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Pulse Genomics Comes of Age

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Keynote Presentation 06 by Rejeev K. Varshney at the International Conference on Pulses in Marrakesh, Morocco, 18-20 April 2016

Published in: Science

Pulse Genomics Comes of Age

  1. 1. Pulse genomics comes of age! Rajeev K. Varshney Research Program Director - Genetic Gains
  2. 2. Contributors Investors Rajeev Varshney Mahendar Thudi, Rachit Saxena, Manish Roorkiwal, K Himabindu, Anu Chitikineni, Vikas Singh, PT Lekha Pallavi Sinha,, Gaurav Agrawal, Aamir Khan, Deepa, Sandip Kale, Abhishek Rathore, Chris Ojiewo, NVPR Gangarao, PM Gaur, HD Upadhayaya, Sameerkumar Shiv Kumar Agrawal, Michael Baum Aladdin Hamwieh, Fida Aloe Ashutosh Serkar, Surendra Barpete IIPR: NP Singh, SK Chaturvedi KR Soren, Aditya Garg IARI: Ch Bharadwaj, Shailesh Tripathi Wang Jun, Xun Xu, Huanming Yang Gengyun Zhang Chi Song, Wenbin Chen, Sheng Yu, Guangyi Fan, Shancen Zha, Ying Wang, Xudong Zhang, Weiming He,, Chunyan Xu, Bicheng Yang KHM Siddique, Dave Edwards, Tim Colmer Jacqui Batley, Pradeep Ruperao Jiayin Pang Bunyamin Taran Amit Deokar Doug Cook R Varma Penmetsa Ming Cheng Luo Asnake Fikre Musa Jarso Million Eshete Paul Kimurto Richard Mulwa Serah Songok Md Yasin Priynaka Joshi S J Singh M S Pithia K N Yamini G Anuradha G Rajani Muniswamy D M Mannur Tim Sutton Jenny Davidson Government of India Ministry of Agriculture & Farmers Welfare DAC ICAR Ministry of Science & Technology DST DBT
  3. 3. Thanks to all colleagues and collaborators ✔ ✔ ✔ ✔ ✔ ✔
  4. 4. Second most important protein rich food legume Cool season tropical legume Self-pollinated Diploid, 2n=16 Genome size ~740 Mbp Key pulse crops Protein rich grain legume A rain-fed crop Often cross-pollinated Diploid, 2n=22 ~835 Mbp Chickpea (Cicer arietinum L.) Pigeonpea (Cajanus cajan L.)
  5. 5. http://www.livemint.com/Opinion/1BcMNygMDdIrgLQsegvNgK/The- pulses-crisis-why-reinvent-the-wheel.html Crop Area (mha) Production (mt) Yield (t/ha) Chickpea 13.54 13.10 0.97 Pigeonpea 6.21 4.74 0.76 Source: FAO 2015 (access on Jun 26,2015) Feeding billions and bringing prosperity in developing countries !!!
  6. 6. Breeding Translational genomics in agriculture (TGA) Genomics (incl. informatics) Genetics -logy disciplines PLoS Biol 2014, Crit Rev Plant Sci 2015
  7. 7. Adzuki bean (PNAS 2015 Sci Rep 2015) Chickpea (Nature Biotechnology- 2013) Pigeonpea (Nature Biotechnology- 2012) Mungbean (Nature Commun.- 2014) Pearl millet- 2016 Genomes sequenced… Groundnut Sesame (Genome Biology- 2014) (Nature Genetics- 2016) (in revision)
  8. 8. • Illumina sequencing used to generate 153.01 Gb • 73.8% of the genome is captured in scaffolds • Genome analysis predicted 28,269 genes • High levels of synteny observed between chickpea and Medicago • > 81,845 SSRs and 4.4 million variants (SNPs and INDELs) The chickpea genome
  9. 9. Integrated physical, genetic and genome map in chickpea Funct Integ Genom 2014
  10. 10.  Illumina sequencing tech used to generate 237.2 Gb  72.7% (605.78 Mb) of the total pigeonpea genome assembled into scaffolds  Genome analysis predicted 48,680 genes  High levels of synteny observed between the pigeonpea and soybean  >50,000 SSR and SNP markers identified  Higher abundance of drought tolerance genes The pigeonpea genome
  11. 11. 1000+ Pulse Genomes Chickpea Pigeonpea Diverse lines (90) Elite varieties (129) Reference set (300) 104 parental lines of hybrids Reference set (300) 157 parental lines/ RIL - 554 lines in chickpea - 526 lines in pigeonpea 1200 MAGIC lines of chickpea, 100 CWRs (18 Cajanus spp.) +
  12. 12.  Capillary electrophoresis: > 3000 SSRs  GoldenGate assays : 1536 SNPs  KASP Assays : >2000 SNPs  DArT/ DArTseq : 15,360 features  Genotyping by sequencing (GBS)  Arachis Axiom Array (Affy): 58,000 SNPs Marker genotyping platforms
  13. 13. Precise and high-throughput phenotyping
  14. 14. Drought tolerance Root traits- root length density, root length, root surface area Yield, harvest index, 100-seed weight, number pods per plant, biomass, specific leaf area, delta carbon ratio, days to flowering, days to maturity Heat tolerance Pods per plant, heat tolerance index, yield, biomass, harvest index, days to flowering, days to maturity Salinity tolerance Pod number, seed number, seed yield, Shoot dry weight, harvest index 100 seed weight Ascochyta blight Seedling resistance and adult plant resistance Helicoverpa Leaf damage rating (flowering), Unit larval weight, Helicoverpa larvae/10 plants, Days to first flowering Botrytis grey mould Heat tolerance ca. 50 traits mapped Pod borer Ascochyta blight Salinity tolerance Drought tolerance Fusarium wilt Fusarium wilt, Botrytis grey mould, Protein content Chickpea
  15. 15. Hybrid related traits Obcordate leaf shape Fertility restoration Seed purity kits CMS seed purity Hybrid seed purity Yield related traits Flowering time Days to maturity Pods per plant 100 seed weight Plant height Seeds per pod Seed yield per plant Primary branches Secondary branches Quality trait Protein content Biotic stress Fusarium wilt Sterility mosaic disease Abiotic stress Drought ca. 20 traits mappedPigeonpea
  16. 16. Mapping and molecular breeding for drought tolerance in chickpea
  17. 17. Intra-specific mapping populations for drought tolerance in chickpea 300 1. ICC 4958 × ICC 1882 - 268 RILs 2. ICC 283 × ICC 8261 - 289 RILs Parental lines Accession Root depth (cm) Root dry wt (g) ICC 4958 116.6 1.06 ICC 1882 83.9 0.71 ICC 283 91.6 0.73 ICC 8261 123.3 1.25 and many other drought tolerance traits! ICC 4958 ICC 1882 ICC 283 ICC 8261
  18. 18. Root related traits Root traits Traits Seasons Root length (cm) 3 Root length density (cm cm -3 ) 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 Experiment of chickpea root growth in ROS Root length screening
  19. 19. Agronomic traits Traits Seasons Traits Seasons Morphological traits Yield related traits 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 Phenotyping under rainfed and irrigated environments
  20. 20. “QTL- hotspot” in two mapping populations TAA170 GA24 STMS11 ICCM0249 CaM0856 LG 4: ICC 4958 × ICC 1882 RLD_06 RLD_08 RDW_06 RDW_08 RT DEPTH_06 RT DEPTH_08 SDW_06 SDW_08 RT VOL_06 RT VOL_08 RSA_06 RSA_08 RL_06 RL_08 STEM DW_06 LDW_06 R-T RATIO_06 LG 4: ICC 283 × ICC 8261 CAM1903 TA130 ICCM0249 TAA170 NC142 209 Theor Appl Genet 2014 13 out of 20 drought tolerance traits explaining 10- 58.20% phenotypic variation
  21. 21. “QTL-hotspot” on CaLG04 ICC 4958 × ICC 1882 Consensus map ICC 283 × ICC 8261 Theor Appl Genet 2014 PVE 58.2%
  22. 22. DNA Quantification Restriction digestion Ligation Pooling and clean up Polymerase chain reaction Cleaning of PCR product QC Check 1] QUBIT fluorometer BR/HS 2] Agilent Bio-Analyzer HS chip GBS – 96 - Plex protocol Good quality libraries are sequenced through Hi-seq-2500 Genotyping-by-sequencing (GBS) SNP Calling ICC4958 x ICC 1882 - 701.1 M reads, 59 Gb data (208 RILs) - 828 SNPs mapped - 49 SNPs integrated to “QTL-hotspot”
  23. 23. Saturating “QTL-hotspot” Mol Genet Genomics 2015 Varshney et al 2014 Jaganathan et al 2015 49 SNPs
  24. 24. Skim sequencing and Bin mapping  Sequencing: parents @ 8X coverage and 222 RILs @ 1X  No. of SNPs called: 53,169 (SGSautoSNP)  Bin construction: sliding window approach (Huang et al 2009)  No. of bins obtained: 1,610  No. of bins on chromosome 4: 281  No. of bins in “QTL-hotspot”: 38 (1,421 SNPs) Bin map of RIL 142
  25. 25. Refining the “QTL-hotspot” Identified 26 candidate genes Kale et al 2015 Varshney et al 2014 Jaganathan et al 2015 Scientific Reports 2015 26 candidate genes
  26. 26. High resolution mapping population 6,000 F2 lines in field @ Dharwad, India
  27. 27. Putative regions/genes associated with 100SDW “QTL-hotspot_a” “QTL-hotspot_b” 100SDW (g) ~113.03 Kb  No of KASPar markers used: 18  Phenotypic data: 100SDW on 59 homozygous F3 lines  ~113.03 Kb region of “QTL-hotspot_a” delimited
  28. 28. Re-sequencing Reference Set (300 lines from 33 countries) 4.9 M SNPs, 596 K indels, 512 K CNVs
  29. 29. Selection sweep, reduction of diversity A significant reduction in diversity was observed from wild genotypes (3.80 × 10−3) to landraces (0.86 × 10−3) and breeding lines (0.84 × 10−3)
  30. 30. Trait Number of MTAs P-value PVE (%) Root length density (RLD, cm cm-3 ) 3 5.73 × 10-6 - 2.1 × 10-8 6.5 - 16.6 Root dry weight (RDW, g plant-1 ) 11 6.81 × 10-6 - 9.18 × 10-10 5.58 - 10.49 Root surface area (RSA, cm2 plant-1 ) 6 9.17 × 10-6 - 1.65 × 10-7 5.9 - 10.12 Root volume (RV, cm3 plant-1 ) 13 7.28 × 10-6 - 1.43 × 10-7 5.77 - 10.41 Days to 50% flowering (DF) 24 8.1 × 10-6 - 7.8 × 10-9 9.09- 20.36 Days to maturity (DM) 48 9.06 × 10-6 - 4.82 × 10-8 8.96 -21.29 100 seed weight (100SDW, g) 98 1.07 × 10-6 - 2.89 × 10-22 10.34 - 14.4 Yield (YLD, Kg/ha) 22 9.42 × 10-6 - 2.77 × 10-7 7.16 - 18.6 Biomass (BM, g) 8 1.6 × 10-6 - 6.35 × 10-8 6.29 -12.02 Harvest index (HI, %) 15 8.87 × 10-6 - 1.46 × 10-8 5.97-14.84 Delta Carbon ratio (δ13 C) 1 6.02 × 10-7 20.7 249 MTAs for drought tolerance
  31. 31. 100 seed weight 166 MTAs total 98 unique (30 in >1 season) 43 SNPs (Ca4)- significant Ca4_15950928 explained 28.1- 43.8% PVE and 1.5 Mb away from “QTL-hotspot_b”
  32. 32. Yield  22 (D), 16 (H) MTAs  6 SNPs with function (D) Ca_12546 in ara1 QTL responsible for yield reduction in Australia & Canada and has 35 haplotypes in the reference set
  33. 33. Partners The 3000 Chickpea Genome Sequencing Initiative
  34. 34. Phenotyping of 3000 chickpeas  6 locations in India  Non-replicated augmented design  Target traits o Days to 50% flowering o Days to maturity o 100 seed weight o Yield of lines  Data on selected 5 lines o Plant height o Primary branches o No of pods/plant o Yield/plant
  35. 35. Introgression of “QTL- hotspot” for root and other drought tolerance related traits through MABC Donors Cultivars JG 11 Chefe KAK 2
  36. 36. 12- 24% higher yield than the elite varieties Enhanced grain yield under rainfed environments in India
  37. 37. 0 50 100 150 200 250 300 Yield Gms/plot Biomss/plot 0 50 100 150 200 250 300 350 400 450 Yield gms/plot Biomass gms/plot 13 superior MABC lines: -17-47% higher seed yield - 12-43% higher biomass Enhanced grain yield under rainfed environments in Kenya
  38. 38. Enhanced grain yield under irrigated conditions in Ethiopia 0 500 1000 1500 2000 2500 3000 3500 MABC11 MABC4 MABC16 MABC13 MABC14 MABC10 ICCV-939554 ICCV-4958 MABC9 MABC7 MABC6 MABC18 MABC19 MABC3 MABC2 MABC22 DALOTA >40% yield above standard check Yield(kg/ha)
  39. 39. Trait mapping and mechanism in pigeonpea
  40. 40. QTL mapping for FW and SMD resistance Features PRIL_A PRIL_B PRIL_C No. of SNPs identified 86,052 2,18,560 73,368 No. of polymorphic SNPs 4,025 2,417 3,843 Final filtered SNPs 1,537 1,789 1,297 Number of SNPs mapped 964 1101 484 Linkage map length (cM) 1120.56 921.20 798.24 PRILs Trait Pedigree Phenotyping PRIL_A FW ICPB 2049 × ICPL 99050 Patancheru, Gulbarga and Tandur PRIL_B FW ICPL 20096 × ICPL 332 Patancheru, Gulbarga and Tandur PRIL_B SMD ICPL 20096 × ICPL 332 Patancheru and Tandur PRIL_C SMD ICPL 20097 × ICPL 8863 Patancheru and Tandur Fusarium wilt (FW) Genotyping and construction of linkage map Sterility mosaic disease (SMD)
  41. 41. QTL mapping for FW resistance (PRIL_A)
  42. 42. QTL mapping for SMD resistance (PRIL_C)
  43. 43. QTL-seq for FW and SMD resistance Plant Biotechnology Journal 2015
  44. 44. Association of nsSNPs to the candidate genes responsive to FW and SMD diseases Linkage group Genes nsSNPs position (bp) Seq-BSA approach nsSNPs substitution approach ICPL20096 (R*toFW&SMD) R-bulka (R*toFW&SMD) S-bulkb (S*toFW&SMD) FW SMD ICPL99050(R*) ICPL20097(R*) ICP8863(R*) ICPB2049(HS†) ICPL99050(R*) ICPL20097(R*) ICPB2049(R*) ICP8863(HS†) FW associated nsSNPs CcLG02 C.cajan_07078 27,426,866 T T G T T T G T T G T CcLG02 C.cajan_07124 27,861,114 G G A G G G A G G A G CcLG11 C.cajan_02962 32,606,065 T T C T T T C T T C T CcLG11 C.cajan_03203 35,228,097 C C A C C C A C C A C SMD associated nsSNPs CcLG02 C.cajan_07067 27,324,239 T T G T T G T T T T G CcLG02 C.cajan_07067 27,324,261 T T G T T G T T T T G CcLG08 C.cajan_15535 2,014,125 G G G C C G C C C C G CcLG11 C.cajan_01839 19,958,148 A A C A A C A A A A C Plant Biotechnology Journal 2015
  45. 45. Markers for purity testing of hybrids and their parental lines Markersfor testingpurity Markers for testing purity
  46. 46. QTL-seq for identification of genomic regions for obcordate leaf type ICP 5529 × ICPL 11605 (Obcordate) (Normal) Pooled 20 F2 plants in extremes Genome wide ∆ SNP Index CcLG08 Days to flowering: (CcLG08) 19.22 to 20.80 Mb 56 SNPs (0 to 1) 7 genes; 2 Exonic 1nsSNP and 1sSNP
  47. 47. Epigenomics for understanding hybrid vigor in pigeonpea
  48. 48. Global Epigenome map of hybrids and parental lines ICPA 2043 ICPH 2671 ICPR 2671 CG CHG CHH C CG CHG CHH 19.14 60.87 46.13 7.13 Global methylation level The epigenome is a multitude of chemical compounds that can tell the genome what to do. Cytosine DNA methylation is a heritable epigenetic mark present in many eukaryotic organisms. Most of plant DNA methylation is restricted to symmetrical CG sequences, but plants also have significant levels of cytosine methylation in the symmetric context CHG (where H is A, C or T) and even in asymmetric sequences.
  49. 49. Canonical DNA methylation profiles of genes
  50. 50. Increased DNA methylation in centromeric regions
  51. 51. Increased DNA methylation in transposable elements (TE) regions
  52. 52. Differentially methylated region (DMR)
  53. 53. What’s next for pulses?
  54. 54. Predict Phenotypes Inbreeding Multi-location, Multi-year testing Seed Increase Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance cheaper to genotype = larger populations for same $$ make selections in ‘off target’ years maintain favorable rare alleles Select years earlier on single plant basis The Breeding Cycle
  55. 55. Selection intensity (i) Large F2 populations Large screening nurseries Large number of crosses y ir R A t  
  56. 56. CG centers have target of 40 Million data points in the next five years Needs a flexible, scalable, cost savings solution Genotyping cost @ US$ 2 per sample Paradigm changing high- throughput solutions
  57. 57. Low genetic gains in classical breeding 50- 100 crosses 5,000- 20, 000 Plant architecture SinglePlantSelections 10, 000 4, 000 1, 000 500 50 entries to regional trial recommended variety (disease, days to flowering, plant stand etc F7 lines Selection intensity (i) : Low Selection efficiency (r) : Low Genetic variance (σ) : Low Years per cycle (Y) : High 100- 200 F2s per cross
  58. 58. US$2 per sample based Forward Breeding F2 F3 F4 F5 F6-8 • 200,000 • Screening for disease, plant habit, quality, yield etc. • ~5,000 homozygous lines based on allelic contributions • ~1000 single plant selection based on other morphological traits • ~50 entires selected based on replicated multi-location/ station trials • ~5-10 superior breeding lines recommended as variety for targeted traits • MAS for homozygocity test • DNA fingerprinting 200F1Crosses(1000F2each) Disease Plant habit Quality YieldPositive alleles Selection intensity (i) : High Selection efficiency (r) : High Genetic variance (σ) : High Years per cycle (Y) : Low
  59. 59. In summary… http://www.pulsecanada.com  Chickpea and piegonpea have become genomic resources rich crops now  Large number of traits mapped using GBS, QTL-Seq, GWAS approaches  Molecular breeding is becoming routine in pulses. Need to target mega-varieties for introgression of needed traits  Forward breeding and digitization of breeding to enhance genetic gain  Analytical and decision support tools need to be implemented in breeding  National and international support essential
  60. 60. InterDrought-V Hyderabad International Convention Center (HICC) Hyderabad, India 21-25 February, 2017  Setting the biophysical context  Maximising dryland crop production  Plant productivity under drought Effective capture of water Transpiration efficiency Vegetative Growth Reproductive development, yield, yield quality  Breeding for water-limited environments  Agronomic management for water-limited environments Conference Topics: InterDrought Chair: Francois Tardieu, INRA, France InterDrought Past Chair: Roberto Tuberosa, Uni Bologna, Italy InterDrought Vice-Chair: J S Sandhu, ICAR, India Conference Organization Chair: Rajeev Varshney, ICRISAT, India Contact: r.k.varshney@cgiar.org, id5.icrisat@gmail.com Website: www.ceg.icrisat.org/idV

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