These slides were presented by Clare Mukankusi

1. TL 1
Objective 3: Improve common bean productivity
for marginal environments in sub-Saharan Africa
Bodo Raatz
CIAT
Lisboa, September 2013

2. • .
Using markers for
biotic stress resistance

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. C-A
BCMV: bc-3 candidate gene: eIF4E
NADERPOUR et al. MOLECULAR PLANT PATHOLOGY (2010)
primer 1
C
primer 2
A
primer reverse

5. BCMV: bc-3 Tms marker
resistent
susceptible
no call
all

6. Storage insects:
bruchid resistance
Bruchids (Zabrotes
subfasciatus)
PV-ATCT001, Yu et al, 2000
Dissociation Curve
APA gene
R
S
80 84

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. CBB resistance QTLs

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. 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. 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. Drought tolerance
and root traits
-1
Yield Root traits
QTL analysis Federico Velasquez
Map for SEA x MD

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. (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. MAGIC
• 8-parental population established
• F4:6 Phenotyping started in July
• Genotyping: DNA ready to send for GBS

16. 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

17. MAGIC
• 648 genotypes in current drought trial

18. 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

19. 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

20. Capacity building
• Workshop with 30+ breeders in
Kawanda
• 2 workshops at CIAT (n ~20)
• Fitsum Alemayehu (phd student,
Ethiopia) visited CIAT for 3 months.

21. 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 …

22. 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

23. Thank you for your attention!
SARI Ethiopia
SARI SELIAN Tanzania
DARS Malawi
DR&SS Zimbabwe
KARI Kenya
CIAT Africa