Package: QTLEMM
Type: Package
Title: QTL Mapping and Hotspots Detection
Version: 1.0.0
Authors@R: c(
person("Ping-Yuan", "Chung", email = "pychung@webmail.stat.sinica.edu.tw", role = "cre"),
person("Chen-Hung", "Kao", email = "chkao@webmail.stat.sinica.edu.tw", role = "aut"))
Description:
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, advanced intercrossed (AI) populations, are
considered in the QTL mapping analysis. For QTL hotpot 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) .
Imports: mvtnorm, utils, stats, graphics
License: GPL-2
Encoding: UTF-8
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2021-09-02 09:44:33 UTC; pingyuan
Author: Ping-Yuan Chung [cre],
Chen-Hung Kao [aut]
Maintainer: Ping-Yuan Chung <pychung@webmail.stat.sinica.edu.tw>
Repository: CRAN
Date/Publication: 2021-09-02 22:30:02 UTC