Package: RAINBOWR 0.1.36

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]

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RAINBOWR.pdf |RAINBOWR.html✨
RAINBOWR/json (API)
NEWS

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

Peer review:

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

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

55 exports 22 stars 2.61 score 70 dependencies 2 mentions 13 scripts 388 downloads

Last updated 6 months agofrom:8d28eb0b7b. Checks:OK: 1 WARNING: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-win-x86_64WARNINGSep 17 2024
R-4.5-linux-x86_64WARNINGSep 17 2024
R-4.4-win-x86_64WARNINGSep 17 2024
R-4.4-mac-x86_64WARNINGSep 17 2024
R-4.4-mac-aarch64WARNINGSep 17 2024
R-4.3-win-x86_64WARNINGSep 17 2024
R-4.3-mac-x86_64WARNINGSep 17 2024
R-4.3-mac-aarch64WARNINGSep 17 2024

Exports:adjustGRMcalcGRMCalcThresholdconvertBlockListcumsumPosdesign.ZEM3.cppEM3.generalEM3.linker.cppEMM.cppEMM1.cppEMM2.cppestNetworkestPhylogenesetmapgenetraitis.diagMAF.cutmake.fullmanhattanmanhattan.plusmanhattan2manhattan3modify.dataparallel.computeplotHaploNetworkplotPhyloTreeqqRGWAS.epistasisRGWAS.multisnpRGWAS.multisnp.interactionRGWAS.normalRGWAS.normal.interactionRGWAS.twostepRGWAS.twostep.episcore.calcscore.calc.epistasis.LRscore.calc.epistasis.LR.MCscore.calc.epistasis.scorescore.calc.epistasis.score.MCscore.calc.intscore.calc.int.MCscore.calc.LRscore.calc.LR.intscore.calc.LR.int.MCscore.calc.LR.MCscore.calc.MCscore.calc.scorescore.calc.score.MCscore.cppscore.linker.cppSeespectralG.cppSS_gwaswelcome_to_RGWAS

Dependencies:apebase64encbslibcachemcliclustercorpcordigestdplyrevaluateexpmfansifastmapfontawesomefsgastongenericsglueherehighrhtmltoolshtmlwidgetsjquerylibjsonliteknitrlatticelifecyclemagrittrMASSMatrixmemoisemimeMM4LMMnlmenloptrnumDerivoptimxpbmcapplypegaspillarpkgconfigpracmapurrrR.methodsS3R.ooR.utilsR6rappdirsRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratRfastrlangrmarkdownrprojrootrrBLUPsassstringistringrtibbletidyselecttinytexutf8vctrswithrxfunyaml

RAINBOWR: Reliable Association INference By Optimizing Weights with R

Kosuke Hamazaki ([email protected]), Hiroyoshi Iwata

Rendered fromRAINBOWR.Rmdusingknitr::rmarkdownon Sep 17 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Function to adjust genomic relationship matrix (GRM) with subpopulationsadjustGRM
Function to calculate genomic relationship matrix (GRM)calcGRM
Function to calculate threshold for GWASCalcThreshold
Function to convert haplotype block list from PLINK to RAINBOWR formatconvertBlockList
Function to calculate cumulative position (beyond chromosome)cumsumPos
Function to generate design matrix (Z)design.Z
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 methodsEMM.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 networkestNetwork
Function to estimate & plot phylogenetic treeestPhylo
Function to generate map for gene setgenesetmap
Generate pseudo phenotypic valuesgenetrait
Function to judge the square matrix whether it is diagonal matrix or notis.diag
Function to remove the minor allelesMAF.cut
Change a matrix to full-rank matrixmake.full
Draw manhattan plotmanhattan
Add points of -log10(p) corrected by kernel methods to manhattan plotmanhattan.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 matchmodify.data
Function to parallelize computation with various methodsparallel.compute
Function to plot haplotype network from the estimated resultsplotHaploNetwork
Function to plot phylogenetic tree from the estimated resultsplotPhyloTree
Draw qq plotqq
RAINBOWR: Perform Genome-Wide Asscoiation Study (GWAS) By Kernel-Based MethodsRAINBOWR-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 GWASRGWAS.menu
Testing multiple SNPs simultaneously for GWASRGWAS.multisnp
Testing multiple SNPs and their interaction with some kernel simultaneously for GWASRGWAS.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 markersRGWAS.twostep
Perform normal GWAS (genome-wide association studies) first, then check epistatic effects for relatively significant markersRGWAS.twostep.epi
Physical map of rice genomeRice_geno_map
Marker genotype of rice genomeRice_geno_score
Physical map of rice genomeRice_haplo_block
Phenotype data of rice field trialRice_pheno
Rice_Zhao_etal:Rice_Zhao_etal
Calculate -log10(p) for single-SNP GWASscore.calc
Calculate -log10(p) of epistatic effects by LR testscore.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 testscore.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 interactionscore.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 testscore.calc.LR
Calculate -log10(p) of each SNP-set and its interaction with kernels by the LR testscore.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 testscore.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 studySS_gwas
Function to greet to userswelcome_to_RGWAS