Tong Zheng ,Chen Hu ,Lei Li ,Zhengxi Sun ,Minmin Yun ,Guihu Bi ,Gvin Humphreys ,To Li ,*

a Key Laboratory of Plant Functional Genomics of the Ministry of Education/Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding/Collaborative Innovation of Modern Crops and Food Crops in Jiangsu/Jiangsu Key Laboratory of Crop Genetics and Physiology,College of Agriculture,Yangzhou University,Yangzhou 225009,Jiangsu,China

b USDA-ARS,Hard Winter Wheat Genetics Research Unit,Manhattan,KS 66506,USA

c Agriculture &Agrifood Canada,Ottawa Research &Development Centre,Ottawa,ON K1A 0C6,Canada

ABSTRACT Fusarium head blight(FHB)is a global wheat disease that devastates wheat production.Resistance to FHB spread within a wheat spike(type II resistance) and to mycotoxin accumulation in infected kernel (type III resistance) are the two main types of resistance.Of hundreds of QTL that have been reported,only a few can be used in wheat breeding because most show minor and/or inconsistent effects in different genetic backgrounds.We describe a new strategy for identifying robust and reliable meta-QTL (mQTL)that can be used for improvement of wheat FHB resistance.It involves integration of mQTL analysis with mQTL physical mapping and identification of single-copy markers and candidate genes.Using metaanalysis,we consolidated 625 original QTL from 113 publications into 118 genetic map-based mQTL(gmQTL).These gmQTL were further located on the Chinese Spring reference sequence map.Finally,77 high-confidence mQTL (hcmQTL) were selected from the reference sequence-based mQTL (smQTL).Locus-specific single nucleotide polymorphism (SNP) and simple sequence repeat (SSR) markers and 17 genes responsive to FHB were then identified in the hcmQTL intervals by combined analysis of transcriptomic and proteomic data.This work may lead to a comprehensive molecular breeding platform for improving wheat resistance to FHB.

Keywords:Wheat Fusarium head blight High-confidence meta-QTL Omics

1.Introduction

Fusarium head blight(FHB)or scab,is a fungal disease of wheat caused primarily byFusarium graminearumand present in most wheat growing areas worldwide.FHB is one of the most devastating wheat diseases,particularly in warm and humid wheat production areas.Because it severely reduces grain yield and impairs grain quality by mycotoxin accumulation in infected kernels,it threatens food security and safety [1].

Host resistance is the most effective and sustainable strategy for combating the disease[2].Five types of wheat FHB resistance have been proposed [3,4],including resistance to initial pathogen infection (type I),to pathogen spread within a spike (type II),to mycotoxin accumulation (type III),and to kernel infection (type IV),and tolerance to yield loss(type V).Types II and III resistances have been extensively studied because they are more stable for phenotyping and can be directly related to disease severity and mycotoxin accumulation in kernel.Recently[5],a modified definition and evaluation method for type IV resistance was reported,and it was recommended that type IV and type V resistance be merged into one type,considering their similarity in nature.

FHB resistance is a quantitative trait that is controlled by multiple quantitative trait loci (QTL).Different resistant cultivars may carry different resistance QTL.To date,several hundred QTL distributed over all 21 wheat chromosomes have been reported [6–8].Fhb1on the short arm of chromosome 3B has been recognized[9,10]as the most effective and robust QTL identified to date.Two different candidate genes forFhb1have been cloned,one (PFT)encoding a gene for pore-forming toxins [11].Two other research teams both identified a candidate gene encoding histidine-rich calcium-binding protein(HRC)but with contrasting functions[12–14],highlighting the complexity of wheat–pathogen interactions.Recently [15],Fhb7,originally fromThinopyrum elongatum,has been cloned.It encodes a glutathione S-transferase (GST) that detoxifies trichothecenes,suggesting thatFhb7mediates resistance of both types II and III.

Although numbers of QTL mapping studies have grown explosively,use of published FHB-resistance QTL in breeding has been limited [16].Most reported QTL showed minor effects and their expressions were greatly affected by environment,genetic background,and inoculation method.It is desirable to identify QTL that show major genetic effects and are consistently expressed in multiple genetic backgrounds and environments.Meta-QTL analysis[17,18] of these QTL can identify mQTL regions that are most frequently associated with trait variation in diverse studies and shrink their confidence intervals (CIs).Biomercator software[19,20] has been developed to facilitate such analysis.mQTL have been identified in maize [21],soybean [22],wheat [23],and many other crops in the course of mining candidate genes for various traits including yield,root genetic architecture,biological stress,and abiotic stress.

Two integrative analyses [7,24] of FHB-resistance QTL published before 2009 provide an overview of different resistance types of QTL in hexaploid wheat.A meta-analysis [25] of the FHB-resistance QTL in durum wheat showed that they largely coincided with resistance QTL from hexaploid wheat,suggesting a common genetic basis for FHB resistance in the two species.Recently [8],FHB resistance QTL from different studies were narrowed to about 50 QTL with unique chromosome locations.New QTL were also found in Chinese landraces by meta-analysis [26].Another study [27] identified 65 mQTL with FHB resistance types I,II,III,and IV by analyzing 556 QTL from 76 studies published before 2017.Although the integration of these data is useful for FHB improvement,most QTL reported from these studies show somewhat inconsistent locations and effects,and span large chromosome regions.Also,molecular markers tightly linked to QTL that can be used directly for breeding selection remain unavailable.

The rapid development of high-throughput genotyping platforms,such as next-generation sequencing-based and SNP chipbased genotyping platforms,together with a published wheat reference genome[28],has permitted the association of DNA markers in genetic maps with reference sequences to physically localize these markers on chromosomes.The integration of QTL for FHB resistance from different studies into meta-QTL allows the comparison of experimental results from independent studies and provides more accurate physical locations for these mQTL to accelerate diagnostic marker identification,QTL fine mapping,and marker-assisted breeding to improve wheat FHB resistance.

The objective of this study was to conduct a meta-analysis of QTL for type II and III resistances that have been published in the last 20 years and to physically map these genetic map-based mQTL on the Chinese Spring reference genome to identify highconfidence mQTL.

2.Materials and methods

2.1.QTL for type II and type III resistance

A set of 113 reports describing QTL mapping of type II and III wheat FHB resistances published between 2000 and 2020 were used as the original data sources for meta-analysis.These publications (Table S1) were retrieved from Web of Science (http://apps.webofknowledge.com).Key parameters described for each original QTL include peak and flanking markers,logarithm of odds (LOD)scores,and percentage of phenotypic variance explained (PVE,R2).Each QTL was initially treated as an independent QTL,even if some were detected in multiple environments or backgrounds.The LOD score was assumed to be 3.0 in reports where onlyPvalue was provided or a minimum LOD score of 3.0 was used to assign QTL.

2.2.Selection of consensus genetic maps for mQTL analysis

An integrated consensus map of the wheat A,B,and D subgenomes was obtained from GrainGenes (https://wheat.pw.usda.-gov/GG3/node/876).The consensus map consisted of 57,112 markers (SSR,SNP,and DART) and spanned 4550 cM with a mean length of 217 cM and mean of 2720 markers per chromosome and marker density of 12.4 per cM (Table S2).

2.3.QTL projection and meta-analysis

QTL were placed according to the markers common to the consensus map and individual genetic maps from independent QTL mapping studies(Table S1).They were projected onto the consensus map using the following criteria:(1) the middle position between the two flanking markers was used as the peak marker position if the peak marker for a QTL was not available;(2) if only one of the flanking markers could be projected onto the genetic map (owing to marker inconsistency),the marker closest to the flanking marker was used instead;(3) if an alternative marker was not available,only one flanking marker was used for the QTL;(4)if neither the flanking markers nor the peak marker could be projected,the QTL was excluded;(5)if only one marker could be projected,the 95% CIs of the QTL on its original map were calculated using empirical formulas[29,30]:for backcross and F2populations,for recombinant inbred line (RIL) populations,for double haploid (DH) populations,whereNis the population size andR2is the PVE by the QTL.

The QTL that were represented by their peak marker positions,along with their CIs,original LOD scores,andR2values,were projected onto the consensus map for the meta-analysis using Biomercator software 4.2.3 [19].On each chromosome,mQTL were calculated using the two-step algorithm [17] in Biomercator.The Akaike information criterion (AIC) was used to select the model best representing the number of mQTL or ‘‘real”QTL.The algorithms and statistical procedures implemented in the software have been described previously [17,20].Among the five QTL models,the one with the lowest AIC value was selected for further analysis.

2.4.Projection of gmQTL onto the wheat reference sequence map

The mQTL obtained from meta-analysis were initially named based on their genetic map positions(gmQTL).In order to position these gmQTL on the reference genome sequence map,the flanking and peak markers for the gmQTL were aligned to the Chinese Spring(CS)reference genome RefSeq V1.1(IWGSC,2018)to generate sequence-based mQTL (smQTL).Searching for these projected marker sequences in the Triticeae Multi-omics Center (http://202.194.139.32/) identified the physical locations (Table S3) and the most likely physical intervals of these smQTL.The distributions of smQTL on chromosomes were illustrated using the R language package Rideogram [31].

2.5.Generation of hcmQTL

A lack of some markers in the genetic maps corresponding to physical maps could result in large physical intervals (>100 Mb in some cases) for the smQTL on the reference sequence map,as a consequence of low marker density,genomic diversity,or chromosome rearrangements in the QTL region.To select hcmQTL,we further refined the smQTL using two additional criteria:the smQTL must have been generated from at least five independent studies and lie in physical intervals of<20 Mb,given that the wheat linkage-disequilibrium decay distance is about 20 Mb[32].

2.6.Identification of single-copy markers in the hcmQTL intervals

Locus-specific(single-copy)SSR markers[33]were identified in the hcmQTL intervals.SNP-containing sequences from two highdensity wheat SNP arrays,the 660K SNP array(http://wheat.pw.usda.gov/ggpages/topics/Wheat660_SNP_array_developed_by_CAAS.pdf)and the 820K SNP array(https://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/axiom_download.php),were aligned against the Chinese Spring reference genome and these singlecopy SNP markers in the hcmQTL intervals were batch-converted to user-friendly PCR-based cleaved amplified polymorphic sequence(CAPS)/derived CAPS(dCAPS)markers with the CAPS/dCAPS Designer Tool[34].

2.7.Co-analysis of transcriptomic and proteomic data associated with wheat responses to FHB

Three sets of transcriptomic data(https://urgi.versailles.inra.fr/download/iwgsc/IWGSC_RefSeq_Annotations/v1.1)were used to identify differentially expressed genes(DEGs)in hcmQTL intervals.The three sets,NCBI ID SRP060670[35],ERP003465[36],and ERP013829[10],were generated from FHB-infected wheat and were obtained using ExpVIP(http://www.wheat-expression.com).The set SRP060670 comprises differential gene expression data in control and the inoculated plants of the susceptible wheat cultivar Chinese Spring at four days afterF.graminearuminoculation.The data set ERP003465 comprises differential expression data in mock-andF.graminearum-inoculated CM-82036 and its four near-isogenic lines(NIL1 with resistance alleles of bothFhb1andQfhs.ifa-5A;NIL2 with the resistance allele ofFhb1;NIL3 with the resistance allele ofQfhs.ifa-5A;NIL4 with susceptibility alleles of both QTL)collected at 30 and 50 h after inoculation.The set ERP013829 comprises differential expression data in mock-andF.graminearum-inoculated two near-isogenic lines(NIL38 with resistance alleles of bothFhb1andQhfs.ifa-5A,NIL51 with susceptibility alleles of both QTL)collected at six time points(3,6,12,24,36,and 48 h after inoculation).The original expression data was reanalyzed with the R language package Deseq2[37],and the log2fold change was corrected using package Apeglm[38].Differentially expressed genes(DEGs)from the three data sets were first filtered using|log2fold change|>1 andP-adjusted<0.05,and then were combined and projected onto the Chinese Spring reference sequence.Gene Ontology(GO)term analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of the DEGs in the hcmQTL intervals were conducted with the GENEDENOVO cloud platform(https://www.omicshare.com/tools/).

Proteomic data were collected from two pools:F.graminearumand mock-inoculated spikes of wheat accessions contrasting in FHB resistance[39].The resistant pool comprised ten wheat accessions:Fhb1-NIL-R,Sumai3,Wangshuibai,Nyubai,Ning7840,Huangfangzhu,Baisanyuehuang,Huangcandou,Taiwan Wheat,and Tokai-66,all of which carryFhb1.FHB resistance QTL were previously mapped on 16 of 21 chromosomes(all but 1D,4A,4D,6B,and 6D)in the 10 accessions when they were treated as one resistant pool.The susceptible pool consisted of anFhb1-susceptible NIL line,Wheaton,Bobwhite,Jagger,Chinese Spring and Clark,which were inoculated withF.graminearumor water.The inoculated spikes were harvested 3 days post-inoculation and the proteins were isolated and identified using a label-free mass spectrometry method [40].Differentially accumulated proteins (DAPs)were identified in the four treatment combinations including RM (mock-inoculated FHB-resistant accessions),SM(mock-inoculated FHB-susceptible accessions),RFg (F.graminearum-inoculated FHB-resistant accessions) and SFg (F.graminearum-inoculated FHB-susceptible accessions) and sequenced.All the sequences of DAPs were blasted against the reference genome sequence,and DAPs located in hcmQTL intervals were further analyzed.

2.8.Mining candidate genes in hcmQTL regions

Candidate genes (CGs) in hcmQTL regions were identified in two steps:selecting DEGs that were mapped within hcmQTL intervals using transcriptomic and proteomic data,and annotating the putative functions of the selected DEGs in the hcmQTL intervals using gene annotations from the reference genome,After integration of the transcriptomic and proteomic data,a circos plot was drawn for the candidate genes in the hcmQTL intervals using the R language package Rcircos [41] to visualize the distribution of the DEGs in different data sets.

3.Results

3.1.QTL for type II and type III resistances reported in the last 20 years

Of 716 QTL for types II and III resistance retrieved from 113 publications (Table S4),625 could be positioned on the consensus genetic map.These QTL were mapped in 105 independent populations,84.9%of them of hexaploid wheat.In general,QTL for type II resistance were the focus of these publications,and QTL for type III resistance often coincided with those for type II resistance.There were 28 papers that reported resistance QTL for both types II and III (Fig.1A).The chromosomal distributions of the 625 original QTL are shown in Fig.1B.The B subgenome contained the highest number of QTL(2 8 0),followed by the A(2 2 8)and D(1 1 7)subgenomes.The highest numbers of QTL were located on chromosomes 3B(85),5A(60),and 2D(46),and the lowest numbers on chromosomes 1D and 6D(Table S1).A total of 529 QTL(85%)were for type II,and only 96 for type III,with 15.3%of the type III QTL being colocalized with type II resistance QTL.Half of the studies assigned LOD scores of 2–4 for significant QTL,but a few assigned LOD values of >30 (Fig.1C).PVE of an individual QTL varied from 1.0% to 64.2% (Fig.1C;Table S1),with the largest PVE reported for QTL on chromosomes 3B (mean PVE=22.3%) and 5A (mean PVE=12.1%).

3.2.Meta-QTL analysis using both genetic and reference sequencebased maps

The chromosomal distribution of the 625 original QTL is shown in Fig.S1.Most of the QTL formed clusters with overlapping CIs,suggesting that QTL in a cluster might represent a single locus.Because the QTL regions on chromosomes 1D (5 original QTL)and 6D (7 original QTL) each contained<10 original QTL,the minimum number of QTL required for meta-analysis,they were excluded.The remaining 613 QTL were used for the final metaanalysis.The mean interval between the original QTL was 17.1 cM.

A total of 118(19.3%)of the gmQTL were identified,lying on all wheat chromosomes but 1D and 6D.The mean gmQTL interval was 3.96 cM,ranging from 0.01 to 23.94 cM and representing a reduction of about 77% in the genetic CIs of the original QTL (Table S5).The number of gmQTL and their mean genetic distances varied across subgenomes:51 gmQTL and 4.05 cM in A,45 gmQTL and 3.87 cM in B,and 22 gmQTL and 4.94 cM in D.

Fig.1.Summary of QTL information for type II and III resistances to wheat Fusarium head blight.(A) Venn diagram depicting relationship between published QTL for type II and III resistances to head blight.(B)Numbers of originally mapped QTL for type II and III resistances on 21 wheat chromosomes.(C)Logarithm of odds(LOD)scores and percentage of phenotypic variation explained (PVE) for FHB resistance of QTL reported in the last 20 years.

The physical positions of the gmQTL were determined based on the reference genome sequence map(Fig.2).Chromosome 3A contained the most smQTL (9),derived from 31 original QTL.Two smQTL each on 3B (smQTL-3B-1from 50 original QTL andsmQTL-3B-2from 44 QTL) and 5A (smQTL-5A-2from 20 QTL andsmQTL-5A-3from 19 QTL) were assembled from more original QTL than those for other smQTL.The mean physical CI of smQTL was 23.18 Mb,ranging from 2 Mb (smQTL-3B-2) to 311 Mb (smQTL-7A-3).EachsmQTLwas given a weight reflecting its reproducibility.For example,the weights forsmQTL-3B-2(12.63–13.28 cM) andsmQTL-3B-3(27.89–28.91 cM) on the short arm of chromosome 3B were 0.55 and 0.15,respectively.Although the two smQTL did not overlap on the genetic map,their physical intervals did overlap,withsmQTL-3B-2at 3–8 Mb andsmQTL-3B-3at 7–9 MB.The two smQTL may be the same,representingFhb1.

3.3.Establishment of hcmQTL intervals

To improve the quality of the smQTL,we further refined the smQTL by removing smQTL with >20 MB intervals and reported in fewer than five studies,leaving 77 hcmQTL (Table 1).The hcmQTL having high R2and weighted values,having short physical intervals,and involving many original QTL should be more reliable.These hcmQTL had a meanR2of 0.12,ranging from 0.056 to 0.223,and a mean physical interval of 11.81 Mb,ranging from 2 to 20 Mb.Chromosome 5B contained the most hcmQTL (6),followed by 2A,2D,4A,5A,6B,7A,and 7B,with five hcmQTL per chromosome,whereas only two hcmQTL were mapped on chromosomes 5D and 7D.hcmQTL-30on 3BS andhcmQTL-41on 4DS had the smallest physical interval of 2 Mb.

3.4.Identification of single-copy markers in hcmQTL intervals

To make effective use of the hcmQTL in marker-assisted breeding and facilitate QTL fine mapping,we searched for locus-specific(i.e.single-copy)sequences in the hcmQTL intervals for potential marker development.A total of 16,096 single-copy SSR sequences were identified in the hcmQTL intervals(Table 1,Table S6).In the 660K SNP array,364,574 probes were from single-copy sequences,of which 7453 were located in hcmQTL intervals,with a mean of 8.24 probes per Mb(Table S7).In the 820K SNP array,177,626 of 784,332 probes were chromosome-specific single-copy sequences,and 3,657 of them were located in hcmQTL intervals,with a mean of 4.05 markers per Mb(Table S8).All the singlecopy sequences harboring the SNP markers in the hcmQTL intervals were transformed into CAPS/dCAPS markers using the CAPS/dCAPS Designer tool[34].

3.5.Predicting candidate FHB-response genes in hcmQTL intervals based on transcriptomic and proteomic data

Re-analyzing three sets of the transcriptomic data(Fig.3A)collected from mock-andF.graminearum-inoculated wheat spikes identified 28,480(SRP060670)(Table S9),2406(ERP003465)(Table S10)and 10,538(ERP013829)DEGs(Table S11).Removal of DEGs duplicated among the three sources left 34,356 unique DEGs,with 17,394 up-regulated genes in at least one source.Aligning these DEGs in all the hcmQTL intervals based on the reference genome sequence revealed 2986 DEGs in these intervals(Table 1).Among the hcmQTL,hcmQTL-34on 4A contained the most(100)DEG hits andhcmQTL-75on 7B the fewest(6).Among the 2986 DEGs in hcmQTL intervals,1553 were up-regulated in response toF.graminearuminfection,1364 genes were down-regulated,and 69 genes showed mixed responses,up-regulated in one source but down-regulated in the others.

Fig.2.Distribution of sequence-based meta-QTL (smQTL) on wheat chromosomes based on the Chinese Spring reference genome sequence.The purple boxes to the right of each chromosome (yellow bar) represent smQTL for type II resistance and the orange dots represent smQTL for both type II and III resistances.The coloring on chromosomes corresponds to the number of original QTL associated with the smQTL:the darker the color,the more original QTL in that interval.The constriction on each chromosome represents the centromere.

The DEGs in hcmQTL intervals were analyzed for GO and KEGG enrichment.The most significantly enriched GO terms associated with biological process were for metabolic(1080 genes,or 36.2%of the DEGs)and cellular processes(1041 genes,or 34.9%of the DEGs).The most significantly enriched GO terms associated with molecular function were for binding site (1360 genes,or 45.5% of the DEGs) and catalytic activities (1124 genes,or 37.6% of the DEGs).In terms of cell components,the genes were enriched mainly in cell membrane components(Fig.3B,Table S12).In KEGG pathways (Fig.3C,Table S13),all DEGs in hcmQTL intervals were significantly enriched in 94 pathways.Among the top 20 pathways,metabolic pathways played an important role in wheat responses to FHB infection.Twelve genes were enriched in the glycerolipid metabolism pathway,13 in the nitrogen metabolism pathway,and nine in alanine,aspartic acid and glutamate metabolic pathways.

In response to inoculation,322 DAPs were identified(Table S14),accounting for 6.9% of 4627 proteins identified [39].These DAPs matching annotated transcripts in the hcmQTL intervals were mapped to the corresponding intervals based on the reference genome.In this way,33 DAPs in hcmQTL intervals were mapped on 16 chromosomes with the highest number of DAPs(representing five genes) on chromosome 2A (Table 1,Table S15).

Combined analyses of DAPs and DEGs in the hcmQTL regions identified 17 putative candidate genes (CGs) (Table 2).The relationships among DAPs,DEGs,and CGs in the hcmQTL regions are illustrated in a circle map(Fig.4).One gene(TraesCS4B02G237300)encoding non-symbiotic hemoglobin was up-regulated in all three sets of transcriptomic data and the proteomic data set.Four genes annotated as tryptophan synthase alpha chain(TraesCS1A02G428400),Fe(ii)-and 2-oxoglutarate-dependent(Fe/2OG) oxygenase (TraesCS5A02G226400),glycosyltransferase(TraesCS5B02G436300),and glutathione S-transferase (TraesCS6A02G059600)were up-regulated in two of the three sets of transcriptomic data and the proteome data set.Two genes(TraesCS2B02G431000,TraesCS3A02G250200)encoding a serine carboxypeptidase family protein and a glycine dehydrogenase decarboxylating protein,respectively,were down-regulated in one of the three sets of transcriptomic data and the proteome data set.

4.Discussion

4.1.mQTL identified using a consensus genetic map (gmQTL)

The consensus map used in the present study has high marker density and contains>80% of the markers identified in the QTL regions reported in multiple studies [42].The projection(Table S3) between the consensus genetic map and the Chinese Spring reference sequence map suggested that the marker positions in the consensus genetic map were in good agreement with their physical order in the reference sequence map,so that the reference map is suitable for localization of smQTL.

Table 1 Summary of 77 high-confidence mQTL (hcmQTL) intervals.

Table 1 (continued)

Table 2 Functional annotations of 17 candidate genes indicated by both transcriptomic and proteomic data in hcmQTL intervals.

Most of the mQTL or clusters (69%,45/65) reported by Venske et al.[27] were identified in the present study.It employed many more new FHB-resistance QTL than previous studies and identified more gmQTL.We found 118 gmQTL from the 613 original QTL,or about three times the number (43 mQTL from 209 original QTL)reported by Liu et al.[7]and twice that(65 mQTL from 323 initial QTL)reported by Venske et al.[27].Because we used more original QTL than Venske et al.[27],we were able to greatly narrow the gmQTL intervals.The gmQTL on chromosomes 3D,5D and 7D havenot been reported previously.However,no gmQTL were found on chromosomes 1D and 6D because each of these chromosomes contained<10 overlapping original QTL,the minimum for mQTL analysis required by the Biomercator software.>8% (50/625) of the genome-wide original QTL were located close toFhb1on chromosome 3BS,a finding consistent with those of previous studies[9,10,24].Of the 118 gmQTL,61(57%)may have pleiotropic effects:for instance,gmQTL on chromosomes 2DS,2DL,3BS,and 5AL showed both type II and type III resistance to FHB.

Fig.3.Transcriptomic data associated with high-confidence mQTL regions for wheat resistance to Fusarium head blight.(A0 Venn diagram depicting the number of differentially expressed genes (DEGs) from three transcriptomic data sets.(B) Level 2 GO terms for DEGs in high-confidence mQTL (hcmQTL) regions.(C) Top 20 KEGG enrichment pathways for DEGs in hcmQTL regions.

4.2.gmQTL on the Chinese Spring reference sequence map

Owing to a lack of a reference sequence map,meta-QTL analysis in earlier studies [7,25–27] located mQTL only on genetic maps using consensus genetic markers in the corresponding map.This kind of mQTL map may not be useful for breeders and geneticists.Analysis of reference sequence map-based mQTL (smQTL) is preferable to that of linkage map-based mQTL.In this study,we located 18 smQTL with CIs shorter than 5 Mb.This achievement will facilitate selection,fine mapping,and cloning of candidate genes by development of high-quality markers.However,the intervals of some smQTL are still large,exceeding 200 Mb interval for smQTL on chromosomes 3D,5A and 7A.The smQTL with large intervals might result from 1)poor overlap among the original QTL regions,2)large intervals of original QTL due to inaccurate phenotypic data and/or low-marker-density maps used for QTL analysis,and 3) gmQTL in a depressed-recombination region where a short genetic distance may correspond to a long physical distance.Given that most tightly linked or partially overlapping smQTL,such as smQTL on chromosomes 1AL,2DS,3AS,3BS,and 4AL,likely represent the same unique locus,fine mapping of these smQTL may improve the QTL resolution and establish their relationships.

4.3.Establishment of hcmQTL

Although smQTL with large confidence intervals may not be informative for practical use in breeding,smQTL with small confidence intervals are more reliable and the tightly linked markers can be more useful for selecting these QTL in breeding.For this reason,we reduced the 118 smQTL to 77 hcmQTL by eliminating smQTL with large CIs and/or minor effects on FHB resistance.For example,Fhb1is a well-known QTL with robust and large effects of FHB resistance[8].After refinement,we identified two partially overlapping hcmQTL regions:hcmQTL-29(5 Mb) andhcmQTL-30(2 Mb) in theFhb1region on 3BS.Fhb2has been mapped on 6BS of Sumai 3 [43].On the same chromosome arm,Li et al.[44]identified a QTL in the Chinese wheat landrace Haiyanzhong.We found that both were located within thehcmQTL-61interval(47–65 Mb),suggesting thathcmQTL-61might beFhb2.>30 original QTL were clustered in a region spanning <4 cM on chromosome 5AS whereFhb5has been frequently mapped [45,46];however,according to the physical map,there were two smQTL intervals (78–86 Mb and 104–351 Mb) in this region.The QTL identified on 5AS of the Chinese landrace Huangfangzhu [47] was assigned tohcmQTL-44between 46 and 85 Mb in the present study.Qfhs.ifa-5Aon 5A near the centromere has been further split into two QTL by fine mapping [48].We found that the locus at 70–105 Mb coincided withhcmQTL-44,and located the other near the centromere.Based on the physical positions of linked markers,hcmQTL-44(78–86 Mb)is most likelyFhb5.These results show that hcmQTL intervals are more informative and reliable than these derived using conventional meta-QTL analysis.Markers derived from these intervals can be more useful for improving selection efficiency in breeding.

4.4.Identification of single-copy markers in hcmQTL intervals

To date,only a few markers for FHB resistance QTL are useful for breeding [49],and because reported markers for most known QTL are not locus-specific,they are not diagnostic.To make most effective use of the hcmQTL identified in this study,we developed locus-specific DNA markers for them.SSR markers have been widely used in QTL mapping and marker assisted selection (MAS)owing to their high levels of polymorphisms,ease of use,and abundance in the wheat genome [28].Li et al.[33] developed 221,911 genome-wide single-copy SSR markers and made them available for public access.With the rapid development of next-generation sequencing technologies,the number of SNP markers has increased immensely.Several high-density SNP arrays including 660K and 820K SNP arrays are available for various genotyping and breeding applications.However,many SNPs in these arrays are present in multiple copies in the wheat genome,resulting in false positive signals when they are used to select linked QTL [50].Accordingly,we determined the copy number of each SNP on the 660K and 820K SNP arrays and selected single-copy SNPs in the hcmQTL intervals.These single-copy SNPs were then converted into userfriendly CAPS/dCAPS markers.The CAPS/dCAPS markers together with the single-copy SSR markers can be easily used for fine mapping,haplotyping,allele identification,and MAS of these hcmQTL.

4.5.FHB-responsive genes in the hcmQTL intervals revealed by transcriptomic and proteomic data

The hcmQTL intervals identified in this study were combined with published transcriptomic data and proteomic data.GO and KEGG enrichment analyses of DEGs in hcmQTL intervals were potentially critical to improvement of FHB resistance.KEGG pathway analysis showed that the glycerolipid metabolism pathway was associated withFusariuminfection because 11 of 12 genes in hcmQTL regions in this pathway were significantly downregulated,indicating a reduction of glycerides in wheat during early Fusarium infection.Martin et al.[51] showed that supplementation with nitrogen increased the infection incidence of Fusarium species.Warth et al.[52] reported that treating wheat plants with deoxynivalenol (DON) significantly modified primary nitrogen metabolism in wheat.KEGG analysis in the present study suggested that FHB infection significantly modified the nitrogen metabolism pathway in wheat.

A putative QTL region may contain tens to hundreds of genes,of which only one or a few may be causal genes for the target trait.Integrative analysis of the hcmQTL with multi-omics data may help identify candidate genes for the target QTL.However,it is impossible to conduct both RNA and protein analysis for all wheat accessions harboring the hcmQTL we identified.In this study,we used three transcriptomic data sets and one proteomic data set to prove the concept and identified 17 FHB-response genes that were differentially expressed at both RNA and protein levels.Candidate genes were identified in only some of the hcmQTL from 10 chromosomes.Five (TraesCS1A02G428400,TraesCS4B02G237300,TraesCS5A02G226400,TraesCS5B02G436300,andTraesCS6A02G059600) were differentially expressed in at least two of the three transcriptomic data sets and the proteomic data set.TraesCS1A02G428400,encoding a tryptophan synthase,was reported to be involved in the two final steps of tryptophan biosynthesis in plants,fungi and bacteria [53].Warth et al.[52] reported that DON treatment resulted in significant elevation in tryptophan levels.TraesCS4B02G237300encodes a non-symbiotic hemoglobin that showed peroxidase-like activity [54] and was involved in nitric oxide(NO)metabolism,possibly protecting the plant against nitrosative stress in plant–pathogen and symbiotic interactions.TraesCS5A02G226400encoding Fe(ii)-and 2-oxoglutaratedependent (Fe/2OG) oxygenases is involved in ethylene synthesis[55],which has been demonstrated[56]to be important for wheat FHB resistance.TraesCS5B02G436300encoding glycosyltransferase may play a pivotal role in mycotoxin detoxification and FHB resistance [57,58].Poppenberger et al.[59] reported that a UDPglycosyltransferase fromArabidopsis thalianadetoxified DON,thereby mediating type III resistance.TraesCS6A02G059600encodes a glutathione S-transferase (GST) that is involved in responses of plants to biotic and abiotic stresses.Dhokane et al.[60] identified a GST in theFhb2region using metabolomic and transcriptomic approaches.Wang et al.[15] cloned fromThinopyrum elongatuma GST as the causal gene forFhb7that confers broad-spectrum resistance toFusariumspecies by detoxifying trichothecenes.In summary,most of the 17 genes identified in the hcmQTL regions can be associated with wheat responses toFusariuminfection,and functional characterization of these genes may increase our understanding of the complex molecular mechanisms underlying wheat FHB resistance.However,the transcriptomic data used in this study were collected from only limited materials,and the wheat accessions used for transcriptomic analysis carry mainlyFhb1andFhb5.Interestingly,putative candidate genes were not identified in two hcmQTL intervals.Putative FHB-responsive genes were identified for only a fraction of the hcmQTL identified,and further transcriptomic and proteomic studies may facilitate the discovery of candidate genes for other hcmQTL identified in this study.

5.Conclusions

By summarizing QTL for type II and III resistance to FHB that have been published in the past 20 years and identifying highconfidence meta-QTL,we consolidated and refined these QTL using an improved meta-analysis strategy.After conventional meta-QTL analysis,genetic-map-based mQTL were projected onto the Chinese Spring reference sequence map to generate sequence-based smQTL,and the smQTL were further refined by elimination of those with large confidence intervals and small effects to obtain hcmQTL.Development of single-copy SSR and CAPS/dCAPS markers using sequences in hcmQTL intervals will provide effective markers tightly linked to these hcmQTL for genotyping,fine mapping,gene cloning,and marker-assisted breeding.The integration of hcmQTL discovery with multi-omics paves a new way towards gene cloning and development of diagnostic markers for improving FHB resistance in wheat.

CRediT authorship contribution statement

Tao Li and Tong Zheng designed the research.Tong Zheng,Chen Hua,Lei Li,Zhengxi Sun,and Minmin Yuan performed data analysis.Tong Zheng,Tao Li,Guihua Bai,and Gavin Humphreys wrote the manuscript.All authors approved the final version of the manuscript.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Key R&D Program,Intergovernmental Key Items for International Scientific and Technological Innovation Cooperation (2018YFE0107700),the National Natural Science Foundation of China(31771772),the Postgraduate Research &Practice Innovation Program of Jiangsu Province(KYCX19_2109),the National Key R&D Program for Breeding of Top-seven Crops (2017YFD0100801),and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).

Appendix A.Supplementary data

Supplementary data for this article can be found online at https://doi.org/10.1016/j.cj.2020.10.006.