Association mapping and allele mining of wheat drought response

Presented by A. Korol

University of Haifa: T. Krugman, Z. Frenkel, T. Fahima,

Hebrew University of Jerusalem: I. Polda, Y. Saranga

Whealbi Final Meeting, Edinburgh, Oct 2018

FP7 European Project

Task 5.2: Allele mining at key genes for adaptive traits

(1) CGs (candidate genes) identified in GWAM will be validated for co-segregation with QTLs identified in T. durum x T. dicoccoides mapping population (Peleg et al. 2009; Fatiukha et al. 2018).

(2) CGs revealed by GWAM will be crossed with CGs for drought tolerance obtained by transcriptome analysis of drought resistant T. dicoccoidesaccession (Krugman et al. 2010, 2011).

(3) These comparisons of CG identified by GWAM, transcriptomic and QTL data will allow to select the most promising CGs for mining of novel alleles in bread wheat and T. dicoccoides.

(4) Screening of wild emmer populations will reveal the spectrum of allelic variation of the best 4-5 candidate genes.

GWAМ of drought resistance: Resources and Data

• Data filtering: excluding potentially wrong phenotypes and low information content SNPs (missing data, low level of polymorphism)

• Taking into account potential admixture effect caused by population structure and correlations caused by kinship

• Physical map positions of genes are based on IWGSC RefSeq v1.0

• Physical map positions of markers are according to WHEALBI (by Michael Seidel, May 2017)

• Genetic map: from population SynOpRIL993 (Sorrells et al. Genome 2011; map draft: from Appels et al. Science 2018). Recalculated for 3,613 best markers (personal communication - Jesse Poland, paper submitted)

• Genetic map positions for genes and exome SNPs: calculated based on physical map positions by interpolation, using markers with consistent positions on physical and genetic maps

Drought resistance experiments in IsraelWP3, Y. Saranga, Hebrew University

• Greenhouse (winter 2015-2016, 353 lines); Field (winter 2016-2017, 251 lines)

• Drought resistance indexes (SI and AI, see next slide) for scored traits:

Yield related traits: grain yield, thousand kernel weight, kernel number per spike, harvest index, grain weight/spike, grains/spike, yield/m2

Biomass related traits: spike dry matter, vegetative dry matter, total dry matter, spike weight/plant, spike weight, efficiency of grain feeling

Plant morphology traits: culm length, spike length, flag leaf length, flag leaf width, number of spikes per plant, plant height, leaf area, waxy

Physiological traits: osmotic potential, flag leaf rolling index

Phenological traits: flowering time (days for heading)

Characterization of drought resistance by difference of trait values in well-watered (WW) and water-limited (WL) conditions:

- Susceptibility index (Fisher and Maurer 1978): SI=1-TWL/TWW

- Association index (AI) taking the effect of flowering time (FT) into account (Bidinger et al. 1982):

𝑨𝑰 =𝑻𝑾𝑳−𝑻𝑾𝑳

(𝑭𝑻)

𝑻𝑾𝑳(𝑭𝑻)

,

where

𝑻𝑾𝑳(𝑭𝑻)

=c0+c1 TWW+c2 FT .

GWAS of drought resistance: Phenotyping

WW

FT

WL

𝑇𝑊𝐿(𝐹𝑇)

𝑇𝑊𝐿 − 𝑇𝑊𝐿(𝐹𝑇)

• Calculating of p-value for each SNP (non-parametric tests, GLM, MLM), taking multiple comparison effects into account (Tassel, EMMAX).

• Multi-locus mixed linear model (MLMM) (Segura et al. Nature 2012).

For each method: (a) select significant SNPs (after FDR correction with 0.15 threshold); (b) identify candidate LD islands (supported by 3-4 methods for maximal number of the SI or AI indices); (c) check gene content of the islands; (d) out of the residing genes, select candidate genes (based on GO, transcriptomics, and QTL mapping of T. dicoccoides x T. durum RILs).

Complications: (i) No population structure was detected (NetStruct, STRUCTURE, InStruct, PCA, …); (ii) Kinship was non-uniform along the genome, thus not accounted in the mixed models (MLM and MLMM); (iii) Low density of polymorphic SNPs (per Mbp and/or cM) in some regions, (iv) Flowering time displayed high between-line variation, can be stress-dependent and by itself affect resistance (drought escape); was accounted by AIs.

GWAM of drought response: Analysis

Results: Testing LD variation along the genome (between SNPs only)

• Total genetic length is about 2,850 cM for ~17 Gbp physical length

• Mbp per cM varies from 1 Mbp (distal) to 150 Mbp (peri-centromeric)

• LD islands were of ≈1 cM (in the figures below, but not in GWAM)

• Kinship varied along genome (hence ignored)

1A

p-value

R2

#Phys

1B

p-value

R2

3D

1D

1B

cM

Results: QTLs for drought resistance (SI and AI) traits

• Some traits are highly correlated (biologically and/or analytically)

• Some markers from the LD islands were seemingly incorrectly placed in v1.0 psedomolecules (inconsistence between the sequence and the linkage map)

• Multiple comparison problem: ~3,000 LD islands → individual p-values should be at least <10-6 (also complicated by genotyping errors and missing data)

Looking for candidate drought resistance genes

Sources of Information:

• GWAM analysis (1 cM LD intervals around significant SNPs). Out of 1 cMLD islands, we restrict the selection of candidates for allele mining to intervals with physical length ≤ 1 Mbp.

• Bi-parental QTL analysis (T. dicoccoides x T. durum RILs)

• Transcriptome meta-analysis of DEGs (Shaar et al. BMC Plant Biology 2015)

• Relevant information from literature (“consensus” drought response candidates across crops)

Searching drought-resistance candidate genes

Genes were selected based on ~120 “keywords” in GO (done by IWGSC). The keyword list was prepared based on broad spectra of literature on drought resistance in wheat, barley and other cereals:

Myb, Patatin, TIP41; Actin cross-linking; Glutamate receptor; SANT-like DNA-binding domain; Squamosa, Calcineurin B; Late embryogenesis abundant; Tubby-like F-box protein; calmodulin binding protein; NAC domain; Gibberellin; Cytochrome P450; etc…

GWAM Candidate SNPs

Physical position

Genetic map

Candidate intervals<1cM & <1 Mbp

Genes with relevant GO DEGs

Potential candidate genes

QTL mapping

Allele mining

Physical

GeneticDEG

Relevant GO

Non-parametric

Non-parametric

QTL mapping

QTL mapping

MLMMGLM

MLMMGLM

Anchored markers

The 4 layers for each GWAM test correspond to SI and AI scores for greenhouse and field experiments

Results: QTLs and drought-response candidate genes

Results: QTLs and drought-response candidate genes

1A 2A 3A 4A 5A 6A 7A

1B 2B 3B 4B 5B 6B 7B

1D 2D 3D 4D 5D 6D 7D

PhysicalGenetic

DEG

Relevant GO

Tentatively best obtained results: presented are significant intervals of 1 Mbp length containing genes with relevant GO and differential expressionin WW vs WL conditions:

• 3A-15: SI for osmotic potential (field) (see next slide)

• 4B-642: AI for flowering time (greenhouse), SI for leaf weight (greenhouse), leaf weight (WL in greenhouse), plant height (WL field), peduncle length (WW field)

• 6A-33: AI for GY/plant (greenhouse)

Additional promising intervals with presumable QTLs for SI and AI traits and two or more other traits: 1A-537, 3B-692, 4D-423, 5B-580 and 7D-556.

Results: QTLs and drought-response candidate genes

Results: Examples of regional association plots

568 571 5751A

50 571B

15 163A

SI for: osmotic potential (field)

SI: for yield/m2

(field)SI for: # seed

per plot (field)

TraesCS3A01G028200(DEG, relevant GO)basic helix-loop-helix (bHLH) DNA-binding superfamily protein

Allele mining: Algorithm

• Clustering of lines using genetic distances calculated on SNPs at LD islands 1 Mbp (~1cM) length carrying the selected candidate genes

• Testing for significant cluster differentiation for SI and AI traits

• Identification of the LD-island haplotypes associated with contrasted values of individual SI and AI trait values

• Increasing the resolution by using multi-trait differentiations

Difficulties:

• Missing data can highly affect clustering of genotypes

• Imputation is problematic for markers surrounded by markers having higher error level

3A15: clustering of genotypes (haplotype mining)

SI for: osmotic potential (field)

SNPs

Lin

es

Strengthen GWAM: Multi-trait analysis (intro)

For single-trait analysis:

ELOD = -½ N log(1- H2),

where H2 is heritability

This also holds in two-trait analysis: H2

x H2xy, where

H2xy = F(dx,dy,x,y,rxy).

H2xy max (H2

x ,H2

y). Hence: ELODxy max(ELODx,ELODy)

a •

• A

A1 •

• a • • Aa

x

y rxy dxdy<0 rxy dxdy>0 dy 0, dx≠0, rxy ≠0 ˜͌

dy

dx dx dx

Higher QTL detection

power and resolution

Higher QTL detection

power and resolutionno improvement

Korol et al. 2001, Genetics 140: 1137

as compared to single-trait analysis

Illustrating by two-trait analysis

Multi-trait analysis: examples on Whealbi dataBoth indices, AI and SI, were calculated for phenotyping data from the greenhouse and the field,

resulting in four groups of traits, FAI, FSI, GAI and GSI. The expected effect of transition from

single-trait to two-trait analysis was assessed by comparison of LOD values (using MultiQTL soft).

SNP

(Mbp)

Traits pair LODxy dx /x dy /y rxy LODx dx /x LODy dy /y

5A(473) GAI & GSI, hP-”- lenPartUP

18.721.5

0.16 0.09

1.26 1.56

-0.74 -0.68

0.342.08

0.300.84

8.042.76

1.340.68

3B(692)-”- lenPed

-”- osmPot14.3

8.60.22

-0.251.470.53

-0.490.69

1.271.06

-0.73-0.33

2.953.25

-0.710.57

5A(473)5B(545)

FAI & FSI, hP-”- lenP

16.6 10.4

0.61-0.74

0.24-0.62

-0.89-0.70

0.35 2.52

0.27-0.86

1.881.94

0.62-0.80

7B(457) GAI & FAI, osmAdj Wax

13.4 -1.63 0.96 -0.08 4.64 -0.98 1.16 0.45

Examples of four-trait analysis

3B692: LOD = 27.3 5A473: LD = 33.8

Multi-trait analysis: examples on Whealbi dataBoth indices, AI and SI, were calculated for phenotyping data from the greenhouse and the field,

resulting in four groups of traits, FAI, FSI, GAI and GSI. The expected effect of transition from

single-trait to two-trait analysis was assessed by comparison of LOD values (using MultiQTL soft).

SNP

(Mbp)

Traits pair LODxy dx /x dy /y rxy LODx dx /x LODy dy /y

5A(473) GAI & GSI, hP-”- lenPartUP

18.721.5

0.16 0.09

1.26 1.56

-0.74 -0.68

0.342.08

0.300.84

8.042.76

1.340.68

3B(692)-”- lenPed

-”- osmPot14.3

8.60.22

-0.251.470.53

-0.490.69

1.271.06

-0.73-0.33

2.953.25

-0.710.57

5A(473)5B(545)

FAI & FSI, hP-”- lenP

16.6 10.4

0.61-0.74

0.24-0.62

-0.89-0.70

0.35 2.52

0.27-0.86

1.881.94

0.62-0.80

7B(457) GAI & FAI, osmAdj Wax

13.4 -1.63 0.96 -0.08 4.64 -0.98 1.16 0.45

Examples of four-trait analysis (plant height)

3B692: LOD = 27.3 5A473: LD = 33.8

Acknowledgments: to leaders and all member of WP1, WP2, WP3, WP4

Thank you for your attention!