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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Significance test and genome selection in bayesian shrinkage analysis.
1Department of Statistics, University of California, Riverside, California 92521, USA.
International Journal of Plant Genomics
|July 16, 2010
Summary
Bayesian shrinkage analysis, now genome selection, effectively detects quantitative trait loci (QTL) with small effects. Incorporating a permutation test enhances QTL detection confidence for whole genome analysis.
Area of Science:
- Genetics
- Statistical Genomics
- Bioinformatics
Background:
- Bayesian shrinkage analysis is the current standard for whole genome analysis of quantitative traits.
- This method, termed genome selection, utilizes dense marker maps to estimate genetic effects across the entire genome.
- It can detect quantitative trait loci (QTL) explaining as little as 2% of phenotypic variance in samples of 300-500 individuals.
Purpose of the Study:
- To enhance the reliability of Bayesian shrinkage analysis for quantitative trait loci (QTL) detection.
- To establish a method for assigning confidence levels to estimated QTL effects within a whole genome context.
- To integrate a significance testing framework with Bayesian genome selection for routine QTL discovery.
Main Methods:
- Applied Bayesian shrinkage analysis for whole genome analysis of quantitative traits.
- Utilized a permutation test to establish empirical thresholds for declaring QTL significance.
- Defined genome-wide type I error rates for significance declaration.
Main Results:
- Bayesian shrinkage analysis demonstrated the ability to detect QTL with small effects (2% phenotypic variance).
- The low false positive rate facilitated QTL detection via visual inspection of genome-wide effects.
- The proposed permutation test provided empirical thresholds for significance, complementing the Bayesian approach.
Conclusions:
- The integration of permutation testing with Bayesian shrinkage analysis enables routine and reliable QTL detection.
- This combined approach enhances confidence in identified QTL effects for whole genome analysis.
- Genome selection, augmented by permutation testing, offers a robust framework for genetic studies of quantitative traits.
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