agronomy

Software (mostly free software) useful to researchers and teachers in agronomy
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 20/01/12
  • Minor correction: 20/01/12

QTLMap : detection of QTL from experimental designs in outbred population

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 0.8.3 - 14 october 2010
  • License(s): CeCILL
  • Status: beta release
  • Support: maintained, ongoing development
  • Designer(s): Pascale Le Roy, Jean-Michel Elsen, Helene Gilbert, Carole Moreno, Andres Legarra, Olivier Filangi
  • Contact designer(s): olivier.filangi@rennes.inra.fr
  • Laboratory, service:

 

General software features

Description

QTLMap is a software dedicated to the detection of QTL from experimental designs in outbred population. QTLMap software is developed by the Animal Genetics Division at INRA (French National Institute for Agronomical Research). The statistical techniques used are linkage analysis (LA) and linkage disequilibrium linkage analysis (LDLA) using interval mapping. Different versions of the LA are proposed from a quasi Maximum Likelihood approach to a fully linear (regression) model. The LDLA is a regression approach (Legarra and Fernando, 2009). The population may be sets of half-sib families or mixture of full- and half- sib families. The computations of Phase and Transmission probabilities are optimized to be rapid and as exact as possible. QTLMap is able to deal with large numbers of markers (SNP) and traits (eQTL).

Functionnalities

  • QTL detection in half-sib families or mixture of full- and half-sib families
  • One or several linked QTL segregating in the population
  • Single trait or multiple trait
  • Nuisance parameters (e.g. sex, batch, weight...) and their interactions with QTL can be included in the analysis
  • Gaussian, discrete or survival (Cox model) data
  • Familial heterogeneity of variances (heteroscedasticity)
  • Can handle eQTL analyses
  • Computation of transmission and phase probabilities adapted to high throughput genotyping (SNP)
  • Empirical thresholds are estimated using simulations under the null hypothesis or permutations of trait values
  • Computation of power and accuracy of your design or any simulated design
Context in which the software is used

QTLMap source code is available under the CeCILL version 2.0 license, a GPL like license.

Utilisateurs

This software is used by genetic researchers to detect a region of the genome that controls an agronomic trait

Cluster Infrastructures

Software dependencies

Installation

  • Suite gcc (>=4.4)
  • CMake 2.6.4

Support

Users mailing list : inscription

Publications related to the software

Legarra A, Fernando RL, 2009. Linear models for joint association and linkage QTL mapping. Genet Sel Evol., 41:43.

Elsen JM, Filangi O, Gilbert H, Le Roy P, Moreno C, 2009. A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps. Genet Sel Evol., 41:50.

Gilbert H., Le Roy P., Moreno C., Robelin D., Elsen J. M., 2008. QTLMAP, a software for QTL detection in outbred population. Annals of Human Genetics, 72(5): 694.

Gilbert H, Le Roy P., 2007. Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families. Genet Sel Evol., 39(2):139-58.

Moreno C.R., Elsen J.M., Le Roy P., Ducrocq V., 2005. Interval mapping methods for detecting QTL affecting survival and time–to–event phenotypes. Genet. Res. Camb., 85 : 139-149.

Goffinet B, Le Roy P, Boichard D, Elsen JM, Mangin B, 1999. Alternative models for QTL detection in livestock. III. Heteroskedastic model and models corresponding to several distributions of the QTL effect.. Genet. Sel. Evol., 31, 341-350.

Mangin B, Goffinet B, Le Roy P, Boichard D, Elsen JM, 1999. Alternative models for QTL detection in livestock. II. Likelihood approximations and sire marker genotype estimations. Genet. Sel. Evol., 31, 225-237.

Elsen JM, Mangin B, Goffinet B, Boichard D, Le Roy P, 1999. Alternative models for QTL detection in livestock. I. General introduction. Genet. Sel. Evol., 31, 213-224

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 23/04/10
  • Minor correction: 23/04/10
  • Index card author: Florian Salipante (IGF - Contrôle de l'apoptose et de la prolifération dans les systèmes neuronaux et endocriniens)
  • Theme leader : Christelle Dantec (CRBM)

GAGG : algorithm (R code) allowing gene clustering

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.1 - 12/01/2010
  • License(s): not yet chosen
  • Status: internal use
  • Support: maintained, ongoing development
  • Designer(s): Florian Salipante, Christelle Reynès, Robert Sabatier
  • Contact designer(s): florian.salipante@univ-montp1.fr
  • Laboratory, service: research team 'Laboratoire de Physique Industrielle et Traitement de l'Information'

 

General software features

GAGG (Genetic Algorithm for Gene Gathering) is a new statistical method which allows to detect differentially expressed genes and to cluster them according to their expression profiles. This is a factorial method based on integer encoding of the projection variables. It allows to take into account the multivariate aspect of data. It requires the use of a genetic algorithm, and combines several statistical methods, such as PCA or k-means. The code is implemented in R language and consists in 5 functions. A main function GAGG, three internal functions GAGG1, GAGG2 and GAGG3 and a function which allows to visualize genes profiles PlotProfiles.

 

Profils
Context in which the software is used

GAGG algorithm is used to realize genes clusters according to their expression profiles.

 

It is essentially intended to biologists, statisticians and bioinformaticians, who have a minimal prerequisite in the use of R software.

Statistical knowledge and in particular in principal component analysis can facilitate the understanding of the graphics, but are not indispensable because the groups are generated in a self organizing manner.

In the same way, default parameters are set for the genetic algorithm:
Tpop and Ngene parameters corresponding respectively to the population size and the number of generations, can be modified by the user. The more these values are high, the more the chance to converge to the optimal solution will be increasing, but the computational time will be increased too.

The algorithm allows to indifferently treat monocolor or bicolor microarrays, the pre-treatment of data is let to the user who can choose his normalization (Quantile normalization, loess, lowess etc..) and standardization technics.

The data matrix will be presented with genes in rows and experimental conditions in columns. If necessary, a pre-treatment step will be added to the algorithm later.

GAGG method gives good results for gene clustering, it uses a genetic algorithm which is greedy in computation, that implies a long execution time (several hours), in function of Tpop and Ngene parameters. At the beginning, a message asks to the user how many components he wants to compute (some information are given to help with this choice), most of the time two components are sufficient. .

The code source may be downloaded.

Publications related to the software

An article will soon be published in the review CSDA

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