Multiple, Single Trait GWAS and Supervised Machine Learning Reveal the Genetic Architecture of <i>Fraxinus excelsior</i> Tolerance to Ash Dieback in Europe

Abstract

Common ash (Fraxinus excelsior) is under intensive attack from the invasive alien pathogenic fungus Hymenoscyphus fraxineus, causing ash dieback at epidemic levels throughout Europe. Previous studies have found significant genetic variation among genotypes in ash dieback susceptibility and that host phenology, such as autumn yellowing, is correlated with susceptibility of ash trees to H. fraxineus; however, the genomic basis of ash dieback tolerance in F. excelsior requires further investigation. Here, we integrate quantitative genetics based on multiple replicates and genome-wide association analyses with machine learning to reveal the genetic architecture of ash dieback tolerance and of phenological traits in F. excelsior populations in six European countries (Austria, Denmark, Germany, Ireland, Lithuania, Sweden). Based on phenotypic data of 486 F. excelsior replicated genotypes we observed negative genotypic correlations between crown damage caused by ash dieback and intensity of autumn leaf yellowing within multiple sampling sites. Our results suggest that the examined traits are polygenic and using genomic prediction models, with ranked single nucleotide polymorphisms (SNPs) based on GWAS associations as input, a large proportion of the variation was predicted by unlinked SNPs. Based on 100 unlinked SNPs, we can predict 55% of the variation in This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</div