QTLEMM: QTL Mapping and Hotspots Detection

For QTL mapping, it consists of several functions to perform various tasks, including simulating or analyzing data, computing the significance thresholds and visualizing the QTL mapping results. The single-QTL or multiple-QTL method that allows a host of statistical models to be fitted and compared is applied to analyze the data for the estimation of QTL parameters. The models include the linear regression, permutation test, normal mixture model and truncated normal mixture model. The Gaussian stochastic process is implemented to compute the significance thresholds for QTL detection onto a genetic linkage map in the experimental populations. Two types of data, the complete genotyping or selective genotyping data, from various experimental populations, including backcross, F2, recombinant inbred (RI) populations, and advanced intercrossed (AI) populations, are considered in the QTL mapping analysis. For QTL hotspot detection, the statistical methods can be developed based on either using the individual-level data or using the summarized data. We have proposed a statistical framework that can handle both the individual-level data and summarized QTL data for QTL hotspots detection. Our statistical framework can overcome the underestimation of threshold arising from ignoring the correlation structure among traits, and also identify the different types of hotspots with very low computational cost during the detection process. Here, we attempt to provide the R codes of our QTL mapping and hotspot detection methods for general use in genes, genomics, and genetics studies. The QTL mapping methods for the complete and selective genotyping designs are based on the multiple interval mapping (MIM) model proposed by Kao, C.-H. , Z.-B. Zeng and R. D. Teasdale (1999) <doi:10.1534/genetics.103.021642> and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) <doi:10.1534/genetics.114.168385>, respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) <doi:10.1093/g3journal/jkab056>.

Version: 1.4.1
Imports: mvtnorm, utils, stats, graphics, grDevices, gtools
Published: 2023-10-23
Author: Ping-Yuan Chung [cre], Chen-Hung Kao [aut], Y.-T. Guo [aut], H.-N. Ho [aut], H.-I. Lee [aut], P.-Y. Wu [aut], M.-H. Yang [aut], M.-H. Zeng [aut]
Maintainer: Ping-Yuan Chung <pychung at webmail.stat.sinica.edu.tw>
BugReports: https://github.com/py-chung/QTLEMM/issues
License: GPL-2
URL: https://github.com/py-chung/QTLEMM
NeedsCompilation: no
CRAN checks: QTLEMM results


Reference manual: QTLEMM.pdf


Package source: QTLEMM_1.4.1.tar.gz
Windows binaries: r-devel: QTLEMM_1.4.1.zip, r-release: QTLEMM_1.4.1.zip, r-oldrel: QTLEMM_1.4.1.zip
macOS binaries: r-release (arm64): QTLEMM_1.4.1.tgz, r-oldrel (arm64): QTLEMM_1.4.1.tgz, r-release (x86_64): QTLEMM_1.4.1.tgz, r-oldrel (x86_64): QTLEMM_1.4.0.tgz
Old sources: QTLEMM archive


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