Ontology-driven International Maize Information System (IMIS) for Phenotypic and Genotypic Data Exchange

The Consultative Group on International Agricultural Research (CGIAR; http://www.cgiar.org/) centres have developed the International Crop Information System (ICIS; http://www.icis.cgiar.org) for the management and integration of global information on genetic resources, and germplasm improvement for any crop. The Maize breeding programs at CIMMYT (http://beta.cimmyt.org/) have different software tools to manage phenotypic, genotypic, and environmental information for their experiments generated worldwide. These tools have the capacity of collecting information in the field, wet lab, and store it into different relational databases. The IMIS (http://imis.cimmyt.org/confluence/display/IMIS/Crop+Finder) is an implementation of the ICIS, which is a computerized database system for general, integrated management and utilization of genealogy, nomenclature, genetic, phenotypic and characterization data for maize. Data exchange within and between databases as well as retrieving information are often hampered by the variability of terms used to describe comparable objects. To overcome this problem, the Crop Ontology (CO) database (http://cropontology.org/) is developed. It provides controlled vocabulary sets for several economically important plant species and facilitates biocurators working in genebanks of plant genetic resources (PGR) and crop breeding data curation and annotation. The maize trait ontology is developed as one of subclasses of CO trait ontology providing standardized trait descriptions, scales and scale values implemented into the IMIS. This ontology-driven IMIS will allow researchers who wish to exploit comparative phenotypic and genotypic information of maize to elucidate functional aspects of each trait

Precision-mapping and statistical validation of quantitative trait loci by machine learning

<p>Abstract</p> <p>Background</p> <p>We introduce a QTL-mapping algorithm based on Statistical Machine Learning (SML) that is conceptually quite different to existing methods as there is a strong focus on generalisation ability. Our approach combines ridge regression, recursive feature elimination, and estimation of generalisation performance and marker effects using bootstrap resampling. Model performance and marker effects are determined using independent testing samples (individuals), thus providing better estimates. We compare the performance of SML against Composite Interval Mapping (CIM), Bayesian Interval Mapping (BIM) and single Marker Regression (MR) on synthetic datasets and a multi-trait and multi-environment dataset of the progeny for a cross between two barley cultivars.</p> <p>Results</p> <p>In an analysis of the synthetic datasets, SML accurately predicted the number of QTL underlying a trait while BIM tended to underestimate the number of QTL. The QTL identified by SML for the barley dataset broadly coincided with known QTL locations. SML reported approximately half of the QTL reported by either CIM or MR, not unexpected given that neither CIM nor MR incorporates independent testing. The latter makes these two methods susceptible to producing overly optimistic estimates of QTL effects, as we demonstrate for MR. The QTL resolution (peak definition) afforded by SML was consistently superior to MR, CIM and BIM, with QTL detection power similar to BIM. The precision of SML was underscored by repeatedly identifying, at ≤ 1-cM precision, three QTL for four partially related traits (heading date, plant height, lodging and yield). The set of QTL obtained using a 'raw' and a 'curated' version of the same genotypic dataset were more similar to each other for SML than for CIM or MR.</p> <p>Conclusion</p> <p>The SML algorithm produces better estimates of QTL effects because it eliminates the optimistic bias in the predictive performance of other QTL methods. It produces narrower peaks than other methods (except BIM) and hence identifies QTL with greater precision. It is more robust to genotyping and linkage mapping errors, and identifies markers linked to QTL in the absence of a genetic map.</p

Molecular Pathogenesis of MALT Lymphoma

Approximately 8% of all non-Hodgkin lymphomas are extranodal marginal zone B cell lymphoma of mucosa associated lymphoid tissue (MALT), also known as MALT lymphoma, which was first described in 1983 by Isaacson and Wright. MALT lymphomas arise at a wide range of different extranodal sites, with the highest frequency in the stomach, followed by lung, ocular adnexa, and thyroid, and with a low percentage in the small intestine. Interestingly, at least 3 different, apparently site-specific, chromosomal translocations and missense and frameshift mutations, all pathway-related genes affecting the NF-κB signal, have been implicated in the development and progression of MALT lymphoma. However, these genetic abnormalities alone are not sufficient for malignant transformation. There is now increasing evidence suggesting that the oncogenic product of translocation cooperates with immunological stimulation in oncogenesis, that is, the association with chronic bacterial infection or autoaggressive process. This review mainly discusses MALT lymphomas in terms of their genetic aberration and association with chronic infections and summarizes recent advances in their molecular pathogenesis

A DArT platform for quantitative bulked segregant analysis

<p>Abstract</p> <p>Background</p> <p>Bulked segregant analysis (BSA) identifies molecular markers associated with a phenotype by screening two DNA pools of phenotypically distinct plants for markers with skewed allele frequencies. In contrast to gel-based markers, hybridization-based markers such as SFP, DArT or SNP generate quantitative allele-frequency estimates. Only DArT, however, combines this advantage with low development and assay costs and the ability to be deployed for any plant species irrespective of its ploidy level. Here we investigate the suitability of DArT for BSA applications using a barley array as an example.</p> <p>Results</p> <p>In a first test experiment, we compared two bulks of 40 Steptoe/Morex DH plants with contrasting pubescent leaves (mPub) alleles on chromosome 3H. At optimized levels of experimental replication and marker-selection threshold, the BSA scan identified 433 polymorphic markers. The relative hybridization contrast between bulks accurately reflected the between-bulk difference in the frequency of the mPub allele (r = 0.96). The 'platform noise' of DArT assays, estimated by comparing two identical aliquots of a DNA mixture, was significantly lower than the 'pooling noise' reflecting the binomial sampling variance of the bulking process. The allele-frequency difference on chromosome 3H increased in the vicinity of mPub and peaked at the marker with the smallest distance from mPub (4.6 cM). In a validation experiment with only 20 plants per bulk we identified an aluminum (Al) tolerance locus in a Dayton/Zhepi2 DH population on chromosome 4H with < 0.8 cM precision, the same Al-tolerance locus that had been mapped before in other barley populations.</p> <p>Conclusion</p> <p>DArT-BSA identifies genetic loci that influence phenotypic characters in barley with at least 5 cM accuracy and should prove useful as a generic tool for high-throughput, quantitative BSA in plants irrespective of their ploidy level.</p

DArT markers for the rye genome - genetic diversity and mapping

<p>Abstract</p> <p>Background</p> <p>Implementation of molecular breeding in rye (<it>Secale cereale </it>L.) improvement programs depends on the availability of high-density molecular linkage maps. However, the number of sequence-specific PCR-based markers available for the species is limited. Diversity Arrays Technology (DArT) is a microarray-based method allowing for detection of DNA polymorphism at several thousand loci in a single assay without relying on DNA sequence information. The objective of this study was the development and application of Diversity Arrays technology for rye.</p> <p>Results</p> <p>Using the <it>Pst</it>I/<it>Taq</it>I method of complexity reduction we created a rye diversity panel from DNA of 16 rye varieties and 15 rye inbred lines, including parents of a mapping population consisting of 82 recombinant inbred lines. The usefulness of a wheat diversity panel for identification of DArT markers for rye was also demonstrated. We identified 1022 clones that were polymorphic in the genotyped ILs and varieties and 1965 clones that differentiated the parental lines L318 and L9 and segregated in the mapping population. Hierarchical clustering and ordination analysis were performed based on the 1022 DArT markers to reveal genetic relationships between the rye varieties and inbred lines included in the study. Chromosomal location of 1872 DArT markers was determined using wheat-rye addition lines and 1818 DArT markers (among them 1181 unique, non-cosegregating) were placed on a genetic linkage map of the cross L318 × L9, providing an average density of one unique marker every 2.68 cM. This is the most saturated rye linkage map based solely on transferable markers available at the moment, providing rye breeders and researches with a better choice of markers and a higher probability of finding polymorphic markers in the region of interest.</p> <p>Conclusion</p> <p>The Diversity Arrays Technology can be efficiently and effectively used for rye genome analyses - assessment of genetic similarity and linkage mapping. The 11520-clone rye genotyping panel with several thousand markers with determined chromosomal location and accessible through an inexpensive genotyping service is a valuable resource for studies on rye genome organization and in molecular breeding of the species.</p

DArT markers: diversity analyses, genomes comparison, mapping and integration with SSR markers in Triticum monococcum

<p>Abstract</p> <p>Background</p> <p><it>Triticum monococcum </it>(2<it>n </it>= 2<it>x </it>= 14) is an ancient diploid wheat with many useful traits and is used as a model for wheat gene discovery. DArT (Diversity Arrays Technology) employs a hybridisation-based approach to type thousands of genomic loci in parallel. DArT markers were developed for <it>T. monococcum </it>to assess genetic diversity, compare relationships with hexaploid genomes, and construct a genetic linkage map integrating DArT and microsatellite markers.</p> <p>Results</p> <p>A DArT array, consisting of 2304 hexaploid wheat, 1536 tetraploid wheat, 1536 <it>T. monococcum </it>as well as 1536 <it>T. boeoticum </it>representative genomic clones, was used to fingerprint 16 <it>T. monococcum </it>accessions of diverse geographical origins. In total, 846 polymorphic DArT markers were identified, of which 317 were of <it>T. monococcum </it>origin, 246 of hexaploid, 157 of tetraploid, and 126 of <it>T. boeoticum </it>genomes. The fingerprinting data indicated that the geographic origin of <it>T. monococcum </it>accessions was partially correlated with their genetic variation. DArT markers could also well distinguish the genetic differences amongst a panel of 23 hexaploid wheat and nine <it>T. monococcum </it>genomes. For the first time, 274 DArT markers were integrated with 82 simple sequence repeat (SSR) and two morphological trait loci in a genetic map spanning 1062.72 cM in <it>T. monococcum</it>. Six chromosomes were represented by single linkage groups, and chromosome 4A^mwas formed by three linkage groups. The DArT and SSR genetic loci tended to form independent clusters along the chromosomes. Segregation distortion was observed for one third of the DArT loci. The <it>Ba </it>(black awn) locus was refined to a 23.2 cM region between the DArT marker locus <it>wPt-2584 </it>and the microsatellite locus <it>Xgwmd33 </it>on 1A^m; and the <it>Hl </it>(hairy leaf) locus to a 4.0 cM region between DArT loci <it>376589 </it>and <it>469591 </it>on 5A^m.</p> <p>Conclusion</p> <p>DArT is a rapid and efficient approach to develop many new molecular markers for genetic studies in <it>T. monococcum</it>. The constructed genetic linkage map will facilitate localisation and map-based cloning of genes of interest, comparative mapping as well as genome organisation and evolution studies between this ancient diploid species and other crops.</p