeriv optimx pbmcapply pegas pillar pkgconfig pracma purrr R.methodsS3 R.oo R.utils R6 rappdirs Rcpp RcppArmadillo RcppEigen RcppParallel Rfast rlang rmarkdown rprojroot rrBLUP sass stringi stringr tibble tidyselect tinytex utf8 vctrs withr xfun yaml zigg

RAINBOWR: Reliable Association INference By Optimizing Weights with R

NOTE!!!! | 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: | 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. | What is RAINBOWR | Installation | Usage | Help | References

Rendered fromRAINBOWR.Rmdusingknitr::rmarkdown

Last update: 2022-01-31
Started: 2019-10-21

Help page	Topics
Function to adjust genomic relationship matrix (GRM) with subpopulations	adjustGRM
Function to calculate genomic relationship matrix (GRM)	calcGRM
Calculate Standard Errors for Variance Parameters	calcParamSE
Calculate Standard Errors for Variance and Correlation Parameters	calcParamSE.cov
Function to calculate threshold for GWAS	CalcThreshold
Function to convert haplotype block list from PLINK to RAINBOWR format	convertBlockList
Function to calculate cumulative position (beyond chromosome)	cumsumPos
Function to generate design matrix (Z)	design.Z
Equation of mixed model for multi-kernel considering covariance structure between kernels	EM3.cov
Equation of mixed model for multi-kernel (slow, general version)	EM3.cpp
Equation of mixed model for multi-kernel including using other packages (with other packages, much faster than EM3.cpp)	EM3.general
Equation of mixed model for multi-kernel (fast, for limited cases)	EM3.linker.cpp
Equation of mixed model for multi-kernel using other packages (much faster than EM3.cpp)	EM3.op
Equation of mixed model for one kernel, a wrapper of two methods	EMM.cpp
Equation of mixed model for one kernel, GEMMA-based method (inplemented by Rcpp)	EMM1.cpp
Equation of mixed model for one kernel, EMMA-based method (inplemented by Rcpp)	EMM2.cpp
Function to estimate & plot haplotype network	estNetwork
Function to estimate & plot phylogenetic tree	estPhylo
Function to generate map for gene set	genesetmap
Generate pseudo phenotypic values	genetrait
Function to judge the square matrix whether it is diagonal matrix or not	is.diag
Function to remove the minor alleles	MAF.cut
Change a matrix to full-rank matrix	make.full
Draw manhattan plot	manhattan
Add points of -log10(p) corrected by kernel methods to manhattan plot	manhattan.plus
Draw manhattan plot (another method)	manhattan2
Draw the effects of epistasis (3d plot and 2d plot)	manhattan3
Function to modify genotype and phenotype data to match	modify.data
Function to parallelize computation with various methods	parallel.compute
Function to plot haplotype network from the estimated results	plotHaploNetwork
Function to plot phylogenetic tree from the estimated results	plotPhyloTree
Draw qq plot	qq
RAINBOWR: Perform Genome-Wide Asscoiation Study (GWAS) By Kernel-Based Methods	RAINBOWR-package RAINBOWR
Check epistatic effects by kernel-based GWAS (genome-wide association studies)	RGWAS.epistasis
Print the R code which you should perform for RAINBOWR GWAS	RGWAS.menu
Testing multiple SNPs simultaneously for GWAS	RGWAS.multisnp
Testing multiple SNPs and their interaction with some kernel simultaneously for GWAS	RGWAS.multisnp.interaction
Perform normal GWAS (test each single SNP)	RGWAS.normal
Perform normal GWAS including interaction (test each single SNP)	RGWAS.normal.interaction
Perform normal GWAS (genome-wide association studies) first, then perform SNP-set GWAS for relatively significant markers	RGWAS.twostep
Perform normal GWAS (genome-wide association studies) first, then check epistatic effects for relatively significant markers	RGWAS.twostep.epi
Physical map of rice genome	Rice_geno_map
Marker genotype of rice genome	Rice_geno_score
Physical map of rice genome	Rice_haplo_block
Phenotype data of rice field trial	Rice_pheno
Rice_Zhao_etal:	Rice_Zhao_etal
Calculate -log10(p) for single-SNP GWAS	score.calc
Calculate -log10(p) of epistatic effects by LR test	score.calc.epistasis.LR
Calculate -log10(p) of epistatic effects by LR test (multi-cores)	score.calc.epistasis.LR.MC
Calculate -log10(p) of epistatic effects with score test	score.calc.epistasis.score
Calculate -log10(p) of epistatic effects with score test (multi-cores)	score.calc.epistasis.score.MC
Calculate -log10(p) for single-SNP GWAS with interaction	score.calc.int
Calculate -log10(p) for single-SNP GWAS with interaction (multi-cores)	score.calc.int.MC
Calculate -log10(p) of each SNP-set by the LR test	score.calc.LR
Calculate -log10(p) of each SNP-set and its interaction with kernels by the LR test	score.calc.LR.int
Calculate -log10(p) of each SNP-set and its interaction with kernels by the LR test (multi-cores)	score.calc.LR.int.MC
Calculate -log10(p) of each SNP-set by the LR test (multi-cores)	score.calc.LR.MC
Calculate -log10(p) for single-SNP GWAS (multi-cores)	score.calc.MC
Calculate -log10(p) of each SNP-set by the score test	score.calc.score
Calculate -log10(p) of each SNP-set by the score test (multi-cores)	score.calc.score.MC
Calculte -log10(p) by score test (slow, for general cases)	score.cpp
Calculte -log10(p) by score test (fast, for limited cases)	score.linker.cpp
Function to view the first part of data (like head(), tail())	See
Perform spectral decomposition (inplemented by Rcpp)	spectralG.cpp
Calculate some summary statistics of GWAS (genome-wide association studies) for simulation study	SS_gwas
Function to greet to users	welcome_to_RGWAS

Package: RAINBOWR 0.1.42

RAINBOWR: Genome-Wide Association Study with SNP-Set Methods

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