gwas of resistance to stem and sheath diseases of uruguayan advanced rice breeding germplasm

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

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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

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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


• Oval lesions with green or gray centers surrounded

by a brown margin


• Disease progress upward the leaf sheath

• Lesions aggregate


• Reaching panicle at booting stage can cause severe

sterility


• 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





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





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


• 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


• 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


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.


641 lines

57K SNP

• PCA:

PC1: inter subspecies variation

PC2: inter indica variation

indica

japonica


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


• Genetic Map:

Many fixed regions, including

all Chr. 11

Indica 316 lines

18K SNP


• PCA:

Over-represented “Olimar-like” lines

from FLAR and INIA

Indica 316 lines

18K SNP

El Paso 144

INIA Olimar FLAR

INIA


• PCA:

PC1 to 8 explain ~50%gv

Indica 316 lines

18K SNP

Results 4: Genotypic data, Japonica, non imputed

• Genotype data:

Haplotype blocks

.


Results 4: Genotypic data, Japonica ssp

• Genetic Map:

Many fixed regions



• PCA: weak intra-subspecies

structure.


L5287

EEA 404

INIA Tacuari


• PCA: More than 10 PC to explain

50% gv


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


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


Q+K ROS SCL ROS SCL


K 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


GH ROS1 GH ROS2 GH ROS3

Results 5: GWAS – SCL in Japonica


• 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


GH SCL1 GH SCL2


• 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!

[email protected]