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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

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Published on: December 10, 2012

Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among

Akio Onogi¹, Masanobu Nurimoto, Mitsuo Morita

¹Maebashi Institute of Animal Science, Livestock Improvement Association of Japan, Inc., 316 Kanamaru, Maebashi, Gunma, 371-0121, Japan. onogi@liaj.or.jp

BMC Bioinformatics

|June 29, 2011

Summary

The Dirichlet process (DP) prior method effectively infers genetic population structures and individual assignments, especially for unbalanced sample sizes. A new program, DPART, enhances its practical application.

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Area of Science:

Population genetics
Computational biology
Statistical genetics

Background:

Bayesian inference using Dirichlet process (DP) prior is valuable for genetic population structure analysis.
DP prior method's properties are not fully understood, limiting its widespread use.
Characterization of the DP prior method is needed to enhance its practical application.

Purpose of the Study:

To characterize the Dirichlet process (DP) prior method for inferring genetic population structures.
To improve the practical utility of DP prior-based methods.
To develop a novel program implementing an enhanced DP prior method.

Main Methods:

Evaluated the sequentially-allocated merge-split (SAMS) sampler for Markov chain Monte Carlo algorithms.
Investigated the effect of a hyperparameter for the prior distribution of allele frequencies.
Compared the DP prior method with existing Bayesian clustering algorithms.

Main Results:

The SAMS sampler effectively improved accuracy and reduced computational time in population structure analysis.
Treating the allele frequency hyperparameter as a variable improved inference.
The DP prior method demonstrated superior accuracy for unbalanced sample sizes compared to methods like STRUCTURE.

Conclusions:

The DP prior method is effective for inferring population number and individual assignments, particularly with unbalanced sample sizes.
A new program, DPART, was developed, implementing the SAMS sampler and a variable allele frequency hyperparameter.
DPART enhances the practical application of DP prior methods in population genetics.