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Doctorate in Agricultural Sciences
Facultad de Agronomía - Universidad de la República
Collaborating Institutions: Cornell University – CIAT - FLAR
GWAS of Resistance to Stem and Sheath Diseases of Uruguayan
Advanced Rice Breeding Germplasm
Juan Rosas
Advisors: Jean-Luc Jannink – Lucía Gutierrez
Special Comittee: Marcos Malosetti (Wageningen University)
Álvaro Roel (INIA)
Funding: MBBISP, INIA (Rice Program, Rice GWAS Project)
Overview
1. Timeline2. Background & Review Why?3. Objectives What?4. Materials & Methods How?5. Preliminary Results Ouch! Wow! 6. Future work7. Schedule When?
Doctorate Program Timeline
2012 2013 2014 2015 2016
Cornell U.
1st. Anual
Committee Meeting
CIAT CU/UW
Field pheno typing
Greenhouse phenotyping (ROS & SCL) GH ph. (R.Solani)
MBBISP Scholarship
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12
Official start Oct 2012 Expected completionThesis Project Defense
Sep 2013
2nd Anual
Committee Meeting
Paper I Paper II
Paper III
Paper IV
Year 1 Year 2 Year 3 Year 4 Year 5
Training in Statistics
Rice factsWhy rice matters to
Uruguay?
– Rice is the 3rd top Uruguayan export.
– It accounts for 7% of country’s total income
Source: www.uruguayxxi.gub.uy
2009 2010 2011 20120
200
400
600
800
1000
1200
1400
1600
SoybeansMeatRiceWheatU
SD x
106
Uruguay factsWhy Uruguay matters to rice?
Uruguay is the 7th major world rice exporter
Source: FAOSTAT
Thailan
d
Viet Nam
Pakistan
U.S.A
.India Ita
ly
Urugu
ayChina
U.A.E
mirates
Benin0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Top Ten World Rice Exporters
t x10
6
Uruguay factsWhy Uruguay matters to rice?
Uruguayan rice yields are amongthe highest of theworld
Source: http://ricestat.irri.org (Alphabetic order)
Cou
ntry
Ave
rage
Yie
ld in
201
0 (t/
ha)
Rice’s biggest adversariesWhat are the major constraints to rice production worldwide? Abiotic:
Water scarcity, poor soil conditions Extreme temperatures
Biotic (fungal diseases):1. Blast (Pyricularia oryzae)
2. Sheath and stem diseases
Worldwide: Uruguay & other temperate areas:Rhizoctonia solani Sclerotium oryzae
Rhizoctonia oryzae-sativae
Stem Rot
Causal agent
Sclerotium oryzae (A. Cattaneo, Italy 1876)
Geographical distribution:
Irrigated rice growing areas worldwide
Stem Rot
• The fungus forms sclerotia
• Sclerotia can survive 1-2 years in soil surface
or water, but prefers rice stubble.
Stem Rot
• Flooding help floating sclerotia reach the stems
Early flooding = early infection = more severity
• Stem surface promotes sclerotia germination
• During the first day of contact, mycelium start developing
• Appresoria penetrates host tissue and hyphae invades it
Stem Rot
• First symptoms at tillering
• Blackish lesions.
Stem Rot
g)
• Stresses (strong wind, herbicides, shadowing) promotes
diseases progression
• The fungus invades outer sheaths and progressively
penetrates the stem.
• High plant stand promotes disease
Stem Rot
Stem Rot
• Stem rotting prevents nutrient translocation
• Bad starch formation
• Chalky and brittle grains
• Bad milling quality
Stem Rot
• Advanced rotting weaken stems and promotes
lodging
• Not easy to harvest!
• The fungus forms new sclerotia
• Sclerotia can survive 1-2 years in soil surface
or water, but prefers rice stubble.
Aggregated Sheath SpotCausal agent
• Rhizoctonia oryzae-sativae (Mordue 1974).
• Geographical distribution:
Irrigated rice growing areas worldwide, most relevant in sub-tropical
and temperate areas.
Aggregated Sheath Spot
• Very similar cycle to that of Stem rot
• First days of infection may be asymptomatic
Aggregated Sheath Spot
• Oval lesions with green or gray centers surrounded
by a brown margin
Aggregated Sheath Spot
• Disease progress upward the leaf sheath
• Lesions aggregate
Aggregated Sheath Spot
• Reaching panicle at booting stage can cause severe
sterility
Aggregated Sheath Spot
• Rhizoctonia oryzae-sativae also produces sclerotia
• Sclerotia can survive in soil surface or water, but prefers rice
stubble.
Rice’s adversaries strike againMajor constraints to rice production Abiotic:
Water scarcity Poor soil conditions Extreme temperatures
Biotic (fungal diseases):1. Blast (Pyricularia oryzae)
2. Sheath and stem diseases
Worldwide: Uruguay & other temperate areas:Rhizoctonia solani Sclerotium oryzae
Rhizoctonia oryzae-sativae
The Uruguayan Rice Defensive Line
How do we face to these constraints to get those high yields? Abiotic:
Water scarcity Poor soil conditions Extreme temperatures
Biotic (fungal diseases):1. Blast (Pyricularia oryzae)
2. Sheath and stem diseases
Worldwide: Uruguay & other temperate areas:Rhizoctonia solani Sclerotium oryzae
Rhizoctonia oryzae-sativae
New high-yield cold tolerant varieties
New molecular markers for cold
tolerance
Resistance genes in high-yielding advanced lines
Extended use of optimum
management practices
100% Irrigated
A Hole in the Defensive Line Top Uruguayan varieties are susceptible to St & Sh diseases
Source: Avila 2000 & 2001.
Sterility, dead sheaths and lodging caused by
Aggregated Sheath Spot in INIA Tacuarí (grown in
15% of the area)Severe lodging caused by Stem Rot in El
Paso 144 (grown in 50% of the area)
Patching the Hole with Fungicide– Varietal susceptibility = Dependence on fungicide
– Dependence on fungicide = higher input costs
= trace levels in grain and environment
– Trace levels = less top markets, lower price, environmental impact
Dependence on fungicide = less economic and environmental
sustainability
Genetic resistance to St&Sh diseases is
environmentally and economically the best option.
Genetics of the resistance to StR
• Quantitatively inherited (Ferreira & Webster 1975)
• RILs with O. rufipogon introgressions (Ni et al 2001):
– QTL in ch. 2, AFLP marker TAA/GTA167 45% phen. var.
– QTL in ch. 3, RM232 - RM251 40% phen. var.
Genetics of the resistance to AShS
• Unknown but most likely quantitatively inherited as for to other Rhizoctonias.
• QTL reported for resistance to R. solani (Srinivasachary et al.
2011):–16 consistent QTL (at least in 2 independent reports)
• 7 QTL for escape mechanism (morphology or cycle, often undesirable traits)
• 9 QTL hypothetically physiologic resistance mechanisms
Importance of phenotyping to detect relevant QTL.
Quantitative Trait Loci Discovery
GWAS
•Uses pre-existent populations
•Simultaneously consider all allele diversity
•Exploits multiple recombination events
•“ready-to-use” SNP into the breeding germplasm
Traditional bi-parental QTL studies
•Population generation is time and resource consuming
•Limited # and significance of detectable QTL (low allelic diversity)
•Low mapping precision (few recombinations)
GWAS
SNP 1
Alelles: 0 or 1
Genotype Phenotype
0 6 9 1 7 5
Disease scores
Do not reject identity between phenotypic means,
p-value >>0.001
-log10(p-value) << 3
Phen
otyp
eGenotype 0 1
No association (negative)
-log10(p-value)
SN
P 1
Loci or position
GWAS
SNP 2
Alelles: 0 or 1
Genotype Phenotype
0 6 9 1 7 5
Disease scores
Phen
otyp
eGenotype 0 1
Reject identity between phenotypic means,
p-value <0.001
-log10(p-value) > 3
-log10(p-value)
SN
P 1
SN
P 2
Association (positive)
Loci or position
GWAS
The same for every SNP
Alelles: 0 or 1
Genotype Phenotype
0 6 9 1 7 5
Disease scores
-log10(p-value)
Manhattan plot
Loci or position
GWASWhat are the key issues for GWAS?
As GWAS relies on correlation between phenotype & allelic
states of marker’s loci
– Non-linkage correlations between loci leads to false positives
– i.e., False positives due to relationship among lines:
• CROASE: Population estructure (sub-species, origin)
• FINE: Kinship or co-ancestry (shared close ancestors)
Correcting for Population Structure
• Pritchard et al. 2000:
• Correlations between unlinked markers to estimate p sub-
populations
• Probabilistic assignation of each n individual to one or
more (admixtures) p.
• STRUCTURE software facilitates to build a Q matrix n x p
(estimates of each n belonging to a p)
Correcting for Population Structure
• Patterson et al.2006
Principal component analysis (PCA)
• Statistically determines the minimum number of
sub-groups (axes) which significantly explain genetic
variation (from genotypic data).
Correcting for Kinship
• Loiselle et al. 1995 and Hardy & Vekemans, 2002
SPAGeDi software
• Estimates the probability of identity-by-state (not by
common ancestry) of alleles of random molecular
markers = kinship coeficient.
GWAS: Unified Mixed Model
y: phenotypic data
S: incidence matrix that relates y with the SNP effects
α : vector of SNP effects
Q: relates y with the p fitting values
v: vector of estimates of fitting to a sub-population (estimated with STRUCTURE)
K: relates y with the estimated kinship coefficients
u : vector of kinship coefficients
e: vector of residual effects
e KuQvSy• Yu et al. 2006
Keys for a succesful GWAS
– Increase power optimizing phenotyping:
• Minimize Phenotypic variance
• Maximize Heritability
– Minimize false positive discovery by correcting causes of marker correlation other than linkage:
• Population structure and kinship (subspecies, common ancestry)
– In rice: consider ancient divergence between subspecies (explore separate analyses)
Recap…• Uruguay is a top rice exporter; Rice is a top Uruguayan
commodity • Top Uruguayan varieties are susceptible to Sclerotium oryzae
(SCL) and Rhizoctonia oryzae-sativae (ROS), suffering losses up to 20%.
• Genetic resistance is the best strategy• Resistance to St & Sh diseases is quantitative• GWAS is a good option for QTL discovery in breeding
population• Good phenotyping is key for GWAS
ObjectivesGeneral Objective: Identify QTL for SCL and ROS that enable breeding new high-
yielding cultivars with improved resistance to these diseases.
Specific Objectives / Papers:
I. Greenhouse phenotyping methodology (Paper 1).
a. Choosing best inoculation method
b. Applying it in high-throughput phenotyping greenhouse experiments
II. QTL for resistance to SCL and ROS in greenhouse and field (Papers 2 and 3).
III. Explore correlations between resistance to the three diseases (SCL, ROS and R.
solani) Paper 4.
Materials & Methods 1: Inoculation Methods
• Inoculation Methods
Method Description
I 5-mm agar disc with growing micellium attached to stems
II Flooded trays spread with sclerotia
III Suspension of sclerotia in CMC
IV Suspension of sclerotia in CMC covered with foil
V Detached stems in test tube with water + sclerotia
Materials & Methods 1: Inoculation Methods
• Plant Materials
Cultivar Subsp. Origin ROS SCL R. Solani
El Paso 144 Indica Uy Int Int ?
INIA Olimar Indica Uy Int Int ?
Tetep Indica Vietnam ? Res Res
INIA Tacuari Trop. Jap. Uy Int Int ?
Parao Trop. Jap. Uy Int Int ?
Lemont Trop. Jap. US ? Sus Sus
Materials & Methods 1: Inoculation Methods
• Greenhouse conditions
• Temperature: 28/18 °C day/night
• RH: 80/90% relative humidity
• Light time: 12 h
• Fungal Isolates
• ROS: soil after INIA Tacuarí in UEPL 200
• SCL: plant Samba cv. In UEPL 2011
• Experimental Design: CRD, 6 rep. EU: pot with 4 plants
• Analysis:
Model with design factors
Method compared by
rH
G
G22
22
e
ijig e ijY
Results 1: Inoculation Methods
• Best IM: I (agarose disk with micellium), for both pathogens
Pathogen Method 2G 2
R H2
ROS I (agar disk) 0.03 0.06 0.75
ROS II (flooded trays) 0.07 0.20 0.67
ROS III (CMC) 0.00 0.31 0.05
ROS IV (CMC+foil) 0.16 0.69 0.58
ROS V (tiller in tube) 1.25 5.24 0.59
SCL I (agar disk) 1.35 0.56 0.94
SCL II (flooded trays) 0.94 0.61 0.90
SCL III (CMC) 0.73 1.05 0.81
SCL IV (CMC+foil) 1.31 1.00 0.89
SCL V (tiller in tube) 0.92 2.04 0.73
2G 2e 2H2G 2e 2H
Results 1: Inoculation Methods• High correlation, low interaction among IM
SCL ROS
M & M 2: Greenhouse Phenotyping• 3 exp. for ROS, 2 exp. for SCL
• Population: 641 advanced INIA’s inbred lines
• 316 indica
• 325 tropical japonica
• Inoculation I (Agar discs)
• Same greenhouse conditions and fungal isolates than IM
• Experimental Design:
• Federer’s unrep, augmented RCBD, 12 blocks
• Replicated checks: El Paso 144, INIA Olimar, Tetep, Parao, INIA Tacuarí and Lemont
• EU: pot with 4 plants
• Stem width measured as covariate.
M & M 2: Greenhouse Phenotyping• Statistical Models:
BAS Compared based
SPA on
(Cullis et al. 2006)
Yij, Yijmn disease score
intercept
g Random block effect with and j={1,...,12}
Gj = gk + cl genotypic effect,
gk random effect of kth
genoline with gk ~N(0,2G), k={1,...,641}
cl fixed effect of lth
check, l={1,…,6}
Rm random row effect, Rm ~N(0,2
r), m={1,...,35}
Cn random column effect , Cn ~N(0,2
c), n={1,...,26}
eij, eijmn residual, gk ~N(0,2
G)
ijjiij GY eg
ijmninimjiijmn CRGY eg )()(
),0(~ 2Bi N g
22
21
G
BLUPg
vH
Results 2: Greenhouse Phenotyping
• Medium to high H2
. GxE interaction. Adapted sources of partial resistance
M & M 3: Field Phenotyping
• Same population than Greenhouse exp.
• 2010, 2011, 2012: “Historical” data
RCBD, 3 rep, natural infection. Checks:
El Paso 144, INIA Olimar, Parao, INIA Tacuarí
• 2013:
Augmented alpha-lattice design, 6 rep, artificial inoculation
• Same fungal isolates than greenhouse experiments.
• Replicated checks: El Paso 144, INIA Olimar, Tetep, Parao, INIA Tacuarí and Lemont
• EU: hill plots with ~10 adult plants
• Length of life cycle measured as covariate.
Materials & Methods 3: Field Phenotyping
• Statistical Models :
BAS Compared based
COV on
SPA (Cullis et al. 2006)
CSP
Yij, Yijmn disease score
overall mean
g block effect, j={1,...,6}
Gj = gk + cl genotypic effect,
gk random effect of kth
genoline, gk ~N(0,2
G), k={1,...,641}
cl fixed effect of lth
check, l={1,…,6}
eij, eijmn residual, gk ~N(0,2
G)
Rm row effect, Rm ~N(0,2
r), m={1,...,90}
Cn column effect, Cn ~N(0,2
c), n={1,...,45}
xij length of life cycle of ith
genotype in jth
block
b regression slope of covariate
ijjiij GY eg
ijijjiij xGY ebg
ijmnnmjiijmn CRGY eg
ijmnnmijjiijmn CRxGY ebg
22
21
G
BLUPg
vH
Results 3: Field Phenotyping (ROS)
• Low to medium H2
. GxE interaction. Adapted sources of partial resistance
H2=0.42
H2=0.15
H2=0.06
H2
=0.43
Results 3: Field Phenotyping (SCL)
• Medium to high H2
. Lesser GxE interaction. Adapted sources of partial R
H2
=0.50
H2
=0.24
H2
=0.45
H2
=0.72
M & M 4: Genotypic data
GBS raw dataHapMaps
130K SNP
Bioinformatic processing
•Tag count (collapse identical reads)
•Alignment with reference genome (Nipponbare)
•Tassel Pipeline
•Hapmap filtering•
Lines with ≥5% SNP
•SNP called in ≥5% lines
•Allele frequency (intra line) ≥5%
Indica 316 lines
94K SNP
641 lines
57K SNP
FILLIN Imputation
Japonica 325 lin.
44K SNP
Indica 316 lines
18K SNP
Japonica 325 lin. 12K SNP
Conjoint SNP
filtering
Separate SNP
filtering
•SNP w/Allele frequency (inter lines) ≥5%•
Lines w/ ≥5% SNP data
< 50% missing
Results 4: Genotypic data, whole, non imputed
641 lines
57K SNP
• Genotype data:
Most of the SNP are between-subesp.
polymorphisms
Results 4: Genotypic data, partial results
Indica 316 lines
94K SNP
641 lines
57K SNP
FILLIN Imputation
Japonica 325 lin.
44K SNP
Indica 316 lines
18K SNP
Japonica 325 lin. 12K SNP
Conjoint SNP
filtering
Separate SNP
filtering
•SNP w/Allele frequency (inter lines) ≥5%•
Lines w/ ≥5% SNP data
< 50% missing
Results 4: Genotypic data, whole population
641 lines
57K SNP
• Genetic Map: dense SNP
evenly distributed in all 12
chr.
Results 4: Genotypic data, whole population
641 lines
57K SNP
• PCA:
PC1: inter subspecies variation
PC2: inter indica variation
indica
japonica
Results 4: Genotypic data, whole population
641 lines
57K SNP
• PCA:
PC1 ~50% gv
PC2 ~5% gv
Results 4: Genotypic data, Indica ssp
• Genotype data:
Some big blocks with low LD decay.
Indica 316 lines
18K SNP
Results 4: Genotypic data, Indica ssp
• Genetic Map:
Many fixed regions, including
all Chr. 11
Indica 316 lines
18K SNP
Results 4: Genotypic data, Indica ssp
• PCA:
Over-represented “Olimar-like” lines
from FLAR and INIA
Indica 316 lines
18K SNP
El Paso 144
INIA Olimar FLAR
INIA
Results 4: Genotypic data, Indica ssp
• PCA:
PC1 to 8 explain ~50%gv
Indica 316 lines
18K SNP
Results 4: Genotypic data, Japonica, non imputed
• Genotype data:
Haplotype blocks
.
Japonica 325 lin. 12K SNP
Results 4: Genotypic data, Japonica ssp
• Genetic Map:
Many fixed regions
Japonica 325 lin. 12K SNP
Results 4: Genotypic data, Japonica ssp
• PCA: weak intra-subspecies
structure.
Japonica 325 lin. 12K SNP
L5287
EEA 404
INIA Tacuari
Results 4: Genotypic data, Japonica ssp
• PCA: More than 10 PC to explain
50% gv
Japonica 325 lin. 12K SNP
Materials & Methods 5: GWAS
y: phenotypic data
b : vector of SNP fixed effects
X: incidence matrix that relates y with the SNP effects
v: vector of fixed estimates of fitting to a sub-population (estimated with STRUCTURE)
Q: incidence matrix for population effects
u : vector of kinship coefficients, Var(u)=K2
, K kinship matrix
Z: relates y with the estimated kinship coefficients
e: vector of residual effects, Var(e)=I2e
eb ZuQvXy
• Mixed model (Yu et al. 2006, Malosetti et al. 2007)
“Q+K”, as implemented in GWAS function from rrBLUP package:
eb QvXy
“Eigenstrat”, as implemented in GWAS.analysis function from
mmQTL package:
y: phenotypic data
b : vector of SNP fixed effects
X: incidence matrix that relates y with the SNP effects
v: vector of random PC scores (eigenvalues).
Q: relates y with the PC scores
e: vector of residual effects, Var(e)=I2e
Results 5: GWAS
Indica 316 lines
94K SNP
641 lines
57K SNP
FILLIN Imputation
Japonica 325 lin.
44K SNP
Indica 316 lines
18K SNP
Japonica 325 lin. 12K SNP
Conjoint SNP
filtering
Separate SNP
filtering
•SNP w/Allele frequency (inter lines) ≥5%
•Lines w/ ≥5% SNP data
< 50% missing
Field GHEigenstrat ROS SCL ROS SCL
Q+K ROS SCL ROS SCL
Eigenstrat ROS SCL ROS SCL
Q+K ROS SCL ROS SCL
Eigenstrat ROS SCL ROS SCL
Q+K ROS SCL ROS SCL
Eigenstrat ROS SCL ROS SCL
K ROS SCL ROS SCL
Eigenstrat ROS SCL ROS SCL
K ROS SCL ROS SCL
Results 5: GWAS – ROS in Japonica
• QTLxE interaction.
• Consistent QTL: chr. 3 ~1 Kb
Field 2010 Field 2011 Field 2012 Field 2013
GH ROS1 GH ROS2 GH ROS3
Results 5: GWAS – ROS in Indica • QTLxE interaction
• Consistent QTL: chr. 3 ~1 Kb
• . QTL chr. 3
Field 2010 Field 2011 Field 2012 Field 2013
GH ROS1 GH ROS2 GH ROS3
Results 5: GWAS – SCL in Japonica
• QTLxE interaction.
• Consistent QTL: chr. 3 ~1 Mb chr. 9 ~14 MbField 2010 Field 2011 Field 2012 Field 2013
GH SCL1 GH SCL2
Results 4: GWAS – SCL in Indica
Field 2010 Field 2011 Field 2012 Field 2013
GH SCL1 GH SCL2
• QTLxE interaction.
• Consistent QTL: chr. 3 ~1 Mb chr. 9 ~14 Mb
Results 4: GWAS
Summary:
• QTL at ~1 Kb Chr. 1 for both pathogens, both subspecies and all environments
• QTL at ~14 Kb Chr. 9 for SCL, both subspecies, almost all environments
Future Work
• Greenhouse phenotyping for resistance to R. solani at CIAT
• Analysis of phenotypic means
• Association analysis:
• LD blocks and haplotypes
• GWAS for R. solani
Coordinación
Victoria Bonnecarrere
Mejoramiento
Pedro Blanco
Fernando Pérez de Vida
Fitopatología
Sebastián Martínez
Bioinformática
Silvia Garaycochea
Schubert Fernández
Marcadores moleculares
Victoria Bonnecarrere
Wanda Iriarte
Bioestadística
Lucía Gutierrez
Gastón Quero
Natalia Berberián
Juan Rosas
Cornell University
Eliana Monteverde
Susan McCouch
Jean-Luc Jannink
Proyecto Mapeo Asociativo en Arroz
Uruguayo
¡MUCHAS GRACIAS!
jrosas@inia.org.uy
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