Pretorius pst symposium 2014

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Pretorius pst symposium 2014

  1. 1. Can crop sensing be used for mapping stripe rust resistance loci in wheat?
  2. 2. • Wheat rust research relies heavily on accurate phenotyping –Pathogen variability • Virulent or avirulent –Host reponse • Resistant or susceptible • R gene phenotype Wheat Rust Phenotyping
  3. 3. • Cobb scale (Cobb, 1892) – Percentages are equal to actual leaf area covered by rust Diagrammatic Scales for Rust Assessment 1% 5% 10% 20% 50%
  4. 4. • Modified Cobb scale I (Melchers & Parker, 1922) • 100% disease severity = 37% area covered by pustules Diagrammatic Scales for Rust Assessment 5% 10% 25% 40% 65% 100%
  5. 5. • Modified Cobb scale II (Peterson et al., 1948) – Retained 100% disease severity = 37% area covered – Used a planimeter to measure area of “pustules” – Introduced • more equally spaced intervals • 4 sets of different pustule sizes – Developed for: • Puccinia graminis • P. rubigo-vera • P. hordei • P. coronata Diagrammatic Scales for Rust Assessment
  6. 6. “The writers do not consider these diagrams suitable for stripe rust …” Peterson et al. (1948) Diagrammatic Scales for Rust Assessment
  7. 7. • Is a hand-held crop sensor sensitive enough to phenotype wheat populations in mapping stripe rust resistance QTL? Research Question
  8. 8. • Population – Francolin#1 x Avocet-YrA (developed at CIMMYT) – Francolin#1 is a spring wheat line with pedigree Waxwing2*/Vivitsi • Locality – Redgates Research Station, Pannar, Greytown, South Africa • Plot layout – 198 F5 RIL entries planted in 1 m rows spaced 75 cm apart – Two replications of Francolin#1 and Avocet-YrA included – JIC871 served as susceptible check at regular intervals • Natural infection by Pst race 6E22A+ – Experiment was part of a larger stripe rust nursery with spreaders and sufficient disease pressure Materials and Methods
  9. 9. • 4 October 2013: –Visual disease severity and host response • Modified Cobb scale (0-100%) • R > RMR > MR > MRMS > MS > MSS > S – (0.1 - 0.7 transformation) –NDVI (scan 1) • 10 October 2013: –NDVI (scan 2) Disease Assessment
  10. 10. • NDVI (Pask et al. 2012 – CIMMYT Field Guide) –Normalized Difference Vegetation Index • Calculated from measurements of light reflectance in the red and near-infrared regions of the spectrum • Regularly used in crop canopy characterisation – Leaf area index, biomass, nutrient status – Healthy green leaves absorb most of the red light and reflect most of the NIR light – NDVI = (RNIR – RRed) / (RNIR + RRed) Disease Assessment
  11. 11. Trimble GreenSeeker™ (model HCS-100) crop sensor
  12. 12. • Relationships between –Severity and host response –NDVI and severity –NDVI and host response –Used means per response class • Population reduced to 180 (eliminating mixtures) Statistical Analysis
  13. 13. • 141 RILs were genotyped with 581 DArT, SSR markers • Phenotyped in Mexico and China QTL Mapping
  14. 14. • Uniform and severe stripe epidemic prevailed • Avocet-YrA = 100S • Francolin#1 = TR Results
  15. 15. Entry 1622 Entry 1620 Entry 1586
  16. 16. R RMR MR MRMS MS MSS S
  17. 17. Y = 0.0063X+0.0483 R² = 0.97 0.00 0.20 0.40 0.60 0.80 0 20 40 60 80 100 Striperustresponsetype Stripe rust severity (%)
  18. 18. • First scan 0.36 to 0.76 • Avocet = 0.48 • Francolin#1 = 0.67 • Second scan 0.34 to 0.79 • Avocet = 0.45 • Francolin#1 = 0.72 NDVI Range
  19. 19. 4 Oct Y = 439.66-614.36X R² = 0.95 10 Oct Y = 259.10-348.84X R² = 0.99 0 20 40 60 80 100 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 Striperustseverity(%) NDVI
  20. 20. 4 Oct Y =3.11-4.26X R² = 0.93 10 Oct Y = 1.87-2.47X R² = 0.99 0.00 0.20 0.40 0.60 0.80 1.00 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 Striperustresponsetype NDVI
  21. 21. QTL Mapping • Lan et al. (2014) –Mean Final Disease Scores identified QTL on • 1BL (Francolin#1) • 2BS (Francolin#1) • 3BS (Francolin#1) • 6AL (Avocet)
  22. 22. QTL Mapping with SA Data Trait name QTL Position Left marker Right marker LOD PVE(%) Add Resistance source YRDS QYr.cim-1BL 20 wPt-1770 wPt-9028 11.61 18.67 14.37 Francolin#1 YRDS QYr.cim-2BS 77 YrF wmc474 12.87 27.88 17.56 Francolin#1 YRDS QYr.cim-3BS 30 wPt-741331 wPt-741750 3.37 5.19 7.71 Francolin#1 YRDS QYr.CIM-6AL 31 gwm356.1 wPt-743476 3.63 5.52 -8.19 Avocet SCAN1 QYr.cim-1BL 8 csLV46 gwm140 3.48 13.7 -2.24 Francolin#1 SCAN1 QYr.cim-2BS 70 wPt-6174 wmc344 7.3 14.09 -2.2 Francolin#1 SCAN1 QYr.cim-3BS 29 wPt-0302 wPt-741331 3.1 5.03 -1.35 Francolin#1 SCAN1 QYr.CIM-6AL 35 wPt-743476 wPt-744881 2.88 4.57 1.26 Avocet SCAN2 QYr.cim-1BL 20 wPt-1770 wPt-9028 5.43 7.85 -2.89 Francolin#1 SCAN2 QYr.cim-2BS 67 barc55 wPt-8548 11.21 22.3 -4.88 Francolin#1 SCAN2 QYr.CIM-6AL 29 wPt-741026 gwm356.1 4.37 6.7 2.83 Avocet
  23. 23. • GreenSeeker™ technology and visual disease severity scores identified the same chromosome regions • Data were comparable with published mapping studies using multi-location phenotyping of the same population • Less variation was explained by NDVI data Conclusions
  24. 24. • Some differences in marker regions occurred for the respective traits • Timing of assessments is important considering the optimal expression windows of different QTL • A uniform epidemic is required • Take measurements at same time of day and during similar weather conditions Conclusions
  25. 25. • Standardise procedures for distance, angle, trigger time, number of samples per entry • Non-subjective crop sensing is suitable for detecting stripe rust resistance loci in the field – Works well for Pst where total leaf damage is most indicative of host response – More experiments with different populations will be conducted in 2014 Conclusions
  26. 26. – Ravi Singh – FxA population – Caixia Lan – mapping – Cornel Bender – disease scores – Neal McLaren – statistical analyses – Rikus Kloppers and Vicky Knight – field facilities and NDVI data Acknowledgements

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