Fine mapping, physical mapping and development of diagnostic markers for the Rrs2 scald resistance gene in barley.

The Rrs2 gene confers resistance to the fungal pathogen Rhynchosporium secalis which causes leaf scald, a major barley disease. The Rrs2 gene was fine mapped to an interval of 0.08 cM between markers 693M6_6 and P1D23R on the distal end of barley chromosome 7HS using an Atlas (resistant) x Steffi (susceptible) mapping population of 9,179 F(2)-plants. The establishment of a physical map of the Rrs2 locus led to the discovery that Rrs2 is located in an area of suppressed recombination within this mapping population. The analysis of 58 barley genotypes revealed a large linkage block at the Rrs2 locus extending over several hundred kb which is present only in Rrs2 carrying cultivars. Due to the lack of recombination in the mapping population and the presence of a Rrs2-specific linkage block, we assume a local chromosomal rearrangement (alien introgression or inversion) in Rrs2 carrying varieties. The variety analysis led to the discovery of eight SNPs which were diagnostic for the Rrs2 phenotype. Based on these SNPs diagnostic molecular markers (CAPS and pyrosequencing markers) were developed which are highly useful for marker-assisted selection in resistance gene pyramiding programmes for Rhynchosporium secalis resistance in barley.

Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork.

BACKGROUND: A typical genetical genomics experiment results in four separate data sets; genotype, gene expression, higher-order phenotypic data and metadata that describe the protocols, processing and the array platform. Used in concert, these data sets provide the opportunity to perform genetic analysis at a systems level. Their predictive power is largely determined by the gene expression dataset where tens of millions of data points can be generated using currently available mRNA profiling technologies. Such large, multidimensional data sets often have value beyond that extracted during their initial analysis and interpretation, particularly if conducted on widely distributed reference genetic materials. Besides quality and scale, access to the data is of primary importance as accessibility potentially allows the extraction of considerable added value from the same primary dataset by the wider research community. Although the number of genetical genomics experiments in different plant species is rapidly increasing, none to date has been presented in a form that allows quick and efficient on-line testing for possible associations between genes, loci and traits of interest by an entire research community. DESCRIPTION: Using a reference population of 150 recombinant doubled haploid barley lines we generated novel phenotypic, mRNA abundance and SNP-based genotyping data sets, added them to a considerable volume of legacy trait data and entered them into the GeneNetwork http://www.genenetwork.org. GeneNetwork is a unified on-line analytical environment that enables the user to test genetic hypotheses about how component traits, such as mRNA abundance, may interact to condition more complex biological phenotypes (higher-order traits). Here we describe these barley data sets and demonstrate some of the functionalities GeneNetwork provides as an easily accessible and integrated analytical environment for exploring them. CONCLUSION: By integrating barley genotypic, phenotypic and mRNA abundance data sets directly within GeneNetwork's analytical environment we provide simple web access to the data for the research community. In this environment, a combination of correlation analysis and linkage mapping provides the potential to identify and substantiate gene targets for saturation mapping and positional cloning. By integrating datasets from an unsequenced crop plant (barley) in a database that has been designed for an animal model species (mouse) with a well established genome sequence, we prove the importance of the concept and practice of modular development and interoperability of software engineering for biological data sets.