Packaged: 2018-10-17 03:38:00 UTC; Administrator

Description: 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>.

Author: Zhou Ya-Hui, Zhang Ya-Wen, Wen Yang-Jun, Wang Shi-Bo, and Zhang Yuan-Ming
Description: Conduct multiple quantitative trait loci (QTL) and QTL-by-environment interaction (QEI) mapping via ordinary or compressed variance component mixed models with random- or fixed QTL/QEI effects. 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 or on each locus curve are viewed as potential main-effect QTLs and QEIs, all their effects are included in a multi-locus model, their effects are estimated by both least angle regression and empirical Bayes (or adaptive lasso) in backcross and F2 populations, and true QTLs and QEIs are identified by likelihood radio test. See Zhou et al. (2022) <doi:10.1093/bib/bbab596> and Wen et al. (2018) <doi:10.1093/bib/bby058>.

Title: QTL Genome-Wide Composite Interval Mapping with Graphical User

Title: Interactive software package for QTL visualization
Depends: utils, lattice, grid, gWidgets, gWidgetsRGtk2
Description: R package for Visualizing QTL mapping results generated by

Title: Tools for mapping of quantitative traits of genetically related
Description: This software provides tools for quantiative trait mapping
in populations such as advanced intercross lines where
relatedness among individuals should be accounted for. It can
estimate background genetic variances, impute missing
genotypes, simulate genotypes, perform a genome scan for
putative quantitative trait loci (QTL), and plot mapping