Bayesian inference to study genetic control of water deficit stress tolerance in wheat by LASSO method

Drought is the main abiotic stress seriously influencing wheat production and quality in Iran. Information about genetic controlling drought tolerance inheritance is necessary to determine the type of breeding program as well as develop tolerant cultivars. In this study, Bayesian inference using LASSO method 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. Bayesian inference is an alternative approach which combines available prior knowledge (prior distribution) with the information contained in the data. The result is the posterior distribution containing all information to interpret genetic structure. LASSO is an effective method to apply shrinkage and selection on model variables. Non-important effects in the model shrunk toward zero and excluded from the model. While for important effects, less shrinkage will be achieved. 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.