Background The recent rapid accumulation of sequence resources of various crop species ensures an improvement in the genetics approach, including quantitative trait loci (QTL) analysis as well as the holistic population analysis and association mapping of natural variations. Because the tribe Triticeae includes important cereals such as wheat and barley, integration of information on the genetic markers in these crops should effectively accelerate map-based genetic studies on Triticeae species and lead to the discovery of key loci involved in plant productivity, which can contribute to sustainable food production. Therefore, informatics applications and a semantic knowledgebase of genome-wide markers are required for the integration of information on and further development of genetic markers in wheat and barley in order to advance conventional marker-assisted genetic analyses and population genomics of Triticeae species. Description: The Triticeae mapped expressed sequence tag (EST) database (TriMEDB) provides information, along with various annotations, regarding mapped cDNA markers that are related to barley and their homologues in wheat. The current version of TriMEDB provides map-location data for barley and wheat ESTs that were retrieved from 3 published barley linkage maps (the barley single nucleotide polymorphism database of the Scottish Crop Research Institute, the barley transcript map of Leibniz Institute of Plant Genetics and Crop Plant Research, and HarvEST barley ver. 1.63) and 1 diploid wheat map. These data were imported to CMap to allow the visualization of the map positions of the ESTs and interrelationships of these ESTs with public gene models and representative cDNA sequences. The retrieved cDNA sequences corresponding to each EST marker were assigned to the rice genome to predict an exon-intron structure. Furthermore, to generate a unique set of EST markers in Triticeae plants among the public domain, 3472 markers were assembled to form 2737 unique marker groups as contigs. These contigs were applied for pairwise comparison among linkage maps obtained from different EST map resources. Conclusions TriMEDB provides information regarding transcribed genetic markers and functions as a semantic knowledgebase offering an informatics facility for the acceleration of QTL analysis and for population genetics studies of Triticeae.

The high-throughput analytical techniques used in genome, proteome and metabolome studies produce large sets of data that must be studied using appropriate tools. The construction of networks linking different genetic elements and/or functions makes it possible to obtain an integrated view of the cell molecular biology and will eventually help us to predict complex phenotypes from molecular data. Genetic networks can be constructed using different types of data such as genes involved in the control of complex phenotypic traits, genes controlling global gene expression, genetic elements involved in the same metabolic process, gene products interacting physically between them. The connections linking these genetic elements in the network reflect the genetic, physical and/or functional interaction among them. All these networks share common properties and reflect the different layers of the cell's complexity. In this review, we will study how different types of networks can be constructed, how the different networks complement each other and how this information can be used to obtain an integrated picture of the cell. Keywords: genetic networks, QTL analysis, expression profiling, synthetic lethality, interactome networks

Summary: quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo

• Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. • Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. • An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F1s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. • Both comparative genomic analyses based on Arabidopsis–Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.

Many endosperm traits are related to grain quality in cereal crops. Endosperm traits are mainly controlled by the endosperm genome but may be affected by the maternal genome. Studies have shown that maternal genotypic variation could greatly influence the estimation of the direct effects of quantitative trait loci (QTLs) underlying endosperm traits. In this paper, we propose methods of interval mapping of endosperm QTLs using seeds of F2 or BC1 (an equal mixture of F1 x P1 and F1 x P2 with F1 as the female parent) derived from a cross between 2 pure lines (P1 x P2). The most significant advantage of our experimental designs is that the maternal effects do not contribute to the genetic variation of endosperm traits and therefore the direct effects of endosperm QTLs can be estimated without the influence of maternal effects. In addition, the experimental designs can greatly reduce environmental variation because a few F1 plants grown in a small block of field will produce sufficient F2 or BC1 seeds for endosperm QTL analysis. Simulation studies show that the methods can efficiently detect endosperm QTLs and unbiasedly estimate their positions and effects. The BC1 design is better than the F2 design.