GRM 2011: Improving cowpea productivity in Africa - J Ehlers
Upcoming SlideShare
Loading in...5
×
 

GRM 2011: Improving cowpea productivity in Africa - J Ehlers

on

  • 326 views

 

Statistics

Views

Total Views
326
Views on SlideShare
326
Embed Views
0

Actions

Likes
0
Downloads
2
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike LicenseCC Attribution-NonCommercial-ShareAlike License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    GRM 2011: Improving cowpea productivity in Africa - J Ehlers GRM 2011: Improving cowpea productivity in Africa - J Ehlers Presentation Transcript

    • Phase II Activities and HighlightsTropical Legume 1: Objective 2Improving cowpea productivity for marginalenvironments in AfricaJeff Ehlers, Phil Roberts, Tim Close, Zhiqiu Hu - UC RiversideNdiaga Cisse, ISRA SenegalIssa Drabo, Jean-Baptist Tignegre - INERA, Burkina FasoRogerio Chiulele - Eduardo Mondlane Univ., MozambiqueOusmane Boukar, Jorge Franco, Sam Ofodile- IITA, NigeriaGCP-GRM, ICRISAT, Hyderabad, September 25,2011
    • 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, bacterialblight, root-knot nematodes
    • Phase II Activities, Highlights• Activity 1: Develop MAGIC population• Activity 2: Develop genomic resources insupport of marker-assisted breeding• Activity 3: Employ MARS and MABC to developimproved breeding lines• Activity 4: Capacity Building• Activity 5: Curate and Store Phase I and II Data
    • 2 DC; 360 double cross seed each4-way (ABCD and EFGH); Fall 20104 single crosses; Spring 2010Develop MAGIC population300+ 8-way individuals formed,being intercrossed now8 diverse parents chosen- NJ analysis- traits
    • Available onwww.HarvEST:cowpea; Lucas et alThe Genome (in press)New CowpeaConsensus Map• 11 RILs, 2 F4• 1293 genotyped• 1107 mappedSNPs,• 856 bins• 33% more bins• 19% more markers• improved order
    • 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_0646AA AA CC CC AA TT -- TT AA AA AA GG AA TTGG TT TT TT GG GG -- CC GG GG GG AA GG AAAA AA CC CC AA TT AA TT AA AA AA GG AA TTGG TT TT TT GG GG TT CC GG GG GG AA GG AALinkage group3 4 5 6LOD0246810(c-1)Linkage group3 4 5 60246810(c-2)(a-1)(a-2)(b)‘Phase Checker’ software to improve map and QTL IDBioinformatics(submitted)GenomeStudioOutput 5 linesCorrectedOutputInferredparentalParentaldata
    • Selecting customized sets of markers for KASPAR• With high density genotyping– Many markers polymorphic ina given cross are tightly linked– no need to genotype all inbreeding• Marker selection by hand – slow &laborious when 200+ candidatemarkers• How to efficiently choose‘background’ and trait-linkedmarkers for given cross to get acost-optimized marker set forKASPAR genotyping?SNP LG cM IT93K-503-1 IT84S-2246 Polymorphic1_0190 4 6.83 AA AA 01_0502 4 10.27 BB AA 11_1148 4 10.36 AA AA 01_0382 4 10.66 AA AA 01_1360 4 12.23 BB AA 11_0504 4 12.76 AA BB 11_1499 4 13.69 AA BB 11_1138 4 14.09 BB AA 11_0155 4 15.56 BB BB 01_0082 4 17.97 AA AA 01_0756 4 18.37 AA AA 01_1413 4 18.38 BB AA 11_1445 4 19.1 BB BB 01_0847 4 19.48 -- -- 01_0774 4 20.16 BB AA 11_1221 4 20.16 BB AA 11_1242 4 20.72 BB BB 01_0027 4 21.49 BB AA 11_0153 4 21.49 BB AA 11_0535 4 22.85 AA BB 11_1261 4 22.92 BB BB 01_1092 4 23.66 BB AA 11_0646 4 24.12 BB AA 11_0874 4 24.4 AA BB 11_1264 4 24.43 -- -- 01_0826 4 24.55 BB AA 11_0692 4 25 AA BB 11_0403 4 25.31 -- AA 01_0106 4 25.57 AA BB 11_0678 4 27.6 BB AA 11_1209 4 27.9 -- BB 0
    • ‘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
    • SNP SNP_ID LG cM IT93K-503-1IT84S-2246 Polymorphic Trait Selected Reason Distance1_0190 16480_663 4 6.83 AA AA 0 01_0502 5268_412 4 10.27 BB AA 1 1 Every 5 cM1_1148 10853_451 4 10.36 AA AA 0 01_0382 6867_337 4 10.66 AA AA 0 01_1360 12393_305 4 12.23 BB AA 1 01_0504 14303_873 4 12.76 AA BB 1 01_1499 12324_917 4 13.69 AA BB 1 1 Flanking 3.411_1138 17303_348 4 14.09 BB AA 1 Flower Thrips 1 Trait 0.41_0155 474_351 4 15.56 BB BB 0 01_0082 13873_544 4 17.97 AA AA 0 01_0756 14622_2490 4 18.37 AA AA 0 01_1413 1078_282 4 18.38 BB AA 1 1 Flanking 4.291_1445 13794_319 4 19.1 BB BB 0 01_0847 8899_1022 4 19.48 -- -- 0 01_0774 16646_118 4 20.16 BB AA 1 01_1221 1202_1215 4 20.16 BB AA 1 1 Flanking 1.771_1242 4217_685 4 20.72 BB BB 0 Foliar Thrips 01_0027 3683_549 4 21.49 BB AA 1 1 Flanking 1.341_0153 14462_1712 4 21.49 BB AA 1 01_0535 1808_342 4 22.85 AA BB 1 01_1261 11736_560 4 22.92 BB BB 0 01_1092 5061_428 4 23.66 BB AA 1 01_0646 12854_535 4 24.12 BB AA 1 01_0874 7102_965 4 24.4 AA BB 1 01_1264 11709_707 4 24.43 -- -- 0 01_0826 9147_1655 4 24.55 BB AA 1 01_0692 8273_1205 4 25 AA BB 1 01_0403 4774_665 4 25.31 -- AA 0 01_0106 8625_1231 4 25.57 AA BB 1 01_0678 13269_270 4 27.6 BB AA 1 1 Every 5 cM 6.11BreedItoutputfile formarkerselection
    • 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 KBioCurrently F3s being phenotyped (Senegal, Burkina, IITA,January 2012-Mozambique)Fall 2011 - Calculate breeding values with OptiMAS• Apply 10% selection intensity, genotype 10individuals/family and intermate
    • 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 (IITAand BF) Genotype at KBio – Dec. 2011CrossnumberCrossNo.BC1F1Donor Traits2011-052 IT84S-2246/IT93K-503-1//IT84S-2246 48 Drought Tol. , Striga2011-053 IT84S-2246/IT98K-1111-1//IT84S-2246 48 Heat Tol., Striga2011-054 SuVita 2/IT93K-503-1//SuVita 2 48 Drought Tol., Striga2011-055 SuVita 2/IT97K-499-35//SuVita 2 48 Drought Tol., StrigaSeveral other MABC efforts at BC1F1 stage
    • 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-2049Aphid Resistance QTL
    • IT93K-503xCB46 RIL2006CVS(57)2006UCR(57)2007CVS(57)2007CVS(124)2007UCR(57)2008CVS(91)2008UCR(108)VuLG4VuLG2VuLG8VuLG10Foliar Thrip Res. QTL Validation LOD>8.4LOD>6.8LOD>4LOD>3.2Validating withCB27/IT82E-18 RIL
    • SNP SNP SNP SNPLine Rank All Weight UC No(+/+) No(-/-) No(+/-) 1_0589 1_0853 1_0604 1_148205066-004 1 1 1 4 4 0 0 1 1 1 105066-065 2 1 1 4 4 0 0 1 1 1 105066-109 3 0.875 0.94 4 3 0 1 1 1 1 0.505066-001 4 0.75 0.88 3 3 1 0 1 1 1 005066-073 5 0.75 0.88 3 3 1 0 1 1 1 005066-087 6 0.75 0.88 3 3 1 0 1 1 1 005066-045 7 0.75 0.87 3.71 2 0 2 0.5 1 1 0.505066-103 8 0.75 0.87 3 3 1 0 0 1 1 105066-015 85 0.25 0.12 1 1 3 0 1 0 0 0Mouride 86 0.25 0.12 1 1 3 0 1 0 0 005066-002 87 0.25 0.12 1.71 0 2 2 0.5 0 0 0.505066-034 88 0.25 0.12 1 1 3 0 0 0 0 105066-054 89 0.125 0.06 1 0 3 1 0 0 0 0.505066-100 90 0 0 0 0 4 0 0 0 0 005066-129 91 0 0 0 0 4 0 0 0 0 0%PV 4.665 19.05 10.15 4.57Favorable DT SNPsDrought QTL ValidationOptiMAS Results - one Pilot MARS Popln10 best and worst lines from 3 HixHi populationsBeing phenotyped now
    • Bayesian QTL detection methodsbeing tried• Improved QTL estimation when QTLs• PROC QTL softwarehttp://statgen.ucr.edu/download/software/PROC%20QTL.pdf• Better markers and marker effects forcalculating selection index weights• Redoing QTL estimates with new map
    • Bayesian vs Interval Mapping2010 MARS Pilot Project Data Senegal - 100 grain weightInterval mapping1 2 3 4 5 6 7 8 9 10 11LODscore0.01.02.03.04.05.0Bayesian analysisChromosome1 2 3 4 5 6 7 8 9 10 11Effectestimate-0.4-0.20.00.20.4
    • Activity 4. Capacity Building• Two PhD students Senegal (Penda Sarr - English at UCR Spring 2010; nowenrolled 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 May2011)
    • 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
    • Thank YouUniversity of California,RiversideMuchero, WellingtonPottorff, MartiWanamaker, SteveEhlers, Jeffrey D.Roberts, Philip A.Close, Timothy J.Lucas, MitchellXu, ShizhongHu, Zhiqui19National Agricultural Research System(NARS)Cisse, Ndiaga, ISRA-SenegalDrabo, Issa, INERA-Burkina FasoTignegre, Jean-Baptist, INERA, Burkina FasoRogerio Chiulele, Eduardo Mondlane U,MozambiqueInternational Institute ofTropical Agriculture (IITA)Muranaka, SatoruBoukar, OusmaneFatokun, ChristianFranco, JorgeSam Ofodile