R/qtl: A QTL mapping environment

Software for mapping quantitative trait loci in experimental crosses

Current version: 1.66 (2023-11-27)

[ Download | FAQ | News | Bugs | Sample graphics | Sample data | Tutorials | Book | Manual | Citation ]

Try the R/qtlcharts package: interactive graphics for QTL data.

Check out our book: A Guide to QTL Mapping with R/qtl, by Karl W. Broman and Śaunak Sen.

See the R/qtl source code on GitHub; licensed under GPL-3.

About R/qtl

About R

R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental crosses. It is implemented as an add-on package for the freely available and widely used statistical language/software R (see the R project homepage). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing.

A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses.

The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression.

R/qtl is distributed as source code for unix or compiled code for Windows or Mac. R/qtl is released under the GNU General Public License. To download the software, you must agree to the terms in that download.

R is an open-source implementation of the S language. As described on the R project homepage:

"R is a system for statistical computation and graphics. It consists of a language plus a run-time environment with graphics, a debugger, access to certain system functions, and the ability to run programs stored in script files.

"The core of R is an interpreted computer language which allows branching and looping as well as modular programming using functions. Most of the user-visible functions in R are written in R. It is possible for the user to interface to procedures written in the C, C++, or FORTRAN languages for efficiency. The R distribution contains functionality for a large number of statistical procedures. Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations. Additional modules are available for a variety of specific purposes."

R is freely available for Windows, unix and MacOS, and may be downloaded from the Comprehensive R Archive Network (CRAN).

Learning R may require a formidable investment of time, but it will definitely be worth the effort. Numerous free documents on getting started with R are available on CRAN. In addition, several books are available on R, S and S-PLUS; for example, see WN Venables, BD Ripley (2002) Modern Applied Statistics with S (4th ed, Springer) or P Dalgaard (2008) Introductory statistics with R (2nd ed, Springer).

See my Introduction to R page for further links.

Contact for problems/questions/suggestions: Karl W Broman

Authors: Karl W Broman and Hao Wu, with ideas from Gary Churchill and Śaunak Sen and contributions from Danny Arends, Timothée Flutre, Ritsert Jansen, Pjotr Prins, Lars Rönnegård, Rohan Shah, Laura Shannon, Quoc Tran, Aaron Wolen, and Brian Yandell

Google Groups: We've created two Google Groups for email announcements regarding software updates (R/qtl announcements) and for discussion about the use of the software (R/qtl discussion). Note that you should join just one of these two groups; all announcements will also be sent to the discussion group.

Other QTL mapping software

MapMaker/QTL GeneNetwork
QTL Cartographer MultiQTL
R/qtlDesign The QTL Cafe
MapQTL Multimapper

[ Download | FAQ | News | Bugs | Sample graphics | Sample data | Tutorials | Book | Manual | Citation ]

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