bayesian inference

Habrobracon hebetor Say (Hymenoptera: Braconidae) is an ectoparasitoid wasp in the family Braconidae and is widely used in biological pest control. Little information is available on the genetic diversity of geographically isolated populations of H. hebetor. In the present study, we assess the genetic structure and diversity of geographically distinct populations of H. hebetor collected from different regions of Iran. To this end, 19 populations of H. hebetor (Dehloran, Hamadan, Minab, Rudan, Ahvaz, Sari, Semnan, Bandar Lengeh, Haji Abbad, Jiroft, Shiraz, Sarpol-e Zahab, Gorgan, Isfahan, Urmia, Kahurestan, Taziyan, Isin, and Sarkhun) were collected from natural niches. For each population, we sequenced a ~660 base pair fragment of Cytochrome Oxidase subunit I (COI) successfully. Analysis of molecular variance revealed sharp differentiation among H. hebetor populations. Populations from Ahvaz, Dehloran, Jiroft and Minab were the most genetically diverged. A Mantel test showed significant positive correlation between genetic and geographic distances (r = 0.47, P < 0.001). The phylogenetic analysis clustered the populations into two major groups (A and B) (100); the major part was assigned to group A. Group B mainly included the populations from southern Iran. Based on these results, we conclude that H. hebetor in Iran is comprised of many diverse populations. These may be successfully applied in innundative release programs.

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.