1. Phase II Activities and Highlights
Tropical Legume 1: Objective 2
Improving cowpea productivity for marginal
environments in Africa
Jeff Ehlers, Phil Roberts, Tim Close, Zhiqiu Hu - UC Riverside
Ndiaga Cisse, ISRA Senegal
Issa Drabo, Jean-Baptist Tignegre - INERA, Burkina Faso
Rogerio Chiulele - Eduardo Mondlane Univ., Mozambique
Ousmane Boukar, Jorge Franco, Sam Ofodile- IITA, Nigeria
GCP-GRM, ICRISAT, Hyderabad, September 25,2011

2. Building on Phase I Key Outputs
• 4 Key Resources for Modern Breeding Developed
– 1. high-throughput genotyping platform
• Based on Illumina 1536 GoldenGate Assay
– 2. high quality consensus genetic map
• 6 RILs, 741 lines, 928 markers
– 3. fingerprints of >500 parental lines
• 370 IITA/GCP mini-core
• 200 IITA, West African lines/cultivars
– 4. QTL and trait-linked markers
• Drought tolerance
• Resistance to flower and foliar thrips, ashy-stem blight, bacterial
blight, root-knot nematodes

3. Phase II Activities, Highlights
• Activity 1: Develop MAGIC population
• Activity 2: Develop genomic resources in
support of marker-assisted breeding
• Activity 3: Employ MARS and MABC to develop
improved breeding lines
• Activity 4: Capacity Building
• Activity 5: Curate and Store Phase I and II Data

4. 2 DC; 360 double cross seed each
4-way (ABCD and EFGH); Fall 2010
4 single crosses; Spring 2010
Develop MAGIC population
300+ 8-way individuals formed,
being intercrossed now
8 diverse parents chosen
- NJ analysis
- traits

5. Available on
www.HarvEST:cowpea; Lucas et al
The Genome (in press)
New Cowpea
Consensus Map
• 11 RILs, 2 F4
• 1293 genotyped
• 1107 mapped
SNPs,
• 856 bins
• 33% more bins
• 19% more markers
• improved order

6. 1_0502 1_1360 1_0504 1_1499 1_1138 1_1413 1_0847 1_0774 1_1221 1_0027 1_0153 1_0535 1_1092 1_0646
AA AA CC CC AA TT -- TT AA AA AA GG AA TT
GG TT TT TT GG GG -- CC GG GG GG AA GG AA
AA AA CC CC AA TT AA TT AA AA AA GG AA TT
GG TT TT TT GG GG TT CC GG GG GG AA GG AA
Linkage group
3 4 5 6
LOD
0
2
4
6
8
10
(c-1)
Linkage group
3 4 5 6
0
2
4
6
8
10
(c-2)
(a-1)
(a-2)
(b)
‘Phase Checker’ software to improve map and QTL ID
Bioinformatics
(submitted)
GenomeStudio
Output 5 lines
Corrected
Output
Inferred
parental
Parental
data

7. Selecting customized sets of markers for KASPAR
• With high density genotyping
– Many markers polymorphic in
a given cross are tightly linked
– no need to genotype all in
breeding
• Marker selection by hand – slow &
laborious when 200+ candidate
markers
• How to efficiently choose
‘background’ and trait-linked
markers for given cross to get a
cost-optimized marker set for
KASPAR genotyping?
SNP LG cM IT93K-503-1 IT84S-2246 Polymorphic
1_0190 4 6.83 AA AA 0
1_0502 4 10.27 BB AA 1
1_1148 4 10.36 AA AA 0
1_0382 4 10.66 AA AA 0
1_1360 4 12.23 BB AA 1
1_0504 4 12.76 AA BB 1
1_1499 4 13.69 AA BB 1
1_1138 4 14.09 BB AA 1
1_0155 4 15.56 BB BB 0
1_0082 4 17.97 AA AA 0
1_0756 4 18.37 AA AA 0
1_1413 4 18.38 BB AA 1
1_1445 4 19.1 BB BB 0
1_0847 4 19.48 -- -- 0
1_0774 4 20.16 BB AA 1
1_1221 4 20.16 BB AA 1
1_1242 4 20.72 BB BB 0
1_0027 4 21.49 BB AA 1
1_0153 4 21.49 BB AA 1
1_0535 4 22.85 AA BB 1
1_1261 4 22.92 BB BB 0
1_1092 4 23.66 BB AA 1
1_0646 4 24.12 BB AA 1
1_0874 4 24.4 AA BB 1
1_1264 4 24.43 -- -- 0
1_0826 4 24.55 BB AA 1
1_0692 4 25 AA BB 1
1_0403 4 25.31 -- AA 0
1_0106 4 25.57 AA BB 1
1_0678 4 27.6 BB AA 1
1_1209 4 27.9 -- BB 0

8. ‘BreedIt’
• Program to choose a marker set for a given cross
– Input SNP data table (genotype or locus file)
– Input trait (QTL, markers) and map files
– Program Options
• Choose 2 parents
• Choose cM interval for background selection
• Choose cM interval for markers flanking traits
• Output list of SNPs for KBiosciences assay
• Can quickly compare cost versus marker density

9. SNP SNP_ID LG cM IT93K-503-1
IT84S-
2246 Polymorphic Trait Selected Reason Distance
1_0190 16480_663 4 6.83 AA AA 0 0
1_0502 5268_412 4 10.27 BB AA 1 1 Every 5 cM
1_1148 10853_451 4 10.36 AA AA 0 0
1_0382 6867_337 4 10.66 AA AA 0 0
1_1360 12393_305 4 12.23 BB AA 1 0
1_0504 14303_873 4 12.76 AA BB 1 0
1_1499 12324_917 4 13.69 AA BB 1 1 Flanking 3.41
1_1138 17303_348 4 14.09 BB AA 1 Flower Thrips 1 Trait 0.4
1_0155 474_351 4 15.56 BB BB 0 0
1_0082 13873_544 4 17.97 AA AA 0 0
1_0756 14622_2490 4 18.37 AA AA 0 0
1_1413 1078_282 4 18.38 BB AA 1 1 Flanking 4.29
1_1445 13794_319 4 19.1 BB BB 0 0
1_0847 8899_1022 4 19.48 -- -- 0 0
1_0774 16646_118 4 20.16 BB AA 1 0
1_1221 1202_1215 4 20.16 BB AA 1 1 Flanking 1.77
1_1242 4217_685 4 20.72 BB BB 0 Foliar Thrips 0
1_0027 3683_549 4 21.49 BB AA 1 1 Flanking 1.34
1_0153 14462_1712 4 21.49 BB AA 1 0
1_0535 1808_342 4 22.85 AA BB 1 0
1_1261 11736_560 4 22.92 BB BB 0 0
1_1092 5061_428 4 23.66 BB AA 1 0
1_0646 12854_535 4 24.12 BB AA 1 0
1_0874 7102_965 4 24.4 AA BB 1 0
1_1264 11709_707 4 24.43 -- -- 0 0
1_0826 9147_1655 4 24.55 BB AA 1 0
1_0692 8273_1205 4 25 AA BB 1 0
1_0403 4774_665 4 25.31 -- AA 0 0
1_0106 8625_1231 4 25.57 AA BB 1 0
1_0678 13269_270 4 27.6 BB AA 1 1 Every 5 cM 6.11
BreedIt
output
file for
marker
selection

10. Activity 3. Main MARS Breeding Effort
• F1 made (March –June 2010)
• F1 grown out to produce F2 (Sept. – Nov. 2010)
• F2 seed (500/cross) sent to partners, Feb. 2011
• Harvested ~300 plants/popln
– Tissue samples taken, mailed to KBio
Currently F3s being phenotyped (Senegal, Burkina, IITA,
January 2012-Mozambique)
Fall 2011 - Calculate breeding values with OptiMAS
• Apply 10% selection intensity, genotype 10
individuals/family and intermate

11. MABC to develop improved breeding lines
 Made F1 - Spring 2010 and BC1F1 Fall 2010
 Made BC2F1 and BC1F2 – Spring 2011
 Currently Phenotyping BC1F2 families for Striga tolerance (IITA
and BF)
 Genotype at KBio – Dec. 2011
Cross
number
Cross
No.
BC1F1
Donor Traits
2011-052 IT84S-2246/IT93K-503-1//IT84S-2246 48 Drought Tol. , Striga
2011-053 IT84S-2246/IT98K-1111-1//IT84S-2246 48 Heat Tol., Striga
2011-054 SuVita 2/IT93K-503-1//SuVita 2 48 Drought Tol., Striga
2011-055 SuVita 2/IT97K-499-35//SuVita 2 48 Drought Tol., Striga
Several other MABC efforts at BC1F1 stage

12. QTL Discovery in Elite RILs
• Heat Tolerance Phenotyping
– Greenhouse Aug. – Oct. 2010 and 2011
– RIL CB27 x IT82E-18
• Aphid Resistance Phenotyping
– Field hotspot, Calif., Summer 2010 &11
– RIL CB27 x IT97K-556-6
• Anthracnose Phenotyping
• Burkina Faso, RIL 524B/IT84S-2049
Aphid Resistance QTL

13. IT93K-503xCB46 RIL
2006CVS(57)
2006UCR(57)
2007CVS(57)
2007CVS(124)
2007UCR(57)
2008CVS(91)
2008UCR(108)
VuLG4
VuLG2
VuLG8
VuLG10
Foliar Thrip Res. QTL Validation LOD>8.4
LOD>6.8
LOD>4
LOD>3.2
Validating with
CB27/IT82E-18 RIL

14. SNP SNP SNP SNP
Line Rank All Weight UC No(+/+) No(-/-) No(+/-) 1_0589 1_0853 1_0604 1_1482
05066-004 1 1 1 4 4 0 0 1 1 1 1
05066-065 2 1 1 4 4 0 0 1 1 1 1
05066-109 3 0.875 0.94 4 3 0 1 1 1 1 0.5
05066-001 4 0.75 0.88 3 3 1 0 1 1 1 0
05066-073 5 0.75 0.88 3 3 1 0 1 1 1 0
05066-087 6 0.75 0.88 3 3 1 0 1 1 1 0
05066-045 7 0.75 0.87 3.71 2 0 2 0.5 1 1 0.5
05066-103 8 0.75 0.87 3 3 1 0 0 1 1 1
05066-015 85 0.25 0.12 1 1 3 0 1 0 0 0
Mouride 86 0.25 0.12 1 1 3 0 1 0 0 0
05066-002 87 0.25 0.12 1.71 0 2 2 0.5 0 0 0.5
05066-034 88 0.25 0.12 1 1 3 0 0 0 0 1
05066-054 89 0.125 0.06 1 0 3 1 0 0 0 0.5
05066-100 90 0 0 0 0 4 0 0 0 0 0
05066-129 91 0 0 0 0 4 0 0 0 0 0
%PV 4.665 19.05 10.15 4.57
Favorable DT SNPs
Drought QTL Validation
OptiMAS Results - one Pilot MARS Popln
10 best and worst lines from 3 HixHi populations
Being phenotyped now

15. Bayesian QTL detection methods
being tried
• Improved QTL estimation when QTLs
• PROC QTL software
http://statgen.ucr.edu/download/softwar
e/PROC%20QTL.pdf
• Better markers and marker effects for
calculating selection index weights
• Redoing QTL estimates with new map

16. Bayesian vs Interval Mapping
2010 MARS Pilot Project Data Senegal - 100 grain weight
Interval mapping
1 2 3 4 5 6 7 8 9 10 11
LODscore
0.0
1.0
2.0
3.0
4.0
5.0
Bayesian analysis
Chromosome
1 2 3 4 5 6 7 8 9 10 11
Effectestimate
-0.4
-0.2
0.0
0.2
0.4

17. Activity 4. Capacity Building
• Two PhD students
 Senegal (Penda Sarr - English at UCR Spring 2010; now
enrolled at Anta Diop Chiekh University, Dakar, Senegal)
 Mozambique (Arsenio Ndeve - English at UCR Spring 2010,
accepted for Winter 2012 enrollment at UCR)
 MABC and MARS breeding
• PhD student at WACCI (started Sept. 2009)
 Burkina Faso (Joseph Batieno)
 Supported by GCP Capacity Building funds
• Mentoring
 Rogerio Chiulele (Mozambique – PhD completed ACCI May
2011)

18. Mentoring WACCI PhDs
• WACCI Cowpea PhD students
– Joseph Batieno – INERA-Burkina Faso; PhD started 2009
– Maureen Nkoumki –IRAD-Cameroon; PhD started 2010
– Mohamad Lawan – IAR-Nigeria; PhD started 2010
– Fafa Egbadzor – Univ. of Ghana; PhD started 2010
• Presented Breeding Training Module March 21 – 25,
2011 at WACCI
• Reviewing WACCI Student PhD Proposals
• Assisting with GCP-GSS Proposal Development

19. Thank YouUniversity of California,
Riverside
Muchero, Wellington
Pottorff, Marti
Wanamaker, Steve
Ehlers, Jeffrey D.
Roberts, Philip A.
Close, Timothy J.
Lucas, Mitchell
Xu, Shizhong
Hu, Zhiqui
19
National Agricultural Research System
(NARS)
Cisse, Ndiaga, ISRA-Senegal
Drabo, Issa, INERA-Burkina Faso
Tignegre, Jean-Baptist, INERA, Burkina Faso
Rogerio Chiulele, Eduardo Mondlane U,
Mozambique
International Institute of
Tropical Agriculture (IITA)
Muranaka, Satoru
Boukar, Ousmane
Fatokun, Christian
Franco, Jorge
Sam Ofodile