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QTLEMM  

QTL Mapping and Hotspots Detection
View on CRAN: Click here


Download and install QTLEMM package within the R console
Install from CRAN:
install.packages("QTLEMM")

Install from Github:
library("remotes")
install_github("cran/QTLEMM")

Install by package version:
library("remotes")
install_version("QTLEMM", "3.0.0")



Attach the package and use:
library("QTLEMM")
Maintained by
Ping-Yuan Chung
[Scholar Profile | Author Map]
First Published: 2021-06-11
Latest Update: 2024-01-23
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) and H.-I Lee, H.-A. Ho and C.-H. Kao (2014) , respectively. The QTL hotspot detection analysis is based on the method by Wu, P.-Y., M.-.H. Yang, and C.-H. Kao (2021) .
How to cite:
Ping-Yuan Chung (2021). QTLEMM: QTL Mapping and Hotspots Detection. R package version 3.0.0, https://cran.r-project.org/web/packages/QTLEMM. Accessed 04 Apr. 2025.
Previous versions and publish date:
0.1.0 (2021-06-11 10:10), 1.0.0 (2021-09-03 00:30), 1.1.0 (2021-10-13 12:10), 1.1.2 (2022-09-08 11:42), 1.1.3 (2022-10-07 09:10), 1.2.0 (2023-06-05 12:10), 1.3.0 (2023-08-08 10:00), 1.3.1 (2023-08-15 12:00), 1.4.0 (2023-10-13 10:50), 1.4.1 (2023-10-23 09:30), 1.5.0 (2024-01-23 04:22), 1.5.1 (2024-02-29 08:40), 1.5.2 (2024-03-13 10:50), 1.5.3 (2024-04-26 13:10), 1.5.4 (2024-05-17 06:40), 2.0.0 (2024-06-21 11:50), 2.1.0 (2024-06-25 11:00), 3.0.0 (2025-02-21 10:50)
Other packages that cited QTLEMM R package
View QTLEMM citation profile
Other R packages that QTLEMM depends, imports, suggests or enhances
Complete documentation for QTLEMM
Functions, R codes and Examples using the QTLEMM R package
Some associated functions: D.make . EM.MIM . EM.MIM2 . EQF.permu . EQF.plot . IM.search . IM.search2 . LOD.QTLdetect . LRTthre . MIM.points . MIM.points2 . MIM.search . MIM.search2 . Q.make . Qhot . progeny . 
Some associated R codes: D.make.R . EM.MIM.R . EM.MIM2.R . EQF.permu.R . EQF.plot.R . IM.search.R . IM.search2.R . LOD.QTLdetect.R . LRTthre.R . MIM.points.R . MIM.points2.R . MIM.search.R . MIM.search2.R . Q.make.R . Qhot.R . progeny.R .  Full QTLEMM package functions and examples
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