Abstract
We utilized 2300 wheat accessions including worldwide landraces, cultivars and primary synthetic-derived germplasm with three Australian cultivars: Annuello, Yitpi and Correll, to investigate field-based resistance to leaf (Lr) rust, stem (Sr) rust and stripe (Yr) rust diseases across a range of Australian wheat agri-production zones. Generally, the resistance in the modern Australian cultivars, synthetic derivatives, South and North American materials outperformed other geographical subpopulations. Different environments for each trait showed significant correlations, with average r values of 0.53, 0.23 and 0.66 for Lr, Sr and Yr, respectively. Single-trait genome-wide association studies (GWAS) revealed several environment-specific and multi-environment quantitative trait loci (QTL). Multi-trait GWAS confirmed a cluster of Yr QTL on chromosome 3B within a 4.4-cM region. Linkage disequilibrium and comparative mapping showed that at least three Yr QTL exist within the 3B cluster including the durable rust resistance gene Yr30. An Sr/Lr QTL on chromosome 3D was found mainly in the synthetic-derived germplasm from Annuello background which is known to carry the Agropyron elongatum 3D translocation involving the Sr24/Lr24 resistance locus. Interestingly, estimating the SNP effects using a BayesR method showed that the correlation among the highest 1% of QTL effects across environments (excluding GWAS QTL) had significant correlations, with average r values of 0.26, 0.16 and 0.55 for Lr, Sr and Yr, respectively. These results indicate the importance of small effect QTL in achieving durable rust resistance which can be captured using genomic selection.
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Acknowledgements
The authors would like to thank Dr. Manisha Shankar for managing both the Carnarvon and Manjimup sites and collected the stripe rust phenotypic data from Manjimup; Dr. Andrew Milgate for managing the Wagga Wagga site and collected the stripe rust phenotypic data from Manjimup; Dr. Gill for managing the Cobbitty trial; Mr. Graham Exell for managing the field operations of the AgVic field nurseries.
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RJ analyzed the data and drafted the manuscript; HD, TG and MH supervised the study; GH phenotyped the germplasm; SK provided and multiplied the germplasm; KF, DW and JP genotyped the germplasm; FS processed the genotypic and phenotypic data; RP did the field experimental design; MH and JT managed the project; HB and UB provided the Watkins germplasm and edited the manuscript; MH and GS gave the final acceptance for the manuscript to be submitted. All authors read and approved the final version of the manuscript.
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Table S1. Passport information for the accessions used in the present study. Column FS_Pop represents the subpopulation inferred from fineStructure analysis; column Major represents the major cluster as shown in Fig. 3a; Column Geo_Pop represents the geographical subpopulation shown in Figure S1; Pop_Color represents the color of the geographical population as shown in Figure S1; column All3BS_QTL defines the genotypes the carry the three QTL on chromosome 3BS located between 9.7 and 14.1 cM. (XLSX 116 kb)
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Table S2. Average max and min temperature, rainfall, evaporation, radiation and vapor pressure every month during 2014 and 2015 in all studied locations. (XLSX 18 kb)
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Table S4. A) Detailed information about the QTL detected in the present study; B) QTL detected using BayesR only. The last 17 columns represent the frequency of the resistance allele within each subpopulation and their colors were scaled based on their values for easier visualization; first number represents the subpopulation ID (column “Geo_Pop” in Table S1), while the second number represents the number of genotypes in the subpopulation. NS, not significant; * the position of the SNP is defined using LD with other SNPs. (XLSX 118 kb)
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Figure S2. Pie charts representing the proportion of resistance, moderate or susceptible accessions within each subpopulation across all environments. R represents scores from 1 to 3, M represents scores from 4 to 6; and S represents scores from 7 to 9. Ca: Carnarvon; Co: Cobbitty; Ho: Horsham; Ma: Manjimup; Wa: Wagga Wagga. (PDF 186 kb)
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Figure S3. Phenotypic correlation between different traits/environments. Above triangle shows the correlation r value and its significance (*p < 0.05, **p < 0.01, and ***p < 0.001). Ca: Carnarvon; Co: Cobbitty; Ho: Horsham; Ma: Manjimup; Wa: Wagga Wagga. (PDF 209 kb)
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Figure S4. Linkage disequilibrium decay for all chromosomes together. The red line represents the Loess 2nd degree smoothing. The blue horizontal line represents the r2 critical value. (PNG 11 kb)
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Figure S5. Linkage disequilibrium decay for individual wheat chromosomes. Each column represents chromosomes of a sub-genome (A, B and D from left to right) and each row represents one chromosomal group (1 to 7 starting from top to bottom). The red line represents the Loess 2nd degree smoothing. The blue horizontal line represents the r2 critical value. (PNG 265 kb)
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Joukhadar, R., Hollaway, G., Shi, F. et al. Genome-wide association reveals a complex architecture for rust resistance in 2300 worldwide bread wheat accessions screened under various Australian conditions. Theor Appl Genet 133, 2695–2712 (2020). https://doi.org/10.1007/s00122-020-03626-9
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DOI: https://doi.org/10.1007/s00122-020-03626-9