Identification of New Leaf Rust Resistance Loci in Wheat and Wild Relatives by Array-Based SNP Genotyping and Association Genetics
Citation
Fatima, F., McCallum, B.D., Pozniak, C.J., Hiebert, C.W., McCartney, C.A., Fedak, G., You, F.M., Cloutier, S. (2020). Identification of New Leaf Rust Resistance Loci in Wheat and Wild Relatives by Array-Based SNP Genotyping and Association Genetics. Frontiers in Plant Science, [online] 11 http://dx.doi.org/10.3389/fpls.2020.583738
Plain language summary
Leaf rust is a wheat disease caused by a fungal pathogen. It is the most widespread rust disease of wheat, causing tremendous annual yield losses worldwide. Fungal populations are constantly changing and new virulence against previously resistant wheat varieties are occurring regularly. The identification of new sources of disease resistance is necessary to stay ahead of this plant-pathogen evolutionary arms race. Here, we aimed to identify novel sources of leaf rust resistance not only in cultivated wheat species but also in wild relatives species. To do so, we characterized 385 lines from 27 different species using genotypic and phenotypic information. The genomic information consisted of defining more than 20,000 DNA markers for each line. The lines were grown in the field for several years at multiple locations to measure the incidence of the leaf rust disease on each line. In addition, the lines were also tested in the greenhouse using six different rust isolates. We then performed in-depth analyses to “associate” the DNA marker data (genotypic) and the data from the two types of disease evaluation (phenotypic) to identify chromosomal regions where putative resistance genes reside, and we identified 96 such locations with high confidence. Such analyses are termed Genome-Wide Association Studies (GWAS). Twenty-one of the locations were found to be near known leaf rust resistance genes, while the remaining were considered to harbor new leaf rust resistance genes, including some that were present in wild relative species and that could be capitalized upon to improve wheat resistance to rust. Analyses of the genes at these locations provided some insight into the nature of the actual resistance gene. These discoveries provide a broad scope of information to improve leaf rust resistance in wheat through many avenues including marker-assisted breeding, gene isolation and editing, introgression and synthetic wheat.
Abstract
Leaf rust caused by Puccinia triticina is the most widespread rust disease of wheat. As pathogen populations are constantly evolving, identification of novel sources of resistance is necessary to maintain disease resistance and stay ahead of this plant-pathogen evolutionary arms race. The wild genepool of wheat is a rich source of genetic diversity, accounting for 44% of the Lr genes identified. Here we performed a genome-wide association study (GWAS) on a diverse germplasm of 385 accessions, including 27 different Triticum and Aegilops species. Genetic characterization using the wheat 90 K array and subsequent filtering identified a set of 20,501 single nucleotide polymorphic (SNP) markers. Of those, 9,570 were validated using exome capture and mapped onto the Chinese Spring reference sequence v1.0. Phylogenetic analyses illustrated four major clades, clearly separating the wild species from the T. aestivum and T. turgidum species. GWAS was conducted using eight statistical models for infection types against six leaf rust isolates and leaf rust severity rated in field trials for 3–4 years at 2–3 locations in Canada. Functional annotation of genes containing significant quantitative trait nucleotides (QTNs) identified 96 disease-related loci associated with leaf rust resistance. A total of 21 QTNs were in haplotype blocks or within flanking markers of at least 16 known Lr genes. The remaining significant QTNs were considered loci that putatively harbor new Lr resistance genes. Isolation of these candidate genes will contribute to the elucidation of their role in leaf rust resistance and promote their usefulness in marker-assisted selection and introgression.