QTL.gCIMapping: QTL Genome-Wide Composite Interval Mapping

Conduct multiple quantitative trait loci (QTL) mapping under the framework of random-QTL-effect mixed linear model. First, each position on the genome is detected in order to construct 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 or by adaptive lasso in F2, and true QTL are identified by likelihood radio test. Wang S-B, Wen Y-J, Ren W-L, Ni Y-L, Zhang J, Feng J-Y, Zhang Y-M (2016) <doi:10.1038/srep29951>.

Version: 3.0
Depends: MASS, qtl, doParallel, foreach, parallel
Imports: Rcpp (≥ 0.12.17), methods, openxlsx, stringr, data.table, parcor
LinkingTo: Rcpp
Published: 2018-06-16
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

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Reference manual: QTL.gCIMapping.pdf
Package source: QTL.gCIMapping_3.0.tar.gz
Windows binaries: r-devel: QTL.gCIMapping_3.0.zip, r-release: QTL.gCIMapping_3.0.zip, r-oldrel: QTL.gCIMapping_3.0.zip
OS X binaries: r-release: QTL.gCIMapping_3.0.tgz, r-oldrel: QTL.gCIMapping_2.0.tgz
Old sources: QTL.gCIMapping archive

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