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Variable selection for large p small n regression models with incomplete data: mapping QTL with epistases.

Min Zhang¹, Dabao Zhang, Martin T Wells

¹Department of Statistics, Purdue University, West Lafayette, IN 47907, USA. minzhang@stat.purdue.edu

BMC Bioinformatics

|May 31, 2008

Summary

This study introduces a novel two-step Bayesian variable selection method to identify quantitative trait loci (QTL) in genetic datasets with many variables and few samples. The approach effectively handles sparse and asymmetric data, improving QTL mapping accuracy.

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Identifying quantitative trait loci (QTL) involves variable selection from numerous candidates with limited observations.
Missing data in traits or markers complicates the use of traditional model selection criteria like AIC and BIC.

Purpose of the Study:

To develop a robust statistical method for QTL detection in "large p small n" datasets.
To address challenges posed by sparse parameter spaces and small sample sizes in genetic analysis.

Main Methods:

A two-step Bayesian variable selection approach is proposed.
Utilizes flexible regression coefficient priors suitable for high-dimensional, low-sample data.
Employs a Gibbs sampling algorithm for stochastic search in low-dimensional subspaces.

Main Results:

The proposed method demonstrates superior performance in simulation studies for QTL mapping.
Successfully applied to real-world QTL mapping datasets, validating its practical utility.
Effectively handles sparse and asymmetric coefficient distributions.

Conclusions:

The two-step Bayesian procedure combined with Bayesian classification offers a flexible solution for "large p small n" data.
This methodology is particularly advantageous for sparse and asymmetric parameter spaces in genetic analyses.
The approach is extendable to other research areas facing high dimensionality and low sample size challenges.