Next Article in Journal
C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features
Next Article in Special Issue
Genome-Wide Analysis of WOX Multigene Family in Sunflower (Helianthus annuus L.)
Previous Article in Journal
Rational Design of Lipase ROL to Increase Its Thermostability for Production of Structured Tags
Previous Article in Special Issue
A Quantitative Genetic Study of Sclerotinia Head Rot Resistance Introgressed from the Wild Perennial Helianthus maximiliani into Cultivated Sunflower (Helianthus annuus L.)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 23
Issue 17
10.3390/ijms23179516
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Insight into Disease Resistance Gene Clusters by Using Sequencing-Based Fine Mapping in Sunflower (Helianthus annuus L.)

by
Guojia Ma
1,†,
Qijian Song
2,
Xuehui Li
1 and
Lili Qi
3,*
1
Department of Plant Sciences, North Dakota State University, Fargo, ND 58102-6050, USA
2
Soybean Genomics and Improvement Laboratory, USDA-Agricultural Research Service, Beltsville, MD 20705-2350, USA
3
USDA-Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND 58102-2765, USA
*
Author to whom correspondence should be addressed.
†
Current address: Ball Horticultural Company, 622 Town Road, West Chicago, IL 60185-2698, USA.
Int. J. Mol. Sci. 2022, 23(17), 9516; https://doi.org/10.3390/ijms23179516
Submission received: 11 July 2022 / Revised: 4 August 2022 / Accepted: 19 August 2022 / Published: 23 August 2022
(This article belongs to the Special Issue Advances in Research for Sunflower Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Rust and downy mildew (DM) are two important sunflower diseases that lead to significant yield losses globally. The use of resistant hybrids to control rust and DM in sunflower has a long history. The rust resistance genes, R13a and R16, were previously mapped to a 3.4 Mb region at the lower end of sunflower chromosome 13, while the DM resistance gene, Pl33, was previously mapped to a 4.2 Mb region located at the upper end of chromosome 4. High-resolution fine mapping was conducted using whole genome sequencing of HA-R6 (R13a) and TX16R (R16 and Pl33) and large segregated populations. R13a and R16 were fine mapped to a 0.48 cM region in chromosome 13 corresponding to a 790 kb physical interval on the XRQr1.0 genome assembly. Four disease defense-related genes with nucleotide-binding leucine-rich repeat (NLR) motifs were found in this region from XRQr1.0 gene annotation as candidate genes for R13a and R16. Pl33 was fine mapped to a 0.04 cM region in chromosome 4 corresponding to a 63 kb physical interval. One NLR gene, HanXRQChr04g0095641, was predicted as the candidate gene for Pl33. The diagnostic SNP markers developed for each gene in the current study will facilitate marker-assisted selections of resistance genes in sunflower breeding programs.

1. Introduction

Sunflower (Helianthus annuus L.) is among the few crops that are native to North America [1,2,3]. Based on the use of its products, sunflower can be classified as confectionary sunflower for human consumption, oilseed sunflower for edible oil, and ornamental sunflower. In addition to these routine uses, environmental scientists have found that sunflower plants can absorb high concentrations of toxic chemicals from soil into their tissues leaves and stems. It has been demonstrated to be a success in industry when scientists use sunflower to clean up land contaminated with lead (https://gardencollage.com/change/sustainability/scientists-using-sunflowers-clean-nuclear-radiation/ (accessed on 10 July 2022)). Although sunflower can tolerate toxic environments and adapt to different agroecological conditions, its growth is still challenged by many biotic and abiotic stresses throughout its life cycle. Downy mildew (DM) and rust are two of the most devastating diseases that impair sunflower production worldwide.
Downy mildew, which is caused by the oomycete pathogen, Plasmopara halstedii (Farl.) Berl. & de Toni, is one of the most damaging diseases in sunflower globally. In epidemic years with cool and wet weather, yield loss can be as high as 95% [4]. As one of the most dynamic pathogens, a total of 44 P. halstedii races have been recorded worldwide, with more than 24 P. halstedii races reported in Europe and 40 in the Americas [5,6,7,8]. The use of resistant hybrids is the first choice for disease management in sunflower for economic and environmental reasons. Host race-specific resistance genes against DM, designated Pl, have been utilized on a commercial scale for sunflower production since the 1970s [9,10]. To date, a total of 37 Pl genes, Pl1–Pl36, and PlArg, have been identified and reported from a resistance gene (R gene) pool that encompasses both cultivated and wild sunflowers (Supplementary Table S1) [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44]. Thirty-one of them have been located on different chromosomes across the sunflower genome: chromosome 1 (PlArg, Pl13, Pl14, Pl16, Pl23–Pl25, and Pl35); chromosome 2 (Pl18 and Pl26); chromosome 4 (Pl17, Pl19, Pl27–Pl29, and Pl33); chromosome 8 (Pl1, Pl2, Pl6, Pl7, Pl15, and Pl20); chromosome 11 (Pl30); and chromosome 13 (Pl5, Pl8, Pl21, Pl22, Pl31, Pl32, Pl34, and Pl36).
Rust, which is caused by the fungus, Puccinia helianthi Schw., is another severe sunflower disease that is present around the world. After infection, sunflower plants can still grow; however, both the yields and seed quality will be reduced. Yield losses as high as 80% can occur in epidemic years [45]. A recent survey coordinated by the USA National Sunflower Association indicated that rust is the most prevalent disease among the common sunflower diseases that have been investigated [46]. A total of 39 P. helianthi races were identified in North America, in which races 334 and 336 were predominant, while race 777 was the most virulent [47]. Identification of P. helianthi races was also reported in Australia, Argentina, China, and South Africa [48,49,50,51]. Similar to that of DM, the use of sunflower host resistance is the top choice for rust management. Rust resistance in sunflower is controlled by single dominant genes. To date, a total of 17 rust resistance genes, R1–R5, R10–R12, R13a, R13b, R14–R18, Pu6 and Radv, have been reported in sunflower, and 15 of them were mapped to various regions across the sunflower genome: chromosome 2 (R5); chromosome 8 (R1 and R15); chromosome 11 (R12 and R14); chromosome 13 (R4, R11, R13a, R13b, R16–R18, Pu6, and Radv); and chromosome 14 (R2) (Supplementary Table S2) [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74].
The rust R genes, R13a, R13b, and R16, were previously mapped to a large gene cluster located at the lower end of chromosome 13 and delimited in a 3.4 Mb region in the XRQr1.0 genome assembly [40,64,75]. No P. helianthi race can differentiate among R13a, R13b, and R16, and no existing markers can be used to saturate the target gene region. High-resolution mapping assisted by whole genome sequencing is needed to further examine this region and to develop diagnostic markers for each of the R genes in the cluster. This would be essential for promptly and accurately incorporating new resistance genes into elite sunflower breeding lines through marker-assisted selection (MAS), as TX16R carrying a rust R gene R16 and DM R gene Pl33 is still resistant to all P. halstedii and P. helianthi races identified thus far after release in 2005 and has not been widely used in sunflower breeding [40].
The DM R gene Pl33 in the TX16R line was initially mapped to the upper end of sunflower chromosome 4 and was co-segregated with two simple sequence repeat (SSR) markers and two single nucleotide polymorphism (SNP) markers [40]. Two additional DM R genes, Pl17 and Pl19, were also found within this interval. The Pl17 and Pl19 markers can differentiate among the three R genes, indicating that the three genes were independent of each other [76]. However, further saturation of the Pl33 interval in chromosome 4 is needed to facilitate specific gene introgressions. In this study, we report on the fine mapping of three R genes, R13a, R16, and Pl33, by using a sequencing-based marker development approach combined with high-density mapping populations. The diagnostic SNP markers that were developed in this study for each targeted gene will facilitate MAS and gene pyramiding in sunflower breeding programs. Our current study provides a foundation and new genetic resource for the cloning of these genes in the future.

2. Results

2.1. Saturation and Fine Mapping of R13a

Previous genetic mapping from a population of the cross between rust susceptible-HA 89 and rust resistant-HA-R6 placed the rust R gene R13a derived from HA-R6 in a 0.59 cM region located at the lower end of chromosome 13. R13a was flanked by SNP marker SFW05743 and a group of co-segregated markers (Figure 1a) [75], which corresponded to a 3.4 Mb physical interval between 193.1–196.5 Mb in the XRQr1.0 genome assembly and a 1.8 Mb physical interval between 236.4–238.2 Mb in the HA412-HO genome assembly, respectively (Table 1). A total of 432 SNP markers were selected from whole-genome sequencing in the target region and were screened for polymorphisms between parents, HA 89 and HA-R6 (R13a). The identified polymorphic markers were further used to genotype the F2 population with 140 individuals, and seven SNPs were mapped to the R13a target region and all were selected from the XRQr1.0 assembly. Due to a small population size of 140 and small number of markers mapped to R13a, the saturation mapping step assigned the R13a gene to co-segregate with a cluster of 14 markers (Figure 1b).
To dissect the marker cluster and increase the map resolution of R13a, a large population consisting of the 2820 F3 individuals selected from the F3 families that were heterozygous for R13a was screened using two flanking markers, the SNP marker SFW01497 and the SSR marker HT382. A total of 312 F3 recombinants were identified in the target region that was delimited by these two markers and advanced to the next generation for rust testing of the recombinant families. Seven SNP markers in the saturation map were used to genotype the 312 recombinants identified from the large population. The combined phenotype and marker data of the recombinants placed R13a in a 0.48 cM interval flanked by SNP marker C13_194757055 (0.13 cM) and a cluster of four SNP markers, C13_195501970, C13_195522913, C13_195526945 and C13_195556768 (0.35 cM) (Figure 1c). This genetic region corresponds to a 745 kb segment in the XRQr1.0 assembly, which decreases the R13a physical interval from 3.4 Mb to 0.745 Mb (Table 1). The genetic positions of the mapped SNP markers of R13a agree well with their physical positions for both XRQr1.0 and HA412-HO, except for the physical position of C13_194735854 on the HA412-HO assembly (Table 1).

2.2. Saturation and Fine Mapping of R16

Genetic mapping of R16 was initially performed in a population derived from the cross between rust and DM susceptible-HA 434 line and rust and DM resistant-TX16R line [40]. The rust R gene R16 from TXR16 was previously mapped into a 2.91 cM interval flanked by public SNP markers, SFW08875 and SFW04317, in a region with R13a and R13b on sunflower chromosome 13 (Figure 2a), which corresponded to 3.4 Mb and 2.0 Mb regions in the XRQr1.0 and HA412-HO assemblies, respectively [40]. A total of 432 SNP markers that were selected based on SNPs/InDels between HA-R6 (R13a)/TX16R (R16) and two reference genomes in the target region of chromosome 13 were first used to screen for polymorphisms between parents HA 434 and TX16R. Polymorphic markers were further selected to genotype the F2 population of HA 434 × TX16R with 146 individuals. Fifteen new SNP markers were mapped around R16, which delimited R16 to a 0.86 cM interval (Figure 2b, Table 2). In the saturation map, R16 co-segregated with four SNP markers, C13_194722668, C13_195512786, C13_195552917, and C13_195605372, and was flanked by two SNP clusters (Figure 2b).
To dissect marker clusters and further improve the map resolution of R16, an F2:3 population of 2256 individuals that were segregated for R16 were screened with two flanking markers, the SSR marker ORS316 and the SNP marker SFW05743. A total of 203 F3 recombinants were identified in the target region that was defined by these two markers and advanced to the next generation for rust testing of the recombinant families. The 10 SNP markers selected from the saturation map were used to genotype the 203 recombinants. The marker data were further analyzed with rust phenotyping data, resulting in R16 being narrowed down to a 0.31 cM interval, which corresponded to a 790 kb region in the XRQr1.0 assembly (Figure 2c, Table 2). After fine mapping, R16 was flanked by SNP marker C13_194722668 (0.18 cM distance) and a cluster of three SNP markers, C13_195512786, C13_195552917 and C13_195605372 (0.13 cM distance) (Figure 2c). The mapped SNP markers for R16 were physically in agreement with their genetic positions on the XRQr1.0 assembly (Table 2).

2.3. Saturation and Fine Mapping of Pl33

As the above population from the cross HA 434 and TX16R also segregated for DM resistance, this population was also used for genetic mapping of the DM R gene Pl33 from TX16R. Pl33 was previously mapped to a similar position as Pl17 on sunflower chromosome 4 and was co-segregated with SSR markers, ORS644 and ORS963, and SNP markers, SFW04052 and SFW04901 (Figure 1a) [40]. In the present study, a total of 157 SNP markers selected in the target region from the variants between TX16R (Pl33)/HA 458 (Pl17) sequences and the XRQr1.0 sequence were first screened for polymorphisms between HA 434 and TX16R. Subsequently, the polymorphic markers were used to genotype the 148 F2 individuals in the HA 434 × TX16R population. A total of 23 SNPs, 15 from variants between HA 458 (Pl17) and the XRQr1.0 reference and 8 from variants between TX16R (Pl33) and the XRQr1.0 reference, were mapped around Pl33, which led to the co-segregation of Pl33 with a cluster of 22 markers (Figure 3b).
To differentiate the co-segregated marker clusters, Pl33 flanking SNP markers, SFW04052 and SFW06856, were selected to screen the same F2:3 population of 2256 individuals that were segregated for both R16 and Pl33 as used above. A total of 111 recombinants located in the target region were identified and advanced to the next generation for DM testing. The 10 mapped SNP markers selected from the saturation map were used to genotype the 111 Pl33 recombinants to increase the Pl33 map resolution. After linkage analysis using DM phenotyping data, the Pl33 gene was placed in a 0.04 cM interval on chromosome 4, which corresponded to a 63 kb interval in the XRQr1.0 genome assembly (Figure 3c, Table 3). Pl33 was co-segregated with three SNP markers, C4_5641353, C4_5671004, and SPB006, and was flanked by C4_5562979 (0.02 cM) and a cluster of three markers, SPB005, C4_5704814, and C4_5738736 (0.02 cM) (Figure 3c, Table 3). The genetic positions of the SNP markers in the fine map agree well with their physical positions on the XRQr1.0 assembly on chromosome 4 but do not agree well with their physical positions on the HA412-HO assembly, in which SNP marker C4_5641353 was mapped to a distant position located outside of the interval, and most of the whole genome sequence-based SNP markers from the XRQr1.0 genome had a reversed order in the HA412-HO assembly (Table 3).

2.4. Comparative Analysis of R13a, R13b, and R16 on Chromosome 13

R13a and R13b were previously mapped together on sunflower chromosome 13 by using the same sets of SSR and SNP markers obtained from published genetic maps, and no marker could differentiate these two genes [64,75]. In the current study, the same set of 432 SNPs that was used in the R13a and R16 saturation mapping was also used to screen polymorphisms between the parents, HA 89 and RHA 397 (R13b). Six SNPs were polymorphic and were subsequently used to genotype the 140 F2 individuals derived from the HA 89/RHA 397 cross. All six SNPs were mapped distal to R13b (Figure 4b). Comparative analysis of the R13a and R13b saturation maps revealed that four SNP markers, C13_195501970, C13_195522913, C13_195526945, and C13_195556768, could differentiate R13a from R13b, while three SNP markers, S13_236323209, S13_236323867, and S13_237169906, could differentiate R13b from R13a, which suggested that they are different genes (Figure 4a,b). Twelve SNP markers in the R16 saturation map developed in the current study could differentiate R16 from R13a and R13b (Figure 4c). The genetic positions of the mapped SNP markers from the saturation maps of R13a, R13b, and R16 and the fine maps of R13a and R16 are summarized in Table 4.

2.5. Comparative Analysis of Pl17, Pl19, and Pl33 on Chromosome 4

Pl17, Pl19, and Pl33 are all located in an R gene cluster on sunflower chromosome 4 [32,34,40,76]. Genetic dissection by sequencing-based fine mapping revealed that Pl19 is located 1 Mb from Pl17 (Table 5) [76]. In the present study, in addition to the SNPs selected between TX16R (Pl33) and the XRQr1.0 reference, a set of 129 SNP markers used in Pl17 fine mapping that was selected between HA 458 (Pl17) and the XRQr1.0 reference was also used for saturation and fine mapping of Pl33. These common markers shared between Pl17 and Pl33 can clearly distinguish the two genes, which indicates that Pl17 is proximal to Pl33 (Table 5). Although Pl17 and Pl33 are located close together in a small region between SNP markers C4_5671004 and SPB001 on chromosome 4, each gene has its own diagnostic markers, which facilitates introduction of each gene into elite sunflower lines and gene pyramiding in sunflower breeding programs (Table 5).

2.6. Candidate Gene Analysis of R13a, R16, and Pl33

In the current study, both R13a and R16 were fine mapped to a 790.5 kb region between nucleotide positions of 194,722,468 and 195,512,986 bp on chromosome 13 of the XRQr1.0 genome assembly (Table 1 and Table 2). The four predicted plant disease defense-related genes were found in the target region, which encodes the putative NB-ARC domain, a signaling motif shared by plant resistance gene products (Table 6). In a 63.4 kb Pl33 target region between nucleotide positions of 5,641,153 and 5,704,545 bp on chromosome 4 of the XRQr1.0 genome assembly (Table 3), only one gene, HanXRQChr04g0095641, was predicted to be a probable disease resistance protein (TIR-NBS-LRR class) family, which is the same candidate gene as Pl17 (Table 6) [77].

2.7. Identification of Diagnostic Markers for R13a, R16, and Pl33

Currently, six rust R genes, R4, R13a, R13b, R16, R17, and R18, are located in a similar region on sunflower chromosome 13 [40,57,64,73,75]. A total of 16 SNP markers that mapped to R13a (7 SNPs), R13b (3 SNPs), and R16 (6 SNPs) were selected to test eight lines, including HA-R6 (R13a), RHA 397 (R13b), TX16R (R16), HA-R3 (R4), HA-R18 (R17), and HA-R19 (R18), and two lines, HA 89 and HA 434, as the respective susceptible parents in the R13a and R16 mapping (Table 7). For the seven markers mapped to R13a, marker C13_194268343 could differentiate R13a from the remaining rust R genes, except for R13b (Figure 5a), while markers C13_195501970, C13_195522913, C13_195526945, and C13_195556768 could distinguish R13a from R13b and R17, but not the other R genes (Table 7a). All three markers mapped to R13b, S13_236323209, S13_236323867, and S13_237169906, could differentiate R13b from the rest of the R genes, except for R4 (Table 7b, Figure 5b). Three of the six SNP markers mapped to R16, C13_194722668, C13_195605372, and C13_195874138, distinguished R16 from the other five R genes (Table 7c; Figure 5c). These three SNP markers were further genotyped in the 96-line evaluation panel. Only SNP marker C13_194722668 could differentiate R16 from the other 95 lines tested and is unique to R16 (Figure 6a). The SNP, C13_195874138, could differentiate R16 from the other 90 lines tested, but five lines, RNID, 803–1, RHA 417, RHA 295, and RHA 426, shared the R16 marker allele with TX16R.
Three DM R genes, Pl17, Pl19, and Pl33, have been located in a gene cluster on chromosome 4 [32,34,40,76]. A total of 17 SNP markers used in the Pl33 saturation mapping were selected to test four lines, TX16R (Pl33), HA 458 (Pl17), HA-DM5 (Pl19), and the susceptible parent, HA 434. Only SNP marker C4_5671004 can distinguish Pl33 from Pl17 and Pl19, while SNP marker C4_5562979 can distinguish Pl33 from Pl19 but not Pl17. Subsequently, these two markers, C4_5671004 and C4_5562979, were tested in a panel with 96 selected sunflower lines. As expected, the C4_5671004 marker allele was present only in the TX16R line, while the C4_5562979 marker allele was present in TX16R and in lines containing the Pl17 gene (Figure 6b).

3. Discussion

Disease resistance genes tend to be clustered in the genome and are common across plants [77,78,79]. An R gene cluster with nine rust and eight DM R genes located on the lower end of sunflower chromosome 13 represents the largest R-gene cluster in sunflower. This R gene cluster can be further divided into two sub-clusters, sub-cluster I containing three rust R genes (Pu6, Radv, and R11) and three fertility restorer genes (Rf1, Rf5, and Rf7) and sub-cluster II including six rust R genes (R4, R13a, R13b, and R16–R18) and eight DM R genes (Pl5, Pl8, Pl21, Pl22, Pl31, Pl32, Pl34, and Pl36) [36,37,38,40,42,64,73,80]. Six rust R genes (R4, R13a, R13b, and R16–R18) in sub-cluster II could be differentiated with race-specific resistance, except for the three, R13a, R13b, and R16, that exhibit resistance to all of the P. helianthi races that have been identified in North America thus far [73]. Polymorphic markers resulting from high-resolution mapping would be able to tackle this challenging region.
In previous studies, no marker could distinguish between R13a and R13b as these two genes are linked to a set of common markers [64,75]. Three genes, R13a, R13b, and R16, originated from different sources, with R13a from the plant introduction line PI 650,362 from France, R13b from an inbred line introduced from South Africa, and R16 from the sunflower-wild H. annuus Texas-16 (Supplementary Table S2). Saturation mapping of R13a, R13b, and R16 using a set of common sequencing-based SNP markers obtained in the current study revealed that four SNP markers, C13_195501970, C13_195526945, C13_195522913, and C13_195556768, could distinguish R13a from R13b and R16, while three SNP markers, S13_237169906, S13_236323867, and S13_236323209, mapped only to the R13b map (Figure 4a,b). Twelve SNP markers in the R16 saturation map differentiated R16 from R13a and R13b (Figure 4c). These results indicate that these three genes are different. Six SNP markers that were selected from whole-genome sequencing of HA-R6 (R13a) and TX16R (R16) were mapped distal to R13b; however, no new marker was mapped downstream of R13b. The lack of SNPs directly obtained from whole-genome sequencing of RHA 397 (R13b) may be a limitation in detecting more polymorphic markers in the HA-89/RHA 397 population.
Although the target regions of R13a and R16 were saturated with the newly developed SNP markers, most markers were co-segregated with the genes in the saturation maps, especially for R13a (Figure 4). Fine mapping using whole-genome sequencing combined with large mapping populations was able to separate the co-segregated markers and place R13a and R16 into a 790 kb region in the XRQr1.0 genome assembly. Molecular studies on disease R gene cloning have demonstrated that most R genes in crops encode nucleotide-binding leucine-rich repeat (NLR) motifs (for a review, see Wersch and Li 2019) [79]. The second largest NLR cluster has been reported on the lower end of chromosome 13, which corresponds to the two gene clusters in this region [81]. Four predicted NLR genes were found in the 790 kb target region of R13a and R16 from the XRQr1.0 gene annotation (Table 6). The PacBio long read target region sequencing of R13a and R16 and further functional analyses of the candidate genes can further help to reveal the molecular mechanism of rust resistance of the clustered genes.
Similar to R13a and R16, the DM R gene, Pl33, is also located in a gene cluster with five other DM R genes, Pl17, Pl19, Pl27, Pl28, and Pl29, at the upper end of sunflower chromosome 4 [32,34,38,40,76]. The differentiation of six DM R genes within this small region was achieved by whole-genome sequencing-based high-resolution mapping when traditional allelic analysis and resistance specificity to different pathotypes could not differentiate them. Based on the markers linked to genes, Pl27 was mapped to a 5.4 Mb location on chromosome 4, while Pl28 and Pl29 were located in a region between nucleotide positions 6.62 and 7.01 Mb, respectively, on the XRQr1.0 assembly, close to Pl19 [38,76]. Our recent fine mapping of Pl17, Pl19, and Pl33 revealed that Pl33 is close to Pl17 in a region between nucleotide positions of 5.69–5.71 Mb on the XRQr1.0 assembly, while Pl19 is located 1 Mb from Pl17 and Pl33 (Table 5). Meanwhile, the diagnostic markers developed for Pl17, Pl19, and Pl33 could clearly distinguish them. A disease defense-related NLR gene, HanXRQChr04g0095641, was found in the target region on the XRQr1.0 genome assembly as a candidate gene for both Pl17 and Pl33. Large-scale sequence analyses of complex R gene haplotypes will shed light on the processes of diversifying resistance specificities in the cluster in the future.
Resistance against DM and rust is controlled by single dominant genes in sunflower. Resistance genes could be ineffective during coevolution with pathogens in which some pathogens can quickly change their genomic components by mutation or recombination when selective pressure is favored [82]. R gene pyramiding is a commonly accepted, effective method to create durable resistance in crops [83,84]. It is more feasible to combine R13a, R13b, R16, and Pl33 with disease resistance genes from other chromosomes or from the same chromosomes but at distal locations due to the increased possibility of linkage. Combining R genes within similar regions is still achievable and, in some instances, induced recombination is required by utilizing a large population to screen for few recombinants. To achieve this, diagnostic molecular markers for each gene are prerequisites. In the present study, the map resolution for each of the genes studied was greatly increased, and the tightly linked diagnostic markers for R13a, R13b, R16, and Pl33 would be important practical implications for tracking gene introgression to elite sunflower lines and pyramiding these genes to slow pathogen evolution to evade R-gene and enhance R-gene durability.

4. Materials and Methods

4.1. Mapping Populations and Evaluation Panel

The F2 populations for R13a and R13b saturation mapping of additional markers in the present study were initially created from crosses between HA 89 and HA-R6 (carrying R13a)/RHA 397 (carrying R13b), respectively, with 140 individuals each, which were previously used to map R13a and R13b to sunflower chromosome 13 [64,75]. HA 89 is an oilseed maintainer line used as a susceptible parent. Both HA-R6 (PI 607509) and RHA 397 (PI 597374) are resistant to rust, and HA-R6 is a confection sunflower line, while RHA 397 is a male fertility restorer line of oilseed sunflower [64]. For the fine mapping, recombinants were screened from 2820 F3 individuals selected from the previously characterized F2:3 families that were heterozygous for R13a. Each selected heterozygous F3 family equates to a segregated F2 population.
Saturation mapping of the rust R gene R16 and DM R gene Pl33 was performed in the F2 population developed from the cross of HA 434 and TX16R (carrying R16 and Pl33) with 146 and 148 F2 individuals, respectively, which were previously used for the initial mapping of R16 and Pl33 to sunflower chromosomes 13 and 4, respectively [40]. HA 434 (PI 633744) is an oilseed line susceptible to DM and rust, while TX16R (PI 642072) is resistant to sunflower DM, rust, and SuMV [40]. For the fine mapping, recombinants were screened from 2256 F3 individuals selected from the previously characterized F2:3 families that were heterozygous for both R16 and Pl33, which was equal to a segregated F2 population for both genes.
The specificity of the DNA markers for R13a, R16, and Pl33 was evaluated among 96 sunflower inbred lines with diverse origins, including 24 and 17 lines harboring different DM and rust R genes, respectively (Supplementary Table S3).

4.2. Whole-Genome Sequencing and SNP/Indel Calling

Sunflower lines HA-R6 (R13a) and TX16R (R16 and Pl33) were sequenced separately at the whole-genome level with 40× genome coverage on the Illumina HiSeq sequencing platform by Novogene Inc. according to their protocols. The genomic DNA of each sample was randomly sheared into short fragments of about 350 bp, respectively. The obtained fragments were subjected to library construction using the NEBNext® DNA Library Prep Kit, with strictly following the instructions. Briefly, as followed by end repairing, dA-tailing, and further ligation with NEBNext adapter, the required fragments (in 300–500 bp size) were PCR enriched by P5 and indexed P7 oligos. After purification and subsequent quality check, pair-end sequencing was performed on Illumina® sequencing platform, with the read length of PE150 bp at each end. The raw reads containing adaptors, reads with >1% ambiguous bases, and reads with low quality (greater than 50% bases less than 15 Q score) were removed and excluded from further analysis. For HA-R6, totally 141.9 G raw data were sequenced from this run, with 141.8 G clean data generated after filtering low-quality data. For TX16R, totally 178.3 G raw data were sequenced from this run, with 178.2 G clean data generated after filtering low-quality data. The clean reads were aligned to the two reference genomes of XRQr1.0 (https://www.heliagene.org/HanXRQ-SUNRISE/ (accessed on 10 April 2019)) and HA412-HO (https://www.heliagene.org/HA412.v1.1.bronze.20141015/ (accessed on 10 April 2019)), respectively. All SNPs and InDels were identified by using the mapped reads. The SNPs in the targeted gene regions were selected based on their physical positions along chromosomes 4 or 13, and the flanking sequences of each SNP were extracted from the XRQr1.0 and HA412-HO reference assemblies (Supplementary Table S4).

4.3. SNP Marker Selection from Whole-Genome Sequencing

Both R13a and R16 were previously mapped to a similar region located at the lower end of sunflower chromosome 13. A total of 308 SNPs were selected based on SNPs/InDels between HA-R6 carrying R13a and the two reference genomes in the target region of chromosome 13 with 116 selected from the HA412-HO genome and 192 selected from the XRQr1.0 genome. Another set of 124 SNPs was selected based on SNPs/InDels between TX16R (R16) and the XRQr1.0 reference in a similar region of chromosome 13. The HA412-HO whole-genome sequence was assembled from Illumina reads (100 bp) and 454 Roche reads (400–1000 bp) of the inbred line HA412-HO, while the XRQ whole-genome sequence was assembled from PacBio sequencing data with an average read length of 10.3 kb of the inbred line XRQ. The two sunflower reference sequences provide alternative opportunities for SNP discovery. The SNP markers were named with the prefixes C13 or S13 followed by a number representing the physical positions of the SNPs along chromosome 13 of each reference genome assembly (Supplementary Table S4). C13 represent the SNPs from the XRQr1.0 reference genome, while prefixes S13 represent the SNPs from the HA412-HO reference genome.
Pl33 was previously mapped to a similar position as Pl17 on chromosome 4 [40]. Thirty-two SNPs from the variants between the TX16R (Pl33) whole genome sequence and the XRQr1.0 reference genome sequence located in the target region of chromosome 4 were selected for marker development. An additional 125 SNPs that were selected from our Pl17 fine mapping project were also used for marker development in the current study [76]. The SNP markers were named with the prefixes C4 or S4 followed by a number representing the physical positions of the SNPs along chromosome 4 of each reference genome assembly. C4 represents the SNPs from the XRQr1.0 reference genome, while prefixes S4 represents the SNPs from the HA412-HO reference genome (Supplementary Table S4).
Other SSR and SNP markers associated with three target genes from previous studies is listed in Supplementary Table S5.

4.4. PCR-Based Genotyping of SNP Markers and Linkage Analysis

PCR-based length polymorphic SNP primers were designed by using the Primer 3-based Primer-BLAST suite embedded within the NCBI website (https://www.ncbi.nlm.nih.gov/tools/primer-blast/ (accessed on 16 August 2019)). The artificial mismatches and length polymorphisms for the SNP primers were created (Supplementary Table S6) as described by Qi et al. (2016) [33] and Long et al. (2017) [85] based on SNP flanking sequences. Polymerase chain reaction (PCR) for SNPs was conducted as described by Ma et al. (2020) [86], and the amplicons were separately visualized and scored on 6.5% polyacrylamide gel using an IR2 4300/4200 DNA analyzer (LI-COR, Lincoln, NE, USA).
After scoring each marker, the genotype data were chi-square (χ2) tested for goodness-of-fit to evaluate whether the segregation ratio for each marker fit the Mendelian ratios, e.g., 1:3 for dominant and 1:2:1 for codominant. Markers fitting the Mendelian ratios were used for linkage analysis with either the respective rust or DM phenotype data by using JoinMap 4.1 software, in which a regression mapping algorithm and Kosambi’s mapping function were selected [87]. The cutoffs for the linkage analysis among markers were set at a likelihood of odds (LOD) ≥ 3.0 and maximum genetic distance ≤ 50 centimorgans (cM).

4.5. Rust Evaluation of Recombinants

The R13a and R16 recombinants, together with their respective parents, HA 89 and HA-R6 for R13a, and HA 434 and TX16R for R16, were evaluated for their reactions to rust infection following the method of Qi et al. (2011) [57]. Plants at the four-leaf stage were inoculated with P. helianthi race 336, and the disease reactions were scored for their infection types (ITs) based on a 0–4 scale and the percentage of leaf area covered with pustules (severity) at 12–14 days after inoculation [88,89]. Infection types 0, 1, and 2, when combined with a pustule coverage of 0–0.5%, were classified as resistant, and ITs 3 and 4 with pustule coverages greater than 0.5%, were considered to be susceptible.

4.6. Downy Mildew Evaluation of Recombinants

The Pl33 recombinants selected from the segregated population using its flanking markers, together with two parents, HA 434 and TX16R, were tested for DM resistance with an isolate of P. halstedii race 734 by using the whole seedling immersion method, as described by Gulya et al. (1999) [90] and Qi et al. (2015) [32]. Briefly, approximately 40 seeds from each recombinant family were germinated, and at least 30 seedlings from each recombinant family were inoculated with P. halstedii race 734 after 2–3 days. After sporulation, the seedlings were evaluated for disease resistance and susceptibility, in which susceptible seedlings showed sporulation on their cotyledons and true leaves, and resistant seedlings showed no sporulation. The genotype of each recombinant was determined as homozygous susceptible if all seedlings in the recombinant family showed sporulation on the cotyledons and true leaves, homozygous resistant if none of the seedlings exhibited sporulation, and segregated if some seedlings showed sporulation on the cotyledons and true leaves while some showed no sporulation.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijms23179516/s1.

Author Contributions

Conceptualization, G.M. and L.Q.; data curation, G.M.; funding acquisition, L.Q. and X.L.; investigation, G.M., Q.S. and L.Q.; supervision, L.Q. and X.L.; writing—original draft, G.M.; writing—review and editing, L.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the USDA–AMS Specialty Crop Block Grant Programs, grant number 19-422 and the USDA–ARS CRIS Project No. 3060-2100-043-00D. The mention of trade names or commercial products in this report is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the US Department of Agriculture. The USDA is an equal opportunity provider and employer.

Acknowledgments

The authors would like to thank Angelia Hogness for technical assistance.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rieseberg, L.H.; Seiler, G.J. Molecular evidence and the origin and development of the domesticated sunflower (Helianthus annuus, Asteraceae). Econ. Bot. 1990, 44, 79–91. [Google Scholar] [CrossRef]
  2. Harter, A.V.; Gardner, K.A.; Falush, D.; Lentz, D.L.; Bye, R.A.; Rieseberg, L.H. Origin of extant domesticated sunflowers in eastern North America. Nature 2004, 430, 201–205. [Google Scholar] [CrossRef] [PubMed]
  3. Blackman, B.K.; Scascitelli, M.; Kane, N.C.; Luton, H.H.; Rasmussen, D.A.; Bye, R.A.; Lentz, D.L.; Rieseberg, L.H. Sunflower domestication alleles support single domestication center in eastern North America. Proc. Natl. Acad. Sci. USA 2011, 108, 14360–14365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Markell, S.G.; Harveson, R.M.; Block, C.C.; Gulya, T.J. Sunflower diseases. In Sunflower: Chemistry, Production, Processing and Utilization; Martínez-Force, E., Dunford, N.T., Salas, J.J., Eds.; AOCS Press: Urbana, IL, USA, 2015; pp. 93–128. [Google Scholar]
  5. Gulya, T.J. Distribution of Plasmopara halstedii races from sunflower around the world. In Advances in Downy Mildew Research, Proceedings of 2nd International Downy Mildew Symposium, Olomouc, Czech Republic; Lebeda, A., Spencer-Phillips, P.T.N., Eds.; Palacky University and JOLA: Olomouc, Czech Republic, 2007; pp. 121–134. [Google Scholar]
  6. Gascuel, Q.; Martinez, Y.; Boniface, M.C.; Vear, F.; Pichon, M.; Godiard, L. The sunflower downy mildew pathogen Plasmopara halstedii. Mol. Plant. Path. 2015, 16, 109–122. [Google Scholar] [CrossRef]
  7. Viranyi, F.; Gulya, T.J.; Tourieille, D.L. Recent changes in the pathogenic variability of Plasmopara halstedii (sunflower downy mildew) populations from different continents. Helia 2015, 38, 149–162. [Google Scholar] [CrossRef]
  8. Trojanovaá, Z.; Sedlaářovaá, M.; Gulya, T.J.; Lebeda, A. Methodology of virulence screening and race characterization of Plasmopara halstedii, and resistance evaluation in sunflower—A review. Plant Pathol. 2017, 66, 171–185. [Google Scholar] [CrossRef]
  9. Vrânceanu, V.; Stoenescu, F. Immunity to sunflower downy mildew due to a single dominant gene. Probl. Agric. 1970, 22, 34–40. [Google Scholar]
  10. Zimmer, D.E.; Kinman, M.L. Downy mildew resistance in cultivated sunflower and its inheritance. Crop Sci. 1972, 12, 749–751. [Google Scholar] [CrossRef]
  11. Gedil, M.A.; Slabaugh, M.B.; Berry, S.; Segers, B.; Peleman, J.; Michelmore, R.; Miller, J.F.; Gulya, T.; Knapp, S.J. Candidate disease resistance genes in sunflower cloned using conserved nucleotide binding site motifs: Genetic mapping and linkage to downy mildew resistance gene Pl1. Genome 2001, 44, 205–212. [Google Scholar] [CrossRef]
  12. Vear, F.; Gentzbittel, L.; Philippon, J.; Mouzeyar, S.; Mestries, E.; Roeckel-Drevet, P.; de Labroube, D.T.; Nicolas, P. The genetics of resistance to five races of downy mildew (Plasmopara halstedii) in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 1997, 95, 584–589. [Google Scholar] [CrossRef]
  13. Vear, F.; Leclercq, P. Deux nouvea ux genes resistance au mildiou du tournesol. Ann. Amelior. Plant. 1971, 21, 251–255. [Google Scholar]
  14. Vear, F. Studies on resistance to downy mildew in sunflower (Helianthus annuus L.). In Proceedings of the 6th International Sunflower Conference, Bucharest, Romania, 22–24 July 1974; pp. 297–302. [Google Scholar]
  15. Vear, F.; Seriveys, H.; Petit, A.; Serre, F.; Boudon, J.P.; Roche, S.; Walser, P.; de Labrouhe, D.T. Origins of major genes for downy mildew resistance in sunflower. In Proceedings of the 17th International Sunflower Conference, Cordoba, Spain, 8–12 June 2008; pp. 125–130. [Google Scholar]
  16. Miller, J.F.; Gulya, T.J. Inheritance of resistance to race 3 downy mildew in sunflower. Crop Sci. 1987, 27, 210–212. [Google Scholar] [CrossRef]
  17. Bert, P.F.; de Labrouhe, T.D.; Philippon, J.; Mouzeyar, S.; Jouan, I.; Nicolas, P.; Vear, F. Identification of a second linkage group carrying genes controlling resistance to downy mildew (Plasmopara halstedii) in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2001, 103, 992–997. [Google Scholar] [CrossRef]
  18. Miller, J.F.; Gulya, T.J. Inheritance of resistance to race 4 of downy mildew derived from interspecific crosses in sunflower. Crop Sci. 1991, 31, 40–43. [Google Scholar] [CrossRef]
  19. Roeckel-Drevet, P.; Gagne, G.; Mouzeyar, S.; Gentzbittel, L.; Philippon, J.; Nicolas, P.; de Labrouhe, D.T.; Vear, F. Colocation of downy mildew (Plasmopara halstedii) resistance genes in sunflower (Helianthus annuus L.). Euphytica 1996, 91, 225–228. [Google Scholar] [CrossRef]
  20. Slabaugh, M.B.; Yu, J.K.; Tang, S.X.; Heesacker, A.; Hu, X.; Lu, G.H.; Bidney, D.; Han, F.; Knapp, S.J. Haplotyping and mapping a large cluster of downy mildew resistance gene candidates in sunflower using multilocus intron fragment length polymorphisms. Plant Biotechnol. J. 2003, 3, 167–185. [Google Scholar] [CrossRef]
  21. Radwan, O.; Bouzidi, M.F.; Vear, F.; Philippon, J.; de Labrouhe, T.D.; Nicolas, P.; Mouzeyar, S. Identification of non-TIR-NBS-LRR markers linked to the Pl5/Pl8 locus for resistance to downy mildew in sunflower. Theor. Appl. Genet. 2003, 106, 1438–1446. [Google Scholar] [CrossRef]
  22. Gulya, T.J.; Sackston, W.E.; Viranyi, F.; Masirevic, S.; Rashid, K.Y. New races of the sunflower downy mildew pathogen (Plasmopara halstedii) in Europe and North and South America. J. Phytopathol. 1991, 132, 303–311. [Google Scholar] [CrossRef]
  23. Seiler, G.J. Registration of 13 downy mildew tolerant interspecific sunflower germplasm lines derived from wild annual species. Crop Sci. 1991, 31, 1714–1716. [Google Scholar]
  24. Dußle, C.M.; Hahn, V.; Knapp, S.J.; Bauer, E. PlArg from Helianthus argophyllus is unlinked to other known downy mildew resistance genes in sunflower. Theor. Appl. Genet. 2004, 109, 1083–1086. [Google Scholar] [CrossRef]
  25. Rahim, M.; Jan, C.C.; Gulya, T.J. Inheritance of resistance to sunflower downy mildew races 1, 2 and 3 in cultivated sunflower. Plant Breed. 2002, 121, 57–60. [Google Scholar] [CrossRef]
  26. Gulya, T.J. Registration of five disease-resistant sunflower germplasms. Crop Sci. 1985, 25, 719–720. [Google Scholar] [CrossRef]
  27. Mulpuri, S.; Liu, Z.; Feng, J.; Gulya, T.J.; Jan, C.C. Inheritance and molecular mapping of a downy mildew resistance gene, Pl13 in cultivated sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2009, 119, 795–803. [Google Scholar] [CrossRef] [PubMed]
  28. Bachlava, E.; Radwan, O.E.; Abratti, G.; Tang, S.; Gao, W.; Heesacker, A.F.; Bazzalo, M.E.; Zambelli, A.; Leon, A.J.; Knapp, S.J. Downy mildew (Pl8 and Pl14) and rust (RAdv) resistance genes reside in close proximity to tandemly duplicated clusters of non-TIR-like NBS-LRR-encoding genes on sunflower chromosomes 1 and 13. Theor. Appl. Genet. 2011, 122, 1211–1221. [Google Scholar] [CrossRef]
  29. de Romano, A.B.; Romano, C.; Bulos, M.; Altieri, E.; Sala, C. A new gene for resistance to downy mildew in sunflower. In Proceedings of the International Symposium “Sunflower breeding on resistance to diseases”, Krasnodar, Russia, 23–24 June 2010. [Google Scholar]
  30. Liu, Z.; Gulya, T.J.; Seiler, G.J.; Vick, B.A.; Jan, C.C. Molecular mapping of the Pl16 downy mildew resistance gene from HA-R4 to facilitate marker-assisted selection in sunflower. Theor. Appl. Genet. 2012, 125, 121–131. [Google Scholar] [CrossRef]
  31. Hulke, B.S.; Miller, J.F.; Gulya, T.J.; Vick, B.A. Registration of the oilseed sunflower genetic stocks HA 458, HA 459, and HA 460 possessing genes for resistance to downy mildew. J. Plant Reg. 2010, 4, 93–97. [Google Scholar] [CrossRef] [Green Version]
  32. Qi, L.L.; Long, Y.M.; Jan, C.C.; Ma, G.J.; Gulya, T.J. Pl17 is a novel gene independent of known downy mildew resistance genes in the cultivated sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2015, 128, 757–767. [Google Scholar] [CrossRef]
  33. Qi, L.L.; Foley, M.E.; Cai, X.W.; Gulya, T.J. Genetics and mapping of a novel downy mildew resistance gene, Pl18, introgressed from wild Helianthus argophyllus into cultivated sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2016, 129, 741–752. [Google Scholar] [CrossRef]
  34. Zhang, Z.W.; Ma, G.J.; Zhao, J.; Markell, S.G.; Qi, L.L. Discovery and introgression of the wild sunflower-derived novel downy mildew resistance gene Pl19 in confection sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2017, 130, 29–39. [Google Scholar] [CrossRef]
  35. Ma, G.J.; Markell, S.G.; Song, Q.J.; Qi, L.L. Genotyping-by-sequencing targeting of a novel downy mildew resistance gene Pl20 from wild Helianthus argophyllus for sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2017, 130, 1519–1529. [Google Scholar] [CrossRef]
  36. Vincourt, P.; As-sadi, F.; Bordat, A.; Langlade, N.B.; Gouzy, J.; Pouilly, N.; Lippi, Y.; Serre, F.; Godiard, L.; Tourvieille de Labrouhe, D.; et al. Consensus mapping of major resistance genes and independent QTL for quantitative resistance to sunflower downy mildew. Theor. Appl. Genet. 2012, 125, 909–920. [Google Scholar] [CrossRef] [PubMed]
  37. Pecrix, Y.; Buendia, L.; Penouilh-Suzette, C.; Maréchaux, M.; Legrand, L.; Bouchez, O.; Rengel, D.; Gouzy, J.; Cottret, L.; Vear, F.; et al. Sunflower resistance to multiple downy mildew pathotypes revealed by recognition of conserved effectors of the oomycete Plasmopara halstedii. Plant J. 2019, 97, 730–748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Pecrix, Y.; Penouilh-Suzette, C.; Muños, S.; Vear, F.; Godiard, L. Ten broad spectrum resistances to downy mildew physically mapped on the sunflower genome. Front. Plant Sci. 2018, 9, 1780. [Google Scholar] [CrossRef] [Green Version]
  39. Jan, C.C.; Gulya, T.J. Registration of a sunflower germplasm resistant to rust, downy mildew, and virus. Crop Sci. 2006, 46, 1829. [Google Scholar] [CrossRef]
  40. Liu, Z.; Zhang, L.; Ma, G.J.; Seiler, G.J.; Jan, C.C.; Qi, L.L. Molecular mapping of the downy mildew and rust resistance genes in a sunflower germplasm line TX16R. Mol. Breed. 2019, 39, 19. [Google Scholar] [CrossRef]
  41. Miller, J.F.; Gulya, T.J.; Seiler, G.J. Registration of five fertility restorer sunflower germplasms. Crop Sci. 2002, 42, 989–991. [Google Scholar] [CrossRef]
  42. Talukder, Z.I.; Ma, G.; Hulke, B.S.; Jan, C.C.; Qi, L. Linkage mapping and genome-wide association studies of the Rf gene cluster in sunflower (Helianthus annuus L.) and their distribution in world sunflower collections. Front. Genet. 2019, 10, 216. [Google Scholar] [CrossRef] [Green Version]
  43. Qi, L.L.; Ma, G.J.; Li, X.H.; Seiler, G.J. Diversification of the downy mildew resistance gene pool by introgression of a new gene, Pl35, from wild Helianthus argophyllus into oilseed and confection sunflowers (Helianthus annuus L.). Theor. Appl. Genet. 2019, 132, 2553–2565. [Google Scholar] [CrossRef]
  44. Qi, L.; Cai, X. Characterization and mapping of a downy mildew resistance gene, Pl36, in sunflower (Helianthus annuus L.). Mol. Breed. 2022, 42, 8. [Google Scholar] [CrossRef]
  45. Markell, S.; Gulya, T.; McKay, K.; Hutter, M.; Hollingsworth, C.; Ulstad, V.; Kock, R.; Knudsvig, A. Widespread occurrence of the aecial stage of sunflower rust caused by Puccinia helianthi in North Dakota and Minnesota in 2008. Plant Dis. 2009, 93, 668–669. [Google Scholar] [CrossRef]
  46. Gulya, T.J.; Harveson, R.; Mathew, F.; Block, C.C.; Thompson, S.; Kandel, H.; Berglund, D.; Sandbakken, J.; Kleingartner, L.; Markell, S. Comprehensive disease survey of U.S. sunflower: Disease trends, research priorities and unanticipated impacts. Plant Dis. 2019, 103, 601–618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Gulya, T.J.; Markell, S. Sunflower Rust Status–2008 Race Frequency across the Midwest and Resistance among Commercial Hybrids. Available online: http://www.sunflowernsa.com/uploads/Gulya_RustStatus_09.pdf (accessed on 4 June 2022).
  48. Sendall, B.; Kong, G.; Goulter, K.; Aitken, E.; Thompson, S.; Mitchell, J.; Kochman, J.; Lawson, W.; Shatte, T.; Gulya, T. Diversity in the sunflower: Puccinia helianthi pathosystem in Australia. Australas Plant Pathol. 2006, 35, 657–670. [Google Scholar] [CrossRef]
  49. Moreno, P.S.; de Romano, A.B.; Romano, M.C.; Vergani., P.; Sposaro, M.; Bulos, M.; Altieri, E.; Ramos, M.L.; Sala, C.A. A survey of physiological races of Puccinia helianthi in Argentina. In Proceedings of the 18th International Sunflower Conference, Mar del Plata, Argentina, 27 February–1 March 2012; pp. 87–90. [Google Scholar]
  50. Guo, D.; Jing, L.; Hu, W.; Li, X.; Navi, S.S. Race identification of sunflower rust and the reaction of hose genotypes to the disease in China. Eur. J. Plant Pathol. 2015, 144, 419–429. [Google Scholar] [CrossRef]
  51. Meyer, W.B.; Boshoff, W.H.P.; Minnaar-Ontong, A.; Young, A.J.; Kong, G.; Thompson, S.; Pretorius, Z.A.; Visser, B. Phenotypic and genotypic variation of Puccinia helianthi in South Africa. Plant Dis. 2021, 105, 1482–1489. [Google Scholar] [CrossRef]
  52. Putt, E.D.; Sackston, W.E. Studies on sunflower rust. IV. Two genes, R1 and R2 for resistance in the host. Can. J. Plant Sci. 1963, 43, 490–496. [Google Scholar] [CrossRef]
  53. Lawson, W.R.; Goulter, K.C.; Henry, R.J.; Kong, G.A.; Kochman, J.K. Marker-assisted selection for two rust resistance genes in sunflower. Mol. Breed. 1998, 4, 227–234. [Google Scholar] [CrossRef]
  54. Yu, J.K.; Tang, S.; Slabaugh, M.B.; Heesacker, A.; Cole, G.; Herring, M.; Soper, J.; Han, F.; Chu, W.C.; Webb, D.M. Towards a saturated molecular genetic linkage map for cultivated sunflower. Crop Sci. 2003, 43, 367–387. [Google Scholar] [CrossRef]
  55. Qi, L.L.; Ma, G.J.; Long, Y.M.; Hulke, B.S.; Markell, S.G. Relocation of a rust resistance gene R2 and its marker-assisted gene pyramiding in confection sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2015, 128, 477–488. [Google Scholar] [CrossRef]
  56. Goulter, K.C. Breeding of a rust differential sunflower line. In Proceedings of the 8th Australian Sunflower Association Workshop, Toowoomba, Australia, 19–22 March 1990; Kochman, J.K., Ed.; Australian Sunflower Association: Toowoomba, Australia; pp. 120–124. [Google Scholar]
  57. Qi, L.L.; Hulke, B.S.; Vick, B.A.; Gulya, T.J. Molecular mapping of the rust resistance gene R4 to a large NBS-LRR cluster on linkage group 13 of sunflower. Theor. Appl. Genet. 2011, 123, 351–358. [Google Scholar] [CrossRef] [Green Version]
  58. Qi, L.L.; Gulya, T.J.; Hulke, B.S.; Vick, B.A. Chromosome location, DNA markers and rust resistance of the sunflower gene R5. Mol. Breed. 2012, 30, 745–756. [Google Scholar] [CrossRef]
  59. Lambrides, C.J.; Miller, J.F. Inheritance of rust resistance in a source of MC 29 sunflower germplasm. Crop Sci. 1994, 34, 1224–1230. [Google Scholar] [CrossRef]
  60. Qi, L.L.; Seiler, G.J.; Vick, B.A.; Gulya, T.J. Genetics and mapping of the R11 gene conferring resistance to recently emerged rust races, tightly linked to male fertility restoration, in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2012, 125, 921–932. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Qi, L.L.; Seiler, G.J. Registration of a male fertility restorer oilseed sunflower germplasm, HA-R9, resistant to sunflower rust. J. Plant Reg. 2013, 7, 353–357. [Google Scholar] [CrossRef]
  62. Gong, L.; Hulke, B.S.; Gulya, T.J.; Markell, S.G.; Qi, L.L. Molecular tagging of a novel rust resistance gene R12 in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2013, 126, 93–99. [Google Scholar] [CrossRef] [PubMed]
  63. Miller, J.F.; Gulya, T.J. Registration of three rust resistant sunflower germplasm populations. Crop Sci. 2001, 41, 601. [Google Scholar] [CrossRef]
  64. Gong, L.; Gulya, T.J.; Markell, S.G.; Hulke, B.S.; Qi, L.L. Genetic mapping of rust resistance genes in confection sunflower line HA-R6 and oilseed line RHA 397. Theor. Appl. Genet. 2013, 126, 2039–2049. [Google Scholar] [CrossRef]
  65. Miller, J.F.; Gulya, T.J. Registration of eight maintainer (HA 393, HA 394 and HA 402 to HA 407) and seven restorer (RHA 395 to RHA 401) sunflower germplasm lines. Crop Sci. 1997, 37, 1988–1989. [Google Scholar] [CrossRef]
  66. Jan, C.C.; Quresh, Z.; Gulya, T.J. Registration of seven rust resistance sunflower germplasms. Crop Sci. 2004, 44, 1887–1888. [Google Scholar]
  67. Zhang, M.; Liu, Z.; Jan, C.C. Molecular mapping of a rust resistance gene R14 in cultivated sunflower line PH3. Mol Breed. 2016, 36, 32. [Google Scholar] [CrossRef]
  68. Ma, G.J.; Song, Q.J.; Markell, S.G.; Qi, L.L. High-throughput genotyping-by-sequencing facilitates molecular tagging of a novel rust resistance gene, R15, in sunflower (Helianthus annuus L.). Theor. Appl. Genet. 2018, 131, 1423–1432. [Google Scholar] [CrossRef]
  69. Yang, S.M.; Dowler, W.M.; Luciano, A. Gene Pu6: A new gene in sunflower for resistance to Puccinia helianthi. Phytopathology 1989, 79, 474–477. [Google Scholar] [CrossRef]
  70. Bulos, M.; Vergani, P.N.; Altieri, E. Genetic mapping, marker assisted selection and allelic relationships for the Pu6 gene conferring rust resistance in sunflower. Breed. Sci. 2014, 64, 206–212. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Miller, J.F.; Gulya, T.J. Registration of six downy mildew resistant sunflower germplasm lines. Crop Sci. 1988, 28, 1040–1104. [Google Scholar] [CrossRef]
  72. Radwan, O. Isolation and expression of an NBS-LRR protein encoding resistance gene candidate that segregates with a rust resistance gene in sunflower. J. Phytopathol. 2010, 158, 433–443. [Google Scholar] [CrossRef]
  73. Qi, L.L.; Talukder, Z.I.; Ma, G.J.; Li, X.H. Discovery and mapping of two new rust resistance genes, R17 and R18, in sunflower using genotyping by sequencing. Theor. Appl. Genet. 2021, 134, 2291–2301. [Google Scholar] [CrossRef] [PubMed]
  74. Qi, L.L.; Ma, G.J.; Seiler, G.J. Registration pf two oilseed sunflower germplasms, HA-R18 and HA-R19, resistant to sunflower rust. J. Plant Reg. 2022, 1–7. [Google Scholar] [CrossRef]
  75. Qi, L.L.; Long, Y.M.; Ma, G.J.; Markel, S.G. Map saturation and SNP marker development for the rust resistance genes (R4, R5, R13a, and R13b) in sunflower (Helianthus annuus L.). Mol Breed. 2015, 35, 196. [Google Scholar] [CrossRef]
  76. Ma, G.; Song, Q.; Underwood, W.R.; Zhang, Z.; Fiedler, J.D.; Li, X.; Qi, L. Molecular dissection of resistance gene cluster and candidate gene identification of Pl17 and Pl19 in sunflower by whole-genome resequencing. Sci. Rep. 2019, 9, 14974. [Google Scholar] [CrossRef]
  77. McDowell, J.M.; Woffenden, B.J. Plant disease resistance genes: Recent insights and potential applications. Trends Biotechnol. 2003, 21, 178–183. [Google Scholar] [CrossRef]
  78. Wiesner-Hanks, T.; Nelson, R. Multiple disease resistance in plants. Annu. Rev. Phytopathol. 2016, 54, 229–252. [Google Scholar] [CrossRef]
  79. van Wersch, S.; Li, X. Stronger when together: Clustering of plant NLR disease resistance genes. Trends Plant Sci. 2019, 24, 688–699. [Google Scholar] [CrossRef] [PubMed]
  80. Ma, G.; Long, Y.; Song, Q.; Talukder, Z.I.; Shamimuzzaman, M.; Qi, L. Map and sequence-based chromosome walking towards cloning of the male fertility restoration gene Rf5 linked to R11 in sunflower. Sci. Rep. 2021, 12, 777. [Google Scholar] [CrossRef] [PubMed]
  81. Radwan, O.; Gandhi, S.; Heesacker, A.; Whitaker, B.; Taylor, C.; Plocik, A.; Kesseli, R.; Kozik, A.; Michelmore, R.W.; Knapp, S.J. Genetic diversity and genomic distribution of homologs encoding NBS- LRR disease resistance proteins in sunflower. Mol. Genet. Genom. 2008, 280, 111–125. [Google Scholar] [CrossRef] [PubMed]
  82. Burdon, J.J.; Thrall, P.H. Coevolution of plants and their pathogens in natural habitats. Science 2009, 324, 755–756. [Google Scholar] [CrossRef] [Green Version]
  83. Malav, A.K.; Chandrawat, I.; Chandrawat, K.S. Gene pyramiding: An overview. Int. J. Curr. Res. Biosci. Plant Biol. 2016, 3, 22–28. [Google Scholar] [CrossRef]
  84. Qi, L.L.; Ma, G.J. Marker-assisted gene pyramiding and the reliability of using SNP markers located in the recombination suppressed regions of sunflower (Helianthus annuus L.). Genes 2020, 11, 10. [Google Scholar] [CrossRef] [Green Version]
  85. Long, Y.M.; Chao, W.S.; Ma, G.J.; Xu, S.S.; Qi, L.L. An innovative SNP genotyping method adapting to multiple platforms and throughputs. Theor. Appl. Genet. 2017, 130, 597–607. [Google Scholar] [CrossRef]
  86. Ma, G.; Song, Q.; Li, X.; Qi, L. High-density mapping and candidate gene analysis of Pl18 and Pl20 in sunflower by whole-genome resequencing. Int. J. Mol. Sci. 2020, 21, 9571. [Google Scholar] [CrossRef]
  87. Van Ooijen, J.W. JoinMap®4, Software for the Calculation of Genetic Linkage Maps in Experimental Populations; Kyazma BV: Wageningen, The Netherlands, 2006. [Google Scholar]
  88. Yang, S.M.; Antonelli, E.F.; Luciano, A.; Luciani, N.D. Reactions of Argentine and Australian sunflower rust differentials to four North American cultures of Puccinia helianthi from North Dakota. Plant Dis. 1986, 70, 883–886. [Google Scholar] [CrossRef] [Green Version]
  89. Gulya, T.J.; Venette, R.; Venette, J.R.; Lamey, H.A. Sunflower Rust; NDSU Extension Service: Fargo, ND, USA, 1990; p. 988. Available online: https://library.ndsu.edu/ir/bitstream/handle/10365/5283/pp998.pdf?sequence=1&isAllowed=y (accessed on 4 June 2022).
  90. Gulya, T.J.; Draper, M.; Harbour, J.; Holen, C.; Knodel, J.; Lamey, A.; Mason, P. Metalaxyl resistance in sunflower downy mildew in North America. In Proceedings of the 21st Sunflower Research Forum, Fargo, ND, USA, 14–15 January 1999; pp. 118–123. [Google Scholar]
Ijms 23 09516 g001 550
Figure 1. Rust R13a gene linkage maps. (a) Map taken from Qi et al., 2015 [75], (b) R13a saturation map, and (c) R13a fine map.
Figure 1. Rust R13a gene linkage maps. (a) Map taken from Qi et al., 2015 [75], (b) R13a saturation map, and (c) R13a fine map.
Ijms 23 09516 g001
Ijms 23 09516 g002 550
Figure 2. Rust R16 gene linkage maps. (a) Map taken from Liu et al., 2019 [40], (b) R16 saturation map, and (c) R16 fine map.
Figure 2. Rust R16 gene linkage maps. (a) Map taken from Liu et al., 2019 [40], (b) R16 saturation map, and (c) R16 fine map.
Ijms 23 09516 g002
Ijms 23 09516 g003 550
Figure 3. DM Pl33 gene linkage maps. (a) Map taken from Liu et al., 2019 [40], (b) Pl33 saturation map, and (c) Pl33 fine map.
Figure 3. DM Pl33 gene linkage maps. (a) Map taken from Liu et al., 2019 [40], (b) Pl33 saturation map, and (c) Pl33 fine map.
Ijms 23 09516 g003
Ijms 23 09516 g004 550
Figure 4. Genetic maps of R13a, R13b and R16. (a) R13a saturation map, (b) R13b saturation map, and (c) R16 saturation map. The underlined markers are the unique markers in each map.
Figure 4. Genetic maps of R13a, R13b and R16. (a) R13a saturation map, (b) R13b saturation map, and (c) R16 saturation map. The underlined markers are the unique markers in each map.
Ijms 23 09516 g004
Ijms 23 09516 g005 550
Figure 5. The polymerase chain reaction (PCR) amplification patterns of the single nucleotide polymorphism (SNP) markers in the eight sunflower lines. (a) SNP C13_194268343 linked to R13a, the arrow indicates the amplified band corresponding to C13_194268343-R13a marker allele, R13a shares the PCR pattern of the marker allele with R13b. (b) SNP S13_236323209 linked to R13b, the arrow indicates the amplified band corresponding to S13_236323209-R13b marker allele, R13b shares the PCR pattern of the marker allele with R4. (c) SNP C13_195874138 linked to R16 can distinguish R16 from all genes in the cluster, the arrow indicates the amplified band corresponding to C13_195874138-R16 marker allele. Lane 1, HA 89; Lane 2, HA 434; Lane 3, HA-R6/R13a; Lane 4, RHA 397/R13b; Lane 5, TX16R/R16; Lane 6, HA-R3/R4; Lane 7, HA-R18/R17; Lane 8, HA-R19/R18.
Figure 5. The polymerase chain reaction (PCR) amplification patterns of the single nucleotide polymorphism (SNP) markers in the eight sunflower lines. (a) SNP C13_194268343 linked to R13a, the arrow indicates the amplified band corresponding to C13_194268343-R13a marker allele, R13a shares the PCR pattern of the marker allele with R13b. (b) SNP S13_236323209 linked to R13b, the arrow indicates the amplified band corresponding to S13_236323209-R13b marker allele, R13b shares the PCR pattern of the marker allele with R4. (c) SNP C13_195874138 linked to R16 can distinguish R16 from all genes in the cluster, the arrow indicates the amplified band corresponding to C13_195874138-R16 marker allele. Lane 1, HA 89; Lane 2, HA 434; Lane 3, HA-R6/R13a; Lane 4, RHA 397/R13b; Lane 5, TX16R/R16; Lane 6, HA-R3/R4; Lane 7, HA-R18/R17; Lane 8, HA-R19/R18.
Ijms 23 09516 g005
Ijms 23 09516 g006 550
Figure 6. The polymerase chain reaction (PCR) amplification pattern of single nucleotide polymorphism (SNP) markers in the 96 selected sunflower lines. The names and pedigrees of 96 selected sunflower lines (lanes) are listed in Supplementary Table S3. (a) SNP marker C13_194722668 diagnostic for R16, the arrow indicates the amplified band corresponding to C13_194722668-R16 marker allele which is only present in the TX16R line in lane 39. (b) SNP marker C4_5671004 diagnostic for Pl33, the arrow indicates the amplified band corresponding to C4_5671004-Pl33 marker allele which is only present in the TX16R line in lane 39. Lane 39: TX16R with R16 and Pl33.
Figure 6. The polymerase chain reaction (PCR) amplification pattern of single nucleotide polymorphism (SNP) markers in the 96 selected sunflower lines. The names and pedigrees of 96 selected sunflower lines (lanes) are listed in Supplementary Table S3. (a) SNP marker C13_194722668 diagnostic for R16, the arrow indicates the amplified band corresponding to C13_194722668-R16 marker allele which is only present in the TX16R line in lane 39. (b) SNP marker C4_5671004 diagnostic for Pl33, the arrow indicates the amplified band corresponding to C4_5671004-Pl33 marker allele which is only present in the TX16R line in lane 39. Lane 39: TX16R with R16 and Pl33.
Ijms 23 09516 g006
Table
Table 1. Genetic and physical positions of the SNP markers linked to R13a on a fine map of sunflower chromosome 13.
Table 1. Genetic and physical positions of the SNP markers linked to R13a on a fine map of sunflower chromosome 13.
MarkerNo. RecombinationPosition in the Fine Map (cM)Physical Position on XRQr1.0 Assembly (bp)Physical Position on HA412-HO Assembly (bp)
SFW0149700193,089,467–193,089,349236,437,096–236,436,978
C13_194268343300.53194,268,143–194,268,543-
C13_19473585450.62194,735,654–194,736,054235,097,621–235,097,389
C13_19475705540.69194,756,855–194,757,255236,982,689–236,982,486
R13a70.82--
C13_195501970201.17195,501,770–195,502,170-
C13_19552291301.17195,522,713–195,523,113-
C13_19552694501.17195,526,745–195,527,145-
C13_19555676801.17195,556,568–195,556,968-
SFW04275211.54196,464,687–196,464,768238,083,828–238,083,909
SFW0431711.56196,474,077–196,473,983238,092,624–238,092,530
SFW0574341.63196,521,145–196,521,026238,196,827–238,196,708
HT3821063.51--
Table
Table 2. Genetic and physical positions of the SNP markers linked to R16 on a fine map of sunflower chromosome 13.
Table 2. Genetic and physical positions of the SNP markers linked to R16 on a fine map of sunflower chromosome 13.
MarkerNo. RecombinationPosition in the Fine Map (cM)Physical Position on XRQr1.0 Assembly (bp)Physical Position on HA412-HO Assembly (bp)
ORS31600-232,376,160 *
SFW88751102.44193,131,235–193,131,123236,146,953–236,147,065
C13_194722668433.39194,722,468–194,722,868
R1683.57--
C13_19551278663.70195,512,586–195,512,986-
C13_19555291703.70195,552,717–195,553,117-
C13_19560537203.70195,605,172–195,605,572-
C13_19583677073.86195,836,570–195,836,970-
C13_19584063403.86195,840,434–195,840,834-
C13_19587413803.86195,873,938–195,874,338-
SFW05743314.54196,521,145–196,521,026238,196,827–238,196,708
* reverse primer aligns to HA412-HO sequences.
Table
Table 3. Genetic and physical positions of the SNP markers linked to Pl33 on a fine map of sunflower chromosome 4.
Table 3. Genetic and physical positions of the SNP markers linked to Pl33 on a fine map of sunflower chromosome 4.
MarkerNo. RecombinationPosition in the Fine Map (cM)Physical Position on XRQr1.0 Assembly (bp)Physical Position on HA412-HO Assembly (bp)
SFW04052004,208,180–4,208,2713,621,090–3,621,181
C4_526141170.165,261,211–5,261,611-
C4_5562979110.405,562,779–5,563,1796,160,956–6,160,556
C4_564135310.425,641,153–5,641,55313,535,870–13,536,270
C4_567100400.425,669,804–5,671,2046,082,367–6,082,024
Pl3300.42--
SBP00600.425,703,949–5,704,0835,947,481–5,947,612
SBP00510.445,704,420–5,704,5455,947,019–5,947,144
C4_570481400.445,704,614–5,705,0146,029,066–6,028,666
C4_573873600.445,738,536–5,738,9365,993,332–5,992,932
SFW06856712.026,978,325–6,978,2067,872,910–7,873,029
Table
Table 4. Map positions of the SNP markers linked to R13a, R13b, and R16 on sunflower chromosome 13.
Table 4. Map positions of the SNP markers linked to R13a, R13b, and R16 on sunflower chromosome 13.
SNP MarkerR13a
Saturation Map (cM)		R13a Fine Map (cM)R13b
Saturation Map (cM)		R16
Saturation Map (cM)		R16 Fine Map (cM)Physical Position on XRQr1.0
Assembly (bp)		Physical Position on HA412-HO
Assembly (bp)
S13_236323867--0.79---236,323,667–236,324,067
S13_236323209--1.01---236,323,009–236,323,409
SFW014970.000.001.01--193,089,467–193,089,349236,437,096–236,436,978
S13_237169906--1.01---237,169,706–237,170,106
C13_1942683430.000.531.015.03NT194,268,143–194,268,543-
C13_194722668---6.753.39194,722,468–194,722,868236,711,421–236,711,022
C13_1947358540.000.621.01--194,735,654–194,736,054235,097,621–235,097,389
C13_1947570550.000.691.01--194,756,855–194,757,255236,982,689–236,982,486
R13b - -1.44----
R13a0.000.82 - ----
C13_1955019700.001.17---195,501,770–195,502,170-
R16 - - - 6.753.57--
C13_195512786 --6.753.70195,512,586–195,512,986-
C13_1955229130.001.17---195,522,713–195,523,113-
C13_1955269450.001.17---195,526,745–195,527,145-
C13_195552917---6.753.70195,552,717–195,553,117-
C13_1955567680.001.17---195,556,568–195,556,968-
C13_195605372---6.753.70195,605,172–195,605,572-
C13_195836770---7.113.86195,836,570–195,836,970-
C13_195840634---7.113.86195,840,434–195,840,834-
C13_195874138---7.113.86195,873,938–195,874,338-
SFW042750.001.544.06--196,464,687–196,464,768238,083,828–238,083,909
SFW043170.001.564.067.83NT196,474,077–196,473,983238,092,624–238,092,530
SFW057430.701.634.068.174.54196,521,145–196,521,026238,196,827–238,196,708
NT: Not test in fine mapping.
Table
Table 5. Map positions of the SNP markers linked to Pl17, Pl19, and Pl33 on sunflower chromosome 4.
Table 5. Map positions of the SNP markers linked to Pl17, Pl19, and Pl33 on sunflower chromosome 4.
SNP MarkerPl17 Fine Map (cM) †Pl19 Fine Map (cM) †Pl33 Fine Map (cM)Physical Position on XRQr1.0 Assembly (bp)Physical Position on HA412-HO Assembly (bp)
C4_5261411--0.15515,261,211–5,261,611-
C4_5562979--0.39895,562,779–5,563,1796,160,956–6,160,556
C4_5641353--0.42125,641,153–5,641,55313,535,870–13,536,270
C4_5671004 *--0.42125,669,804–5,671,2046,082,367–6,082,024
Pl33--0.4212--
C4_5696413 **0.26595--
C4_5704814--0.44355,704,614–5,705,0146,029,066–6,028,666
C4_5705018 **0.26595--5,704,818–5,705,2186,028,462–6,028,862
C4_5705841 **0.28257--5,705,641–5,706,0416,027,639–6,028,039
C4_5709499 **0.28257--5,709,299–5,709,6996,021,349–6,021,749
C4_57115240.28257--5,711,324–5,711,7246,627,884–6,628,284
Pl170.31581----
SPB0001 **0.34905--5,696,076–5,696,1815,950,918–5,951,024
SPB00060.34905-0.42125,703,949–5,704,0835,947,481–5,947,612
SPB00050.34905-0.44355,704,420–5,704,5455,947,019–5,947,144
C4_5738736--0.44355,738,536–5,738,9365,993,332–5,992,932
C4_6675662 ***-0.4212-6,675,462–6,675,8626,972,167–6,972,567
C4_6676629 ***-0.4212-6,676,429–6,676,8296,971,201–6,971,601
Pl19-0.4655---
C4_6711381-0.6428-6,711,181–6,711,5817,089,348–7,089,748
C4_6730143-0.7536-6,729,943–6,730,3437,073,422–7,073,822
S4_7964876 ***-1.1304-6,914,409–6,914,8097,964,676–7,965,076
† map data for Pl17 and Pl19 were taken from Ma et al., 2019 [76]. * diagnostic SNP markers specific to Pl33, ** diagnostic SNP markers specific to Pl17, and *** diagnostic SNP markers specific to Pl19.
Table
Table 6. Predicted plant disease defense-related genes in the interval of R13a, R16, and Pl33 in the XRQr1.0 genome assembly.
Table 6. Predicted plant disease defense-related genes in the interval of R13a, R16, and Pl33 in the XRQr1.0 genome assembly.
Candidate GeneDescriptionPhysical Position (bp)Length (bp)
R13a and R16 interval 194,722,468–195,512,986790,518
HanXRQChr13g0425851Putative NB-ARC; P-loop containing nucleoside triphosphate hydrolase; Leucine-rich repeat domain, L domain-like194,725,998–194,753,53127,534
HanXRQChr13g0425891Putative NB-ARC; P-loop containing nucleoside triphosphate hydrolase; Leucine-rich repeat domain, L domain-like194,800,201–194,803,6843484
HanXRQChr13g0425931Putative NB-ARC; P-loop containing nucleoside triphosphate hydrolase; Leucine-rich repeat domain, L domain-like195,196,820–195,210,74513,926
HanXRQChr13g0425941Putative NB-ARC; P-loop containing nucleoside triphosphate hydrolase; Leucine-rich repeat domain, L domain-like195,250,038–195,252,7032666
Pl33 interval 5,641,153–5,704,54563,392
HanXRQChr04g0095641Probable disease resistance protein (TIR-NBS-LRR class) family5,672,715–5,705,04432,330
Table
Table 7. (a) Specificity test of SNP markers linked to R13a in eight lines, (b) Specificity test of SNP markers linked to R13b in eight lines, (c) Specificity test of SNP markers linked to R16 in eight lines.
Table 7. (a) Specificity test of SNP markers linked to R13a in eight lines, (b) Specificity test of SNP markers linked to R13b in eight lines, (c) Specificity test of SNP markers linked to R16 in eight lines.
(a)
MarkerHA 89HA 434HA-R6/R13aRHA 397/R13bTX16R/R16HA-R3/R4HA-R18/R17HA-R19/R18
C13_194268343AABBCHHH
C13_194735854AABBBBHB
C13_194757055AABBBBHB
C13_195501970AABABBAB
C13_195522913AABABBAB
C13_195526945 AABABBAB
C13_195556768 AABABBAB
A represents HA 89 marker allele; B represent HA-R6 marker allele; C represents the marker allele different A and B, H represents heterozygous.
(b)
MarkerHA 89HA 434HA-R6/R13aRHA 397/R13bTX16R/R16HA-R3/R4HA-R18/R17HA-R19/R18
S13_236323209AAABABAC
S13_236323867AAABABAC
S13_237169906AAABABAC
A represents HA 89 marker allele; B represent RHA 397 marker allele; C represents the marker allele different A and B.
(c)
MarkerHA 89HA 434HA-R6/R13aRHA 397/R13bTX16R/R16HA-R3/R4HA-R18/R17HA-R19/R18
C13_194722668AAAABAAA
C13_195552917AABABCAB
C13_195605372AAAABAAA
C13_195836770AABBBBAB
C13_195840634AABBBBAB
C13_195874138AAAABAAA
A represents HA 89 marker allele; B represent TX16R marker allele; C represents the marker allele different A and B.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ma, G.; Song, Q.; Li, X.; Qi, L. Genetic Insight into Disease Resistance Gene Clusters by Using Sequencing-Based Fine Mapping in Sunflower (Helianthus annuus L.). Int. J. Mol. Sci. 2022, 23, 9516. https://doi.org/10.3390/ijms23179516

AMA Style

Ma G, Song Q, Li X, Qi L. Genetic Insight into Disease Resistance Gene Clusters by Using Sequencing-Based Fine Mapping in Sunflower (Helianthus annuus L.). International Journal of Molecular Sciences. 2022; 23(17):9516. https://doi.org/10.3390/ijms23179516

Chicago/Turabian Style

Ma, Guojia, Qijian Song, Xuehui Li, and Lili Qi. 2022. "Genetic Insight into Disease Resistance Gene Clusters by Using Sequencing-Based Fine Mapping in Sunflower (Helianthus annuus L.)" International Journal of Molecular Sciences 23, no. 17: 9516. https://doi.org/10.3390/ijms23179516

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop