Genomic basis of European ash tree resistance to ash dieback fungus

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Abstract

Populations of European ash trees (Fraxinus excelsior) are being devastated by the invasive alien fungus Hymenoscyphus fraxineus, which causes ash dieback. We sequenced whole genomic DNA from 1,250 ash trees in 31 DNA pools, each pool containing trees with the same ash dieback damage status in a screening trial and from the same seed-source zone. A genome-wide association study identified 3,149 single nucleotide polymorphisms (SNPs) associated with low versus high ash dieback damage. Sixty-one of the 192 most significant SNPs were in, or close to, genes with putative homologues already known to be involved in pathogen responses in other plant species. We also used the pooled sequence data to train a genomic prediction model, cross-validated using individual whole genome sequence data generated for 75 healthy and 75 damaged trees from a single seed source. The model’s genomic estimated breeding values (GEBVs) allocated these 150 trees to their observed health statuses with 67% accuracy using 10,000 SNPs. Using the top 20% of GEBVs from just 200 SNPs, we could predict observed tree health with over 90% accuracy. We infer that ash dieback resistance in F. excelsior is a polygenic trait that should respond well to both natural selection and breeding, which could be accelerated using genomic prediction.

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Fig. 1: Summary of variation among the sequenced DNA pools using correspondence analysis.
Fig. 2: Manhattan plot for pool-seq genome-wide association study of tree health under natural ash dieback inoculation.
Fig. 3: Manhattan plots for contigs containing genes with missense variants associated with tree health under natural ash dieback inoculations.
Fig. 4: Performance of genomic prediction models for health under ash dieback pressure.
Fig. 5: Performance of genomic prediction models for selection.

Code availability

The gppool pipeline developed as part of the project to run GP trained on pool-seq data can be found at https://github.research.its.qmul.ac.uk/btx330/gppool. All software used (Trimmomatic v.0.38, BWA MEM v.0.7.17, SAMtools v.1.9, BCFtools v.1.8, VCFtools v.0.1.15, PoPoolation2, R v.3.5.3, Repeatmasker v.4.0.5, Bowtie v.2.3.0, Blobtools v.1.1, SNPeff v.4.3 T, Haploview, rrBLUP v.4.6, NCBI BLAST, RaptorX-Binding, Q-value 2.16.0, SWISS-MODEL Phyre2, SMILES, Autodock Vina v.1.1.2, PyRx v.0.8, PyMOL v.2.0, DRONA, SignalP 4.1 server, Phobius server and NetPhos 3.1 Server) are commercially or freely available.

Data availability

All trimmed reads are available at the European Nucleotide Archive with primary accession number: PRJEB31096. A guide to these is given in Supplementary Table 7b. The reference F. excelsior genome is available for download at www.ashgenome.org and is Assembly GCA_900149125.1 at the European Nucleotide Archive. Biological Materials from the Forest Research Mass Screening trials are available through negotiation of a Materials Transfer Agreement with Forest Research, Northern Research Station, Roslin, Midlothian EH25 9SY.

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Acknowledgements

This study was supported by Forest Research, Queen Mary University of London and the Royal Botanic Gardens, Kew. J.J.S. was funded by a Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) studentship 202790/2014-2 and was part of the Brazilian Scientific Mobility Program—Science without Borders (SwB). S.J.L. and R.J.A.B. were partly funded by Living with Environmental Change (LWEC) Tree Health and Plant Biosecurity Initiative—Phase 2 grant BB/L012162/1, funded jointly by the Biotechnology and Biological Sciences Research Council, the Department for Environment, Food and Rural Affairs (Defra), the Economic and Social Research Council, the Forestry Commission, the Natural Environment Research Council and the Scottish Government. R.J.A.B. and L.J.K. were also supported in this work by funding from the Defra Future Proofing Plant Health scheme and the Erica Waltraud Albrecht Endowment Fund. Sequencing was paid for by a direct grant from Defra to the Royal Botanic Gardens, Kew. W.J.P. was supported by a Walsh Fellowship from the Department of Agriculture, Food and the Marine, Ireland. C.L.M. was supported by a studentship funded by Defra. Forest Research designed and set up the field trials with funding supplied by Defra, contract number TH032, ‘Rapid screening for Chalara resistance using ash trees currently in commercial nurseries’, with additional financial contribution from the Department of Agriculture, Food and the Marine, Ireland. The ash trees were all British-grown and sourced from various participating nurseries in England and Scotland. Maelor Forest Nurseries donated free of charge around half the total number of trees planted.

Author information

J.J.S. performed the field assessments and sampling, data analysis for the GWAS, GP trained on Dataset B and wrote the manuscript. R.J.A.B. supervised field work, data analysis and interpretation and wrote the manuscript. L.J.K. analysed genetic data. S.J.L. designed the field trials. R.A.N. designed the statistical approaches. C.L.M. developed and performed methods for GP with training on pool-seq data. W.J.P. modelled the proteins. All authors reviewed the manuscript.

Correspondence to Richard J. A. Buggs.

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Extended data

Extended Data Fig. 1 Schematic overview of the study design.

Showing sampling and pooling strategies and dependencies of analyses for genome-wide association study and genomic prediction.

Extended Data Fig. 2 Circle plot of major allele frequency correlation values between all 31 pools in the Pool-seq dataset.

Numbers after seed source code correspond to health status (1 - healthy or 2 - damaged by ADB). Pool NSZ204:1 (with low ADB damage) was technically replicated (NSZ204:1R) using the same set of trees. Both pools from NSZ106 and NSZ107 were biologically replicated for both high and low damage pools, using different sets of trees. High correlation for both technical (NSZ204:1R) and biological replicates (NSZ 106 & 107) can be seen.

Extended Data Fig. 3 Detection of contamination in the F. excelsior reference genome (BATG0.5).

Blobtools plot for the showing taxonomic affiliation at the phylum rank level, distributed according to GC content and base coverage. Contigs that were not classified as streptophyta corresponded to 0.5% of the genome assembly and 0.24% of all mapped reads.

Extended Data Fig. 4 Pool-seq GWAS p-value density histogram with line plots of the q-values and local False Discovery Rate (FDR) values versus p-values.

The π0 estimate is also displayed.

Extended Data Fig. 5 Predicted protein structures for genes containing amino acid changes associated with tree health status under ADB pressure.

The protein structures to the left were more common in damaged trees, and those to the right were more common in healthy trees. Variant amino acids are coloured in magenta and indicated with a black arrowhead. (a) Gene FRAEX38873_v2_000003260, a BED finger-NBS-LRR resistance protein, where position 157 is a leucine (left) versus tryptophan (right) variant. Two ATP molecules are shown in orange to indicate the location of nucleotide binding sites. (b) Gene FRAEX38873_v2_000164520, a F-box/kelch-repeat, where position 13 is a glutamine (left) versus arginine (right) variant. (c) FRAEX38873_v2_000180950, a Protein DAMAGED DNA-BINDING, where position 99 is a proline (left) versus leucine (right) variant. DNA molecules are shown in orange docked at the proteins’ DNA binding sites. (d) Gene FRAEX38873_v2_000116110, a 60S ribosomal protein L4-1, where position 251 is an arginine (left) versus glycine (right) variant, position 285 is a methionine (left) versus arginine (right) variant, position 287 is an asparagine (left) versus lysine (right) variant and position 297 is a threonine (left) versus alanine (right) variant.

Extended Data Fig. 6 Genomic prediction results using the 150 individually genotyped samples (Dataset B) as both training and testing set, showing little difference between GWAS SNPs and random SNPs in correlations between GEBVs and health statuses.

(A) GEBV-health status correlation using GWAS candidate SNPs with all data filters applied (mapping quality, indel and repeat removal); (B) GEBV-health status correlation using GWAS candidate SNPs only filtering by mapping quality and indel removal; (C) GEBV-health status correlation using random selection of SNPs and all data filters (mean and standard error shown for N=10 runs, each of 500 iterations); (D) GP allocation accuracy using GWAS candidate SNPs with all data filters applied. The scale on the left hand vertical axis is for correlation, and the scale on the right hand vertical axis is for accuracy. 100 to 5 million SNPs used to train and test the rrBLUP model.

Extended Data Fig. 7 Genomic prediction using Pool-seq data for training and 150 NSZ 204 individuals for testing.

Dashed lines show results excluding Pool-seq data from NSZ 204 (the test seed source) from the training dataset, whereas solid lines show results with NSZ 204 included. The left column shows correlation of observed phenotype and GEBV and the right column shows accuracy of phenotypic assignment from GEBV.

Supplementary information

Supplementary Information

Supplementary Tables 1–6.

Reporting Summary

Supplementary Table 7

a, Contigs that may be contamination from other organisms in the Fraxinus excelsior BATG0.5 genome as identified by Blobtools. b, Guide to read data on ENA. c, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 100 SNPs from the pool-seq GWAS. d, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 200 SNPs from the pool-seq GWAS. e, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 500 SNPs from the pool-seq GWAS. f, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 1,000 SNPs from the pool-seq GWAS. g, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 5,000 SNPs from the pool-seq GWAS. h, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 10,000 SNPs from the pool-seq GWAS. i, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 25,000 SNPs from the pool-seq GWAS. j, Estimated effect sizes from genomic prediction model trained on the pool-seq data using the top 50,000 SNPs from the pool-seq GWAS.

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Stocks, J.J., Metheringham, C.L., Plumb, W.J. et al. Genomic basis of European ash tree resistance to ash dieback fungus. Nat Ecol Evol 3, 1686–1696 (2019) doi:10.1038/s41559-019-1036-6

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