QTLBIM

QTLBIM performs Bayesian interval mapping of quantitative trait loci (QTL) to estimate the number, locations, and effects of QTLs—including main effects, epistatic interactions, and gene–environment interactions—in experimental crosses for continuous, binary, and ordinal traits.

Key Features:

• Bayesian Analysis Framework: Employs a Bayesian approach for model selection and evaluates posterior distributions of the number and locations of QTLs, their main effects, epistatic interactions, and gene–environment interactions.
• Markov Chain Monte Carlo (MCMC) Algorithms: Incorporates MCMC algorithms to sample posterior probabilities of competing genetic models.
• Comprehensive Summaries and Diagnostics: Provides graphical and numerical summaries along with model selection and convergence diagnostics to assess MCMC outputs.
• Support for Trait Types and Experimental Crosses: Handles continuous, binary, and ordinal traits and is tailored for analysis of multiple interacting QTL models in experimental crosses.
• Integration with R/qtl: Built upon the R/qtl framework for QTL data structures and analysis routines.

Scientific Applications:

• Mapping interacting QTLs in experimental crosses: Identification and localization of multiple interacting QTLs underlying complex traits in experimental cross populations.
• Detection of epistasis and gene–environment interactions: Estimation and inference of epistatic effects and gene–environment interactions contributing to phenotypic variation.
• Analysis of diverse trait types: Application to continuous, binary, and ordinal phenotypes for broad genetic studies.
• Probabilistic inference of QTL parameters: Estimation of posterior distributions for QTL number, positions, and effect sizes to support probabilistic statements about genetic architecture.

Methodology:

Bayesian statistical modeling using Markov Chain Monte Carlo (MCMC) to explore parameter space, estimate posterior distributions of genetic models, perform Bayesian model selection, and generate convergence diagnostics.