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What are we learning from genome-wide association studies (GWAS) in rice?
Susan McCouch, Chih Wei Tung, Mark Wright, Adam Famoso, Randy Clark, Anthony Greenberg, Janelle Jung, Hyujung Kim,
Josh Cobb, Moni Singh, Kazi Akther, Pavel Korniliev, Genevieve DeClerck, Francisco Agosto-Perez, Ken McNally,
Georgia Eizenga, Anna McClung, Leon Kochian, Jason Mezey
• Genetic variation is key to accomplishing that goal
• Most breeders focus on locally adapted, elite germplasm
• Gene Banks contain thousands of diverse strains, but they are largely uncharacterized, and most are never used
• Utilizing more diverse germplasm requires time, money, and a good roadmap
< Crossing parents >
Too diverged =>
Sterility
Too similar =>
No genetic gain
Need to double rice production in next 30 years
Building a road map of natural variation in rice
• How much genetic variation is there in O. sativa, how is it partitioned and where is it found?
• How can we use that diversity to identify genes and QTLs associated with traits important to breeders?
• How can knowledge from GWAS increase the rate of genetic gain in rice improvement?
Traditionally, plant breeders
recognized 3 major groups:
• ecological adaptation,
• ease of crossing,
• geographic origin,
• grain shape,
• plant type, etc.
Geneticists identify groups based on shared ancestry • molecular polymorphisms (isozymes, RFLP, SSRs, SNPs)
Do we see the same story?
Where is the diversity within O. sativa?
Germplasm from: IRRI’s Genetic Resources Center (GRC) & USDA-GRIN
Sample the diversity of O. sativa
O. sativa - 1500 landrace & elite varieties from 80 countries
Subpopulation groups in O. sativa
Garris et al. (2005) Genetics
indica
tropical japonica
temperate
japonica
aus
basmati
169 SSRs
McCouch et al. (2015) Submitted
TRJ TEJ IND AUS ARO
700,000 SNPs
Two Varietal Groups (sub-species) • Japonica • Indica
O. sativa (80)
O. rufipogon (10)
O. nivara (4)
O. glaberrima (7)
O. barthii (7)
indica
aromatic
tropical japonica
O. glaberrima
O. barthii
O. rufipogon
AFRICA
ASIA
temperate japonica
16 M SNPs Re-sequencing of 125 genomes
Rice SNP Consortium www.ricesnp.org; Data analysis by Mark Wright.
• Clear divergence of Asian and African species
• Deep population structure in O. sativa (Fst=0.37)
• O. rufipogon mimics structure of O. sativa
• Unique types of variation within & among clusters
• Template for developing smaller SNP assays
Data: 16M SNPs
Funded by the Rice SNP Consortium, www.ricesnp.org; Computational analysis by Mark Wright
Diversity within & between subpopulations
Indica
Japonica
0
2
4
6
8
10
12
14
16
18
20
O. rufipogon aus indica aromatic(GroupV)
tropical japonica temperatejaponica
π (
aver
age
pai
rwis
e d
iffe
ren
ce/k
b)
Ancestor
LD = 50-120 kb LD = 100-500 kb LD = 5-50 kb
Germplasm used for GWAS
~500 O. sativa, O. rufipogon • 87 indica
• 57 aus
• 97 tropical japonica
• 96 temp. japonica
• 14 aromatic
• 49 admix
• 100 wilds
Rice Diversity Panel 2
“RDP2” (IRRI)
~1200 O. sativa • 571 indica
• 203 aus
• 428 trop. japonica
• 152 temp. japonica
• 83 aromatic
• 7 admix
Total: ~1500 publically available, purified accessions O. sativa
Rice Diversity Panel 1
“RDP1” (USDA)
Geographic distribution of diversity in RDP1 Rice Diversity Panel 1 (400 O. sativa accessions)
admixed ( 62 )
aromatic ( 14 )aus ( 57 )
indica ( 87 )
temperature
japonica ( 96 )
tropical
japonica ( 97 )
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0.04
0.06
PC2 (9.8%)
PC1 (34.3%)
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−0.10 −0.05 0.00 0.05
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0.00
0.05
0.10
0.15
0.20
0.25
PC3 (5.9%)
PC4 (2.3%)
a
b
0
1
cm
indica aus temperatejaponica
aromatic tropicaljaponica
admixed ( 62 )
aromatic ( 14 )aus ( 57 )
indica ( 87 )
temperature
japonica ( 96 )
tropical
japonica ( 97 )
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0.00
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0.06
PC2 (9.8%)
PC1 (34.3%)
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−0.10 −0.05 0.00 0.05
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0.00
0.05
0.10
0.15
0.20
0.25
PC3 (5.9%)
PC4 (2.3%)
a
b
0
1
cm
indica aus temperatejaponica
aromatic tropicaljaponica
temperate japonica (96)
admixed ( 62 )
aromatic ( 14 )aus ( 57 )
indica ( 87 )
temperature
japonica ( 96 )
tropical
japonica ( 97 )
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0.00
0.02
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0.06
PC2 (9.8%)
PC1 (34.3%)
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−0.10 −0.05 0.00 0.05
−0.05
0.00
0.05
0.10
0.15
0.20
0.25
PC3 (5.9%)
PC4 (2.3%)
a
b
0
1
cm
indica aus temperatejaponica
aromatic tropicaljaponica
indica (87)
aus (57)
admixed (62)
aromatic (14)
temperate japonica (96)
tropical japonica (97)
Zhao et al. (2011) Nature Communications 2:476
E W
N
S
Many local varieties are maintained within a community, some are shared only through traditional networks, others are traded
Isolated pockets of diversity persist in the hills & valleys
Sub-population
Diverse origins of fragrance
• Implications for breeding?
Diverse alleles found in locally adapted landraces in SE Asia-
Predominant allele from basmati (japonica) shared with Thai Jasmine (indica)
Kovach et al., (2009) The origin and evolution of fragrance in rice (Oryza sativa L.) PNAS 106(34):14444
BADH2 allele IN
Sub-populat’n
Aroma [2AP] IMutation
One BADH2 allele predominant.
SNP genotyping and analysis platforms for rice
700K-SNP Array
44K-SNP Array
Affymetrix
Illumina Hi-Seq Bar-coded re-Sequencing
Genotyping by Sequencing GBS
Re-sequenced rice genomes
Low-Resolution assays
High-Resolution arrays
384-SNP
“Breeder’s Chips”
1536-SNP assays
Illumina
Nipponbare Genome (temperate japonica)
Indica genome Aus genome
Population structure & admixture in O. sativa
temperate japonica
tropical japonica
aromatic/ GroupV
indica
aus
O. rufipogon
admixed
semi-dwarf 1 (sd1) Fragrance (BADH2) Blast R (Pi-ta)
Gharib
IR64
IR8
JC91
Jasmine85
Minghui_63
Mudgo
Tainung 72
Vavilovi
Arias
Azucena
Bengal
Bowman
Canella_de_Ferro
Analysis using NAKARA algorithm by Koni Wright
multiple locations, environments, collaborators
• Whole plant phenotypes in the field
• Seed & grain quality characters
• Disease and insect resistance
• Abiotic stress tolerance
• Root and panicle phenotypes
• Ionomics
Phenotypic Evaluation
What is the most efficient approach to phenotyping?
• Minimize environmental variation => increase genetic signal
• Increase precision of critical measurements
• Screen more seedlings at young age
• Decrease cost => automate & standardize
• Develop hypotheses => test a targeted set of lines in the field
• Maximize relevance to breeders and farmers
• Characterize target population of environments
• Evaluate over years and locations
• Estimate G x E effects
• Increase efficiency => automate & standardize
• Refine hypotheses and develop new screening protocols
Field conditions Controlled conditions
3D Root System Architecture
Clark et al., 2011, Plant Phys.
3D Phenotyping Platform
• Image & Analysis
- Sequence of 40 images per plant
- Imaged at Day 3, 6, 9,
- RootReader3D Software
Janelle Jung Randy Clark
In collaboration with Kochian Lab, USDA/ARS
RootReader 3D root system models from 10 day measurement sequence
Time Course - 3D Root Images
Clark et al., 2011, Plant Phys.
Azucena – upland variety
IR64 – irrigated variety
Genome Wide Association Analysis
3D Root System Architecture (RSA)
GWAA
QTL
• 380 individual single trait analyses: - 13 traits x 3 days x 4 subpopulations
• Significant regions found for each analysis.
• Global, local and dynamic characteristics
Randy Clark
Region significantly correlated with rooting depth in indica
GWAA
QTL
GWAS for Root Architecture
• Significant SNP associated with four traits in the Indica subpopulation:
Peak SNP -66kb -33kb 0 +33kb +66kb
• Centroid
• Maximum Depth
• Maximum Width
• Volume Distribution
GWAA
QTL
GWAS for Root System Architecture
“A” “B” SNP Allele SNP Allele
n=39 n=118
Peak SNP -66kb -33kb 0 +33kb +66kb
GWAA
QTL
Peak SNP -66kb -33kb 0 +33kb +66kb
“A” “B” SNP Allele SNP Allele
n=44 n=107
GWAS for Root System Architecture
Variation by subpopulation at candidate SNP
Aus Indica
Temperate Japonica Tropical Japonica
All Subpops
n=211 n=360 n=9 n=80 n=44 n=107
n=10 n=169 n=7 n=162
Multi-variate modeling & Co-localization of QTLs from high throughput genome-wide ‘genomic prediction’ and field-based experiments
Integration of GWAS & QTL data to identify useful targets for selection
• Integrate single trait analyses
- >1,000 traits evaluated
- 5 Subpopulations + wild species
- Time course (days, years)
- Multiple environments
• Model G X G to assess impact of introgressions
• Evaluate G X E to measure trait stability across environments
Rice Diversity Research Platform
Diversity Database
Genotype • captures wide range of
polymorphisms • supports multiple platforms •connects to ref genome(s)
Field/Plant Observation • tracks planting, treatment, locality • links to individual plant sample • metadata for environment
Germplasm • seed stock information • pedigree relationships
• provenance
Phenotype • quantitative or qualitative traits
• integrates ontology terms • reps, units, seasons, years
GDPDM: www.maizegenetics.net/gdpdm
Track genotypes, phenotypes, environments; seed stocks; experiments, reps; use emerging information to select parental lines for crossing, test hypotheses about
performance, mine the gene bank
DRO1 & Pistol1, rice genes controlling rooting depth, angle & vigor => enhance yield under drought & phosphorus uptake in low-fertility soils
Co-localization of genes/QTLs - field & hydroponics
Uga et al. (2011) TAG; Gamuyao et al. (2012) Nature
Deeper Rooting 1 (DRO1)
DRO1 was first identified as a QTL that explained 67% of the phenotypic variation for root angle in a population of RILs
Uga et al. (2011) Journal of Experimental Botany, Vol. 62, No. 8, pp. 2485–2494.
IR64 (indica) DRO1 – NIL Kinandang Patong
A single bp deletion in exon 4 of DRO1 changes root angle & enhances grain yield under drought
Uga et al. (2013) Nature Genetics 45(9):1097-102
Different rooting angles and depths are appropriate for different soils, nutrient profiles, and hydrological conditions
Pre-breeding to enhance utilization of wild species
Using genome-wide SNPs, identify divergent wild donors and systematically backcross them into elite cultivars (indica & japonica).
TEJ INDICA AUS TRJ ARO O. rufipogon
IR64
494A and B
India RU 50 1 50 93 218 47.06
462A
India RU 35 26
Aus-like
506A
Bangladesh
NI 100 3 50 54.21
397 US -Cybonnet
SA 300 100 Rec. parents 644 IRRI-IR64 SA 300
planted every 6
days 99.93
*SP: O.spontanea, RU: O.rufipogon, NI: O.nivara, SA: O.sativa **: based on SNP data
Importing seed:
Enhancing representation by wild japonica-like accessions: During 2008, we imported five new O. rufipogon accessions (765, 766, 767, 768, and 769) from the National
Institute of Genetics in Japan to enlarge our opportunities to obtain F1¡s with the pool of japonica-like accessions. These accessions originated from China and are known as
ancestors or close relatives to temperate japonica cultivars (Cai and Morishima 2000, 2002, Yano et al. 2008, 2009). All these imported materials were planted at Cornell to make F1 hybrids during the fall of 2008 (Figure_1_Kim).
Figure_1_Kim. New japonica-like O. rufipogon germplasm
Selection of donor parents: We created standards for selecting prospective donors from the 21, wild parents, listed in Table_1_Kim based on morphological traits, geographical distribution, based on SSR and SNP data.
a. Morphological traits: We evaluated morphological characteristics including hull color, pericarp color, flowering time, plant type, plant height, flag leaf length, culm length, panicle length, shattering, crossing ability, germination ability, and reproductive ability (Table_2_Kim and Figure_2_Kim). We gave preference to accessions with a dark hull and red pericarp, seed shattering type, diverse plant type, good crossing and germination and good self fertility determined by the availability of 25-50 selfed seeds harvested in green house condition. Some F1
765: Spreading 766: Upright 767: Spreading 769: Upright
Figure_2_Kim. Morphological traits of CSSL donors
(A): seed color and shape, (B): one month after planting, (C): two months after planting, (D): adult stage after flowering, (E): panicle shape, (F) two days after germination; (1) 763 from japonica-like: dark hull, red pericarp, long awn, long grain length, tall height, upright plant
type, closed panicle type and good germination ability, (2) 686C from indica-like: light-dark hull, red pericarp, long awn, long grain length, weak appearance
during vegetative growth, spreading plant type and good germination, (3) 490 from Group1: dark hull, red pericarp, long awn, long grain length and spreading plant type, (4) 757A from Group1: light hull, red pericarp, long awn, long grain length and spreading plant type, (5) 494A from group1: dark hull, red pericarp, long awn, long grain length and spreading plant type, (6) 503A from group1: dark hull, red pericarp, long awn, long grain length, spreading plant type and open panicle type, (7) 549A from group1: dark hull, red pericarp, long awn, long grain length, tall height, spreading ~ creeper plant type, semi-closed panicle type and good germination ability,
Geographical distribution: The geographic distribution for O. rufipogon accessions was reported last year. We added five wild accessions originating from China
(A (B (C
(4) 757A
(5) 494A
IR64
(A (D (E (F
(7) 549A
Cybonne
(A (B (D (F) (C
(2) 686C
(6) 503A
(A (B (C (D (E
(A (C(B (D757 LAOS
494A and B
India RU 50 1 50 93 218 47.06
462A
India RU 35 26
Aus-like
506A
Bangladesh
NI 100 3 50 54.21
397 US -Cybonnet
SA 300 100 Rec. parents 644 IRRI-IR64 SA 300
planted every 6
days 99.93
*SP: O.spontanea, RU: O.rufipogon, NI: O.nivara, SA: O.sativa **: based on SNP data
Importing seed:
Enhancing representation by wild japonica-like accessions: During 2008, we imported five new O. rufipogon accessions (765, 766, 767, 768, and 769) from the National
Institute of Genetics in Japan to enlarge our opportunities to obtain F1¡s with the pool of japonica-like accessions. These accessions originated from China and are known as
ancestors or close relatives to temperate japonica cultivars (Cai and Morishima 2000, 2002, Yano et al. 2008, 2009). All these imported materials were planted at Cornell to make F1 hybrids during the fall of 2008 (Figure_1_Kim).
Figure_1_Kim. New japonica-like O. rufipogon germplasm
Selection of donor parents: We created standards for selecting prospective donors from the 21, wild parents, listed in Table_1_Kim based on morphological traits, geographical distribution, based on SSR and SNP data.
a. Morphological traits: We evaluated morphological characteristics including hull color, pericarp color, flowering time, plant type, plant height, flag leaf length, culm length, panicle length, shattering, crossing ability, germination ability, and reproductive ability (Table_2_Kim and Figure_2_Kim). We gave preference to accessions with a dark hull and red pericarp, seed shattering type, diverse plant type, good crossing and germination and good self fertility determined by the availability of 25-50 selfed seeds harvested in green house condition. Some F1
765: Spreading 766: Upright 767: Spreading 769: Upright
490 INDONESIA
763 CHINA
2 Recurrent Parents IR64
WILD DONORS
O. rufipogon
757A LAOS
(indica-like)
490A INDONESIA
(independent)
Diverse wild donors
763 CHINA
(japonica-like)
Six inter-specific CSSL libraries
Chromosome 1 tropical japonica
background
Chromosome 1 indica background
PRE-BREEDING RESOURCES: Inter-mated populations & CSSLs => novel “admixtures”
Cybonnet (tropical japonica)
Recurrent parents
IR64 (indica)
What are we learning from GWAS in rice?
• GWAS is helping develop a G-> P road map to accelerate trait /gene discovery and genetic gain rice;
• Alleles underlying complex traits are highly subpopulation-specific
• Admixture is common and contributes significantly to trait variation in different lines
• Targeted introgression of GWAS-QTLs can help breeders harvest high-value alleles from poorly adapted germplasm
• GWAS can provide fixed variables to improve prediction of Genomic Selection (GS) models
Acknowledgements
Hei Leung
Ken McNally
Ruaraidh Hamilton
USDA- Cornell
Leon Kochian Randy Clark Jon Shaff
BSCB- Cornell
Jason Mezey
Francisco Agosoto-Perez
Pavel Korniliev
Keyan Zhao
Adam Famoso
Juan David Arbelaez
Lyza Marón
Koni Wright
Chih Wei Tung
Genevieve DeClerck
Hyunjung Kim
Sandy Harrington
Kazi Akther
PB&G- Cornell USDA-Stuttgart
Georgia Eizenga Anna McClung
NIAS - Japan
Yusaku Uga Masahiro Yano
RiceSNP Consortium
