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1Utilization of the Genome-wide Wheat55K SNP Array for Genetic
2Analysis of Stripe Rust Resistance in Common Wheat Line P9936
3Shuo Huang, Jianhui Wu, Xiaoting Wang, Jingmei Mu, Zhi Xu, Qingdong Zeng, Shengjie
4Liu, Qilin Wang, Zhensheng Kang*, and Dejun Han*
5First, second, third, fourth, sixth, seventh, eighth, ninth and tenth authors: State Key Laboratory of Crop
6Stress Biology for Arid Areas, Northwest A&F University, Yangling, Shaanxi 712100, P. R. China; and
7fifth author: Department of Plant Disease, Institute of Plant Protection, Sichuan Academy of
8Agricultural Sciences, Jingjusi Road 20, Jinjiang District, Chengdu, Sichuan610066, P.R. China
9First and second authors made equal contribution to this study.
10
ABSTRACT
11
12 Huang, S., Wu, J. H., Wang, X. T., Mu, J. M., Xu, Z., Zeng, Q. D., Liu, S. J., Wang, Q. L.,
13 and Kang, Z. S., and Han, D. J.2018. Utilization of the Genome-wide Wheat55K SNP
14 Array for Genetic Analysis of Stripe Rust Resistance in Common Wheat Line P9936.
15 Phytopathology 000: 000-000.
16 Corresponding authors: D. J. Han. E-mail: handj@nwsuaf.edu.cn; Z. S. Kang. E-mail:
17 kangzs@nwsuaf.edu.cn
18
19 Breeding for resistance to stripe rust (caused by Puccinis striiformis f. sp. tritici) is
20 essential for reducing losses in yield and quality in wheat. To identify genes for use in
21 breeding, a biparental population of 186 recombinant inbred lines (RILs) from a cross of the
22 Chinese landrace Mingxian 169 and CIMMYT-derived line P9936 was evaluated in field
23 nurseries either artificially or naturally inoculated in two crop seasons. Each of the RILs and
24 parents was genotyped with the Wheat55K single nucleotide polymorphism (SNP) ‘Breeders’
25 array and a genetic linkage map with 8,225 polymorphic SNP markers spanning 3,593.37 cM
26 was constructed. Two major quantitative trait loci (QTL) and two minor QTL were identified.
27 The major QTL QYrblu.nwafu-3BS.2 and QYrblu.nwafu-7BL on chromosomes arms 3BS and
28 7BL were detected in all field locations and explained an average 20.4% and 38.9% of
29 phenotypic variation stripe rust severity, respectively. QYrblu.nwafu-3BS.2 likely
30 corresponds to the locus Yr30/Sr2 and QYrblu.nwafu-7BL may be a resistance allele
31 identified previously in CIMMYT germplasm. The other minor QTL had limited individual
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32 effects, but increased resistance when in combinations with other QTL. Markers linked to
33 QYrblu.nwafu-7BL were converted to kompetitive allele-specific PCR (KASP) markers and
34 validated in a panel of wheat accessions. Wheat accessions carrying the same haplotype as
35 P9936 at the identified SNP loci had lower average stripe rust severity than the average
36 severity of all other haplotypes.
37 Additional keywords: Adult-plant resistance; Yellow rust
38
39 Stripe rust, or yellow rust, caused by Puccinia striiformis Westend. f. sp. tritici Erikss.
40 (Pst), occurs on wheat (Triticum aestivum L.) worldwide, and causes significant economic
41 losses by increasing the cost of disease-management and reduced grain yield (Beddow et al.
42 2015; Hovmøller et al. 2010). As one of the most destructive diseases, itoccurs periodically in
43 almost all winter wheat-growing regions in China (Chen et al. 2014; Li and Zeng 2002).
44 Chemicals can control stripe rust, however, the application of chemicals can substantially
45 increase the cost of wheat production and may pollute the environment. Planting resistant
46 wheat varieties is considered as a top priority to control this disease (McIntosh et al. 1995;
47 Wiesner-Hanks and Nelson 2016).
48 There are two general categories of resistance to stripe rust: race specific and race
49 non-specific. Race specific resistance is generally effective at all growth stages and is often
50 described as all-stage or seedling resistance, whereas race non-specific resistance tends to
51 develop at post-seedling growth stages (adult plant resistance (APR) or high-temperature
52 adult plant (HTAP) resistance) and is more likely to be quantitative in effect (Chen 2013).
53 The advantage of the latter type is that it is more likely to be durable (Johnson 1981;
54 McDonald and Linde 2002); Race non-specific resistance maybe controlled by several genes
55 with either major or minor effects (Chen 2013). Many wheat cultivars are reported to have
56 durable resistance or APR (Boyd 2005; Ellis et al. 2014; McIntosh 1992). More than 300
57 genes or QTL (quantitative trait loci) for resistance to stripe rust have been mapped on all 21
58 wheat chromosomes and most are listed in the Wheat Catalogue of Gene Symbols or
59 summarized in other publications (Bulli et al. 2016; Maccaferri et al. 2015; McIntosh et al.
60 2017; Rosewarne et al. 2013). However, most all-stage resistance genes are no longer
61 effective against prevalent races in China and only several APR genes, such as Yr18, Yr29,
62 Yr30, Yr36, Yr39, Yr46, Yr52, Yr54, Yr59, Yr62, Yr78, and Yr79, are reported to reduce stripe
63 rust among wheat varieties (Feng et al. 2018, Wu et al. 2016, 2018b; Zeng et al. 2015; Zhou
64 et al. 2015). Unfortunately, not all of the effective genes or QTL have molecular markers that
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65 could be useful for marker-assisted selection (MAS). Therefore, it is necessary to identify
66 more resistance genes or QTL with user-friendly markers.
67 QTL analysis using molecular markers is an efficient way to dissect complex traits.
68 However, a lack of high resolution polymorphic markers was the biggest constraint in QTL
69 mapping of hexaploid wheat in the past. As a consequence, target QTL are often located in
70 large chromosomal regions and MAS is limited to relatively low numbers of well validated
71 markers (Wang et al. 2015). Recently, next generation sequencing and high-throughput
72 genotyping technologies have revolutionized the QTL analysis of complex traits and enabled
73 rapid marker development and map construction (Wang et al. 2018). Several high-density,
74 SNP chips and platforms, such as Affymetrix Gene ChipTM, Illumina Bead ArrayTM, and
75 kompetitive allele specific PCR (KASPTM, http://www.Lgcgenomics.com) are now available
76 for wheat genetic studies (Rasheed et al. 2016, 2017; Wu et al. 2017b, 2018c). SNP chips
77 containing different numbers of markers, including 9K, 35K, 90K, and 660K are
78 commercially available (Allen et al. 2017; Cavanagh et al. 2013; Cui et al. 2017; Wang et al.
79 2014). The newly designed Axiom® Wheat55K SNP Array is more appropriate for wheat
80 genetic research as all 53,063 SNP probes were carefully selected from the Wheat660K SNP
81 array for polymorphisms across a set of global wheat varieties (Jia and Zhao 2016). Based on
82 the advantages of lower costs, less “ascertainment bias”, and more scorable marker sites, this
83 new developed SNP array has been used in QTL studies (Liu et al. 2018; Ren et al. 2018; Wu
84 et al. 2018a).
85 One of the objectives of the International Maize and Wheat Improvement Center
86 (CIMMYT) is to strengthen the genetic diversity of resistance in its wheat germplasm
87 (Guzmán et al. 2017). Many CIMMYT wheat have been introduced to China. In an earlier
88 study, more than one thousand common wheat accessions, including many from CIMMYT,
89 were assessed for stripe rust response in both greenhouse and field environments (Han et al.
90 2012). Among these, line P9936 with pedigree Bluebird / Gallo × Cajeme // F35.70 ×
91 Kalyansona/Bluebird (CIMMYT 1983) consistently displayed high resistance against stripe
92 rust at the adult-plant stage (Han et al. 2012). However, the genetic basis of its resistance
93 was unknown. The goals of this study were to (1) map the stripe rust resistance of P9936
94 using a high-density SNP map developed for a Mingxian 169 × P9936 recombinant inbred
95 line (RIL) population, (2) identify QTL for stripe rust APR across multiple environments
96 using SNP-based genome-wide scanning, and (3) develop user-friendly markers for use in
97 breeding programs.
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98 MATERIALS AND METHODS
99 Wheat materials. The biparental population comprised 186 F6-derived F7 RILs from the
100 cross Mingxian 169 × P9936. Mingxian 169 (MX169) is a stripe rust susceptible Chinese
101 winter wheat landrace. The susceptible controls used in this study throughout the experiment
102 were MX169, Avocet S (AvS), and Xiaoyan 22 (XY22). A diverse panel of 361 common
103 wheat varieties was assessed for SNP-based haplotypes. The goal was to indentify useful
104 SNP markers linked to stripe rust resistance QTL for use in MAS. The wheat accessions
105 comprising the panel were kindly provided by the China Agriculture Research System
106 (CARS).
107 Greenhouse trials. Evaluation of seedling reactions to different Pst races was conducted
108 under controlled greenhouse conditions in a previous study (Han et al. 2012). In this study,
109 for seedling test, 10 seeds of each parents was sown 9 × 9 × 9 cm pots, and for adult-plant
110 tests, three plants were grown in larger pots (20 cm diameter × 15 cm height). Seedlings at
111 the two-leaf stage (14 days after planting) and adult-plant at the booting stage were separately
112 inoculated with urediniospores of Pst races CYR32, CYR33, and CYR34 mixed with talc at a
113 ratio of approximately 1:20. Infection types (IT) were recorded 18–21 days after inoculation
114 using a 0–9 scale (Line and Qayoum, 1992). Plants with ITs 0–6 were considered resistant,
115 and plants with ITs 7–9 were considered susceptible. In order to confrm and clarify ITs of the
116 entries, all tests were repeated three times.
117 Field trials. Stripe rust responses were assessed at three locations over two years. The
118 locations were Yangling in Shaanxi province, Tianshui in Gansu province and Jiangyou in
119 Sichuan province and the trails were conducted during the 2016-2017 and 2017-2018 crop
120 seasons, (here after 2017 and 2018, respectively). All field plots were sown between
121 mid-September and early November. After every 20 rows in the field, the susceptible check
122 XY22 and parents were used to increase and check stripe rust uniformity. The susceptible
123 lines AvS and MX169 were planted as spreader rows surroundingthe fields to ensure
124 adequate inoculum for infection . At the jointing stage (usually in late March) in the fields at
125 Yangling, the susceptible checks were artificially inoculated with a mixture of urediniospores
126 of prevalent races CYR32 and CYR34 suspended in liquid paraffin (1:300). The experiments
127 at Tianshui and Jiangyou were subjected to natural infection as both locations are hotspot
128 regions for stripe rust. A randomized complete block design with two replications was used
129 in all experiments. Thirty seeds of each line were planted in 120 cm rows spaced 30 cm apart.
130 Parents and progenies were visually evaluated for disease severity using modified Cobb scale
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131 (Peterson et al. 1948) when stripe rust severity levels of susceptible checks reached
132 maximum levels of 90-100% at 18-20 days post-flowering (around 15-20 May at Yangling,
133 10-15 April at Jiangyou, and 10-15 June at Tianshui). Disease assessment was made at least
134 twice, and generally the final dataset (the maximum disease severity, MDS) was used for
135 phenotypic and QTL analyses.
136 Phenotypic analysis. Analysis of variance (ANOVA) was conducted using the MDS data
137 to determine the effects of genotypes, environments, and genotype × environment interaction.
138 Pearson’s correlation coefficient (PCC) analysis and ANOVA were conducted using the
139 “AOV” function in QTL IciMapping software (Version 4.1) with the default parameters
140 (Meng et al. 2015). Estimation of broad-sense heritability (h2 b) of resistance to stripe rust
141 was based on the equation h2 b= σ2 g/(σ2 g+ σ2 ge/e + σ2 ε/re), where σ2 g is (MSf – MSfe)/re,
142 σ2 ge is (MSfe – MSe)/r and σ2 ε is MSe; σ2 g= genetic variance, σ2 ge = genotype ×
143 environment interaction variance, σ2 ε= error variance, MSf = mean square of genotypes,
144 MSfe = mean square of genotype × environment interaction, MSe = mean square of error, r =
145 number of replications, and e = number of environments.
146 Average phenotypic values for RILs in each environment were used for analyses. The
147 genetic effects from six environments were evaluated using the R package lme4 of BLUP
148 (best linear unbiased prediction), where lines, environments, line × environment interaction
149 and replicates nested in environments were all treated as random effects (Bates et al. 2015).
150 Genotyping of the RIL population. Fresh leaves of RILs and parents were sampled in the
151 field at the early jointing stage. Genomic DNA was extracted using the sodium dodecyl
152 sulfonate (SDS) method (Song et al. 1994). The two parents and RIL population (N = 186
153 genotypes) were genotyped with the 55K iSelect SNP array. All genotyping using the SNP
154 array was carried out in CapitalBio Corporation (Beijing; http://www.capitalbio.com). The
155 polyploid version of Affymetrix Genotyping Console™ (GTC) software was used for SNP
156 genotype calling and allele gathering. Low quality SNPs with >10% missing values and
157 major allele frequencies (MAF) >95% were removed. The polymorphic SNPs within QTL
158 peaks were converted to KASP markers (Table S1). The procedures used for SNP conversion
159 to KASP markers and selective KASP assaying described in Ramirez-Gonzalez et al. (2015)
160 and Wu et al. (2017b).
161 Linkage groups construction and QTL mapping. The chi-squared test for goodness of
162 fit was used to test the segregation of SNP markers (P > 0.001). Markers significantly
163 different from a one-locus segregation ratio were excluded. Redundant, co-segregating
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164 markers were first binned using the “BIN” function in the software QTL IciMapping V4.1
165 (Meng et al. 2015; Wang 2009). Then one marker was randomly selected in case markers had
166 the samegenotype in each bin. Linkage groups construction were based on the filtered
167 markers using the “MAP” function in QTL IciMapping V4.1, and Mapchart V2.3 was used to
168 draw the maps (Voorrips 2002). The identity positions of linkage groups were determined
169 according to the wheat 660K integrated maps (Cui et al. 2017).
170 The average values of RILs in each environment and BLUP values were used for QTL
171 detection. Inclusive composite interval mapping of additive tool (ICIM-ADD) in IciMapping
172 V 4.1 and composite interval mapping (CIM) in Windows QTL Cartographer V2.5 were
173 carried out to detect QTL. When the LOD score was greater than the calculated threshold
174 value (LOD = 2.5-4), the corresponding QTL was considered significant. Because a single
175 QTL peak has minor differences among LOD contours across different environments, QTL
176 with either overlapping confidence intervals or sharing flanking markers using different
177 programs were considered to be identical. The proportion of phenotypic variance explained
178 (PVE) was used to estimate the effect of an individual QTL.
179 KASP marker validation. SNP markers closely linked to stripe rust resistance QTL were
180 converted to KASP markers following the previous described method (Ramirez-Gonzalez et
181 al. 2015 and Wu et al. 2017b). The KASP markers were used to genotype a panel of 361
182 wheat lines, which were evaluated for reaction to stripe rust using the methods described in
183 Zeng et al. (2014), were used for marker validation. Univariate analysis of variance was used
184 to analyze marker-trait associations in Office Excel 2016 (Microsoft, Redmound, WA).
185 RESULTS
186 Stripe rust responses of the parents and RILs. In the seeding experiments with Pst races
187 CYR32, CYR33, and CYR34, P9936 was susceptible (IT 8–9) but highly resistant (IT 1–2)
188 in the adult-plant stage tests. The susceptible parent MX169 was susceptible (IT 8–9) at both
189 seeding and adult-plant growth stages. In the five field environments, stripe rust developed to
190 adequate levels for scoring high quality phenotypic data. MX169 was susceptible (IT 9, DS ≥
191 90) and P9936 was resistant (IT 1–2, DS ≤ 20). Mean DS for the 186 RILs were 49, 27, and
192 35.0% at Yangling, Jiangyou, and Tianshui in 2017, respectively, and 40.6, 42.0, and 38.9%
193 at the same locations in 2018 (Table 1). The DS distributions showed continuous variation in
194 all environments (Fig. S1), indicating the quantitative inheritance of APR. Pearson’s
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195 correlation coefficients of pairwise comparison for the six environments ranged from 0.44 to
196 0.78 (P <0.01) (Table 2). P values in the ANOVA of RIL scores showed significant
197 phenotypic variation in DS among lines, environments, and line × environment interactions
198 whereas there was no significant variation between the replications. Lines were the main
199 source of phenotypic variation. Heritabilities were high, ranging from 0.73 to 0.96 for all
200 data sets (Table 1). Expression of APR was uniform across all environments.
201 Genome-wide SNP scan and linkage map. To test for the presence of resistance QTL, we
202 generated a linkage map for the MX169 × P9936population. The 55K SNP array was used to
203 genoype all 186 RIL and parents. A total of 10,133 SNP loci were polymorphic between the
204 parents, from which 1,840 were removed due to >10% missing data or distorted segregation.
205 The remaining 8,293 SNP fell into 2,075 bins. After removing redundantSNPs, 2075 SNPs
206 were used to construct a linkage map of 28 linkage groups, representing all 21 chromosomes.
207 Fewer SNP markers were present in the D genome (186 markers) than in the A (974 markers)
208 and B (915 markers) genomes. Only 22 markers were ungrouped. Chromosomes 2B, 2D 3D,
209 4B, 5D, 6A, and 6D were represented by two or more linkage groups, whereas the remaining
210 14 chromsomes each was represented by a single linkage group. The entire linkage map was
211 3,593.37 cM, with an average bin interval of 1.73 cM (Table 3). Only linkage groups with
212 significant stripe rust resistance QTL are presented below.
213 QTL analysis. Using ICIM and CIM, four QTL for stripe rust resistance were detected in
214 chromosome arms 3BS, 3BL, 3DS, and 7BL; all were derived from P9936. QTL
215 QYrblu.nwafu-3BS and QYrblu.nwafu-7BL were identified in all environments and in the
216 BLUP analysis, and were therefore considered stable. QYrblu.nwafu-3BS flanked by SNP
217 markers AX-111487728 and AX-109919508 was located in a 0.6 cM overlapping confidence
218 interval and explained average values of 13.0% and 20.4% of the phenotypic variation in CIM
219 and ICIM, respectively (Table 4; Fig. 1A). The largest effect QTL, QYrblu.nwafu-7BL,
220 explained an average 38.9% (ICIM) and 36.0% (CIM) of the phenotypic variation across
221 environments (Table 4, Fig. 1B). This QTL was mapped to a 9.04 cM region and the
222 overlapping confidence interval was a 1.0 cM region spanned by markers AX-108819274 and
223 AX-110470708 (Fig. 1B). Mean DS for RILs carrying QYrblu.nwafu-7BL ranged from 1 to
224 65%, compared to 10 to 100% for RIL lacking the allele (Fig. 2A, B). Two QTL with small
225 effects (less than 10%) were each identified in only one location and were considered unstable
226 or environmentally dependent. QYrblu.nwafu-3BL was linked to markers AX-109329844 and
227 AX-111030999, and QYrblu.nwafu-3DS was linked to markers AX-109280169 and
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228 AX-110686188 (Table 4).
229 To determine interaction effects, the QTL were used to classify the RILs into different
230 genotypic groups according to the presence of the linked markers (Table S2). As
231 QYrblu.nwafu-7BL was the largest effect QTL, it was individually combined with the other
232 QTL. Significant additive effects were detected. Lines with higher numbers of resistance
233 QTL had lower average DS. Lines with all four had low DS values ranging from 13.9 to 19.0%
234 across all environments, similar to the resistant parent (Fig. 2C; Table S2). RILs with no QTL
235 were highly susceptible (DS >80%) similar to the susceptible parent. The phenotypic
236 variance explained in the BLUP analysis accounted for 45.3 to 69.5% of the total phenotypic
237 variation.
238 Location of the major QTL in chromosome 7BL. The QTL on 7BL was mapped to an
239 overlapping confidence interval 1.0 cM between SNP markers AX-108819274 and
240 AX-110470708 based on CIM and ICIM, which corresponded to a 2.0 Mb physical interval
241 (Fig. 3A, B; Table A in S3). Compared with the positions of expressed sequence tags (ESTs)
242 in the 7B deletion bin map, deletion bin 7BL5-0.86-1.00 likely contained QYrblu.nwafu-7BL
243 (https://wheat.pw.usda.gov/cgibin/westsql/maplocus.cgi; Fig. 3C, Table B in S3).
244 To deduce the position of QYrblu.nwafu-7BL relative to previously mapped stripe rust
245 resistance genes/QTL on chromosome 7BL, two integrated genetic maps were used to make
246 comparisons (Maccaferri et al. 2015; Cui et al. 2017; Fa Cui, pers. comm.). Based on the
247 QTL of flanking markers, all genes/QTL were arranged in the integrated genetic map. Most
248 were concentrated in two intervals, 224.0 to 282.4 cM and 328.5 to 402.3 cM (total map
249 length 455.8 cM), and QYrblu.nwafu-7BL spanned an interval from 341.4 to 344.2 cM (Fig.
250 3D).
251 Development and validation user-friendly markers for QYrblu.nwafu-7BL. To
252 determine the robustness of identified markers for QYrblu.nwafu-7BL, the flanking markers
253 were converted into KASP markers and assayed in both the RIL population and the diversity
254 panel (Table S4; Fig. 2A). The genotyping assays generated two groups for each marker,
255 enabled by bi-allelic scoring of each SNP (Fig. 4A). Single-marker analyses showed that
256 AX-109317388 and AX-108987034 were significantly associated with the DS scores of the
257 wheat panel (P < 0.001) (Fig. 4B). Several CIMMYT wheat lines known to possess QTL on
258 7BL carried ‘T–A’ haplotypes at both SNP loci (Table S4). In fact the ‘T–A’ haplotype at
259 both loci in wheat accessions were associated with resistance (Table S4), suggesting that
260 selection on basis of both markers was more accurate than when based on either marker alone.
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261 Pedigrees indicated that most of the accessions with the same haplotypes as P9936 were
262 CIMMYT-derived lines (CIMMYT, 1983). Thus, these KASP markers could be used for
263 MAS in wheat breeding programs.
264 DISCUSSION
265 Line P9936 has shown resistance for ten years to stripe rust in China despite exposure to a
266 changing and variable Pst population in experimental fields. The APR in P9936 was
267 attributable to two major and two minor QTL with additive effects, and was consistently
268 identified by both CIM and ICIM analyses across all six environments.
269 QYrblu.nwafu-7BL, located in chromosome 7BL with flanking markers AX-108819274
270 and AX-110470708, probably has the highest potential value for use in breeding. First,
271 QYrblu.nwafu-7BL has the largest effect across environments in reducing DS, explaining up
272 to 38.9 (ICIM) and 36% (CIM) of the phenotypic variation. Second, this gene is probably not
273 common in current Chinese wheat cultivars and, therefore, represents a new source of
274 resistance for local wheat breeders. Third, QYrblu.nwafu-7BL acted additively with the other
275 identified QTL to enhance resistance, and gene combinations likely extend the potential
276 durability of resistance or at least prevent extreme susceptibility in the event of a single gene
277 failure.
278 Previous studies have identified several permanently and tentatively designated Yr genes
279 on chromosome 7B, including Yr39 (Lin and Chen 2007), Yr52 (Ren et al. 2012a), Yr59
280 (Zhou et al. 2014), Yr67 (YrC591 and YrSuj) (Lan et al. 2015; Li et al. 2009; Xu et al. 2014),
281 Yr79 (Feng et al. 2018), and YrZH84 (Li et al. 2006). In addition, there are several QTL, such
282 as QYrtir.nsw-7B in Tiritea (Imtiaz et al. 2004), QYratt.csiro-7BL in Attila (Rosewarne et al.
283 2008), QYrkuk.sun-7BL in Kukri (Bariana et al. 2010), QYrpas.cim-7BL in Pastor
284 (Rosewarne et al. 2012), QYrsha.caas-7BL.1 and QYrsha.caas-7BL.2 (Ren et al. 2012b),
285 QYrstr-7BL in Strongfield (Singh et al. 2013), QYrchi.cim-7BL in Chilero (Ponce-Molina et
286 al. 2018), and two QTL from a genome-wide associate study (GWAS) (Zegeye et al. 2014).
287 On basis of the integrated genetic map, QYrblu.nwafu-7BL was located at a similar region to
288 QYratt.csiro-7BL, QYrchi.cim-7BL, and QYrpas.cim-7BL. Attila shares the same alleles,
289 whereas Pastor and Chilero sources share some common alleles, when assayed with KASP
290 markers used in the present study. Rosewarne et al. (2008) found that both Pastor and Attila
291 contain closely linked QTL for reducing stripe rust and leaf rust severity in the terminal end
292 of chromosome 7BL. Ponce-Molina et al. (2018) also mapped QYrchi.cim-7BL in the same
293 chromosome location as the QTL in Attila, closely linked to SSR marker Xgwm344. Pedigree
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294 analyses reveal that Attila has a close relationship to Pastor through the common parent Seri
295 M82. Veery is one of parents of Chilero, which shares a common pedigree with Seri M82
296 (Kavkaz/Buho//Kalyansona/Bluebird); P9936 is related to Kalyansona/Bluebird,
297 “Kalyansona/Bluebird” is a well-known carrier with slow-rusting loci (Rosewarne et al.
298 2012). The QTL on 7BL in all the above of varieties may be derived from this source.
299 However, further studies are required to dissect the chromosomal region and confirm the
300 genetic relationships among the stripe rust resistance genes or QTL on 7BL.
301 The second major QTL QYrblu.nwafu-3BS effective in all environments likely corresponds
302 to Sr2/Yr30. In addition to the mapping results, the Sr2 marker csSr2 was present in P9936,
303 which also displayed the dark glumes characteristic of pseudo-black chaff associated with
304 Sr2 at the late grain-filling stage. Sr2/Yr30 is common in the CIMMYT wheat lines
305 (Dedryver et al. 2009; William et al. 2006; Wu et al. 2017a; Yang et al. 2013). The other
306 minor QTL QYrblu.nwafu-3DS and QYrblu.nwafu-3BL were environmentally dependent and
307 explained under 10% of the phenotypic variation. These latter two QTL have limited value, if
308 exploited individually, but potential interactive effects require further assessment. In the
309 present study, these QTL enhanced the level of resistance conferred by the two major QTL,
310 since RILs combining QTL showed higher resistance than those with one QTL (Table S2).
311 Nevertheless when all QTL were combined only 45.3 to 69.5% of the total phenotype
312 variation was explained.
313 . P9936 also carried the allele of Yr18 in our previous molecular detection assay (Han et al.
314 2012). The slow-rusting gene Yr18 confers partial, durable resistance to stripe rust for more
315 than 50 years (Krattingeret al. 2009). The commonly used Yr18/Lr34 markers, including the
316 positive alleles of markers cssfr3 and cssfr5, were present in both MX169 (Zeng et al. 2010)
317 and P9936 (Han et al. 2012) as well as the RIL population. If these markers directly indicated
318 the presence of Yr18/Lr34, then MX169 should not have been susceptible, and no RIL should
319 have been fully susceptible. The first possibility is that the frequently used markers for
320 Yr18/Lr34 are not ‘perfect’ and that multiple haplotypes are present, only some of which are
321 associated with resistance. On the other hand the presence of the markers and stripe rust
322 resistance attributable to Yr18/Lr34 in MX169 has been obtained in other RIL populations
323 involving MX169 as a parent (Yuan et al. 2018). The diagnostic marker csLV34 for
324 Yr18/Lr34 is present in many highly susceptible varieties (Yang et al. 2008; Zeng et al. 2010;
325 Wu et al. 2015). Wu et al. (2015) provided preliminary evidence for the presence of a
326 suppressor of Yr18, but no further supporting information has emerged. To date, three Yr18
327 haplotypes were identified and named as +Lr34/Yr18, -Lr34/Yr18, and Yr18-Jagger. Based
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328 on the haplotypes of Yr18, gene-specific PCR markers cssfr3 and cssfr5 (Lagudah et al.
329 2009), KASP markers Yr18_TCCIND, and Yr18_jagge (Rasheed et al. 2016) were developed.
330 Given that there is evidence that Yr18 in MX169 is suppressed, we can consider the alternate
331 possibilities of whether Yr18 in P9936 is, or is not, suppressed. If P9936 does not carry a
332 suppressor, segregation at the suppressor locus would be detected as resistance associated
333 with another locus or chromosome. If this was true one of the major resistance genes reported
334 here should be the suppressor, but this seems unlikely. If P9936 also carried the suppressor
335 then neither the suppressor nor Yr18 would be detected.
336 SNP markers tightly linked to QYrblu.nwafu-7BL were converted to the high-throughput,
337 cost-effective SNP genotyping format known as KASP for use by geneticists and breeders.
338 Validation with a large number of diverse wheat varieties revealed that the markers were
339 significantly related to stripe rust resistance, but some accessions with single markers were
340 susceptible. However, when these two markers were used together, all aceesions carrying the
341 ‘T–A’ haplotype at the identified SNP loci displayed resistance, indicating that selection for
342 the resistance QTL will be better based on the favourable haplotype, rather than individual
343 markers.
344 ACKNOWLEDGMENTS
345 The authors are grateful to Prof. R. A. McIntosh, Plant Breeding Institute, University of
346 Sydney, for review of this manuscript. This study was financially supported by the
347 Genetically Modified Organisms Breeding Major Project (2016ZX08002001), the National
348 Key Research and Development Program of China (Grant No. 2016YFE0108600), the
349 Earmarked Fund for Modern Agro-industry Technology Research System (No.
350 CARS-3-1-11), and the National Science Foundation for Young Scientists of China (Grant
351 31701421).
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562 Table 1. Summary of adult responses to stripe rust in the Mingxian 169 (MX169) × P9936
563 recombinant inbred line (RIL) population in 2017 and 2018 at Yangling (YL), Jiangyou (JY),
564 Tianshui (TS) and BLUP
Disease severity (%)
Environment
MX 169
P9936
Mean
Range
σ2 g
SD
h2 b
2017YL
100.0
15.0
49.0
3-100
891.8
29.9
0.96
2018YL
100.0
10.0
40.6
1-95
1036.9
32.2
-a
2017JY
95.0
7.5
27.0
0.5-80
672.1
25.9
0.94
2018JY
97.5
10.0
31.8
0-90
613.9
24.8
0.92
2017TS
95.0
10.0
42.0
5-97
838.8
29.0
0.89
2018TS
95.0
12.5
43.1
5-97.5
819.0
28.6
0.73
BLUP
100.0
10.0
38.9
7.7-86
478.1
21.9
-
565 a Not calculated.
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
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584 Table 2. Correlation coefficients (r) of mean disease severities (DS) for the MX169 × P9936
585 RIL population across six environments
Environment
2017YL
2017JY
2017TS
2018YL
2018JY
2018TS
2017YLa
1.00
-
-
-
-
-
2017JY
0.52***b
1.0000
-
-
-
2017TS
0.63***
0.78***
1.0000
-
-
-
2018YL
0.53***
0.46***
0.50***
1.0000
-
-
2018JY
0.63***
0.51***
0.49***
0.66***
1.0000
-
2018TS
0.65***
0.44***
0.46***
0.68***
0.72***
1.0000
586 a YL, TS and JY, Yangling Tianshui and Jiangyou during the 2016-2017 and 2017-2018
587 cropping seasons, respectively.
588 b *** All r values were significant at P = 0.001.
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
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604 Table 3. Single-nucleotide polymorphism (SNP) marker statistics for distribution and density
605 on 21 wheat chromosomes derived from cross between MX169 and P9936
Chromosome
Linkage
group
No. of
markers
% of all
markers
No. of
bins
Length
(cM)
Average
markers/bin
Marker
densitya
1A
G1
587
6.03
184
216.59
1.18
2.71
1B
G2
363
4.13
105
148.55
1.41
2.44
1D
G3
54
2.55
15
91.77
6.12
0.59
2A
G4
403
4.85
80
174.22
2.18
2.31
2B
G5+G6b
206
4.87
91
175.03
1.92
1.18
2D
G7+G8
126
3.68
33
132.32
4.01
0.95
3A
G9
641
7.44
177
267.34
1.51
2.40
3B
G10
1022
8.10
197
291.16
1.48
3.51
3D
G11+G12
37
1.55
12
55.57
4.63
0.67
4A
G13
240
5.23
101
187.92
1.86
1.28
4BS/6AS
G14+ G15
116
3.14
54
112.84
2.09
1.03
4D
G16
34
2.06
24
74.17
3.09
0.46
5A
G17
855
9.26
174
332.61
1.91
2.57
5B
G18
618
6.93
169
249.15
1.47
2.48
5D
G19+G20
72
1.67
30
60.16
2.01
1.20
4BL/6AL
G21+ G22
123
2.88
49
103.46
2.11
1.19
6B
G23
769
3.83
120
137.62
1.15
5.59
6D
G24+G25
76
2.46
50
88.23
1.76
0.86
7A
G26
892
8.71
209
312.88
1.50
2.85
7B
G27
958
6.95
179
249.88
1.40
3.83
7D
G28
33
3.67
22
131.9
6.00
0.25
A genome
8
3741
44.39
974
1595.02
1.64
2.35
B genome
9
4052
37.97
915
1364.23
1.49
2.97
D genome
11
432
17.65
186
634.12
3.41
0.68
Total
28
8225
2075
3593.37
1.73
2.29
606 a Marker density namely locus/cM was calculated by dividing their added number of unique
607 loci by their added genetic length.
608 b Two separated linkage groups.
609
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610 Table 4. Summary of stripe rust resistance QTL detected in the MX169 × P9936 RIL
611 population across six environments using inclusive composite interval mapping (ICIM) and
612 composite interval mapping (CIM)
613
614 aYL, TS, and JY, Yangling, Tianshui and Jiangyou, respectively.
615 b LOD, logarithm of odds score.
616 c PVE, percentages of the phenotypic variance explained by individual QTL.
617 d R2, percentages of the phenotypic variance explained by individual QTL.
618
619
620
621
622
QTL
Environmenta
Marker interval
ICIM
Closest
marker
CIM
LODb
PVEc
LOD
R2d
2017JY-DS
AX-109280169
AX-110686188
2.6
3.3
AX-110686188
2.5
0.03
2017TS-DS
AX-109280169
AX-110686188
4.3
6.1
AX-109280169
3.4
0.05
2018TS-DS
AX-109280169
AX-110686188
5.1
7.5
AX-109280169
4.0
0.08
QYr.nwaf
u-3DS
DS-BLUP
AX-109280169
AX-110686188
3.2
4.2
AX-109280169
3.2
0.04
2017JY-DS
AX-109329844
AX-111030999
2.6
6.2
AX-111030999
3.5
0.06
2018JY-DS
AX-109329844
AX-111030999
2.4
5.5
AX-111030999
4.1
0.08
QYr.nwaf
u-3BL
DS-BLUP
AX-109329844
AX-111030999
2.9
6.0
AX-111030999
3.7
0.06
2017YL-DS
AX-111487728
AX-109919508
14.2
27.5
AX-111487728
16.8
0.23
2018YL-DS
AX-109919508
AX-111107404
7.4
17.1
AX-109919508
8.6
0.10
2017JY-DS
AX-111487728
AX-109919508
3.9
9.2
AX-109919508
3.2
0.04
2018JY-DS
AX-111487728
AX-109919508
4.5
9.6
AX-111487728
4.3
0.06
2017TS-DS
AX-111487728
AX-109919508
11.1
24.3
AX-111487728
13.7
0.20
2018TS-DS
AX-108849053
AX-94545746
2.1
5.5
AX-94545746
3.2
0.05
QYr.nwaf
u-3BS
DS-BLUP
AX-111487728
AX-109919508
9.3
20.4
AX-111487728
11.4
0.13
2017YL-DS
AX-108819274
AX-110470708
9.2
20.7
AX-110470708
9.8
0.22
2018YL-DS
AX-108819274
AX-110470708
18.2
35.9
AX-108806378
16.5
0.31
2017JY-DS
AX-108818685
AX-111703868
16.5
33.3
AX-110611968
19.7
0.36
2018JY-DS
AX-108819274
AX-110470708
14.2
30.9
AX-110028937
15.6
0.29
2017TS-DS
AX-108819274
AX-110470708
10.7
25.1
AX-110611968
13.1
0.27
2018TS-DS
AX-111510102
AX-109395779
9.7
21.7
AX-110470708
10.3
0.22
QYr.nwaf
u-7BL
DS-BLUP
AX-108819274
AX-110470708
19.1
38.9
AX-110470708
19.2
0.36
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623
624 Figure legends
625 Fig. 1. Graphical display of positions of QTL for stripe rust resistance across all environments.
626 Overlapping confidence intervals of QTL are based on IciMapping 4.1 (upper) and Windows
627 QTL Cartographer 2.5 (lower). Genetic linkage maps of QYrblu.nwafu-3BS (A) and
628 QYrblu.nwafu-7BL (B). Overlapping confidence intervals are shown in red, and markers
629 surrounding the QTL are in bold underlined font.
630 Fig. 2. Location of QYrblu.nwafu-7BL on wheat chromosome 7B. Genetic linkage map of
631 QYrblu.nwafu-7BL on wheat chromosome arm 7BL produced from results from RILs (A).
632 Markers surrounding the QTL are indicated with blue bold font and underlining. (B) The
633 physical map of wheat chromosome 7B according to the Chinese Spring IWGSC RefSeq
634 v1.0 reference genome sequence was constructed using polymorphic markers from selected
635 SNPs. (C) Deletion bin map of wheat chromosome 7BL. (d) Identified QTL (red bar with
636 underlined font and red region on chromosome 7B) in this study and previously mapped Pst
637 resistance genes and QTL (blue bars) were positioned based on integrated genetic maps
638 (Maccaferri et al. 2015; Fa Cui, personal communication). Centromere region is colored
639 black. Confidence intervals of QTL are indicated with blue lines. References [1] Zegeye et al.
640 2014; [2] Ren et al. 2012b; [3] Zegeye et al. 2014; [4] Imtiaz et al. 2004; [5] Bariana et al. 2010;
641 [6] Rosewarne et al. 2012; [7] Singh et al. 2013; [8] Rosewarne et al. 2008; [9]Ponce-Molina et
642 al. 2018; [10] Ren et al. 2012b.
643 Fig. 3. Effects of QYrblu.nwafu-7BL and QTL combinations on stripe rust scores illustrated
644 by mean disease severity scores of RILs from the MX169 × P9936 population at Yangling,
645 Tianshui, Jiangyou, and combined environments (A, B, C). The box plots (quartiles are boxes,
646 medians are continuous lines, means are crosses, whiskers extend to the farthest points that
647 are not outliers, and outliers are black dots) for disease severity associated with the identified
648 QTL and their combination
649 Fig. 4. Genotyping cluster plots for linked SNP markers (A, C), and QTL validation results
650 (B, D), showing the mean disease severities associated with alleles assayed across a set of
651 361 wheat lines. At the AX-109317388 locus, resistant lines carried the ‘T’ allele, and
652 susceptible lines, the ‘C’ allele (B); at the AX-108987034 locus, resistant lines carried the ‘A’
653 allele and the susceptible lines were ‘C’s (D)
654
655
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656 Supplemental Materials
657 Fig. S1. Distribution of mean disease severities for the 186 RILs from MX169 × P9936
658 evaluated at Yangling, Tianshui, and Jiangyou in 2017 (A) and 2018 (B). Values for the
659 parents are indicated by arrows.
660 Table S1. Kompetitive allele apecific PCR (KASP) primers for QYrblu.nwafu-7BL
661 Table S2. Effects of different combinations on number of QTL in combination in the RILs
662 in the Mingxian 169 × P9936 RIL population based on mean DS in six field experiments
663 (Yangling, Tianshui, and Jiangyou during the 2016-2018 cropping seasons)
664 Table A in S3 Dataset. The physical positions of SNP markers in the consensus map
665 Table B in S3 Dataset. The physical positions of ESTs in different chromosomal bins
666 Table S4. Phenotype and SNPs for wheat parental lines used for validation of identified
667 QYrblu.nwafu-7BL.
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Fig. 1
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Fig. 1
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Fig. 2
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Fig. 3
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Fig. 4
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0
10
20
30
40
50
60
70
0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
No. of F7liens
Disease severity
Yangling 2017
Jiangyou 2017
Tianshui 2017
P9936 MX169
0
10
20
30
40
50
60
70
80
0-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100
No. of F8liens
Disease severity
Yangling 2018
Jiangyou 2018
Tianshui 2018
P9936
MX169
A
B
Fig. S1
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