2013 GRM: Improve chickpea productivity for marginal environments in sub-Saharan Africa and South Asia -- RK Varshney

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  • Several abiotic factors are responsible to reduce production among the chickpea growing countries. Among abiotic stresses drought is a major constraints that lead to more than 50% losses. As chickpeas are grown on residual soil moisture, due to low precipitation, high evaporation and increase in temperature during maturity and harvest stage called as terminal drought leads to major proportion of yield losses
  • As a significant achievement, chickpea genome has been decoded recently. This has provided boost to research efforts for chickpea improvement.
  • For dissecting drought tolerance we have used two intra-specific mapping populations and reference set. These populations were phenotyped for drought tolerance
  • This phenotyping was conducted in Kenya, Ethiopia in sub-saharanafrica
  • In India phenotyping was done at Patancheru, Kanpur and Banglore
  • The reference set was genotyped with ~2000 markers. Nine candidate genes involved in conferring drought tolerance in different crop plant species were also used for candidate gene based association studies
  • Like segregating mapping populations phenotyping data were generated for 12 root related traits at two to three seasons. Root traits were phenotyped in the semi-automated root screening facility.
  • Reference set/mini-core collection was phenotyped for several morphological, transpiration efficiency related and yield related traits in 2-3 replications and 2-14 seasons in two locations in India and three locations in sub-Saharan Africa

Transcript

  • 1. Improve chickpea productivity for marginal environments in sub-Saharan Africa and South Asia
  • 2.  Introduction  Genomic resources  Genetic resources  Trait mapping- QTLs and GWAS  Molecular breeding for drought tolerance  Enhancing Capacity of NARS  Data management and decision support tools Contents
  • 3. Introduction…
  • 4. Global distribution of chickpea area Grown on about 12 m ha across 54 countries >6 m ha 0.5 to 1.0 m ha 150,000 to 210,000 ha 50,000 to 100,000 ha 10,000 to 40,000 ha
  • 5. Production constraints Abiotic stresses 1. Drought 2. Heat 3. Salinity 4. Cold Max. Temperature (0C) Mean Temp Min. Temperature (0C) Harvest 0 50 100 150 200 250 300 350 400 450 1 2 3 4 5 6 7 8 9 10 11 12 Month mm 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 Sowing Terminal drought Precipitation Evaporation Million(US$) Totalproduction Drought Lowtemperature Wilt Ascochytablight Podborer
  • 6. Constraints in molecular breeding  Very few molecular (SSR) markers  Either no genetic map or maps with low marker density  Non-availability of appropriate germplasm, mapping populations and phenotyping data  Non-availability of trait-associated markers in breeding  Capacity and skills in molecular breeding
  • 7. Genomic resources...
  • 8. Resource Amount SSRs ~2,000 DArT arrays 15,360 features SNPs 9,000 GoldenGate assays 768 SNPs KASPar assays 2,068 SNPs Veracode assays 96 SNPs Summary of marker resources
  • 9. LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 Marker Loci: 1,291 Coverage: 845.56 cM Comprehensive genetic map TAG 2010, 2011 PLoS One 2011 Plant Biotech J 2012
  • 10. KASPars assays integrated in transcript map Markers mapped : 1328 Map distance : 788.6cM Average number of markers/LG : 166 Average inter-marker distance : 0.59cM TAG 2010, 2011 PLoS One 2011 Plant Biotech J 2012
  • 11. Towards genome-wide physical map Clones CAH library CAE library Old library Total 1st 2nd 1st 2nd 1st 2nd Clones targeted 29,664 5,376 29,568 5,376 337 773 71,094 (12X ) Clones with usable data 28,492 5,160 28,272 5,240 319 765 68, 248 Clones in FPC 18,285 3,502 22,571 3,926 319 765 49,368 Old library, 1st instance are the clones from which BES-SSR were developed Old library, 2nd instance are the RGH hybridizing clones Fingerprinting statistics of different BAC-libraries
  • 12. Clone statistics in contigs: Total no. clones in 1,174 contigs 46,112 Range of clone in contigs 2 to 3,007 Average no. of clones in each contig 39.27 Genome coverage 8X Genome represented 615 Mb Band statistics in clones: Total no. of bands in clones 318,971 Average no. of bands in clones 271.69 Range of bands in clones 34 to 2,268 Minimum tiling path (MTP): Total no. of contigs 1,174 No of clones in MTP 4,290 Statistics of physical map Collaboration: NIPGR, India and UC-Davis, USA
  • 13. Integrating sequence, physical and genetic maps
  • 14. The chickpea genome
  • 15. Re-sequencing in chickpea Samples: - 5 wild species - 25 landraces - 60 breeding lines/ released varieties Or - 5 wild species - 57 Desi genotypes - 28 Kabuli genotypes Sequencing: -RAD sequencing (35 Gb) - 61 genotypes (40 desi, 16 kabuli, 5 wild) -WGRS (205 Gb) - 29 genotypes (17 desi, 12 kabuli)
  • 16. Genetic resources…
  • 17. Harnessing alleles from genebanks  1,700 genebanks  By 1997, the world economy had accrued annual benefits of ca. $115 billion from use of crop wild relatives  Genomic characterization of gene bank material is essential  Needs to associate allelic value with phenotype  Transfer in elite varieties
  • 18. Genetic resources 300 (A) Intra-specific mapping populations: 1. ICCV 283 × ICC 8261 - 289 RILs 2. ICCV 4958 × ICC 1882 - 268 RILs (B) Chickpea reference set -300 genotypes (C) Pre-breeding populations ICRISAT, Patancheru - 17 ICRISAT, Nairobi - 20 Egerton Uni. Kenya - 5 EIAR, Ethiopia - 2
  • 19. MAGIC populations Genotypes Remarks ICC 4958 Drought tolerant genotype found promising in Ethiopidrought tolerant parent of two mapping populations ICCV 10 Widely adapted drought tolerant cultivar found promising in India and Kenya JAKI 9218 Farmer-preferred cultivar in central and southern India JG 11 Farmer-preferred cultivar in southern India and also performing well in Kenya JG 130 Farmer-preferred cultivars from central India JG 16 Farmer-preferred cultivar in northern and central India ICCV 97105 Farmer-preferred elite line identified in Kenya and Tanzania ICCV 00108 Farmer-preferred elite line identified in Tanzania Eight well performing elite chickpea lines (TLI & TLII)
  • 20. Trait mapping…
  • 21. Experiment of chickpea root growth in rain out shelter (ROS) Crane for lifting root cylinders for moisture under water stress determinations Semi-automated precise high-throughput phenotyping
  • 22. Phenotyping in rainfed and irrigated conditions
  • 23. Phenotyping in Sub-Saharan Africa (Kenya, Ethiopia)
  • 24. Phenotyping in India (Pantcheru, Kanpur, Bangalore)
  • 25. “QTL- hotspot” on CaLG04 ICC 4958 × ICC1882 Consensus map ICC283 × ICC8261 PVE 58.2%
  • 26. Saturating “QTL- hotspot” region  701.049 million reads  59030.7 Mbp  828 SNPs
  • 27. Genotyping data on Reference set Homozygote KASPar assays Homozygote Heterozygote SSR analysis (CE) SNPs in candidate genes Marker data on reference set  SSR - 35  DArT - 1157  SNPs - 651  Gene based SNPs - 113 Total 1,956 Candidate genes  Abscisic acid stress and ripening gene (ASR)  DREB2A  ERECTA  Sucrose synthase (SuSy)  Sucrose phosphate synthase (SPS)  AKIN (SNF1 related protein kinase)  Aminoaldehyde dehydrogenase (AMADH)  Dehydrin (DHN),  Myb transcription factor
  • 28. Root related traits Root traits Traits Seasons Root length (cm) 3 Root length density (cm cm -3 ) Root volume (cm 3 ) 3 Root dry weight (g) 3 Rooting depth (cm) 3 Root surface area 3 R-T ratio(%) 3 Shoot dry weight (g) 3 Stem dry weight (g) 3 Leaf dry weight (g) 3 Projected area 2 Average diameter 2 Water stress determination Exp. of chickpea root growth in ROS
  • 29. Agronomic traits Morphological traits Yield related traits Traits SeasonsTraits Seasons Plant height (cm) 14 Pods/plant 2 Plant width (cm) 7 100 SDW (g) 10 Plant stand 7 Yield (g/m 2 ) 3 Apical primary branch 7 Yield (Kg/ha) 10 Apical secondary branch 7 Yield per plant 7 Basal primay branch 7 Production 7 Basal secondary branch 7 Biomass 6 Teritiary branches 7 Biomass/plant 2 Phenological traits Harvest index 6 Days to flowering 13 TDM weight (g/m 2 ) 2 Days to maturity 9 Transpiration efficiency Seeds per pod 7 13 C 2 Seeds/plant 2 SPAD 2 Field phenotyping under rainfed and irrigated environments Heat tolerance phenotyping
  • 30. Δ Carbon on LG4 Yield under rainfed on LG4 . …. GWAS for ΔC and Yield
  • 31. Root length density Harvest index GWAS for RLD and HI
  • 32. GWAS for 100 seed weight
  • 33. Molecular breeding for drought tolerance…
  • 34.  MABC, MARS and GS approaches seem to most promising for crop improvement  Need to have genomic resources and cost-effective genotyping platforms  Precise phenotyping platforms required  Breeders-friendly pipelines and decision support tools required for prediction of phenotype Modern breeding approaches for enhancing genetic gains
  • 35. Marker-assisted backcross (MABC) method for introgressing genomic region controlling root and other drought tolerance related traits Donors Cultivars JG 11 Chefe KAK 2
  • 36. Phenotyping of MABC products in ROS BC3F3 lines phenotyped in ROS for assessing root traits
  • 37. 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Yield(kg/ha) Irrigated Rainfed QTL introgression into JG 11 Enhanced root length density Enhanced yield The Plant Genome, 2013
  • 38. Institution Cross/parents Current status ICRISAT, India JG 11 × ICC 4958 20 BC3F5 lines Chefe × ICC 8261 8 BC3F5 lines KAK2 × ICC 8261 2 BC3F5 lines ICCV 10 × ICC 4958 22 BC3F5 IIPR, India DCP92-3 × ICC 4958 60BC1F1 KWR 108 × ICC 4958 7 BC1F1 IARI, India Pusa 362 × ICC 4958 170 BC2F1 EIAR, Ethiopia Ejere × ICC 4958 384 BC2F1 Arerti × ICC 4958 27 BC3F4 lines EU, Kenya ICCV 97105 × ICC 4958 33 BC3F1 ICCV 95423 × ICC 4958 10 BC3F5 lines MABC for enhancing drought tolerance in Asia and sub-Saharan Africa
  • 39. MABC status @ IARI (Indian project)  4 BC2F1 plants (with >96% genome recovery) were used in making backcrosses with recurrent parent Pusa 362 to generate BC3F1 (105 seeds)  BC2F2 seeds also harvested from plants with more than 96% genome recovery
  • 40. PopulationdevelopmentRecombinationPopulationdevelopment 1st Recombination cycle 2nd Recombination cycle 3rd Recombination cycle Multilocation phenotyping Genotyping Parent 1 × Parent 2 F1 F2 F3 F3:4 F3:5 Single seed descent 282 F3 progenies 282 progenies Multilocation phenotyping A B C D E F G H F1 F1 F1 F1 F1 F1 F1 F2 F3 F3:4 10 plants/family (A-H), 6 sets of 8 families/cross QTL detection JG 11 × ICC 04112 JG 130 × ICC 05107 Kenya, Ethiopia and India Rainfed and irrigated environments (2010-11) 70 marker 92 markers QTL analysis completed Marker-assisted recurrent selection (MARS) MARS lines for RC selected OptiMAS Indian project TLI Phase II First RC completed Second RC in progress
  • 41. × JG 130 X ICCV 05107
  • 42. × JG 11 X ICCV 04112
  • 43. Exploring genomic selection in chickpea  320 elite-breeding lines;>3000 markers
  • 44. GS ISMU V2 Raw Reads Reference Assemble& Align Raw Reads Mine SNPs Generate Marker Matrix Visualizein TABLET and FLAPJACK Export in FLAT Files GDMS Genotypic Matrix & QTLs Lines selected for further crossing in GS External Genotyping Platforms Called SNPs ISMU V2.0 44
  • 45. Enhancing capacity of NARS in modern breeding…
  • 46. NARS partners practicing modern breeding Serah Alice Paul examining the crossesRobert
  • 47. Musa Jarso marker analysis for MABC crosses Mosses oyier marker analysis for MABC crosses 4th International Workshop on Next generation genomics and integrated breeding for crop improvement, Feb 19-21,2014, NARS partners practicing modern breeding
  • 48. Products  Reference set, pre-breeding populations, MAGIC lines  >3000 SSRs, 2068 KASPar, DArT arrays  High density genetic maps and physical map  Draft genome sequence and re-sequencing of 90 lines  MABC lines with enhanced drought tolerance in the genetic background of JG11, KAK2 and Chefe
  • 49. Summary  Well characterized reference set and 39 pre-breeding populations developed  1200 F8 MAGIC lines developed ready for genotyping and phenotyping  Large scale markers (SSRs, SNPs, DArTs) and several maps developed  A genome-wide physical map developed (574 Mb) that contributed to genome sequencing of chickpea  A most promising “QTL-hotspot” with about 50 markers for MABC for drought tolerance  Promising introgression (BC3F5) lines with higher yield in rainfed conditions  Next generation of molecular breeders (4 PhD and 8 MSc students from NARS, >50 researchers) trained  TLI Phase I and Phase II datasets curated into IBWS
  • 50. ICRISAT, Patancheru, India: Pooran Gaur, Hari Upadhyaya, L Krishnamurthy, Junichi Kashiwagi, Abhishek Rathore, Trushar Shah, Mahendar Thudi, Manish Roorkiwal, Rachit Saxena, Ashish Kumar, Murali Mohan, HimaBindu, Shailesh, Pavana, Neha, Serah, Gafoor UC-Davis, USA: Doug Cook, R Varma Penmetsa NCGR, Santa Fe, USA: Greg May, Andrew Farmer Uni Georgia, USA: Scott Jackson; JCVI, USA: Chris Town, Yongli Xiao DArT Pty Ltd, Australia: Andrzej Killian Uni Frankfurt, Germany: Peter Winter, Guenter Kahl Osmania University: PB Kavikishor NRCPB, New Delhi, India: NK Singh, TR Shrma, R Srinivasan, PK Jain NIPGR, New Delhi, India: AK Tyagi, Sabhyata Bhatia IIPR-Kanpur, India: SK Chaturvedi, Aditya Garg, S Datta IARI, New Delhi, India: Jitendra Kumar, C Bharadwaj, S MPKV, Rahuri, India: L Mhase, P N Harer, PL Kulwal JNKVV, Jabalpur, India: A Babbar, N Saini, O Gupta ARS-Gulburga, India: D Mannur, Jayalaksmi UAS-Bangalore, India: Sheshashayee, M Udayakumar, KP Vishwanath Egerton University, Kenya: Paul Kimurto, Richard Mulwa EIAR, Addis Abba, Ethiopia: Asnake Fikre, Million Eshete LZARDI, Debre Zeit, Tanzania: Robert Kileo Many thanks to all contributors
  • 51. Collaborators and partners