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>.
Version:
0.1.38
Depends:
R (≥ 3.5.0)
Imports:
Rcpp,
Matrix,
cluster,
MASS,
pbmcapply,
optimx, methods,
ape,
stringr,
pegas,
rrBLUP,
expm,
here,
htmlwidgets,
Rfast,
gaston,
MM4LMM,
R.utils
Suggests:
knitr,
rmarkdown,
plotly,
haplotypes,
adegenet,
ggplot2,
ggtree,
scatterpie,
phylobase,
ggimage,
furrr,
future,
progressr,
foreach,
doParallel,
data.table
Published:
2025年05月21日
Author:
Kosuke Hamazaki [aut, cre],
Hiroyoshi Iwata [aut, ctb]
Maintainer:
Kosuke Hamazaki <hamazaki at ut-biomet.org>
NeedsCompilation:
yes
Documentation:
Downloads:
Linking:
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