Physiological Model greatly enhances GWAS when used to dissect Multi-Environment Field Phenomics for Climate Adaptation Traits

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1 GRiSP Global Rice Phenotyping Network Physiological Model greatly enhances GWAS when used to dissect Multi-Environment Field Phenomics for Climate Adaptation Traits Michael Dingkuhn, Julie M.C. Pasuquin, Baboucarr Manneh, Abdoulaye Sow, Julie Dusserre, Brigitte Courtois, Jean Damo, Richard Pasco, Jean-Christophe Soulié Physiologist/modeler GRiSP phenotyping network manager Rice breeder Physiologists Geneticists Informatics/modeling Cirad IRRI Africa Rice

2 Physiological detail Choices Approach: Control E variability Approach: Use E variability to extract response Number of environments

3 Contents GRiSP Rice Global Phenotyping Network Rice phenology & sterility vs. climate Panel structure Senegal and Madagascar experiments Extraction of hidden traits with RIDEV GWAS Candidate mechanisms and genes

4 GRiSP Global Rice Phenotyping Network NARS PhilRice Ph CAAS Ch EMBRAPA Br GRiSP Centers Africa Rice IRRI CIAT Cirad IRD JIRCAS QTLs Trait loci C4- Rice Private sector Syngenta Flinders Au Cornell US UPLB Ph Newcastle Au Universities Newcastle Au Saudi Univ. Univ. Bonn De Colorado SU US Trait measurements - Field - Controlled Environments - Dedicated platforms Phenomics resource GWAS Genomics resource Saturating SNP maps GBS (DART, 22k SNPs) - GBS (>Cornell, 43k SNPs) - ORYZA-Chip (700k SNPs) Shared diversity panels among partners

GRiSP Global Rice Phenotyping Network GBS YP, NSC, Aerobic rice Panicle structure Drought, Blast Root architecture, Cardinal temperatures, Modeling YP, Heat, Cold, Phenology, Salinity Salinity YP,

5 GRiSP Global Rice Phenotyping Network GBS YP, NSC, Aerobic rice Panicle structure Drought, Blast Root architecture, Cardinal temperatures, Modeling YP, Heat, Cold, Phenology, Salinity Salinity YP, Heat Cold, Phenology (ORYTAGE) YP, Photosynthesis, Phenology, Salinity, Anaerobic Panicle structure, Lodging, Heterosis, YP P & Zn deficiency, Antioxidants, NSC, RYMV Aerobic NSC, prot., Grain Lignin, Si chem GBS Anaerobic Aerobic & upland Analytics Projects: ORYTAGE (Cirad) Indica (200) TropJap (200) Nucleus (25) PRAY (IRRI/GRiSP) Indica (300) TropJap (300) Aus (250) SKEP-Lodging (IRRI/Syngenta) Indica (300)

Phenology Organ T at critical time Spikelet sterility Objective Discover genes controlling phenology and thermal

6 Rice responses to photo-thermal environment Thermal sterility is a major yield loss mechanism in rice systems Rice is cold sensitive (microspore stage) Rice is heat sensitive (anthesis) but escapes thru thermal cooling Climate Phenology Organ T at critical time Spikelet sterility Objective Discover genes controlling phenology and thermal sterility P a Flagl n eaf1 Flagl eaf2 1 P a n 2 Flagl eaf3 P a n 3 Leaf5 Flagl eaf4 P a n 4 Crop-generated micro-climate

7 Approach Measure on diversity panel (1) flowering date, (2) spikelet sterility and (3) meteorology Multiple environments (altitude, season) Heuristic extraction of component traits by ecophysiological model RIDEV Genome-wide association study

8 Temp. japonica Trop. Japonica Aus, Boro Indica Diversity nucleus (25) ORYTAGE Indica panel for GWAS (189) incl. Aus + Boro 53% Asia (11% IRRI) 18% Madagascar 23% West Africa 6% Latin America Indica The Panel

9 Sowing date Sowing date Field trials Senegal : seasonal variation Senegal river delta 6 sowing dates X 220 acc. 6-block augmented design with 4 checks Madagascar: altitudinal variation 2 sites (857 and 1497 m alt.) 2 years (2009, 2010) 3 reps x 220 acc. Temperature ( o C) and PET (mm) A HDS 1/2/2009 1/1/2009 1/6/2009 1/5/2009 1/4/2009 1/3/2009 WS 1/9/2009 1/8/2009 1/7/2009 Tmax Tmean Tmin RH PET /12/2009 1/11/2009 1/10/ Minumum relative humidity (%) Astronomic day length (h) B Date 1 (HDS) Date 2 Date 3 1/6/2009 1/5/2009 1/4/2009 1/3/2009 1/2/2009 1/1/2009 Date 4 (WS) Date 5 Date 6 1/12/2009 1/11/2009 1/10/2009 1/9/2009 1/8/2009 1/7/2009 Mid altitude 857m Mean Tmin 18.9 C 13.9 C High altitude 1497m Mean Tmax 30.4 C 25.7 C

10 Variation in time to flowering Senegal, time to Flowering (F) vs. sowing date Days sowing to flowering A Jan Feb Mar Apo Nipponbare Tequing Kalinga III Betsilaizina BR24 Dec Nov Oct Sep Aug Jul Apr May Jun B Jan Feb Mar Nipponbare Carreon Jengar Nona Bokra Pokkali Dec Nov Oct Sep Aug Jul Apr May Jun C Jan Feb Mar De Abril Menahoditra 1234 Pelita Janggut Vato Matsoamalona Nipponbare Danau Laut Tawar Dec Nov Oct Sep Aug Jul Apr May Jun Duration to flowering, Madagascar high-alt. (d) Madagascar, F (high vs. md alt.) Duration to flowering, Madagascat mid-alt. (d) Date of sowing (2009) A 180 B 1.0 C F, Madagascar F (mid.alt.) vs. Senegal (July sowing) R 2 or slope (unitless) Date of sowing (2009) R Altitude delays 0.2 flowering in Madagascar 1/12/09 1/11/09 1/10/09 1/9/09 1/8/09 1/7/09 1/6/09 1/5/09 1/4/09 1/3/09 1/2/09 1/1/09 Slope Date of sowing (2009) Day length X thermal effects on flowering in Senegal Duration to flowering, Madagascar mid-alt. (d) Duration to flowering, Senegal July crop (d) Date of sowing (Senegal)

11 Phenotypic panel clusters according to phenology

12 Fraction sterile spikelets Fraction sterile spikelets Sterility V133 MTU9 India Madagascar 0.2 Senegal V60 CICA8 Colombia 0.0 A B C D E V135 NAM SA GUI 19 Thailand 124 LATSIBOZAKA Madagascar V34 B6144-MR-6 Indonesia V196 Tokabany 665 Madagascar V28 Adny 11 Mali V165 Rojomena B48 Madagascar V240 WAY RAREM Indonesia V25 Tequing China 1 2 Diversity of sterility responses to cold Fraction sterile spikelets Fraction sterile spikelets Fraction sterile spikelets V105 IR8 Philippines V19 M202 temperatejaponica USA Estimated minimum Tw ( o C) V103 IR72 Philippines V13 Giza 171 temperatejaponica Egypt Estimated minimum Tw ( o C) V86 IR20 Philippines V52 C21 tropicaljaponica Philippines Estimated minimum Tw ( o C) V61 CT Colombia V40 Betsilaizina tropicaljaponica Madagascar Estimated minimum Tw ( o C) V121 Kogoni 121 Mali V17 Khao Dam tropicaljaponica Laos 1.0 V14 IAC165 V8 Dom Sofid V6 Basmati 370 V114 Kasalath All cvs tropicaljaponica Iran India India aromatic aromatic AUS 0.6 Brazil Estimated minimum Tw ( o C) C 18C Tmin(apex) at booting

13 RIDEV model simulates crop-generated microclimate, phenology and thermal sterility TOA Day length Panicle T at anthesis => Heat stress Development rate fn(thermal time) BVP PSP Reproductive Maturation Tmin (W) => Cold stress Apex is below water line Apex emerges Apex at canopy top Parameter estimation by iterative optimization (R-Genoud)

14 Spikelet sterility (components, as fraction) Spikelet sterility (total, as fraction) Crop duration (d) cv. Sahel 108, Ndiaye, Senegal Extracting Flowering (sim.) component traits with Maturity (sim.) Flowering (obs., ORYTAGE) Flowering (obs., thesis Stuerz) Maturity(obs., ORYTAGE) RIDEV model Maturity (obs., thesis Stuerz) Phenology Tbase, Topt Basic vegetative phase (BVP) PP-sensitivity Total sterility (sim.) Total sterility (obs., ORYTAGE) Total sterility (obs., thesis Stuerz) Sterility Error bars = SE (ORYTAGE data) Cold sterility Critical T (at microspore) Response slope KColdHardening (acclimation potential) Model parameters: Tbase = 10.6 o C Topt = 31.7 o C PPexp = PPsens = SumBvp = 748 o C.d SumRepr = 400 o C.d (preset) SumMatu = 409 o C.d CritSterCold1 = 10.6 o C CritSterCold2 = 18.7 o C CritSterHeat = 29,7 o C SterBase = 0.08 (=8%) Heat sterility Critical T (at anthesis) Baseline sterility ( garbage parameter ) Method: Parameter optimization to match prediction with observation (10 environments) SterCold1 (sim.) SterCold2 (sim.) SterHeat (sim.)

15 A Trait: BVPindex GWAS B6i Empirical BVP: Shortest observed duration sowing-pi B Trait: SumBVP B6m B7m RIDEV parameter SumBVP: Thermal time to end of BVP (= onset of PSP) C Trait: BVPmin B2m B6m Phenology (basic vegetative phase, BVP) RIDEV trait extraction improves associations

16 A Trait: PPindex P1(1)i P6i P7i P11(2)i Phenology (day length effects) RIDEV trait extraction improves associations Empirical PP-senstivity: Senegal March vs. July sowing B Trait: 1/PPsens P1(2)m P2m P3m P4m P6m P9m P11(1)m P11(3)m RIDEV parameter 1/PPsens: Photoperiod sensitivity using Impatience model

17 Phenology (thermal effects) RIDEV trait extraction improves associations A Trait: COLDindex C1i Empirical thermal sensitivity: October (cold) vs. July (warm) sowing B Trait: Tbase C6m HD3a RIDEV parameter Tbase

18 Sterility Trait: TCritSterCold Sc7m Sc1m Sc6m Critical T for cold sterility D Trait: TCritSterHeat Sh4m Critical T for heat sterility

Acclimation for cold sterility, fn(t during BVP) Some entries more cold tolerant in Madagascar than Senegal Hyp: Acclimation at high altitude during BVP Model with acclimation

19 Acclimation for cold sterility, fn(t during BVP) Some entries more cold tolerant in Madagascar than Senegal Hyp: Acclimation at high altitude during BVP Model with acclimation explains sterility 2X better Positive alleles only in trad. Madagascar highland accessions Trait: KColdHardening H6m H8m H11m Acclimation QTLs H1(2)m H1(1)m H2(1)m H2(2)m H7m H8m I3

20 Candidate genes (examples) Cold hardening (acclimation) 10 QTLs (-logp = 5.0 to 10.0) +Alleles mainly in group I3 (Madagascar) Annotated candidate genes in QTL: Methyl transferases (6 QTLs) Histone acetyl transferase (1 QTL) Dimethyl allyl transferase (1 QTL) Histone-like TF (1 QTL) Heat shock protein (1 QTL) Base temperature 1 QTL on chr6 (-logp = 6.7) Annotated candidate genes in QTL: HD3a (Heading time) [SNP is within gene] RFT1 flowering gene [tail-to-back w/ HD3] Gomez-Ariza et al. (2015) in JXP: HD3 & RFT1 convey adaptation to cool climates => Almost all acclimation QTLs are associated with epigenetic genes => Tbase for duration to flowering may be controlled by HD3/RFT1 florigens

Conclusion Large diversity for phenology & cold tolerance Multi-E trait variation dissected by RIDEV model Genotypic model parameters as traits Model

21 Conclusion Large diversity for phenology & cold tolerance Multi-E trait variation dissected by RIDEV model Genotypic model parameters as traits Model parameters give much stronger GWAS signals than raw data Epigenetic acclimation to cold in Madagascar I3 group HD3/RFT1 gene may control Tbase for flowering

22 Thank you Merci Salamat po

Phenotyping rice response to heat: Methodology to discriminate spikelet sterility and grain quality. Tanguy Lafarge. Orytage, 11 December 2009, Cirad

Phenotyping rice response to heat: Methodology to discriminate spikelet sterility and grain quality Tanguy Lafarge Orytage, 11 December 29, Cirad Options for canopy adaptation for heat tolerance and avoidance