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Research Program Genetic Gains (RPGG) Review Meeting 2021: A crop of prodigious opportunities By Dr Rakesh K. Srivastava

Pearl millet is a staple food for more than 90 million farmers in arid and semi-arid regions of sub-Saharan Africa, India and South Asia. ICRISAT highlight the substantial enrichment for wax biosynthesis genes, which may contribute to heat and drought tolerance in this crop. ICRISAT resequenced and analyzed 994 pearl millet lines, enabling insights into population structure, genetic diversity and domestication. We use these resequencing data to establish marker trait associations for genomic selection, to define heterotic pools, and to predict hybrid performance.

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Research Program Genetic Gains (RPGG) Review Meeting 2021: A crop of prodigious opportunities By Dr Rakesh K. Srivastava

  1. 1. Pearl millet: a crop of prodigious opportunities Rakesh K. Srivastava Principal Scientist (Genomics & Trait Discovery) ICRISAT, Hyderabad 05 Jan, 2020 r.k.srivastava@cgiar.org
  2. 2. Genetic resources • Word association mapping panel (PMiGAP) • Bi-parental mapping populations (23 pairs) • Chromosome Segment Substitution Lines (CSSLs) • TILLING population • NAM population • Alloplasmic-isonuclear population Genomic resources • World reference genome Tift23D2B1-P1-P5 • ~1,000 genomes re-sequenced • DM genome (Pathotype 1, India) sequenced • PMiGAP with >29.5 million WGRS-SNPs & 3.8 million InDels • Panel of ~61K genic SNPs for GWAS on PMiGAP • Virtual 60K SNP array • RAD/GbS SNPs for GS • Consensus map • Transcriptome assemblies • QC panel • Improvement and new reference genomes Available genetic & genomic resources
  3. 3. Mapped traits in pearl millet Grain and forage quality traits  Grain Fe and Zn content  In-vitro organic matter digestibility  Metabolizable energy  Neutral detergent fiber (cellulose, hemicellulose, lignin)  Nitrogen on dry matter basis  Gas volume  Sugar content on dry matter basis  Fresh and dry stover yield Biotic constraints  DM resistance (12 pathotype- isolates)  Blast resistance  Rust resistance Abiotic constraints  Terminal drought tolerance (tiller number, panicle diameter, total biomass dry weight, leaf dry weight, root dry weight, shoot dry weight, stem dry weight, leaf area, specific leaf weight, transpiration efficiency, transpiration rate, absolute transpiration, leaf rolling, delayed leaf senescence, low VPD transpiration rate, high VPD transpiration rate) Yield and yield-related traits  Flowering time  Plant height  Panicle length  Seed weight  Panicle harvest index  Grain harvest index  Grain number per panicle  Harvest index  Biomass  Nitrogen use efficiency & related traits  Grain yield under moisture stress and irrigated conditions Other traits  Heterotic gene pools for hybrid parental lines  General and specific combining ability for grain yield under drought stress and irrigated conditions
  4. 4. Product concept Estimated area (m ha) % area/ effort Target and spillover agroecologies Maturity (days) Resistance/tolerance required Other criteria Product development goals (1) Early-duration pearl millet (OPV/hybrids) for adaptation to Sahelian zone of West Africa 8 25 Target: Niger, Mali, Burkina Faso, Senegal and Nigeria Spillover: Parts of Sudan, Chad, Cameroon and India 70-80 Biotic stresses: Downy mildew and head miner Abiotic stresses: Drought, flowering period heat stress, low P tolerance Must have traits: Grain yield:1.5- 2.0 t/ha; plant height: 170-200 cm; panicle length: 30-50 cm; panicle width: 8-10 cm; test grain weight: 10-15 g; high grain Fe and Zn content 10% increase in grain yield and stover yield over local and improved check (2) Medium gero pearl millet (OPV/hybrids) for adaptation to better endowed environments of West Africa 7.5 20 Target: South of Niger, Mali Nigeria, Burkina Faso, Ghana and Senegal Spillover: Parts of Sudan, Chad and Cameroon 85-100 Biotic stresses: Downy mildew and Striga Abiotic stress: Drought Must have traits: Grain yield: 2.0- 2.5 t/ha; plant height:170 - >200 cm; panicle length: 60-75 cm; panicle width: 7-10 cm; test grain weight: 10-15 g 10% increase in grain yield and stover yield over local and improved check (3) Dual-purpose maiwa pearl millet (OPV/hybrids) for adaptation to better endowed environments of West Africa 3-4 10 Target: Nigeria, Mali, Senegal and Burkina Faso Spillover: Parts of Sudan, Chad and Cameroon 110-120 Biotic stresses: Downy mildew and Striga Abiotic stresses: Drought tolerance; flowering period heat stress Must have traits: Grain yield: 2.0- 2.5 t/ha; plant height: >200 cm, panicle length: 70->85 cm; panicle width: 8-12 cm; high grain Fe and Zn content 10% increase in grain yield and stover yield over local check with >40 ppm Fe (4) Early- to medium- maturity high-yielding varieties and hybrids for Eastern and Southern Africa 3.0 10 Target: Sudan, Tanzania and Uganda Spillover: Kenya, Zimbabwe, Namibia, Eritrea, Malawi, Somalia and Mozambique 65-90 Biotic stresses: Striga, downy mildew, covered and kernel smut, stem borer Abiotic Stresses: Drought Must have traits: High yield: 1.5- 2.0 t/ha (varieties) and 2.0-2.5 t/ha (hybrids), high grain Fe and Zn 10% grain yield increase compared to the commercial check (5) Parent lines of medium- to late-maturing, dual- purpose hybrids for adaptation to better endowed environments of South Asia 6.0 25 Target: India: East Rajasthan, Central and South Gujarat, Haryana, Uttar Pradesh, Maharashtra and Peninsular India Spillover: Tanzania, Kenya and Uganda (ESA) 75-90 Biotic stresses: Downy mildew and blast Abiotic stresses: Flowering period heat stress tolerance (summer season) Must have traits: Parents with high productivity and good GCA for grain yield, hybrids with grain yield of 3-4 t/ha, high grain Fe and Zn content, better fodder quality Hybrid parents to develop hybrids with 10% increase in grain yield over representative checks (6) Parent lines of early- maturing, dual-purpose hybrids for adaptation to drought prone environments in South Asia 1.5 5 Target: India: Western Rajasthan and drier parts of Gujarat and Haryana (200-400 mm/annum) Spillover: Sudan (ESA), Northern Niger and Senegal (WCA) 65-75 Biotic stresses: Downy mildew and blast Abiotic stress: Drought Must have traits: Parents with high productivity and good GCA for grain yield, hybrids with grain yield of 2.0-2.5 t/ha Hybrid parents to develop hybrids with 10% increase in grain yield over representative (7) Cultivars and hybrid parents exclusively for forage and high biomass in South Asia 1.0 5 Target: India: Gujarat, Punjab, Rajasthan, Uttar Pradesh, Madhya Pradesh, Peninsular India (summer and rainy season) Spillover: Central Asian countries and Brazil Single cut (50-80); Multicut (50- 110) Biotic stresses: Downy mildew, blast and rust Must have traits: Green biomass of 40-55 t/ha, dry biomass of 15-20 t/ha, non-hairy, leaf: stem ratio of 3-5, IVDMD of 50-55% with protein of 10-12% 5% increase in biomass yield over best check
  5. 5. • Mapped combining ability loci using chromosome segment substitution lines (CSSLs). Major loci for general combining ability (GCA) and specific combining ability (SCA) were mapped for yield and yield-related traits (Kumari et al. 2019. PLOS ONE) • Mapped major effect QTLs for downy mildew resistance (DMR) for the three new pathotype- isolates. The largest amount of observed phenotypic variation (R2 of 76.6%) was contributed by the QTL on LG4 for the Sg519 isolate (Durgaraju et al., 2019. European J. Plant Path.) Trait mapping A B C
  6. 6. Heterotic Gene Pools defined  343 lines (160 B- and 182 R- lines along with world reference germplasm Tift 23D2B1-P1-P5 as control) used in the study  B10R5, B3R5, B3R6, B4UD, B5R11, B2R4, and B9R9 represent putative heterotic gene pools in pearl millet.
  7. 7. pgpb6112 pgpb9106 Xipes0042 pgpb11431 pgpb7814 pgpb10695 pgpb9498 pgpb9152 pgpb7328 Xipes0017 pgpb6756 pgpb12900 pgpb13184 pgpb10531 pgpb7001 pgpb9927 pgpb13376 pgpb9130 pgpb12275 pgpb7069 pgpb9393 pgpb5822 pgpb11882 pgpb12077 pgpb12612 pgpb12664 pgpb11990 pgpb12385 pgpb7349 pgpb7938 pgpb11716 pgpb11894 pgpb6149 pgpb6981 pgpb10653 pgpb11467 pgpb7094 pgpb6723 pgpb9205 Xipes0226 Xicmp3032 Xicmp3017 Xipes0146 Xipes0197 Xipes0079 Xipes0203 Xpsmp2273 Xipes0098 LG1 pgpb120940.0 pgpb10685 pgpb102177.8 sts32223.5 Xipes018136.7 Xipes000745.9 Xpsmp208851.3 Xpsmp207257.8 pgpb10293 pgpb10563 pgpb9172 pgpb741369.7 pgpb9822 pgpb10840 pgpb960675.0 Xipes016288.6 Xipes0163102.8 Xipes0117120.0 Xipes0160128.3 pgpb8902137.2 pgpb8978138.6 pgpb8139 pgpb7284 pgpb6665 pgpb7736140.0 Xipes0236143.0 Xpsmp2059146.5 Xpsmp2206147.0 Xpsmp2231 Xpsmp2013149.2 pgpb9747156.1 Xipes0210163.4 pgpb7861 pgpb7028173.1 pgpb10959193.4 Xipes0118208.0 Xipes0221263.7 LG2 Xipes01610.0 Xipes009536.0 pgpb663454.7 pgpb1164761.5 Xipes016672.3 pgpb11874 pgpb10791 pgpb1313580.7 pgpb1007681.8 Xipes021386.7 pgpb7379 pgpb9688 pgpb1123589.6 pgpb7983 pgpb10464 pgpb12994 pgpb827293.5 Xpsmp2227107.5 Xctm10109.0 Xpsmp2214112.9 Xipes0142118.1 pgpb10327 pgpb7799 pgpb10182130.7 pgpb10926132.6 pgpb5938 pgpb7699136.8 pgpb10033161.5 pgpb12395178.2 pgpb6174196.1 pgpb6901203.6 pgpb8757212.3 LG3 Xpsmp20850.0 Xipes02255.5 Xipes012911.1 Xpsmp207621.4 Xipes017429.9 pgpb13161 pgpb11422 pgpb12125 pgpb12964 pgpb11170 pgpb6413 39.2 pgpb1132542.0 pgpb1292950.3 pgpb990354.6 pgpb9967 pgpb876468.1 pgpb739070.8 pgpb11527 pgpb1107079.3 pgpb12505 pgpb9424 pgpb1149683.8 pgpb1145985.4 pgpb938990.6 pgpb9189 pgpb7539 pgpb9351 pgpb796693.4 pgpb1235496.3 pgpb10500 pgpb10228 pgpb6267100.2 pgpb12538106.8 pgpb6707112.8 pgpb7394122.6 pgpb5883 pgpb6988 pgpb12366125.0 pgpb9788126.4 pgpb6590 pgpb6827 pgpb12278129.8 pgpb9328135.9 pgpb6967142.6 pgpb11249 pgpb8864143.6 pgpb7925 pgpb9653 pgpb10230144.6 pgpb9450 pgpb6246146.6 sts305154.0 Xipes0219165.1 LG4 Xpsmp22290.0 Xpsmp227627.6 Xpsmp227729.4 pgpb934343.4 pgpb7186 pgpb602860.2 pgpb9755 pgpb846369.6 pgpb6766 pgpb1300270.6 Xpsmp207887.0 Xipes021790.2 Xipes017592.8 Xipes021495.2 Xipes0152104.4 pgpb11029 pgpb5908123.0 pgpb8456 pgpb6774 pgpb10577130.5 pgpb10505133.4 pgpb12521 pgpb9241135.6 Xipes0230143.8 Xipes0093148.7 pgpb9647158.0 pgpb8532 pgpb8909167.9 Xicmp3027191.7 LG5 F MLG3 Fe Zn 50%FT 50%FT Fe-Zn QTL location for (ICMB 841-P3 × 863B- P2)-based RILs Zn- R2 50.1, LOD 14.9 Fe- R2 19.4, LOD 4.7 Co-localized QTLs Kumar et al., 2016. Frontiers in Plant Sciences
  8. 8. Genomic positions of significantly associated SSR markers with grain iron and zinc content in the consensus map of Rajaram et al. (2013). Color code: Orange for iron; Blue for zinc; and Green for both iron and zinc. Front. Plant Sci. 2017. 8:412. Association mapping of Fe Zn content
  9. 9. pgpb91660.0 pgpb12666 pgpb6468 pgpb73282.6 pgpb675614.0 pgpb10565 pgpb1336217.2 Xipes01729.3 pgpb1290034.3 pgpb1053149.7 pgpb913054.3 pgpb695560.4 pgpb9393 pgpb12275 pgpb706978.6 pgpb6874 pgpb9135 pgpb968485.1 Xipes09897.0 Xipes079103.6 Xipes091110.4 Xipes146114.4 pgpb7504 pgpb11518120.4 pgpb10307 pgpb5896 pgpb6818120.8 pgpb8836 pgpb7553125.5 pgpb9433 pgpb10653145.9 pgpb12525 pgpb11693167.1 pgpb10397177.5 pgpb10394 pgpb12204183.6 pgpb5859201.7 pgpb5822217.8 LG1 Xipes0270.0 pgpb10526 pgpb12897 pgpb9302 pgpb13198 pgpb13146 pgpb9338 pgpb6665 7.0 pgpb73308.6 pgpb72849.9 pgpb1111214.0 pgpb787915.0 pgpb5966 pgpb1042416.3 pgpb1171716.9 pgpb9970 pgpb12444 pgpb1063722.1 Xipes06028.6 pgpb8259 pgpb1155245.1 pgpb683249.6 pgpb1145254.7 pgpb960656.7 pgpb817760.9 pgpb10685 pgpb1138861.4 pgpb10684 pgpb1063862.3 pgpb13083113.8 LG2 Xipes1800.0 pgpb109267.8 pgpb1019811.1 Xipes18815.6 pgpb906923.4 pgpb1231026.5 pgpb779930.1 pgpb737934.0 pgpb6812 pgpb849040.9 pgpb6754 pgpb845743.7 pgpb1313545.6 pgpb1123547.6 Xipes16648.3 pgpb12497 pgpb1174851.8 pgpb1195754.4 pgpb724162.3 pgpb1042565.2 pgpb1225972.6 pgpb1054684.0 LG3 0.0 23.6 29.8 38.8 42.2 44.0 49.9 56.9 65.3 66.7 72.1 93.9 97.6 101.5 125.3 139.1 140.7 144.5 150.7 151.5 153.7 155.2 157.4 178.1 LG4 LG1 M Fe Zn Co-localization Fe-Zn QTL positions for (ICMS 8511 × AIMP 92901-08)-based RILs Fe- R2 9.0, LOD 25.4 Zn- R2 30.0, LOD 23.9 Kumar et al., Genes, 2018
  10. 10. CIRCOS of highly expressed genes in grain of different genotypes (AIMP, ICMS and MRC) thick ness of ribbon showing the expression levels Candidate genes for Fe and Zn metabolism Sci Rep. 2020. 10, 16562
  11. 11. SubstrateEnzymes Environment Lipase Lipoxygenases Polyphenol oxidase (PPO) Peroxidase (POD) Lipids Glycosylflavones Mining candidate genes for flour rancidity
  12. 12. LG2 Terminal drought tolerance (Kholová et al. 2010) • From a total of 52,028 ddRAD-SNPs that were generated, a total of 6,821 SNPs were used for mapping • A panel of 10 SNPs is being used in forward breeding • All the A1 zone material from ICRISAT and NARS are being genotyped Early drought stress tolerance (Debieu et al. 2018) • 11 SNPs under validated on breeding lines from WCA • May be applicable to Indian breeding programs Grain Fe and Zn content (Kumar et al. 2016) • Validated LG3 high grain Fe-Zn QTL interval mined for SNPs • 4-SNP panel is being used in breeding programs Currently available markers
  13. 13. Blast • Bi-parental mapping populations (7 populations, F3/F4) • QTL-Seq using F3:4 mapping populations • TILLING approach in ICMR 11019 genetic background • GWAS using PMiGAP data from major hot-spot locations (5 environments) Fe-Zn • Positive selection, high Fe (~110 ppm) Zn (~70 ppm) (Kumar et al., 2018. Front Pl Sci) • DArT-seq genotyping of a F10 mapping population • Phenotyping in three environments • Candidate genes for grain Fe and Zn discovered (Mahendrakar et al., 2020. Sci Rep) Fertility restoration • In-silico approach • Alloplasmic-isonuclear mapping population GWAS for Nitrogen Use Efficiency (NUE) • Association mapping, three season data on 400 lines • Initial MTA analysis being improved Markers available by this year
  14. 14. • Blast resistance (2022) • DM resistance (2022) • NUE (2022) • WUE (2022) • Striga • Head-miner • Seedling-stage heat tolerance • Lodging tolerance • Forage quality • Flour rancidity • Low GI Markers available by next year and beyond
  15. 15. Optimization of genomic prediction models Varshney et al. 2017 • Phenotyped 64 pearl millet hybrids (23 × 20) in five environments for 15 traits • 302,110 high-quality SNP marker data from 580 B- and R- lines, were used to predict hybrid performance • 170 promising hybrid combinations found, 11 combinations are existing, 159 combinations have never been used Liang et al. 2018 • Evaluated two potential genotyping strategies RAD-seq and tGBS • 320 hybrids and 37 inbreds at field trials in four locations • Prediction accuracy was equivalent for RAD-seq/tGBS- SNPs • tGBS generated greater number of high MAF SNPs per million reads Optimization of SNPs (under preparation) • Prediction accuracy evaluation of different classes of SNPs for 20 traits, 4 season data, 250 PMiGAP inbreds, 250 hybrids • SNP types studied: exonic, intronic, upstream, downstream • A total of 276,267 SNPs used for this study Varshney et al. 2017. Nature Biotechnology Liang et al. 2018. G3 Under preparation
  16. 16. Optimization of genomic prediction models AICRP-PM/ICRISAT-Asia Centre (on-going) • 370 inbreds (elite hybrid parental lines), 75 hybrids, 3 season data from A1, A and B zones • Genotying of the training population with mid-density panel (~4K SNPs) • Development of genomic prediction model ICRISAT- WCA (on-going) • 250 inbreds (elite parental lines- OPVs, hybrids), 150 hybrids, 4 season data from WCA countries • Genotying of the training population with mid-density panel • Development of genomic prediction model
  17. 17. • Used SNP data from the ~1,000 genomes project from PMiGAP and B-/R- lines used (Varshney et al., 2017) • A set of common SNPs between PMiGAP and B- and R- lines were identified (~20,000) • Using ICRISAT’s and Corteva’s proprietary pipeline a QC panel of 48 and 54 (28) SNPs have been developed • The 48 and 54 SNP set from ICRISAT and Corteva respectively, used for assay development with Intertek • Validation of the SNPs completed on breeding lines from ICRISAT India & Africa, NARS Corteva Agriscience: 15+4 plates • A total of 30 SNPs finalized post validation on 15 plates • Breeding samples from ICRISAT (Asia/WCA), AICP-PM 30 SNPs (~16+4+16+12 plates) Development of SNP-based QC Panel
  18. 18. Mid-density marker panel • A marker panel of ~4,000 SNPs • Being developed from the ~1,000 genomes project • ICRISAT-Corteva Agriscience • Exploring separate pipeline for the African breeding programs • Good support form the ICRISAT breeders and AICRP-PM for integration in their respective breeding programs • To also be used in MABC program especially for the A1 zone Mid-density array
  19. 19. Development of additional reference genomes  ICRISAT In association with Corteva Agriscience  Two additional genomes 843B & CMR 06777  Improvement of genome assembly of Tift23 D2B1P1-P5  Comparative genomics with iso-seq data  HiC for one of the lines  Platinum standard genomes
  20. 20. • HHB 67 Improved (grown in >10% area) second cycle improvement completed with stacking of downy mildew resistance (DMR) and high grain iron and zinc density QTLs (AICRP-PM 2019). • Double DMR QTL introgression lines in GHB 538 hybrid with promising results in the Indian national trials (AICRP-PM 2019). Molecular Breeding Products
  21. 21. Diabetes- a global threat Source: WHO
  22. 22. • Two low glycemic index (GI) hybrids with high slowly digestible starch (SDS) and resistant starch (RS) fractions performed very well in the multi- location trials during 2019. • These hybrids recorded high grain yield superiority and blast and downy mildew resistance over the checks (Project report, Innovate UK Project No: 102726). Molecular Breeding Products: Developing low GI millets
  23. 23. • Working closely for development optimization and implementation of whole-genome prediction models, QC panel and mid-density array • Working with SAUs for release of DM improved version of hybrid • Working with AICRP-PM for evaluation and release of HHB 67 improved version • Support the National and Seed Companies for breeder seed of the parental line of HHB 67 Improved • Developed a mega proposal on the A1 zone with AICRP-PM well complementing the breeding programs • Similar proposal with Corteva-EiB Engagement with the AICRP-PM/SAUs
  24. 24. • Discovery work is not a high priority for many funders • Affected by the big-crop syndrome • No allocation of money in proportion to the product profiles • Depend on special project funding • Skewed distribution of money from projects such as ICAR-ICRISAT, CtEH • Duplication of genomics and trait discovery work • Opportunities to work with ICRISAT and regional breeders • Complementing the breeding programs of National Systems such as the A1 zone- upstream science support Challenges & Opportunities
  25. 25. Thank you!

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