Gramene QTL database: development, content and applications

Abstract

Gramene is a comparative information resource for plants that integrates data across diverse data domains. In this article, we describe the development of a quantitative trait loci (QTL) database and illustrate how it can be used to facilitate both the forward and reverse genetics research. The QTL database contains the largest online collection of rice QTL data in the world. Using flanking markers as anchors, QTLs originally reported on individual genetic maps have been systematically aligned to the rice sequence where they can be searched as standard genomic features. Researchers can determine whether a QTL co-localizes with other QTLs detected in independent experiments and can combine data from multiple studies to improve the resolution of a QTL position. Candidate genes falling within a QTL interval can be identified and their relationship to particular phenotypes can be inferred based on functional annotations provided by ontology terms. Mutations identified in functional genomics populations and association mapping panels can be aligned with QTL regions to facilitate fine mapping and validation of gene-phenotype associations. By assembling and integrating diverse types of data and information across species and levels of biological complexity, the QTL database enhances the potential to understand and utilize QTL information in biological research.

Figure 1.

Number of published QTL papers for rice, wheat, maize, Arabidopsis and tomato between 1987 and 2007. Graph showing the steady increase in publications reporting QTLs in five major plant species between 1987 and 2007 based on nonredundant data from four publicly available literature databases, PubMed, Agricola, CAB Abstracts and BIOSIS Previews.

Figure 2.

Generalized model for candidate gene discovery. The diagram shows a simplified view of the information modules in the Gramene database. The figure has been designed to show the path of forward genetic dissection of complex traits above the dotted line and reverse genetic investigation below the heavy dotted line. In reality, as indicated by the arrows, the database can be accessed from any point of entry and users can navigate to the required information. Gramene provides automated analytical tools shown to the left of the pale grey box, and manual curation using ontologies and controlled vocabularies shown to the right of the dotted box, to enhance the data mining potential of the database. The association between the QTL and EST modules contained within the pale grey box highlights the fact that a user can systematically zero in on information about the relationship between a complex phenotype and a candidate gene/s of interest. As depicted here, a trait or complex phenotype is associated through maps and markers to a genomic region identified as a QTL and to ESTs and gene models previously annotated to the genome region; the QTL database thus performs the crucial function of connecting molecular or genotypic information with complex phenotypes, and more generally connecting the field of molecular biology with that of quantitative genetics and plant breeding.

Figure 3.

Iron-uptake pathway in rice: cloned gene to complex traits, via pathway, genes and QTLs. Leaf senescence QTLs aligned to CMap view of rice chromosome 3 shown in the top left corner; Genome Browser view of QTL region of rice chromosome 3 with associated gene models shown in the center; DMA metabolic pathway shown in the top right; rice plants expressing iron chlorosis tolerant phenotype and iron toxicity phenotype shown at the bottom. The Mugenic acids (MAs) or phytosiderophores are end products of the DMA metabolic pathway. Biosynthesis of MAs is closely linked to iron deficiency and is highly regulated to avoid toxicity owing to excess iron uptake. The figure represents parts of the curated DMA pathway; the genomic positions of the cloned genes from this pathway are used to trace suitable QTL candidates in that region. The populations from which these QTLs were analyzed can then be used in breeding programs aimed at developing rice varieties that are tolerant to soil environments characterized by deficiencies or excesses of iron.