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Research Program Genetic Gains (RPGG) Review Meeting 2021: Groundnut genomic tools and integration in breeding By Manish K. Pandey

  1. Groundnut genomic tools and integration in breeding Manish K. Pandey Senior Scientist-Groundnut Genomics Email Id: m.pandey@cgiar.org
  2. Key areas of research  Genomic resources and genotyping assays  Trait discovery and diagnostic markers  Integration of genomics in groundnut breeding
  3. Genomic resources and genotyping assays…
  4. Genomic Resources: Reference genomes, gene expression atlas and genetic markers in cultivated groundnut  From only ~1000 SSRs in 2012 to >300K genome- wide SSR markers  >100K InDel markers and primers  Millions of genome-wide SNP markers  These genetic markers are now in use for conducting genetic, genomics and breeding research Global gene expression atlas of cultivated groundnut (Pl Biotech J 2020 18:2187-2200 ICRISAT co-led (4)/contributed (3) in 7 of 8 genome sequencing efforts. These high quality genomes are global resource and are being used by all the genomics and breeding researchers across the world including ICRISAT 2016 2018 2019 2020
  5. High-density genotyping assays/platforms Scientific Reports 2016, 7:40577; Molecular Plant 2016;10:309-322; Theor Appl Genet 2019, 132(4):1001-1016; The Crop Journal 2020, 8:1-15; Theor Appl Genet 2020, 133:3101–3117; Pl Biotechnol J 2020; 18:992-1003; Pl Biotechnol J 2020; 18:1457-1471; Genes 2021, 12:37. High density genotyping assays developed and currently been deployed for generating high throughput and high density genotyping data on germplasm and breeding lines 58K SNPs Axiom_Arachis assay Genotyping-by-Sequencing (GBS) Applications Fingerprinting & genetic diversity Trait mapping Genomic selection  Marker-based varietal adoption Whole genome sequencing
  6. Mid-density genotyping assays/ platforms Mid-density genotyping assay with 5000 SNPs including 160 associated SNPs for 8-10 traits as part of bilateral project. This assay will become available for use from Feb 2021 onwards with just USD 6/sample. SNP calling from WGRS data on a set of 264 cultivated genotypes using cultivated tetraploid genome completed  10,000 highly informative uniformly distributed SNPs identified and shared with EiB  Assay with 4K SNPs with financial support from EiB in very early stage Thermo Fisher’s AgriSeq Targeted GBS KASP assay @ Intertek platform Mid-density genotyping assay with 4000 SNPs @Intertek platform with tentative cost of genotyping of US$11/ sample (384 samples) and US$10/ sample (1536 samples). Hopefully this assay will become available in early 2022.
  7. Quality check (AhQC) panel to check hybridity in SA, WCA & ESA groundnut breeding programs  Sequencing, SNP discovery and shortlisting of 48 most promising SNPs in addition to 10 more informative SNPs for KASP assay`.  A set of 20 SNPs showed high level of discriminative features and thus forms the first set of QC SNPs.  Tested successfully the initial quality check (AhQC) panel with 20 SNPs among founder parents of IC-Asia and IC-WCA GN breeding programs; while genotyping of samples from IC-ESA and ICAR-DGR is in progress at Intertek.  IC-Asia GN breeding program deployed QC panel of 20 SNPs for checking hybridity among a set of 700 F1 plants from 35 cross combinations and their parental genotypes during post-rainy 2020. In addition to IC-Asia, this QC panel is ready for deployment in IC-WCA groundnut breeding program while it will become available from Feb 2021 for ESA as well. Also developing for 3 major breeding programs in India (ICAR- DGR, UAS-Dharwad and RARS-Tirupati) during 2021. S. No. Intertek SNP ID S. No.Intertek SNP ID 1 snpAH0030 11 snpAH00053 2 snpAH0031 12 snpAH00059 3 snpAH0033 13 snpAH00064 4 snpAH0035 14 snpAH00066 5 snpAH0037 15 snpAH00067 6 snpAH0038 16 snpAH00071 7 snpAH0041 17 snpAH00079 8 snpAH0042 18 snpAH00087 9 snpAH0048 19 snpAH00088 10 snpAH0049 20 snpAH00091
  8. Trait discovery & diagnostic markers…
  9. Groundnut PCN1 Groundnut PCN2 Groundnut PCN3
  10. Target traits for marker discovery Must have traits Nice to have traits Leaf rust resistance Seed coat color and shelling percentage Late Leaf spot resistance Iron deficiency tolerance Oil & protein content (NIRS phenotyping) Fresh seed dormancy Haulm weight Aflatoxin contamination Stem rot resistance Blanchability, sugar content, salinity tolerance Heat tolerance (wilting@ high temp) Others/New traits Growth habit Fatty acid (Oleic, linoleic, and palmitic acids) Seed weight Validated markers available at Intertek platform Under validation process at Intertek platform Significant lead
  11. Marker (GMRQ517) for rust resistance Marker (GMLR975) for late leaf spot resistance
  12. 10-SNP panel for FDR and high oleic acid SNP ID Position (bp) Chrom Resistant parent allele Susceptible parent allele Trait GKAMA03QR517 131739517 A03 A C Rust GKAMA03QR786 133497786 A03 T A Rust GKAMA03GR173 134613173 A03 C A Rust GKAMA03GR429 134225429 A03 C A Rust GKAMA03QR843 131788843 A03 C T Rust GKAMA03QL975 131784975 A03 G A Rust GKAMA02GL975 789755 A02 G C LLS GKAMA02GL582 1271582 A02 G C LLS GKAMA02GL779 436779 A02 T C LLS GKAMFAD2B 63041191 B09 A - Oleic acid Genotyping cost reduced from 13 to 1.5 USD/sample for FDR and oil quality Used so far generating ~704, 256 datapoints (Dec 2020)..highest among ICRISAT crops ICRISAT India ICAR-DGR India UAS-Dharwad & Raichur India RARS-ANGRAU, Tirupati India ICRISAT Mali ICRISAT Malawi ICRISAT Nairobi NaSARRI Uganda CRI, CERAAS Ghana ISRA/ CERAAS Senegal Uni of Southern Queensland Australia Peanut Company of Australia (PCA) Australia AgResearch Consultants Georgia
  13. Validated diagnostic markers for seed weight available in 2020 @Intertek platform
  14. Diagnostic markers under validation @Intertek  Sequencing-based analysis for QTLseq samples completed with tetraploid genome; associated SNPs identified for foliar disease resistance and verification in progress with Intertek for 38 SNPs  Sequenced and analysed high oleic varieties Girnar 4 (ICGV 15083) and Girnar 5 (ICGV 15090); new marker developed for FAD2A and verification in progress with Intertek for 4 SNPs.  Associated SNPs identified and verification is in progress with Intertek for: flowering duration (1 SNP), fresh seed dormancy (13 SNPs), plant habit (3 SNPs), shelling percentage (5 SNPs), seed weight (8 SNPs), testa color (3 SNPs), net blotch resistance (13 SNPs) and blanchibility (13).
  15. Project Title: Identifying the genomic regions and genes for drought and heat tolerance P Latha, AN Kumar, RP Vasanthi, KVN Madhuri, ARS-ANGRAU, Tirupati, Andhra Pradesh Ramesh Bhat, Spurthi Nayak, Babu Motagi, VP Chimmad UAS-Dharwad, Karnataka Manish Pandey, Rajeev Varshney, P Janila ICRISAT, Hyderabad, Telangana  MAGIC population and two RIL populations Field before heat stress Field during heat stress Field after heat stress Susceptible parent Tolerant parent
  16. Exploring haplotypes for key traits Bevan et al. 2017, Nature Example: Haplotype discovery for A. flavus infection
  17. Project Title: A strategy to exploit genomic selection for achieving higher genetic gains in groundnut T Radhakrishnan, Chandramohan ICAR-DGR, Junagadh, Gujarat Ramesh Bhat, Babu Motagi UAS-Dharwad, Karnataka Manish Pandey, Rajeev Varshney, P Janila, Abhishek Rathore ICRISAT, Hyderabad, Telangana John Hickey The Roslin Institute, The University of Edinburgh, UK  4-part GS prediction strategy for crops…optimized for groundnut  2-part GS prediction strategy with available dataset of groundnut training population & 2000 advanced breeding lines  5K mid-density genotyping assay for routine use in groundnut genomic selection
  18. Optimization of genomic prediction based selection in groundnut  Naïve interaction model (E+L+G+GE) and naïve and informed interaction model (E+L+G+LE+GE) identified as promising GS models for improving traits with high G x E.  High prediction accuracies (>0.600) were observed for days to hundred seed weight, oil content, rust@90days, rust@105days and late leaf spot@90days while medium prediction accuracies (0.400-0.600) could be obtained for pods/plant, shelling %, total yield/plant.  Groundnut training population includes 440 elite breeding lines from ICRISAT & UAS-Dharwad breeding program  Has variation for key agronomical traits focused by the Indian groundnut breeding programs.  High density genotyping data generated with 58K Axiom_Arachis SNP array
  19. Genomic selection implementation plan in groundnut  Working with GN-breeding and SBDM to initiate GS using training population more relevant to ICRISAT GN-breeding  DNA from 2000 advanced breeding lines from ICRISAT & NARS breeding programs (including AICRP lines) ready for genotyping with mid-density assay which will help in expanding existing training population
  20. Parmar et al. 2021. Unpublished Single seed-based (chipping) genotyping  Optimized in groundnut breeding @ICRISAT Cost saving Time saving
  21. Integration of genomics in groundnut breeding…
  22. Improved rust and late leaf spot resistance lines in groundnut 56- 96% Yield advantage in improved groundnut lines Theor Appl Genet, 2014 TAG 24 Susceptible GPBD 4 Resistant TAG 24 + rust QTL Resistant introgression
  23. Three MABC lines completed final year of testing in Indian National Trial (AICRP-G) for varietal release Six best MABC lines (ICGV 13192, ICGV 13193, ICGV 13200, ICGV 13206, ICGV 13228 and ICGV 13229 selected with 39– 79% higher mean pod yield and 25–89% higher mean haulm yield over their respective recurrent parents.
  24. Improved lines with high oleic acid @ICRISAT  Girnar 4 (ICGV 15083) and Girnar 5 (ICGV 15090) released as first high oleic varieties in six major groundnut growing states of India  Also first molecular breeding bred variety among oilseed crops  ICGV 15074 set to release as first high oleic varieties in Gujarat State of India
  25. GJG 9 (Spanish bunch), GG 20 (Virginia Bunch), GJGHPS 1 (Virginia Runner). Marker-assisted improvement of GJG 9, GG 20 and GJGHPS 1 for resistance to rust and LLS and high oleic acid
  26.  Kadiri 6 (K 6) is a popular Spanish Bunch groundnut variety has high pod yield, uniform pod size, preferred pod and kernel features  K 6 covers over 46% Breeder Seed demand in India through public sector seed distribution (AICRP-G, 2018) and is the most popular variety. Marker-assisted improvement of K6 for resistance to rust and LLS and high oleic acid
  27. Traits improved Improved varieties Reference (ICRISAT & NARS-India) Rust resistance TAG 24, JL 24 and ICGV 91114 ICRISAT (Theor Appl Genet 2016, 127(8):1771-81; Plant Breeding 2016, 135(3): 355-366; Euphytica 2020 216:85) High oleic acid ICGV 06110, ICGV 06142 and ICGV 06420 ICRISAT (Plant Science 2016, 242:203-213) High oleic acid, resistance to rust and late leaf spot (LLS) GJG 9, GG 20 and GJGHPS 1 ICRISAT (The Crop Journal 2020, 8:1-15; doi.org/10.1016/j.cj.2019.07.001 2020) High oleic acid, resistance to rust and LLS Dh86, ICGV 87846, ICGV 00351 and Kadiri 6 ICRISAT (Frontiers in Genetics 2020, 11:514) Rust and LLS resistance JL24 UAS-Dharwad, India (Electron J Plant 2016 7:37–41 ) Rust and LLS resistance TMV 2 UAS-Dharwad, India (Plant Breed 2017 136(6):948–953) High oleic acid ICGV 05141 ICAR-DGR, Junagadh, India (Euphytica 2018, 214:1622018) High oleic acid and resistance to rust and LLS GPBD 4 ICAR-DGR, Junagadh, India (Nawade et al. 2019) High oleic acid ICGV 06100 ICAR-DGR, Junagadh, India (PLoS ONE 2019 14(12): e0226252) Molecular breeding products @ICRISAT and NARS: Genes/QTLs from genome to farmers field
  28. ICRISAT bred molecular breeding products  Three high oleic Spanish Bunch lines ICGV 16668, ICGV 16697 and ICGV 16690 completed AVT-2 trial and found promising based on yield superiority over checks and have >80% oleic acid  Three rust resistant Spanish Bunch lines ICGV 14421, ICGV 13189 and ICGV 13207 completed AVT-2 trial and found promising based on yield trial and superiority over recurrent parent and checks  >300 breeding lines from marker-based early generation selection for resistance to rust and LLS and high oleic acid at different stage of selection and evaluation  High-oleic Virginia Bunch varieties Girnar 4 (ICGV 15083) and Girnar 5 (ICGV 15090) released in 2020 and dedicated to nation by Prime Minister Modi on 16th Oct, 2020
  29. WGRS 2017 58K SNPs array 2017 Progenitor Genomes 2016 DArT 2014 KASP 2013 GBS 2016 5K SNPs assay 2020 Seed chipping 2020 QC-SA & WCA 2020 4X genomes 2019 10 SNP panel 2017 Gene expression atlas 2020 Where we are today…
  30. QC IC varieties ? 2022 Haplotype assay 2022 QC HOL 2021 New Diagnostic markers 2021 4K SNPs @Intertek 2021 ??? Custamized assays 2023 Planned/possible assays... QC ESA NARS 2021 New Diagnostic markers 2022 New Diagnostic markers 2023
  31. Mid-density assay with 4/5K SNPs (2021) for genetic purity testing among founder parents 10-SNPs panel (2017) for rust and LLS resistance and high oleic acid for early generation selection Seed weight & fresh seed dormancy 2021 Mid-density assay with 4K/5K SNPs (2021) for genomic selection breeding Integration of genomic tools in breeding QC panel with 20 SNPs (2020/21) for hybridity testing in F1 plants Mid-density assay with 4K/5K SNPs (2021) for checking homozygosity within line • Mid-density assay with 4K/5K SNPs (2021) for tracking ICRISAT bred material in seed chain/varietal adoption in farmers field • Genetic purity testing kit with 10 SNPs for high oleic varietiesSeed chipping preference Customized assay for foreground selection, background selection and genomic selection
  32. Data management  Datasets are being regularly made publicly available by submitting data in open access platforms including NCBI, CEGSB and ICRISAT Dataverse
  33. Challenges & Opportunities… • Current structure is multi-layered and it should be flattened by bringing crop scientists together • Non-inclusiveness in core institutional activities • Lack of recognition and funding support from institutional initiatives such as CtEH, AVISA, ICAR-ICRISAT and CRP- GLDC • Further opportunities in modernization of breeding program such as multipart GS & haplotype-based breeding
  34. Team, Collaborators & Funders Rajeev Varshney P Janila Hari Sudini Abhishek Rathore Anu Chitikineni Prasad Bajaj Damaris Odeny Sunil Gangurde Vinay Sharma Pushpesh Joshi Sejal Parmar Aamir Khan T Radhakrishnan KL Ratnakumar SK Bera Scott Jackson Peggy Ozias-Akins David Bertioli Soraya Bertioli Richard Michelmore Lutz Froenicke Baozhu Guo Corley Holbrook Steven Cannon Guahao He Xuanquiang Liang Xiaoping Chen Yanbin Hong Xing Jun Shanlin Yu Shi Hua Shan Mei Yuan Xiao Yuan Chi Wang Shuping Honge Li Xun Xu Liu Xin Obarley Yu Wei Zhang Changhoon Kim Shaun An Bellbull Kim Sachiko Isobe Kenta Shirashawa Ramesh Bhat Spurthi Nayak HL Nadaf Hasan Khan R Vasanthi D Latha K NagaMadhuri
  35. Thank You
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