Open Access

Identification of bakanae disease resistance loci in japonica rice through genome wide association study

  • Andrea Volante1,
  • Alessandro Tondelli2,
  • Maria Aragona3,
  • Maria Teresa Valente3,
  • Chiara Biselli2,
  • Francesca Desiderio2,
  • Paolo Bagnaresi2,
  • Slavica Matic4,
  • Maria Lodovica Gullino4, 5,
  • Alessandro Infantino3,
  • Davide Spadaro4, 5 and
  • Giampiero Valè1, 2Email authorView ORCID ID profile
Rice201710:29

https://doi.org/10.1186/s12284-017-0168-z

Received: 20 March 2017

Accepted: 30 May 2017

Published: 8 June 2017

Abstract

Background

Bakanae disease, caused by seed-borne Fusarium species, mainly F. fujikuroi, is a rice disease whose importance is considerably increasing in several rice growing countries, leading to incremental production losses.

Results

A germplasm collection of japonica rice was screened for F. fujikuroi resistance, allowing the identification of accessions with high-to-moderate levels of resistance to bakanae. A GWAS approach uncovered two genomic regions highly associated with the observed phenotypic variation for response to bakanae infection on the short arm of chromosome 1 (named as qBK1_628091) and on the long arm of chromosome 4 (named as qBK4_31750955). High levels of phenotypic resistance to bakanae were associated to the cumulated presence of the resistant alleles at the two resistance loci, suggesting that they can provide useful levels of disease protection in resistance breeding. A fine comparison with the genomic positions of qBK1_628091 and qBK4_31750955 with respect to the QTLs for bakanae resistance reported in the literature suggests that the resistant loci here described represent new genomic regions associated to F. fujikuroi resistance. A search for candidate genes with a putative role in bakanae resistance was conducted considering all the annotated genes and F. fujikuroi-related DEGs included in the two genomic regions highlighting several gene functions that could be involved in resistance, thus paving the way to the functional characterization of the resistance loci.

Conclusions

New effective sources for bakanae resistance were identified on rice chromosomes 1 and 4 and tools for resistance breeding are provided.

Keywords

Bakanae Disease resistance loci Genome wide association study (GWAS) Candidate genes

Background

Bakanae disease is one of the most serious and oldest problems affecting rice production, first described in 1828 in Japan (Ito and Kimura 1931) and currently identified in Europe, Asia, Africa, and North America (Ou 1985; Pra et al. 2010). In various rice growing countries, significant yield losses caused by the disease can range from 50% to more than 70% (Ou 1985; Rood 2004). Increasing bakanae disease incidence has been reported in Italy (Amatulli et al. 2012) and major growing areas of Asia such as Pakistan, South Korea, Bangladesh, Northern India, and Taiwan (Khan et al. 2000; Park et al. 2009; Haq et al. 2011; Gupta et al. 2014; Chen et al. 2016).

Bakanae is caused by one or more seed-borne Fusarium species, mainly F. fujikuroi (Wulff et al. 2010), and the disease may infect rice plants from the pre-emergence stage to the mature stage, with severe infection of rice seeds resulting in poor germination or withering (Iqbal et al. 2011). F. fujikuroi belongs to hemibiotrophs fungi, whose initial infection relies on a living host (biotrophic), and progressive infection involves a consumption and destruction of the host cells (necrotroph; Ma et al. 2013). Seeds contaminated with the fungus provide initial foci for primary infection. Under favorable environmental conditions, infected plants have the capacity to produce numerous conidia that subsequently infect proximate healthy panicles through aerial conidial diffusion by wind, producing infected seeds (Ou 1985; Ora et al. 2011; Matic et al. 2017). During primary infection, mature rice plants are tall, frequently stunted, with an angle of leaf insertion wider than in healthy seedlings. Moreover, infected plants eventually die, while panicles on surviving plants do not develop any grains, thus resulting in yield loss (Desjardins et al. 2000; Mew and Gonzales 2002; Ou 1985).

The altered plant morphology is due to the ability of F. fujikuroi to produce and secrete gibberellic acids (GAs) (Bearder 1983; Ou 1985). Although GAs are considered as secondary metabolites (SMs) in Fusarium because they are not essential for fungal growth and development, they are thought to contribute to the virulence of F. fujikuroi, the only Fusarium species capable of GAs biosynthesis, by controlling jasmonic acid-responsive gene expression and jasmonic acid-mediated plant immune responses (Wiemann et al. 2013; Siciliano et al. 2015). GA production was also associated with fungicide sensitivity of different F. fujikuroi isolates (Tateishi et al. 1998; Tateishi and Suga 2015).

The most common management practices to limit bakanae are based on thermal seed treatment (hot water immersion) or fungicides. The hot water immersion method (Hayasaka et al. 2001) was demonstrated ineffective on severely infected rice seeds, because thermal effect is not efficiently transmitted to the pericarp layers. Also seed dressing with fungicides has restricted efficiency in destroying the spores of the fungus, owing their resistance to several fungicides (Iqbal et al. 2011; Park et al. 2009; Kim et al. 2010; Lee et al. 2011). Promising results have only recently been obtained through a combination of antagonistic yeasts and thermotherapy (Matic et al. 2014). However, the current incidence of bakanae disease is increasing, leading to serious concerns in the main rice-producing areas worldwide (Wahid et al. 1993; Ma et al. 2008) and there is a strong request for alternative disease control measures, such as the identification of rice bakanae resistant cultivars (cvs.). However, only a few accessions were reported to effective source of resistance to bakanae. An extensive search carried out on more than 400 rice accessions identified only one and 12 cvs. with high and moderate resistance, respectively (Li et al. 1993). Similarly, in other studies only a few resistant varieties were identified after application of different screening procedures (Lv 1994; Khokhar and Jaffrey 2002; Kim et al. 2014).

Knowledge on mapped loci conferring resistance to bakanae is very limited. Two QTLs for bakanae resistance derived from the Chinese japonica cv. Chunjiang 06 were identified on chromosomes 1 and 10, explaining each one about 13% of phenotypic variation (Yang et al. 2006). Hur et al. (2015), using near-isogenic lines (NILs) derived from a cross between the highly resistant indica variety Shingwang and the japonica susceptible variety Ilpum, identified a major QTL, named as qBK1, on the long arm of chromosome 1 explaining 65% of the phenotypic variation and not coincident with the QTL identified in Chunjiang 06. More recently, three QTLs were identified on chromosome 1 (Fiyaz et al. 2016). Two of them (qBK1.2 and qBK1.3), detected on the short arm of chromosome 1, represent novel QTLs, while the third one (qBK1.1) was mapped in coincidence with the qBK1 QTL described by Hur et al. (2015).

A large genetic diversity of the pathogen population has been highlighted for strains isolated from Asia, Africa and Europe (Wulff et al. 2010; Jeon et al. 2013; Valente et al. 2016), therefore supporting the necessity of additional loci conferring rice resistance to bakanae. Whole-genome association mapping (GWAS) has recently demonstrated to offer better resolution than QTL mapping, thus reducing the QTL interval of confidence and, consequently, the number of candidate genes underlying individual QTLs (Huang et al. 2010, 2011; Courtois et al. 2013). Linkage disequilibrium (LD) decay, which determines the expected resolution in the GWAS approach, has been reported to range from 500 kb in the temperate japonica rice background to 75 kb in the indica background (Huang et al. 2010; Mather et al. 2007), even considering that in germplasm collection of more related temperate japonica rice accessions values of LD decay of 1250 kb were also observed (Biscarini et al. 2016).

The main objective of the present study was the screening of a japonica rice germplasm collection for bakanae resistance after artificial inoculation with a virulent isolate of F. fujikuroi, in order to map the genetic polymorphisms underlying rice resistance against this disease. Genome-wide association study allowed the identification and localization of two new QTLs conferring rice resistance to bakanae disease in the rice japonica background.

Results

Bakanae disease resistance in the Rice Germplasm Collection

A virulent F. fujikuroi isolate was used to inoculate seeds of 138 rice accessions adapted to the Italian growing conditions, in order to explore their resistance to bakanae disease. A weak skewing of the frequency distribution towards high levels of susceptibility was observed in the collection (Fig. 1); nevertheless, some accessions showed high-to-moderate resistance (e.g. the tropical japonica genotypes: Greppi, L205, Arsenal, IAC32_52 and Adair with a disease index (I*) value <20, Table 1). Highly significant genotypic variation for the trait under study was detected by ANOVA (Table 2), and confirmed by the calculated H2 value of 0.84. Temperate japonica accessions showed a higher incidence of the disease with respect to tropical japonica accessions (average I* values 42.8 and 40.1, respectively). However the difference was found to be not statistically significant (data not shown).
Fig. 1

Frequency distribution of bakanae disease resistance (I*) in the Rice Germplasm Collection analyzed in the present study

Table 1

List of accessions in the Rice Germplasm Collection assembled for this work

Accession Name

Sub-population

Origin

I*

S1_628091

S4_31750955

Structure group

GREPPI

Tropical Japonica

Italy

13.2

A

C

adm

L205

Tropical Japonica

USA

16.5

A

C

2

ARSENAL

Tropical Japonica

Italy

17.9

A

C

adm

IAC32 52

Tropical Japonica

Brazil

18.4

A

C

2

ADAIR

Tropical Japonica

USA

19.1

A

C

2

KING

Tropical Japonica

Italy

22.8

A

C

2

MAIORAL

Temperate Japonica

Portugal

23.0

A

C

adm

GRALDO

Tropical Japonica

Italy

24.0

T

C

adm

BENGAL

Temperate Japonica

USA

25.2

A

C

adm

ALAN

Tropical Japonica

USA

25.7

A

C

2

MAYBELLE

Tropical Japonica

USA

26.0

A

C

2

A201

Tropical Japonica

USA

26.8

A

C

2

ITALPATNA x MILYANG

Temperate Japonica

Portugal

28.9

T

C

adm

FLIPPER

Temperate Japonica

Italy

29.5

T

C

1

DELLROSE

Tropical Japonica

USA

29.7

A

C

2

ERCOLE

Temperate Japonica

Italy

29.9

T

C

1

AUGUSTO

Temperate Japonica

Italy

30.0

T

C

1

DUCATO

Temperate Japonica

Italy

30.1

T

C

1

LUXOR

Temperate Japonica

Italy

30.1

T

C

1

CARRICO

Temperate Japonica

Portugal

30.6

T

C

1

HARRA

Temperate Japonica

Australia

30.6

T

C

1

LAGRUE

Tropical Japonica

USA

31.2

A

-

2

LORD

Temperate Japonica

Italy

31.4

T

C

1

LIDO

Temperate Japonica

Italy

31.4

T

C

1

HAREM

Temperate Japonica

Portugal

31.6

T

C

1

CASTELMOCHI

Temperate Japonica

Italy

32.6

T

C

1

GZ8367

Temperate Japonica

Egypt

32.7

T

C

1

JUBILIENI

Temperate Japonica

Bulgary

32.8

T

C

1

DELFINO

Temperate Japonica

Italy

34.1

T

C

1

BRAZOS

Tropical Japonica

USA

34.3

T

-

2

VENERE

Temperate Japonica

Italy

34.4

T

C

1

CT36

Temperate Japonica

Colombia

34.6

T

C

1

FRANCES

Temperate Japonica

Spain

34.9

T

C

1

GRITNA

Temperate Japonica

Italy

35.2

T

C

1

CT58

Temperate Japonica

Colombia

35.2

T

C

1

LUNA

Temperate Japonica

USA

35.4

T

C

1

CRESO

Temperate Japonica

Italy

35.5

A

C

1

BARAGGIA

Temperate Japonica

Italy

35.6

T

G

1

COLINA

Temperate Japonica

Spain

35.7

T

C

1

ITALMOCHI

Temperate Japonica

Italy

35.7

T

C

1

GRAAL

Tropical Japonica

France

35.9

T

C

adm

PECOS

Tropical Japonica

USA

36.0

T

G

adm

CIGALON

Temperate Japonica

France

36.2

T

C

1

SALVO

Tropical Japonica

Italy

36.5

T

C

adm

BIANCA

Temperate Japonica

Italy

36.7

T

C

1

LUSITO IRRADIADO

Temperate Japonica

Portugal

36.8

T

C

1

KULON

Temperate Japonica

Russia

36.8

T

C

1

GIGANTE VERCELLI

Temperate Japonica

Italy

36.9

T

G

1

LADY WRIGHT

Tropical Japonica

USA

37.1

T

G

adm

MANTOVA

Temperate Japonica

Italy

37.6

T

C

1

BAHIA

Temperate Japonica

Spain

38.4

T

C

1

ALICE

Temperate Japonica

Italy

38.5

T

C

1

CENTAURO

Temperate Japonica

Italy

38.7

T

C

1

EUROPA

Temperate Japonica

Italy

38.8

T

C

1

ESTRELA

Temperate Japonica

Portugal

38.9

T

C

adm

IBO 400

Temperate Japonica

Portugal

38.9

A

C

1

DIMITRA

Temperate Japonica

Greece

38.9

T

C

1

CLOT

Temperate Japonica

Spain

39.0

T

C

1

L201

Tropical Japonica

USA

39.1

A

-

2

JEFFERSON

Tropical Japonica

USA

39.3

A

G

2

L202

Tropical Japonica

USA

40.0

A

C

2

EUROSE

Temperate Japonica

Italy

40.3

T

C

1

L204

Tropical Japonica

USA

40.5

A

C

2

BALILLA

Temperate Japonica

Italy

40.5

T

C

1

CHIPKA

Temperate Japonica

Bulgary

40.6

T

C

1

LUCERO

Temperate Japonica

Italy

40.7

T

C

1

GIANO

Tropical Japonica

Italy

41.0

T

C

adm

DRAGO

Temperate Japonica

Italy

41.0

T

C

1

CINIA 40

Temperate Japonica

Cile

41.1

T

C

1

SAKHA 103

Temperate Japonica

Egypt

41.1

T

C

1

GARDE SADRI

Temperate Japonica

Turkey

41.7

T

C

1

BELLE PATNA

Tropical Japonica

USA

41.7

A

C

2

LAMONE

Tropical Japonica

Italy

41.8

T

C

2

FAMILIA 181

Temperate Japonica

Portugal

42.0

T

C

1

CORBETTA

Temperate Japonica

Italy

42.1

T

C

1

EUROSIS

Temperate Japonica

Italy

42.3

A

C

adm

ALPE

Temperate Japonica

Italy

42.3

T

C

1

CARIOCA

Tropical Japonica

Italy

42.4

T

C

adm

VIALONE NANO

Temperate Japonica

Italy

42.5

T

G

1

LOMELLINO

Temperate Japonica

Italy

42.7

T

C

1

KORAL

Temperate Japonica

Italy

42.7

T

C

1

SAKHA 102

Temperate Japonica

Egypt

43.3

T

C

1

ERMES

Tropical Japonica

Italy

43.4

T

G

adm

BURMA

Tropical Japonica

Italy

43.4

T

C

adm

CALMOCHI 101

Temperate Japonica

USA

43.5

T

C

1

ANTONI

Temperate Japonica

Bulgary

44.1

T

C

1

GUADIAMAR

Temperate Japonica

Spain

44.2

T

C

1

ARGO

Temperate Japonica

Italy

44.4

T

C

1

CARNISE

Temperate Japonica

Italy

44.5

T

G

1

AMERICANO 1600

Temperate Japonica

Italy

44.9

T

C

1

ARBORIO

Temperate Japonica

Italy

45.1

T

G

1

DIXIEBELLE

Tropical Japonica

USA

45.3

T

C

2

M203

Temperate Japonica

USA

45.6

T

C

1

BALDO

Temperate Japonica

Italy

45.6

T

C

1

ARTEMIDE

Tropical Japonica

Italy

46.0

T

C

adm

BOMBON

Temperate Japonica

Spain

46.1

T

C

1

CARINA

Temperate Japonica

Bulgary

46.4

T

G

1

GANGE

Tropical Japonica

Italy

46.5

T

C

2

ALEXANDROS

Tropical Japonica

Greece

46.7

A

C

2

LACASSINE

Tropical Japonica

USA

46.7

T

C

2

M6

Temperate Japonica

Italy

48.3

T

G

1

MARATELLI

Temperate Japonica

Italy

48.3

T

C

1

FORTUNA

Tropical Japonica

Italy

49.0

T

G

adm

AIACE

Tropical Japonica

Italy

49.5

T

C

2

BAIXET

Temperate Japonica

Spain

49.8

T

C

1

GIZA 177

Temperate Japonica

Egypt

50.3

T

C

1

CRIPTO

Temperate Japonica

Italy

50.7

T

C

1

AGOSTANO

Temperate Japonica

Italy

51.0

T

C

1

CAPATAZ

Temperate Japonica

Spain

51.3

T

C

1

ANSEATICO

Temperate Japonica

Italy

51.3

T

C

adm

BONNI

Temperate Japonica

Italy

51.4

T

C

1

BALZARETTI

Temperate Japonica

Italy

52.2

T

C

1

BERTONE

Temperate Japonica

Italy

52.2

T

C

1

TEXMONT

Tropical Japonica

USA

52.6

T

-

2

KARNAK

Temperate Japonica

Italy

54.3

T

G

1

CARNAROLI

Temperate Japonica

Italy

54.3

T

G

1

AKITAKOMACHI

Temperate Japonica

Japan

54.5

T

C

1

IBO 380–33

Temperate Japonica

Portugal

54.5

T

G

1

BOMBILLA

Temperate Japonica

Spain

54.5

T

-

1

ALLORIO

Temperate Japonica

Italy

54.6

T

C

1

DREW

Tropical Japonica

USA

54.8

T

C

2

M202

Temperate Japonica

USA

55.1

T

C

1

HANDAO 297

Temperate Japonica

China

55.3

T

G

1

ITALPATNA 48

Temperate Japonica

Italy

55.4

T

C

1

GIOVANNI MARCHETTI

Temperate Japonica

Italy

55.7

T

G

1

ARIETE

Temperate Japonica

Italy

56.0

T

C

1

COCODRIE

Tropical Japonica

USA

57.0

T

C

2

HONDURAS

Tropical Japonica

Spain

59.9

T

G

2

CAMPINO

Temperate Japonica

Portugal

60.1

T

C

1

CARMEN

Temperate Japonica

Italy

60.8

T

C

1

ALPHA

Temperate Japonica

Italy

61.2

T

G

1

CALENDAL

Temperate Japonica

France

61.6

T

C

1

M204

Temperate Japonica

USA

62.9

T

C

1

SELENIO

Temperate Japonica

Italy

66.6

T

C

1

A301

Tropical Japonica

USA

68.7

T

C

2

GLADIO

Tropical Japonica

Italy

73.4

T

C

2

ESCARLATE

Temperate Japonica

Portugal

77.0

T

G

1

JACINTO

Tropical Japonica

USA

77.7

T

C

2

Values of the disease scoring (I*) and the allelic status for the peak SNP markers (S1_628091 and S4_31750955) of the two identified bakanae resistance QTL is reported. Structure groups = groups defined by Structure (membership ≥70%)

Table 2

Analysis of variance for the bakanae disease resistance test carried-out on the Rice Germplasm Collection

Source

DF

SS

MS

F - value

P(F)

Replicate

2

65.35

32.67

1.31

0.272

Genotype

137

52,013.95

379.66

15.2

<0.001

Residual

240

5992.82

24.97

  

Total

379

58,072.12

153.22

  

Population structure of the Rice Germplasm Collection and genetic diversity analysis

The model based analysis of the panel structure was performed with Structure coupled with Structure Harvester analysis of the results, taking into account also the number of admixed varieties identified at each K value, as proposed by Courtois et al. (2012). Both the plot of Ln(K) and the analysis of ΔK against increasing K values indicated K = 2 as the most probable value (Additional file 1: Figure S1). At K = 2, the percentage of varieties classified as admixed was 13.7%, while for higher K values the percentage raised to over 43% (data not shown). The Structure analysis at K = 2 identified a subpopulation (91 accessions) constituted by varieties derived from the temperate japonica subspecies, and a second group (28 accessions) including tropical japonica-derived varieties (Table 1).

The information obtained from Structure and integrated with the neighbor - joining tree, together with the information available in the literature are presented in Fig. 2. The three sources of information are in good accordance, with few exceptions. Artemide, Ermes, Giano and Graldo were grouped in the temperate japonica cluster, although these varieties were classified in the literature as tropical japonica. In the Structure analysis these varieties were considered as admixed. A significant proportion of European varieties documented as tropical-derived were classified as admixed in the Structure analysis, probably reflecting the contribution of cvs. from different groups in their breeding programs. The Structure output and the information taken from the literature were also crossed with results from a Principal Coordinate Analysis (PCoA) (Additional file 2: Figure S2). The first and second coordinates accounted together for 42% of the total variability (35.9 and 5.9% respectively). Principal coordinate 1 separated the sub-populations defined by the Structure analysis at K = 2 and corresponding to the temperate and tropical japonica groups, with the admixed accessions clustering in between. No further clustering was evident with this analysis. According to the results of this whole dataset, the LD analysis was performed assuming the panel to be structured in two subgroups.
Fig. 2

Neighbor-joining tree of the Rice Germplasm Collection. On each branch the blue circles show the results of the bootstrap analysis, when higher than 0.7. The outer white-to-black coded cycle represents the clustering of the different varieties of the panel according to O. sativa classification; the inner cycle (three-color scaled) reports the cluster organization resulting from the STRUCTURE analysis

Analysis of the genetic diversity indicated that the rice panel as a whole explained a genetic diversity of H = 0.31 while among temperate and tropical japonica (H = 0.23 and H = 0.27 respectively) as well as between the groups obtained by Structure analysis (0.22 and 0.23 respectively) the values were comparable (Additional file 3: Table S1 A). Within the tropical accessions, the European genotypes explained a higher diversity compared to USA accessions (H = 0.32 and H = 0.22 respectively). The genetic divergence between the temperate and tropical japonica of the rice panel estimated as FST (Additional file 3: Table S1 B), identified a value equal to 0.38. Considering the two tropical japonica subgroups (European and USA), the higher divergence was detected between the temperate japonica and tropical japonica USA (FST = 0.47). Similar divergence estimates were computed considering the two groups identified by the Structure analysis, since the FST value was equal to 0.49. All comparisons performed were significant at p = 0.01.

Analysis of linkage disequilibrium and association mapping of bakanae resistance loci

The analysis of LD decay for each chromosome evidenced an average value of 1992 Kb (ranging from 1015 Kb for chromosome 6 to 2725 Kb for chromosome 12, Table 3). The set of markers available for GWAS after filtering by call rate and minor allele frequency, consisted of 31,752 SNPs, with a number of SNP markers per chromosome ranging from 1585 (chromosome 9) to 3970 (chromosome 1) (Table 3). Considering a total estimated genome size of 373 Mbp, we calculated a marker density of 0.09 SNP/Kbp in the whole population, with this value decreasing to 0.05, 0.06 and 0.04 in the temperate japonica, tropical japonica from Europe and tropical japonica from USA, respectively (Additional file 3: Table S1 C). Taking into account the extent of LD decay observed, this panel can be considered suitable to find markers associated to the resistance/susceptibility phenotype.
Table 3

For each chromosome the number of markers used for GWAS analysis and the corresponding average LD is indicated

Chromosome #

Number of SNPs

Average LD (Kb)

1

3970

1155

2

1997

2005

3

2389

2185

4

2746

2575

5

2438

2555

6

2505

1015

7

2703

1635

8

2530

2105

9

1585

1785

10

3169

1745

11

2696

2425

12

3024

2725

Total

31,752

 
Genome-wide association analysis revealed two genomic regions highly associated with the observed phenotypic variation on the short arm of chromosome 1 (qBK1_628091) and the long arm of chromosome 4 (qBK4_31750955), respectively (Fig. 3). On chromosome 1, 56 SNPs encompassing 413 Kb passed a stringent FDR threshold of 0.01 [−log10(p-value) = 4.87] (Table 4). The most associated marker mapped at the distal border of this region, at position 628,091; more distally, the observed marker coverage was low, with only two additional SNPs detected in the panel under study, at positions 170,244 and 330,484 (Additional file 4: Figure S3); none of them was associated to bakanae resistance. Twenty-two accessions out of 138 (15.9%) carried the resistance “A” allele at position 628,091 and they showed an average I* value of 30. This allele was more abundant in tropical japonica accessions (17 out of 41, 41.5%) than in temperate japonica ones (5 out of 97, 5.1%). Lines carrying the alternative “T” allele had an average I* score of 44.4 (Table 4; Additional file 5: Figure S4).
Fig. 3

Manhattan plot showing the results of the Genome-Wide Association scan for bakanae disease resistance in the Rice Germplasm Collection. The −log10(p) from the GWA scan is plotted against the physical SNP positions on the 12 rice chromosomes. Two different FDR thresholds are indicated by dashed horizontal lines

Table 4

Summary of significant marker-trait associations identified for bakanae disease resistance

Marker

Chr

Position (bp)

MAF

-log10(p)

Average I* value

Minor allelea

Major alleleb

S1_628091

1

628,091

0.16

5.84

30.0

44.4

S4_31750955

4

31,750,955

0.15

6.06

48.8

41.1

S4_1212877

4

1,212,877

0.13

4.06

50.9

40.6

S6_4318697

6

4,318,697

0.37

3.89

43.1

41.5

S7_3180670

7

3,180,670

0.36

4.41

44.7

40.6

S8_19138386

8

19,138,386

0.24

3.92

46.6

40.6

S12_24321230

12

24,321,230

0.15

4.14

33.1

43.7

SNP markers passing the 0.01 FDR threshold are in bold

aAverage I* value of the accessions carrying the allele at lower frequency

bAverage I* value of the accessions carrying the allele at higher frequency

On chromosome 4, a genomic region of 595 Kb (from position 31,162,467 to position 31,757,436) was delimited by four significant SNPs (Additional file 4: Figure S3). The peak marker [−log10(p-value) = 6.06] mapped at 31,750,955, with the “C” allele associated to a lower incidence of the disease (average I* = 41.1) and harbored by 112 accessions (i.e. 85% of the whole population, 82% of the temperate japonica and 83% of the tropical japonica). The average I* value associated to the alternative “G” allele was 48.8 (Table 4; Additional file 5: Figure S4).

Eleven out of the 12 most resistant genotypes with I* value <27 carried the “A”-“C” combination at the two major loci detected. With the exception of the temperate japonica cvs. Bengal and Maioral, the other 10 genotypes belonged to the tropical japonica sub-population and were originating from the United States (5 accessions), Italy (4 accessions) and Brazil (1 accession). Sequences surrounding the SNPs associated to bakanae resistance on chromosomes 1 and 4 are provided in Additional file 6: Figure S5.

Additional SNPs on chromosomes 4, 6, 7, 8 and 12 were significantly associated to bakanae resistance at a FDR threshold <0.05 (with −log10(p-value) ranging from 3.89 to 4,41; Table 4). They may represent further loci responsible for partial levels of resistance and contributing to the observed quantitative variation for F. fujikuroj resistance.

Identification of candidate genes for the major bakanae resistance loci

SNP markers with −log10(p-value) above the considered FDR threshold delimited the two major bakanae resistance loci to about 413 kbp on chromosome 1 (from position 628,091 to 1,040,823) and about 595 kbp on chromosome 4 (from position 31,162,467 to 31,757,436) (Additional file 4: Figure S3). A search for candidate genes with a putative role in bakanae resistance was carried out considering all the annotated genes included in the above indicated genomic regions through the screening of the O. sativa genomic reference sequence (Os-Nipponbare-Reference-IRGSP-1.0).

For qBK1_628091, 129 genes were identified in the genomic region surveyed for candidates, of which 45 were functionally annotated (Additional file 7: Table S2). No candidate genes were identified in the reference genome for the position of the most associated SNP marker (position 628,091 bp), as well as in the interval from 628,091 to 645,598 bp, this last position corresponding to the region in which the first candidate was identified; indeed in this interval only 5 genes with unknown function are annotated on the Nipponbare reference genome. Among the genes with known function, 33 loci encoded for protein kinases and, in particular, 22 for receptor-like kinases, of which 3 represented the receptor kinase LRK14 (Os01g0114900, Os01g0115600 and Os01g0117700) and other 3 the receptor kinase LRK10 (Os01g0117100, Os01g0117300 and Os01g0117500). These last receptors are assigned to the wheat leaf rust kinase (WLRK) receptor family, implicated in leaf rust resistance response in wheat (Feuillet et al. 1998). In addition, 2 genes (Os01g0112800 and Os01g0113150) corresponded to disease resistance proteins domain containing protein (Additional file 7: Table S2).

A total of 56 functionally annotated genes were identified in the genomic interval analyzed for qBK4_31750955. Also in this genomic region, several genes with functions compatible with disease resistance were identified (Additional file 7: Table S2). These included Os04g0620800, encoding for a NB-LRR resistance protein; Os04g0621500, encoding for a disease resistance domain containing protein; 3 genes (Os04g0616300, Os04g0616400 and Os04g0616500), representing SHR5-receptor-like kinases, belonging to the VIII-2 subclass of LRR receptor kinases, induced by fungal and bacterial infection in sugarcane and predicted to be involved in plant defense response (Vinagre et al. 2006); Os04g0620000, an ABC transporter, member of a class of transporters implicated in detoxification after fungal infection in wheat (Krattinger et al. 2009; Sucher et al. 2016); three genes (Os04g0624400, Os04g0624450 and Os04g0624500) encoding for polyphenol oxidases, representing a class of enzymes whose over-expression reduced leaf blast severity in rice (Ng et al. 2016); Os04g0615900, a FAR1 domain containing protein, representing a light signaling factor which regulates plant immunity by modulating chlorophyll biosynthesis (Wang et al. 2016); Os04g0616100, corresponding to a tetratricopeptide repeat domain containing protein, and Os04g0618050 encoding for a pentatricopeptide repeat domain containing protein. Both these two domains have been demonstrated to be present in proteins implicated in plant resistance (Spoel and Dong 2012; Sekhwal et al. 2015).

Additional candidate genes for the bakanae resistance loci were searched among the Differentially Expressed Genes (DEGs) identified in a previous RNA-Seq comparative transcriptomic analysis, of resistant and susceptible rice cvs. (Selenio and Dorella, respectively), in response to F. fujikuroi (Matic et al. 2016). DEGs were selected according to the four criteria listed in the Materials and methods and only those showing a genomic position within or near the two major bakanae resistance loci defined above were considered. A single locus (Os01g0112600), encoding for a protein of unknown function and induced by infection in the resistant cv. Selenio (log2Fold Change (log2FC) values = 2.17), was discovered for qBK1_628091, while 18 candidate loci were identified for qBK4_31750955 (Additional file 8: Table S3). Among them, the Aldo/keto reductase encoding locus Os04g0594400 was less expressed in Selenio, with respect to Dorella, in the presence of the fungus at 1 wpg (log2FC = −15.21). However, it was repressed by infection in Dorella at 3 wpg (log2FC = −2.03), while induction by infection in Selenio at 3wpg was detected (log2FC = 1.3), thus leading to a higher level of transcription in the resistant genotype during infection at 3 wpg (log2FC = 2.72). Plant aldo-keto reductases are enzymes involved in the response to stresses, including abiotic and biotic challenges (Sengupta et al. 2015). Os04g0598900 encodes for a protein similar to wall-associated kinases and was more expressed in Selenio vs. Dorella in both conditions at 1 wpg (log2FCs = 1.01 and 14.275, in mock and infected samples respectively) and during infection at 3 wpg (log2FC = 3.11). Moreover, at 3 wpg the gene was repressed by infection in Dorella (log2FC = −2.55) and induced by infection in Selenio (log2FC = 1.14). Os04g0616400 encoded for a protein similar to a receptor-like serine/threonine kinase and was more expressed in Selenio, with respect to Dorella, in both treatments and time-points of germination (log2FCs ranging from 5.37 to 66). Finally, Os04g0652400 represented a protein similar to a sulphate transporter and was more expressed in infected Selenio vs. infected Dorella at both time-points of growth (log2FCs = 16.98 and 1.265, respectively at 1 and 3 wpg).

Discussion

In this work, a panel of rice temperate and tropical japonica accessions from different origins, mainly from Italy (67), USA (28), Portugal (11) and Spain (10) was assembled in order to identify new loci associated with resistance to the rice bakanae disease through a GWA mapping approach. The analysis of LD decay for each chromosome in this panel showed an average value of 1992 Kb, which is considerably higher than those commonly reported in the literature of about 150–180 kb for japonica backgrounds (Mather et al. 2007, Huang et al. 2011, Courtois et al. 2013). However, higher LD values ranging from 600 kb up to 2 Mb were also observed in a number of cases for japonica and indica rice (Xu et al. 2011, Kumar et al. 2015); moreover, a germplasm collection of more related temperate japonica rice accessions recorded values of LD decay of 1250 kb (Biscarini et al. 2016). Since the panel used in this study represents a sub-group of the panel used in Biscarini et al. (2016), it is conceivable that the higher values of LD observed here are most likely due to a lower level of diversity among the varieties included in the present collection, suggesting that few historical recombination events occurred in this population. Moreover, SNP density applied in our study can contribute to the higher LD value estimated; in the present work a total of 31,752 SNPs were used to estimate LD (and for GWAS analysis) while Courtois et al. (2013) used 16,664 markers (both SNPs and DArTs) and Mather et al. (2007) used only 522 markers. LD estimates tend to be higher with denser SNP panels (Khatkar et al. 2008, O’Brien et al. 2014), and LD patterns tend to emerge clearly only at higher SNP densities (Bacciu et al. 2012). The resulting higher LD detected may eliminate true positives if in one region in LD more than one significant association is present, however considering both, the extent of LD decay observed and the expected average marker density (calculated as 0.09 SNP/Kbp in the whole population), we were confident that this panel represented an excellent resource for investigating bakanae resistance in japonica rice.

Screening of the GWAS panel allowed the identification of accessions with a low disease index (I*); even considering that the disease incidence among temperate and tropical japonica accessions was not statistically different, ten of the 12 more resistant accessions (i.e. those showing I* values <27) were identified within the tropical japonica background, raising the possibility that higher frequency of effective bakanae resistance loci is present in tropical with respect to temperate japonica. However, since no screenings for bakanae resistance involving relevant numbers of accessions belonging to the different rice groups (temperate and tropical japonica, indica, aromatic, aus) have been carried out so far, these conclusions cannot be adequately supported. The GWAS analysis was therefore carried out using a restricted number of related sub-populations (temperate and tropical japonica). As previously observed, this approach from one side increases the possibility to detect associations for alleles that are segregating only in one or two populations while are fixed in others, but from the other side the resulting higher LD may eliminate true positives (Famoso et al. 2011; Zhao et al. 2011). However, the high frequency of the resistant phenotypes in the tropical japonica sub-population detected in this work, leveraged power to detect alleles that were segregating within this sub-population.

To our knowledge, the present work represents the first report on the utilization of a GWAS approach for the identification of resistant loci effective against the bakanae disease of rice. Two genomic regions were associated to bakanae resistance and delimited to about 0.41 Mb on chromosome 1 (from position 628,091 to 1,040,823) and 0.59 Mb on chromosome 4 (from position 31,162,467 bp to 31,757,436 bp), and were named as qBK1_628091 and qBK4_31750955, respectively. Different bakanae resistance QTLs have been previously located on rice chromosome 1 (Fiyaz et al. 2016; Hur et al. 2015; Yang et al. 2006). Of these, qBK1.2 and qBK1.3 (Fiyaz et al. 2016) and qB1 (Yang et al. 2006) were located on the short arm of chromosome 1. The comparison between our resistance-associated region on chromosome 1 and qBK1.2, identified in a 0.26 Mb region between RM10153 and RM5336 (from position 3,105,042 to 3,367,533; Fiyaz et al. 2016), demonstrated that qBK1_628091 was located apart from qBK1.2. Similarly, the region associated to qBK1.3, ranging from RM10271 and RM35 (from position 4,657,288 to 8,411,302 bp; Fiyaz et al. 2016), and qB1, spanning from RM7180 to RM486 (from position 34,105,454 and 34,956,597 bp; Yang et al. 2006) resulted different from qBK1_628091. Moreover, previous studies (Ma et al. 2008) indicated that rice varieties with the sd1 gene, a semi-dwarf gene resulting in defective 20-oxidase GA biosynthetic enzyme and localized from 38,382,382 to 38,385,504, are susceptible to bakanae disease. The detected position for qBK1_628091 indicates that this resistance locus is not related to the sd1 allele. All these observations therefore indicate that the QTL we have detected on chromosome 1 represents a novel unknown locus involved in bakanae resistance. Moreover, no bakanae-resistance loci have been previously mapped on chromosome 4, suggesting that, also in this case, qBK4_31750955 represents a new genomic region associated to F. fujikuroi resistance.

Alternative alleles for SNPs representing peak markers for the two resistance loci were identified, where the “A” and “C” alleles for qBK1_628091 and qBK4_31750955 respectively, were associated to a lower bakanae disease incidence. Noteworthy, when the 12 most resistant accessions (with I* value <27) were analyzed, 11 of them carried the combination of the “A” and “C” alleles (for Adair, the C allele at qBK4_31750955 has been imputed from neighbor markers, data not shown), suggesting that pyramiding of the two loci should provide effective levels of resistance. Within these accessions, the tropical japonica sub-group was predominant (10 accessions out of 12); this result, together with the observation that tropical japonica have a lower level of average disease incidence, may indicate that higher breeding pressure for bakanae resistance was applied in the tropical japonica than on the temperate sub-group. This observation is further supported by the higher frequency of the resistant allele (“A”) at the qBK1_628091 peak marker observed in tropical japonica (41.5%) compared to temperate japonica (5.1%). Sequences corresponding to the peak markers for qBK1_628091 and qBK4_31750955 are here provided (Additional file 6: Figure S5). These sequences can be used to develop SNP-based high-throughput markers to be used in marker-assisted selection for pyramiding the two resistant loci in bakanae susceptible lines. Moreover, effective resistance loci were also identified in several different commodity classes, including round (Greppi), long A (Maioral and Bengal) and long B (Arsenal, Adair, King), an aspect that should facilitate the introgression of bakanae resistance into rice lines addressing different market requirements.

Several annotated genes encoding functions compatible with resistance were identified for both resistance loci genetic intervals. These included receptor-like kinases, such as LRK10 and LRK14, known to participate to wheat leaf rust resistance (Feuillet et al. 1998) for the qBK1_628091 region, while for qBK4_31750955, a NB-LRR gene, receptor kinases and an ABC transporter were identified. A second approach for identification of candidates was based on the integration of mapping position and RNA-Seq data previously obtained in a comparative transcriptome analysis of resistant and susceptible rice cvs., Selenio and Dorella respectively, in response to F. fujikuroi (Matic et al. 2016). DEGs located within or near the two QTLs regions were analyzed according to the criteria indicated in Methods. The analysis did not lead to identification of candidates for qBK1_628091, as only one locus encoding for a protein of unknown function fitting the criteria was located on this QTL region. For the qBK4_31750955, a SHR5-receptor kinases was listed among the candidate genes using the combined DEGs and map position approaches. Finally, also a gene encoding for a sulphate transporter, transcribed at higher rates in Selenio during infection (Matic et al. 2016), was located in the qBK4_31750955 region. It is well known that sulphur increases resistance in different plant-fungal pathogen interactions, inducing the production of a number of sulphur compounds implicated in defense responses like glucosinolates, phytoalexins, H2S, cysteine and glutathione (Walters and Bingham 2007). Thus, another possible qBK4_31750955 function might be related to S uptake and related production of S-resistance compounds.

Overall, the in silico search for candidate genes, in the two QTL regions (qBK1_628091 and qBK4_31750955) identified in this work, highlighted several genes with functions associated to disease resistance that could represent candidates for bakanae resistance. It should however be considered that these genes were identified on the Nipponbare genome and that, currently, the reaction of this rice cv. to bakanae infection is not known. Additional investigations involving targeted resequencing of the two QTL regions in resistant and susceptible accessions here identified and the comparison of these regions with the available Nipponbare sequence are therefore required. To address the final identification of the genes responsible for bakanae resistance we are developing high-resolution mapping populations for qBK1_628091 and qBK4_31750955 through crossing accessions bearing only one of the two loci with highly susceptible accessions. These materials will allow a fine mapping of the two loci and a more detailed and precise assessment of the candidate genes here reported until the identification of the genes underlying the QTL involved in resistance.

Conclusions

Screening of a japonica rice germplasm collection carried out with a virulent F. fujikuroj isolate allowed the identification of accessions bearing relevant levels of resistance. The subsequent GWAS approach under stringent conditions identified two previously un-identified bakanae resistance loci on the short arm of chromosome 1 and on the long arm of chromosome 4. Since high levels of phenotypic resistance to bakanae was associated to the cumulated presence of the peak markers resistance alleles at the two loci, it is expected that they can have an additive effect that could be exploited also in resistance breeding. Candidate genes with a putative role in bakanae resistance were identified in the two genomic regions highlighting several gene functions that could be involved in resistance opening the way for the functional characterization of the resistance loci.

Methods

Plant materials and genotyping

The accession panel used in this study included 138 O. sativa varieties from the Rice Germplasm Collection maintained at the CREA-Rice Research Unit (Vercelli, Italy). The sampled collection included 41 tropical japonica and 97 temperate japonica accessions. Most of these were temperate rice developed in Italy (67 accessions), selected from larger collections with the aim of including the broadest range of genetic and phenotypic variation (Faivre-Rampant et al. 2011; Biscarini et al. 2016). The remaining 71 genotypes were developed elsewhere but they are considered adapted to Italian agro-climatic conditions. The complete list of accessions used in this study, with information on taxonomic group and geographic origin, is reported in Table 1.

All the accessions were subjected to genotyping-by-sequencing (GBS) as described by Biscarini et al. (2016). The analysis yielded a set of 166,418 SNP markers, which were filtered for call rate (1 - percentage of missing data) and minor allele frequency (MAF) with the PLINK software (http://zzz.bwh.harvard.edu/plink/; Purcell et al. 2007). Different filtering thresholds were chosen depending on the analysis performed.

Phenotyping for bakanae resistance

The collection of 138 rice genotypes was evaluated for bakanae resistance after seed inoculation. For the inoculum production, the ER 2103 F. fujikuroi isolate from the CREA-PAV collection was used. This isolate was previously tested for its virulence by seed inoculation of the susceptible rice cv. Galileo, by the same method described below for phenotyping of the whole rice collection. The fungus was grown for 3 days on 20% V8 juice liquid medium (Miller 1955) with shaking (120 rpm) at 23 °C, microconidia were harvested and the concentration was adjusted to 106 spores ml−1. Rice seeds were surface sterilized in 70% ethanol for 1 min. With shacking, then in 1.5% sodium hypochlorite for 30 min and subsequently rinsed 5 times in sterile water. Surface sterilized seeds were inoculated by dipping the seeds in the inoculum for 30 min, immediately before sowing in pots containing soil; therefore, temperatures applied for inoculums were the same as those applied to growth the rice plantlets, below indicated. A complete randomized block design with three replicates and 25 seeds for each replicate was used. Plants were kept in the greenhouse at 20–25 °C with a 12 h photoperiod. After 30 days, seedlings were evaluated for symptoms by using the 0–4 disease scale of Mohd Zainudin et al. (2008) with the following modifications: 0 = no symptoms; 1 = normal growth but leaves beginning to show yellowish-green, small necrosis localized at the crown level; 2 = abnormal growth, elongated, thin and yellowish-green leaves; seedlings also shorter or taller than normal, necrosis on main root and crown; 3 = abnormal growth, elongated, chlorotic, thin and brownish leaves; seedlings also shorter or taller than normal, reduced root system with necrosis on secondary roots and on basal stem; 4 = dead plants. Based on scoring values of each plant, gravity index was assessed by using McKinney index (I; McKinney 1923) calculated as:
$$ I=\frac{\sum \left( f\ast v\right)}{N\ast X}\ast 100 $$

where: f = value of the scoring class (0, 1, 2, 3, 4), v = number of plants of each class, N = total of observed plants, X = highest value of the evaluation scale. Data were arcsin transformed (disease index, I*) prior to analysis of variance (ANOVA). Two-factor ANOVA was carried out with the software GenStat (Payne et al. 2009) to evaluate differences for bakanae resistance between genotypes. Broad sense heritability (H2) was estimated from the variance components obtained by fitting both replications and genotypes as random terms as H2 = σ2 g/(σ2 g + σ2 e), where σ2 g is the genotypic variance component and σ2 e is the residual variance component.

Analysis of population structure and genetic diversity

The panel of rice varieties was screened for the presence of sub-populations using a model-based approach integrated by neighbor joining phylogenetic and Principal Coordinate (PCoA) analyses. For this purpose we used a subset of 10,000 SNP markers randomly selected from the whole dataset by applying the following thresholds: call rate 95%, MAF 5%.

The model-based analysis was performed using Structure v2.3.4 (Pritchard et al. 2000). The data were analyzed as haploids (a correct approach for a highly autogamous species such as rice; Nordborg et al. 2005). The following parameters were used: presence of admixture admitted; allele frequencies among sub-populations correlated; 20,000 burn-in cycles followed by 10,000 Monte Carlo – Markov Chain (MCMC) iterations; a number of sub-populations (K) ranging between 1 and 8; 5 runs per K value. The results of the Structure analysis were analyzed according to Evanno et al. (2005) with the Structure Harvester program (Earl and vonHoldt 2012) to identify the most probable number of clusters in the population. The changing of the population clustering (number of sub-populations, number of admixed accessions) was also evaluated at increasing values of K, as proposed by Courtois et al. (2012). Once defined this parameter, one single run of the Structure analysis was repeated at the most probable K value to maximize the accuracy in determining the membership of each accession. The same parameters as above were used, except for the number of burn-in and MCMC iterations (150,000 and 100,000 respectively). Accessions with membership coefficients ≥0.7 were assigned to a specific sub-population, whereas the remaining genotypes were identified as admixed.

A neighbor-joining tree was built with the MEGA v7 software (Kumar et al. 2016), based on the Jukes-Cantor model which is appropriate for sequence data when the rate of nucleotide substitution is expected to be equal for all pairs of the four nucleotides. Bootstrap values (300×) were computed and added to the tree branches when higher than 70% (Hillis and Bull 1993). The resulting tree was imported in iTOL (http://itol.embl.de/; Letunic and Bork 2016) and implemented with the provenience information (Faivre-Rampant et al. 2011; Biscarini et al. 2016; Table 1) and with the results of the above Structure analysis.

Finally, a PCoA was performed with the PAST v3.11 software (Hammer et al. 2001) with the Jukes-Cantor algorithm; sub-population attributions derived from Structure analysis and taxonomic groups defined in the literature (Biscarini et al. 2016; Courtois et al. 2012) were projected onto the final output.

For the genetic diversity analyses, the number of polymorphic loci, the expected heterozygosity (He; Nei 1978) and the number of transitions and transvertions were computed using the Arlequin v3.5 software (Excoffier and Lischer 2010). The whole sample and the following partitions of the accessions were considered for the analyses: temperate japonica, tropical japonica and, within the tropical japonica, sampling was done according to the provenience (Europe and USA). The genetic diversity statistics described above were also computed for the genetic groups highlighted by the Structure analysis. The divergence among the populations defined a priori according to the subspecies, within tropical japonica and among groups identified by Structure, was estimated as FST (Weir and Cockerham 1984). The significance of the estimates was obtained through permutation tests, using 1000 permutations. The Arlequin v3.5 software (Excoffier and Lischer 2010) was used.

Analysis of linkage disequilibrium and association mapping

The expected resolution of the association mapping panel was evaluated by calculating the linkage disequilibrium (LD) as the correlation (R2) between loci on each chromosome, after filtering the SNP markers with the following threshold values: call rate > 95%; MAF > 5%.

The R2 computation was performed with the package LDcorSV v1.3.1 (https://cran.r-project.org/web/packages/LDcorSV/index.html) implemented in R; the values were therefore plotted against physical distance and fitted to a second degree LOESS curve (Cleveland 1979, Marroni et al. 2011) using the R language. A critical value of 0.2 was set as R2 between unlinked loci. The value of physical distance corresponding to a LOESS curve value of 0.2 was assumed as an estimate of the LD extent in each chromosome.

Genome-wide association between markers with call rate > 95% and minor allele frequency > 10% and the phenotypic data was performed by fitting a Mixed Linear Model (MLM) in Tassel v5.0 (Bradbury et al. 2007), that includes a kinship matrix as random term to account for genetic relatedness due to population structure. MLM was run with the optimal compression and genetic and residual variances were estimated for each SNP marker. False Discovery Rate (FDR) was calculated with the R package q-value (http://qvalue.princeton.edu) in order to detect significant SNP associations. Finally, the R package qqman (https://cran.r-project.org/package=qqman) was used to draw Manhattan plots.

Search for candidate genes

The genomic regions associated to bakanae resistance have been selected on the base of FDR value (i.e. regions defined by significantly associated markers) and used as starting point to explore the genomic context of the Oryza sativa reference sequence (Os-Nipponbare-Reference-IRGSP-1.0, http://rapdb.dna.affrc.go.jp/download/irgsp1.html). All annotated genes included in the selected genomic windows have been scanned to identify candidate genes.

Additional candidate resistance genes were identified among the Differentially Expressed Genes (DEGs), located on the selected genomic regions, from a RNA-Seq comparative transcriptome analysis of resistant and susceptible rice cvs. Selenio and Dorella respectively, in response to F. fujikuroi at one and 3 weeks post-germination (Matic et al. 2016). DEGs were selected according to the following criteria: a) induction by infection in the resistant genotype only and higher expression in the resistant cv. with respect to the susceptible during infection; b) induction by infection in both genotypes and higher expression in the resistant cv., with respect to the susceptible during infection; c) higher expression in the resistant genotype with respect to the susceptible in mock conditions, but not infection responsiveness; d) induction by infection in the resistance genotype and repression by infection in the susceptible cv. and higher expression in resistant vs. susceptible comparisons.

Declarations

Acknowledgements

This study was funded by grants from AGER Foundation, (RISINNOVA project grant n. 2010–2369)

Authors’ contributions

This study was conceived by GV and AT. AV, MA, MTV, CB, FD, PB, SM, MLG, AI and DS performed the experiments and data analysis. AV, AT, MA, FD, MLG, DS and GV prepared the manuscript. All authors approved the manuscript.

Competing interests

The authors declare that they have no competing interests.

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Authors’ Affiliations

(1)
Council for Agricultural Research and Economics (CREA), Rice Research Unit
(2)
Council for Agricultural Research and Economics (CREA), Genomics Research Centre
(3)
Council for Agricultural Research and Economics (CREA), Plant Pathology Research Centre
(4)
AGROINNOVA, Università di Torino
(5)
DISAFA, Università di Torino

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