QTL.gCIMapping: QTL Genome-Wide Composite Interval Mapping

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect linear mixed model. First, each position on the genome is detected in order to obtain a negative logarithm P-value curve against genome position. Then, all the peaks on each effect (additive or dominant) curve are viewed as potential QTL, all the effects of the potential QTL are included in a multi-QTL model, their effects are estimated by empirical Bayes in doubled haploid population or by adaptive lasso in F2 population, and true QTL are identified by likelihood radio test. See Wen et al. (2018) <doi:10.1093/bib/bby058>.

Version: 3.2
Depends: R (≥ 3.5.0), MASS, qtl
Imports: Rcpp (≥ 0.12.17), methods, openxlsx, stringr, data.table, parcor, doParallel, foreach
LinkingTo: Rcpp
Published: 2019-11-11
Author: Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Maintainer: Yuanming Zhang <soyzhang at mail.hzau.edu.cn>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: QTL.gCIMapping results

Downloads:

Reference manual: QTL.gCIMapping.pdf
Package source: QTL.gCIMapping_3.2.tar.gz
Windows binaries: r-devel: QTL.gCIMapping_3.2.zip, r-devel-gcc8: QTL.gCIMapping_3.2.zip, r-release: QTL.gCIMapping_3.2.zip, r-oldrel: QTL.gCIMapping_3.2.zip
OS X binaries: r-release: QTL.gCIMapping_3.2.tgz, r-oldrel: QTL.gCIMapping_3.2.tgz
Old sources: QTL.gCIMapping archive

Reverse dependencies:

Reverse imports: QTL.gCIMapping.GUI

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