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Research Program Genetic Gains (RPGG) Review Meeting 2021: Current status and planning for integration of genomics applications in Sorghum breeding By Dr Santosh Deshpande

  1. Current status and planning for integration of genomics applications in breeding Sorghum RP Genetic Gains meeting with DDG-R 05th January 2021
  2. Current status and planning for integration of genomics applications in Sorghum breeding Background Current status Applications • Trait-linked SNP panels • QC panel • Mid-density panel • Trait introgression Integration Strategy Data management Resource mobilization – challenges, opportunities
  3. Background • Contents as per current understanding of Product Concept Notes (PCNs) • Over all – 8 PCNs; WCA – 2; ESA – 2 and SA – 4 (?) • Invited to several interactions with EiB Module#2 – breeding optimization discussions • Interactions with breeders, RBLs and ESA-Biotechnology • Genomics tools/technologies – EiB Module#3 – shared services • CRP-GLDC FP5 Current status and planning for integration of genomics applications in Sorghum breeding
  4. Current Status – genomics tools/technologies Discovery, development Verification/ validation Routine breeding application Trichome density – SBI10, Leaf glossiness – SBI05 & 10 (Shoot fly resistance traits) Transpiration Efficiency–SBI02 Water Use Efficiency – SBI02 (Post-Flow Drought Tol) Fertility restoration (Rf) genes – several (Hybrid breeding trait) Brown mid-rib genes – bmr6 and bmr12 (biofuel, biomass and forage) ME and IVMOD for fodder digestibility + NUE (Fodder Quality traits) Anthracnose, leaf blight and rust (Disease resistance) Aphid resistance and FAW (Insect resistance) Develop SNP assays, screen breeding pools, verification in current breeding population Screening breeding lines and formulating 10 SNP panels for routine use Strategy, planning for wider use QC panel - for routine use Mid-density SNP panel – EiB Module#3 shared services
  5. SNP panel for routine selection of (Shoot fly resistance component trait) leaf glossiness, lower leaf trichome density Sl. No. SNP ID Chromoso me Trait QTL Favorable allele Unfavorable allele 1 snpSB0106 SBI-05 Leaf Glossines s QTL-J1 C T 2 snpSB0109 C G 3 snpSB110 T G 4 snpSB0143 SBI-10 Leaf Glossines s QTL-G C G 5 snpSB0144 G A 6 snpSB0146 T C 7 snpSB0148 A C 8 snpSB0158 SBI-10 Trichome density (lower side) QTL-J2 C G 9 snpSB0164 G A 10 snpSB0169 C T Rurrent Parent F 1 Donor Parent Negative Control SNPs development … to routine breeding utilization (shoot fly resistance) • To confirm efficacy of designed KASP SNPs, over 5000 breeding lines screened with 10 SNPs (Intertek platform – HTPG) • This 10 SNP set useful for screening early generation breeding population for selecting individuals with shoot fly resistance (SFR) • >800 SA, WCA and ESA breeding lines ; 2350 breeding material from ICAR-IIMR- AICRPS Applications – trait linked SNP panels • Current season - >1800 F2s from SA, 16 plates breeding material from current off- season nursery in WCA and ESA breeding Gorthy et. al. (2017)
  6. SBI-02 1 2 3 4 5 1 2 3 4 5 • SNPs development … to routine breeding utilization (post- flowering drought tolerance – TE = stg3A and WUE = stg3B) • To confirm efficacy of designed KASP SNPs, over 5000 individuals in MABC project involving 12 genetic backgrounds (Intertek platform – HTPG) • A set of 10 SNP set useful for screening early generation breeding population is identified and being validated in over 1000 breeding lines (from breeding programs across SA, ESA and WCA); 2350 breeding stocks from ICAR-IIMR-AICRPS • Currently being used in MBAC projects in Tanzania (through HTPG); Ghana, Burkina Faso and Senegal (through SMIL) Applications – trait linked SNP panels cont … • Current season - >800 F2s from SA, 16 plates breeding material from current off- season nursery in WCA and ESA breeding
  7. SNPs development … to routine breeding utilization (post- flowering drought tolerance – TE = stg3A and WUE = stg3B) 0 50 100 150 200 250 Grainyield(gm-2) Staygreen introgessed lines in two genetic backgrounds (CRS 4 & RSLG 262 B Grain yield WS 0 200 400 600 800 1000 1200 1400 C5-R C11 C9 C7-R C2 C13 C4 R1-W R7 R3 R10 R8 R5-R B35 CSV29 Totalbiomas(gm-2) Staygreen introgessed lines in two genetic backgrounds (CRS 4 & RSLG 262 B Total Biomass WS Figure 2: Average biomass accumulation of three locations in 24 staygreen introgressed lines in two genetic background (CRS 4- 13 lines; RSLG 262 -11 lines), recipient parents and ckecks grown under well watered and (WW) and water stressed conditions at three locations (Hyderabad, Solapur and Parbhani). Figure 1: Average grain yield of three locations in 24 staygreen introgressed lines in two genetic background (CRS 4- 13 lines; RSLG 262 -11 lines), recipient parents and ckecks grown under well watered and (WW) and water stressed conditions at three locations (Hyderabad, Solapur and Parbhani).
  8. SNPs development … to routine breeding utilization (fertility restoration genes -Rf genes) • We designed 33 SNPs from own studies and published information • SNPs designed for – Rf1, Rf2, Rf5, Rf6 • Validated in mapping populations and breeding lines (Intertek platform – HTPG) • A set of 20 SNP set useful for screening early generation breeding population will be finalized • >800 breeding lines from SA, ESA and WCA breeding programs; 2350 lines from ICAR-IIMR-AICRPS • Current season - >F2s from RxR nurseries from SA program; on-going discussion with ESA and WCA Applications – trait linked SNP panels cont …
  9. QC panel -status • 72 loci identified from GBS data from a set of >1900 breeding lines; • ICRISAT breeding program- Mali, Kenya, Nigeria, India; ICAR-IIMR; SAUs from India; national program from Mali; few lines TAMU • Verification/first run with 376 breeding lines completed (235 from SA-breeding program; 101 from WCA-breeding program and 40 from ESA) • A set of 49 SNPs short-listed; and second run 800 lines from SA, ESA and WCA along with 2350 breeding lines from ICAR-IIIMR- AICRPS SNP Quality No of SNPs Very Good 30 Good 10 Medium 12 Inconclusive 20 Bad 0 Total 72
  10. Status – mid-density panel • Variant calling using - 1985 Genotypes and 2.6Million SNPs • Variant calling criterion • Pre-defined filtering criteria resulted in 41,844 SNPs • Functional annotation for each locus Criteria • Remove intergenic markers, Tandem repeats on the flanking (mono, di) • Specificity of the flanking sequences • GC content on the flanking sequences • PIC • Proportion of Missing • Markers on upstream and downstream windows (30bp) • A/T inversions • Including the trait linked SNPs and QC sets Current season – designing and development in first quarter; first run, validation in second quarter; short–listing smaller set per breeding program
  11. Status – Genomic prediction • Utilize the mid-density panel • Sorghum breeding program – currently running through breeding optimization exercise with EiB M#2 • After breeding pipelines are finalized; the stages and PCNs for applying the mid-density panel for genomic prediction • Plan – • Use the mid-density panel to genotype current parent pool • Identify stages and populations within each PCN for implementing mid-density panel • A min of two-years 3-4 location each data; e.g. current AYT, PYT selected
  12. Application – Trait introgression • Working with CMBE and breeding groups for Rapid Generation Advancement (RGA) optimization • Sorghum breeding program – currently two generations • With RGA, at least 3.5 to 4 generations expected • Plan – develop further this plan for trait x PCN Trait discovery and validation strategies • Usually mapping, but focused on using breeding material from PCN; QTLseq, transcriptomics • Working with CMBE and breeding groups for trait validation • Strategy to develop segregating breeding populations
  13. Integration with breeding – thought process • Currently the breeding optimization process going on • Dealing with breeding process, populations ……selection indices etc. • Molecular markers and applications – • Trait linked SNPs – stage, which population and population size • Role in each PCN ….. • Capacity of breeding program to adopt …. Generation Breeding process PCN-1 Rainy- Hybrid A/B-line PCN-1 Rainy- Hybrid R-line PCN-2 PostRainy - OPV PCN-3 Forages A/B-line PCN-3 Forages R-line PCN-4 Biofuel/ Sweet Sor PCN-1 Sahel (dry) PCN-2 Sudan (semi- humid) PCN-3 Guinea (humid) PCN-1 (Semi- arid) PCN-2 (Sub- humid) Parents Selection for recycling and crossing P1 X P2 Crossing 1, 2, 4 (SFR, Rf) 1, 2, 4 (SFR, Rf) 1, 2, 4 (SFR, TE, WUE) 1, 2 1, 2 1, 2 1, 2, 4 1, 2, 4 1, 2 1, 2, 4 1, 2, 4 F1 Selfing true-tp-types 3 3 3 3 3 3 3 3 3 3 3 F2 Some selection 2 2 2 2 2 2 2 2 2 2 2 SSD F3 SSD F4 SSD F5 PYT, Stage1-trial; test cosses 2, 4 (SFR, Rf) 2, 4 (SFR, Rf) 2, 4 (SFR, TE, WUE) 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 F6 AYT, Stage1-trial; test cosses 2, 4 (SFR, Rf) 2, 4 (SFR, Rf) 2, 4 (SFR, TE, WUE) 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 2, 4 F7 AYT, Stage 2-trial; test crosses F8-desiganted Product development process 5 5 5 5 5 5 5 5 5 5 5 Asia WCA ESA • Genomics Applications - 1. Marker Assisted Quality Assurance/Quality Control; 2. Trait Linked SNPs; 3. Marker Assisted Hybridity Confirmation; 4. Mid-Density SNP panels; and 5. Introgression of target traits
  14. Improved – genetically diverse cultivars ADAPTED to target agro-ecology Germplasm/adapted cultivars Environment characterization Trait variability Trait characterization and dissection 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.50 1.00 1.50 2.00 2.50 3.00 3.50 Evaporative demand (VPD) Canopyconductance Target location testing Advanced/HTP phenotyping Development of BCNAM population(s) along with Genomics & informatics tools Advanced breeding strategies Improved genetics for adaptation Drought Insect Disease Adaptation based integrated crop improvement scheme by developing BCNAM populations CMBGE
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