Skip to main content

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



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.


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.


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


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.


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
Table 2 Analysis of variance for the bakanae disease resistance test carried-out on the Rice Germplasm Collection

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

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

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).


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.


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.


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 (; 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 (; 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 ( 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 ( in order to detect significant SNP associations. Finally, the R 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, 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.


  1. Amatulli MT, Spadaro D, Gullino ML, Garibaldi A (2012) Conventional and real-time PCR for the identification of Fusarium fujikuroi and Fusarium proliferatum from diseased rice tissues and seeds. European J Plant Pathol 134(2):401–408

    CAS  Article  Google Scholar 

  2. Bacciu N, Bomba L, Nicolazzi E, Milanesi M, Ajmone-Marsan P (2012) Linkage disequilibrium in BTA6 for the italian holstein breed using a 54k and 800k SNP-chip. In: Proceedings of the 16th, QTL-MAS workshop, pp 24–25

    Google Scholar 

  3. Bearder JR (1983) In vivo diterpenoid biosynthesis in Gibberella fujikuroi: the pathway after ent-kaurene. In: Crozier A (ed) The biochemistry and physiologyof gibberellins. Praeger, New York, pp 251–387

    Google Scholar 

  4. Biscarini F, Cozzi P, Casella L, Riccardi P, Vattari A, Orasen G, Perrini R, Tacconi G, Tondelli A, Biselli C, Cattivelli L, Spindel J, McCouch S, Abbruscato P, Valé G, Piffanelli P, Greco R (2016) Genome-wide association study for traits related to plant and grain morphology, and root architecture in temperate rice accessions. PLoS One 11:e0155425

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    CAS  Article  PubMed  Google Scholar 

  6. Chen YC, Lai MH, Wu CY, Lin TC, Cheng AH, Yang CC, Wu HY, Chu SC, Kuo CC, Wu YF, Lin GC, Tseng MN, Tsai YC, Lin CC, Chen CY, Huang JW, Lin HA, Chung CL (2016) The genetic structure, virulence, and fungicide sensitivity of Fusarium fujikuroi in Taiwan. Phytopathology 106(6):624–635

    CAS  Article  PubMed  Google Scholar 

  7. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836

    Article  Google Scholar 

  8. Courtois B, Frouin J, Greco R, Bruschi G, Droc G, Hamelin C, Ruiz M, Clément G, Evrard J, Van C, Katsantonis D, Oliveira M, Negrão S, Matos C, Cavigiolo S, Lupotto E, Piffanelli P, Ahmadi N (2012) Genetic diversity and population structure in a European collection of Rice. Crop Sci 52:1663–1675

    Article  Google Scholar 

  9. Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, Droc G, Frouin J, Rouan L, Gozé E, Kilian A, Ahmadi N, Dingkuhn M (2013) Genome-wide association mapping of root traits in a japonica rice panel. PLoS One 8:e78037

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Desjardins AE, Manandhar HK, Plattner RD, Manandhar GG, Poling SM, Maragos CM (2000) Fusarium species from Nepalese rice and production of mycotoxins and gibberellic acid by selected species. Appl Environ Microbiol 66:1020–1025

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Earl DA, vonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Cons Gen Res 4(2):359–361

    Article  Google Scholar 

  12. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620

    CAS  Article  PubMed  Google Scholar 

  13. Excoffier L, Lischer HEL (2010) Arlequin suite version 3.5: a new series of programs to perform population genetics analyses under linux and windows. Mol Ecol Resour 10:564–567

    Article  PubMed  Google Scholar 

  14. Faivre-Rampant O, Bruschi G, Abbruscato P, Cavigiolo S, Picco AM, Borgo L, Lupotto E, Piffanelli P (2011) Assessment of genetic diversity in Italian rice germplasm related to agronomic traits and blast resistance (Magnaporthe oryzae). Mol Breed 27(2):233–246

    Article  Google Scholar 

  15. Famoso AN, Zhao K, Clark RT, Tung C-W, Wright MH, Bustamante C, Kochian LV, McCouch SR (2011) Genetic architecture of aluminum tolerance in rice (Oryza sativa) determined through genome-wide association analysis and QTL mapping. PLoS Genet 7:e1002221

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Feuillet C, Reuzeau C, Kjellbom P, Keller B (1998) Molecular characterization of a new type of receptor-like kinase (wlrk) gene family in wheat. Plant Mol Biol 37:943–953

    CAS  Article  PubMed  Google Scholar 

  17. Fiyaz AR, Yadav AK, Krishnan SG, Ellur RK, Bashyal BM, Grover N, Bhowmick PK, Nagarajan M, Vinod KK, Singh NK, Prabhu KV, Singh AK (2016) Mapping quantitative trait loci responsible for resistance to Bakanae disease in rice. Rice 9(1):45

    Article  PubMed  PubMed Central  Google Scholar 

  18. Gupta AK, Singh Y, Jain AK, Singh D (2014) Prevalence and incidence of bakanae disease of rice in northern India. J Agri Search 1:233–237

    Google Scholar 

  19. Hammer Ø, Harper DAT, Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontolo Electr 4:1–9

    Google Scholar 

  20. Haq M, Taher Mia MA, Rabbi MF, Ali MA (2011) Incidence and severity of rice diseases and insect pests in relation to climate change. In: Lal R, MVK S, Faiz MA, AHM MR, Islam KR (eds) Climate change and food security in South Asia. Springer, Netherlands, pp 445–457

    Google Scholar 

  21. Hayasaka T, Ishiguro K, Shibutani K, Namai T (2001) Seed disinfection using hot water immersion to control several seed-borne diseases of rice plants. J Phytopathol 67:26–32

    Article  Google Scholar 

  22. Hillis DM, Bull JJ (1993) An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Syst Biol 42:182–192

    Article  Google Scholar 

  23. Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z, Li M, Fan D, Guo Y, Wang A, Wang L, Deng L, Li W, Lu Y, Weng Q, Liu K, Huang T, Zhou T, Jing Y, Li W, Lin Z, Buckler ES, Qian Q, Zhang QF, Li J, Han B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet 42:961–967

    CAS  Article  PubMed  Google Scholar 

  24. Huang X, Zhao Y, Wei X, Li C, Wang A, Qiang Zhao Q, Li W, Guo Y, Deng L, Zhu C, Fan D, Lu Y, Weng Q, Liu K, Zhou T, Jing Y, Si L, Dong G, Huang T, Lu T, Feng Q, Qian Q, Li J, Han B (2011) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet 44:32–39

    CAS  Article  PubMed  Google Scholar 

  25. Hur YJ, Lee SB, Kim TH, Kwon T, Lee JH, Shin DJ, Park SK, Hwang UH, Cho JH, Yoon YN, Yeo US, Song YC, Kwak DY, Nam MH, Park DS (2015) Mapping of qBK1, a major QTL for bakanae disease resistance in rice. Mol Breed 35:78

    Article  Google Scholar 

  26. Iqbal M, Javed N, Sahi ST, Cheema NM (2011) Genetic management of bakanae disease of rice and evaluation of various fungicides against Fusarium moniliforme in vitro. J Phytopathol 23:103–107

    Google Scholar 

  27. Ito S, Kimura J (1931) Studies on the ‘bakanae’ disease of the rice plant. Rep Hokkaido Natl Agric Exp Stat 27:1–95

    Google Scholar 

  28. Jeon YA, Yu SH, Lee YY, Park HJ, Lee S, Sung JS, Kim YG, Lee HS (2013) Incidence, molecular characteristics and pathogenicity of Gibberella fujikuroi species complex associated with rice seeds from Asian countries. Mycobiol 41:225–233

    CAS  Article  Google Scholar 

  29. Khan JA, Jamil FF, Gill MA (2000) Screening of rice germplasm against Bakanae and bacterial leaf blight. J Phytopathol 12:6–11

    Google Scholar 

  30. Khatkar MS, Nicholas FW, Collins AR, Zenger KR, Cavanagh JAL, Barris W, Schnabel RD, Taylor JF, Raadsma HW (2008) Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel. BMC Genomics 9:187

    Article  PubMed  PubMed Central  Google Scholar 

  31. Khokhar LK, Jaffrey AH (2002) Identification of sources of resistance against bakanae and foot rot disease in rice. Pakistan J Agric Res 17:176–177

    Google Scholar 

  32. Kim JM, Hong SK, Kim WG, Lee YK, Yu SH, Choi HW (2010) Fungicide resistance of Gibberella fujikuroi isolates causing rice bakanae disease and their progeny isolates. Kor J Mycol 38:75–79

    Article  Google Scholar 

  33. Kim MH, Hur YJ, Lee SB, Kwon T, Hwang UH, Park SK, Yoon YN, Lee JH, Cho JH, Shin D, Kim TH, Han SI, Yeo US, Song YC, Nam MH, Park DS (2014) Large-scale screening analysis for the evaluation of bakanae disease inrice. J Gen Plant Pathol 80:408–414

    Article  Google Scholar 

  34. Krattinger SG, Lagudah ES, Spielmeyer W, Singh RP, Huerta-Espino J, McFadden H, Bossolini E, Selter LL, Keller B (2009) A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323(5919):1360–1363

    CAS  Article  PubMed  Google Scholar 

  35. Kumar V, Singh A, Mithra SVA, Krishnamurthy SL, Parida SK, Jain S, Tiwari KK, Kumar P, Rao AR, Sharma SK, Khurana JP, Singh NK, Mohapatra T (2015) Genome-wide association mapping of salinity tolerance in rice (Oryza sativa). DNA Res 22(2):133–145

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33:1870–1874

    CAS  Article  PubMed  Google Scholar 

  37. Lee YH, Lee MJ, Choi HW, Kim ST, Park JW, Myung IS, ParkK LSW (2011) Development of in vitro seedling screening method for selection of resistant rice against bakanae disease. Res Plant Dis 17:288–294

    Article  Google Scholar 

  38. Letunic I, Bork P (2016) Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res doi. doi:10.1093/nar/gkw290

  39. Li D, Luo K, Chen Z (1993) Studies on resistance of rice varieties to bakanae disease and pathogenicity of pathogen Fusarium moniliforme. Acta Phytopathol Sin 23:315–319

    Google Scholar 

  40. Lv B (1994) Preliminary study of identification for resistance to rice bakanae disease in rice resource. Plant Prot 3:20–21

    Google Scholar 

  41. Ma L, Ji Z, Bao J, Zhu X, Li X, Zhuang J, Yang C, Xia Y (2008) Responses of rice genotypes carrying different dwarf genes to Fusarium moniliforme and Gibberellic acid. Plant Prod Sci 11:134–138

    CAS  Article  Google Scholar 

  42. Ma LJ, Geiser DM, Proctor RH, Rooney AP, O'Donnell K, Trail F, Gardiner DM, Manners JM, Kazan K (2013) Fusarium pathogenomics. Annu Rev Microbiol 67:399–416

    CAS  Article  PubMed  Google Scholar 

  43. Marroni F, Pinosio S, Zaina G, Fogolari F, Felice N, Cattonaro F, Morgante M (2011) Nucleotide diversity and linkage disequilibrium in Populus nigra cinnamyl alcohol dehydrogenase (CAD4) gene. Tree Genet Genom 7(5):1011–1023

    Article  Google Scholar 

  44. Mather KA, Caicedo AL, Polato NR, Olsen KM, McCouch S, Purugganan MD (2007) The extent of linkage disequilibrium in rice (Oryza sativa L.) Genetics 177:2223–2232

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. Matic S, Spadaro D, Garibaldi A, Gullino ML (2014) Antagonistic yeasts and thermotherapy as seed treatments to control Fusarium fujikuroi on rice. Biol Control 73:59–67

    Article  Google Scholar 

  46. Matic S, Bagnaresi P, Biselli C, Orrù L, Amaral Carneiro G, Siciliano I, Valé G, Gullino ML, Spadaro D (2016) Comparative transcriptome profiling of resistant and susceptible rice genotypes in response to the seedborne pathogen Fusarium fujikuroi. BMC Genomics 17(1):608

    Article  PubMed  PubMed Central  Google Scholar 

  47. Matic S, Gullino ML, Spadaro D (2017) The puzzle of bakanae disease through interactions between Fusarium fujikuroi and rice. Front Biosci 9:333–344

    Article  Google Scholar 

  48. McKinney HH (1923) Influence of soil temperature and moisture on infection of wheat seedlings by Helminthosporium sativum. J Agr Res 26:195–217

    Google Scholar 

  49. Mew TW, Gonzales P (2002) A handbook of rice seedborne fungi. Int Rice Res Inst, Los Baños: 31-34

  50. Miller PM (1955) V-8 juice agar as a general purpose medium for fungi and bacteria. Phytopathology 45:461–462

    Google Scholar 

  51. Mohd Zainudin NAI, Razak AA, Salleh B (2008) Bakanae disease of rice in Malaysia and Indonesia: etiology of the causal agent based on morphological, physiological and pathogenicity characteristics. J Plant Protect Res 48:475–485

    Google Scholar 

  52. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Ng LC, Sariah M, Sariam O, Radziah O, Zainal Abidin MA (2016) PGPM-induced defense-related enzymes in aerobic rice against rice leaf blast caused by Pyricularia oryzae. Eur J Plant Pathol 145(1):167–175

    CAS  Article  Google Scholar 

  54. Nordborg M, Hu TT, Ishino Y, Jhaveri J, Toomajian C, Zheng H, Bakker E, Calabrese P, Gladstone J, Goyal R, Jakobsson M, Kim S, Morozov Y, Padhukasahasram B, Plagnol V, Rosenberg NA, Shah C, Wall JD, Wang J, Zhao K, Kalbfleisch T, Schulz V, Kreitman M, Bergelson J (2005) The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol 3(7):e196

    Article  PubMed  PubMed Central  Google Scholar 

  55. O’Brien AMP, Mészáros G, Utsunomiya YT, Sonstegard TS, Fernando Garcia JF, Van Tassell CP, Carvalheiro R, da Silva MVB, Sölkner J (2014) Linkage disequilibrium levels in Bos indicus and Bos taurus cattle using medium and high density SNP chip data and different minor allele frequency distributions. Livest Sci 166:121–132

    Article  Google Scholar 

  56. Ora N, Faruq AN, Islam MT, Akhtar N, Rahman MM (2011) Detection and identification of seed borne pathogens from some cultivated hybrid rice varieties in Bangladesh. Middle-East J SciRes 10:482–488

    Google Scholar 

  57. Ou SH (1985) Bakanae disease and foot rot. In: Ou SH (ed) Rice diseases. Surrey, Commonwealth Mycological Institute, pp 262–272

    Google Scholar 

  58. Park WS, Choi HW, Han SS, Shin DB, Shim HK, Jung ES, LeeSW LCK, Lee YH (2009) Control of bakanae disease of rice by seed soaking into the mixed solution of procholraz and fludioxnil. Res Plant Dis 15:94–100

    CAS  Article  Google Scholar 

  59. Payne RW, Murray DA, Harding SA, Baird DB, Soutar DM (2009) GenStat for windows (12th edition) introduction. VSN International, Hemel Hempstead

    Google Scholar 

  60. Pra MD, Tonti S, Pancaldi D, Nipoti P, Alberti I (2010) First report of Fusarium andiyazi associated with rice bakanae in Italy. Plant Dis 94:1070

    Article  Google Scholar 

  61. Pritchard JK, Stephens M, Donnelly PJ (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, de Bakker PIW, Daly MJ, Sham PC (2007) PLINK: a toolset for whole-genome association and population-based linkage analysis. American J Human Genet 81(3):559–575

    CAS  Article  Google Scholar 

  63. Rood MA (2004) Bakanae in field yield loss. Rice J 15:8–10

    Google Scholar 

  64. Sekhwal MK, Li P, Lam I, Wang X, Cloutier S, You FM (2015) Disease resistance Gene analogs (RGAs) in plants. Int J Mol Sci 16(8):19248–19290

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. Sengupta D, Naik D, Reddy AR (2015) Plant aldo-keto reductases (AKRs) as multi-tasking soldiers involved in diverse plant metabolic processes and stress defense: a structure-function update. J Plant Physiol 179:40–55

    CAS  Article  PubMed  Google Scholar 

  66. Siciliano I, Amaral Carneiro A, Spadaro D, Garibaldi A, Gullino ML (2015) Jasmonic acid, abscisic acid and salicylic acid are involved in the phytoalexin responses of rice to Fusarium fujikuroi, a high gibberellin producer pathogen. J Agr Food Chem 63:8134–8142

    CAS  Article  Google Scholar 

  67. Spoel SH, Dong X (2012) How do plants achieve immunity? Defence without specialized immune cells. Nat Rev Immunol 12:89–100

    CAS  Article  PubMed  Google Scholar 

  68. Sucher J, Boni R, Yang P, Rogowsky P, Büchner H, Kastner C, Kumlehn J, Krattinger SG, Keller B (2016) The durable wheat disease resistance gene Lr34 confers common rust and northern corn leaf blight resistance in maize. Plant Biotech J 15(4):489-496

  69. Tateishi H, Suga H (2015) Species composition, gibberellin production and sensitivity to ipconazole of the Fusarium fujikuroi species complex isolates obtained before and after its launch. J Pestic Sci 40:124–129

    CAS  Article  Google Scholar 

  70. Tateishi H, Saishoji T, Suzuki T, Chida T (1998) Antifungal properties of the seed disinfectant ipconazole and its protection against “bakanae” and other diseases of rice. Ann Phytopathol Soc Jpn 64:443–450

    CAS  Article  Google Scholar 

  71. Valente MT, Desiderio F, Infantino A, Valè G, Abbruscato P, Aragona M (2016) Genetic variability of Fusarium fujikuroi populations associated with bakanae of rice in Italy. Plant Pathol DOI. doi:10.1111/ppa.12575

  72. Vinagre F, Vargas C, Schwarcz K, Cavalcante J, Nogueira EM, Baldani JI, Ferreira PC, Hemerly AS (2006) SHR5: a novel plant receptor kinase involved in plant–N2-fixing endophytic bacteria association. J Exp Bot 57:559–569

    CAS  Article  PubMed  Google Scholar 

  73. Wahid A, Saleem M, Khan MU, Tariq AR, Saleem A (1993) Seed-borne mycoflora of rice. Pak J Agric Res 31:95–100

    Google Scholar 

  74. Walters DR, Bingham IJ (2007) Influence of nutrition on disease development caused by fungal pathogens: implications for plant disease control. Ann Appl Biol 151(3):307–324

    CAS  Article  Google Scholar 

  75. Wang W, Tang W, Ma T, Niu D, Jin JB, Wang H, Lin R (2016) A pair of light signaling factors FHY3 and FAR1 regulates plant immunity by modulating chlorophyll biosynthesis. J Integr Plant Biol 58:91–103

    CAS  Article  PubMed  Google Scholar 

  76. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370

    CAS  PubMed  Google Scholar 

  77. Wiemann P, Sieber CMK, von Bargen KW, Studt L, Niehaus E-M, Espino JJ, Hu K, Michielse CB, Albermann S, Wagner D, Bergner SV, Connolly LR, Fischer A, Reuter G, Kleigrewe K, Bald T, Wingfield BD, Ophir R, Freeman S, Hippler M, Smith KM, Brown DW, Proctor RH, Münsterkötter M, Freitag M, Humpf HU, Güldener U, Tudzynsk B (2013) Deciphering the cryptic genome: genome-wide analyses of the rice pathogen Fusarium fujikuroi reveal complex regulation of secondary metabolism and novel metabolites. PLoS Pathog 9:e1003475

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. Wulff EG, Sørensen JL, Lübeck M, Nielsen KF, Thrane U, Torp J (2010) Fusarium spp associated with rice Bakanae: ecology, genetic diversity, pathogenicity and toxigenicity. Environ Microbiol 12:649–657

    Article  PubMed  Google Scholar 

  79. Xu X, Liu X, Ge S, Jensen JD, Hu F, Li X, Dong Y, Gutenkunst RN, Fang L, Huang L, Li J, He W, Zhang G, Zheng X, Zhang F, Li Y, Yu C, Kristiansen K, ZhangX WJ, Wright M, McCouch S, Nielsen R, Wang J, Wang W (2011) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nat Biotechnol 30:105–111

    CAS  Article  PubMed  Google Scholar 

  80. Yang CD, Guo LB, Li XM, Ji ZJ, Ma LY, Qian Q (2006) Analysis of QTLs for resistance to rice bakanae disease. Chin J Rice Sci 6:657–659

    Google Scholar 

  81. Zhao K, Tung C-W, Eizenga GC, Wright MH, Liakat Ali M, Price AH, Norton GJ, Rafiqul Islam M, Reynolds A, Mezey J, McClung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2:467

    Article  PubMed  PubMed Central  Google Scholar 

Download references


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.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information



Corresponding author

Correspondence to Giampiero Valè.

Additional files

Additional file 1: Figure S1.

Structure Harvester analysis. Four different parameters are reported to evaluate the most probable number of subpopulations in the panel used for GWAS. Ln(k) = mean of the likelihood distribution LnP(D) over 5 runs for each K value from 1 to 7; Ln’(K) = rate of change of the likelihood function with respect to K; |Ln”(K)| = second order rate of change of Ln(K) with respect to K; Delta(K) = mean(|Ln”(K)|)/DevSt(L(K)). (TIFF 205 kb)

Additional file 2: Figure S2.

Principal Coordinate Analysis of the Rice Germplasm Collection; each point shape represents a different cluster obtained in the STRUCTURE analysis at K = 2 (top panel) or a different taxonomic group defined in the literature (bottom panel) (see the text for details). (TIFF 355 kb)

Additional file 3: Table S1.

Genetic diversity (A), Genetic divergence (FST) (B) and marker density (C) estimates computed for the whole rice panel and for the major subdivisions of the panel. (PDF 49 kb)

Additional file 4: Figure S3.

Details of the two QTL (on chromosomes Os1 and Os4) associated to bakanae disease resistance in the Rice Germplasm Collection. The horizontal line represents the 0.01 FDR threshold and defines the genomic regions where candidate genes were searched for. (TIFF 401 kb)

Additional file 5: Figure S4.

Box-plots showing the phenotypic distributions for the alternative alleles at the most significant SNPs detected in this work. (TIFF 132 kb)

Additional file 6: Figure S5.

Sequences encompassing the peak SNP markers detected for bakanae disease resistance on chromosomes 1 and 4. The alleles associated with the resistant phenotype are in bold. (PDF 372 kb)

Additional file 7: Table S2.

List of candidate genes, functionally annotated, selected on the base of the FDR value. (XLSX 14 kb)

Additional file 8: Table S3.

List of candidate genes selected from Differentially Expressed Genes (DEGs). (XLSX 13 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Volante, A., Tondelli, A., Aragona, M. et al. Identification of bakanae disease resistance loci in japonica rice through genome wide association study. Rice 10, 29 (2017).

Download citation


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