Package: statgenQTLxT 1.0.3

statgenQTLxT: Multi-Trait and Multi-Trial Genome Wide Association Studies

Fast multi-trait and multi-trail Genome Wide Association Studies (GWAS) following the method described in Zhou and Stephens. (2014), <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Authors:Bart-Jan van Rossum [aut, cre], Willem Kruijer [aut], Fred van Eeuwijk [ctb], Martin P Boer [ctb], Daniela Bustos-Korts [ctb], Emilie J Millet [ctb], Joao Paulo [ctb], Maikel Verouden [ctb], Ron Wehrens [ctb], Choazhi Zheng [ctb]

statgenQTLxT_1.0.3.tar.gz
statgenQTLxT_1.0.3.zip(r-4.7-x86_64)statgenQTLxT_1.0.3.zip(r-4.6-x86_64)statgenQTLxT_1.0.3.zip(r-4.5-x86_64)
statgenQTLxT_1.0.3.tgz(r-4.6-x86_64)statgenQTLxT_1.0.3.tgz(r-4.6-arm64)statgenQTLxT_1.0.3.tgz(r-4.5-x86_64)statgenQTLxT_1.0.3.tgz(r-4.5-arm64)
statgenQTLxT_1.0.3.tar.gz(r-4.7-arm64)statgenQTLxT_1.0.3.tar.gz(r-4.7-x86_64)statgenQTLxT_1.0.3.tar.gz(r-4.6-arm64)statgenQTLxT_1.0.3.tar.gz(r-4.6-x86_64)
statgenQTLxT_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
statgenQTLxT/json (API)

# Install 'statgenQTLxT' in R:

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

gDataDropsRestr - Subset of DROPS data for use in examples

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp openmp

2.70 score 3 scripts 283 downloads 1 exports 34 dependencies

Last updated from:e8a8806b23. Checks:13 OK. Indexed: yes.

Target	Result	Time	Files	Syslog
linux-devel-arm64	OK	165
linux-devel-x86_64	OK	198
source / vignettes	OK	298
linux-release-arm64	OK	176
linux-release-x86_64	OK	209
macos-release-arm64	OK	100
macos-release-x86_64	OK	273
macos-oldrel-arm64	OK	120
macos-oldrel-x86_64	OK	213
windows-devel	OK	155
windows-release	OK	161
windows-oldrel	OK	144
wasm-release	OK	147

Exports:runMultiTraitGwas

Dependencies:cli codetools cpp11 crayon data.table dotCall64 enhancer farver foreach ggplot2 glue gtable isoband iterators labeling lattice lifecycle LMMsolver MASS Matrix R6 RColorBrewer Rcpp RcppArmadillo RcppProgress rlang S7 scales sommer spam statgenGWAS vctrs viridisLite withr

Multi-trait GWAS with the statgenQTLxT package

Multi-Trait GWAS | Theoretical background | Hypotheses for the SNP-effects | Models for the genetic and residual covariance | The class gData | Worked example 1: multiple traits in one trial | Create gData object | Recoding and cleaning of markers | Running Multi-trait GWAS | GWAS Summary | GWAS Plots | QQ plots | Manhattan plots | QTL plots | Kinship matrices | Worked example 2: one trait measured in multiple trials | Multi-trial GWAS | Further options | Significance thresholds | Variance components | Covariates | Minor Allele Frequency | Estimation of common SNP effects and QTL×E effects. | References

Rendered fromstatgenQTLxT.Rmdusingknitr::rmarkdown

Last update: 2023-02-23
Started: 2023-02-23

Citation

Readme and manuals

Help Manual

Help page	Topics
Subset of DROPS data for use in examples	gDataDropsRestr
Perform multi-trait GWAS	runMultiTraitGwas

Usage by other packages (reverse dependencies)