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Bayesian hierarchical regression models for detecting QTLs in plant experiments

Author

Listed:
  • Edward Boone
  • Susan Simmons
  • Haikun Bao
  • Ann Stapleton

Abstract

Quantitative trait loci (QTL) mapping is a growing field in statistical genetics. In plants, QTL detection experiments often feature replicates or clones within a specific genetic line. In this work, a Bayesian hierarchical regression model is applied to simulated QTL data and to a dataset from the Arabidopsis thaliana plants for locating the QTL mapping associated with cotyledon opening. A conditional model search strategy based on Bayesian model averaging is utilized to reduce the computational burden.

Suggested Citation

  • Edward Boone & Susan Simmons & Haikun Bao & Ann Stapleton, 2008. "Bayesian hierarchical regression models for detecting QTLs in plant experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(7), pages 799-808.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:7:p:799-808
    DOI: 10.1080/02664760802005910
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    References listed on IDEAS

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    1. Karl W. Broman & Terence P. Speed, 2002. "A model selection approach for the identification of quantitative trait loci in experimental crosses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 641-656, October.
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