Dr. Richard Harrison

Head of department; Genetics, Genomics and Breeding

Research Focus

Plant-microbe interactions, improving food security and plant health.

Scientific Activities

Dr. Harrison leads a multidisciplinary research group that seeks to understand the evolutionary process that occur between pathogens and plants, specifically the evolution of host resistance and pathogen virulence and in the case of strawberry, the role that polyploidy plays in these interactions. His group studies the genomic architecture of bacteria, fungi and oomycetes at the molecular and the population level. The group works with genetically complex crops such as strawberry, apple and broadleaf tree species and work in these crop systems is directed towards identifying the molecular mechanisms controlling resistance to a range of plant pathogens.

Many of the current research projects that he leads have a translational component that aims to utilise information about the pathogen’s effector gene complement to enable plant breeders to select and pyramid resistance. To carry out translational genomics work in non-model crops, part of the group develop bioinformatics tools for the analysis of complex, highly heterozygous genomes. All research sits within the broad area of improving food security and plant health.

Significant Publications

Gomez-Cortecero A, Saville RJ, Scheper RWA, Bowen JK, Agripino de Medeiros H, Kingsnorth J, Xu Xiangming and Harrison RJ (2016) Variation in host and pathogen in the Neonectria/Malus interaction; towards an understanding of the genetic basis of resistance to European canker. Front Plant Sci. 2016;7: 1365.

Baroncelli R., Buchvaldt-Amby D., Zapparata A., Sarrocco S., Vannacci G., Le Floch G., Harrison R.J., Holub E., Sukno S.A., Sreenivasaprasad S. and Thon M.R. (2016) Gene family expansions and contractions are associated with host range in plant pathogens of the genus Colletotrichum – BMC Genomics 17-555 Li,

B., Hulin, M. T., Brain, P., Mansfield, J.W., Jackson, R.W. and Harrison, R.J. (2015). Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis. Plant Methods, 11(1), p.57.

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Collaborators