Qin You,Sushm Sood,Ziling Luo,Hongo Liu,d,Md.Sriful Islm,Muqing Zhng,Jinping Wng,e,*

a State Key Lab for Conservation and Utilization of Subtropical Agric-Biological Resources,Guangxi University,Nanning 530005,Guangxi,China

b Agronomy Department,University of Florida,Gainesville,FL 32610,USA

c USDA-ARS,Sugarcane Field Station,Canal Point,FL 33438,USA

d Sugarcane Research Institute,Yunnan Academy of Agricultural Sciences,Kaiyuan 661699,Yunnan,China

e Genetics Institute &Plant Molecular and Cellular Biology Program,University of Florida,Gainesville,FL 32610,USA

Keywords:Quantitative trait loci (QTL)GACD IciMapping SNP marker Sugarcaneratoon stunting disease

ABSTRACT The ratoon stunting disease(RSD)of sugarcane,caused by the bacterium Leifsonia xyli subsp.xyli,is one of the major concerns to sugarcane production and breeding programs worldwide.Due to no obvious external symptoms,RSD cannot be easily detected by the growers,hence has reduced the world’s sugarcane production significantly.This study aimed to identify quantitative trait loci (QTL) associated with RSD resistance and to assist in the development of linked molecular markers for marker-assisted breeding to minimize the reduction in sugarcane yield by the RSD infection.A set of 146 individuals derived from a self-crossing of CP80-1827 were evaluated for RSD resistance in a mechanically duplicated inoculated field trial from 2014 to 2017 using tissue blot immunoassay.Leveraging the genetic data and the four years phenotyping data of CP80-1827 selfing population,linkage map construction and QTL analysis were conducted based on clonal F1 and F2 mapping population types with GACD V.1.2 and IciMapping V.3.3,respectively.A total of 23 QTL associated with RSD resistance were identified,which explained 6% to 13% of the phenotypic variation with the two types of software.A total of 82 disease resistance genes were identified by searching these 23 QTL regions on their corresponding regions on the Sorghum bicolor genome (44 genes),sugarcane R570 genome (20 genes),and S.spontaneum genome (18 genes),respectively.Compared with IciMapping V.3.3,GACD V.1.2 identified more major (6 vs.3) and stable QTL (2 vs.0),and more disease resistance genes (51 vs. 31),indicating GACD V.1.2 (clonal F1 mapping type) is most likely to be more efficient than IciMapping (F2 mapping type) for QTL analysis of a sefling population or clonal F1 population in clonal species.The identified QTL controlling RSD resistance along with the associated SNP markers will assist sugarcane molecular breeding programs in combating this disease.

1.Introduction

Sugarcane is one of the most economically important crops for sugar and bioenergy production,which contributed to 80%of sugar and 60% of bioethanol production worldwide [1].Bagasse,a byproduct of sugar production,can be used for livestock feed,fertilizer,energy generation,etc.[2].Sugarcane is grown in more than 100 countries in the tropical and subtropical areas,with a worldwide cane production of 1.84 billion tons [3].However,various biotic and abiotic stresses substantially reduce sugarcane production.As for biotic stress,the ratoon stunting disease (RSD) of sugarcane,caused by the bacteriumLeifsonia xylisubsp.xyli(Lxx)[4,5],is one of the most economically important disease of the sugarcane and present in all sugarcane-growing regions worldwide [6–8].RSD can cause yield reduction up to 60%under drought conditions and 12% to 37% under normal conditions [9–11].Sugarcane plants infected by bacteriumLxxusually result in reduced stalk height(stunting),smaller stalk diameter,and less tiller number [11],which are mainly caused by the interference with the transportation of water and nutrients [12,13].The stunted growth caused by RSD can be misdiagnosed as the symptoms resulted from drought or poor field management.Therefore,theLxxbacterium could be transmitted from one field to another by propagating cuttings from these RSD infected sugarcane plants [14].In addition,the bacterium is also spread by harvesting equipment,depending on the susceptibility of the sugarcane cultivars[15,16].Efforts have been made to develop and apply fast and reliable RSD diagnostic methods,including microscopic detection [17–19],serological tests [20–22],and DNA-based molecular detection [11,23,24].The selection of varietal resistance is the most cost-effective and environmentally friendly strategy to control the RSD.

Modern sugarcane cultivars (2n=100–130) are derived from interspecific hybridization between the high-sugar speciesSaccharum officinarumand the wild speciesS.spontaneum,followed by several backcrosses withS.officinarum[25,26].Sugarcane has a complex and large genome(approximately 10 Gb),which contains 78%–80%genome fromS.officinarum,10%–20%fromS.spontaneumand remaining 10% recombinant genome from these two species[27].Therefore,genetic and genomic studies of sugarcane cultivars have many challenges [28,29].Identification of quantitative trait loci (QTL) is an efficient way to unravel the genetic basis of interesting traits for marker-assisted selection (MAS) in sugarcane breeding.Due to the complex inheritance of sugarcane,a singledose (SD) marker strategy has been applied to construct linkage maps and to identify QTL associated with traits of interest in sugarcane segregating populations [30].

Although sugarcane genotyping is challenging due to its complex genome,with the advancement of next generation sequencing(NGS) technologies,a large number of single nucleotide polymorphisms (SNPs) markers were discovered from sugarcane genomes using the sorghum (Sorghum bicolor) genome as reference [31–33].Since sorghum genome is available,which is a closely related crop containing 95.2% identical coding regions with sugarcane[34].These SNP markers have important value for sugarcane high throughput genotyping.With their genome abundance and costefficiency in genotyping,SNP markers have been extensively used for sugarcane QTL mapping studies.Recently,three QTL associated with sugarcane orange rust resistance were identified in CP95-10 39 × CP88-1762 segregating population,which were genotyped by SNPs through genotype-by-sequencing [35].Two QTL related to sugarcane yellow leaf virus (SCYLV) resistance were identified using a genetic map from the same segregating population,which could reduce SCYLV disease incidence by 31% [3].Nowadays,SNP array is becoming one of the most common approaches for SNP genotyping.Compared to other genotyping methods,SNP array has some major advantages such as high accuracy,consistent data generation,and comparatively straightforward downstream analysis [36,37].Only very recently,several SNP arrays were developed for polyploid crops [33,38–41],which were proven to be an efficient SNP genotyping approach in crop QTL mapping and genomic selection studies [37,42].Using the Axiom Sugarcane100K SNP array,we previously detected 18 QTL controlling SCYLV resistance in two mapping populations,which harbored 27 disease resistance genes [33].

Several studies on diagnosis or detection of RSD have been reported [11,43],but there is no study reporting QTL associated with RSD resistance in sugarcane yet.Earlier,a genetic map was developed based on a selfing population derived from CP80-1827 genotyped data using the Axiom Sugarcane100K SNP array [33].Since CP80-1827 is susceptible to RSD [44],the same population segregating for RSD resistance has been observed and phenotyped in multiple years.This mapping population is a clonal F1population derived from selfing one heterozygous clonal line,and also could be considered as a traditional F2population derived from selfing of a homozygous F1individual(theoretical),but could have some issues caused by an unknown linkage phase in a selfing heterozygous clone [45,46].Taking advantage of the genetic data generated for the population,this study aimed (1) to identify the QTL and candidate genes associated with sugarcane resistance to RSD;(2) to select the appropriate mapping population type for genetic analysis of this mapping population by comparing two mapping types(clonal F1and F2)using inclusive composite interval mapping (ICIM) method with GACD V.1.2 [47] and IciMapping V.3.3 [48] software,respectively.

2.Materials and methods

2.1.Plant materials

The 146 individuals were from a population derived from the self-pollination of a sugarcane cultivar CP80-1827 (moderately susceptible to RSD) at the Sugarcane Field Station,United States Department of Agriculture (USDA),Agricultural Research Service(ARS),Canal Point,FL,United States.The whole population was planted in a duplicated trial in 2013 following a randomized complete block design with each trial as one block.Each individual in each trail was placed in a 3.3 m single row plot.The stalks of the 146 individuals along with the parent CP80-1827 were inoculated by RSD pathogenLxxprior to the planting following a simple mechanic method.Briefly,sugarcane stalks of each individual were cut into several pieces using a machete that had been dipped in the juice collected from the infected stalks of CP53-1,which consists of a very high population(5.18×108cells mL-1)of theLxxin the sap extracts [4].

2.2.Disease scoring

The plot of each individual was sampled and evaluated for RSD infection in plant cane,first ratoon,and second ratoon in 2014,2015,and 2016,respectively.At approximately ten months after inoculation,five stalks from each plot in plant cane crop were randomly selected and cut close to the ground.Then 15 cm long basal portion from each of the five stalks consisting of an internode was cut and used for the RSD evaluation.RSD was evaluated by the tissue bot immunoassay method (TBIA) [49,50].A stalk tissue piece was cored from the internode using a 1-cm diameter Cork borer.The core piece was cut into 1-cm section and placed into the custom-made plastic holder (holding a nitrocellulose membrane placed on the top of a blotting paper).The plates were centrifuged at 1300×gfor 10 min.Centrifugation made vascular bundles imprints on the nitrocellulose membranes and bacteria deposited in the imprints.After immunoassay,vascular bundles colonized with bacteria turned blue.These blue dots per 1-cm diameter section were counted.The average number of colonized vascular bundles (CVB) was calculated.The rating scale for RSD was based on the average number of CVB [49,50].Individuals with an average of 0–2.0 CVB were considered resistant;2.1–5.0 CVB moderately resistant;5.1–10.0 CVB moderately susceptible;10.1–15.0 CVB susceptible and 15.1 and more CVB highly susceptible (Fig.1).The population was replanted in 2016 in a similar way as mentioned earlier and RSD data was collected in 2017 in plant cane crop from the replanted field.Thus a total of four sets of RSD scoring data were collected.

2.3.Genotyping and linkage map construction

Fig.1.Rating scale for sugarcane ratoon stunting disease resistance based on the average number of colonized vascular bundles(CVB).(a)Resistant with 0–2.0 CVB scores;(b)Moderately resistant with 2.1–5.0 CVB scores;(c)Moderately susceptible with 5.1–10.0 CVB scores;(d)Susceptible with 10.1–15.0 CVB scores;(e)Highly susceptible with 15.1 and more CVB scores.

The extraction of DNA,SNP genotyping,and construction of linkage maps were reported by You et al.[33].In brief,young leaf tissues were used for genomic DNA extraction with MagJET Plant Genomic DNA Kit (Thermo Fisher Scientific,MA,USA).Then qualified DNA samples were adjusted to a concentration of 15 ng μL-1for Axiom Sugarcane100K SNP array genotyping by Affymetrix(Santa Clara,CA,USA).A 97% call rate (CR) threshold was applied to call genotypes of all samples.The SNPs from poly high resolution class and rescued SNPs (≥80% CR and ≥10 heterozygous genotypes in one cluster) from no minor homozygote and call rate below threshold classes were treated as high-quality SNP markers for using in the downstream analysis [33].Only SD SNP markers based on CP80-1827 genotype calls segregating with 1:2:1 ratio(P<0.01) in population,were used to build linkage map.Clonal F1mapping population type with GACD V.1.2 [45,47] and F2mapping population type with IciMapping V.3.3[48]were used to construct linkage map of CP80-1827,respectively.In order to obtain the comparable results,the same mapping function (Kosambi)and parameter setting were used for map construction [51].For example,logarithm of odds (LOD) value of 10,nnTwoOpt algorithm,and the sum of adjacent recombination frequency (SARF)were used for map grouping,ordering,and ripping,respectively.The linkage groups (LGs) were removed if their length was<2 cM or with less than two markers on one map.Additionally,the SNPs in Axiom Sugarcane100K SNP array were identified based on alignment to sorghum genome and were named as Chromosome number+Position (such as Chr03P63829504) based on sorghum genome information.Therefore,SNP markers positions on sorghum genome were provided.

2.4.Statistical analysis of phenotypic data

A pairwise Pearson correlation among the four RSD scoring data sets was calculated.The RSD incidence(proportion of infected stalk samples) was estimated on a plot basis.Broad sense heritability(H2) was calculated using the formula as follows:

2.5.QTL analysis and candidate gene search

For QTL analysis,the SNP genotyping results (on the linkage map) were used as genotype data,and the average RSD infection score of each individual was used as phenotypic data.Inclusive composite interval mapping (ICIM) [46,52] with the default LOD threshold of 2.5 were used to identify QTL using GACD V.1.2 [47]and IciMapping V.3.3 [48],respectively.The consistent QTL was defined as QTL with the consistent two flanking markers,which could be identified in different years or by different software packages.For GACD,additive effects (a) were calculated according to the formulation of Muchero,a=[m(a c)-m(b d)]/2 [53],based on the computed results [m (ac) and m (bd)] by GACD.The m(ac) and m (bd) represent the mean phenotypical value of the heterozygous genotypes at the QTL,AC and BD,respectively [53].However,the dominance effects were provided directly by GACD.For IciMapping,the additive and dominance effects were provided directly.Then,related disease resistance genes were detected by searching the identified QTL regions according to the annotation of sorghum genome [54],sugarcane hybrid scaffold (R570-monoploid reference) [28],andS.spontaneumgenome (AP85-441)[29].Since each identified QTL had two flanking SNP markers,we BLASTed the flanking SNP probe sequences (71-bp) to the sorghum genome with an E-value <1e-06 and identity ≥90%,and to sugarcane andS.spontaneumgenomes with an E-value<1e-06 and identity ≥95%,respectively.An identity<95%is commonly used to filter hits of SNP probe sequences with BLAST analysis[55],therefore 95% identity was used as a threshold for BLAST hit filter on sugarcane andS.spontaneumgenomes.In order to get as many QTL corresponding regions as possible on sorghum genome,90%identity was used as a cut off for probe sequence BLAST results against sorghum genome considering that sugarcane and sorghum belong to different genera.Regions with the best BLAST hit on these three available genomes were considered as identified QTL corresponding regions.

3.Results

3.1.Phenotypic data analysis of RSD

The four sets of RSD scores in the mapping population showed continuous distribution(Fig.2)indicting that the RSD resistance is a quantitative trait in sugarcane.RSD scores of four data sets in the mapping population were highly correlated(P<0.05)with an average Pearson Correlation Coefficient of 0.71 (Table S1).The heritability of RSD resistance was 0.40 by conducting four years’ data together,while if each year’s data were used separately for heritability calculation,the heritability values of RSD resistance were 0.79 in 2014,0.83 in 2015,0.73 in 2016,and 0.80 in 2017(Table 1).The average RSD scores of each individual in each year (Table S2)were used as phenotypic data for QTL analysis below.

3.2.Linkage map constructed from the mapping population

A total of 842 SD SNP markers were validated following 1:2:1 segregation ratio in CP80-1827 selfing population[33].The genetic map of CP80-1827 was constructed with 487 SD SNP markers forming 106 LGs,which covered 3752 cM genetic distance and had a marker density of 7.7 cM per marker using GACD (Table 2).Similarly,507 SD SNP markers were mapped on 94 LGs containing 3651 cM genetic distance with a marker density of 7.2 cM per marker using IciMapping (Table 2).The majority of mapped markers(482 common SNP markers) were consistent,which meant the markers appearing on the two linkage maps were the same,including 99.0%SNP markers(482/487)on the linkage map developed by GACD and 95.1% SNP markers (482/507) by IciMapping.

Table 1 The comparison of the total PVE values for all QTL and the heritability in each environment from GACD and IciMapping.

3.3.QTL detected for RSD

A total of 13 QTL associated with sugarcane RSD resistance were detected in the CP80-1827 selfing population with GACD,of which six were considered as major QTL explaining 10%to 13%of the phenotypic variance (Table 3).In addition,two QTL (consistent QTL)were considered as stable QTL,which were detected in different years with the consistent two flanking SNP markers,includingqRSDR11in 2014 and 2015,andqRSDR37in 2015 and 2017.Similarly,10 QTL were identified to the sugarcane RSD resistance in this mapping population with IciMapping (Table 3).Three of them were detected as major QTL with 10% to 12% of phenotypic variance explained (PVE).Three QTL out of 23 QTL were consistently detected by both softwares.While nine QTL were exclusively identified by GACD and seven QTL exclusively detected by IciMapping.To be specific,the three QTL,qRSDR1,qRSDR31,andqRSDR37identified by GACD were consistent withqRSDR32.25,qRSDR17,andqRSDR35identified by IciMapping,respectively.Furthermore,the total PVE of all QTL detected by GACD turned to be higher than those by IciMapping in each environment (Table 1).To compare the total PVE values with the heritability values in each year,it showed that in general the dataset with higher heritability could detect QTL with higher PVE values by using both software packages.

Fig.2.Ratoon stunting disease phenotypic score distributions in CP80-1827 population from 2014 to 2017.

Table 2 Comparison of the linkage maps of CP80-1827 constructed by GACD and QTL IciMapping software separately.

The consistent QTLqRSDR1from GACD andqRSDR32.35from IciMapping,andqRSDR11from GACD on data from 2014 and 2015 had the negative additive effects (-1.3 and -1.7;-0.1 and-0.3,respectively) on the RSD resistance (Table 3).The other two consistent QTL containing the same QTL region have opposite additive effects.For example,the consistent QTLqRSDR31in GACD(leftmarker:AX-171306866 and rightmarker:AX-171260978)had negative additive effects (-2.0),while the QTLqRSDR17(leftmarker:AX-171260978 and rightmarker:AX-171306866) in IciMapping have the positive additive effects (2.0) on the RSD resistance.Similarly,for consistent QTLqRSDR37identified by GACD and QTLqRSDR35by IciMapping,the opposite additive effects (-1.5 vs.1.1) for RSD resistance were also detected.

The majority of the dominance effects showed different directions at the consistent QTL identified by the two different software,such asqRSDR1(1.1) andqRSDR32.35(-1.8),qRSDR31(-0.9) andqRSDR17(1.7) (with flanking markers in reversed direction),qRSDR37(0.6 in 2015 and -0.2 in 2017) andqRSDR35(-2.0),except for the QTLqRSDR11in 2014 and 2015 (-1.3 and -1.6).

3.4.Candidate genes for the resistance to RSD

To identify candidate genes,the probe sequences (71-bp long,Table S3) of the 22 SNP markers flanking those 11 unique QTL regions with GACD and of the 20 SNP markers flanking 10 QTL regions with IciMapping were BLASTed to the sorghum genome,sugarcane scaffolds,andS.spontaneumgenome,separately.

On the sorghum genome,six corresponding QTL regions were located by searching the 11 QTL flanking marker sequences with GACD,which contained two stable QTL (qRSDR11andqRSDR37)(Table S4).A total of 2094 genes were found in these six QTL regions observed on sorghum Chr.2,Chr.3,Chr.4,Chr.6,and Chr.8,of which 29 were disease resistance genes within the four QTL intervals on Chr.2 (qRSDR69),Chr.3 (qRSDR37),Chr.6(qRSDR47),and Chr.8 (qRSDR11) (Table S4;Fig.3a).In addition,23 of these 29 disease resistance genes were identified within a stable QTL (qRSDR11) on Chr.8.Likewise,nine corresponding QTL regions were identified by searching the 10 QTL flanking marker sequences with IciMapping on the sorghum genome (Table S4),which contained 2713 genes located on Chr.1,Chr.3,and Chr.4.Among these genes,15 were disease resistance genes found in four QTL regions on Chr.1 (qRSDR8.162andqRSDR23) and Chr.3(qRSDR32.53andqRSDR49) (Table S4;Fig.3a).Since there were some sequences overlapped between these four QTL regions,two disease resistance genes inqRSDR8.162were included in the six ofqRSDR23,and another two disease resistance genes inqRSDR32.53were also covered in the nine ofqRSDR49.Furthermore,two of 29 disease resistance genes identified by GACD were also present in 15 disease resistance genes identified by IciMapping.

On the sugarcane R570 scaffolds,seven corresponding QTL regions were found by searching these 11 flanking marker sequences with GACD,which included two stable QTL (qRSDR11andqRSDR37)(Table S4).A total of 1896 genes were located within these seven QTL regions on sugarcane scaffold Sh.1,Sh.2,Sh.3,Sh.6,and Sh.8,of which 10 disease resistance genes were identified in four QTL intervals on Sh.2 (qRSDR44),Sh.3 (qRSDR53),Sh.6(qRSDR47),and Sh.8(qRSDR11)(Table S4;Fig.3b).Similarly,seven of these 10 QTL were found in the corresponding regions by searching the sugarcane scaffolds with IciMapping (Table S4),which contained 1415 genes on sugarcane scaffold Sh.1 and Sh.3.A total of 10 disease resistance genes were found in three QTL regions on Sh.1(qRSDR8.162)and Sh.3(qRSDR32.53andqRSDR49)(Table S4;Fig.3b).One disease resistance gene inqRSDR32.53was included in the nine ofqRSDR49as overlapped sequences in their QTL regions.In addition,it is worth noting that three disease resistance genes inqRSDR53(Sh.3) identified by GACD were also observed in the nine disease resistance genes ofqRSDR49(Sh.3)with IciMapping.Totally,three of 10 disease resistance genes identified with GACD were also found within the 10 disease resistance genes identified with IciMapping.

Table 3 Quantitative trait loci (QTL) associated with ratoon stunting disease resistance of the CP80-1827 selfing population using GACD and IciMapping software.

Fig.3.QTL mapping of sugarcane ratoon stunting disease resistance and identification of disease resistance genes to the corresponding QTL intervals with GACD and IciMapping software.(a)Corresponding QTL intervals and R genes on sorghum genome with GACD and IciMapping;(b)Corresponding QTL intervals and R genes on sugarcane scaffolds with GACD and IciMapping;(c) Corresponding QTL intervals and R genes on S.spontaneum genome with GACD and IciMapping.

Parallelly,eight corresponding QTL regions were located on theS.spontaneumgenome by searching the flanking marker sequences of 11 QTL identified by GACD,including the two stable QTL(qRSDR11andqRSDR37) (Table S4).Among eight QTL regions,1934 genes were identified on Chr.1B,Chr.2C,Chr.3B,Chr.3C,Chr.3D,Chr.4B,and Chr.5D,of which 12 disease resistance genes were identified in three QTL intervals on Chr.2C (qRSDR11andqRSDR44),and Chr.5D (qRSDR47) (Table S4;Fig.3c).For the 10 QTL identified by IciMapping,eight corresponding QTL regions were identified by searching the 10 QTL regions’flanking markers’sequences on theS.spontaneumgenome,which included 2857 genes located on Chr.1A,Chr.1D,Chr.3B,Chr.3C,Chr.3D,and Chr.4B (Table S4).Of those genes,only six resistance genes were observed in three QTL regions on Chr.1A (qRSDR23andqRSDR92)and Chr.3C (qRSDR49) (Table S4;Fig.3c).There was no common resistance genes onS.spontaneumgenome corresponding to the QTL intervals detected by both QTL mapping software packages.

4.Discussion

To develop the genetic markers linked to the RSD resistance for MAS of RSD resistance in the breeding programs,we took the advantages of the genetic data available [33] for a population derived from CP80-1827 selfing segregating on RSD resistance to construct the linkage map and identify the RSD resistance QTL.We compared mapping data processed by two different softwares,GACD and IciMapping.Our results could potentially help understanding the resistance mechanism of RSD and accelerate the sugarcane breeding process toward combating this disease and could assist in the selection of genetic software for clonal F1mapping population in asexually propagated crops.

4.1.Phenotypic evaluation of RSD

In this study,RSD resistance in the same population was assessed for four years,including both plant cane and ratoon crops,which captured the effects of physiological factors as well as environmental factors on the disease resistance.These factors are crucial for ratoon stunting disease as yield losses are more prominent in ratoon crops [56,57].RSD rating in this study was based on an average number of colonized vascular bundles,which was highly correlated to pathogen densities in stalk tissues [58].The higher heritability of RSD resistance in each year than in all four year together indicated that the environmental factors contributed significantly to the variation of RSD resistance in this mapping population.

4.2.Comparison of linkage map of CP80-1827 with GACD and IciMapping

The genetic maps of CP80-1827 were constructed based on clonal F1and F2mapping population types with GACD and IciMapping,respectively.Generally,the two maps were very similar,containing 99.0% (GACD) and 95.1% (IciMapping) identical SNP markers on the maps.The mapped SNP marker number(507)with IciMapping was slightly more than the mapped SNP marker number (487) with GACD.Therefore,these two mapping population types can be considered to have the comparable efficiency for construction the linkage map of a selfing population derived from a heterozygous clone or a clonal F1population.

The mapping rate (mapped marker number/total input marker number for constructing linkage map) in this study was comparable to other SNP marker based genetic map studies in sugarcane.For example,the mapping rate were 57.8% (487/842) with GACD and 60.2% (507/842) with IciMapping on CP80-1827 selfing population in our study,while 11.8% (680/5757) with OneMap V.2.0[59] on a bi-parental population derived from SP80 × RB83,and 52.3% (627/1198) and 51.59% (638/1236) on two different maps with JoinMap 4 [60] and a mapping population developed from cross between CP95-1039 and CP88-1762 [61,62].

The relative low mapping rates in sugarcane (compared with other crops) could be caused by several reasons,such as genome complexity,genotyping platforms,mapping population size,mapping strategies and mapping tools [33].SNP array was used for genotyping in this study,while GBS (genotyping by sequence)were applied for other two studies [61,62].Furthermore,a small population size would lead to less observations of recombination events,which would impact the number of mapped markers as well.

4.3.Identified QTL related to RSD resistance

This study is the first report of QTL associated with sugarcane RSD resistance.Although only 487 (GACD) and 507 (IciMapping)SD SNP markers were placed on the CP80-1827 map,they were still effective to identify 13 and 10 RSD resistance QTL,respectively.However,these 23 identified QTL all showed relatively small PVE values (6% to 13%) with 60.9% (14/23) of them as minor QTL.The low PVE of these QTL may be due to the low heritability(0.40)of this trait in the mapping population.In addition,low marker density (7.2 or 7.7 cM/SNP marker) or uneven distribution of the mapped markers of the CP80-1827 maps may weaken the effect of mapping RSD resistance QTL.Increasing the saturation of the map may enhance the effect of these QTL.Furthermore,changing environmental conditions,smaller population size,and genotyping or phenotyping errors could be attributed to the appearance of minor QTL[63].In the case of sugarcane brown rust,the marker (acgcta16) closest to the new rust resistance gene was found,showing only 13%of PVE[64].In our study,with similar PVE values,these major QTL and stable QTL can be validated and potentially used in MAS and candidate gene approach.

We noticed that some of the dominance effects showed different directions between the consistent QTL.The possible reasons causing the different directions of dominance effects for the consistent QTL could be that different mapping models were used,specifically,the ‘‘Clonal F1” population was used in GACD and F2was used in IciMapping.Additionally,the different genes may control the resistance at different environments even in the consistent QTL regions.Similarly,we also found the inconsistent dominance effects at the consistent QTL in other studies [65,66].Therefore,although dominance effects were reported in our study,additive effects (the focus of current study) were mainly considered when referring to consistent QTL.Compared to QTL identified with IciMapping,more major QTL(6 vs.3)and stable QTL(2 vs.0)were found with GACD.However,there were three stable QTL observed in both software.Therefore,either F2or clonal F1mapping population type in the two software could be applied for QTL mapping,while GACD may have higher efficiency to identify major QTL or stable QTL in a clonal F1population.

4.4.Searched corresponding QTL regions and identifed disease resistance genes

By BLASTing the probe sequences of QTL flanking SNP markers to the three available sugarcane related genomes,the majority of the QTL were located to their corresponding QTL regions,including 62% to 77% QTL with GACD and 70% to 90% QTL with IciMapping(Table S5).In addition,all of the corresponding QTL regions were consistent at the chromosome level for these three reference genomes,except forqRSDR11andqRSDR47(Table S4).qRSDR11was aligned toS.spontaneumChr.2C,sugarcane Sh.8 and sorghum Chr.8.Similarly,qRSDR47was aligned toS.spontaneumChr.5D,sugarcane Sh.6 and sorghum Chr.6.However,these inconsistent QTL regions are due to the genome recombination betweenS.spontaneumand sorghum chromosomes[29],which showed thatS.spontaneumChr.2 and Chr.7 were aligned to sorghum Chr.8,andS.spontaneumChr.5 was aligned to sorghum Chr.6.Therefore,these consistent corresponding QTL intervals indicate the SNP array hybridization results were accurate and reliable to a certain extent.

By searching candidate genes in the 27(GACD)and 24(IciMapping) QTL corresponding regions to the three related genomes available in both software,a total of 11 (GACD) and 10 (IciMapping) corresponding QTL intervals contained disease resistance genes (Table S5).Interestingly,intervals ofqRSDR11identified by GACD contained 31 disease resistance genes,andqRSDR49identified by IciMapping contained 19 disease resistance genes.These two QTL intervals with high density of disease resistance gene had a high chance of containing the candidate genes controlling the RSD resistance.According to above results,GACD showed more corresponding QTL intervals,more disease resistance genes,and more related chromosome regions than IciMapping,which suggested that GACD (clonal F1) may be more effective than IciMapping (F2) for QTL analysis in a clonal F1population.

4.5.Future applications of RSD candidate genes

The disease resistance genes are involved in the preliminary recognition of pest or pathogen and are considered as candidate genes [67].If these candidate genes are themselves involved in resistance,they could be useful as molecular markers in MAS breeding programs.Comparing the available candidate gene sequences from multiple species,it can help to locate candidate genes on chromosomal regions,thus providing an effective way to narrow down candidate genetic regions related to resistance,which can be validated by using appropriate mapping populations[68].

The RSD is caused by bacteriumLxx,whose genome sequence was published based on a Brazilian sugarcane RSD isolate (L.xylissp.xyli CTCB07)in 2004[69].Later,Young et al.[43]reported that no variation was detected in the intergenic spacer (IGS) region of the ribosomal RNA genes or 16S ribosomal RNA genes among 105 isolates of sugarcane RSD pathogen collected from nine countries,indicating that a single pathogenic clone was spread worldwide.The results were consistent with the study by Li et al.[70].Since,there is a single pathogenic clone ofLxxpresent in the sugarcane growing areas worldwide,the development of reliable diagnostic molecular makers based on identified QTL orRgenes in our study would benefit the worldwide sugarcane production.Development of diagnostic markers for RSD,SSR markers can be identified in the QTL region with a focus on candidateRgenes or can be collected from published literature [35].Furthermore,using Kompetitive Allele Specific PCR (KASP),SD SNP markers in the QTL region can also be considered as diagnostic marker development.These markers will be helpful to identify RSD resistant cultivars or germplasm for crossing and selecting resistance progeny,similar to the use of MAS in sugarcane brown and orange rust resistance breeding [35,71–73].

Of course,the QTL identified in this study only explained the phenotypic variations of 6% to 13%,which indicated that other genomic regions or environmental factors also influenced the RSD phenotypic variations in the population.Some other genes related to RSD resistance and their regulatory mechanism might not be investigated in this study.It is well known thatLxxinfection on sugarcane affects the photosynthetic carbon sequestration function,thus the integrity of vascular bundle sheath cells in the ‘‘garland structure”of sugarcane leaves is damaged,and the expression activity of key enzymes PEPC,PPDK and NADP-ME in C4 cycle are all reduced[74],which further affect the growth of sugarcane.The interaction between key enzyme genes of the C4 cycle and resistance genes located within the QTL should be further investigated in future studies.

CRediT authorship contribution statement

Jianping Wangconceived and supervised the study.Qian You,Sushma Sood,and Ziliang Luoanalyzed the data.Qian You,Sushma Sood,and Hongbo Liuprepared the manuscript draft.Sushma Soodcollected the phenotypic data.Md.Sariful Islamprovided the plant materials and DNA samples.Muqing Zhangsupported the research.All authors reviewed and approved the manuscript draft.

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 Florida Sugarcane League,United States Department of Agriculture-Agricultural Research Service CRIS Project 6030-21000-005-00D and USDA National Institute of Food and Agriculture,Hatch Project 1011664.

Appendix A.Supplementary data

Supplementary data for this article can be found online at https://doi.org/10.1016/j.cj.2020.10.010.