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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Mapping quantitative trait loci using the MCMC procedure in SAS.

S Xu1, Z Hu

  • 1Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA. shizhong.xu@ucr.edu

Heredity
|June 17, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces SAS/MCMC for Bayesian analysis, applying it to wheat QTL mapping. A key QTL was found to control female fertility switching, not seed count.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Bayesian analysis is crucial for complex statistical modeling.
  • Markov Chain Monte Carlo (MCMC) algorithms are powerful tools for Bayesian inference.
  • Quantitative Trait Locus (QTL) mapping requires robust statistical methods.

Purpose of the Study:

  • To introduce and demonstrate the SAS/MCMC procedure for complex statistical analyses.
  • To apply SAS/MCMC for QTL mapping in a wheat fertility trait experiment.
  • To model fertility phenotypes using Poisson, Bernoulli, and zero-truncated Poisson models.

Main Methods:

  • Utilized the SAS/MCMC procedure for Bayesian analysis.
  • Applied Markov chain Monte Carlo (MCMC) algorithms.
  • Employed Poisson, Bernoulli, and zero-truncated Poisson models for phenotype description.
  • Performed QTL mapping on a wheat dataset.

Main Results:

  • Successfully applied SAS/MCMC to a real-life QTL mapping experiment.
  • Identified a significant QTL on the second chromosome in wheat.
  • The identified QTL regulates the initiation of seed production in female wheat plants.

Conclusions:

  • SAS/MCMC is a versatile tool for complex Bayesian analyses, including QTL mapping.
  • A novel QTL influencing wheat female fertility switching behavior was discovered.
  • This finding provides insights into the genetic control of reproductive traits in plants.