Application of Gibbs Variable Selection Approach to Study Genetic Control of Water Deficient Stress Tolerance in Wheat

Drought is the main abiotic stress seriously influencing wheat production and quality in Iran. Information about genetic controlling drought tolerance inheritance for grain yield is necessary to determine the type of breeding program as well as develop tolerant cultivars, enabling breeders to choose the most appropriate strategy to breeding trait of interest. In this study, Bayesian inference using Gibbs variable selection (GVS) approach used to identify the most important gene effects related to drought tolerance in context generation mean analysis. For this purpose, field experiments consist of two pairs of crosses with non-tolerant and tolerant cultivars and generations derived from them were carried out across two years as split plot designs based on RCBD with three replications in which main plots assigned to irrigation treatment consist of two levels (well watered and cessation of irrigation at pollination stage) and sub-plots given to the generations. To study the inheritance of any trait in generation mean analysis, joint scaling test is applied. Restrictions of degrees of freedom to number of parameters of model and over- or underestimation of the main and epistatic effects are disadvantages of this method. An alternative approach to obviate these limitations is to perform Bayesian inference and model selection strategies like GVS. GVS using estimation of posterior inclusion probabilities of effects identifies the most discriminant effects in the model. Since the additive, dominance and epistatic gene actions involved in drought tolerance inheritance, methods which utilize all type of gene effects, like recurrent selection followed by pedigree method may be useful for drought tolerance stress improvement. Also hybrid seed production, which utilizes all types of gene effects, may be useful in improving yield in wheat.