Context

Ash dieback is a highly destructive disease of ash trees caused by an invasive fungus called Hymenoscyphus fraxineus. This fungus has spread widely across Europe and has killed large numbers of native European ash trees. However, a small number of ash trees have experienced less damage from the fungus, and previous research has found certain genetic markers (locations in the tree’s DNA) that are linked to better health when exposed to the fungus.

Being able to select genetically ‘superior’ trees when they are seedlings, could speed up the efforts to breed trees that are more resistant to the disease. This project will build on existing research by studying older ash trees and those that have been exposed to the disease for a longer period.

Research aims and objectives

Aim:

Better understand how natural resistance works over time, and to develop tools that can predict a tree’s resistance to ash dieback just from its DNA.

Objectives:

Re-analysis of existing ash tree resources

• Use the latest genetic tools and methods to reanalyse DNA from 1,200 ash trees collected in 2017, which will provide a more accurate view of which genetic traits are linked to ash dieback resistance over a short period. 

• Compare differences in the traits between younger and older trees from four natural woodlands to confirm that natural selection is already favouring more resistant trees within a single generation.

Improved understanding of the genetic basis of ash dieback resistance

• Undertake a study on 500 healthy, and 500 dead ash trees, compare their DNA to identify genetic differences linked to long-term survival, and compare against earlier results that focused on short-term ash dieback resistance. 

Generation of an ash dieback prediction model using whole-genome data

• Use this new genetic data to build a prediction model to identify how resistant a tree is to ash dieback based on its DNA.  

• Measure the accuracy of the model by how closely its predictions match the health of 400 trees planted in a 1995 ash trial.  

Expected outcomes

• Improved understanding of the genetics behind why some ash trees resist ash dieback.

• A more accurate prediction model to identify which trees are likely to stay healthy over the next five years, which will help identify the best trees to use in breeding programmes.

• A new prediction model to identify trees which are likely to survive 13 years or more with ash dieback.

• Testing the accuracy of these prediction tools using 30-year-old trees.

• Develop new methods for analysing tree DNA.

• Develop new tools to speed up the breeding of disease-resistant ash trees.