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Understanding forage resources using Genotyping-by-sequencing approach


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Presented by Alemayehu Teressa Negawo, Meki Shehabu, Ermias Habte, Alieu Sartie and Chris Jones (ILRI), at the National Natural Science Foundation of China (NFSC) Meeting, China, 4-9 December 2018

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Understanding forage resources using Genotyping-by-sequencing approach

  1. 1. Alemayehu Teressa Negawo, Meki Shehabu, Ermias Habte, Alieu Sartie and Chris Jones (ILRI) Understanding forage resources using Genotyping-by-sequencing approach National Natural Science Foundation of China (NFSC) Meeting China, 4-9 December 2018
  2. 2. Overview of smallholder feeding systems • Feed quantity and quality - a key limitation to livestock production in the tropics • Estimated to be 50 to 80% of total production costs • Particularly an issue during the dry season • Poor quality feeds (crop residues) constitute the major component of the diet • Pasture lands/rangelands declining in size and fertility • Land tenure issues and lack of enabling policies
  3. 3. Strategies to meet feed demands Optimizing feeding practices • Prioritizing and targeting forage interventions using decision-support tools • Making better use of existing feed resources • Developing new feed and forage options • More and higher quality feeds and forages • Exploiting inherent natural resource management characteristics
  4. 4. Planted forages • Increase livestock production by alleviating feed constraints/shortages • Improve soil fertility through nitrogen fixation/leaf drop • Reduce erosion through increased ground cover, especially on slopes • Help control insect pests • Provide environmental services - carbon sequestration, enhanced system water productivity • Improve system resilience - alternative land use strategy for marginal lands and steep slopes
  5. 5. Livestock production benefits • Modelling increases in milk yields in response to different interventions in Ethiopia (Herrero et al., 2016)
  6. 6. Demand for forages is growing This is linked to: • Market intensification for milk and meat • Global food prices making grain an expensive feed • Feed scarcity from competition, biofuels, reducing rangelands and natural pastures and drought incidence • Rising feed prices • Demands for re-vegetation, soil and water management • Alternative income generation opportunities
  7. 7. Forage development to delivery Forage germplasm Characterization and evaluation Promising genotypes Variety registration Basic seed production Delivery to producers Forage grasses, herbaceous legumes and fodder trees Phenotype Genotype Agronomic Nutrition Adaptation use Selection of best bet genotypes for a range of environments and livestock systems National trials and selection compared to best local checks Information sharing Production of starter and basic high quality seeds Formal and informal seed sector involvement Laboratory Farmer fields On station research plots Private and public sector Livestockfarmers Genebank
  8. 8. Forage genetic resources in ILRI genebank • A unique resource for exploring and capturing forage diversity • 1,400 species representing over 600 genera Forage type Total accessions Accessions under Treaty Annex 1 accessions Forage cultivars Browse 3,720 3,684 45 25 Grasses 4,397 4,377 313 11 Legumes 10,518 10,440 2,646 275 Total 18,635 18,501 3,004 311 • Around 2,000 accessions distributed annually
  9. 9. Four forage field genebanks Site Altitude (m.a.s.l.) Rainfall (mm) Soil Compatibility Highland (Gurd Shola) 2400 1000 Vertisol, rainfed Tropical highland and temperate species Mid-altitude (Bishoftu) 1800 800 Alfisol/Vertisol, irrigated Tropical highland and temperate species Mid-altitude (Soddo) 1800 1200 Acid Nitosol, rainfed Acid soil adapted species Lowland (Zwai) 1650 500 Sandy alkaline, irrigated Tropical lowland species, especially grasses
  10. 10. Some important forages: Oats Rhodes (Chloris gayana) Buffel grass(Cenchrus ciliaris) Desho (Pennisetum pedicellatum) Napier grass (Pennisetum purpureum) Vetch
  11. 11. Diversity assessment of the germplasm collections Phenotypic trait characterization Nutritional trait characterization Agronomic traits Disease resistance traits Genotypic characterization
  12. 12. Facilities available in ILRI Ethiopia • Molecular lab • Biosafety level 2 lab and greenhouse • Seed viability lab • Seed health lab (virus, phytoplasma and fungi detection) • Feed quality lab (proximate and in vitro feed analysis) • Herbarium and taxonomy lab • Large collection of over 1400 species of over 600 forage genera in the ILRI genebank
  13. 13. Qualitative trait prediction based on Near Infrared Spectroscopy (NIRS) Non-invasive c. 200 samples/day >30 traits Physico-chemical c. $60 000 Calibration Validation Establishing an International NIRS feed analysis platform
  14. 14. Genetic diversity of forage crops Genotyping-by-sequencing (GBS) approach • Little information at molecular level • GBS using DArTseq platform (Kilian et al., 2012) – combines genome complexity reduction using a combination of restriction enzymes and next-generation sequencing. – Produces thousands of markers that could be useful for diversity assessment, trait discovery, subsetting, etc.
  15. 15. Genetic diversity of forage crops… Three Forage crops were genotyped so far using GBS: • Large number of marker data has been generated. • Others: lablab and Sesbania sesban Species Number of markers generated Silico DArT markers SNP markers Total Napier grass 116,190 85,452 201,642 Cenchrus ciliaris 111,917 93,501 205,418 Chloris gayana 93,128 65,529 158,657
  16. 16. Challenges in orphan crops like forages • Lack of genetic map and genomic sequence to select genome representative markers • What can be done (Opportunities) • Use genomic information of closely related species.  Pearl millet for Napier grass, Setaria italica for Buffel grass, Teff for Rhodes gras, …
  17. 17. Genome and ploidy information of related species Crop Scientific name family subfamily CN Ploidy X GS WG Reference Target species : Pennisetum purpureum Napier grass Pennisetum purpureum Syn. Cenchrus purpurem Poaceae 28 Tetraploid 7 No Pearl millet Pennisetum glaucum Poaceae 14 Diploid 7 ∼1.79 Gb Yes Varshney et al., 2017 Target species: Cenchrus ciliaris Buffel grass Cenchrus ciliaris Poaceae Panicoideae 36 Tetraploid 9 NO Foxtail millet Setaria italica Poaceae Panicoideae 18 Diploid 9 ~515 Mbp Yes Muthamilarasan et al., 2013 Sorghum Sorghum bicolor Poaceae Panicoideae 20 Diploid 10 730 Mbp Yes Maize Zea mays Poaceae Panicoideae 20 Diploid 10 2106 Mbp Yes Target species: Chloris gayana Rhodes grass Chloris gayana Poaceae Chloridoideae 20 Diploid 10 No Rhodes grass Chloris gayana Poaceae Chloridoideae 40 Tetraploid 10 No Finger millet E. coracana Poaceae Chloridoideae 36 Tetrapolid 9 1196 Mbp Yes Hittalmani et al., 2017 Manila grass Zoysia matrella Poaceae Chloridoideae 40 Tetraploid 10 380Mbp Yes Huang et al., 2016; Tanaka et al., 2016 Teff Eragrostis tef Poaceae Chloridoideae 20 Tetraploid 10 772 Mbpp Yes Cannarozzi et al., 2014 Note: CN= Chromosome number; X= Base chromosome number; GS: Genome size; WG= Whole genome
  18. 18. Phylogenetic tree of Poaceae: Species for which whole genome sequence is available Poaceae ++Oryzoideae | -Oryza sativa Japonica Group +-Panicoideae | ++Andropogoneae | | -Sorghum bicolorSorghum (X=10) | -Paniceae | ++Dichantheliinae | | -Dichanthelium oligosanthes (X=9) | -Cenchrinae | +-Setaria | | -Setaria italicFoxtail millet(X=9) | -Cenchrus | -Cenchrus americanusPearl millet(x=7) +-Pooideae | ++Brachypodieae | | -Brachypodium distachyon(X=5) | -Triticeae | +-Triticinae | | +-Aegilops | | | -Aegilops tauschii | | | +-Aegilops tauschii subsp. strangulata | | | -Aegilops tauschii subsp. tauschii | | -Triticum | | -Triticum aestivum | +Hordeinae | -Hordeum vulgare | -Hordeum vulgare subsp. vulgare -Chloridoideae ++Eragrostideae | -Eragrostis tef Teff(X=10) ++Zoysieae | -Zoysia matrella (X=10) -Cynodonteae +-Tripogoninae | -Oropetium | -Oropetium thomaeum(X=9) +Eleusininae -Eleusine coracanaFinger millet(X=9) Cenchrus ciliaris  Buffel (X=9) Chloris gayana  Rhodes grass (X=10) Bennetzen et al.(2012)
  19. 19. Mapping markers on the selected reference genome Species Number of Mapped markers (%) Reference genome Silico DArT SNP Pennisetum purpureum 17 % 33% Pennisetum glaucum (Pearl millet) Cenchrus ciliaris 7.2 % 16.3 % Setaria italica (foxtail millet) Chloris gayana 0.23 % 2.07 % Setaria italica (foxtail millet) 0.74 % 5.86 % Teff 0.56 % 5.13 % Zoysia matrella
  20. 20. Napier or elephant grass • Pennisetum purpureum, the major forage species for smallholder dairy in East Africa • High yielding lines produce 5 times more biomass than natural pastures in Tanzania1 • Yield shown to increase by intercropping with legumes and can be harvested 6 to 9 times per year under irrigation in Ethiopia2 • Smut and stunt disease resistant lines identified from the in trust collection at ILRI and being adopted by farmers 1Lukuyu et al. High yielding improved forages. 7th Multi-Stakeholder Partnership Meeting of the Global Agenda for Sustainable Livestock, Addis Ababa, Ethiopia, 8–12 May 2017. 2Adie et al. Lessons from pilot trials with small-scale irrigated forage production in the Amhara Region: potential of integrating the perennial forage Napier grass with Desmodium and Pigeon Pea in cropping systems. The second Amhara Agricultural Forum. 16 January 2018, Bahir Dar.
  21. 21. Genotyping-by-sequencing of Napier grass (Peniessetum purperum) Clusters of the 104 Napier grass accessions using 980 selected SNPs. (A) UPGMA tree showing seven groups; (B) PCA plot for PC1 and PC2; (C) The delta K suggesting two major groups and up to 5 subgroups; (D) Bar plots based on the admixture model in STRUCTURE, for K = 2 and K = 5. The colours are according to the STRUCTURE k = 5. ( Muktar et al., submitted manuscript) A B C D
  22. 22. Buffelgrass • Cenchrus ciliaris, one of the best pasture grasses for the Africa subtropics • An apomictic, perennial C4 grass • Good forage potential, and particularly a candidate for drought tolerance • Also helps to prevent soil erosion
  23. 23. Genotyping-by-sequencing of Buffel grass (Cenchrus ciliaris)  185 genotypes Collected from different countries  GBS data (DArTseq) 111,917 SilicoDArT markers 2,773 markers have data for all genotypes 93,501 SNP markers 4,054 markers have data for all genotypes Figure 1. Origin of buffelgrass collection (Jorge et al. 2008)
  24. 24. Genotyping-by-sequencing of Buffel grass.. PIC for all markers for Buffel grass
  25. 25. Genotyping-by-sequencing of Buffel grass… Silico DArT SNP Distribution of the DArT silico and SNP markers across the Setaria italica genome
  26. 26. Genotyping-by-sequencing of Buffel grass..  Genome distribution of the selected markers for in-depth diversity studies Silico DArT SNP
  27. 27. Genotyping-by-sequencing of Buffel grass..  Population structure and diversity of buffelgrass collection
  28. 28. Rhodes grass • Chloris gayana, a major C4 forage in the tropics and subtropics. • Known for its wide adaptability and ease of establishment • A stoloniferous perennial, usually of creeping habit but with plenty of variation • Cross-pollinating, with diploid and tetraploid forms, usually propagated by seed • Vigorous root system confers reasonable drought tolerance and makes it suitable for erosion control
  29. 29. Genotyping-by-sequencing of Rhodes grass (Chloris gayana)  94 Accessions Collected from different countries  GBS data (DArTseq) data was received in excel (csv) format 93,128 Silico DArT markers 3,590 markers have data for all genotypes 65529 SNP markers 10,111 markers have data for all genotypes Ponsens (2009)
  30. 30. Genotyping-by-sequencing of Rhodes grass … 0 5000 10000 15000 20000 25000 30000 Numberofmarkers PIC ranges DArT markers SNP markers PIC for all markers of Rhodes grass
  31. 31. Genotyping-by-sequencing of Rhodes grass … Silico DArT markers SNP markers  Phylogenetic tree of the accessions
  32. 32. Genotyping-by-sequencing of Rhodes grass…  Cluster analysis of Rhodes grass genotypes Silico DArT markers SNP markers
  33. 33. Creating subsets of forage crops Napier grass subsets representing the diversity in ILRI gene bank collection (Muktar et al.)
  34. 34. Creating subsets of forage crops … -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 Coord.2(8.23%) Coord. 1 (22.19 %) PCA indicating accessions selected for core collection based on silicoDArT markers Selected for core collection Not selected for core collection  Buffel grass subsets representing the diversity in the genebank collection
  35. 35. Creating subsets of forage crops … -5 -4 -3 -2 -1 0 1 2 3 4 5 6 -6 -4 -2 0 2 4 6 Coord.2(10.47%) Coord. 1 (13.67 %) PCA indicating accessions selected for core collection based on SNP DArT markers Not selected for core collection Selected for core collection Buffel grass subsets representing the diversity in the genebank collection
  36. 36. Creating subsets of forage crops … -5 -4 -3 -2 -1 0 1 2 3 4 5 -7 -5 -3 -1 1 3 5 7 Coord.2(22.2%) Coord. 1 (50.4 %) Cluster analysis of buffelgrass collection based on nutrition data Selected for core collection Not selected for core collection Buffel grass subsets representing the diversity in the genebank collection
  37. 37. Summary:  Forage genetic resources are less understood at molecular level  GBS provide cheapest and quick approach to study forage genetic resources  Genomic information of well studied closely related model crops are important resource to better understand forage genetic resource  Presence of significant variation in the collections (Napier grass, buffelgrass and Rhodes grass)  The genotypic data will be useful for trait association study
  38. 38. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence. better lives through livestock ILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system