QTLNetMiner - Demonstration of Candidate Gene Discovery in Plant and Animal Knowledge Networks

Correct identification of causative genes for an important agronomic trait can be very valuable for effective marker assisted breeding. Forward genetic approaches such as linkage analysis and association mapping can determine genomic regions (QTL) associated with a phenotype. However, even well-defined QTL often span genomic regions that can contain hundreds of positional candidate genes. Evaluation of potential functional candidates from such long lists is often time-consuming and requires the integration of information from many different sources such as gene function annotations, biochemical pathways, gene expression data, comparative information from related organisms, gene knock-out and the scientific literature.  We will demonstrate how to use the open-source Ondex framework (www.ondex.org) to integrate data gathered from multiple databases into a semantically consistent knowledge network.

Data mining and visualisation tools are required to mine custom knowledge networks in a systematic and automated manner to discover trait related candidate genes. We will demonstrate QTLNetMiner – a user-friendly web application to interrogate plant and animal knowledge networks and to show candidate genes and QTL associated with given input terms (e.g. early flowering, disease resistance). The relevance of a gene to particular query terms is weighted using information retrieval and network inference methods. The supporting evidence networks for selected candidate genes are visualized in the Ondex Web Java-applet. QTLNetMiner is designed in a generic way and can be created for any organism with a sequenced genome and an integrated Ondex knowledge network. We have currently developed QTLNetMiner instances for Arabidopsis, poplar, potato, pig, cattle and chicken, see http://ondex.rothamsted.ac.uk/QTLNetMiner/.