| Name : R-QTLEMM
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| Version : 2.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp155.1.1
| Date : 2024-06-26 10:15:36
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| Group : Development/Libraries/Other
| Source RPM : R-QTLEMM-2.1.0-lp155.1.1.src.rpm
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| Size : 1.16 MB
| |
| Packager : https://www_suse_com/
| |
| Summary : QTL Mapping and Hotspots Detection
|
Description :
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>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/15.5/x86_64 |
