PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowpeas – BL Huynh

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PAG XXII 2014 – Genomic resources applied to marker-assisted breeding in cowpeas – BL Huynh

  1. 1. GCP Tropical Legumes 1: “Improve cowpea productivity for marginal environments in sub-Saharan Africa” Genomic resources applied to marker-assisted breeding in cowpea Philip Roberts, Timothy Close, Bao Lam Huynh Mitchell Lucas, Arsenio Ndeve, Steve Wanamaker – UC Riverside Ousmane Boukar, Christian Fatokun, Sam Ofodile – IITA, Nigeria Ndiaga Cisse, Penda Sarr – ISRA, Senegal Issa Drabo, Jean-Baptiste Tignegre – INERA, Burkina Faso Rogerio Chiulele – E. Mondlane U, Mozambique Batieno T. Benoît Joseph – WACCI, Ghana Ndeye N. Diop, Xavier Delannay et al. – IBP, CIMMYT, Mexico PAG XXII – GCP Workshop, Jan 2014
  2. 2. Cowpea – Vigna unguiculata • • • • • Tropical legume. Nutritious, high-protein food. Major food crop in sub-Saharan Africa. Nitrogen fixation to enrich soil fertility. Tolerance to drought, heat, poor soils. Blackeye bean Diverse seed types Yard-long bean/asparagus bean
  3. 3. Cowpea production zones in Africa 200mm 800mm ISRA INERA IITA 200mm 800mm EMU
  4. 4. Yield constraints Seedling Flowering Flower thrips Aphids Pod filling Pod-sucking bugs Heat Pod borer Post-harvest Weevils Root-knot nematodes Drought Bacterial blight Viruses Macrophomina African cowpea varieties yield ~20% of potential Striga
  5. 5. Genetic variation – basis for QTL discovery and marker-assisted breeding Heat Aphid T R S S Root-knot nematodes Striga R S R S Jean-Baptiste, Burkina Faso
  6. 6. QTLs for biotic and abiotic traits LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 Cowpea consensus genetic map 11 RIL populations, 1091 SNPs, 815 bins, 680 cM V1: 2009 PNAS 106:18159-18164 V4: 2011 Plant Genome 4:218-225 V6: 2013 http://harvest.ucr.edu/
  7. 7. SNP database for germplasm collection > 400 accessions from Africa and the world 1 5 3 6 S N P s SNP 1_1431 1_0721 1_1392 1_1157 1_0595 1_0741 1_0482 1_0791 1_1490 1_1033 1_0144 1_0328 1_0240 1_0985 1_0041 1_1470 1_1535 1_1230 1_1108 1_0670 TVu-9522 GG CC GG AA GG AA GG AA GG GG GG GG CC AA GG AA GG AA CC AA TVu-9556 GG CC GG AA GG AA CC AA GG GG GG GG CC TT GG AA GG AA CC GG TVu-9557 GG CC GG AA GG AA CC AA GG GG GG GG CC TT AA AA GG AA CC GG TVu-9620 GG CC GG AA GG AA GG AA GG GG GG GG CC TT GG AA GG AA CC AA TVu-9651 GG CC GG AA CC AA CC AA GG GG GG GG CC TT GG AA GG AA CC AA TVu-969 AA CC GG AA CC AA CC AA GG GG GG GG CC AA AA AA GG AA CC AA TVu-972 GG CC GG AA CC GG GG GG CC AA AA GG CC AA GG AA GG AA AA GG 2013 The Plant Genome 6(3) TVu-9749 GG CC GG AA CC AA CC AA GG GG GG GG CC TT GG AA -AA CC GG TVu-9761 GG CC GG AA CC AA CC AA GG GG GG GG CC AT GG AA GG AA CC AG TVu-9801 AA CC GG GG CC AA GG GG GG GG AA GG CC AA GG GG AA AA CC AA TVu-9820 AA CC GG GG CC AA GG GG GG AA AA AA CC AA GG GG AA AA AA AA
  8. 8. Select informative SNPs for a MAS project Consensus map QTL SNP genotypes of parents SNPs selected for MAS BreedIt (breedit.org), GDMS (IBP tool, ICRISAT)
  9. 9. SNP genotyping (KASP platform-LGC Genomics) 1 2 3 8 Selection Crossing Planting New genotyping 4 7 6 5
  10. 10. Employ marker-assisted backcrossing (MABC) to improve local cowpea varieties Country 11 MABC populations (Recurrent/Donor) Main donor Traits Nigeria IT93K-452-1/IT97K-499-35 IT89KD-288/IT97K-499-35 SuVita 2/IT97K-499-35 Striga Striga Drought, Striga Burkina Faso Moussa/IT93K-693-2 KVX745-11P/KVX414-22 Striga Striga, Large Seed Senegal Melakh/IT97K-499-39 Striga Mozambique IT85F-3139/CB27 CB27/INIA-41 Large seed, Heat Nematode, Drought USA/ Mozambique CB27/IT97K-556-6 CB46/IT97K-556-6 CB50/IT97K-556-6 Aphid
  11. 11. MABC example – Aphid resistance Blackeyes IT97K-556-6 (susceptible) (resistant)
  12. 12. Aphid resistance screening Kearney, CA Recombinant inbred lines CB27 (S) x IT97K-556-6 (R) Big Buff Big Buff S R
  13. 13. Aphid-resistance QTLs LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 Major QTL 66% phenotypic variance Minor QTL 9% phenotypic variance QTL IciMapping: http://www.isbreeding.net/
  14. 14. QTL introgression Resistant RIL x Recurrent P 1 2 3 BCnF1 plants Leaf sampling 04 AA AB AB AB AB AA AB AA AA AA AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 05 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 5 6 Backcrossing 11 AA AA AB AB AB AA AB AB AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 12 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AB AA AA AA AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA 24 AA AA AB AB AB AA AA AA AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 26 AA AB AB AB AB AA AB AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA 35 AA AB AB AB AB AA AA AB AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 38 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 40 AA AB AB AB AB AA AA AB AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA -AA 46 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 48 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 53 AA AB AB AB AB AA AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 57 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 62 AA AA AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 63 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 66 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AA AA AA AA AB AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA SNP genotyping 67 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AA AA AA AA AA AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA 69 AA AB AB AB AB AA AB AB AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 71 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AB AB AA AA AA AA AA AA AA AA Background selection 79 AA AB AB AB AB AA AA AB AA AA AA AA AA AA AB AA AA AA AB AB AA AA AA AA AA AA AB AA AA AA AA AA AA AA AA 80 AA AB AB AB AB AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA CB46A AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA AA 4 Resistant genotypes Foreground selection
  15. 15. Parent 1 × Parent 2 QTL Recombination Population development F1 F2 Multilocation phenotyping QTL detection –estimate marker effects Cycle 1 Cycle 2 Cycle 3 A B C D E F1 F1 F G H 4 populations Traits Burkina Faso (Issa Drabo et al.) F1 F1 Ideotype Line development Genotyping F3 Employ marker-assisted recurrent selection (MARS) to develop improved breeding lines Yield, Drought, Striga, Macrophomina Nigeria (Ousmane Boukar et al.) Earliness, Striga, Heat Senegal (Ndiaga Cisse et al.) Drought, Striga, Nematode, Macrophomina Mozambique (Rogerio Chiulele et al.) Heat, Large seed, Grain quality F1 F1 F1 F2 F3 Single seed descent F3:4 Multilocation phenotyping
  16. 16. Burkina Faso – MARS example • Developed F2 from elite parents (Suvita 2, IT97K-499-35). • Genotyped 300 F2s with 164 poly SNPs every 2 cM interval. • Phenotyped F2:3 families ₋ Pobe (low-yielding site) Pope 300 mm ₋ Saria (high-yielding site) Saria 1000 mm
  17. 17. QTL detection VuLG1 VuLG2 VuLG3 VuLG4 VuLG5 VuLG6 Yield Grain size VuLG7 VuLG8 VuLG9 VuLG10 VuLG11 Yield Yield Grain size Favorable alleles from Suvita 2 Favorable alleles from IT97K-499-35 QTL IciMapping: http://www.isbreeding.net/
  18. 18. Select best families with OptiMAS (IBP tool, INRA) F2:3 Molecular family score 243 0.83 71 0.83 281 0.79 51 0.75 190 0.75 154 0.71 112 0.42 235 0.42 251 0.42 199 0.19 285 0.17 99 0.17 (Example list) YLD-4 1.00 1.00 1.00 1.00 0.50 0.50 1.00 0.50 0.50 0.62 0.00 0.00 Yield QTLs YLD-6 0.50 1.00 0.50 0.50 1.00 1.00 0.50 0.00 0.00 0.00 0.00 0.00 YLD-8 0.50 0.50 0.98 1.00 0.98 1.00 0.50 0.50 0.50 0.00 0.50 0.00 Kernel-size QTLs GDW-3 GDW-11 1.00 1.00 1.00 0.50 0.50 1.00 1.00 0.50 0.50 1.00 0.50 0.50 0.50 0.00 0.50 0.50 0.50 0.50 0.00 0.50 0.00 0.00 0.00 0.00 Striga Rsg 3 1.00 1.00 0.73 0.50 0.50 0.73 0.00 0.50 0.50 0.00 0.50 1.00 Selected Yes Yes Yes Yes Yes Yes No No No No No No The striga QTL is incorporated from prior publication 2002 Genome 45:787-793 (Ouédraogo et al.)
  19. 19. QTL combination 1 2 3 10 members per family/cross Leaf sampling 5 6 Plant ID Cycle MS Yield QTLs KASP genotyping Kernel-size QTLs Striga YLD-4 YLD-6 YLD-8 GDW-3 GDW-11 Rsg 3 Selected 71 0.83 1.00 1.00 0.50 1.00 0.50 1.00 71-03 F4 0.92 1.00 1.00 0.50 1.00 1.00 1.00 Yes 71-02 F4 0.92 1.00 1.00 1.00 1.00 0.50 1.00 Yes 71-10 F4 0.92 1.00 1.00 1.00 1.00 0.50 1.00 Yes 71-01 F4 0.75 1.00 1.00 0.52 1.00 0.00 1.00 No 71-04 F4 0.75 1.00 1.00 0.00 1.00 0.50 1.00 No 71-05 F4 0.75 1.00 1.00 0.50 1.00 0.00 1.00 No 71-07 Intercrossing F2 F4 0.67 1.00 1.00 0.00 1.00 0.00 1.00 Plant Cycle 1_0853 1_0447 1_0146 1_0937 1_0031 2010-057-190 F2 GG GG AA GG GG 2010-057-190-01 F4 GG GG AA GG GG 2010-057-190-02 F4 GG -AA -GG 2010-057-190-03 F4 GG GG AA GG GG 2010-057-190-04 F4 GG GG AA GG GG 2010-057-190-05 F4 GA GC GA GC GA 2010-057-190-06 F4 GG GG AA GG GG 2010-057-190-07 F4 GG GG AA GG GG 2010-057-190-08 F4 GG GG AA GG GG 2010-057-190-09 F4 GG GG AA GG GG 2010-057-190-10 F4 GA GC GA GC GA 4 No OptiMAS to select best plants Outcrosses eliminated
  20. 20. OptiMAS summary: Frequency of favorable alleles at different selection steps in Burkina Faso MARS (on average and for each QTL) C2 C1 F5 F4 F2 F1 P
  21. 21. MARS activities in Burkina Faso (Issa Drabo et al.) Leaf sample for genotyping Intercross to recombine QTLs Intercross progeny “Ideotype” Local check
  22. 22. University of California, Riverside Roberts, Philip A. Close, Timothy J. Huynh, Bao Lam Wanamaker, Steve Lucas, Mitchell Ndeve, Arsenio Jansen Santos Xu, Shizhong Ehlers, Jeff D. Diop, Ndeye N. Muchero, Wellington Pottorff, Marti Hu, Zhiqiu Acknowledgements National Agricultural Research System (NARS) Cisse, Ndiaga, ISRA-Senegal Drabo, Issa, INERA-Burkina Faso Tignegre, Jean-Baptiste, INERA-Burkina Faso Joseph, Batieno T. Benoît – WACCI Chiulele , Rogerio, EMU-Mozambique International Inst. of Tropical Agriculture (IITA) Boukar, Ousmane Fatokun, Christian Ofodile, Sam IBP-GCP Delannay, Xavier et al. LGC Genomics Vyas, Darshna et al. 22

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