Leaf rust, caused by Puccinia triticina Eriks. is a common and widespread disease of bread wheat (Triticum aestivum L.), in Argentina. Host resistance is the most economical, effective and ecologically sustainable method of controlling the disease. Gene postulation helps to determine leaf rust resistance genes (Lr genes) that may be present in a large group of wheat germplasm. Additionally presence of Lr genes can be determined using associated molecular markers. The objective of this study was to identify Lr genes that condition leaf rust resistance in 66 wheat cultivars from Argentina. Twenty four differential lines with individual known leaf rust resistance genes were tested with 17 different pathotypes of leaf rust collected from Argentina. Leaf rust infection types produced on seedling plants of the 66 local cultivars were compared with the infection types produced by the same pathotypes on Lr differentials to postulate which seedling leaf rust genes were present. Presence of Lr9, Presence of Lr21, Lr25, Lr29, and Lr47 could not be determined with the seventeen pathotypes used in the study because all were avirulent to these genes. Eleven cultivars (16.7%) were resistant to all pathotypes used in the study and the remaining 55 (83.3%) showed virulent reaction against one or more local pathotypes. Cultivars with seedling resistance gene combinations including Lr16 or single genes Lr47 (detected with molecular marker), Lr19 and Lr41, showed high levels of resistance against all pathotypes or most of them. On the opposite side, cultivars with seedling resistance genes Lr1, Lr3a, Lr3a + Lr24, Lr10, Lr3a + Lr10, Lr3a + Lr10 + Lr24 showed the highest number of virulent reactions against local pathotypes. Occurrence of adult plant resistance genes Lr34, Lr35 and Lr37 in local germplasm was evaluated using specific molecular markers confirming presence of Lr34 and Lr37. Our data suggest that combinations including seedling resistance genes like Lr16, Lr47, Lr19, Lr41, Lr21, Lr25 and Lr29, with adult plant resistance genes like Lr34, SV2, Lr46 will probably provide durable and effective resistance to leaf rust in the region.

BackgroundIncreasing wheat (Triticum aestivum L.) production is required to feed a growing human population. In order to accomplish this task a deeper understanding of the genetic structure of cultivated wheats and the detection of genomic regions significantly associated with the regulation of important agronomic traits are necessary steps. To better understand the genetic basis and relationships of adaptation and yield related traits, we used a collection of 102 Argentinean hexaploid wheat cultivars genotyped with the 35k SNPs array, grown from two to six years in three different locations. Based on SNPs data and gene-related molecular markers, we performed a haplotype block characterization of the germplasm and a genome-wide association study (GWAS).ResultsThe genetic structure of the collection revealed four subpopulations, reflecting the origin of the germplasm used by the main breeding programs in Argentina. The haplotype block characterization showed 1268 blocks of different sizes spread along the genome, including highly conserved regions like the 1BS chromosome arm where the 1BL/1RS wheat/rye translocation is located. Based on GWAS we identified ninety-seven chromosome regions associated with heading date, plant height, thousand grain weight, grain number per spike and fruiting efficiency at harvest (FEh). In particular FEh stands out as a promising trait to raise yield potential in Argentinean wheats; we detected fifteen haplotypes/markers associated with increased FEh values, eleven of which showed significant effects in all three evaluated locations. In the case of adaptation, the Ppd-D1 gene is consolidated as the main determinant of the life cycle of Argentinean wheat cultivars.ConclusionThis work reveals the genetic structure of the Argentinean hexaploid wheat germplasm using a wide set of molecular markers anchored to the Ref Seq v1.0. Additionally GWAS detects chromosomal regions (haplotypes) associated with important yield and adaptation components that will allow improvement of these traits through marker-assisted selection.

BackgroundResearch projects often involve observation, registration, and data processing starting from information obtained in field experiments. In many cases, these tasks are carried out by several persons in different places, times, and ways, adding different levels of complexity and error in data collecting. Furthermore, data processing can be time consuming, and input errors may produce unwanted results. ResultsWe have developed a novel, open source software called Phenobook, an easy, flexible, and intuitive tool to organize, collect, and save experimental data for further analyses. Phenobook was conceived to collect phenotypic observations in a user-friendly, cost-effective way. It consists of a web-based software for experiment design, data input and visualization, and exportation, combined with a mobile application for remote data collecting. We provide in this article a detailed description of the developed tool. ConclusionPhenobook is a software tool that can be easily implemented in collaborative research and development projects involving data collecting and forward analyses. Adopting Phenobook is expected to improve the involved processes by minimizing input errors, resulting in higher quality and reliability of the research outcomes.

Seed storage proteins (gliadins and glutenins) play a key role in the determination of dough and bread-making quality in bread wheat. This is due to the interaction between high and low molecular weight glutenins subunits and gliadins, via complex inter-and intramolecular bondings. In contrast to high molecular weight glutenins, low molecular weight glutenins and gliadins analysis is difficult due to the large number of expressed subunits and coding genes. For these reasons the role of individual proteins/subunits in the determination of wheat quality is less clear. In this work we studied the effect of gene clusters Glu-A3/ Gli-A1 and Glu-D3/Gli-D1 in bread-making quality parameters using 20 F4-6 families from the cross Prointa Guazú × Prointa Oasis, both cultivars carrying identical high molecular weight glutenins subunits composition and presence of 1BL/1RS wheat-rye translocation, but differing in Glu-A3/Glu-D3 low molecular weight glutenins subunits and Gli-A1/Gli-D1 gliadins patterns. ANCOVA analysis showed a significant contribution of the Glu-D3/Gli-D1 gene cluster provided by Prointa Guazú to gluten strength explained by mixograph parameters MDS and PW, and Zeleny Test. Markers tagging Prointa Guazú Glu-D3/Gli-D1 alleles are available for strong gluten selection in breeding programs.