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Doonan, J. H.

Publications and source records attributed to Doonan, J. H..

2 recordsLinked to original sources

Increased and ectopic expression of Triticum polonicum VRT-A2 underlies elongated glumes and grains in hexaploid wheat in a dosage-dependent manner

Flower development is a major determinant of yield in crops. In wheat, natural variation for the size of spikelet and floral organs is particularly evident in Triticum polonicum, a tetraploid subspecies of wheat with long glumes, lemmas, and grains. Using map-based cloning, we identified VRT2, a MADS-box transcription factor belonging to the SVP family, as the gene underlying the T. polonicum long-glume (P1) locus. The causal P1 mutation is a sequence re-arrangement in intron-1 that results in both increased and ectopic expression of the T. polonicum VRT-A2 allele. Based on allelic variation studies, we propose that the intron-1 mutation in VRT-A2 is the unique T. polonicum species defining polymorphism, which was later introduced into hexaploid wheat via natural hybridizations. Near-isogenic lines differing for the P1 locus revealed a gradient effect of P1 across florets. Transgenic lines of hexaploid wheat carrying the T. polonicum VRT-A2 allele show that expression levels of VRT-A2 are highly correlated with spike, glume, grain, and floral organ length. These results highlight how changes in expression profiles, through variation in cis-regulation, can impact on agronomic traits in a dosage-dependent manner in polyploid crops. One-sentence summaryAn intron-1 rearrangement in the MADS-box transcription factor VRT-A2 leads to its misexpression and defines the long-glume phenotype of Polish wheat (T. polonicum).

plant biology

KnetMiner - Intelligent search and visualisation of connected data to explain complex traits and diseases

Generating new ideas and scientific hypotheses is often the result of extensive literature and database reviews, overlaid with scientists own novel data and a creative process of making connections that were not made before. We have developed a comprehensive approach to guide this technically challenging data integration task and to make knowledge discovery and hypotheses generation easier for plant and crop researchers. KnetMiner can digest large volumes of scientific literature and biological research to find and visualise links between the genetic and biological properties of complex traits and diseases. Here we report the main design principles behind KnetMiner and provide use cases for mining public datasets to identify unknown links between traits such grain colour and pre-harvest sprouting in Triticum aestivum, as well as, an evidence-based approach to identify candidate genes under an Arabidopsis thaliana petal size QTL. We have developed KnetMiner knowledge graphs and applications for a range of species including plants, crops and pathogens. KnetMiner is the first open-source gene discovery platform that can leverage genome-scale knowledge graphs, generate evidence-based biological networks and be deployed for any species with a sequenced genome. KnetMiner is available at http://knetminer.org.

bioinformatics