Parviz Safari - Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
محمد مقدم واحد - Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
سیامک علوی کیا - Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Majid Norouzi - Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
Babak Rabiei - Department of Agronomy and Plant Breeding, Faculty of Agriculture Science, University of Guilan, Rasht, Iran.

Drought is the main abiotic stress seriously influencing wheat production and quality in the world. Information about the inheritance of drought tolerance is necessary to determine the type of breeding program and to develop tolerant cultivars. In this study, Bayesian inference was used to explore the nature and amount of gene effects controlling yield and its components under water deficit and normal conditions by assessment of contrasting bread wheat parents (Bam and Arta) and derived generations from them. Bayesian inference using the Gibbs Variable Selection (GVS) approach and the Deviance Information Criterion (DIC) were applied to identify the most important gene effects and to compare models including different gene effects. The GVS and DIC provided an efficient way to perform the analysis and to introduce the more appropriate models. It can be inferred from the results that the Bayesian analysis provides a robust inference of genetic architecture of yield and yield components in wheat. Since the additive, dominance and epistatic gene actions involved in the inheritance of agronomic characters under both water stress and normal conditions, methods which utilize all types of gene effects, such as hybrid seed production, may be useful in improving yield and its stability in wheat.

Bayesian inference, DIC, GVS, MCMC, Water Deficit, Wheat