Context

In this final stage of the project we will estimate how much susceptibility to AOD is inherited, look for genes that may confer resistance and predict individuals most likely to be resistant. We will also use genomic data to look for associations with traits of interest to foresters, and combine our data with data from Europe to predict environmental adaptation in oaks.

Research aims and objectives

• Estimate the heritability of susceptibility of oak trees to AOD, and identification of parts of the genome associated with AOD resistance or susceptibility.
• Find possible parts of the genome related to phenotypes of interest to foresters and ecologists.
• Find parts of the genome related to environmental variables across UK and Europe oak populations.

Project description

In this project we will do the following:

Tissue and phenotype collection: Forest Research (FR) will select 400 trees, take swabs from AOD lesions, take leaf samples and phenotype the trees. FR will analyse swab samples to confirm AOD infection.

Genome sequencing: Kew will arrange the extraction of DNA from the samples and the whole genome re-sequencing of the DNA. This novel data will be made available on a public repository. Together with data already generated we will have  approx. 30TB of total oak genomic data for around 2000 individuals. We will align raw reads to the oak genome and call genome-wide variants (points of difference between individuals).

GWAS and genome prediction: We will carry out Genome Wide Association Studies (GWAS) to  test for associations between genome-wide variants and presence/absence of AOD. If this evidence indicates a genetic component to susceptibility, we will carry out ‘genomic prediction’. This is a way of calculating breeding values for individual trees and can identify oak trees that should be used for future plantings. GWAS will also be carried out for useful phenotypic traits.

Genome-environment association: Additionally, we will carry out GEA studies, which look for associations between points on the genome and environmental variables. To augment our dataset, we will download published datasets from Europe.

Outputs