What are we learning from genome-wide association studies (GWAS) in rice?

Transcription

1 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

2 Need to double rice production in next 30 years 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 Too similar => No genetic gain < Crossing parents > Too diverged => Sterility

3 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?

4 Where is the diversity within O. sativa? 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?

5 Sample the diversity of O. sativa O. sativa landrace & elite varieties from 80 countries Germplasm from: IRRI s Genetic Resources Center (GRC) & USDA-GRIN

6 Subpopulation groups in O. sativa Two Varietal Groups (sub-species) Japonica Indica tropical japonica temperate japonica basmati aus indica TRJ TEJ IND AUS ARO 169 SSRs 700,000 SNPs Garris et al. (2005) Genetics McCouch et al. (2015) Submitted

7 16 M SNPs Re-sequencing of 125 genomes tropical japonica AFRICA O. barthii O. glaberrima 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 Rice SNP Consortium temperate japonica aromatic ASIA O. rufipogon Data analysis by Mark Wright. indica O. sativa (80) O. rufipogon (10) O. nivara (4) O. glaberrima (7) O. barthii (7)

8 π (average pairwise difference/kb) Diversity within & between subpopulations Data: 16M SNPs Ancestor Indica 10 8 Japonica O. rufipogon aus indica aromatic (GroupV) LD = 5-50 kb LD = kb tropical japonica temperate japonica LD = kb Funded by the Rice SNP Consortium, Computational analysis by Mark Wright

9 Germplasm used for GWAS Rice Diversity Panel 1 RDP1 (USDA) ~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

10 a Geographic distribution of diversity in RDP1 Rice Diversity Panel 1 (400 O. sativa accessions) N a W E indica ( 87 ) aus ( 57 ) aromatic ( 14 ) admixed ( 62 ) temperature temperate japonica japonica ( 96 (96) indica (87) ) b temperate japonica (96) indica ( 87 ) 1 cm aus ( 57 ) temperature japonica ( 96 ) b aus (57) aromatic tropical (14) japonica ( 97 ) admixed (62) 0 aromatic ( 14 ) admixed ( 62 ) tropical japonica (97) tropical japonica ( 97 ) 1 cm indica0 aus temperate japonica indica aus temperate japonica Zhao et al. (2011) Nature Communications 2:476 S aromatic aromatic tropical japonica tropical japonica

11 Isolated pockets of diversity persist in the hills & valleys Many local varieties are maintained within a community, some are shared only through traditional networks, others are traded

12 Diverse origins of fragrance Diverse alleles found in locally adapted landraces in SE Asia- BADH2 allele IMutation IN population Subpopulat n Aroma [2AP] One BADH2 allele predominant. Predominant allele from basmati (japonica) shared with Thai Jasmine (indica) Implications for breeding? Kovach et al., (2009) The origin and evolution of fragrance in rice (Oryza sativa L.) PNAS 106(34):14444

13 SNP genotyping and analysis platforms for rice Indica genome Nipponbare Genome (temperate japonica) Aus genome Re-sequenced rice genomes Illumina Low-Resolution assays High-Resolution arrays Affymetrix 384-SNP Breeder s Chips 700K-SNP Array 1536-SNP assays Illumina Hi-Seq Bar-coded re-sequencing Genotyping by Sequencing GBS 44K-SNP Array

14 Population structure & admixture in O. sativa semi-dwarf 1 (sd1) Fragrance (BADH2) Tainung 72 Vavilovi Arias Azucena Bengal Bowman Canella_de_Ferro Blast R (Pi-ta) admixed temperate japonica tropical japonica aromatic/ GroupV Gharib IR64 IR8 JC91 Jasmine85 Minghui_63 Mudgo indica aus O. rufipogon Analysis using NAKARA algorithm by Koni Wright

15 Phenotypic Evaluation 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

16 What is the most efficient approach to phenotyping? Field conditions 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 Controlled conditions 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

17 Janelle Jung 3D Root System Architecture 3D Phenotyping Platform Randy Clark Image & Analysis - Sequence of 40 images per plant - Imaged at Day 3, 6, 9, - RootReader3D Software In collaboration with Kochian Lab, USDA/ARS Clark et al., 2011, Plant Phys.

18 Time Course - 3D Root Images Azucena upland variety IR64 irrigated variety RootReader 3D root system models from 10 day measurement sequence Clark et al., 2011, Plant Phys.

19 3D Root System Architecture (RSA) Randy Clark Genome Wide Association Analysis 380 individual single trait analyses: - 13 traits x 3 days x 4 subpopulations Significant regions found for each analysis. Global, local and dynamic characteristics Region significantly correlated with rooting depth in indica GWAA QTL

20 GWAS for Root Architecture Peak SNP -66kb -33kb 0 +33kb +66kb Significant SNP associated with four traits in the Indica subpopulation: Centroid Maximum Depth Maximum Width Volume Distribution GWAA QTL

21 GWAS for Root System Architecture Peak SNP -66kb -33kb 0 +33kb +66kb A SNP Allele B SNP Allele GWAA QTL n=39 n=118

22 GWAS for Root System Architecture Peak SNP -66kb -33kb 0 +33kb +66kb A SNP Allele B SNP Allele GWAA QTL n=44 n=107

23 Variation by subpopulation at candidate SNP All Subpops Aus Indica n=211 n=360 n=9 n=80 n=44 n=107 Temperate Japonica Tropical Japonica n=10 n=169 n=7 n=162

24 Integration of GWAS & QTL data to identify useful targets for selection Multi-variate modeling & Co-localization of QTLs from high throughput genome-wide genomic prediction and field-based experiments 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

25 Diversity Database 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 Genotype captures wide range of polymorphisms supports multiple platforms connects to ref genome(s) Germplasm seed stock information pedigree relationships provenance Field/Plant Observation tracks planting, treatment, locality links to individual plant sample metadata for environment Phenotype quantitative or qualitative traits integrates ontology terms reps, units, seasons, years

26 Co-localization of genes/qtls - field & hydroponics DRO1 & Pistol1, rice genes controlling rooting depth, angle & vigor => enhance yield under drought & phosphorus uptake in low-fertility soils Uga et al. (2011) TAG; Gamuyao et al. (2012) Nature

27 Uga et al. (2011) Journal of Experimental Botany, Vol. 62, No. 8, pp Deeper Rooting 1 (DRO1) IR64 (indica) DRO1 NIL Kinandang Patong DRO1 was first identified as a QTL that explained 67% of the phenotypic variation for root angle in a population of RILs

28 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):

29 Different rooting angles and depths are appropriate for different soils, nutrient profiles, and hydrological conditions

30 SP: **: based SNP data (CO.spontanea, RU: O.rufipogon, (D NI: O.nivara, SA: O.sativa (F) *: based on SNP data Importing Pre-breeding seed: to enhance utilization of wild species porting Enhancing seed: representation by wild japonica-like accessions: During 2008, we i nhancing five Using new genome-wide representation O. rufipogon SNPs, by accessions wild identify japonica-like (765, divergent 766, accessions: wild 767, donors 768, During and systematically 769) 2008, from we the impo ve Institute new O. of rufipogon Genetics backcross accessions in them Japan into enlarge (765, elite cultivars 766, our opportunities 767, (indica 768, & and japonica). to 769) obtain from F1 s the with Nat the stitute japonica-like of Genetics accessions. in Japan These to enlarge accessions our opportunities originated to from obtain China F1 s and with are the po kn ponica-like ancestors accessions. or close relatives These to accessions WILD temperate DONORS originated japonica cultivars from China (Cai and are Morishim know ncestors 2002, Yano or close et al. relatives 2008, 2009). to temperate All these japonica imported cultivars materials (Cai were and planted Morishima at C2 002, make O. Yano rufipogon F1 et hybrids al. INDICA 2008, during 2009). the fall All AUS of these 2008 imported (Figure_1_Kim). TEJ TRJ materials ARO were planted O. rufipogon at Corne ake F1 (Chybrids during the fall of 2008 (Figure_1_Kim). 765: Spreading 766: Upright 767: Spreading 769: Uprig 765: Spreading 766: Upright 767: Spreading 769: Upright IR CHINA 757 (C LAOS (D Figure_1_Kim. New japonica-like O. rufipogon germplasm Figure_1_Kim. New japonica-like O. rufipogon germplasm Selection IR64 of donor 2 Recurrent parents: Parents 490 INDONESIA

31 PRE-BREEDING RESOURCES: Inter-mated populations & CSSLs => novel admixtures 763 CHINA (japonica-like) Diverse wild donors Recurrent parents Six inter-specific CSSL libraries 757A LAOS (indica-like) Cybonnet (tropical japonica) Chromosome 1 tropical japonica background 490A INDONESIA (independent) IR64 (indica) Chromosome 1 indica background

32 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 subpopulationspecific 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

33 Acknowledgements PB&G- Cornell Adam Famoso Juan David Arbelaez Lyza Marón Koni Wright Chih Wei Tung Genevieve DeClerck Hyunjung Kim Sandy Harrington Kazi Akther BSCB- Cornell Jason Mezey Francisco Agosoto-Perez Pavel Korniliev Keyan Zhao Hei Leung Ken McNally Ruaraidh Hamilton USDA-Stuttgart Georgia Eizenga Anna McClung USDA- Cornell Leon Kochian Randy Clark Jon Shaff NIAS - Japan Yusaku Uga Masahiro Yano

34 RiceSNP Consortium

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