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QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

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Deviance Information Criterion (DIC) in Bayesian Multiple QTL Mapping.

Daniel Shriner¹, Nengjun Yi

¹Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294.

Computational Statistics & Data Analysis

|February 18, 2010

Summary

This summary is machine-generated.

This study introduces the Deviance Information Criterion (DIC) for Bayesian multiple quantitative trait loci (QTL) mapping. The DIC effectively balances model fit and complexity for improved genome-wide QTL analysis.

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Multiple quantitative trait loci (QTL) mapping is often framed as a model selection problem.
Existing model selection criteria are primarily non-Bayesian.
The Deviance Information Criterion (DIC) is a popular Bayesian model selection tool but has not been applied to multiple QTL mapping.

Purpose of the Study:

To derive and apply the DIC for Bayesian multiple interacting QTL models.
To demonstrate DIC calculation using Markov chain Monte Carlo (MCMC) algorithms.
To provide a computationally efficient method for sensitivity analysis in QTL mapping.

Main Methods:

Derivation of the DIC for multiple interacting QTL models.
Calculation of DIC using posterior samples from MCMC algorithms.
Implementation of the DIC within the R/qtlbim package.

Main Results:

The DIC measures posterior predictive error by penalizing model deviance with effective number of parameters.
The effective number of parameters accounts for various factors including sample size, genetic design, chromosome characteristics, covariates, and QTL properties.
The DIC facilitates evaluation of the necessity of including environmental and interaction effects.

Conclusions:

The DIC is a valuable and computationally efficient criterion for Bayesian multiple QTL mapping.
The R/qtlbim package enables widespread use of Bayesian methods for genome-wide interacting QTL analysis.
This approach enhances the quantitative evaluation of complex genetic models.