EQF.plot

Generate an EQF plot based on the result of the permutation process
used to detect the QTL hotspot.

For QTL mapping, this package comprises several functions designed to execute diverse
tasks, such as simulating or analyzing data, calculating significance thresholds, and
visualizing QTL mapping results. The single-QTL or multiple-QTL method, which enables
the fitting and comparison of various statistical models, is employed to analyze the
data for estimating QTL parameters. The models encompass linear regression, permutation
tests, normal mixture models, and truncated normal mixture models. The Gaussian stochastic
process is utilized to compute significance thresholds for QTL detection on a genetic
linkage map within experimental populations. Two types of data, complete genotyping, and
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, statistical methods can
be developed based on either utilizing individual-level data or summarized data. We have
proposed a statistical framework capable of handling both individual-level data and
summarized QTL data for QTL hotspot detection. Our statistical framework can overcome the
underestimation of thresholds resulting from ignoring the correlation structure among
traits. Additionally, it can identify different types of hotspots with minimal computational
cost during the detection process. Here, we endeavor to furnish the R codes for our QTL
mapping and hotspot detection methods, intended 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>.

Ping-Yuan Chung

QTLEMM

QTL EM Algorithm Mapping and Hotspots Detection

Chen-Hung Kao

Y.-T. Guo

H.-N. Ho

H.-I. Lee

P.-Y. Wu

M.-H. Yang

M.-H. Zeng

EQF.plot function

<dl><dt>result</dt>
<dd>list. The data list of the output from LOD.QTLdetect(),
EQF.permu(), or Qhot.EQF().</dd>
<dt>plot.all</dt>
<dd>logical. When set to TRUE, it directs the function to
output one figure of the EQF values over the bins.</dd>
<dt>plot.chr</dt>
<dd>logical. When set to TRUE, it instructs the function to
output the figures of the EQF values over the bins for each chromosome.</dd>
<dt>plot.main</dt>
<dd>logical of character. When set to TRUE, it will use the
default title on the plot. When set to FALSE, it will be no title on the
plot. Users can also use this argument to set their own title.</dd></dl>

Arguments

EQF plot — EQF.plot

<dl>

<dt>result</dt>
<dd>list. The data list of the output from LOD.QTLdetect(),
EQF.permu(), or Qhot.EQF().</dd>


<dt>plot.all</dt>
<dd>logical. When set to TRUE, it directs the function to
output one figure of the EQF values over the bins.</dd>


<dt>plot.chr</dt>
<dd>logical. When set to TRUE, it instructs the function to
output the figures of the EQF values over the bins for each chromosome.</dd>


<dt>plot.main</dt>
<dd>logical of character. When set to TRUE, it will use the
default title on the plot. When set to FALSE, it will be no title on the
plot. Users can also use this argument to set their own title.</dd>

</dl>

EQF.plot: EQF plot

Description

Usage

Value

Arguments

References

See Also

Examples