slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
New genetic tools to improve dryland crop adaptation to abiotic stress and improve crop resistance to pests and diseas PowerPoint Presentation
Download Presentation
New genetic tools to improve dryland crop adaptation to abiotic stress and improve crop resistance to pests and diseas

New genetic tools to improve dryland crop adaptation to abiotic stress and improve crop resistance to pests and diseas

223 Vues Download Presentation
Télécharger la présentation

New genetic tools to improve dryland crop adaptation to abiotic stress and improve crop resistance to pests and diseas

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. New genetic tools to improve dryland crop adaptation to abiotic stress and improve crop resistance to pests and diseases C.T. Hash et al. Presented at the symposium: DRYLAND CROP PRODUCTION AND CLIMATE VARIABILITY: 40 YEARS OF RESEARCH PARTNERSHIPS WITH ICRISAT IN WCA during CORAF Science Week, 14-18 May 2012, in Ndjamena, Tchad

  2. Co-workers • ICRISAT colleagues: S.P. Deshpande, S. Chandra, S. de Villiers, R.T. Folkertsma, F. Hamidou, M. Kolesnikova-Allen, J. Ndjeunga, T. Nepolean, P. Ramu, O. Riera-Lizarazu, H.F.W. Rattunde, F. Sagnard, S. Senthilvel, T. Shah, S.D. Singh, R.K. Srivastava, Supriya, M. Thudi, V. Vadez, R.K. Varshney, & E. Weltzien; • Other CGIAR colleagues: M. Blümmel (ILRI), & H. Leung (IRRI); • WCA NARS partners: I. Angarawai, I.D.K. Atokple, F. Padi, M.D. Sanogo, O. Sy, & R. Zangré; • American ARI partners: J. Bennetzen, E.S. Buckler, K.M. Devos, S. Kresovich, S.E. Mitchell, A.H. Paterson, & J.P. Wilson; • Australian ARI partners: A. Borrell& D.R. Jordan • British ARI partners: W.A. Breese, C.J. Howarth, E.S. Jones, J. Scholes, D.S. Shaw, J.R. Witcombe, & R.S. Yadav; • French ARI partners: G. Bezançon, C. Billot, M. Deu, J-C. Glaszmann, J-F. Rami, D. This, & Y. Vigouroux; and • German ARI partners: A. Buerkert, H.H. Geiger, B.I.G. Haussmann, & H.K. Parzies

  3. Presentation outline • ICRISAT-mandate crops • Molecular marker development • Genetic diversity assessment • Molecular marker-based linkage maps & aligned genome sequences • QTL mapping • Conventional bi-parental populations • Association mapping with inbred germplasmpanels • QTL validation • Marker-assisted selection • Farm-level impact to date • Opportunities

  4. ICRISAT-mandate crops in WCA Groundnut Sorghum Pearl millet

  5. 1980s 2012 Molecular marker development Restriction Fragment Length Polymorphisms (RFLPs) Genotyping-by-Sequencing Single-Nucleotide Polymorphism Haplotypes Current technology Quicker, cheaper & more complete genome coverage US$40 for 80,000+ data points DNA isolation DNA digestion DNA fragment ligation 95X or 383X pooling Skim sequencing 0.1X to 0.3X Automated SNP allele scoring ca. 275,000 polymorphic GBS-SNP loci for pearl millet • 1980s technology • Slow, laborious, expensive & incomplete genome coverage • US$2.50 per data point • DNA isolation • DNA digestion • Electrophoretic separation • Probe with labels clones • Develop image • Score polymorphism • 300+ polymorphic RFLP loci for pearl millet

  6. Genetic diversity assessment New tools for sorghum Full data set by origin East Asia,India,Middle East, Western Africa, Central Africa,Eastern Africa,Southern Africa, North America, Latin America, &Australia 3365- entry GCP Sorghum Composite Germplasm Collection

  7. Genetic diversity assessment wild bicolor caudatum durra guinea margaritiferum kafir intermediate

  8. Molecular marker-based linkage maps & aligned genome sequences Sorghum genome sequence Kresovichet al. (2005) Plant Physiology 138:1898–1902 Paterson et al. (2009) Nature 457:551–556 Physical map of sorghum SSRs Ramu, Deshpandeet al. (2010) Molecular Breeding 26:409–418 Millets: genetic & genomic resources Dwivediet al. (2011) Plant Breeding Reviews 35:247–375 Groundnut genome sequence Peanut-CRISP led consortium w/ ICRISAT as partner Pearl millet genome sequence ICRISAT led consortium building on rice, sorghum, & Setariaitalica aligned genome sequences

  9. Physical map of sorghum SSRsRamu, Deshpandeet al. (2010) Molecular Breeding 26: 409–418

  10. QTL mapping Conventional bi-parental populations Association mapping w/ germplasm panels Identification of PhyC as a major gene controlling flowering in pearl millet, with major shifts in allele frequency in Niger between 1976 and 2003 Vigourouxet al. (2011) PLoSONE 6(5):e19563 Candidate-gene approach to mapping flowering genes in West African sorghum Bhosaleet al. (2012) BMC Plant Biology 12:32 • Downy mildew resistance mapping in pearl millet • Jones et al. (1995) Theoretical & Applied Genetics 91:448–456 • Strigahermonthica resistance mapping in sorghum • Haussmann et aI. (2004) Theoretical & Applied Genetics 109: 1005–1016

  11. QTL validation by MABC & phenotyping Sorghum stay-green • Trait mapped independently in Australia & USA (Purdue & TAMU) • MABC to assess utility of 6 QTLs from donor B35 = BTx642 in different genetic backgrounds • Hash et al. (2003) Field Crops Research 84:79–88 • SARI-led project (Water for Food Challenge Programme), & ICRISAT-led project (Generation Challenge Programme ) • ICSV 111 & S 35 • ISIAP Dorado • IRAT 204 • R 16 Subsequently tested in Ethiopia (release pending for 4 introgression lines), Ghana (again), India, & Sudan

  12. QTL validation by MABC & phenotyping Sorghum Striga resistance • QTLs mapped based on phenotyping in Kenya & Mali • Marker-assisted backcrossing to introgress resistance from donor N13 into locally-preferred varieties from • Eritrea: ??? • Kenya: Failed as breeding program got too far ahead of marker-data generation • Mali: Successful • Sudan: Successful  advancing towards cultivar release

  13. Marker-assisted selection Backcrossing Genome-wide selection (GWS) Testing GWS for downy mildew resistance, Striga resistance, & grain yield in pearl millet w/ support from the McKnight Foundation Marker-assisted back-crossing (MABC) • Pearl millet • Downy mildew resistance • Terminal drought tolerance • Stover nutritional quality (foliar disease resistance) • Sorghum • Shoot fly resistance • Stay-green component of drought tolerance & ruminant nutritional value Backcross nested association mapping (BCNAM) • Jordan et al. (2011) Crop Science 51:1444–1457 Testing GWS for sorghum in improvement in Mali w/ support from the Generation Challenge Programme

  14. Farm-level impact to date Nothing in WCA to date, but early-generation MABC products in farmer-preferred backgrounds are in pipeline An excellent example from India: • 15 years of ARI/ICRISAT/ NARS collaboration led to release of pearl millet hybrid “HHB 67 Improved” in 2005 • By 2011 this maintenance breeding product was grown on >950,000 ha in Rajasthan & Haryana states, with annual net benefits to farmers estimated at US$20 million, with US$13.5 m to growers there and US$6 m to seed producers in Andhra Pradesh

  15. Emerging opportunities GbS-SNPs as a tool for orphan crops Aligned crop genome sequences White fonio accessions from Mali Pearl millet Groundnut

  16. Mapping pearl millet Strigaresistance • Recently remade cross of wild & inbred parents as mapping population received from US-based partner was mixed up • Produced new plant x plant F1s & advanced these to F3 progenies with DNA sampling of 300 F2 plants • New population segregates for a single recessive gene for male-sterility • Also likely to segregate for root traits, including P-acquisition ability

  17. Mapping pearl millet tolerance to low soil P • Assessing performance of 150+ diverse inbreds, & their testcross hybrids, under low and high soil P conditions • Genotyping with SSR, DArT, & GbS-SNP markers • Merge data sets for Association Mapping Similar approach taken in India to identify new QTLs for terminal drought tolerance using a newly developed Pearl Millet inbred Germplasm Association Panel (PMiGAP)

  18. Value-chain participatory genome-wide selection • GbS-SNP markers saturate genome enough to permit effective marker-assisted selection for any heritable trait in any species • Need greater than ever for prioritization of breeding targets, use of appropriate experimental designs, generation of high quality phenotype data, and thorough statistical analysis of the resulting data sets

  19. Thankyou! • Nagodé! • Fofo! • Merci de votre attention!