Speaker: Lic. JUAN ROSAS, (MSc.) Programa de Arroz INIA-Uruguay y estudiante de Doctorado en Ciencias Agrarias de la Universidad de la República de Uruguay

1. 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

2. Overview
1. Timeline
2. Background & Review Why?
3. Objectives What?
4. Materials & Methods How?
5. Preliminary Results Ouch! Wow!
6. Future work
7. Schedule When?

3. 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
completion
Thesis 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

4. Rice facts
Why 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
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2009 2010 2011 2012
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Soybeans
Meat
Rice
Wheat

5. Uruguay facts
Why Uruguay matters to rice?
Uruguay is the 7th major world rice exporter
Source: FAOSTAT
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Top Ten World Rice Exporters

6. Uruguay facts
Why Uruguay matters to rice?
Uruguayan rice
yields are among
the highest of the
world
Source: http://ricestat.irri.org
(Alphabetic order)
CountryAverageYieldin2010(t/ha)

7. Rice’s biggest adversaries
What 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

8. Stem Rot
Causal agent
Sclerotium oryzae (A. Cattaneo, Italy 1876)
Geographical distribution:
Irrigated rice growing areas worldwide

9. Stem Rot
• The fungus forms sclerotia
• Sclerotia can survive 1-2
years in soil surface or water,
but prefers rice stubble.

10. 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

11. Stem Rot
• First symptoms at tillering
• Blackish lesions.

12. 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

13. Stem Rot

14. Stem Rot
• Stem rotting prevents
nutrient translocation
• Bad starch formation
• Chalky and brittle grains
• Bad milling quality

15. 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.

16. Aggregated Sheath Spot
Causal agent
• Rhizoctonia oryzae-sativae (Mordue
1974).
• Geographical distribution:
Irrigated rice growing areas worldwide,
most relevant in sub-tropical and
temperate areas.

17. Aggregated Sheath Spot
• Very similar cycle to that of Stem rot
• First days of infection may be
asymptomatic

18. Aggregated Sheath Spot
• Oval lesions with green or gray
centers surrounded by a brown
margin

19. Aggregated Sheath Spot
• Disease progress upward
the leaf sheath
• Lesions aggregate

20. Aggregated Sheath Spot
• Reaching panicle at booting
stage can cause severe sterility

21. Aggregated Sheath Spot
• Rhizoctonia oryzae-sativae also
produces sclerotia
• Sclerotia can survive in soil surface or
water, but prefers rice stubble.

22. Rice’s adversaries strike again
Major 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

23. 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

24. 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)

25. 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.

26. 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.

27. 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.

28. 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)

29. 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
Phenotype
Genotype0 1
No association (negative)
-log10(p-value)
SNP1
Loci or position

30. GWAS
SNP 2
Alelles: 0 or 1
Genotype Phenotype
0 6 9 1 7 5
Disease scores
Phenotype
Genotype0 1
Reject identity between
phenotypic means,
p-value <0.001
-log10(p-value) > 3
-log10(p-value)
SNP1
SNP2
Association (positive)
Loci or position

31. 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

32. GWAS
What 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)

33. 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)

34. 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).

35. 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.

36. 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

37. 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)

38. 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

39. Objectives
General 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.

40. 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

41. 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

42. 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
r
H
G
G
22
2
2
e



ijig e ijY

43. Results 1: Inoculation Methods
• Best IM: I (agarose disk with micellium), for both pathogens
Pathogen Method 2
G 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
2
G 2
e 2
H2
G 2
e 2
H

44. Results 1: Inoculation Methods
• High correlation, low interaction among IM
SCL ROS

45. 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.

46. 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,2
G), 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(~ 2
Bi N g
2
2
2
1
G
BLUP
g
v
H



47. Results 2: Greenhouse Phenotyping
• Medium to high H2. GxE interaction. Adapted sources of partial resistance

48. 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.

49. 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 
2
2
2
1
G
BLUP
g
v
H



50. 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

51. 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

52. M & M 4: Genotypic data
GBS raw
data
HapMaps
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

53. Results 4: Genotypic data, whole, non imputed
641 lines
57K SNP
• Genotype data:
Most of the SNP are
between-subesp.
polymorphisms

54. 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

55. Results 4: Genotypic data, whole population
641 lines
57K SNP
• Genetic Map:
dense SNP
evenly distributed
in all 12 chr.

56. Results 4: Genotypic data, whole population
641 lines
57K SNP
• PCA:
PC1: inter subspecies
variation
PC2: inter indica variation
indica
japonica

57. Results 4: Genotypic data, whole population
641 lines
57K SNP
• PCA:
PC1 ~50% gv
PC2 ~5% gv

58. Results 4: Genotypic data, Indica ssp
• Genotype data:
Some big blocks with
low LD decay.
Indica 316 lines
18K SNP

59. Results 4: Genotypic data, Indica ssp
• Genetic Map:
Many fixed
regions, including
all Chr. 11
Indica 316 lines
18K SNP

60. 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

61. Results 4: Genotypic data, Indica ssp
• PCA:
PC1 to 8 explain
~50%gv
Indica 316 lines
18K SNP

62. Results 4: Genotypic data, Japonica, non imputed
• Genotype data:
Haplotype blocks
.
Japonica 325 lin.
12K SNP

63. Results 4: Genotypic data, Japonica ssp
• Genetic Map:
Many fixed
regions
Japonica 325 lin.
12K SNP

64. Results 4: Genotypic data, Japonica ssp
• PCA: weak intra-
subspecies structure.
Japonica 325 lin.
12K SNP
L5287
EEA 404
INIA Tacuari

65. Results 4: Genotypic data, Japonica ssp
• PCA: More than 10
PC to explain 50% gv
Japonica 325 lin.
12K SNP

66. 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)=K2 , K
kinship matrix
Z: relates y with the estimated kinship coefficients
e: vector of residual effects, Var(e)=I2
e
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)=I2
e

67. 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 GH
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
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

68. 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

69. Results 5: GWAS – ROS in Indica
• QTLxE interaction
• Consistent QTL: chr. 3 ~1 Kb
•. QTL chr. 3Field 2010 Field 2011 Field 2012 Field 2013
GH ROS1 GH ROS2 GH ROS3

70. Results 5: GWAS – SCL in Japonica
• QTLxE interaction.
• Consistent QTL: chr. 3 ~1 Mb chr. 9 ~14 Mb
Field 2010 Field 2011 Field 2012 Field 2013
GH SCL1 GH SCL2

71. 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

72. 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

73. 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

74. 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