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Tropical maize genome: what do we know so far and how to use that information

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Tropical maize genome: what do we know so far and how to use that information. Yunbi Xu

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Tropical maize genome: what do we know so far and how to use that information

  1. 1. Tropical maize genome: what do we know so far and how to use that information Yunbi Xu CIMMYT-China, Institute of Crop Science, CAAS, Beijing, China 13th Asian Maize Conference and Expert Consultation on “Maize for Food, Feed, Nutrition and Environmental Security” Radisson Blu Hotel, Ludhiana, India, October 8‐10, 2018
  2. 2. Outline  Introduction  Why tropical maize genomics  What do we know so far about tropical maize genomes  What can do with the genomics information from tropical maize  Molecular breeding driven by big data and artificial intelligence
  3. 3. (1) An intermediate genome size compared to rice and wheat; (2) Typical outbreeding system with flexibility for inbreeding; (3) Multiple breeding products (inbreds, hybrids, synthetic varieties, open pollinated varieties and improved landraces); (4) Wide adaptability, especially for stressed environments; (5) Multiple-purpose crop: 5Fs food (grain), feed (grain and stalk), fuel (grain and stalk), forage (young grain and stalk) fruit (sweetcorn, baby corn, fresh corn) Xu and Crouch 2008 In: Genomics of Tropical Crop Plants Maize as an economically important crop and suitable as an experimental model
  4. 4.  Temperate maize in cooler climates beyond 34°N and 34°S  Tropical maize in warmer environments located between 30°N and 30°S latitudes  Lowland (sea level to ≤1000 masl)  Mid-altitude (1000 to 1600 masl)  Highland (≥1600 masl)  Subtropical maize between the 30° and 34° latitudes. Maize is adapted to diverse environments Tropical maize is grown in over 60 countries, occupying about 60% of the area harvested and representing 40% of the world production.
  5. 5.  Wild relatives (teosinte, Tripsicum)  Landraces  Open-pollinated varieties (OPV)  Synthetic varieties  Inbreds  Hybrids  Germplasm complexes  Pools  Populations  Genetic stocks (mutant, permanent populations, near- isogenic lines, introgression lines, etc). Xu et al 2017. J Exp Bot 68: 2641–2666 Genetic variation is largely hosted by tropical maize
  6. 6. Most of the tropical germplasm may not be considered as manageable resources, as they could be too diverse to be used directly and have to undergo a pre-breeding process. Utilization of genetic variation in tropical maize Xu and Crouch 2008 In: Genomics of Tropical Crop Plants Genetic variation can be unlocked from tropical maize germplasm through genetic approaches such as large-scale and systematic identification and characterization of QTL
  7. 7. Outline  Introduction  Why tropical maize genomics  What do we know so far about tropical maize genomes  What can do with the genomics information from tropical maize  Molecular breeding driven by big data and artificial intelligence
  8. 8. Genomic Gaps Left by Reference Genomes Single genomes that have been sequenced up to 90% are used as reference genomes Resequencing indicates that 20-50% of the original reads from different ecotypes cannot be mapped to the reference genome 50% Hi-seq reads from tropical maize cannot be mapped; while only 20% of the SNPs from landraces can be mapped to the B73 reference (Peter Wenzel, CIMMYT) Transcriptome sequencing of 503 maize inbred lines identified 8681 representative transcripts 16.4% were expressed in all lines 50% being absent in the B73 reference (Hirsch et al. 2014)
  9. 9. Single-genome based references provide only a partial genome coverage, which results in  Got lost in map-based cloning  Missing of 40% or more important QTL/genes in AM  Biased estimation (Ascertainment bias)  Genetic diversity ‘Population structure  LD and IBD  Haplotypes  MAS  Inefficient procedures  Unpredictable results The Results of Partial Genome Coverage
  10. 10. A multiple genome-based pangenome, is needed for unlocking genetic variation that is hidden in diverse tropical maize collections, to provide a complete profile of genetic variation, including favorable alleles and haplotypes, at various omics levels across elites, landraces, and wild relatives. Technology transfer from temperate maize to tropical maize and capacity building in tropical countries are needed for improvement of tropical maize. Comparative genomics across tropical maize germplasm and temperate maize will help identify novel genes and alleles required for improvement of both temperate and tropical maize. More attention should be given to tropical maize genomes
  11. 11. SNP1 SNP2 SNP3 Chromosome 1 AACACGCCA …. TTCGGGGTC….AGTCGACCG …. Chromosome 1 AACACGCCA …. TTCGAGGTC….AGTCAACCG …. Chromosome 1 AACATGCCA …. TTCGGGGTC….AGTCAACCG …. Chromosome 1 AACACGCCA …. TTCGGGGTC….AGTCGACCG …. Individual 01 CTCAAAGTACGGTTCAGGCA Haplotype 1 Individual 02 CTCAAAGTACGGTTCAGGCA Individual 03 CTCAAAGTACGGTTCAGGCA Individual 04 CTCAAAGCACGGTTGAGGCA Haplotype 2 Individual 05 CTCAAAGCACGGTTGAGGCA Individual 06 CTCAAAGCACGGTTGAGGCA Individual 07 CTCGAAGTACGGTTCAGGCA Haplotype 3 Individual 08 CTCGAAGTACGGTTCAGGCA Individual 09 CTCGAAGTACGGTTCAGGCA Individual 10 CTCAAAGCACGGTTCAGGCA Haplotype 4 Individual 11 CTCAAAGCACGGTTCAGGCA Individual 12 CTCAAAGCACGGTTCAGGCA A T C / / / G C G Tag SNPs SNPs The concept of haplotype Haplotype Revised from Xu et al 2017. J Exp Bot 68: 2641–2666
  12. 12. Contribution of tropical maize genomics to potential-increasing and gap-closing Xu et al. 2017. J. Exp. Bot. 68: 2641–2666
  13. 13. Outline  Introduction  Why tropical maize genomics  What do we know so far about tropical maize genomes  What can do with the genomics information from tropical maize  Molecular breeding driven by big data and artificial intelligence
  14. 14. Maize HapMaps HapMap 1 Gore et al 2009 Science 326: 1115-1117 27 diverse maize lines (7 tropical lines) Array-Maize SNP50 developed 56,110SNPs chosen from >840,000 SNPs Covering 2/3 predicted genes HapMap 2 Chia et al 2012 Nat Genet 7:803-807 103 maize lines 25 tropical inbreds 23 landraces 19 wild relatives 55M SNPs
  15. 15. Bukowski et al. 2018. GigaScience 7: 1-12 HapMap3
  16. 16. Pan-genomes Building pan-genome sequence anchors using genetic mapping approaches combined with machine learning algorithm 14,129 maize inbred lines 26 M tags Lu et al 2015 Nature Communications 6:6914
  17. 17. Pangenomic information can be incorporated by databases, by which the core genome, variable genome and the expression levels can be linked (Golicz et al. 2016). The exome is many times smaller than that of the whole genome, making exome sequencing data more easily manageable and applicable in plant breeding (Warr et al. 2015). Pangenomic information and exome can be integrated with other functional genomics approaches and used to discover genes and their functions. Pangenomic information and the exome
  18. 18. 30133 SNPs from 600K Affymextrix Axion Maize Genotyping Array 1068 SNPs from Illumina MaizeSNP50 BeadChip 9395 SNPs from RNA-seq data of 368 maize lines 4067 SNPs for filling gaps in the B73 reference 734 SNPs for heterotic grouping 132 SNPs for transgenic eventsZou et al 2017 Mol Breed 37: 20 In collaboration with CapitialBio Technology and Daxiong Seed Co. A 55K-SNP chip with improved genome coverage by large-scale resequencing tropical maize
  19. 19.  Selection and development of 20K SNP markers from the 55K SNP array with improved genome coverage  Target sequences are enriched by in-solution probes  Using the same panel of 20K SNP markers to generate 10K, 5K and 1K SNP markers by sequencing at different depths  Test and validation using two genotype panels:  96 diverse maize germplasm from China, USA and CIMMYT  387 breeding lines generated in CAAS maize breeding programs  Suitability: 50-40000 SNPs, SSRs or InDels  Genotyping cost:10 USD/2K, 20USD/ 20K, 30 USD/40K Developed by CIMMYT-China, Institute of Crop Science (CAAS) and Shijiazhuang Molecular Breeding Incorporation Multiple panels of SNP markers developed by Genotyping by target sequencing (GBTS) The system can be used for all organisms (plants and animals) Guo et al 2018 in preparation
  20. 20. Potential applications of the marker panels in genomics, genetics and plant breeding MAGE +++ ++ + + Heterotic grouping +++ +++ ++ ++ Marker linkage map construction +++ ++ + + Linkage mapping for major traits +++ +++ ++ + Genomewide association study ++ + Selection in selfed populations +++ +++ +++ +++ +++ Gene transfer by backcrossing +++ +++ +++ ++ + Gene pyramiding +++ +++ ++ + + Variety protection and IP issues +++ +++ ++ ++ + Applications ≥20K* 10K 5K 1K <200 MAGE: marker-assisted germplasm evaluation, including differentiating cultivars and classifying inbed lines into heterotic or ecological groups; identifying gaps and redundancy in germplasm collections; monitoring genetic shifts that occur during germplasm conservation, regeneration, domestication, and breeding; identifying novel and superior alleles for improvement of agronomic traits; and constructing representative subsets or core collections (Xu 2003, 2010). Guo et al 2018 in preparation
  21. 21. Outline  Introduction  Why tropical maize genomics  What do we know so far about tropical maize genomes  What can do with the genomics information from tropical maize  Molecular breeding driven by big data and artificial intelligence
  22. 22.  Dissect the genetic structure of their germplasm to understand gene pools and germplasm (heterotic) groups  Provide insights into allelic content of potential germplasm for use in breeding  Screen early generation breeding populations to select segregants with desired combinations of marker alleles associated with beneficial traits (in order to avoid costly phenotypic evaluations)  Establish genetic identity (fingerprinting) of their products The applied genomics information and tools routinely used by large multi-national seed companies Are also utilized by small- and medium-seed companies and developing countries for breeding tropical maize
  23. 23. GWAS using Multi-Hybrid Populations Resequenced maize inbreds can be used to cross with each other to develop multiple hybrid populations for GWAS, and their genotypes inferred from the sequenced parental lines. The parental lines can be easily shared and used to produce different subsets of multiple hybrids based on the objectives of gene target and discovery. An example has been provided for GWAS of flowering time using 55K SNP markers and 724 hybrids (Wang et al 2017). Similarly, two approaches, geographic associations and F-one association mapping (FOAM), were integrated to characterize the diversity of 4,471 maize landraces, with 1,005 genes identified across 22 environments (Navarro et al 2017).
  24. 24. Navarro et al 2017 Nat Genet 49: 476-480 Significance for flowering time, and overlap between flowering time and latitude- and altitude- associated SNPs.
  25. 25. Target traits  Yield and heterosis  Quality (e.g., QPM from tropical maize)  Abiotic stresses (with tropical maize as donors)  Biotic stresses Diseases as examples  Global diseases (most maize growing environments): leaf blights, leaf rusts, leaf spots, stalk rots and ear rots.  Regional diseases  Asia - downy mildews, which are also spreading to some parts of Africa and the Americas;  Africa – MLN, maize streak virus and the parasitic weed Striga;  Latin America - maize stunt and tar spot. Marker-assisted recurrent selection and genomic selection for tropical maize
  26. 26. Integrated plant breeding platforms for breeding tropical maize  Efficient breeding pipeline  Integrated DH and MAS procedures  Excellency in Breeding resources  Open-source breeding networks Xu et al 2017. J Exp Bot 68: 2641–2666
  27. 27. Maize molecular breeding initiatives in China supported by integrated plant breeding platforms Tongzhou International Seed GS Breeding Initiatives Genotyping 100 GS populations supported by Beijing governmental funds One + Eight Breeding Initiatives Including one institute (Institute of Crop Science, CAAS) and eight seed companies, with genotyping cost subsidized by Ministry of Agriculture and Rural Affairs Jiusuo Breeding Initiatives Seed companies in the winter nursery Sanya, China, through fully open source breeding by sharing everything
  28. 28. Outline  Introduction  Why tropical maize genomics  What do we know so far about tropical maize genomes  What can do with the genomics information from tropical maize  Molecular breeding driven by big data and artificial intelligence
  29. 29. Plant breeding is increasingly driven by big data Medium:field book => EXCEL => databases Scale:k=> m => b => t Dimension:one(phenotype)=> two(phenotype + genotype)=> three(phenotype + genotype + envirotype)=> four( phenotype + genotype + envirotype + time) Throughput (data generated in one experiment or unit time): 1=> 100(1*96)=> 10000 (96*96) => 1m(384*3072)=> 100M(384*300K) Precision:repeatability, duplicability, compatibility, additivity, predictability Data revolution
  30. 30. Multi-omics data Multi-phenotypic data Multi-environmental data Integrated data Empirical breeding Selection indices Parental selection and mating Combining ability Heterosis and hybrid performance Parental relationship Genetic distance Long-term selection data Growth and development Dynamic changes Varietal transition Quality and nutrients Abiotic stresses Biotic stresses … … … Multiple sources of plant breeding data
  31. 31. Xu 2016 Theor Appl Genet 129: 653–673 More attention should be given to envirotypic data
  32. 32. Experimental design and data analysis are more costly and time consuming, compared to data generation CIMMYT and donors are eager to maximize the use and impact of data Kate Drehe 2013 CIMMYT Science Week
  33. 33.  AI-assisted breeding system will play significant roles in theoretical study, evaluation, selection, breeding procedure development, and field management.  AI will have significant influence on breeding information system because AI-equipped robots will interact with all the processes relevant to data collection, storage, analysis, sharing and utilization.  AI system will benefit from historical experience and relevant knowledge achieved and accumulated in breeding programs.  The breeding system driven by big data and AI will have great capacity of designing and predicting in breeding programs with improve breeding efficiency and enhanced genetic gain, through machine learning, optimization and simulation. Plant breeding will be driven by artificial intelligence
  34. 34. Acknowledgements ccMaize Group: Wen-Xue Li, Cheng Zou, Shanhong Wang CIMMYT: Daniel Jeffers, Mike Olsen, B. M. Prasanna International Collaborators CAAS, Sichuan Agric Univ, China Agric Univ Cornell University, Cold Spring Harbor Laboratory Funds  CGIAR MAIZE  Bill and Melinda Gates Foundation  China National Natural Science Foundation  Ministry of Science and Technology of China  The Agricultural Science and Technology Innovation Program (ASTIP) of Chinese Academy of Agricultural Sciences Thank you for your attention

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