Integrated Breeding in Dryland Cereals @ ICRISATStefania Grando on behalf of the Research Program
4th International Workshop on Next Generation Genomics and Integrated Breeding for Crop Improvement
February 19– 21, 2013
ICRISAT Headquarters, Patancheru, India
• Essential staple for poor smallholder farmers in some of the most marginal areas of the world
• Lack of choices of crops• Food and nutritional security• Multiple uses • Poor market opportunities• Several production constraints
Dryland Cereals Increasing productivity to help end hunger
Focus countries
SA WCA ESA
Millet India (11.3 m ha) Niger (6.9)Nigeria (4.1)Mali (1.5)Burkina Faso (1.4)Senegal (1.0)
Sudan (2.2)Uganda (0.5)
Sorghum India (7.7) Nigeria (5.7)Burkina Faso (1.8)Mali (1.1)
Sudan (6.3)Ethiopia (1.6)Tanzania (0.9)Mozambique (0.5)
• World’s largest collections of sorghum and millets genetic resources
• Exploiting within‐crop diversity• Effective and efficient transfer of traits
of interest from genetic resources into varieties
• Breeding for yield potential, biotic and abiotic stress resistances, nutritional quality, multiple uses
• Policy research on sorghum and millet commodity and seed issues
Longstanding commitment on sorghum and millet research
• Genomic tools (QTL, genome sequencing, MABC, GS, MAS)
• Harnessing genetic resources
• Precise phenotyping• Data management and analysis platforms
Enhance breeding efficiency
Impact of HHB 67 Improved
• First marker‐assisted field crop bred in the public domain to reach farmers’ fields in India
• Cultivated in 875,000 ha in 2011 • Net additional benefits to farmers in
Haryana in 2011 reached US$ 13.5 million.
• Seed production of HHB 67 Improved provided net income of US$ 6.4 million in 2011 to smallholder seed producers in Andhra Pradesh and Gujarat
• Generated at least 900,000 person days of employment (45% for women)
• Partnership with ARC, University of Khartoum, in Sudan
• Three released OPVs ‐susceptible
• Five striga resistance QTLs
• Role of the QTLs validated in the field
• Four lines released in Sudan
Molecular breeding for Strigaresistance
• Six QTLs for stay‐green • Introgression in two diverse genetic backgrounds
• QTL role validated with field evaluation and lysimetric system
• 2 QTLs effective across genetic background
Molecular breeding for stay‐green trait
Sorghum• Genome sequenced (Paterson et al.,
2009)• Re‐sequencing efforts
– QAFFI, Australia group – mostly breeding lines
– CIRAD, France – domestication studies– University of Georgia, USA – racial diversity
and transcriptomes for improved annotations
• Very little or no re‐sequencing efforts relevant to food and feed research
• Genotyping‐by‐Sequencing • Several applications – genetic diversity to
genomic selection
Sorghum GbS current status
Chromosome No of SNPs
1 83,4602 74,1923 74,9764 64,1995 52,4016 51,6337 28,1538 30,2219 48,14310 49,595Total 556,973
Material N of lines/RILs
Reference set 384Mini core collection 243
Sorghum Association Panel 410Mapping populations 3800Sweet sorghum lines 109B‐ and R‐ lines 350Photoperiod sensitive material 140Mali sorghum lines 240Other germplasm 3000Total Accessions 7173
• Global Diversity of Sorghum: genetic diversity in relation to racial and geographic origin ‐~265,000 SNPs by utilizing GBS approach (Morris et al. PNAS, 2013)
• Genome Wide Association Studies for several target traits such stem borer and pre‐flag leaf plant height
Germplasm Characterization and GWAS by using GBS
Association Mapping by GWAS ‐Grain Fe and Zn
• Association mapping – 364 samples– 556,973 SNPs
• 45 SNPs associated with grain Fe identified
• 36 genes related to Fe and Zn concentration identified based on gene search
3 genes 11 genes 16 genes 6 genes
Pearl Millet • Very few molecular markers
– Genomic SSRS ‐152 – EST‐SSRs – 208 – CISP‐ 24 – SNPs‐ 5800
• Genetic maps with low marker densitiesCross Marker loci ReferenceLDG‐1‐B‐10 × ICMP 85410 181 Liu et al, 1994Consensus map 418 Qi et al, 2004ICMB 841‐P3 × 863B‐P2 87 Senthilvel et al, 2008ICMB 841‐P3 × 863B‐P2 196 Pedraza‐Garcia et al, 2010 H 77/833‐2 × PRLT 2/89‐33 321 Supriya et al, 2011Consensus 174 Rajaram et al, 2013
BAC library ‐ John Innes CenterTranscriptomic resourcesLarge genome size
International Pearl Millet Genome Sequencing Consortium
India
Next………..• 500‐600 lines re‐sequenced (landraces, varieties, and parental lines of hybrids from Asia and Africa)
• Completion of genetic maps• Develop Final Assembly• Use BAC sequence and genetic maps
• Downstream analysis• GWAS and heterotic group analysis
• Two mapping populations (LGD‐1‐B‐10 ×ICMB 85410‐P7, and IP 18293‐P152 × Tift 238D1‐P158) under development for identification of QTLs responsible for off‐flavor in pearl millet flour
• Populations segregate for two key enzymes (POD, PPO) responsible for rancidity and off‐flavor
Towards mapping QTLs for flour rancidity
GPU 28
• Limited genomic resources
• Several initiatives underway
‒ Molecular marker repository‐new generation sequencing technologies (NGS); ICRISAT & AICSMIP, University of Georgia (UGA)
‒ Transcriptomic and sequencing resources
Finger millet genomics resources
• Genetic map construction ‐cultivated tetraploid finger millet using NGS
• Collaborating with UGA for generating information on A‐genome [Eleusine indica (2n = 18)]
• Sequencing at 5x – 8x coverage
• Transcriptomic resources
• Sequencing data sets
Next………..
Phenotyping for drought adaptation –Lysimetric facility at ICRISAT
A system intended to assess time pattern of water use
Capacity: 2400 small PVC1300 large PVC
major stress patterns
0
0.2
0.4
0.6
0.8
1
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600
thermal time (oD)
S/D
vegetative
pre-flowering
post-flowering
post-flowering relieved
mild
Which traits confer advantage in the most frequent environment?
Kholova et al., 2013
Characterization of the stress environments
Environmental patterns
Sorghum area in India
Integrated SNP mining and utilisation pipeline (ISMU)
High‐throughput sequencing analysis
• GBS analysis pipeline
• In collaboration with Cornell University (NSF Bread)
• Workshop held in March 2013
High‐throughput sequencing analysis
The Integrated Breeding Workflow System
Breeding Activities
Parental selectionCrossingPopulation development
GermplasmManagement
Open ProjectSpecify objectivesIdentify teamData resourcesDefine strategy
Project Planning
Experimental DesignFieldbook productionData collectionData loading
GermplasmEvaluation
Marker selectionFingerprintingGenotypingData loading
MolecularAnalysis
Quality AssuranceTrait analysisGenetic AnalysisQTL AnalysisIndex Analysis
DataAnalysis
Selected linesRecombinesRecombination plans
BreedingDecisions
AdministrationProject conf.
Workbenchadministration
&configuration
Breeding Management
System
Trial FieldbookEnvironment characterizationsystem
Field Trial Management
System
GenotypicDataManagementSystem
Samplemanagement
Analytical Tools
Sel. IndicesBreeding App.MARS App.Cross PredictionSimulation
Decision Support Tools
Breeding Applications Analytical Pipeline
Breeding mana.Genealogy mana.Query tools Sample mana.
Data managementStatistical analysisMolecular Genetic
The way forward – enhancing genetic gains• High‐throughput and cost‐effective genotyping platform ‐ efficient use of markers in breeding programs
• Large scale, fully automated phenotypingplatform
• Network of locations, representative of stress scenario, for testing breeding materials
• Enhance the informatics capacity and infrastructure to deploy modern breeding approaches
This work is undertaken as part of the
Thank you!
ICRISAT is a member of the CGIAR Consortium