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

Abstract: The endosperm of plants is a major source of food, feed and industrial raw materials. The genetic analysis of en-dosperm traits poses numerous challenges due to its complex genetic composition and unique physical and developmental proper-ties. 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 of 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 t