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Research Program Genetic Gains (RPGG) Review Meeting 2021: Forward Breeding: tools and technologies for accelerating rate of genetic gain By Dr Manish Roorkiwal

  1. Forward Breeding: tools and technologies for accelerating rate of genetic gain Manish Roorkiwal Senior Scientist, Forward Breeding Research Program- Genetic Gains 5 Jan, 2021
  2.  Establish and use of high throughput and cost-effective genotyping platforms  Forward breeding-based breeding solutions to crop improvement  Deploy molecular breeding approaches e.g. MAS/MARS/GS in collaboration with the themes on Crop Improvement and Genomics and Trait Discovery  Collaborate with the theme on Genomics and Trait Discovery to validate identified markers  Convert candidate molecular markers to high-throughput enable marker system  Lead/engage in developing analytical tools, platforms and databases for molecular breeding  Meta-analysis using sequencing data for haplotypes-based selection Forward Breeding  Integration of various molecular breeding approaches (MABC, MARS, and GS) in the product development process at ICRISAT.  Accelerated rate of genetic gain across all mandate crops by leveraging expertise from various groups inside and outside ICRISAT. Key responsibilities:
  3. Markervalidation Convertmarkers HTPG Biomarkers GAB Identify research needs Precision and high throughput phenotyping Use of markers to increase efficiency Define baseline Strategic planning of experiments Improved lines Modernize the breeding Enhanced rate of genetic gain Inputs OutputsForward Breeding Forward Breeding Strategies y ir R A t  
  4. Forward Breeding  Many lines having undesirable alleles are discarded  Opportunity to the evaluation of fewer lines in later generations Provides tools, technologies and platforms to deploy markers in breeding programs for developing improved lines in cost- and time-effective manner
  5. Gene Pyramiding Single gene/QTL introgression Single backcross DH Scheme Multi-trait introgression MES: Marker-evaluated selection SLS-MAS: Single large scale MAS Forward Breeding based solutions
  6. building a global community of knowledge through workshops, hackathons and cross-training to transform breeding http://cbsugobii05.tc.cornell.edu/wordpress/ GOBii: A Global Community A stable, high quality, easy to deploy, genomic data management system, with a web service layer incorporating BrAPI for integration Developed and released key tools for breeders To improve data availability, working to integrate GOBii with breeding management systems and tools
  7. GOBii Tools GOBii Genomics Data Manager (GDM) Scalable genomic data management system, and a marker tools portal to access all GOBii-GDM tools for data loading, QC, extract & breeding. GOBii DArT Tools The GOBii-QC (quality control) module run by KDCompute is fully integrated with the GOBii-GDM system. GOBii Genomic Selection (GS) Genomic Selection (GS-Galaxy) Analysis Pipeline is under active development by GOBii team and CGIAR contributors using open-source Galaxy platform. GOBii JHI Tools Marker-Assisted Back Crossing (MABC) module is an open-source platform for conducting marker-assisted backcross visualization and selection analysis. http://cbsugobii05.tc.cornell.edu:6084/x/GQDcAQ MABC module: http://flapjack.hutton.ac.uk/en/latest/mabc.html F1 pedigree verification module: http://flapjack.hutton.ac.uk/en/latest/pedver_f1s_known_ parents.html http://galaxy-demo.excellenceinbreeding.org/
  8. Collaboration space with Open-Source, BrAPI enabled analytics Analytics M5: Common vision of system deployment, aligned training Source: Elizabeth Jones
  9. 8 CG Centers 30+ NARS 28+ Countrie s 4+ Private partners 18+ Crops 100+ Traits 1000+ SNPS 500+ QC SNPS Lead Institute Service Provider Funding Support Coordination Support Partner Institute(s) High Through-Put Genotyping (HTPG) project
  10. Circa. 2.0 Million USD Genotyping Volume Circa. 7.5 Million Data points AUG 2016 - DEC 2019 With 4 million US$ investment, HTPG saved significant resources for CGIAR & NARS and enabled generation of genotyping data at 1/3rd- 1/4th price. By assuming a minimum cost of US$1 per data point, CG and NARS might have spent about US$ 7.5 Million on the data generation. $0.26 per data point The project has saved about US$ 5.0 Million, with total investment of about US$ 3.25 Million by 2019 Groundnut, 662160 Pigeon Pea, 49104Chick Pea, 82320 Finger Millet, 145536 Pearl Millet, 193536 Sorghum, 348768 ICRISAT Crops - Data point AUG 2016 - JUNE 2020 Barley, 46080 cassava,515040 Common Bean, 736512 Cowpea,166272 Maize,837792 Potato,306720Rice, 2316576 Wheat,1137120 Other CGIAR Crops - Data point AUG 2016 - DEC 2019 High Throughput Genotyping Project (HTPG)
  11.  1.8 Tb sequence data generated (5 X to 14 X)  4.9 million SNPs, 596K Indels, 4.9K CNVs, 60.7K PAVs & 70K SVs  Domestication analysis reported 122 CDR regions that underwent selection  Selection sweep analysis reported a significant reduction in diversity from wild to landraces to breeding lines  Candidate genes for yield, heat and flowering time identified
  12. Global chickpea genetic variations based on sequencing of 3,366 genome
  13. Axiom®CicerSNP array: a high-throughput SNP genotyping platform Plant Biotechnol J. 2018
  14. Axiom®CicerSNP array based high- density genetic map (ICC 4958 × ICC 1882) (ICC 283 × ICC 8261)
  15. Mid Density – Targeted GBS with AgriSeqTM NGS Platform  Targeted GBS—a flexible, powerful, highly accurate genotyping system  Sequencing based identification of Novel SNPs in addition to known SNPs
  16.  ~4.9 million markers on diverse chickpea lines  Based on different QC criteria, a set of 8654 highly polymorphic markers tested  5000 markers in 4349 amplicons  Primer panel in manufacturing Mid Density – Targeted GBS panel for chickpea Priority Total Markers Dropped in preDesignQC No Design Available Remaining Design (not included in the 5000 markers final panel) Included in Final Panel Priority 1 422 53 126 0 243 Priority 2 8232 149 381 2945 4757 Total 8654 202 507 2945 5000  Validation of mid-density panel by Dec 2020  Genotyping of chickpea breeding material for possible deployment in routine chickpea breeding  ICRISAT chickpea breeding specific panel
  17. Sample genotype table: AgriSum Toolkit 17 Sample Matrix Table Output Top/Bottom Output Sample 1;IonCode_0601 Sample 2;IonCode_0602 Sample 3;IonCode_0603 Sample 4;IonCode_0604 Sample 5;IonCode_0605 Target-1 C/G G/G C/C C/G C/G Target-2 C/G G/G C/C ./. C/G Target-3 A/G G/G A/A A/G A/G Target-5 G/A A/A G/G G/A G/A Target-6 G/A A/A G/G G/A G/A Target-9 G/A A/A G/G G/A G/A Target-11 G/A A/A G/G G/A G/A Target-14 C/G G/G C/C ./. C/G Target-17 C/T T/T C/C C/T C/T Sample 1;IonCode_0601 Sample 2;IonCode_0602 Sample 3;IonCode_0603 Sample 4;IonCode_0604 Sample 5;IonCode_0605 Target-1 AB BB AA AB AB Target-2 AB BB AA ./. AB Target-3 AB BB AA AB AB Target-5 AB AA BB AB AB Target-6 AB AA BB AB AB Target-9 AB AA BB AB AB Target-11 AB AA BB AB AB Target-14 AB BB AA ./. AB Target-17 AB AA BB AB AB Target-20 AB BB AA AB AB
  18. Chrom Position Ref Variant Allele Call Filter Frequency Quality Filter Type Allele Source Allele Name Ca1 389832 T C Heterozygous - 53.2 330.717 - SNP Novel tvc.novel.1 Ca1 389920 C G Heterozygous - 48.5 236.899 - SNP Hotspot Target-1 Ca1 391254 C G Heterozygous - 46.5 166.596 - SNP Hotspot Target-2 Ca1 393860 TGGTC - Heterozygous - 52.8 316.761 - DEL Novel tvc.novel.2 Ca1 393871 G A Heterozygous - 52.8 319.98 - SNP Novel tvc.novel.3 Ca1 393894 A G Heterozygous - 54 346.287 - SNP Hotspot Target-3 Ca1 395378 G C Heterozygous - 48.2 232.03 - SNP Novel tvc.novel.4 Ca1 395496 G A Heterozygous - 48.6 238.193 - SNP Hotspot Target-5 Ca1 396587 G A Heterozygous - 49.2 247.027 - SNP Hotspot Target-6 Ca1 401320 G A Heterozygous - 49.2 250.298 - SNP Hotspot Target-9 Ca1 405686 G A Heterozygous - 49.7 257.24 - SNP Hotspot Target-11 Ca1 407777 G A Heterozygous - 34.8 52.0189 - SNP Novel tvc.novel.5 Ca1 407795 C T Heterozygous - 34.8 51.8514 - SNP Novel tvc.novel.6 Ca1 407849 C G Heterozygous - 33.5 41.8833 - SNP Hotspot Target-14 Ca1 407887 C T Heterozygous - 34.5 49.9275 - SNP Novel tvc.novel.7 Ca1 407897 G A Heterozygous - 37.1 73.0879 - SNP Novel tvc.novel.8 Ca1 407901 T C Heterozygous - 39.6 101.75 - SNP Novel tvc.novel.9 Ca1 407928 CTC AT Heterozygous - 42.3 136.08 - COMPLEX Novel tvc.novel.10 Novel SNP Detection 18 Novel Markers - 2899
  19. Chickpea QC (CaQC) SNP panel  Available re-sequencing data on 66 chickpea parental lines from chickpea breeding (1.9 million markers) were used  Based on different criteria and analysis, a set of 48 markers selected & used for genotyping  Marker data was analyzed on 94 different cross combination  Set of 14 markers for testing on larger set of lines for validation  2-12 polymorphic markers polymorphic for all the crosses except 2 cross combinations  The panel is also being tested on parental lines from NARS partners An affordable and effective genotyping platform for hybridity testing and seed quality control & assurance SNP panel ready for deployment during crop season 2020-21
  20.  Selected 14 SNPs deployed in ICRISAT chickpea breeding program snpCA00171; snpCA00177; snpCA00178; snpCA00181; snpCA00184; snpCA00188; snpCA00192; snpCA00193; snpCA00197; snpCA00203; snpCA00206; snpCA00207; snpCA00209; snpCA00216  32 plates for genotyping (2020-2021 chickpea crop season) Deployment of CaQC SNP panel  Sequencing of chickpea parental lines with Chickpea breeding team for identification of more markers  Upload of data on all parental lines to GOBii and provide access to chickpea team
  21. Optimization of genomic prediction based selection strategy in chickpea Frontiers in Plant Science 2016; Scientific Reports 2018 Frontiers in Plant Science 2020  Restructuring training population  Genotyping of new training population with new mid-density genotyping platform  Optimization and establishment of GS models
  22.  Total individuals in training set: 315 (162 Desi, 153 Kabuli)  5000 F5 plants from IARI and ICRISAT genotyped using LD DArT  Comparison of visual selection vs selection based on GEBV: two set of ~200 lines (based on GEBVs and visual selection) were evaluated in the field conditions for yield and yield related traits during crop season 2019-2020  Lines selected based on GEBVs performed better in terms of yield and 100 seed weight as compared to lines selected based on visual selection Deployment of genomic prediction based selection strategy in chickpea All Predict All Desi Predict Desi Kabuli Predict Kabuli Desi Predict Kabuli Kabuli Predict Desi Seed Yield 0.48 (0.015)a 0.26 (0.029) 0.25 (0.020) 0.08b 0.04c Seed Weight 0.92 (0.002) 0.76 (0.012) 0.74 (0.014) 0.20 0.58 Biomass 0.50 (0.013) 0.39 (0.019) 0.26 (0.026) 0.11 0.16 Plant Height 0.65 (0.011) 0.75 (0.010) 0.42 (0.038) -0.13 0.16 Days to Flower 0.68 (0.007) 0.63 (0.016) 0.56 (0.031) -0.34 0.07 Days to Maturity 0.70 (0.003) 0.53 (0.021) 0.53 (0.038) -0.16 0.09 Frontiers in Plant Science 2020
  23. Based on initial results from pilot experiments AICRP Chickpea initiated efforts to deploy GS in routine breeding program Included additional parental lines from national chickpea breeding program to extend the training population Training population genotyped using newly developed Mid- density SNP arrays Training population evaluated at NARS locations for yield and quality traits Based on initial analysis ICRISAT suggested new sets of crosses to AICRP centres These crosses are being made in the ongoing crop season Deployment of Genomic selection in Indian national chickpea breeding program
  24. BGM 10216 a drought tolerant MABC line in field  BGM 10216 First MABC line released in India for commercial cultivation in central zone  Pusa Chickpea 10216 (BGM 10216) is developed after introgression QTL-hotspot in “Pusa 372” genetic background at IARI in coll. with ICRISAT  16% yield advantage over recurrent parent across all the centers tested under AICRP  It’s grain protein content is 22.6% Support to NARS: Pusa Chickpea 10216 (BGM 10216), drought tolerant variety - 2019
  25. Support to NARS: Pusa Chickpea Manav (BGM 20211) enhanced fusarium wilt resistance - 2020  Pusa Chickpea Manav developed by introgression of “QTL region” for wilt resistance from WR 315 to recurrent parent Pusa 391  28 % yield advantage over recurrent parent in National WRIL Trials under AICRP under wilt stress conditions  Its average 100-seed weight is 19.5 g.  It’s grain protein content is 18.92%.
  26. Trial Entries Recurrent Parent AVT1 (2019-20) Mean yield (Kg/ha) Yield increase (%) over recurrent parent DTIL AVT 2 BGM 10218 RSG 888 1342 21.23 NBeG 506 JG 11 1826 10.13 BG 4005 Pusa 362 1604 18.64 AVT 1 BGM 10220 JAKI 9218 IPC (L4-25) DCP 92-3 IPC (L22-33-2) DCP 92-3 IPC (L4-14) DCP 92-3 BGM 10221 JG 16 (IARI) WRIL AVT 2 BG 20213 Pusa 391 2280 29.69 AVT 1 KCD-2019-5 JG 11 KCD-2019-7 JG 11 More products in pipeline… In collaboration with ICAR-IARI, ICAR-IIPR, UAS-Raichur and AICRP-Chickpea
  27. AX-1236443280.0 AX-1236439940.1 AX-12361765212.7 AX-12364483313.4 AX-12364417015.0 AX-12364511346.8 AX-123615540113.0 AX-123615611113.1 AX-123643054121.9 AX-123615557122.0 CaLG01 AX-1236392650.0 AX-1236478340.1 AX-12362263914.3 AX-12362272914.4 AX-123621900153.4 AX-123621911167.6 AX-123639136178.7 AX-123621873178.8 AX-123659615194.4 AX-123621627194.5 CaLG03 AX-1236649210.0 AX-1236309010.1 AX-12363014686.4 AX-12364203887.5 AX-123623881216.2 AX-123615349217.6 AX-123624139223.1 AX-123648479230.3 AX-123624245230.4 CaLG04 AX-1236333160.0 AX-1236541170.1 AX-1236333310.8 AX-1236334501.3 AX-12364258543.6 AX-12366333443.7 AX-12363439587.9 AX-12363451988.0 AX-12363436989.5 AX-12366310489.6 AX-123634069103.5 AX-123634068103.8 CaLG06 qYPP1.1 qPLHT1.1 qYPP1.2 AX-1236443280.0 AX-1236439940.1 AX-12361765212.7 AX-12364483313.4 AX-12364417015.0 AX-12364511346.8 AX-123615540113.0 AX-123615611113.1 AX-123643054121.9 AX-123615557122.0 CaLG01 AX-1236392650.0 AX-1236478340.1 AX-12362263914.3 AX-12362272914.4 AX-123621900153.4 AX-123621911167.6 AX-123639136178.7 AX-123621873178.8 AX-123659615194.4 AX-123621627194.5 CaLG03 AX-1236649210.0 AX-1236309010.1 AX-12363014686.4 AX-12364203887.5 AX-123623881216.2 AX-123615349217.6 AX-123624139223.1 AX-123648479230.3 AX-123624245230.4 CaLG04 AX-1236333160.0 AX-1236541170.1 AX-1236333310.8 AX-1236334501.3 AX-12364258543.6 AX-12366333443.7 AX-12363439587.9 AX-12363451988.0 AX-12363436989.5 AX-12366310489.6 AX-123634069103.5 AX-123634068103.8 CaLG06 qPB3.1 q100SW3.1 q100SW3.2 AX-1236443280.0 AX-1236439940.1 AX-12361765212.7 AX-12364483313.4 AX-12364417015.0 AX-12364511346.8 AX-123615540113.0 AX-123615611113.1 AX-123643054121.9 AX-123615557122.0 CaLG01 AX-1236392650.0 AX-1236478340.1 AX-12362263914.3 AX-12362272914.4 AX-123621900153.4 AX-123621911167.6 AX-123639136178.7 AX-123621873178.8 AX-123659615194.4 AX-123621627194.5 CaLG03 AX-1236649210.0 AX-1236309010.1 AX-12363014686.4 AX-12364203887.5 AX-123623881216.2 AX-123615349217.6 AX-123624139223.1 AX-123648479230.3 AX-123624245230.4 CaLG04 AX-1236333160.0 AX-1236541170.1 AX-1236333310.8 AX-1236334501.3 AX-12364258543.6 AX-12366333443.7 AX-12363439587.9 AX-12363451988.0 AX-12363436989.5 AX-12366310489.6 AX-123634069103.5 AX-123634068103.8 CaLG06 qPLHT4.1 q100SW4.1 qPLHT4.2 qYPP4.1 AX-1236443280.0 AX-1236439940.1 AX-12361765212.7 AX-12364483313.4 AX-12364417015.0 AX-12364511346.8 AX-123615540113.0 AX-123615611113.1 AX-123643054121.9 AX-123615557122.0 CaLG01 AX-1236392650.0 AX-1236478340.1 AX-12362263914.3 AX-12362272914.4 AX-123621900153.4 AX-123621911167.6 AX-123639136178.7 AX-123621873178.8 AX-123659615194.4 AX-123621627194.5 CaLG03 AX-1236649210.0 AX-1236309010.1 AX-12363014686.4 AX-12364203887.5 AX-123623881216.2 AX-123615349217.6 AX-123624139223.1 AX-123648479230.3 AX-123624245230.4 CaLG04 AX-1236333160.0 AX-1236541170.1 AX-1236333310.8 AX-1236334501.3 AX-12364258543.6 AX-12366333443.7 AX-12363439587.9 AX-12363451988.0 AX-12363436989.5 AX-12366310489.6 AX-123634069103.5 AX-123634068103.8 CaLG06 qPPP6.1 qPLHT6.1 q100SW6.1 q100SW6.2 Theor Appl Genet (2014); Plant Biotechnol J. 2018 Int J Mol Sci (2020) Submitted… High resolution trait mapping Drought Salinity Seed Size Plant Genome (2020) Helicoverpa component traits
  28. Wide phenotypic variability observed for the 11 nutritional traits Trait (PVE %) Candidate gene analysis results for significant MTAs Beta carotene (15-20) CA_4 (Ca_03822) Iron (10-17) CA_6 (Ca_08678) Phytic acid (12-17) CA_1 (Ca_02905) ; CA_4 (Ca_03574) Vitamin B1 (25-31) CA_1 (Ca_26128) ; CA_4 (Ca_12127) CA_4 (Ca_03836) ; CA_5 (Ca_13399) CA_6 (Ca_09604) Zinc (11-15) CA_3 (Ca_12279) Candidate gene analysis GWAS for nutrition traits in chickpea  Chickpea reference set analyzed for 11 nutritional traits  237K markers from WGRS
  29. Data management  All the chickpea genotyping data in stored in GOBii database and public repositories (NCBI; CEGSB open access)  All the chickpea datasets uploaded in ICRISAT dataverse
  30. Challenges and way forward…  Lack of funding support and recognition in different institutional initiatives including CRP-GLDC, AVISA and CtEH  No-clarity in activity alignment with GTD and Crop Improvement themes with defined role and responsibilities and due recognition  Need to support ICRISAT and NARS breeding programs through team of specialists in genomics and molecular breeding, and information technology to design breeding process  Primary focus would be cost effective genotyping, pedigree verification system, genome-wide marker based prediction and haplotype based breeding through identification of novel superior haplotypes for target traits
  31. Strong collaboration – chickpea research
  32. Thank you!
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