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Acta Agron Sin ›› 2009, Vol. 35 ›› Issue (9): 1569-1575.doi: 10.3724/SP.J.1006.2009.01569


Bayesian Statistics-Based Multiple Interval Mapping of QTL Controlling Endosperm Traits in Cereals

WANG Ya-Min1,2,TANG Zai-Xiang1,LU Xin1,XU Chen-Wu1,*   

  1. 1Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology/Key Laboratory of Plant Functionality Genomics of Ministry of Education,Yangzhou University,Yangzhou 225009,China;2Basis Course of Lianyugang Technical College,Lianyungang 222006,China
  • Received:2008-12-18 Revised:2009-04-25 Online:2009-09-12 Published:2009-07-03
  • Contact: XU Chin-Wu, E-mail: qtls@yzu.edu.cn; Tel: 0514-87979358


The endosperm of plants is a major source of food, feed and industrial raw materials. The genetic analysis of endosperm traits poses numerous challenges due to its complex genetic composition and unique physical and developmental properties. Modern molecular techniques and statistical methods have greatly improved the mapping of quantitative trait loci (QTL) underlying endosperm traits. In recent years, Bayesian statistics-based analyzing methods have been developed for mapping QTL underlying diploid quantitative traits, but these methods have not been effective to the mapping of triploid endosperm characters. On the basis f Bayesian statistics and quantitative genetic model of triploid endosperm traits, a Bayesian multiple interval method for mapping QTL underlying endosperm traits was proposed. This method used the DNA molecular marker genotypes of each plant in F2 segregation population and the single endosperm observation of a few endosperms of each plant as data set to analyze endosperm QTL. After constructing the multiple-QTL model, the Bayesian estimates of multiple QTL position and effects were obtained through MCMC algorithm implementing via Gibbs and Metropolis-Hastings sampling. The validation of the statistical procedure was verified through chromosome level simulation studies. The results showed that the proposed Bayesian method can estimate the multiple QTL positions and effects as well as distinguish the two dominance effects.

Key words: Bayesian statistics, Endosperm traits, Markov chain Monte Carlo, Quantitative trait loci

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