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. 2020 Feb 4;15(2):e0222375. doi: 10.1371/journal.pone.0222375

Mapping of quantitative trait loci for traits linked to fusarium head blight in barley

Piotr Ogrodowicz 1, Anetta Kuczyńska 1,*, Krzysztof Mikołajczak 1, Tadeusz Adamski 1, Maria Surma 1, Paweł Krajewski 1, Hanna Ćwiek-Kupczyńska 1, Michał Kempa 1, Michał Rokicki 2, Dorota Jasińska 2
Editor: Ajay Kumar3
PMCID: PMC6999892  PMID: 32017768

Abstract

Fusarium head blight (FHB) is a devastating disease occurring in small grain cereals worldwide. The disease results in the reduction of grain yield, and mycotoxins accumulated in grain are also harmful to both humans and animals. It has been reported that response to pathogen infection may be associated with the morphological and developmental traits of the host plant, e.g. earliness and plant height. Despite many studies, effective markers for selection of barley genotypes with increased resistance to FHB have not been developed. In the present study, we investigated 100 recombinant inbred lines (RIL) of spring barley. Plants were examined in field conditions (three locations) in a completely randomized design with three replications. Barley genotypes were artificially infected with spores of Fusarium culmorum before heading. Apart from the main phenotypic traits (plant height, spike characteristic, grain yield), infected kernels were visually scored and the content of deoxynivalenol (DON) mycotoxin was investigated. A set of 70 Quantitative Trait Loci (QTLs) were detected through phenotyping of the mapping population in field conditions and genotyping using a barley Ilumina 9K iSelect platform. Six loci were detected for the FHB index on chromosomes 2H, 3H, 5H, and 7H. A region on the short arm of chromosome 2H was detected in which many QTLs associated with FHB- and yield-related traits were found. This study confirms that agromorphological traits are tightly related to FHB and should be taken into consideration when breeding barley plants for FHB resistance.

Introduction

Fusarium head blight (FHB) or scabs affects different species of crops around the world. The infection is caused by several fungal pathogens, including Fusarium culmorum (W. G. Sm.) Sacc and Fusarium graminearum (teleomorph stage: Gibberella zeae). Fusarium culmorum has been found to dominate in regions with warm and humid conditions, whereas Fusarium graminearum has been associated with cool, wet, and humid conditions [1]. Fusarium spp. produce trichothecene—deoxynivalenol (DON) [2]. This mycotoxin disrupts normal cell function by inhibiting protein synthesis [3], which results in reduced grain quality and yield performance. Floret sterility and deformed kernels contribute to significant yield loss [4]. In Europe, 15–55% of barley products are contaminated with DON [5].

DON poses a genuine threat to human and livestock health. This mycotoxin is also known as "vomitoxin" due to its emetic effects after consumption [6]. DON levels present in barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.) infected with FHB vary according to the time of infection and environmental factors. It is well known that infection is favored by moist and warm conditions [7, 8]. While the presence of scab can be determined through visual inspection, the presence of DON cannot. Assessment of disease severity is based on the ratio of symptomatic spikelets on each spike and the proportion of infected spikes in tested plants [9]. Although this method is widely used in the screening of resistant germplasms, the results are subjective. For identification and quantification of mycotoxins in barley grain different types of chromatography are commonly used [10, 11]. However, due to the time-consuming and costly nature of these methods, commercial immunometric assays, such as enzyme-linked immunosorbent assay (ELISA), are frequently applied for monitoring of mycotoxin content [12, 13].

Disease control is achieved by the deployment of resistant cultivars. However, breeding for FHB resistance has proven to be difficult due to the complex inheritance of resistance genes [14] and the strong genotype-by-environment interaction [15].

One of the several crop species most vulnerable to FHB infection is barley (Hordeum vulgare L.). This species is a cereal crop of major importance, and is ranked as the fourth grain crop worldwide in terms of production volume [16]. Its major uses include both animal feed and as a component of human nutrition [17, 18]. In addition, barley is a model plant in genetic studies due to colinearity and synteny across rye, barley, and wheat genomes [19].

Fusarium poses a tangible threat for barley plants, especially in regions that are prone to periods of wet weather during the flowering stage [4]. Host plants are most vulnerable to infection during anthesis due to development of fungal spores on anthers and pollen containing nutrients [20]. Numerous morphological traits have been shown to be associated with FHB resistance in barley [21], and in this regard heading date, plant height, and spike traits (linked to spike compactness) are mostly investigated [22, 23]. Days to heading is often negatively correlated with FHB susceptibility and usually results in disease escape [24]. Hence, using the least susceptible varieties with different flowering dates may reduce the risk of FHB. Two categories of resistance to FHB are generally recognized: type I (resistance to initial infection) and type II (resistance to fungal spread within the spike) [25]. Another type of resistance has been described as a third type and is related to accumulation of mycotoxins within the grain [26].

Studies designed to determine the number and chromosomal location of loci contributing to FHB resistance and the accumulation of DON are urgently needed for resistance breeding efforts. It is known that resistance to FHB is a complex trait controlled by multiple genes and affected by several environmental factors [27, 28]. Quantitative trait loci (QTLs) have been identified for quantitative disease resistance in wheat and barley [4]. Furthermore, resistance to both FHB and DON levels have been mapped to all seven barley chromosomes [29, 30], and the most common regions related to FHB resistance have been previously reported to be located on chromosomes 2H and 6H [3, 25, 31]. Other traits, including awned/awnless ears [26] and spike compactness [32], have also been studied. Plant height is another frequently investigated parameter, and a negative correlation of this trait with type I FHB susceptibility has been frequently documented [33].

Molecular markers have become increasingly important for plant genome analysis, and different classes of DNA markers have been developed and implemented over time [34]. A new genotyping platform was introduced in 2009 that contained larger numbers of markers based on SNP discovery from Next Generation Sequencing data using the oligo pool assay from Illumina as a marker platform [35] to improve the genotyping process. Based on these analyses, the 9K iSelect chip was developed which contains 7864 SNPs [36] and enables genotyping with high efficiency and reduced costs. Indeed, in the current study, this chip was employed due to the favorable tradeoff between genotyping costs and marker density.

The overall aim of the present study was to map quantitative trait loci for agronomic properties in a biparental population grown in field conditions and subjected to artificial infection with Fusarium. Evaluation of disease severity was based on visual assessment of infection and content of deoxynivalenol.

Materials and methods

Plant material

A 100-RIL population of spring barley (hereafter referred to as LCam) obtained from the cross between the Polish cultivar Lubuski and a Syrian breeding line—Cam/B1/CI08887//CI05761 (hereafter referred to as CamB) was studied in field conditions, together with both parental forms. The plant materials were described in detail in Ogrodowicz et al. [37] and parental genotypes were chosen on the basis of earlier studies conducted by Górny et al. [38].

Field experiments

Experimental fields belonging to the Poznan Plant Breeding Company (PPB) in three locations were used for the present studies: Nagradowice (NAD–Western Poland, 52°19′14″N, 17°08′54″E), Tulce (TUL—Western Poland, 52°20′35.2″N 17°04′32.8″E), and Leszno (LES—Western Poland, 51°50′45″N 16°34′50″E). At each location, experiments were performed in randomized blocks with three replications. The effects of Fusarium infection were evaluated during the 2016 growing season. The two experimental variants consisted of un-inoculated (control) and inoculated plants. Seeds were sown on 1 m2 plots. Control rows were established at a distance of 20.0 m from the plots designated for inoculation. This isolation was necessary to protect plants against infection during inoculation.

Inoculum preparation

Fusarium culmorum isolates were incubated on wheat grain (50 g) in 300 ml Erlenmeyer glass flasks for five weeks. The colonies were covered with 15 ml of sterile distilled water. Inoculum was prepared just before the inoculations by liquid cultures of Fusarium culmorum (isolate KF846) and 0.0125% TWEEN®20 (Sigma-Aldrich Chemie GmbH). Inoculum concentration was adjusted to 105 spore/ml. Inoculation was performed at the flowering stage (Zadoks scale 65). Mist irrigation to promote fungal infection was performed for three days in the field using a sprinkler system with DN881A-type sprinkler heads equipped with 1.50-mm-diameter nozzles (Sun Hope Inc., Meguro-ku). Water was applied three times daily (at 07.00, 13.00, and 19.00) for 15 min at each interval.

Agronomic traits

Agronomic traits were classified into three categories: traits associated with spike characteristics [number of spikelets (NSS), number of kernels (NGS), length of spike without awns (LS), numbers of sterile spikelets per spike (sterility), spike density (density), grain traits (grain weight per spike (GWS), grain yield per plot (GY), average weight of 1000 grains (TGW)], heading day and plant height [heading date (HD) and length of main stem (LSt)]. The traits measured with ontology annotation are listed in Table 1.

Table 1. List of phenotypic traits with description, abbreviations, measured units and ontology annotation.

Trait (unit) Trait description Abbrev. Annotation
Number of spikelets per spike Number of spikelets in spike from 10 randomly selected spikes in a plot NSS http://purl.obolibrary.org/obo/TO_0000456
Number of grains per spike Number of grains collected from 10 randomly selected spikes in a plot NGS http://purl.obolibrary.org/obo/TO_0002759
Length of spike (cm) Length of spike from 10 randomly selected spikes in a plot (without awns) LS http://purl.obolibrary.org/obo/TO_0000040
Rate of sterile spikelets per spike Fraction of sterile spikelets per spike, calculated as a ratio of number of spikelets
per spike (NSS) to number of grains per spike (NGS)		 Sterility http://purl.obolibrary.org/obo/TO_0000436
Spike density Number of spikelets per unit length (centimeter) of spike calculated by dividing the
number of spikelets per spike by the length of the spike		 Density http://purl.obolibrary.org/obo/TO_0020001
Grain weight per spike (g) Average weight of grain per spike, calculated from 10 randomly selected spikes
in a plot		 GWS http://purl.obolibrary.org/obo/TO_0000589
Grain yield (g) Weight of grain harvested per plots GY http://purl.obolibrary.org/obo/TO_0000396
1000-grain weight (g) Average weight of 1000 grains, calculated as 1000 * average weight of one grain for 10 spikes in a plot TGW http://purl.obolibrary.org/obo/TO_0000382
Heading date (number of days) Number of days from beagining of year to emergence of inflorescence (spike) from
the flag leaf(51 BBCH), assessed when spikes emerged on at least 50% of plants		 HD http://purl.obolibrary.org/obo/TO_0000137
Length of main stem (cm) Average of measurements of length of stem from ground level to the end of spike
(without awns) for 10 randomly selected plans in a plot		 LSt http://purl.obolibrary.org/obo/TO_0000576
FHB index (%) Spike infection, calculated as (the percentage of spikelets affected within a spike *
the percentage of infected spikes per plot)/100		 FHBi http://purl.obolibrary.org/obo/TO_0000662
DON concentration (ppb) Deoxynivalenol content of the grain DON http://purl.obolibrary.org/obo/TO_0000669
Number of damaged kernels Number of kernels classified as damaged (pinkish or discoloured) per 10 randomly
selected spikes per plot		 FDKn
Weight of damaged kernels (g) Weight of kernels classified as damaged (pinkish or discolored) per 10 randomly
selected spikes per plot		 FDKw
Number of healthy kernels Number of kernels classified as healthy per 10 randomly selected spikes per plot HLKn
Weight of healthy kernels (g) Weight of kernels classified as healthy per 10 randomly selected spikes per plot HLKw
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Evaluation of disease symptoms

Disease development was visually scored (Table 1) using the Fusarium Head Blight index (FHBi) computed as (percentage of infected spikelets within a spikes * percentage of infected spikes per plot)/100. The assessments were performed 20 days after inoculation. After harvest, Fusarium-damaged kernels (FDK) were observed as the number (FDKn) and weight (FDKw) of kernels—classified as pinkish or discolored (S1 and S2 Figs). Kernels that appeared to be healthy were scored as healthy-looking kernels (HLKn and HLKw). The FDK and HLK rates were estimated for infected and controlled kernels at one location (NAD). DON content (ppm) from infected grain samples (in each experiment with three replications) was assessed using a Ridascreen®DON competitive enzyme immunoassay kit (R-Biopharm AG, Darmstadt, Germany) according to the manufacturer’s instructions. For the DON assay, 5 g samples of kernel were ground and 100 ml of distilled water was added. Samples were shaken vigorously for three minutes (manually). After incubation, samples were filtered through Whatman No. 1 filters; 50 μl of the filtrate per well was used in the test. Absorbance was measured at 450 nm with a spectrophotometer (Chromate Microplate Reader), and data were evaluated with RIDA®SOFT Win software. Within a single location (NAD, TUL, LES), samples obtained from plants grown under controlled conditions (exposed to natural infection) were pooled together as one sample and assayed as above. For each sample, three repetitions (biological replicates) were performed (three repetitions for each inoculated condition and three repetitions for one representative controlled condition).

Genotyping

Genomic DNA was extracted from young leaf tissue as described in Mikołajczak et al. 2016 [39]. DNA quantity and concentration were measured with a NanoDrop 2000 spectrophotometer (Thermo Scientific™). DNA samples were diluted to ~ 50 ng/μL and sent to Trait Genetics, Gatersleben, Germany (http://www.traitgenetics.com) for genotyping using the barley iSelect SNP chip. This chip contains 7,842 SNPs that comprise 2,832 of the existing barley oligonucleotide pooled assay (BOPA1 and BOPA2) SNPs discovered and mapped previously [40, 41], as well as 5,010 new SNPs developed from Next Generation Sequencing data [36, 42]. SNPs which were not polymorphic between the parents, contained more than 10% of missing values, or with minor allele frequency < 15% were removed from the data set.

Linkage map

Genetic map was calculated using JoinMap 4.1 software [43]. All markers were analyzed for goodness of fit using a chi-square test with α  = 0.05. A segregation ratio of 1:1 was expected. Markers with other segregation ratios were categorized as markers with segregation distortion. The localization of markers was designated using the maximum likelihood algorithm. Markers were assigned to linkage groups by applying the independence LOD (logarithm of the odds) parameter with LOD threshold values ranging from 6.0 to 9.0. The recombination frequency threshold was set at level 0.4. Recombination fractions were converted to map distances in centimorgans (cM) using the Kosambi mapping function. A map was drawn using MapChart 2.2.

Data analysis and QTL mapping

Observations for RILs were processed by analysis of variance in a mixed model with fixed effects for location, treatment, and location × treatment interaction, and with random effects for line and interaction of line with location and interaction of line with location and treatment. The residual maximum likelihood algorithm was used to estimate variance components for random effects and the F-statistic was computed to assess the significance of the fixed effects. Pearson correlation coefficients between all the analyzed traits were calculated. QTL analysis was performed for the linkage map with the mixed model approach described by Malosetti et al. [44], including optimal genetic correlation structure selection and significance threshold estimation. The threshold for the−log10(P-value) statistic was computed using the method of Li and Ji [45] to ensure that the genome-wide error rate was < 0.01. Selection of the set of QTL effects in the final model was performed at P < 0.05; P-values for the Wald test were computed as the mean from the values obtained by adding and dropping the QTL main and interaction effects in the model. All the above computations were performed with Genstat 18 [46]. RILs with a fraction of missing genotypic data smaller than 20% were used to map QTL. QTL identification was performed for all traits.

The detected QTLs were labeled using a system described for wheat and Arabidopsis [47, 48], with minor modifications. The QTLs names consist of the prefix Q followed by a two- or three-letter descriptor of the phenotype (abbreviation of the trait name), an indicator for the laboratory, the chromosome number, and a serial number. For traits linked to FDK and HLK, the QTL names were extended by adding the letter “w” or “n” for loci found for trait weight of FDK, HLK, and number of FDK and HLK, respectively.

QTL effects in individual trials were considered major if the fraction of explained variance exceeded 12.32% (upper quartile of the distribution of explained variance) according to the rules employed by [49] and [50] (with minor modifications).

The barleymap pipeline (http://floresta.eead.csic.es/barleymap) [51] was used to identify SNP positions in the reference Morex genome and gene annotations linked to potential candidate genes located in the vicinity (intervals around markers extended by ±2cM) of the particularly robust QTL. Overrepresentation analysis of Gene Ontology (GO) terms in QTL regions was performed using the GO annotation of barley genes downloaded from Ensembl Plants Genes (rel. 43) database in Bingo 3.0.3 [52] (hypergeometric test, Benjamini-Hochberg [53] FDR corrected p-values < 0.05).

Results

Phenotypic analysis

The parents of the LCam population were characterized with 10 agronomical traits under two different conditions (infection and control treatments). Evaluation of disease severity was studied by using measurements of six FHB-related traits in both previously mentioned conditions. The distributions of trait values among RILs are visualized in Fig 1. Raw data are available at data repository Ćwiek-Kupczyńska et al. [54].

Fig 1. Violin plots for traits measured in the LCam population in control (V1, green) and infected (V2, red) conditions in three locations.

Fig 1

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Black symbols: triangle, Lubuski; dot, CamB.

Lubuski showed higher mean values of traits linked to yield performance (e.g. GWS, GY) (S1 Table) than CamB. CamB showed a lower mean value of HD in all trials and under both types of treatments (heading for CamB was 11 days earlier than for Lubuski). A substantial GY decline was observed for Lubuski in conditions of infection (40.1%). In comparison, for CamB a lower relative decline for GY was observed (17.3%). Both mean values for FHBi and traits associated with visual evaluation of Fusarium symptoms (FDK) increased during infection. For CamB, a higher mean value of DON concentration was noted in comparison to that of Lubuski. For both parental forms low concentrations of mycotoxin were also observed in control conditions than under infection.

The mean values of the studied traits for RILs are presented in S2 Table. Relatively high values of variation coefficients were observed in the NAD location under infection for the following traits: NSS, NGS, Density, GWS, and TGW. In the LES location, very high values of CV were noted for traits FHBi and DON under control conditions. FHBi varied across locations with the mean value ranging from 1.89 to 2.26 under infection and from 0.62 to 0.99 in the control conditions (S2 Table). The amount of DON, measured in grains from infected plants, varied from 8.06 ppm (NAD) to 39.99 ppm (TUL). Mean DON values of 26.43, 25.68, and 27.14 ppm for infection at LES, NAD, and TUL were observed, respectively. In control conditions, relatively high coefficients of variation were noted for DON and FHBi.

Analysis of variance indicated significant effects of location and treatment for all traits (P<0.001) with some exceptions (S3 Table). In all cases, variance components for all types of interactions were smaller than those for lines. For FHBi, a significant line × location interaction was noted, while no signicant interaction was observed for line × treatment in this case. An insigificant effect was noticed in terms of the interaction line × location for DON content. The values of correlation coefficients between the studied traits and FHBi were generally low (Fig 2). FHBi was negatively correlated with all studied traits (exeptions: FDKw and Sterility). In LES location positive significant correlation between FHBi and Density was recorded, whereas negative correlation coefficients were noted between these traits in other two locations. Positive correlation was recorded between DON content and FHBi in one of three locations (TUL) for both type of treatments. No significant correlations between DON content and other agronomic traits were observed.

Fig 2. Correlation coefficients between FHBi and studied traits recorded under two treatments at three locations (n.s.- not significant; correlations shown are significant at the P < 0.01 level).

Fig 2

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Linkage map construction

The constructed genetic map comprised 1947 SNPs distributed in seven linkage groups. The map length was 1678 cM with an average marker interval of 0.86 cM. The shortest chromosome was 6H, which harbored 250 markers with a genetic length of 141 cM and an average interloci distance of 0.56 cM. The longest chromosome was 2H, harboring 368 markers with a genetic length of 291 cM and an average interloci distance of 0.79 cM. The number of markers, marker density, and map length for individual chromosomes are listed in Table 2.

Table 2. Map details across each chromosome.

Characteristic Chromosome Total
1H 2H 3H 4H 5H 6H 7H
Number of mapped markers 156 368 324 329 324 250 196 1947
Number of loci 3 13 5 5 13 4 6 49
Map length (cM) 232 291 241 215 295 141 263 1678
Mean distance between markers (cM) 1.48 0.79 0.74 0.65 0.91 0.56 1.30 0.86
Number of distorted markers (%) 11.1 9.4 13.1 15.8 6.2 11.7 8.5
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QTL analysis

A total of 70 QTLs for all studied traits were found for the LCam population (S3, S4 and S5 Figs). The numbers of QTLs were 7, 24, 5, 6, 17, 4, and 7 for chromosomes 1H, 2H, 3H, 4H, 5H, 6H, and 7H, respectively. Moreover, 46 QTLs presented major effects and 38 presented a QTL × E interaction. The largest number of QTLs was detected for NSS and TGW (eight QTLs were identified for each trait), and the smallest for FDK (two QTLs were detected for FDKn and FDKw). Fourteen QTLs were classified as major loci and 56 QTLs were described as minor loci. Detailed information, including location, peak marker, additive effects, and explained phenotypic variance for each QTL and trait is presented in S4 Table.

Spike characteristics

For the number of spikelets per spike, eight QTLs were detected in chromosomes 1H, 2H, 3H, and 5H. The major QTL (QNSS.IPG-2H_1) on chromosome 2H (SNP marker BK_12) showed the most significant effect for this trait and explained a large proportion of phenotypic variance (4.79–71.81%). In this case, significant QTL × E interaction was noted and Lubuski alleles conferred a positive effect in increasing this trait. A second locus positioned at 98.67 cM on chromosome 5H also showed a highly significant association with NSS (LogP statistics = 16.95). Chromosome 1H was the location of the last major QTL (QNSS.IPG-1H_1) in the vicinity of marker BOPA1_4625–1413. The remaining five NSS QTLs showed minor effects. Out of the eight QTLs detected for NSS, three (QNSS.IPG-1H_2, QNSS.IPG-2H_2 and QNSS.IPG-3H_2) were associated with a significant increase in this trait contributed by CamB alleles.

Five QTLs were found for the number of grains per spike. QNGS.IPG-2H was found in the vicinity of marker BK_12. This locus was positioned at 22 cM on chromosome 2H. The second major QTL was detected on chromosome 5H in the vicinity of marker BOPA2_12_30929. For this QTL, no significant additive effects were recorded in the NAD location. The other QTLs (QNGS.IPG-1H_1, QNGS.IPG-1H_2 and QNGS.IPG-5H_2) were classified as minor QTLs. All QTLs for NGS were with alleles of Lubuski, contributing to an increasing of number of grains per spike.

Three QTLs were reported for the length of spike (QLS.IPG-1H, QLS.IPG-2H, and QLS.IPG-5H). All detected loci were classified as major (≥12.32% PVE) and the effects of these QTLs were stable over environments (treatments). All QTLs were associated wih a significant increase in LS contributed by Lubuski. The main QTL was found on chromosome 2H in the vicinity of marker BK_13. In total, five QTLs were identified for sterility. On chromosome 2H, two major QTLs were detected (QSte.IPG-2H_1 and QSte.IPG-2H_2). The first, QSte.IPG-2H_1, was located in the vicinity of marker SCRI_RS_154030 and showed the highest LogP value of all detected QTLs controlling this trait. The second sterility QTL was located on chromosome 2H 5.6 cM from marker SCRI_RS_230497. One major QTL (QSte.IPG-5H_2) was detected on chromosome 5H. None of the mentioned QTLs had significant additive effects in the control condition at the LES location or in the infection condition at the NAD location. On chromosome 7H, a minor QTL for sterility was identified, namely QSte. IPG-7H. All QTLs detected for this character were with alleles of CamB contributing to the increase in sterility with the exception of QSte.IPG-5H_1, where Lubuski alleles determined the increase. Interaction with the environment was found for all but one detected QTL (an exeption was QSte.IPG-7H).

Six QTLs controlling density were detected on chromosomes 2H and 5H with a PVE ranging from 0.01 to 31.43%. Half of those QTLs displayed significant QTL × E interaction. The major QTL (LogP = 15.17) was found on the upper arm of chromosome 2H mapped in marker BK_22. Concurrently, this QTL was the only locus associated with Density, where Lubuski alleles conferred a positive effect in increasing this trait, while CamB alleles at the other five QTLs contributed positively to Density. The second major QTL (QDen.IPG-2H_2) was also found on chromosome 2H at position 113.9 cM. On chromosome 2H two other minor QTLs were identified for Density QTL (QDen.IPG-2H_2 and QDen.IPG -2H_4) with a stable effect, mapped in the vicinity of BOPA1_5537–283. QDen.IPG -5H_1 was also found on chromosome 5H at position 93.9 cM. The additive effects of this QTL were significant at only two locations (NAD and TUL). For Density, two minor QTLs were found, QDen.IPG-2H_3 and QDen.IPG-5H_2, for which the smallest LogP values were recorded for Density.

Grain traits

Grain weight per spike was mapped to seven loci. The major GWS QTL (QGWS.IPG-2H_1) was found on chromosome 2H in the vicinity of marker BK_22. This locus, with a PVE ranging from 31.74–57.92%, was the only GWS QTL where no significant QTL × E interaction was detected. QGWS.IPG-5H was found on chromosome 5H and the nearest marker (BOPA1_4795–782) was 1.37 cM away from the corresponding QTL peak. Two other major QTLs (QGWS.IPG-7H_1 and QGWS.IPG-7H_2) controlling GWS were reported on chromosome 7H. Both of these QTLs had a significant additive effect at only in single location. A minor QGWS.IPG-4H_2 was found on chromosome 4H at position 127.40 cM. Lubuski contributed to the increase in GWS for all detected QTLs for this trait (except for two QTLs; minor QGWS.IPG -2H_2 and major QGWS.IPG-4H_1 on chromosomes 2H and 4H, respectively).

Of the four QTLs found for grain yield, only one was classified as major (PVE > 12.32%). In additon, no significant additive effects were noticed for any loci in infection conditions for the NAD location. The major QGY.IPG-2H was located on chromosome 2H and linked to marker BK_22. No QTL × E interaction was found for GWS QTLs detected in the mapping population and in all cases positive alleles were attributed to Lubuski.

Eight QTLs were reported for thousand grain weight. QTGW.IPG-2H_1 and QTGW.IPG-4H_1 were identified on chromosomes 2H and 4H, respectively, but their additive effects were significant only in infection (LES) and control conditions (NAD). On chromosome 4H, TGW QTL was found with a stable and positive effect from the Lubuski genotype. QTGW.IPG-6H_2 locus on chromosome 6H was determined by CamB alleles contributing positively to TGW. In this locus, no significant QTL × environment interaction for TGW was observed. Major QTGW.IPG-7H_1 with stable effects from the CamB allele significantly increasing TGW was identified on chromosome 7H. On the same chromosome QTGW.IPG-7H_2 was found, but the additive effects of this QTL were significant in only three treatments. QTGW.IPG-2H_2 and QTGW.IPG-6H_1, detected on chromosome 2H and 6H, respectively, were classified as minor QTLs.

Heading day and height

Two QTLs (QHD.IPG-2H and QHD.IPG-5H) were reported for heading date. The major QTL was located on chromosome 2H in the vicinity of marker BK_22. The “late” allele (high HD value) was contributed by Lubuski. In contrast, at the second locus, classified as a minor QTL, the CamB alleles conferred a positive effect by increasing this trait. For both loci, no QTL × E interaction was detected.

Seven loci for length of the main stem were found in the LCam population. The major locus (QLSt.IPG-2H_1) was detected on chromosome 2H in the vicinity of marker BK_13 at position 21 cM. This QTL explained a large portion of the variance for LSt (from 13.88 to 41.68%). The Lubuski alleles contributed to the increase in LSt at this locus. The second major QTL was reported on chromosome 1H with stable and positive effects on the length of the main stem contributed by Lubuski. QLSt.IPG-4H_2 and QLSt.IPG-5H were identified on chromosomes 4H and 5H, respectively. These QTLs were classified as major loci, but their additive effects were not significant in some treatments (e.g. control conditions in NAD location). Three minor LSt loci were found: QLSt.IPG-2H_2, QLSt.IPG-3H, and QLSt.IPG-4H_1 on chromosomes 2H, 3H and 4H, respectively.

Fusarium symptoms and DON content

Six QTLs were reported for the FHB index. The major QTL (QFHBi.IPG-2H_1) was found on chromosome 2H in the vicinity of marker BOPA1_5880–2547 at position 23.10 cM. The CamB alleles positively contributed to the increase in the FHB index at this locus and a significant QTL × E interaction was detected for QFHBi.IPG-2H_1. On the same chromosome, another FHBi QTL was reported which was located at position 87.70 cM, but additive effects of this locus were significant in only one location (TUL). The next major locus (QFHBi.IPG-2H_3) was also detected on chromosome 2H with stable and positive effects of CamB alleles responsible for increasing the FHBi. In contrast, the EuropLubuski alleles conferred a positive effect in increasing the FHBi at the locus found on chromosome 5H (QFHBi.IPG-5H). No significant additive effects were detected in the LES location for this QTL. Two minor loci − QFHBi.IPG-3H and QFHBi.IPG-7H − were found on chromosomes 3H and 7H, respectively.

Four QTLs were found for traits linked to Fusarium-damaged kernels. These loci were located on chromosomes 5H and 6H. The major QFDKn.IPG-5H was detected in the vicinity of SCRI_RS_165578, where Lubuski genotype significantly increased the FDKn. The second major locus (QFDKw.IPG-5H) was identified at position 87.80 cM and showed positive effects on this trait contributed by Lubuski alleles. Two remaining loci on chromosome 6H (QFDKn.IPG-6H and QFDKw.IPG-6H) were classified as minor QTLs.

Five QTLs were detected for traits associated with HLKw and HLKn. The major QHLKn.IPG-2H_2 was found on the short arm of chromosome 2H (marker BK_13) and showed stable and positive effects of Lubuski genotype alleles which contributed to the increase in HLKn. Two minor QTLs were recorded for HLKn on chromosomes 2H and 5H. No significant QTL × E interaction was detected for either locus. Two major loci (QHLKw.IPG-2H and Q_HLKw.IPG-7H) were found for the trait HLKw. The Lubuski alleles were responsible for increasing HLKw in both loci, but only one QTL (QHLKw.IPG-2H) had stable effects.

In this study, no QTL for DON content was detected.

Co-localized or pleiotropic QTLs

All QTLs linked to FHB on chromosomes 2H and 5H co-localized with other agronomic traits. A total of eight chromosomal regions (named A-G) harboring QTLs for the studied traits were defined. These regions (hotspots), listed in Table 3, were designed based on inter-QTL distances smaller than 2 cM. Five QTLs were reported in region A located on the long arm of chromosome 1H, and associated with SNP BOPA1_4625–1413. Region B identified on the short arm of chromosome 2H contained 10 loci. In most cases, QTLs from region B were detected in the vicinity of marker BK_12 (S4 Fig). Out of the five QTLs detected in region C assigned to the same chromosome, four were found in the vicinity of marker BOPA2_12_10937. Region D harbored two QTLs found at the same position (127.40 cM). Region E (chromosome 5H) harbored six QTLs; QDen.IPG-5H_1 and QFHB.IPG-5H were found in this region in the vicinity of marker SCRI_RS_184066 and both QNGS.IPG-5H_1 and QLSt.IPG-5H were detected in the vicinity of marker BOPA2_12_30929. On the same chromosome, the next region was noted (named region F). Of the four QTLs reported on this region, two were found in the vicinity of marker SCRI_RS_206867. Region G on chromosome 7H harbored two loci associated with marker SCRI_RS_159555.

Table 3. Regions harboring QTLs for traits with the names of the nearest SNP markers.

Name of region Trait QTL ID Chromosome Position (cM) Nearest marker
A
 NSS QNSS.IPG-1H_1 1H 0.00 BOPA1_4625–1413
NGS QNGS.IPG-1H_1 1H 0.00 BOPA1_4625–1413
LS QLS.IPG-1H 1H 0.00 BOPA1_4625–1413
LSt QLSt.IPG-1H 1H 0,00 BOPA1_4625–1413
GY QGY.IPG-1H 1H 0.00 BOPA1_4625–1413
B
 LS QLS.IPG-2H 2H 21.00 BK_13
LSt QLSt.IPG-2H_1 2H 21.00 BK_13
NSS QNSS.IPG-2H_1 2H 22.00 BK_12
NGS QNGS.IPG-2H 2H 22.00 BK_12
Density QDen.IPG-2H_1 2H 22.00 BK_12
GWS QGWS.IPG-2H_1 2H 22.00 BK_12
GY QGY.IPG-2H 2H 22.00 BK_12
HD QHD.IPG-2H 2H 22.00 BK_12
HLKw QHLKw.IPG-2H 2H 22.00 BK_12
FHBi QFHB.IPG-2H_1 2H 23.10 BOPA1_5880–2547
C
 Density QDen.IPG-2H_3 2H 225.26 BOPA2_12_10937
LSt QLSt.IPG-2H_2 2H 225.26 BOPA2_12_10937
GWS QGWS.IPG-2H_2 2H 228.70 BOPA2_12_10937
TGW QTGW.IPG-2H_2 2H 228.70 BOPA2_12_10937
NSS QNSS.IPG-2H_2 2H 229,80 SCRI_RS_174051
D
 TGW QTGW.IPG-4H_2 4H 127.40 BOPA1_2196–195
GWS QGWS.IPG-4H_2 4H 127.40 BOPA1_2196–195
E
 Density QDen.IPG-5H_1 5H 93.90 SCRI_RS_184066
FHBi QFHB.IPG-5H 5H 95.60 SCRI_RS_184066
NGS QNGS.IPG-5H_1 5H 97.30 BOPA2_12_30929
LSt QLSt.IPG-5H 5H 97.30 BOPA2_12_30929
LS QLS.IPG-5H 5H 97.30 BOPA2_12_30929
NSS QNSS.IPG-5H_1 5H 98.67 SCRI_RS_235055
F
 GY QGY.IPG-5H 5H 285.20 BOPA2_12_30533
NSS QNSS.IPG-5H_2 5H 286.90 SCRI_RS_206867
NGS QNGS.IPG-5H_2 5H 286.90 SCRI_RS_206867
HLKn QHLKn.IPG-5H 5H 288.00 SCRI_RS_165919
G
 GWS QGWS.IPG-7H_1 7H 119.80 SCRI_RS_159555
TGW QTGW.IPG-7H_2 7H 119.80 SCRI_RS_159555
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QHLKw.IPG-2H was found in the vicinity to marker BK_12. In the same position a set of QTLs linked to different agronomic traits was found (Density, GWS, GY, HD, NGS and NSS—QDen.IPG-2H_1, QGWS.IPG-2H_1, QGY.IPG-2H, QHD.IPG-2H, QNGS.IPG-2H and QNSS.IPG-2H_1, respectively). QHLKn.IPG-2H_2, other QTL related to FHB, was detected in the vicinity of marker BK_13 –in the same position as QTLs related to LS (QLS.IPG-2H) and LSt (QLSt.IPG-2H_1). In both cases Lubuski contributed positively to the increase of the trait linked to HLK (HLKn and HLKw).

An overrepresentation analysis was performed (S5 Table) to identify enriched Gene Ontology (GO) terms–cellular component, molecular function, and biological process–associated with genes in three regions B, E, and F containing QTLs for FHB-related traits (Table 3). Genes annotated with overrepresented GO terms associated with FHB responses (glucuronosyltransferase activity, galactosylgalactosylxylosyl protein 3-beta-glucuronosyltransferase activity, transferase activity, transferring hexosyl groups) are listed in Table 4.

Table 4. Genes annotated with selected overrepresented GO terms from regions B, E and F.

Gene Gene description Position in region
B E F
HORVU2Hr1G013590 Glycosyltransferase + - -
HORVU2Hr1G013630 Glycosyltransferase + - -
HORVU5Hr1G095010 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096240 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096260 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096310 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096320 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096340 UDP-Glycosyltransferase superfamily protein - + -
HORVU5Hr1G096360 UDP-Glycosyltransferase superfamily protein - + -
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Discussion

It is widely known that a mapping population derived from parents divergent in genetic composition allows high performance QTL analysis. In this study, RILs (named LCam) derived from a European variety (Lubuski) and a Syrian breeding line (CamB) were used for QTL analysis. Both parental forms were differentiated in terms of height, grain yield, HD, and resistance/tolerance to abiotic stress [37, 39]. CamB is unadapted to the Central European region and has undesired agonomical traits such as early heading and tall stature. Lubuski is an old cultivar with agro-morphological-physiological characters adapted to climatic conditions in Poland during a long cultivation period. With the aim of providing the genetic variability between the parents of the mapping populaion and increasing the chance of indentifying loci linked to FHB, we conducted field experiments using RILs derived from a cross between Lubuski and CamB genotypes.

FHB, caused by Fusarium culmorum, is a very important disease affecting crops on a global scale [9]. The pathogen is dominant in cooler areas like north, central and western Europe [55]. F. graminearum predominates in the warmer, humid areas of the world such as USA [56, 57]. Damage caused by Fusarium fungus includes reduced grain yield and grain functional quality, and results in the presence of the mycotoxin deoxynivalenol in FDK (even in grains without any visible symptoms). The development of FHB resistant crop cultivars is an important component of integrated breeding management [58, 59]. The objective of this investigation was to identify QTLs for traits linked to yield performance in a recombinant inbred line population grown under a disease-free environment and under conditions of Fusarium infection.

FHB infection can be evaluated in different ways. In field conditions, FHB can be determined by visual inspection of the percentage of infected spikelets [60], and can be used to determine an FHB index [61]. After harvest, percentage of both FDK and HLK as described by the visual symptom score and weight of kernels were evaluated. In addition, DON concentration was quantified. In this study, Lubuski was less susceptible to FHB than CamB in all conditions in terms of DON accumulation. On the other hand, we observed a higher FHBi value for Lubuski plants during infection. This can be explained by the fact that symptomless grains may contain significant amounts of mycotoxins, while symptomatic grains within the same samples may not [62]. DON tests of grains harvested from the LCam population showed that some RILs showed lower DON content values than CamB, while other RILs were more susceptible than Lubuski. In all conditions, the mean values of agronomic traits were as expected, i.e. biotic stress conditions led to impaired yields. In control conditions, DON contamination represents the natural occurrence of FHB [63] and the level of mycotoxin accumulation varied significantly from those observed in LCam plants grown in conditions of infection.

Most of the correlation coefficients among the FHBi and other characteristics studied were negative and statistically significant (P<0.01). The Pearson correlation coefficient between FHBi and the two main traits of our interest, HD and LSt, was also significantly negative, which is in agreement with previous studies [64–66] where plants with lower FHB severities have usually been characterized by late heading and tall stature. Late-maturing plants may head during a time in the summer that is less suitable for infection, and tall plants avoid higher concentrations of inoculum near the surface of the soil [67]. Another study by Mesfin et al. [24] concluded that late HD may be linked to FHB resistance since the heads experience less exposure time to fungal spores. Moreover, a negative correlation coefficient was recorded between FHBi and Density, which can be explaned by the fact that lax spikes dry faster and it is difficult for the pathogen to spread upward and downward on the spike [68].

Visual ratings for FHB in barley plants are usually conducted just before the spikes begin to lose chlorophyll, and thus disease symptoms can be easily scored. In some years, there are favorable conditions for Fusarium growth and DON accumulation throughout plant senescence, reducing correlations between FHB and DON because the FHB score does not accurately reflect the final disease level [69]. It is well known that symptomless spikes can be contaminated with DON [70]. Traditionally, mycotoxin determination has mainly been performed by chromatographic techniques [71, 72], although ELISA has been proposed as an alternative method to visual scoring and DON quantification for measuring FHB [73]. The relationship between visual symptoms of FHB and DON content is highly variable ranging from none to a very strong positive relationship [69]. The difference in relationships may be due to differences among plant varieties, weather conditions, pathogen population and disease management practices [74]. As a consequence, FHB and DON values are not always closely correlated. In our study, positive significant correlation was found between FHBi and levels of DON only in one location.

Agronomic traits related to spike traits (e.g., spike density and sterility) have been reported to be linked to FHB resistance, but the association between traits and FHB vulnerability seems to be unclear. Steffenson et al. [75] reported that FHB severity was apparently higher in dense spike NILs than in lax spikes. A negative correlation between FHB severity and spike density was recorded in an experiment on a population derived from two-row and six-row barley plants [76]. In contrast, spike density had little or no effect in the study by Yoshida et al. [77] on barley NILs. Ma et al. [78] also reported an association between lax spike and the FHB reaction. Lax spikes may be related to FHB resistance due to their specific architecture that retains, presumably, less moisture within the whole spike (lax spike dry faster and it is difficult fo the pathogen to spread upward and downward of the spike). This decreases the pace of fungus spread [4]. Herein, negative correlations were detected between the traits Density and FHBi, indicating that spike compactness may be one of the factors enhancing FHB susceptibility. A positive correlation was also recorded between Sterility and FHBi in LCam plants, which means that FHB infection had negative effects on seed development, as expected.

The polymorphic SNP markers found in this study were distributed across all seven linkage groups in the LCam mapping population. Marker order and distances for SNPs generally matched previously published barley maps [40, 79]. The genetic map consisting of 1947 SNPs developed in this study, covering 1678 cM, is larger than other maps (e.g., that constructed by Wang et al. [80] covered 1375.8 cM).

Many bi-parental mapping studies have been carried out on barley to explain the genetic architecture of resistance to FHB and DON accumulation and to identify molecular markers that could be useful in breeding [24, 30, 81, 82]. FHB resistance has frequently been found to be associated with plant morphology parameters, and especially plant height, spike architecture, anther extrusion and HD. For this reason, the LCam population was also evaluated for HD, plant height, spike compactness, and other traits, which seem to be important from an agronomic point of view. Numerous QTL mapping studies in different crop species have revealed that QTLs associated with FHB resistance are coincident with QTLs linked to various agronomic and morphological traits [4, 24, 82]. Previous studies have used population sizes comparable to this study and successfully identified FHB QTL [24, 31, 83]. In our investigation, 70 QTLs were detected on seven barley chromosomes. A higher number of QTLs for agronomic traits was found on chromosome 2H, where the greatest number of FHB-linked QTLs was also identified.

In our study different tools for FHB evaluation have been used: among others: DON content estimation. No QTLs for DON content were detected but visual assessment of FHB severity like FHBi, FDK and HLK were employed here for evaluation of the level of FHB severity. In this study six, four and five QTLs were found for FHBi, FDK and HLK, respectively. The association between Fusarium head blight (FHB) intensity and DON accumulation in harvested grain is not fully understood. Varying degrees of association between Fusarium head blight intensity and DON accumulation in harvested grain have been reported in the literature, including situations with high positive correlations, low significant correlations, and negative correlations, as well as correlations close to zero [84–87]. Visual assessments of disease were usually made at Feekes GS 11.2, based on the proportion of the spike diseased, while DON was quantified in this study after harvest as the amount of DON per unit weight of a bulked sample of ground kernel. The measurement of DON in an assay typically is a composite value for seeds with different levels of DON (including those with 0 ppm) and different levels of fungal colonization. In our study positive correlation between DON content and FHBi was observed only in one location which can be explained by the fact that the growth of the fungus and the production of DON are highly weather dependent [88, 89]. Moreover, DON concentration may have increased at differential rates in the different studies, affecting the relationship between DON sampled at harvest and disease assessed different developmental stage of the plant.

QTLs for FHB resistance have previously been found on all seven barley chromosomes [24, 31, 77, 81, 82]. For most of the resistance varieties, QTLs associated with FHB were detected on the long arm of chromosome 2H [30, 31, 83]. In addition, the QTLs for disease resistance and reduced DON concentration have been linked to spike morphology controlled by vrs1 and a major HD locus (Ppd-H1) [90]. The number of detected QTLs varies in different reports, ranging from only one in the study by Mesfin et al. [24], two [4, 31, 78], and up to to 10 [22]. For many FHB regions in the barley genome, QTLs for DON concentration have been detected for both barley [83, 91] and wheat [87, 92], although such a relationship is not reported as significant in all studies [30]. Identification of QTLs linked to FHB symptoms can be confounded by agronomic traits such as HD, plant height, and properties associated with spike morphology [24, 82]. Hence, mapping of traits characterized by strong phenotypic correlations constitutes a challenge in terms of pleiotropy/linkage. Massman et al. [90] summarized previously described FHB regions and showed all detected QTLs associated with genome location (bin). The QTLs were located on chromosome 2H at three different spots (bin 8, bin 10, and bin 13–14). In our study, six QTLs related do FHBi were found. Of these QTLs, three were identified on chromosome 2H at positions 23.1, 87.7, and 216.7 cM, corresponding to the previously mentioned bin locations. Three other loci–QFHB.IPG-3H, QFHB.IPG-5H, and QFHB.IPG-7H –were found on chromosomes 3H, 5H, and 7H, respectively. QFHB.IPG-2H_1 was found on the short arm of chromosome 2H in the vicinity of SNP marker BOPA1_5880–2547, which explains the largest percentage of phenotyping variance (3.69–30.69) of all FHB QTLs detected. The CamB alleles positively contributed to the increase in FHBi at this locus, which is in accordance with previous studies in which early heading plants were vulnerable to FHB symptoms. In our study, the main QTL for HD was located on chromosome 2H in the vicinity of marker BK_12 at position 22 cM, shifted 1.1 cM from marker BOPA1_5880–2547. According to Turner at al. [93], the most significant SNP marker (BK_12) is directly located within the Ppd-H1 gene, which is the main determinant of response to long day conditions in barley. The 2Hb8 QTL is also considered to be a major locus for resistance to FHB and DON accumulation [94]. Delayed head emergence may increase the likelihood that the host will escape infection by the pathogen [76, 95]. On the other hand, late heading is undesirable in breeding programs addressed to arid regions [96]. Plants with lower FHB severities usually have one or more of the following traits: late heading, increased height, and two-rowed spike morphology [64, 65, 75]. Although tall plants are usually more resistant to disease than short plants [78], the heading date can be either negatively [76, 78] or positively correlated with DON content in seeds [22, 24]. The major QTL associated with heading and located on chromosome 2H (Q.HD.LC-2H) was also identified at SNP marker 5880–2547 in our previous study [37]. SNP 5880–2547 was the closest marker to QTLs associated with plant architecture, spike morphology, and grain yield in those experiments.

Plant height is under polygenic control and represents one of the most important agronomic traits for barley [97, 98]. The right timing of flowering time allows optimal grain development with regards to the availability of heat, light, and water, while semi-dwarf cereals allocate more resources into grain production than taller plants and show reduced losses through lodging [99, 100]. In addition, due to increasing of moisture content of plants, lodging causes expansion of the infection [101]. In the current study, seven loci for LSt were detected. The main locus (QLSt.IPG-2H_1) was on chromosome 2H in the vicinity of marker BK_13, which coincided with the main HD QTL. In this study, only one locus was found on chromosome 3H, where sdw1/denso gene has been located in our previous investigations [97, 102]. There is a gradient in ascospore concentration from the soil surface to upper part of plant stem. Thus, short plants tend to have higher level of FHB infection [103], which is in accordance with our results.

In barley, spike length and spike characters such as number of grains and spikelets per spike are perceived as important agromorphological traits due a direct impact on crop yield [104]. Spike architecture has significant influence on yield and less dense spike alters the spike microenvironment by making it less favorable for fungal infection [105]. In the current study, six QTLs linked to Density were found. Of the six QTLs detected, four loci were found on chromosome 2H. The major QTL (QDen.IPG-2H-1) was located on the short arm of 2H in the vicinity of marker BK_12. Two QTLs related to the density of the spike were found on chromosome 5H. In most cases, CamB alleles contributed positively to this trait. In many studies, plants with lax spikes have been reported as being less vulnerable for fungal infection [91, 105]. On the other hand, Yoshida et al. [77] found no differences between genotypes when comparing barleys with normal and dense type of spikes. Steffenson et al. [75] showed that FHB severity was higher in dense spike NILs vs. lax spike plants, but no significant differences were found. Langevin et al. [106], in a study using barley with two- and six-row types of spikes, concluded that the high level of DON contamination observed in dense spikes occurred mainly because of direct contact with florets. To summarize, the results for an association between disease severity and spike architecture of barley plants are not consistent.

In our study FHB QTLs coincidence with traits connected with spike morphology, HD and height (LSt) on chromosomes 2H and 5H was found. The underlying mechanism of coincident HD, LSt, Density and disease QTL could be due to tight linkage or pleiotropy. However, late-heading plants may serve as an escape mechanism from infection due to a lack of overlapping periods in plant development and fungus life cycle. Plant height could contributed to physically avoiding pathogens as well as inflorescence structure [83].

The GO term overrepresentation analysis combines information from regions containing QTLs for a given trait and gene function terms. Thus, we investigated the GO term over-representation of three hotspots containing, among others, QTLs for FHB. Overrepresentation analysis revealed GO annotations linked to glycosylation process. Two annotations were assigned to region B (with GO-ID: 15018 and 15020). Both annotations were referred to genes associated with glycosyltransferase (HORVU2Hr1G013590 and HORVU2HHr1G013630). Another two annotations in the E region were related to genes referred to UDP-glycosyltransferase superfamily protein (HORVU5Hr1G095010, HORVU5Hr1G096240, HORVU5Hr1G096260, HORVU5Hr1G096310, HORVU5Hr1G096320, HORVU5Hr1G096340, HORVU5Hr1G096360). Glycosylation is a widespread cellular modification reaction in all living organisms, attaching a carbohydrate to the hydroxyl or other functional group of a molecule in a biosynthetic pathway [107]. Glycosylation modifications are catalyzed by glycosyltransferase enzymes (GTs), which are highly divergent, polyphyletic and belong to a multigene family [108]. Plant uridine diphosphate (UDP)-glucosyltransferases (UGT) catalyze the glucosylation of xenobiotic, endogenous substrates and phytotoxic agents produced by pathogens such as mycotoxins [109, 11]. The studies have shown that plant UDP-glucosyltransferase genes have significant role in plant resistance both to biotic and abiotic stresses [111, 112]. Poppenberger et al. [113] demonstrated that DON resistance can be achieved by the enzymatic conversation (a natural detoxification process in plants called glycosylation) of the toxin into the non-toxic form (DON-3-0-glucoside) by UDP-glucosyltransferase. Recently the HvUGT-10 W1 gene has been isolated from an FHB resistant barley variety conferred FHB tolerance [110]. It is also worth to mention that in our study these GO terms have been annotated for two regions, where FHBi QTLs were found on chromosomes 2H and 5H in this study.

Supporting information

S1 Fig. Seeds, observed in Lcam plants, with moderate or severe Fusarium symptoms.

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S2 Fig. Abundant mycelial growth observed on the grain surface.

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S3 Fig. The positions of QTLs (chromosomes 1H and 2H) detected for studied traits.

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S4 Fig. The positions of QTLs (chromosomes 3H, 4H and 5H) detected for studied traits.

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S5 Fig. The positions of QTLs (chromosomes 6H and 7H) detected for studied traits.

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S1 Table. The mean values for studied traits for parental cultivars.

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S2 Table. The mean values for studied traits for RILs.

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S3 Table. ANOVA results, variance components and heritability estimated for studied traits.

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S4 Table. QTLs identified in the LCam population for the observed traits.

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S5 Table. An overrepresentation analysis associated with three regions (hotspots B, E, and F).

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Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was financially supported by Polish Ministry of Agriculture and Rural Development (grant no HOR hn-501-19/15 Task 88).

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Decision Letter 0

Ajay Kumar

19 Sep 2019

PONE-D-19-23971

Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight Symptoms Evaluation in Barley RILs

PLOS ONE

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: I reviewed the manuscript entitled “Mapping of quantitative trait loci for traits linked to Fusarium head blight symptoms evaluation in barley RILs” submitted by Ogrodowicz et al. The authors studied the association between a number of agronomic traits and resistance/susceptibility to Fusarium head blight (FHB) in 100 RILs from a barley bi-parental population Lubuski x Cam/B1/ CI08887///CI05761. A total of 70 QTL for agronomic and FHB resistance were mapped in this study. The authors used only 100 RILs for mapping QTL for complex traits like FHB resistance and agronomic traits which is something I do not support in this work. I recommend representing all the 70 QTL in a Figure (a map) rather than a table and add the SNP physical positions. The authors need to give more emphasis to the FHB/DON QTL that are co-localized with agronomic trait QTL and give their recommendations to breeders based on their findings. The authors need to improve the English and the discussion section of this manuscript. Please avoid repeating results in the discussion. These are additional comments to the authors.

1) The manuscript title is not good

2) Abstract: very long introduction in the abstract, summarized it and focus more on the findings of your study. Include the species name of Fusarium used for inoculation (line #24)

3) Introduction: there are some unnecessary details and some spelling mistakes

4) Materials and Methods:

a. Include why you chose these specific two parents for your study in materials and methods and be consistent with the names of the parents. The authors sometime mention the names of the parents and the origins of the parents (Syrian, European) in other cases. 100 RILs is a small number for mapping QTL for FHB resistance and agronomic traits.

b. Line # 116: I think you can use un-inoculated and inoculated plots instead of “V1-variant-control” and “V2-Variant-inoculation”

c. Change “Methodology” to something like “Inoculum preparation” and give more details on inoculum preparation. Why you did not use F. graminearum for inoculation. Why did you use F. Culmorum? Any reason? What the authors mean by micro-irrigation? Give more details.

d. Line 129: 10 randomly selected plants per plot? if so add “per plot”in Line# 30: can you define “stature” of plants and how that is different from plant height and if there is a scale, please describe it.

e. Describe all the traits you measure in the text and do not just refer the readers to table1.

f. Include in the methods when did you score for FHB severity (how many days after heading)

g. Adjust column width of table 1. I believe you mean “plot” not “pot” in table 1

h. Include more details on how you extracted DON (how much grain were used to quantify the toxin, method of DON extraction, more details on the ELISA methodology, if you included controls in your ELISA plates, were the samples duplicated or just included once, etc)

i. Fig1 and Fig 2 can go to supplementary

j. Line 176 and 177: replace “7.842 SNPs” by “7,842 SNPs” and same for “2.832”

k. Change “map construction” to something like “linkage map….”

l. Line 186: “markers with other segregation ratios were categorized as odd” what do you mean by “odd” do you mean markers with segregation distortion?

m. Line 186: Not clear what you mean by “incorrect regions of the chromosomes….”

n. Line 191: “recombination frequency was set at level <4”, it should be 0.4 or 40%

o. For “P” values. The “P” should be italics

p. Line 209: “exceed 20/15 %” is this a typing mistake?

5) Results:

a. Figure 3. Is very blurry: provide better quality figure

b. Line 240: “The parental forms were differentiated in terms of all studied characters”: This statement is not accurate because in Fig 3 there was no much difference in these traits LSt, FHBi, FDKn, FDKw, HLKn between the parents.

c. Better have the DON values in ppm

d. Line 268: FHBi was positively correlated with sterility. Please correct

e. Did you check for normality of traits before doing correlation ?

f. Why you did not do correlation of agronomic traits with DON levels

g. It will be good if you calculate the heritability of each trait

h. Table2. ANOVA should go to supplementary

i. Is table 3 for correlation between FHB severity or FHBi with other traits?

j. Table 2: I expected NSS and density to be positively correlated with FHB. How do you explain the negative correlations in your study?

k. Linkage map construction and table 4: add more statistics on the map. How many loci these 1,947 SNPs represent? How many markers have segregation distortion? Table 4 “map lenght” misspelled

l. All markers and QTL names should be italics

m. You should include the physical position of the markers linked to your QTL

n. Line 313: for the QTL “QNSS.IPG-2H_1” indicate which parent provides the resistant allele.

o. Line 340: font difference in the QTL name

p. Where are the QTL for DON?

q. For the co-localized QTL. I would like to see more emphasis on what FHB/DON QTL co-localized with QTL for spike and agronomic traits.

r. Table 4 could be better represented in a map so it will be easier to see which QTL are co-localized and put the physical positions of the markers.

s. I don’t see the meaning of looking for gene candidates within ± 2 cM of the FHB QTL. It is a very huge physical distance especially that the resolution of your map wouldn’t be good enough knowing that you used only 100 RILs for mapping the QTL.

6) Discussion

a. Authors should work better on the discussion of this manuscript and avoid repeating results in discussion.

b. Line 501-514: Lubuski is less susceptible to FHB in terms of DON but you have higher FHBi for Lubuski under inoculation: how do you explain that? Lines 501-509 are results not discussion

c. Line 526-530: what is the relationship between antibody specific mycelial proteins and DON measures with ELISA? The antibody in the ELISA are specific to DON not to the fungal mycelium. Your statement was not clear.

d. Line 540-542: you have negative correlation between density and FHBi which means compactness is negatively correlated with FHB but you are discussing that compactness is positively correlated with FHB. There is contradiction here.

e. What is the difference between your present study and your previous study: line 545-549: was it just the density of mapping by increasing the number of markers used?

f. Change “investigation” to “study”

g. Discuss the type of linkage between alleles providing resistance to FHB and the other agronomic traits in your study.

Reviewer #2: The manuscript does present interesting results related to FHB in barley. However, apart from few technical comments, the manuscript needs to be rewritten (except discussion portion) completely in an intelligible fashion and standard english communication skills.

**********

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Reviewer #2: Yes: Ravinder Singh, Asstt. Prof., SKUAST-Jammu, India (180009)

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PLoS One. 2020 Feb 4;15(2):e0222375. doi: 10.1371/journal.pone.0222375.r002

Author response to Decision Letter 0

6 Nov 2019

Reviewer Report

Manuscript # PONE-D-19-23971

Full Title: Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight Symptoms Evaluation in Barley RILs

General outline of the manuscript

The manuscript titled “Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight Symptoms Evaluation in Barley RILs” presented results of QTL analysis conducted for fusarium head blight (FHB) in barley. The experiments were conducted at three locations (with three replicates each) for two conditions – control and inoculated conditions (taken as treatments). The genetic mapping for QTL analysis carried out using 9K Illumina infinium arrays has led to the identification of 70 QTLs for various traits related to FHB on different chromosomes of barley.

Comments

Overall, the research has relevance to the identification of genetic loci controlling FHB in barley. The QTLs identified constitute an important resource for the improvement of FHB in barley. However, it still has enough room for improvement before making it available to public.

Technical comments: (i) The words ‘symptoms evaluation’ and ‘RILs’ can be deleted from the title – it has been done. The new title is: Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight In Barley

(ii) It is not clear from the text whether the variance components reported in Table 2 were calculated within each location or across all the locations combined. Ideally, it would make more sense if variance components for line x location, line x treatment interactions are calculated within each location. – Variance components reported in Table 2 were computed in the mixed linear model that is described in Methods and was applied to original data at replication level, without combining or averaging over any factor. Please note that variance components for interaction of lines and locations cannot be computed at each location.

(iii) In Table 3, the correlation coefficients were reported between FHB and traits recorded for the current study. Again, the data for individual location might be more informative than combining it across all location and subsequently using for correlation analysis. Moreover, the trait Stature was most likely recorded as an ordinal data and therefore cannot be used for calculation of correlation coefficients. – We computed correlations of traits within each location and treatment; they are reported in new Table 2. The corresponding fragment in Results has been changed correspondingly (lines 250-256):

The values of correlation coefficients between the studied traits and FHBi were generally low (Table 2). FHBi was negatively correlated with all studied traits (exeptions: FDKw and Sterility). In LES location was recorded positive significant correalation between FHBi and Density, whereas negative correlation coefficients were noted between these traits in other two locations. Positive correalation was recorded between DON content and FHBi in one of three locations (TUL) for both type of treatments. No significant correlations between DON content and other agronomic traits were observed.

The authors must look into the fact that traits like NSS, NGS, Density, GWS and GY were negatively correlated with FHB; yet the correlation coefficient between FHB and TGW was found to be non-significant. Conducting location specific correlation analysis might point to something more important that was probably missed in the combined analysis.- it has been done. Thank you for this important remark. Indeed, detailed correlation analysis has provided some additional data (Table 2).

Through the manuscript, the authors have been non-consistent in the usage of the names of the genotypes.he two parents have been referred to by various names like Syrian breeding line has been written as Syrian genotype, Syrian parent and CamB; and similarly Polish Cultivar has been referred to as Lubuski, European parent, European genotype and European parent genotype. – it has been done.

The methodology in material and methods needs to be explained a bit more. Is 20mts a standard isolation distance for fungal spores (Line 117)?- the description of methodology has been extended. Distance between plots with different treatments were established based on our previous pilot experiment determining the safe distance between areas subjected to inoculation and plots used for control experiments.

The figure of concentration of inoculum on Line 124 is not clear. – it has been done (line 118):

Conidia concentration was adjusted to 105/ml.

The number mentioned on Line 209 are not clear, as what do they refer to.- it has been clarified.

The ‘LCam population’ on Line 229 is not defined in materials and methods.- The studied population has been defined (lines 96-98):

A 100-RIL population of spring barley (hereafter referred to as LCam) obtained from the cross between the Polish cultivar Lubuski and a Syrian breeding line - Cam/B1/CI08887//CI05761 (hereafter referred to as CamB) was studied in field conditions, together with both parental forms.

Throughout the manuscript, the words ‘main QTLs’ have been used but not explained in the materials; probably, it referred to major QTLs.- it has been corrected.

The words ‘trait’ and ‘character’ have been used interchangeably; the authors should have restricted to the use of one type of word only.- it has been done.

The discussion part is well written (requires little changes only) and results have discussed in the right context. It has been done.

Comments related to written English: It seems the authors have paid very little attention to the use of good written communication skills. - There are numerous instances throughout the manuscript of misspelling very commonly used words like disease, classified, pollen containing, described etc (please refer to lines 21, 72, 87, 100, 305, 330, 332, 369, 383 and 388).- it has been corrected.

Overall, the sentences have been written without focusing on the subject and objects of the sentences. The second paragraph on Page 4 needs to re-written.- it has been done.

The sentence beginning on line 179 needs to be re-written.- it has been done.

On Line 304, the sentence should begin with numbers in words only. – it has been done.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I reviewed the manuscript entitled “Mapping of quantitative trait loci for traits linked to Fusarium head blight symptoms evaluation in barley RILs” submitted by Ogrodowicz et al. The authors studied the association between a number of agronomic traits and resistance/susceptibility to Fusarium head blight (FHB) in 100 RILs from a barley bi-parental population Lubuski x Cam/B1/ CI08887///CI05761. A total of 70 QTL for agronomic and FHB resistance were mapped in this study. The authors used only 100 RILs for mapping QTL for complex traits like FHB resistance and agronomic traits which is something I do not support in this work. – references of research using similar number of plants have been added (lines 528-529):

Previous studies have used population sizes comparable to this study and successfully identified FHB QTL [24, 31, 82].

I recommend representing all the 70 QTL in a Figure (a map) rather than a table and add the SNP physical positions. – A new figure (S3 Figure) showing SNP positions in the reference genome has been made.

The authors need to give more emphasis to the FHB/DON QTL that are co-localized with agronomic trait QTL and give their recommendations to breeders based on their findings. – it has been done (lines 402-404):

All QTLs linked to FHB on chromosomes 2H and 5H co-localized with other agronomic traits. A total of eight chromosomal regions (named A-G) harboring QTLs for the studied traits were defined. These regions (hotspots), listed in Table 4.

(lines 576-580):

Delayed head emergence may increase the likelihood that the host will escape infection by the pathogen [75, 94]. On the other hand, late heading is undesirable in breeding programs addressed to arid regions [95]. Plants with lower FHB severities usually have one or more of the following traits: late heading, increased height, and two-rowed spike morphology [63, 64, 74].

The authors need to improve the English and the discussion section of this manuscript. Please avoid repeating results in the discussion - it has been done.

These are additional comments to the authors.

1) The manuscript title is not good – the title has been changed.

2) Abstract: very long introduction in the abstract, summarized it and focus more on the findings of your study. Include the species name of Fusarium used for inoculation (line #24) – it has been done.

3) Introduction: there are some unnecessary details and some spelling mistakes – the unnecessary details have been removed and spelling mistakes have been corrected.

4) Materials and Methods:

a. Include why you chose these specific two parents for your study in materials and methods and be consistent with the names of the parents. The authors sometime mention the names of the parents and the origins of the parents (Syrian, European) in other cases. 100 RILs is a small number for mapping QTL for FHB resistance and agronomic traits. –

b. Line # 116: I think you can use un-inoculated and inoculated plots instead of “V1-variant-control” and “V2-Variant-inoculation”- it has been done.

c. Change “Methodology” to something like “Inoculum preparation” and give more details on inoculum preparation. Why you did not use F. graminearum for inoculation. Why did you use F. Culmorum? Any reason? What the authors mean by micro-irrigation? Give more details. – more details about inoculum preparation have been added and the explanation of F. culmorum using has been added to Discussion (lines 451-454):

FHB, caused by Fusarium culmorum, is a very important disease affecting crops on a global scale [9]. The pathogen is dominant in cooler areas like north, central and western Europe [54]. F. graminearum predominates in the warmer, humid areas of the world such as USA [55, 56].

d. Line 129: 10 randomly selected plants per plot? if so add “per plot”in Line# 30: can you define “stature” of plants and how that is different from plant height and if there is a scale, please describe it.- it has been done and the analysis of trait Stature has been removed from the paper.

e. Describe all the traits you measure in the text and do not just refer the readers to table1.- the traits have been divided into subgroups and brief descriptions have been given in methodology.

f. Include in the methods when did you score for FHB severity (how many days after heading)-

it has been done (line 143):

The assessments were performed 20 days after inoculation.

g. Adjust column width of table 1. I believe you mean “plot” not “pot” in table 1- it has been done.

h. Include more details on how you extracted DON (how much grain were used to quantify the toxin, method of DON extraction, more details on the ELISA methodology, if you included controls in your ELISA plates, were the samples duplicated or just included once, etc) –

it has been done (lines 114-122):

Fusarium culmorum isolates were incubated on wheat grain (50 g) in 300 ml Erlenmeyer glass flasks for 5 weeks. The colonies were covered with 15 ml of sterile distilled water and autoclaved twice for 30 min within 2 days. Inoculum was prepared just before the inoculations by liquid cultures of Fusarium culmorum (isolate KF846) and 0.0125% TWEEN®20 (Sigma-Aldrich Chemie GmbH). Conidia concentration was adjusted to 105/ml. Inoculation was performed at the flowering stage (Zadoks scale 65). Mist irrigation to promote fungal infection was performed for three days in the field using a sprinkler system with DN881A-type sprinkler heads equipped with 1.50-mm-diameter nozzles (Sun Hope Inc., Meguro-ku). Water was applied three times daily (at 07.00, 13.00, and 19.00) for 15 min at each interval.

i. Fig1 and Fig 2 can go to supplementary- it has been done.

j. Line 176 and 177: replace “7.842 SNPs” by “7,842 SNPs” and same for “2.832”- it has been done.

k. Change “map construction” to something like “linkage map….”- it has been done.

l. Line 186: “markers with other segregation ratios were categorized as odd” what do you mean by “odd” do you mean markers with segregation distortion?- Yes, the sentence has been corrected.

m. Line 186: Not clear what you mean by “incorrect regions of the chromosomes….”- The statement was confusing and has been removed form manuscript.

n. Line 191: “recombination frequency was set at level <4”, it should be 0.4 or 40%- it has been done.

o. For “P” values. The “P” should be italics- it has been done.

p. Line 209: “exceed 20/15 %” is this a typing mistake?- Yes, the sentence has been corrected.

5) Results:

a. Figure 3. Is very blurry: provide better quality figure- it has been done. The new figure 1 has been added in better quality.

b. Line 240: “The parental forms were differentiated in terms of all studied characters”: This statement is not accurate because in Fig 3 there was no much difference in these traits LSt, FHBi, FDKn, FDKw, HLKn between the parents.- This sentence has been removed.

c. Better have the DON values in ppm- it has been done.

d. Line 268: FHBi was positively correlated with sterility. Please correct- it has been corrected.

e. Did you check for normality of traits before doing correlation ?- We did not perform a formal normality test of the observations. However, Figure 1 indicates that the distributions in most cases are not very nonsymmetric or far from normal. So, we think that the statistical test that were used in ANOVA and correlation analysis are justified.

f. Why you did not do correlation of agronomic traits with DON levels- no correlations between studied traits and DON content have been observed in our study. This has been reported in Results (lines 255-256):

No significant correlations between DON content and other agronomic traits were observed.

g. It will be good if you calculate the heritability of each trait- Heritabilities has been computed, separately for control and infected variants, over locations, in an appropriate linear mixed model. They are reported in S3 Table.

h. Table2. ANOVA should go to supplementary- it has been done.

i. Is table 3 for correlation between FHB severity or FHBi with other traits?- yes, it has been corrected.

j. Table 2: I expected NSS and density to be positively correlated with FHB. How do you explain the negative correlations in your study?- the association between two traits strongly linked to NSS (Density and Sterility) and FHB has been explained (lines 599-610):

Spike architecture has significant influence on yield and might alter the spike microenvironment by making it less favorable for fungal infection [104]. In the current study, six QTLs linked to Density were found. Of the six QTLs detected, four loci were found on chromosome 2H. The major QTL (QDen.IPG-2H-1) was located on the short arm of 2H in the vicinity of marker BK_12. Two QTLs related to the density of the spike were found on chromosome 5H. In most cases, CamB alleles contributed positively to this trait. In many studies, plants with lax spikes have been reported as being less vulnerable for fungal infection [90, 104]. On the other hand, Yoshida et al. [76] found no differences between genotypes when comparing barleys with normal and dense type of spikes. Steffenson et al. [74] showed that FHB severity was higher in dense spike NILs vs. lax spike plants, but no significant differences were found. Langevin et al. [105], in a study using barley with two- and six-row types of spikes, concluded that the high level of DON contamination observed in dense spikes occurred mainly because of direct contact with florets.

k. Linkage map construction and table 4: add more statistics on the map. How many loci these 1,947 SNPs represent? How many markers have segregation distortion? Table 4 “map lenght” misspelled- all possible information has been included in “Materials and methods”

l. All markers and QTL names should be italics- it has been done

m. You should include the physical position of the markers linked to your QTL – it has been included

n. Line 313: for the QTL “QNSS.IPG-2H_1” indicate which parent provides the resistant allele.- it has been done (lines 288-289):

In this case, significant QTL × E interaction was noted and Lubuski alleles conferred a positive effect in increasing this trait.

o. Line 340: font difference in the QTL name- it has been corrected.

p. Where are the QTL for DON?-

No QTLs for DON content were found in our study and the comments have been given in Discussion (lines 532-549):

In our study different tools for FHB evaluation have been used: among others: DON content estimation. No QTLs for DON content were detected but visual assessment of FHB severity like FHBi, FDK and HLK were employed here for evaluation of the level of FHB severity. In this study six, four and five QTLs were found for FHBi, FDK and HLK, respectively. The association between Fusarium head blight (FHB) intensity and DON accumulation in harvested grain is not fully understood. Varying degrees of association between Fusarium head blight intensity and DON accumulation in harvested grain have been reported in the literature, including situations with high positive correlations, low significant correlations, and negative correlations, as well as correlations close to zero [83–86]. Visual assessments of disease were usually made at Feekes GS 11.2, based on the proportion of the spike diseased, while DON was quantified in this study after harvest as the amount of DON per unit weight of a bulked sample of ground kernel. The measurement of DON in an assay typically is a composite value for seeds with different levels of DON (including those with 0 ppm) and different levels of fungal colonization. In our study positive correlation between DON content and FHBi was observed only in one location which can be explained by the fact that the growth of the fungus and the production of DON are highly weather dependent [87, 88]. Moreover, DON concentration may have increased at differential rates in the different studies, affecting the relationship between DON sampled at harvest and disease assessed different developmental stage of the plant.

q. For the co-localized QTL. I would like to see more emphasis on what FHB/DON QTL co-localized with QTL for spike and agronomic traits.- it has been done (lines 613-618):

In our study FHB QTLs coincidence with traits connected with spike morphology, HD and height (LSt) on chromosomes 2H and 5H was found. The underlying mechanism of coincident HD, LSt, Density and disease QTL could be due to tight linkage or pleiotropy. However, late-heading plants may serve as an escape mechanism from infection due to a lack of overlapping periods in plant development and fungus life cycle. Plant height could contributed to physically avoiding pathogens as well as inflorescence structure [82].

r. Table 4 could be better represented in a map so it will be easier to see which QTL are co-localized and put the physical positions of the markers.- A new figure (S3 Figure) has been made

s. I don’t see the meaning of looking for gene candidates within ± 2 cM of the FHB QTL. It is a very huge physical distance especially that the resolution of your map wouldn’t be good enough knowing that you used only 100 RILs for mapping the QTL.-

This section in Discussion has been changed. Genomic and functional genomic studies normally generate large lists of interesting genes, and translating such lists into biologically meaningful information is critical to understand the underlying regulatory mechanisms of the related biological processes. That’s why the overrepresentation analysis of gene functions based on Gene Ontology term was employed in our study. The basis assumption underlying the overrepresentation approach is that in several of multiple QTL regions for a given trait, a causal gene is present, and that several causal genes have related or similar gene functions (Beissbarth and Speed, 2004). We believe this type of analysis will be useful for looking up a cues about the potential factors contributing to traits importance.

Ref: Beissbarth T, Speed TP. GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics. 2004;20:1464–1465.

6) Discussion

a. Authors should work better on the discussion of this manuscript and avoid repeating results in discussion.- it has been done.

b. Line 501-514: Lubuski is less susceptible to FHB in terms of DON but you have higher FHBi for Lubuski under inoculation: how do you explain that? Lines 501-509 are results not discussion- it has been done (lines 469-470):

This can be explained by the fact that symptomless grains may contain significant amounts of mycotoxins, while symptomatic grains within the same samples may not [61].

c. Line 526-530: what is the relationship between antibody specific mycelial proteins and DON measures with ELISA? The antibody in the ELISA are specific to DON not to the fungal mycelium. Your statement was not clear.- The statement was confusing. It has been removed from paper and the explanation of low correlations between FHB and DON content has been added.

d. Line 540-542: you have negative correlation between density and FHBi which means compactness is negatively correlated with FHB but you are discussing that compactness is positively correlated with FHB. There is contradiction here.- it has been corrected.

e. What is the difference between your present study and your previous study: line 545-549: was it just the density of mapping by increasing the number of markers used?- the density of mapping is the main difference between those maps but the previous statement has been removed form manuscript because of its unimportance for discussion.

f. Change “investigation” to “study”- it has been done.

g. Discuss the type of linkage between alleles providing resistance to FHB and the other agronomic traits in your study.-

results and discussion have been changed:

QHLKw.IPG-2H was found in the vicinity to marker BK_12. In the same position a set of QTLs linked to different agronomic traits was found (Density, GWS, GY, HD, NGS and NSS - QDen.IPG-2H_1, QGWS.IPG-2H_1, QGY.IPG-2H, QHD.IPG-2H, QNGS.IPG-2H and QNSS.IPG-2H_1, respectively). QHLKn.IPG-2H_2, other QTL related to FHB, was detected in the vicinity of marker BK_13 – in the same position as QTLs related to LS (QLS.IPG-2H) and LSt (QLSt.IPG-2H_1). In both cases Lubuski contributed positively to the increase of the trait linked to HLK (HLKn and HLKw).

In our study FHB QTLs coincidence with traits connected with spike morphology, HD and height (LSt) on chromosomes 2H and 5H was found. The underlying mechanism of coincident HD, LSt, Density and disease QTL could be due to tight linkage or pleiotropy.

Reviewer #2: The manuscript does present interesting results related to FHB in barley. However, apart from few technical comments, the manuscript needs to be rewritten (except discussion portion) completely in an intelligible fashion and standard english communication skills.- manuscript has been rewritten. The English language paper was revised by mother tongue.

Attachment

Submitted filename: Response to reviewers.docx

Click here for additional data file. (28.7KB, docx)

Decision Letter 1

Ajay Kumar

6 Dec 2019

PONE-D-19-23971R1

Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight In Barley

PLOS ONE

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Reviewer #1: All comments have been addressed

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Reviewer #1: The authors improved the quality of their manuscripts and did most of the modifications I requested in my first revision. However the authors still need to improve the writing of this manuscript.

Additional comments

1- “parental genotypes were 107 chosen on the basis of earlier studies conducted by Górny and co-workers (lit)” put the reference here instead of “(li)”

1- “Fusarium culmorum isolates were incubated on wheat grain (50 g) in 300 ml Erlenmeyer glass flasks for 5 weeks. The colonies were covered with 15 ml of sterile distilled water and autoclaved twice for 30 min within 2 days”.

Is the water that was autoclaved or the mixture of fungal conidia suspended in H2O? I believe you don’t want to autoclave your inoculum so please rewrite this sentence. “5 weeks” and “2 days” should be “five weeks” and “two days”.

2- “Spike architecture has significant influence on yield and might alter the spike microenvironment by making it less favorable for fungal infection [104]”:

What spike architecture is influencing yield and in what direction (positive or negative?) and what spike architecture is influencing microenvironment. I believe you mean less dense spike make the microenvironment less favorable for fungal infection.

3- “In many studies, plants with lax spikes have been reported as being less vulnerable for fungal infection [90, 104]. On the other hand, Yoshida et al. [76] found no differences between genotypes when comparing barleys with normal and dense type of spikes. Steffenson et al. [74] showed that FHB severity was higher in dense spike NILs vs. ……………...”

So your data opposes all of these previous studies? (check the negative correlations between FHB and density and NSS in Table2, does your data mean that more dense spikes are less susceptible to FHB?).

4- Add number of loci per chromosome (markers mapped in the same location represent a single locus) and add percentage of distorted markers per chromosome in Table4

5- I suggest presenting table 2 as figure (correlation plots) if possible

6- “Conidia concentration was adjusted to 105/ml”. Change this to “inoculum concentration was adjusted to 105 spore/ml”

7- Table1 clolum1 and column2: please adjust the width because there some missing words.

8- Looking for candidates genes based on QTL mapping is not adequate at this stage because of the low resolution of your QTL mapping. Looking for candidate genes is appropriate only after fine mapping.

9- the authors need to provide the phenotypic and genotypic data of the this population

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PLoS One. 2020 Feb 4;15(2):e0222375. doi: 10.1371/journal.pone.0222375.r004

Author response to Decision Letter 1

16 Jan 2020

Dear Editor,

We would like to thank you for all suggestions and comments, which have been useful for improving the quality of the manuscript. All objections were properly faced and a suitable answer/modification was provided.

Reviewer #1: The authors improved the quality of their manuscripts and did most of the modifications I requested in my first revision. However the authors still need to improve the writing of this manuscript.

Additional comments

1- “parental genotypes were 107 chosen on the basis of earlier studies conducted by Górny and co-workers (lit)” put the reference here instead of “(li)”

Thank you for the suggestion. It has been done (lines 99-100):

“The plant materials were described in detail in Ogrodowicz et al. [37] and parental genotypes were chosen on the basis of earlier studies conducted by Górny et al. [38].”

1- “Fusarium culmorum isolates were incubated on wheat grain (50 g) in 300 ml Erlenmeyer glass flasks for 5 weeks. The colonies were covered with 15 ml of sterile distilled water and autoclaved twice for 30 min within 2 days”.

Is the water that was autoclaved or the mixture of fungal conidia suspended in H2O? I believe you don’t want to autoclave your inoculum so please rewrite this sentence. “5 weeks” and “2 days” should be “five weeks” and “two days”.

It has been corrected. The sentence about sterilisation of water has been removed (insignificant detail).

2- “Spike architecture has significant influence on yield and might alter the spike microenvironment by making it less favorable for fungal infection [104]”:

What spike architecture is influencing yield and in what direction (positive or negative?) and what spike architecture is influencing microenvironment. I believe you mean less dense spike make the microenvironment less favorable for fungal infection.

Thanks for the suggestion. It has been corrected:(601)

“Spike architecture has significant influence on yield and less dense spike might alters the spike microenvironment by making it less favorable for fungal infection [104].”

3- “In many studies, plants with lax spikes have been reported as being less vulnerable for fungal infection [90, 104]. On the other hand, Yoshida et al. [76] found no differences between genotypes when comparing barleys with normal and dense type of spikes. Steffenson et al. [74] showed that FHB severity was higher in dense spike NILs vs. ……………...”

So your data opposes all of these previous studies? (check the negative correlations between FHB and density and NSS in Table2, does your data mean that more dense spikes are less susceptible to FHB?).

4- Add number of loci per chromosome (markers mapped in the same location represent a single locus) and add percentage of distorted markers per chromosome in Table4.

It has been done.

5- I suggest presenting table 2 as figure (correlation plots) if possible.

Thank you for suggestion. It has been done.

6- “Conidia concentration was adjusted to 105/ml”. Change this to “inoculum concentration was adjusted to 105 spore/ml”

It has been changed.

7- Table1 clolum1 and column2: please adjust the width because there some missing words.

It has been done.

8- Looking for candidates genes based on QTL mapping is not adequate at this stage because of the low resolution of your QTL mapping. Looking for candidate genes is appropriate only after fine mapping.

The candidate gene strategy has shown promise for bridging the gap between quantitative genetic and molecular genetic approaches to study complex traits (Ingvarsson and Street 2011). A GO annotation is regarded as a statement about gene function of a particular gene. That’s why we changed “potential candidate genes” term on “ (gene annotations linked to potential candidate genes located in the vicinity (intervals around markers extended by ±2cM) of the particularly robust QTL.) (207-210).

Low resolution of the estimated chromosomal location of quantitative trait loci (QTL) is a major obstacle in application of QTL linkage mapping results for. Up to a certain point, mapping resolution can be improved by increasing marker density (Darvasi et al. 1993). However, for given sample size and standardized QTL substitution effect, ultimate map resolution is fixed and cannot be improved even with infinite marker density (Darvasi et al. 1993; Ronin et al. 2003). In our study, genetic map spanned 1678 cM and contained 1947 single nucleotide polymorphism markers. We believe this resolution is sufficient for a Gene Ontology analysis. Similar mapping resolution was used i.a. in study concerning wheat 2D chromosome (Deng et al. 2019). Effective sample size can also be increased by accumulating recombinants in advanced generations (Darvasi and Soller 1995). That`s why in our study a set of recombinant inbred lines (F10) was used.

Ref:

Ingvarsson, P. K. and Street, N. R. (2011) Association genetics of complex traits in plants, New Phytologist,189(4), 909-922

A. Darvasi, A. Weinreb, V. Minke, J. I. Weller, and M. Soller (1993). Detecting marker-QTL linkage and estimating QTL gene efect and map location using a saturated geneticmap.Genetics,134, 943±951

Ronin Y.I., Korol A.B., Shtemberg M., Nevo E. & Soller M.(2003) High resolution mapping of quantitative trait lociby selective recombinant genotyping.Genetics164, 1657–66

Darvasi, A. and Soller, M. 1995. Advanced intercross lines: An experimental population for fine genetic mapping. Genetics 141: 1199-1207

Deng M, Wu F, Zhou W, Li J, Shi H, Wang Z, Lin Y, Yang X, Wei Y, Zheng Y Liu Y (2019) Mapping of QTL for total spikelet number per spike on chromosome 2D in wheat using a high-density genetic map. Genet Mol Biol. Jul-Sep;42(3):603-610. doi: 10.1590/1678-4685-GMB-2018-0122. Epub 2019 Nov 14.

9- the authors need to provide the phenotypic and genotypic data of the this population.

The phenotypic and genotypic data have been provided (line 220):

Raw data are available at [www.polapgen.pl/eksplan/dataset_FHB_in_LCamRIL.zip].

Decision Letter 2

Ajay Kumar

21 Jan 2020

Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight In Barley

PONE-D-19-23971R2

Dear Dr. Kuczyńska,

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PLOS ONE

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Reviewers' comments:

Acceptance letter

Ajay Kumar

28 Jan 2020

PONE-D-19-23971R2

Mapping of Quantitative Trait Loci for Traits linked to Fusarium Head Blight In Barley

Dear Dr. Kuczyńska:

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Fig. Seeds, observed in Lcam plants, with moderate or severe Fusarium symptoms.

    (DOCX)

    Click here for additional data file. (288.7KB, docx)
    S2 Fig. Abundant mycelial growth observed on the grain surface.

    (DOCX)

    Click here for additional data file. (179.2KB, docx)
    S3 Fig. The positions of QTLs (chromosomes 1H and 2H) detected for studied traits.

    (DOCX)

    Click here for additional data file. (97.3KB, docx)
    S4 Fig. The positions of QTLs (chromosomes 3H, 4H and 5H) detected for studied traits.

    (DOCX)

    Click here for additional data file. (127.7KB, docx)
    S5 Fig. The positions of QTLs (chromosomes 6H and 7H) detected for studied traits.

    (DOCX)

    Click here for additional data file. (69.9KB, docx)
    S1 Table. The mean values for studied traits for parental cultivars.

    (DOCX)

    Click here for additional data file. (14.4KB, docx)
    S2 Table. The mean values for studied traits for RILs.

    (DOCX)

    Click here for additional data file. (19.3KB, docx)
    S3 Table. ANOVA results, variance components and heritability estimated for studied traits.

    (DOCX)

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    S4 Table. QTLs identified in the LCam population for the observed traits.

    (DOCX)

    Click here for additional data file. (39.2KB, docx)
    S5 Table. An overrepresentation analysis associated with three regions (hotspots B, E, and F).

    (DOCX)

    Click here for additional data file. (25.6KB, docx)
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