Molecular Breeding

, 36:127 | Cite as

Genomic regions conferring resistance to multiple fungal pathogens in synthetic hexaploid wheat

  • Abdulqader Jighly
  • Manickavelu Alagu
  • Farid Makdis
  • Murari Singh
  • Sukhwinder Singh
  • Livinus C. Emebiri
  • Francis C. Ogbonnaya


Fungal diseases are among the most devastating biotic stresses and often cause significant losses in wheat production worldwide. A set of 173 synthetic hexaploid wheat (SHW) characterized for resistance against fungal pathogens that cause leaf, stem and yellow rusts, yellow leaf spot, Septoria nodorum and crown rot were used in genome-wide association study (GWAS). Diversity Arrays Technology (DArT) and DArTSeq markers were employed for marker–trait association in which 74 markers associated with 35 quantitative trait loci (QTL) were found to be significantly linked with disease resistances using a unified mixed model (P = 10−3 to 10−5); Of these 15 QTL originated from D genome. Six markers on 1BL, 3BS, 4BL, 6B, and 6D conferred resistance to two diseases representing 10 of the 35 QTL. A further set of 147 SHW genotyped with DArT only markers validated 11 QTL detected in the previous 173 SHW. We also confirmed the presence of the gene Lr46/Yr29/Sr58/Pm39/Ltn2 on 1BL in the SHW germplasm. In addition, gene–gene interactions between significantly associated loci and all loci across the genome revealed five significant interactions at FDR <0.05. Two significant leaf rust and one stem rust interactions were thought to be synergistic, while another two QTL for yellow leaf spot involved antagonistic relations. To the best of our knowledge, this is the first GWAS for six fungal diseases using SHW. Identification of markers associated with disease resistance to one or more diseases represents an important resource for pyramiding favorable alleles and introducing multiple disease resistance from SHW accessions into current elite wheat cultivars.


Linkage disequilibrium Multiple disease resistance Synthetic hexaploid wheat Genome-wide association study Gene–gene interaction Genotyping by sequencing 



Multiple disease resistance


Synthetic hexaploid wheat


Mixed linear model


Quantitative trait loci


Genome-wide association study


Linkage disequilibrium


Leaf rust


Stem rust


Yellow rust


Yellow leaf spot


Septoria Nodorum glume blotch


Septoria Nodorum leaf blotch


Crown rot


Diversity Arrays Technology



The authors acknowledge financial support from the Grains Research and Development Corporation, International Centre for Agricultural Research in the Dry Areas (ICARDA), the International Maize and Wheat Improvement Centre (CIMMYT) and Department of Environment and Primary Industries, Victoria. They thank J. Wilson, M. S. McLean, S. P. Taylor, J. P. Thompson, H. S. Bariana, M. M. Shankar, and A. Milgate for their assistance with disease phenotyping.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interest.

Supplementary material

11032_2016_541_MOESM1_ESM.xlsx (35 kb)
Table S1 List of the 320 synthetic hexaploid wheat genotypes used in this study, their pedigrees and their phenotypes scored from 1 (susceptible) to 9 (resistant) (XLSX 34 kb)
11032_2016_541_MOESM2_ESM.docx (13 kb)
Table S2 The number of mapped DArT and DArTSeq markers on each wheat chromosome (DOCX 13 kb)
11032_2016_541_MOESM3_ESM.xlsx (7.8 mb)
Table S3 The full genotypic data for both main and validation sets (XLSX 7994 kb)
11032_2016_541_MOESM4_ESM.xlsx (69 kb)
Table S4 Genotypes of the associated marker for the main set (XLSX 69 kb)
11032_2016_541_MOESM5_ESM.docx (14 kb)
Table S5 QTL detected when analyzing the validation and the main panels as one set (DOCX 13 kb)
11032_2016_541_MOESM6_ESM.png (3 kb)
Figure S1 Results of the response of 320 SHWs to each of the six diseases evaluated. YLS = yellow leaf spot, Cr = crown rot, Lr = leaf rust, Sr = stem rust, Yr = yellow rust, SNL = Stagonospora nodorum leaf blotch, SNG = Stagonospora nodorum glume blotch. S = susceptible, MS = moderately susceptible, MR = moderately resistant, and R = resistant (PNG 2 kb)
11032_2016_541_MOESM7_ESM.pdf (58 kb)
Figure S2 Pseudo-heritability estimation inferred from the mixed model for the studied traits (PDF 58 kb)
11032_2016_541_MOESM8_ESM.png (8 kb)
Figure S3 Map position of both DArT (red) and DArTSeq (black) markers on wheat genome (PNG 8 kb)
11032_2016_541_MOESM9_ESM.png (124 kb)
Figure S4 Phylogenetic tree of the 320 SHWs, red genotypes represent the main set while blue genotypes represent the validation set (PNG 123 kb)
11032_2016_541_MOESM10_ESM.png (315 kb)
Figure S5 Kinship relations for the 320 SHWs (PNG 314 kb)
11032_2016_541_MOESM11_ESM.png (199 kb)
Figure S6 Scatter plot for the genetic distance against R2 value for each pair of markers on the same chromosome (LD decay) for a) whole genome; b) genome A; c) genome B; d) genome D. Red lines represent the LOESS second degree smoothing while the blue horizontal lines represents the R2 cut off 0.2 (PNG 199 kb)
11032_2016_541_MOESM12_ESM.png (3 kb)
Figure S7 Inter-chromosomal R2 values for each pair of markers for each genome (PNG 2 kb)
11032_2016_541_MOESM13_ESM.pdf (1.3 mb)
Figure S8 Manhattan and QQ plots for studied traits. Cr = crown rot, Lr = leaf rust, Sr = stem rust, Yr = yellow rust, SNL = Stagonospora nodorum leaf blotch, SNG = Stagonospora nodorum glume blotch and YLS = yellow leaf spot. Chromosomes were numbered starting from the homoeologous chromosome group one to seven with within group order of A, B and D genome, respectively. Chromosome 22 represents the unmapped markers (PDF 1379 kb)
11032_2016_541_MOESM14_ESM.png (43 kb)
Figure S9 The average disease score (the allelic effect) for the alleles of the markers with multiple associations. For the 6D QTL, we used only the marker 1126778 (PNG 42 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Abdulqader Jighly
    • 1
    • 2
    • 3
  • Manickavelu Alagu
    • 1
    • 4
  • Farid Makdis
    • 1
  • Murari Singh
    • 1
  • Sukhwinder Singh
    • 5
  • Livinus C. Emebiri
    • 6
  • Francis C. Ogbonnaya
    • 1
    • 7
  1. 1.International Centre for Agricultural Research in the Dry Areas (ICARDA)RabatMorocco
  2. 2.Department of Economic Development, Jobs, Transport and ResourcesAgriBio, Centre for AgribioscienceBundooraAustralia
  3. 3.School of Applied Systems BiologyLa Trobe UniversityBundooraAustralia
  4. 4.Kihara Institute for Biological ResearchYokohama City UniversityMaioka, YokohamaJapan
  5. 5.International Maize and Wheat Improvement Center (CIMMYT)TexcocoMexico
  6. 6.Graham Centre for Agricultural Innovation (NSW Department of Primary Industries and Charles Sturt University)Wagga WaggaAustralia
  7. 7.Grains Research and Development CorporationKingstonAustralia

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