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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Bayesian hierarchical model for analysis of SNP diversity in multilocus, multipopulation samples.

Feng Guo1, Dipak K Dey, Kent E Holsinger

  • 1Feng Guo is Assistant Professor of Statistics, Department of Statistics, Virginia Tech, Blacksburg, VA 24061 (email: feng.guo@vt.edu ), Dipak K. Dey is Professor and Head, Department of Statistics (email: dipak.dey@uconn.edu ), and Kent E. Holsinger is Professor of Biology, Department of Ecology and Evolutionary Biology (email: kent@darwin.eeb.uconn.edu ), University of Connecticut, Storrs, CT 06269.

Journal of the American Statistical Association
|July 23, 2009
PubMed
Summary

This study introduces Bayesian models to identify genetic loci under selection by analyzing population differentiation (FST). The models detect outlier loci, revealing genes influenced by stabilizing or diversifying selection.

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Area of Science:

  • Population Genetics
  • Genomic Analysis
  • Statistical Bioinformatics

Background:

  • Wright's F(ST) measures genetic variation distribution among populations.
  • Allelic differences at most loci are presumed selectively neutral.
  • Demographic parameters (population size, migration, mutation) influence genetic variation.

Purpose of the Study:

  • To develop Bayesian hierarchical models for estimating locus-specific effects on F(ST).
  • To identify genomic regions under diversifying or stabilizing selection.
  • To apply these models to single-nucleotide polymorphism (SNP) data from the HapMap project.

Main Methods:

  • Proposed Bayesian hierarchical models to estimate locus-specific F(ST) effects.
  • Incorporated conditional autoregressive (CAR) models for local correlation among loci.
  • Utilized Markov chain Monte Carlo (MCMC) simulations for parameter estimation.
  • Employed Kullback-Leibler divergence to detect statistical outliers.

Main Results:

  • A model with locus- and population-specific effects outperformed other models.
  • CAR models were superior for high-resolution SNP data.
  • Identified statistical outlier loci associated with known genes in HapMap data.
  • Simulation studies confirmed the approach's effectiveness in detecting selection.

Conclusions:

  • The proposed Bayesian models effectively estimate locus-specific F(ST) effects.
  • Statistical outlier detection using Kullback-Leibler divergence successfully identifies loci under selection.
  • The methodology provides a robust framework for analyzing genomic variation and detecting selection signatures.