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: |
3.3 |
| Depends: |
R (≥ 3.5.0), MASS, qtl |
| Imports: |
Rcpp (≥
0.12.17), methods, openxlsx, stringr, data.table, glmnet, doParallel, foreach |
| LinkingTo: |
Rcpp |
| Published: |
2020-05-01 |
| 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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