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High-density SNP mapping reveals closely linked QTL for

resistance to Stagonospora nodorum blotch (SNB) in flag leaf and glume of hexaploid wheat.

Journal: Genome

Manuscript ID gen-2017-0203.R1

Manuscript Type: Note

Date Submitted by the Author: 28-Nov-2017

Complete List of Authors: Francki, Michael; Department of Agriculture and Food Western Australia, ;

Murdoch University, State Agricultural Biotechnology Centre Walker, Esther ; Western Australia Department of Agriculture and Food, Grains R&D Transformation Li, Dora ; DAFWA, Biotechnology Forrest, Kerrie; Agriculture Victoria Research, Department of Economic Development, Jobs, Transport and Resources

Is the invited manuscript for consideration in a Special

Issue? : N/A

Keyword: Stagonospora nodorum blotch, quantitative trait loci, adult plant resistance, single nucleotide polymorphism

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High-density SNP mapping reveals closely linked QTL for resistance to 1

Stagonospora nodorum blotch (SNB) in flag leaf and glume of hexaploid 2

wheat. 3

4

Michael G. Francki • Esther Walker • Dora A. Li • Kerrie Forrest 5

6

Michael G. Francki � • Esther Walker 7

Department of Agriculture and Food Western Australia, South Perth WA 6151, Australia 8

Michael G. Francki � • Esther Walker • Dora A. Li 9

State Agricultural Biotechnology Centre, Murdoch University, Murdoch WA, 6150, Australia 10

Kerrie Forrest 11

Department of Economic Development, Jobs, Transport and Resources, Agriculture Victoria Research, Agribio, Bundoora, 12

VIC, Australia 13

e-mail: michael.francki@agric.wa.gov.au 14

15

16

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Abstract 17

The genetic control of adult plant resistance to Stagonospora nodorum blotch (SNB) is complex 18

consisting of genes with minor effects interacting in an additive manner. Earlier studies detected 19

quantitative trait loci (QTL) for flag leaf resistance in successive years on chromosomes 1B, 2A, 2D, 20

5B using SSR- and DArT-based genetic maps of progeny from the crosses EGA Blanco/Millewa, 21

6HRWSN125/WAWHT2074 and P92201D5/P91193D1. Similarly, QTL for glume resistance detected 22

in successive years and multiple environments were identified on chromosomes 2D and 4B from 23

genetic maps of P92201D5/P91193D1 and 6HRWSN125/WAWHT2074, respectively. The SSR- and 24

DArT-based genetic maps had an average distance of 6.5, 7.8 and 9.7 cM between marker loci for 25

populations EGA/Millewa, P92201D5/P91193D1 and 6HRWSN125/WAWHT2074, respectively. This 26

study used single nucleotide polymorphism (SNP) markers from the iSelect Infinium 90K genotyping 27

array to fine map genomic regions harbouring QTL for flag leaf and glume SNB resistance reducing 28

the average distance between markers to 2.9, 3.3 and 3.4 -3.4 cM for populations 29

P92201D5/P91193D1, EGA/Millewa and 6HRWSN125/WAWHT2074, respectively. Increasing the 30

marker density of the genetic maps with SNPs did not identify any new QTL for SNB resistance but 31

discriminated previously identified co-located QTL into separate but closely linked QTL. 32

33

Key words: Stagonospora nodorum blotch; quantitative trait loci; adult plant resistance; single 34

nucleotide polymorphism 35

36

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Introduction 37

Stagonospora nodorum blotch (SNB) is a nercrotophic fungal disease of wheat caused by the 38

pathogen Parastagonospora nodorum (synonyms Stagonospora nodorum, Phaeosphaeria nodorum). 39

Symptoms of infection in adult plants generally develop in wet and humid environments and include 40

red-brown lesions surrounded by a yellow halo on flag leaves or pale to brown lesions on the glume. 41

SNB infection on adult plants reduces the photosynthetic capacity of flag leaves, heads and peduncle 42

causing significant losses in grain yield (Krupinsky et al 1973). Although fungicide application and 43

crop rotation are common approaches for managing SNB, the use of resistant wheat varieties 44

provides a complementary strategy to maintain grain yields under SNB epidemics. 45

Breeding for SNB resistance in wheat is a challenging task due to complex quantitative 46

genetic control. Genetic analysis for adult plant resistance in spring and winter wheat has identified 47

several minor quantitative trait loci (QTL) interacting in an additive manner, where genes expressing 48

resistance in flag leaves are independent to those in glumes (reviewed in Francki 2013). QTL for flag 49

leaf resistance on chromosomes 1B, 2A, 2D and 5B were detected in successive years under 50

Australian field conditions using spring wheat doubled-haploid populations EGA Blanco/Millewa 51

(Francki et al. 2011) and 6HRWSN125/WAWHT2074 (Shankar et al. 2008), while QTL for glume 52

resistance were detected on chromosome 2D in the latter population (Shankar et al. 2008). Analysis 53

of glume response in a P92201D5/P91193D1 recombinant-inbred population across multiple 54

environments detected the same QTL on chromosome 2D (Uphaus et al. 2007), suggesting similar 55

genes may be responsible for expression of resistance to the pathogen in USA and Australia. It was 56

therefore reasonable to assume that co-located QTL for flag leaf and glume resistance in the EGA 57

Blanco/Millewa, 6HRWSN125/WAWHT2074 and P92201D5/P91193D1 populations contained the 58

same genes responding to SNB infection in different environments. However, the genetic maps 59

underpinning those studies were based on a maximum of 492 DNA markers, the average distance 60

between marker loci was as high as 9.7 cM (Francki et al. 2009; Shankar et al. 2008), and QTL 61

intervals spanned 10-30 cM and accounted for 8-38% of the phenotypic variation (reviewed in Francki 62

2013). 63

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QTL spanning large genome intervals (i.e. >5 cM) can have restrictions when transferred to 64

breeders germplasm. In particular is the increased risk of chromosome recombination between 65

marker loci and the gene when QTL intervals are large which reduces the accuracy of selection for 66

desirable phenotype. To mitigate these problems, fine mapping can be used to reduce QTL intervals 67

and thereby increase efficiencies for QTL introgression in breeding programs (Salvi and Tuberosa 68

2005). Recently, several high-density genotyping arrays providing wide genome coverage have been 69

developed for wheat including the iSelect Infinium 9K and 90K bead chip arrays (Cavanagh et al. 70

2013; Wang et al. 2014). Here, we used the iSelect 90K wheat SNP array to genotype the EGA 71

Blanco/Millewa, 6HRWSN125/WAWHT2074 and the P92201D5/P91193D1 populations to create a 72

high-density integrated SNP, SSR and DArT genetic map for each population to help refine QTL 73

intervals for flag leaf and glume resistance to SNB. 74

Methods 75

SNP genotyping and genetic map construction 76

DNA samples from 241, 250 and 239 individuals of doubled-haploid (DH) populations EGA 77

Blanco/Millewa and 6HRWSN125/WAWHT2074 and recombinant-inbred (RI) line population 78

P92201D5/P91193D1, respectively, were assayed using the 90K iSelect SNP array Agriculture 79

Victoria Research, Agribio, Bundoora, VIC, Australia. SNP clustering and assignment of samples to 80

cluster was performed using custom scripts. In brief, sample theta and normR values for each SNP 81

were exported from GenomeStudio v1.9.4. Assignment of a sample to a cluster required that the 82

sample data point fell within two standard deviations of the sample cluster, and that the confidence 83

score for assignment to that cluster versus the next closest cluster exceeded the value of 0.7, where 0 84

and 1 represent lowest and highest confidence, respectively. Cluster assignments were converted to 85

binary parental phasing using a custom perl script. Genotyping data for each population was 86

preliminarily mapped using MSTmap (Wu et al. 2008) at LOD6 to assess the quality of genotype 87

calling. Markers with either less than 80% call rate or segregation distortion (p-value >0.10 calculated 88

from a chi-square test) were removed. Moreover, samples with a high crossover rate (indicating a 89

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mixed sample) or duplicate samples (>95% identical genotypes) were also removed. The datasets for 90

each population were then merged with SSR, DArT and other markers previously genotyped for these 91

populations (Francki et al. 2011; Shankar et al. 2008; Uphaus et al. 2007) and re-mapped using 92

MSTmap at LOD6, without redundant markers, selecting “DH” as Population Type for populations 93

EGA Blanco/Millewa and 6HRWSN125/WAWHT2074, and “RIL at generation 8” for population 94

P92201D5/P91193D1. The merged datasets were also mapped using QTL Ici Mapping Version 95

4.0.6.0 (Meng et al. 2015) to confirm marker order. Redundant markers were removed by selecting 96

the BIN parameter within QTL IciMapping using “Delete redundancy By Missing Rate (%)” and 97

missing data was set at 20% using the “Delete markers By missing rate (%)” function. MAP 98

parameters were set at LOD6, specifying Algorithm: nnTwoOpt and Rippling:SARF. 99

QTL mapping with SNP-based genetic maps 100

The high-density genetic maps containing SNP markers and mean flag leaf and glume 101

disease scores from multi-environments (Francki et al. 2011; Shankar et al. 2008; Uphaus et al. 2007) 102

were used to identify any new QTL not previously detected using SSR and DArT-based genetic maps 103

and to fine map existing genomic regions for SNB resistance. QTL IciMapping was used to identify 104

QTL regions using inclusive composite interval mapping with additive effect (ICIM-ADD). Parameters 105

for all traits and environments were set to 0.1cM steps, Probability in Stepwise Regression (PIN) of 106

0.001, minimum LOD threshold of 2.5 and deleting missing phenotypes. Final maps and associated 107

QTL were drawn using MapChart Version 2.3 (Voorrips, 2002). 108

Results and Discussion 109

The 90K iSelect SNP array substantially increased the number of markers with identification of 110

8,760-16,411 polymorphic SNP loci (Table 1) representing 22.8-33.3 times the number of existing 111

markers for each genetic map reported in Francki et al. (2009) and Shankar et al. (2008). However, 112

the number of non-redundant SNP markers polymorphic between parents of the mapping populations 113

ranged from 1,183-1,767 (Table 1) representing 8-20% of the total polymorphic SNP markers and 114

were used to integrate with other markers in genetic maps. Although the SNP markers cover the 115

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majority of the wheat genome, the high SNP redundancy reflects the marker density to the limited 116

recombination for each DH and RIL population. The total number of SSR, DArT, non-redundant SNP 117

and other markers mapped in the EGA Blanco/Millewa, 6HRWSN125/WAWHT2074 and 118

P92201D5/P91193D1 populations were 1,459, 1,713 and 2,047, respectively, where the integration of 119

non-redundant polymorphic SNP markers expanded the total genetic map distance 4,906-6,022 cM 120

(Table 1) from the SSR- and DArT-based map reported in Francki et al. 2009 and Shankar et al. 121

2008. The average genetic map distance between SNP and other marker loci was 2.94 cM, 3.36 cM 122

and 3.43 cM for populations P92201D5/P91193D1, EGA/Millewa and 6HRWSN125/WAWHT2074, 123

respectively (Table 1). The intervals were reduced from 6.5, 7.8 and 9.7 cM between marker loci for 124

populations EGA/Millewa, P92201D5/P91193D1 and 6HRWSN125/WAWHT2074, respectively, when 125

predominantly SSR and DArT markers were used in map construction (Francki et al. 2009; Shankar 126

et al. 2008). SNP marker locus intervals were comparable to previously published high-density SNP-127

based marker maps derived from the DH mapping populations of hexaploid wheat, Avalon/Cadenza 128

and Savannah/Rialto, with average intervals of 2.8 and 3.8 cM, respectively (Allen et al. 2011; Allen et 129

al. 2013). The smaller interval size was, therefore, suitable for fine mapping QTL for SNB resistance 130

in flag leaf and glumes. 131

The co-location of QTL using SSR and DArT marker-based genetic maps indicated that 132

genome regions controlling SNB resistance contain similar genes expressed in different 133

environments. QTL analysis of maps supplemented with SNP markers did not identify any new QTL 134

for flag leaf resistance. It was anticipated that existing QTL for flag leaf resistance detected in 135

different environments would co-locate in the same genomic region as previously described in Francki 136

et al. (2011) and Shankar et al. (2008) using genetic maps supplemented with SNP markers. Instead, 137

the higher density genetic maps discriminated two closely linked QTL for flag leaf resistance detected 138

in different environments on chromosome 2A and 2D from the populations P92201D5/P91193D1 and 139

6HRWSN125/WAWHT2074, respectively (Fig. 1). The distinct QTL provided unambiguous evidence 140

that multiple flag leaf genes on chromosomes 2A and 2D are linked within 17 cM and 5 cM, 141

respectively, and express resistance in response to different environmental signals. The proportion of 142

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phenotypic variation accounted by each QTL using genetic maps supplemented with SNP markers 143

ranged from 7.8% to 20.8% depending on the environment (Fig. 1). The proportion of variation 144

accounted for by each QTL and environment using composite interval mapping was the same as 145

previously reported in Francki et al. (2011) and Shankar et al. (2008). 146

Similarly, SNP markers were able to discriminate at least two QTL for flag leaf resistance on 147

chromosomes 5B in the EGA Blanco/Millewa population where each QTL contained genes that 148

express resistance in response to different environments (Fig. 1). However, additional SNP markers 149

within a 10-cM interval were unable to discriminate between the Qsnl08.daw QTL for flag leaf SNB 150

resistance detected in 2008 and Qhd07.daw and Qhd08.daw for heading date in 2007 and 2008 on 151

chromosome 5B (Fig. 1). As heading date was unlikely to have pleiotropic effects on flag leaf 152

resistance in the EGA Blanco/Millewa population (Francki et al. 2011) it is anticipated, therefore, that 153

further fine mapping within this region would discriminate QTL for flag leaf resistance from heading 154

date. Although high-density SNP markers were mapped on chromosome 1B in the EGA 155

Blanco/Millewa population (Fig. 1), it remains unclear whether single or linked QTL controlling flag leaf 156

resistance in different environments reside at this locus. Analysis using a larger size of the EGA 157

Blanco/Millewa population would determine whether single or multiple QTL for flag leaf resistance 158

reside on chromosome 1B. 159

The genetic maps supplemented with SNP markers were used in QTL analysis for SNB 160

resistance in glumes for the 6HRWSN125/WAWHT2074 population using mean glume disease 161

scores from two environments reported in Shankar et al (2008). Glume resistance on chromosome 162

4B identified two linked QTL within 12 cM (Fig. 2), indicating at least two genes underpin resistance 163

and are expressed in this genomic region. The QTL explained similar phenotypic variance to that 164

reported previously by Shankar et al. (2008). However, high-resolution SNP mapping detected QTL 165

co-located for glume resistance on chromosome 2D within the same SNP marker interval of 0.3 cM 166

(IWB31450- IWB21124) when the P92201D5/P91193D1 population was evaluated in three 167

environments in the USA (Fig. 2). Distinct but tightly linked QTL were also detected for glume 168

resistance in two environments in Australia and resided in the (Xgwm526a - IWB53436) marker 169

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interval of <1.0 cM (Fig. 2). Therefore, it appears that the same genes control glume resistance to 170

SNB across different USA environments but are different to resistance genes that consistently 171

respond to variable Australian environments. 172

The significant increase in map density provided by SNP markers in three genetic maps in this 173

study provided a new insight on the QTL underpinning SNB resistance contributing to the genetic 174

complexity of this trait in wheat. It was reasonable to assume in earlier studies (Francki et al. 2011; 175

Shankar et al. 2008; Uphaus et al 2007) that similar genes controlled expression of flag leaf and 176

glume resistance to SNB in different environments based on the co-location of QTL using medium-177

resolution SSR- and DArT-based genetic maps. Instead, fine mapping using SNP markers in this 178

study revealed multiple but closely linked QTL, indicating distinct clusters of concomitant disease-179

related genes in the host that respond differently to pathogen infection across environments. Plant 180

resistance (R) multi-gene families are typically tandem or segmentally duplicated for large stretches 181

of contiguous DNA and contain diverse copies within clusters that have evolved through selective 182

forces generating novel or diverse pathogen recognition capabilities (Gururani et al. 2012; Leister 183

2004; Liu et al. 2007; Meyers et al. 2005). Therefore, it is conceivable that multiple linked QTL for 184

SNB resistance identified in this study through high-density SNP mapping may have detected disease 185

response from clusters of R genes or R gene-mediated signalling pathway factors that respond to 186

specific isolates in mixed inoculum that predominate in specific environments. The identified SNP 187

markers in this study would assist in aligning QTL intervals to the sequenced genome of wheat (The 188

International Wheat Genome Sequencing Consortium, 2014) and provide further knowledge of the 189

underlying genes involved in SNB resistance. In the meantime, the knowledge of the genetic control 190

and SNP markers identified in this study are exemplary tools to introgress clusters of disease related 191

genes for breeding improved SNB resistance in wheat. 192

Acknowledgements: Funding from the Grains Research Development Corporation through 193

DAW00248 and DAV00127 is gratefully acknowledged. 194

References 195

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Allen, A., Barker, G., Berry, S., Coghill, J., Gwilliam, R., Kirby, S., Robinson, P. et al. 2011. Transcript-196

specific, single-nucleotide polymorphism discovery and linkage analysis in hexaploid bread wheat 197

(Tricticum aestivum L.). Plant Biotech. J. 9:1086-1099 198

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Discovery and development of exome based, co-dominant single nucleotide polymorphism 200

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loci for flag leaf resistance to Stagonospora nnodorum blotch. Phytopath. 101:1278-1284 211

Gururani, M., Venkatesh, J., Upadhyaya, C., Nookaraju, A., Pandey, S., Park, S. 2012. Plant disease 212

resistance genes: current status and future directions. Physiol. Mol. Plant Pathol. 78:51-65 213

Krupinsky, J., Scharen, A., Schillinger, J. 1973. Pathogenic variation in Septoria nodorum (Berk.) 214

Berk. in relation to organ specificity, apparent photosynthetic rate and yield of wheat. Physiol. 215

Plant Path. 3:187-194 216

Leister, D. 2004. Tandem and segmental duplication and recombination in the evolution of plant 217

disease resistance genes. Trends Genet. 20:116-122 218

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map construction and quantitative trait locus mapping in biparental populations. Crop J. 3: 269-222

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Myers, B., Kaushik, S., Nandety, R. 2005. Evolving disease resistance genes. Curr. Op. Plant Biol. 224

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Salvi, S., Tuberosa, R. 2005. To clone or not to clone plant QTLs: present and future challenges. 226

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Shankar, M., Walker, E., Golzar, H., Loughman, R., Wilson, R., Francki, M. 2008. Quantitative trait 228

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Table 1 Summary of total and non-redundant (NR) SNP and other DNA markers mapped in three homozygous bi-parental mapping

populations used for QTL analysis for SNB resistance in hexaploid wheat.

EGA Blanco/Millewa 6HRWSN125/WAWHT2074 P92201D5/P91191D1

SNP markers 13,308 16,411 8,760 Other markers 4681 4922 3851 Total markers 13,776 16,903 9,145 NR SNP markers 1,183 1,346 1,767 NR other markers 276 367 280 Total NR markers 1,459 1,713 2,047 Total map distance 4,906 cM 5,885 cM 6,022 cM Average map distance between markers 3.36 cM 3.43 cM 2.94 cM

1 Other markers described in Francki et al (2009) 2Other markers described in Shankar et al (2008)

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Fig. 1 QTL analysis for flag leaf resistance to SNB from populations EGA Blanco/Millewa

(chromosomes 1B and 5B), 6HRWSN125/WAWHT2074 (chromosome 2D) and P92201D5/P91191D1

(chromosome 2A). Chromosomal location of SNP integrated with other DNA markers are shown to

the left and QTL traces based on phenotypic data for flag leaf response from successive years are

shown on the right. Coloured solid bars in traces represent QTL regions detected in different

environments (Qnl designations) and threshold LOD are represented by dotted lines. The phenotypic

variance accounted for each QTL is shown in parentheses.

Fig. 2 QTL analysis for glume resistance to SNB from populations P92201D5/P91191D1

(chromosome 2D) and 6HRWSN125/WAWHT2074 (chromosome 4B). Chromosomal location of SNP

integrated with other DNA markers are shown to the left and QTL traces based on phenotypic data for

glume response from successive years screened in Australia and USA are shown on the right.

Coloured solid bars in traces represent QTL regions detected in Australian and USA environments

(Qng.daw and Qng.pur designations, respectively) and threshold LOD are represented by dotted

lines. The phenotypic variance accounted for each QTL is shown in parentheses.

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Chromosome 1B Chromosome 5B

Chromosome 2A

Chromosome 2D

wPt-356516.8IWB1912317.0IWB2226817.7wPt-846418.2IWB2910318.4IWB1793718.7IWB1531518.9IWB779219.7IWB3244920.7wPt-244822.3wPt-832823.8IWA312224.4IWB5268726.1IWB144027.7IWB3565630.3IWA621932.6IWB2339733.1IWB5109933.6IWB3247434.5IWB920635.0IWB581335.6IWB964037.3IWB1207038.3IWB4251938.8Xbarc124b40.2IWB2875042.3IWB5727542.8IWB7299044.2IWB5787944.5

QSnl0

4.d

aw

-2A

QSnl0

5.d

aw

-2A

0

5

10

15

20

2ALOD

wPt-3457240.9IWB55622241.4IWB15463241.9IWA5478242.5IWB12583243.4IWB22904244.3

Xfcp620248.5

IWA4103264.4

IWA1780267.3wPt-1733268.2IWB39412268.7IWA1797269.6IWB73666270.6IWB8906271.1

IWB3744273.7

VrnB1278.9

IWB25238283.0IWA7910284.5IWB27379285.1IWB46235286.1IWB40222287.0IWB11075287.9IWB62746288.9IWB56998289.4

QSnl0

7.d

aw

-5B

QSnl0

8.d

aw

-5B

QH

d07.d

aw

-5B

QH

d08.d

aw

-5B

0

5

10

15

20

25

30

35

40

5B LOD

IWB72737.2IWB117887.6IWB454378.1IWB237118.6

IWB6436812.0IWB5560712.5

wPt-894914.9wPt-826715.8wPt-709416.3

IWB2057018.9wPt-275119.9Xstm542a20.9

IWB4176923.9

IWB2950725.8

QSnl0

7.d

aw

-1B

QSnl0

8.d

aw

-1B

0

5

10

15

20

1BLOD

IWB3544115.6

IWB6610719.4IWB4129521.3wPt-833023.1IWA815124.4IWB3872424.9IWB2809925.4IWB6583425.8IWB4639626.7IWB5390328.1

IWB761631.5IWB3868732.0

Xgwm15735.3

QSnl0

3.d

aw

-2D

QSnl0

4.d

aw

-2D

0

5

10

15

20

2DLOD

(22.7%)

(18.4%) (13.3%)

(7.8%) (38.0%)

(38.0%)

(20.8%)

(12.8%) (11.2%)

(8.3%)

Fig. 1

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Fig. 2

LOD

Chromosome 4B

IWB5607895.2

IWB7325898.0IWB7295598.4IWB472098.9IWB6993999.3IWB71675100.8IWB7612102.2IWB62190103.0wPt-3991105.3

wPt-7569108.4

Xbarc340112.7

IWA3325116.6

IWA4055118.4IWB28921119.3Xgwm495120.6

IWB41476125.8

QSng03.d

aw

-4B

QSng04.d

aw

-4B

0

5

10

15

20

4B

LOD

Xcfd50b0.0

IWB269544.2

IWB505327.9

IWB7379111.3IWB7379912.3IWB4531213.2IWB1198714.5IWB3528715.0IWB3145016.2IWB2112416.5Xgwm526a17.0IWB5343617.5IWB2579119.1

IWB908823.8

QSng04.d

aw

-2D

QSng05.d

aw

-2D

QSng03.p

urE

-2D

QSng03.p

urL

-2D

QSng03.p

urV

-2D

0

5

10

15

20

2D

Chromosome 2D

(13.3%)

(7.7%)

(35.8%) (25.4%)

(11.2%) (16.9%)

(17.3%)

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https://mc06.manuscriptcentral.com/genome-pubs

Genome