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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 identifying significant polygenic effects while controlling for confounding and

Christopher McMahan1, James Baurley1, William Bridges1

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Statistical Applications in Genetics and Molecular Biology
|November 16, 2017
PubMed
Summary

This study introduces a new regression method to identify key genetic factors for rice yield, accounting for environmental conditions and plant genetics. The approach helps develop climate-resilient crops by understanding complex trait influences.

Keywords:
Bayesian hierarchical modelsEM algorithmMAP estimatorgenomic studiesrice scienceshrinkage prior

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Area of Science:

  • Plant genetics
  • Agricultural science
  • Statistical modeling

Background:

  • Marginal analysis of genetic markers may miss polygenic and environmental influences on plant traits.
  • Confounding factors like growing conditions and genetic similarity can affect study conclusions.
  • Understanding complex trait influences is crucial for developing resilient plant varieties.

Purpose of the Study:

  • To develop a regression method for identifying significant genomic factors influencing plant traits.
  • To simultaneously control for environmental and genetic confounding factors.
  • To analyze rice yield data from a study in Indonesia.

Main Methods:

  • A Bayesian maximum a posteriori probability (MAP) estimator was developed using a generalized double Pareto shrinkage prior.
  • A novel and computationally efficient expectation-maximization (EM) algorithm was created for variable selection and estimation.
  • The method was validated through simulations and applied to real-world rice yield data.

Main Results:

  • The proposed regression method effectively identifies significant genomic factors.
  • The approach successfully controls for field factors and genetic similarities among plant varieties.
  • The analysis provided insights into factors influencing rice yield in the Indonesian pilot study.

Conclusions:

  • The developed Bayesian regression method offers a robust approach for genomic analysis in plants.
  • This method enhances the understanding of genetic and environmental interactions influencing desirable traits.
  • The findings support the development of improved crop varieties for optimal yield and resilience.