GRM 2013: Improve common bean productivity for marginal environments in sub-Saharan Africa -- B Raatz

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GRM 2013: Improve common bean productivity for marginal environments in sub-Saharan Africa -- B Raatz

  1. 1. TL 1 Objective 3: Improve common bean productivity for marginal environments in sub-Saharan Africa Bodo Raatz CIAT Lisboa, September 2013
  2. 2. • . Using markers for biotic stress resistance
  3. 3. SNP genotpying: Tm shift assay A G from Wang et al. 2005°C resistant susceptible• Based on SNP-specific primers • Genotyping by melting point analysis • No fluorescent primers required
  4. 4. C-A BCMV: bc-3 candidate gene: eIF4E NADERPOUR et al. MOLECULAR PLANT PATHOLOGY (2010) primer 1 C primer 2 A primer reverse
  5. 5. BCMV: bc-3 Tms marker resistent susceptible no call all
  6. 6. Storage insects: bruchid resistance Bruchids (Zabrotes subfasciatus) PV-ATCT001, Yu et al, 2000 Dissociation Curve APA gene R S 80 84
  7. 7. Common Bacterial Blight (CBB) MAS with • SU91 on Chr8 (Pedraza et al. 1997) • SAP6 on Chr10 (Miklas et al. 2006) Bacterial blight: Xanthomonas
  8. 8. CBB resistance QTLs
  9. 9. Marker conversions Chr Position G35346 AFR298 AND696 G10474 G40001 G5686 MD23-24 SEA5 VAX1 Chr01 219,278.00 T A A A A A A A A Chr01 219,650.00 T C C C C C C C C Chr01 219,722.00 T C C C C C C C C Chr01 219,749.00 G A A A A A A A A Chr01 219,950.00 C T T T T T T T T Chr01 219,952.00 T A A A A A A A A Chr01 220,668.00 T A A A A A A A A Chr01 221,020.00 G C C C C C C C C Chr01 252,386.00 C G G G G G G G G Chr01 252,420.00 T A A A A A A A A Chr01 253,519.00 A T T T T T T T T Chr01 253,526.00 T G G G G G G G G Chr01 253,542.00 C T T T T T T T T Chr01 258,082.00 C G G G G G G G G Chr01 264,339.00 C T T T T T T T T Chr01 265,119.00 T G G G G G G G G Chr01 265,168.00 T G G G G G G G G Chr01 265,336.00 A G G G G G G G G Chr01 265,418.00 C G G G G G G G G Chr01 265,713.00 C T T T T T T T T Chr01 265,722.00 C T T T T T T T T Chr01 266,882.00 C A A A A A A A A Chr01 267,398.00 A G G G G G G G G Chr01 267,562.00 C A A A A A A A A Chr01 267,688.00 T C C C C C C C C Chr01 267,727.00 T A A A A A A A A Chr01 268,418.00 C G G G G G G G G Chr01 268,648.00 A C C C C C C C C Chr01 271,111.00 A G G G G G G G G Chr01 271,132.00 T C C C C C C C C Chr01 271,437.00 A G G G G G G G G Chr01 271,644.00 T C C C C C C C C Chr01 272,223.00 G C C C C C C C C Chr01 272,579.00 G A A A A A A A A Chr01 272,645.00 G C C C C C C C C Jorge et al SNP sources: • 1500 SNP set BeanCAP • 9 genotypes sequenced
  10. 10. Angular Leaf Spot Marker: Sc267437 Chr 8 G10474 Marker: P50 Chr. 4 G5686 • SNP based on SSR markers confirmed and converted • Finemapping on Chr 4 – Resistance gene 43.5 – 43.9 Mbp 42.0 42.5 43.0 43.5 44.0 44.5 45 0 2 4 6 8 Chromosome 4 (Mbp) LODscore Beat Keller, 2013
  11. 11. Bean Stem Maggot • QTL mapping underway • 100 additional SNPs genotyped (Daniel Ambachew) • BSM evaluations in Zimbabwe, Malawi and Ethiopia tolerant susceptible Maps for DOR x BAT SER16 x G35346
  12. 12. Drought tolerance and root traits -1 Yield Root traits QTL analysis Federico Velasquez Map for SEA x MD
  13. 13. MARS • PhD thesis of Fitsum Alemayehu SARI, Ethiopia • Population of ~200 Lines • Phenotyping drought & irrigated at 4 locations – 2x Palmira, 2x Ethiopia • Genotyping: – Kbio/LGC ~150 SNP markers – additional 14.000 fluidigmdata points • Marker Analysis in progress – DNAs sent, extracted from seed
  14. 14. (CAL 143xSAB 620) X SAB 626 (SAB 628xCAL 143) X SAB 659 (SAB 628xCAL 143) X SAB 686 G 12229 x AND 277 CAL143 (ABA 58x AFR298)F1xSAB258(COS 16 x AFR298)F1 x SAB258 SAB258 ABA58AFR298COS16 41 lines 54 lines 30 lines 26 lines 42 lines F5 F5F1: CAL 143xSAB 620 F1: SAB 628xCAL 143 F1: ABA 58x AFR298 F1: COS 16 x AFR298F1F1F1 F1 F1 SAB620 SAB626 SAB628 SAB659 SAB686 MARS
  15. 15. Evaluation EvaluationEvaluation Evaluation Population development F1 F2 F4 • QTL Analysis • Select markers F5 MARS
  16. 16. MARS Evaluation EvaluationEvaluation Evaluation Population development F1 F2 F4 • QTL Analysis • Select markers F5
  17. 17. MARS • QTL Analysis • Select markers
  18. 18. Genotype F3 MARS Genotype evaluate Select worst linesSelect best lines • QTL Analysis • Select markers F4 F5 evaluate F4 F5 F4 F5 evaluate MARS Conventional selection Select worst linesSelect best lines F3 F3 2nd MARS crossing cycle •Select best lines •Make new crosses
  19. 19. MAGIC • 8-parental population established • F4:6 Phenotyping started in July • Genotyping: DNA ready to send for GBS
  20. 20. SXB 412 INB 827 ALB 213 SEN 56 SCR 2 MIB 778 SCR 9 INB 841 79 F1 5 F1 38 F1 99 F1 323 F1 272 F1 728 F1 500 selected 499 F2 996 F3 families 926 F4.5 families 926 F4:6 families MAGIC population in common bean (mesoamerican gene pool) Genotype individuals Drought phenotyping
  21. 21. MAGIC • 648 genotypes in current drought trial
  22. 22. Breeding products Relationship between irrigated and rainfed grain yield of 36 common bean genotypes grown under field conditions at CIAT-Colombia (2013) AFR298 CAL143 CAL96 DAB 231 DAB 233 DAB 236 DAB 244 DAB 251 DAB 252 DAB 256 DAB 258 DAB 267 DAB 277 DAB 295 DAB 344 DAB 353 DAB 366 DAB 374DAB 380 DAB 384 DAB 396 DAB 398 DAB 402 DAB 441DAB 489 DAB 494 DAB 514 DAB 520 DAB 525 DAB 528 DAB 534 DAB 541 DAB 545 DAB 549 DAB 555 KATB1 0 200 400 600 800 1000 1200 2000 2500 3000 3500 4000 4500 droughtYDHA irrigated YPDA 2013 YDHA
  23. 23. Data management • Adopting defined file formats • Adopting barcoding • Adopting use of barcoding for seed storage organization • Training of partners to adopt defined file formats Sheet 1 Sheet 2 Trial data Controlled vocabulary trait dictionary Metadata • Sowing date • Location • Person in charge
  24. 24. Capacity building • Workshop with 30+ breeders in Kawanda • 2 workshops at CIAT (n ~20) • Fitsum Alemayehu (phd student, Ethiopia) visited CIAT for 3 months.
  25. 25. Conclusions • MAS for biotic stress moving toward routine • MAGIC and MARS moving forward • Advanced lines show improved yield under drought • Data Management moving towards adoption • Capacity building ongoing Anticipated impact: improved germplasm …
  26. 26. View to the future • Unite TL 1 and TL2 activities • Technology changes – GBS: for fingerprinting, population genotyping – genomic selection • Fingerprinting / Forensics – Specific projects need to be defined – Impact studies – software • Organizational changes – Extend work with partners – Data management -software – Links to • Soils work, agronomy • Other downstream activities
  27. 27. Thank you for your attention! SARI Ethiopia SARI SELIAN Tanzania DARS Malawi DR&SS Zimbabwe KARI Kenya CIAT Africa

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