Published March, 2012 O R I G I N A L R ES E A R C H
Association Mapping of Quantitative Trait Loci in Spring Wheat Landraces Conferring Resistance to Bacterial Leaf Streak and Spot Blotch Tika B. Adhikari,* Suraj Gurung, Jana M. Hansen, Eric W. Jackson, and J. Michael Bonman
Abstract Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa (Smith et al.) Bragard et al., and spot blotch (SB), caused by Cochliobolus sativus (S. Ito & Kurib.) Drechs. ex Dastur, are two emerging diseases of wheat (Triticum aestivum L.). To achieve sustainable disease management strategies and reduce yield losses, identifying new genes that confer quantitative resistance would benefit resistance breeding efforts. The main objective of this study was to use association mapping (AM) with 832 polymorphic Diversity Arrays Technology (DArT) markers to identify genomic regions associated with resistance to BLS and SB in 566 spring wheat landraces. From data analysis of this diverse panel of wheat accessions, we discovered five novel genomic regions significantly associated with resistance to BLS on chromosomes 1A, 4A, 4B, 6B, and 7D. Similarly, four genomic regions were found to be associated with resistance to SB on chromosomes 1A, 3B, 7B, and 7D. A high degree of linkage disequilibrium (LD) decayed over short genetic distance in the set of wheat accessions studied, and some of these genomic regions appear to be involved in multiple disease resistance (MDR). These results suggest that the AM approach provides a platform for discovery of resistance conditioned by multiple genes with quantitative effects, which could be validated and deployed in wheat breeding programs.
Published in The Plant Genome 5:1–16. Published 7 Feb. 2012. doi: 10.3835/plantgenome2011.12.0032 © Crop Science Society of America 5585 Guilford Rd., Madison, WI 53711 USA An open-access publication All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. THE PL ANT GENOME
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HEAT (Triticum aestivum L.) is a major crop worldwide. Both abiotic and biotic stresses limit successful wheat production. Among biotic stresses, plant diseases have a negative impact on crop health and are strongly influenced by climate change (Sharma et al., 2007). Bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa (Bragard et al., 1997), is emerging as a potential threat to wheat production in the Upper Midwest of the United States in recent years (Adhikari et al., 2011a) because most commercial wheat varieties grown in the region appeared to be susceptible to BLS (Adhikari et al., 2009). Bacterial leaf streak can cause up to 40% yield loss (Forster and Schaad, 1988). The pathogen is seed borne (Forster and Schaad, 1985, 1988; Fourest et al., 1990) and can disperse long distance via wheat germplasm exchange (Maraite et al., 2007). Spot blotch (SB), caused by Cochliobolus sativus (Ito & Kuribayashi) Drechsler ex Dastur [anamorph: Bipolaris sorokiniana (Sacc.) Shoemaker], is another emerging disease of wheat and barley (Hordeum vulgare L.) (Knight et al., 2010; Duveiller et al., 2005; Zhong
T.B. Adhikari, S. Gurung, and J.M. Hansen, Dep. of Plant Pathology, North Dakota State Univ., NDSU Dep. 7660, P.O. Box 6050, Fargo, ND 58108; E.W. Jackson and J.M. Bonman, USDA-ARS, Small Grains and Potato Germplasm Research Unit, Aberdeen, ID 83210. Current address of S. Gurung: Dep. of Plant Pathology, Univ. of California, Davis, c/o U.S. Agricultural Research Station, 1636 E. Alisal Street, Salinas, CA 93905. Received 28 July 2011. *Corresponding author (
[email protected]). Abbreviations: AM, association mapping; AUDPC, area under disease progress curve; BLS, bacterial leaf streak; DArT, Diversity Arrays Technology; FDR, false discovery rate; GS, growth stage; IR, infection response; LD, linkage disequilibrium; MAF, minor allele frequency; MDR, multiple disease resistance; NDSU, North Dakota State University; NSGC, National Small Grains Collection; PC, principal component; PCA, principal component analysis; PCR, polymerase chain reaction; pFDR, p value determined with the false discovery rate; QTL, quantitative trait loci; RIL, recombinant inbred line; SB, spot blotch; SNP, single nucleotide polymorphism; SSR, simple sequence repeat.
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and Steffenson, 2001) in North America (Ghazvini and Tekauz, 2007, 2008; Windels et al., 1992) and in South Asia (Duveiller and Sharma, 2009). The disease has a significant impact on wheat production (Joshi et al., 2007; Siddique et al., 2006; Sharma et al., 2007) and can cause up to 30% yield loss (Duveiller et al., 2005). Chemical control to manage these diseases (Forster and Schaad, 1985, 1988; Fourest et al., 1990; SharmaPoudyal et al., 2005; Duveiller et al., 2005) is neither economical nor environmentally friendly. Therefore, identifying genes that condition quantitative resistance and developing durably resistant cultivars is the best approach to manage both BLS and SB in wheat. Evaluation of different plant populations at the molecular levels and utilization of such information in varietal improvement programs are imperative to effectively use the genetic resources in wheat breeding programs (Chao et al., 2007; Zhang et al., 2010). Additionally, analysis of genetic diversity, population structure, and linkage disequilibrium (LD) in populations also provides valuable information for association mapping (AM) and marker-assisted breeding (Chao et al., 2007, 2010; Zhang et al., 2010). Wheat is a selfpollinated crop and recombination frequencies of specific genetic regions appear to be low in selective breeding populations undergoing a rapid rate of inbreeding with selfing (Tester and Langridge, 2010; Zhang et al., 2010). Compared to other cereal crops (e.g., barley, maize [Zea mays L.], rice [Oryza sativa L.], and sorghum [Sorghum bicolor (L.) Moench]), wheat has a large genome and therefore it has the poorest marker coverage (Zhang et al., 2010). Importantly, LD estimates in wheat also vary with chromosomes across genome in a population (Breseghello and Sorrells, 2006; Chao et al., 2007). Although linkage mapping has been successfully used to map genes or loci in biparental populations (e.g., recombinant inbred lines [RILs], doubled haploid, or backcrosses), the AM approach could be an efficient strategy to detect quantitative trait loci (QTL) particularly in genetically diverse germplasm or wild relatives (Flint-Garcia et al., 2003). In this approach, no prior information on marker–trait associations is necessary, and multiple loci can be readily identified. Several robust statistical tools and modeling methods such as mixed linear models (Yu et al., 2006; Zhu et al., 2008), Bayesian clustering (Pritchard et al., 2000), principal component analysis (PCA) (Price et al., 2006), and Q+K (terms that abbreviate gross structure based on given number of principle components [Q] and finer structure based on kinship [K]) mixed models (Yu et al., 2006) have been developed and used to enhance our understanding of complex traits in animal and plant genetic systems. These techniques have been utilized to reduce the occurrence of false positive marker–trait associations (Kang et al., 2008; Tommasini et al., 2007) and have been successfully applied to detect QTL associated with disease resistances in barley (Roy et al., 2010) and wheat (Ghavami et al., 2011; Massman et al., 2011). 2
To detect potential QTL using the AM approach, degree of marker polymorphism and genome coverage are important aspects in wheat. Because of chromosome specificity, codominance, high reproducibility, and high polymorphism, simple sequence repeat (SSR) or microsatellite markers have been extensively used in wheat (Huang et al., 2002; Roder et al., 1998). Recently, Diversity Arrays Technology (DArT) (Canberra, Australia; http:// www.diversityarrays.com) markers were developed and used in mapping different traits in wheat and other crops. Diversity Arrays Technology markers are a cost-effective high throughput technology that can detect all types of DNA variations such as single nucleotide polymorphism (SNP), copy number variation, and methylation in plant genomes (Diversity Arrays Technology, 2011). A few wheat cultivars (e.g., Turaco, Alondra, Angostura, Mochis, and Pavon 76) identified in greenhouse inoculations and field conditions were useful sources of resistance to BLS (Duveiller et al., 1993; Forster and Schaad, 1985). Reactions of wheat germplasm to X. translucens pv. undulosa also were found to be similar both in field and in greenhouse conditions (Akhtar and Aslam, 1986). Most recently, several winter accessions resistant to multiple strains of X. translucens pv. undulosa were identified (Adhikari et al., 2011a). However, molecular mapping of genes or loci for resistance to BLS is not yet complete. Resistance to SB in wheat was reported to be both quantitatively and qualitatively inherited. Initially, four QTL conferring resistance to the disease were mapped in the Chinese wheat variety ‘Yangmai 6’ on chromosomes 2AS, 2BS, 5BL, and 6DS (Kumar et al., 2009). Using SSR markers and area under disease progress curve (AUDPC) based on disease severity (%) at adult plant stage, Kumar et al. (2010) identified four QTL for resistance to SB on chromosomes 2AS, 2BS, 5BL and 7DS in the ‘Ning 8201’ × ‘Sonalika’ population (Kumar et al., 2010) and five other QTL on chromosomes 2BS, 2DS, 3BS, 7BS, and 7DS in another population ‘Chirya 3’ × Sonalika. Bacterial leaf streak and SB are caused by different plant pathogen groups: prokaryotic and eukaryotic, respectively. Pathogenesis and mode of nutrient uptake by the two pathogens are distinct. Xanthomonas translucens pv. undulosa enters through the stomata and grows in the parenchyma (Cunfer, 1987). During infection, elongated streaks limited by the veins are developed and later yellow exudates are formed on the surface of streaks (Cunfer, 1987). Infected glumes are discolored or blackened (Broadfoot and Robertson, 1993; Smith et al., 1919). For C. sativus, infection takes place when spores land and appressoria and infection cushions form on the host surface before penetration. Infection proceeds from the epidermis to the cortex and endodermis, resulting in breakdown of these tissues. The fungus produces phytotoxins that can kill host cells (Aggarwal et al., 2011). Subsequently, this necrotrophic pathogen derives nutrients from dead host tissues, which enables pathogen growth and disease development. Therefore, several aspects of THE PL ANT GENOME
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pathogenesis are unique to each disease and could require distinct host defense mechanisms, signal transduction pathways, transcription factors, and genes that confer disease resistance. Available evidence suggested that multiple disease resistance (MDR) genes exist in plants (Wisser et al., 2005, 2011) and that a locus conditions quantitative resistance to multiple diseases (Wisser et al., 2011). Positional cloning of a putative ABC transporter gene in wheat (Krattinger et al., 2009), silencing of a germin-like protein gene family in rice (Manosalva et al., 2009), and multivariate analytical approach for structured AM in maize (Wisser et al., 2011) have verified genes involved in MDR. Both BLS and SB are emerging diseases of wheat in the Upper Midwest of the United States possibly due to climate change. Despite their economic importance, quantitative resistance to these diseases is poorly understood. Wheat landrace accessions from the USDA National Small Grains Collection (NSGC) are a rich source of disease resistance genes (Bonman et al., 2006, 2007). Our previous studies demonstrated that the combined DArT markers and AM strategy were effective for identifying QTL associated with resistance to Phaeosphaeria nodorum (Adhikari et al., 2011b) and Pyrenophora tritici-repentis in wheat (Gurung et al., 2011). These findings have prompted us to test the hypothesis that there also exists naturally occurring allelic variation for resistance to BLS and SB in these wheat landraces representing diverse origin. Ultimately, QTL associated with resistances that map to new loci within the wheat genome would likely represent new resistance sources and would be beneficial in efforts to breed resistance to these two diseases either for cultivar release or use as parents in breeding programs. Association mapping should be a useful tool to identify such loci within the collection materials. The main objective of this study was to discover novel loci for resistance to BLS and SB using AM in a set of 566 spring wheat landraces from the NSGC. Once it is known that novel resistance is present within the set of accessions evaluated, further work will be needed to validate the resistance and transfer it to adapted genetic backgrounds.
MATERIALS AND METHODS Wheat Accessions In total, 566 wheat accessions with spring habit from the NSGC used in previous association analysis for P. nodorum (Adhikari et al., 2011b) and P. tritici-repentis (Gurung et al., 2011) were analyzed in this study. These accessions were classified as landraces and represent diverse geographic origin. To avoid any effects of confounding factors (e.g., growth stages, dual infection of leaf spot pathogens, environmental factors), all experiments were conducted in controlled environments at North Dakota State University (NDSU), Fargo, ND, during the fall of 2009 and the winter of 2010. AD H I K ARI ET AL .: Q UANTITATIVE RESISTAN CE I N SPRI N G WH EAT
Phenotypic Evaluation Planting Wheat Accessions and Experimental Designs For BLS, three seeds of each accession were planted in each of three cones (Stuewe and Sons, Inc., Corvallis, OR). To the soil in each container, 2.5 g of multicote slow release commercial fertilizer (14–14–16 N–P–K; Sungro Horticulture Distribution Inc.) was applied at the time of planting. Wheat breeding line ND495 (Adhikari et al., 2009) and winter wheat cultivar Magnum (Milus et al., 1996) served as susceptible and resistant controls, respectively. Wheat accessions were arranged in a randomized complete block design (RCBD) with three replications. Each treatment consisted of three plants per replication. Each cone was considered as an experimental unit, and the flag leaf of each plant was regarded as a sampling unit. Greenhouse temperatures were maintained at a temperature of 24 to 28°C with a 16 h photoperiod using 400 W Lucalox LU400 sodium vapor lamps (General Electric Co.) to supplement natural light until disease scoring. For SB evaluation, two experiments were conducted in growth chambers at the same time using procedures as described previously (Gurung et al., 2011). Planting and experimental design were the same as described above. Wheat cultivars Sonalika (Sharma et al., 2007) and Chirya 7 (Kumar et al., 2010) served as susceptible and resistant controls, respectively. Each cone was considered an experimental unit and each single plant in a cone was regarded as a sampling unit. Wheat accessions were arranged in a RCBD with three replications. In each experiment, the wheat accessions were fi xed effects and the replications were random effects. Inoculum and Inoculation The highly virulent strain BLS-CS42 (formerly BLSW16) of X. translucens pv. undulosa from Casselton, ND, was used in this study. Inoculum preparation and inoculation procedures were similar to those described previously (Adhikari et al., 2011a). Briefly, the bacterial strain was cultured on peptone sucrose agar (Ou, 1985), suspended in water, and the inoculum density was adjusted to approximately 1 × 107 colony forming units ml–1 using a spectrophotometer. Ten to 15 μL of inoculum was infiltrated into a fully expanded flag leaf using a needleless disposable syringe, which infiltrates without wounding (Milus and Mirlohi, 1994). The infiltrated areas were marked by a permanent marker and the plants were placed in trays of water and left in the greenhouse. Greenhouse temperatures were maintained at 26 to 28°C with 16 h of supplemental light provided by 400 W Lucalox LU400 sodium vapor lamps (General Electric Co.) until disease assessment. An isolate CS45 of C. sativus originally collected from Bhairahawa, Nepal, was imported (USDA-APHIS permit no. P526P-09-02536) to NDSU, Fargo, ND. Inoculum preparation and inoculation procedures were the same as described previously (Mahto et al., 2010, 2011). Briefly, a mycelium plug was placed on V8 potato dextrose agar consisting of 150 mL of V8 juice (Campbell Soup 3
Company), 10 g of potato dextrose (Becton, Dickinson and Company), 10 g of Difco agar, 3 g of calcium carbonate, and 850 mL of sterile distilled water in 10-cm petri dishes. The plates were incubated for 8 to 10 d at 24°C and conidia were suspended in water and the inoculum was adjusted to 3.5 × 103 conidia ml–1 using a hemacytometer. Two-weekold seedlings were spray inoculated until run-off with the suspension and seedlings were immediately incubated in a mist chamber for 24 h at 100% relative humidity at 24°C. Finally, plants were moved to a growth chamber and programmed for a 23/19°C diurnal cycle (day/night) with a 16-h photoperiod.
Phenotypic Data Analysis In the previous study (Adhikari et al., 2011b), we used stringent disease assessment methods such as high bacterial inoculum concentration (107 colony forming unit ml–1) and disease rating scale (0–6 score) under controlled environments. We found that median disease scores of ≤2.0 (i.e., less than 10% water-soaking and chlorosis symptoms on the flag leaf) was a reliable criterion to classify resistant and susceptible wheat accessions. Therefore, the same approach was used here. For BLS, disease reactions within infiltrated areas (0.2 to 0.4 cm2) on the flag leaves were assessed between 7 and 9 d after infiltration using a 0 to 6 rating scale (Milus and Chalkly, 1994), in which 0 equals no visible symptoms, 1 equals chlorosis without watersoaked lesions, 2 equals water soaking less than 10%, 3 equals water soaking 10 to 30%, 4 equals water soaking 31 to 70%, 5 equals water soaking 71 to 100%, and 6 equals water soaking extending beyond the infiltrated area. Median disease scores of 0 to 2 were considered resistant (Tillman et al., 1996) whereas median disease scores of >2 to 6 were regarded susceptible. Although AUDPC based on disease severity (%) has been used to assess resistance to SB at adult plant stage (Kumar et al., 2010), our goal was to evaluate resistance to SB at seedling stage in a controlled environment. For SB, we used the rating scales recommended by Fetch and Steffenson (1999). In this method, infection responses (IRs) on wheat seedlings infected with C. sativus were estimated based on the type (presence of necrosis and chlorosis) and relative size of lesions developed on the second leaves (Fetch and Steffenson, 1999). For phenotypic analysis, two interactions were assessed 9 d after inoculation. Minute to small necrotic lesions (with no or very slight diffuse marginal chlorosis) characteristic of IRs 1, 2, 3, and 4 were considered resistant while medium-sized to large necrotic lesions with a distinct but restricted chlorotic margin or even expanding diffuse chlorosis characteristic of IRs 5, 6, 7, 8, and 9 were considered susceptible to highly susceptible (Fetch and Steffenson, 1999). Nonparametric analysis (Shah and Madden 2004) was performed to estimate median disease scores and relative treatment effects (pij) for BLS and SB using Proc Mixed of SAS (SAS Institute, 2010). Additionally, Bartlett’s χ2 were calculated to test the hypothesis of homogeneity of variances as described in previous work (Gurung et al., 2011). 4
DNA Extraction and Genotyping The same DNA used previously for P. nodorum (Adhikari et al., 2011b) and P. tritici-repentis (Gurung et al., 2011) was tested for AM of resistance to X. translucens pv. undulosa and C. sativus in this study. Deoxyribonucleic acid samples were shipped to Triticarte Pty. Ltd. for DArT genotyping. In total, 2500 DArT markers developed from a PstI and BstNI combination were used to identify QTL in spring wheat landraces as described previously (Akbari et al., 2006). A DArT consensus map was developed using a set of 80 wheat populations (RILs, doubled haploid, and F2) (Akbari et al., 2006; A. Kilian, personal communication, 2011).
Association Mapping Analysis All computer soft ware and data analyses used in this study were similar as described in previous works (Adhikari et al., 2011b; Gurung et al., 2011). Briefly, the JMP Genomics soft ware (SAS Institute, 2010) and the TASSEL program, version 2.1 (Bradbury et al., 2007) were used to analyze the marker properties, LD, principal component (PC) matrix, hierarchical clustering, and Q+K mixed model. From the original DArT marker data set, loci with minor allele frequencies (MAFs) < 0.06 or with more than 0.13 missing genotypes were removed from further analysis as were markers lacking map positions. In total, the 832 polymorphic DArT markers were detected across the wheat accessions. Among these, the 625 had specific chromosomal locations (Dr. A. Kilian, Diversity Arrays Technologies, Canberra, Australia, personal communication, 2011). The MAFs and the proportion of missing genotypes were evaluated for 832 polymorphic markers. Linkage disequilibrium statistics (R2) were calculated using the TASSEL program. For each chromosome, LD values were calculated separately and later were combined across three wheat genomes A, B, and D as described previously (Emebiri et al., 2010). The 95 percentile of unlinked LD estimates was used to derive the critical value of R2 (Breseghello and Sorrells, 2006). The intergenic R2 values were plotted against genetic distance and a curve was fitted to the plot in SAS 9.2 (SAS Institute, 2010). The intrachromosomal R2 values against the genetic distance were plotted, and later a distance at which the critical R2 was determined by plotting a LOESS curve (Neumann et al., 2011). All pairwise marker-to-marker (n = 625) correlation coefficients were calculated and used to perform (Q) PCA and develop a pairwise allele sharing similarity matrix (K). To visualize structure within the 566 wheat accessions, Ward’s clustering method, which employs an agglomerative clustering algorithm (Ward, 1963), was used. Data from the appropriate number of principle components (3) and the allele sharing similarity matrix account for Q and K, respectively, in a linear model to associate numeric DArT genotypes with ordinal phenotypes using residual denominator degrees of freedom. The residual pseudo-likelihood estimation (RSPL) method in PROC GLIMMIX was used with no THE PL ANT GENOME
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Figure 1. Disease reactions of 566 spring wheat landraces to Xanthomonas translucens pv. undulosa, the causal agent of bacterial leaf streak (BLS) (A), and Cochliobolus sativus, which causes spot blotch (SB) (B). For BLS, disease reactions on infiltrated areas (0.2 to 0.4 cm2) on flag leaves were assessed 7 to 9 d after infiltration using a 0 to 6 rating scale (Milus and Chalkly, 1994) in which 0 equals no visible symptoms, 1 equals chlorosis without water-soaked lesions, 2 equals water soaking less than 10%, 3 equals water soaking 10 to 30%, 4 equals water soaking 31 to 70%, 5 equals water soaking 71 to 100%, and 6 equals water soaking extending beyond the infiltrated area. Disease scores of the nine infiltrated areas on flag leaves (one site on each flag leaf) were analyzed. Median disease scores of 0 to 2 were considered resistant (Adhikari et al., 2011a; Tillman et al., 1996) whereas median disease scores of >2 to 6 were regarded as susceptible. Wheat breeding line ND 495 (Adhikari et al., 2009) and winter wheat cultivar Magnum (Milus et al., 1996) served as susceptible and resistant controls, respectively. For SB, infection responses (IRs) on wheat seedlings infected with Cochliobolus sativus were estimated according to the type (presence of necrosis and chlorosis) and relative size of lesions developed on the second leaves (Fetch and Steffenson, 1999). Minute to small necrotic lesions (with no or very slight diffuse marginal chlorosis) characteristic of IRs 1, 2, 3, and 4 were considered resistant while the medium-sized to large necrotic lesions with a distinct but restricted chlorotic margin or even expanding diffuse chlorosis characteristic of IRs 5, 6, 7, 8, and 9 were considered susceptible to highly susceptible (Fetch and Steffenson, 1999) 9 d after inoculation. Wheat cultivars Sonalika (Sharma et al., 2007) and Chirya 7 (Kumar et al., 2010) were used as susceptible and resistant controls, respectively.
constraint of the K matrix covariance parameter because the discrete distribution of the ordinal trait was not normal. Since dominance or additive effects were not assumed, association tests were performed by treating genotypes as categorical variables in ANOVA (dominance model) and as quantitative variables in regression (additive model) analyses. The negative Log10 (NegLog10) conversion was used on all calculated p-values. All of the tests were performed with and without the false discovery rate (FDR) multiple testing correction. Based on the F statistic and numerator and denominator df value, the phenotypic variation explained by a particular marker (R2) was calculated (Edwards et al., 2008).
RESULTS Phenotypic Analysis Artificial infections on wheat accessions under controlled conditions for both diseases were highly reproducible and consistent across replications or experiments, and also were comparable to resistant and susceptible checks. For BLS, wheat accessions were highly significant (df = 565, F-value 11.92, and p ≤ 0.001) and no significant interaction (p ≤ 0.0001) between wheat accessions and replications was observed (data not shown). Since phenotypic data were homogeneous (Bartlett’s χ2 = 11.13 and p = 0.112), AD H I K ARI ET AL .: Q UANTITATIVE RESISTAN CE I N SPRI N G WH EAT
BLS data were combined and overall means were used for AM to identify putative genomic regions associated with. For SB, no interaction was found between two experiments (Bartlett’s χ2 = 4.28 and p ≤ 0.05; data not shown). Therefore, data from both experiments were pooled, and overall means were used in AM study. Nearly 31.9 and 25.0% of the accessions were resistant to BLS and SB, respectively (Fig. 1A). Median disease scores of resistant and susceptible checks to BLS were 1 and 4, respectively (Supplemental Table S1). Among the BLS-resistant accessions, 43 accessions had disease scores between 0 and 1 while 138 accessions had disease scores between 1.1 and 2 (Fig. 1A). Median disease scores of resistant and susceptible checks to SB were 3, and 8, respectively (Supplemental Table S2). Of the SB-resistant accessions identified, four of the resistant accessions had mean disease score less than 3 and the remaining resistant accessions had mean disease scores between 3.1 and 4 (Fig. 1B).
Phenotypic Analysis Based on Geographic Origin Significant differences were observed for the proportion of resistant accessions when classified by geographic origin (Table 1). The percent of BLS resistant accessions from Africa was significantly higher than for those originating from Asia. In contrast, SB resistance was more frequent 5
Table 1. Disease reactions of 566 spring wheat landraces of diverse geographic origin to bacterial leaf streak (BLS) and spot blotch (SB) in the greenhouse in North Dakota. Region The Americas Europe Asia Africa Total§
n 38 63 390 72 566
No. of resistant Percent No. of resistant Percent resistant‡ for SB resistant to BLS† 12 31.6 ab 22 57.9 b 27 42.9 ab 17 27.0 a 105 26.9 a 82 21.0 a 36 50.7 b 19 26.4 a 180 140
†
For BLS, disease reactions on infiltrated areas (0.2 to 0.4 cm2) on flag leaves were assessed 7 to 9 d after infiltration using a 0 to 6 rating scale (Milus and Chalkly, 1994). Disease scores of 0 to 2 (or less than 10% water soaking within the infiltrated areas) were considered resistant (Tillman et al., 1996) whereas disease scores of >2 to 6 (or greater than 10% water soaking) were regarded susceptible. For SB, second leaves were rated 9 d postinoculation using 1 to 9 disease rating scale (Fetch and Steffenson, 1999), in which disease scores ≤4 were considered resistant and those >4 were considered susceptible. ‡ Percentages followed by the same letter not significantly different by nonoverlap of the 95% binomial confidence interval. § Two accessions from the former Soviet Union and one accession of unknown origin included in total.
in accessions from the Americas versus Asia and Africa. Some countries bordering the Mediterranean showed a high percentage of BLS-resistant accessions. For example, collectively 58.7% of the accessions (44 of 75) from Tunisia (9 of 9), Libya (1 of 1), Syria (4 of 5), Macedonia (3 of 4), Egypt (3 of 5), Greece (7 of 12), Israel (2 of 4), and Turkey (12 of 29) were resistant (Supplemental Table S1). Among the accessions from the Americas, all entries from Peru (n = 6), Guatemala (n = 3), and Honduras (n = 2) were resistant to SB (Supplemental Table S2).
Marker Statistics and Linkage Disequilibrium Among 832 polymorphic DArT markers, 85 with MAFs of 0.06 and less than 0.13 missing genotypic data were used in the analysis. The majority of DArT markers were distributed across wheat A and B genomes (39.7 and 38.1%, respectively) while the D genome had the fewest (22.2%). The highest number of DArT markers was distributed on chromosome 6A followed by chromosomes 1A, 3B, and 7D (Fig. 2). The average number of DArT markers per chromosome was 54.5; however, the distribution was uneven with only one marker present on chromosomes 5A and 5D and no markers present on chromosome 4D (Fig. 2). A critical value for significance that was also considered as an upper limit was fixed at R2 = 0.049. The genomewide R2 estimate was found to decline rapidly to 0.2 within 5 cM of genetic distance during the LD analysis (data not shown). Specifically, LD decayed rapidly (≤2 cM) on 9 of the 17 chromosomes having more than one unique position while chromosomes 1D, 6B, and 6D had the highest LD when more than 12 unique DArT positions were used. Only three unique DArT positions (11, 37.5, and 151.8 cM) were retained for the analysis on chromosome 3D although it had the highest extent of LD (18 cM).
Population Structure and Relationship Matrix Three PCs accounted for 59.7% of the genetic variation in 566 spring wheat landraces. The first PC explained 40.5% while the second and third PC explained 10.5 and 8.7% of the variation, respectively (Fig. 3A). Structure shown by the allele sharing similarity matrix was in line with
Figure 2. Distribution of 625 Diversity Arrays Technology (DArT) markers across wheat chromosomes. Chromosomal locations and positions of DArT markers were based on information from Diversity Arrays Technology PTL (Canberra, Australia).
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Figure 3. Population structure of 554 spring wheat accessions analyzed based on principal component (PC) (A) and allele sharing similarity (B) analyses. Three PCs were used in the analysis and the overall graph showed two dimension comparisons using PC1 in the first row and column, PC2 in the second row and column, and PC3 in the third row and column. Hierarchical clustering was performed using Ward’s method (Ward, 1963). Heat plots were developed based on allele sharing coefficients. Heat plots refer to pairwise marker-to-marker correlation between two wheat accessions. Red color within the heat plot indicates the highest proportion of shared alleles while blue color indicates the lowest. Dot and cluster colors are consistent between figures. Twelve clusters (subpopulations) were grouped by different colors in the entire population. Value in each cluster represented cluster number or subpopulation. Number of wheat accessions identified in each cluster can be visualized in Supplemental Table S3. Prin, principle component.
Table 2. Bacterial leaf streak (BLS) and spot blotch (SB) phenotypic variation explained by significant Diversity Array Technology (DArT) loci based on association analysis in a set spring landraces from the USDA National Small Grains Collection. Disease BLS
SB
DArT allele Chromosome† wPt-4886 1A wPt-665259 wPt-671483 wPt-2780 4A wPt-8292 4B wPt-663764 6B wPt-5674 7D wPt-730148 1A wPt-668214 wPt-1159 3B wPt-5769 wPt-2838 7B wPt-664459 7D
Position† 60.7 75.2 75.2 90.6 110.8 31.7 174.2 58.0 58.0 53.2 86.1 223.5 175.1
p-value‡ R2 (%)§ 2.29* 1.4 2.51** 1.6 2.56** 1.6 2.37* 1.5 2.39* 1.5 2.59*** 1.6 3.82*** 2.6 3.08*** 2.1 2.51*** 1.7 2.40*** 1.6 3.12*** 2.2 3.21*** 2.2 2.37* 1.5
*Significant at the 0.05 probability level. **Significant at the 0.01 probability level. ***Significant at the 0.001 probability level. † Chromosomal position of the significantly associated DArT markers based on WheatDArTmaps version (Diversity Arrays Technology, 2011). ‡ The p-value adjusted after multiple tests (experimentwise p value). The significance was assessed using 10,000 permutations (Roy et al., 2010). § Phenotypic variation explained by the markers and not including the other items in the model.
the three PCs during PCA (Fig. 3A; Supplemental Table S3) and the three main groups corresponded roughly to geographic origin with most of the Eastern Africa, Eastern Asia, South America, and Southern Europe accessions grouping in one cluster, most of the Western Europe accessions in a second cluster, and most of the South-Central Asia accessions in a third cluster. Cluster analysis (Ward, 1963) using the allele sharing similarity matrix and the three PCs (data not shown) showed 12 subpopulations or clusters in the entire population (Fig. 3B; Supplemental Table S3). Cluster 12 contained the greatest number of accessions (n = 110) followed by cluster 1 (n = 68) and cluster 5 (n = 64), respectively. Clusters 11 (n = 16) and cluster 8 (n = 20) had the fewest accessions while the number of accessions in the remaining clusters ranged from 26 to 63 (Supplemental Table S3). The results based on pairwise allele sharing, as displayed in the heat map (Fig. 3B), showed a large portion of accessions in clusters 9, 10, and 11 shared similar alleles. Accessions in these clusters also shared about 60% of alleles with those in clusters 5, 6, and 7 (Fig. 3B).
Association Analysis of QTL Associated with Resistance to Bacterial Leaf Streak Putative genomic regions conferring resistance to BLS were detected on chromosomes 1A, 4A, 4B, 6B, and 7D after correcting for multiple testing using the p value determined with the false discovery rate (pFDR) criterion. One distinct genomic region on chromosome 1A 8
spanning the interval of 60.7 to 75.2 cM was found significantly associated with three DArT markers (Table 2; Fig. 4A). Among the DArT markers, two markers (wPt-665259 and wPt-671483) were co-segregating and phenotypic variations ranged from 1.4 to 1.6% (Table 2). The other four DArT markers (wPt-2780, wPt-8292, wPt-663764, and wPt-5674) on chromosomes 4A, 4B, 6B, and 7D, respectively, also were found significantly (p < 0.01 or p < 0.05) associated with resistance and explained 1.5 to 2.6% of the phenotypic variation (Table 2; Fig. 4C through 4G).
Association Analysis of QTL Associated with Resistance to Spot Blotch Putative genomic regions conferring resistance to SB were detected on chromosomes 1A, 3B, 7B, and 7D after correcting for multiple testing using the pFDR criterion. One candidate genomic region on chromosome 1A at 58 cM was found significantly (p < 0.01 and p < 0.001) associated with two co-segregating DArT markers, wPt-730148 and wPt-668214 (Fig. 4A), and phenotypic variations ranged from 1.7 to 2.1% (Table 2). In addition, markers wPt-1159 and wPt-5769 on chromosome 3B and wPt-2838 and wPt664459 on chromosome 7B and 7D were significantly associated with resistance to SB (Table 2; Fig. 4F and 4G).
DISCUSSION Although traditional breeding strategies have significantly contributed to crop improvement, there has been slow progress in developing wheat cultivars resistant to BLS and SB (Sharma et al., 2007; Duveiller et al., 1993) possibly due to resistance being complex and polygenic (Duveiller, 1990; Duveiller et al., 1993). Other confounding problems for assessing resistance to BLS and SB are the influence of environmental factors, relying on natural infection, and the effects of multiple leaf spot pathogen infections on the expression of resistance under field conditions (Duveiller et al., 2005; Sharma and Duveiller, 2007). In this study, the combination of highly reproducible phenotyping data obtained from artificial infections under the controlled conditions, DArT genotyping technology, and the AM approach were applied to identify QTL responsible for BLS and SB, the two emerging bacterial and fungal diseases of wheat, respectively. Considering population structure and FDR, five and four genomic regions were found significantly associated with resistance to BLS and SB, respectively. Linkage disequilibrium decayed over short distance in the panel of wheat accessions analyzed and some of the novel genomic regions found to be involved in resistance to the BLS and SB isolate used. Sources of resistance to X. translucens pv. undulosa were previously identified in wheat cultivars Turaco, Alondra, Angostura, Mochis, and Pavon 76 (Duveiller et al., 1993; Forster and Schaad, 1985). However, no further efforts were made to develop mapping populations from these resistant cultivars and to map the resistance QTL or genes on wheat chromosomes. The QTL responsible for resistance to BLS identified in this study are novel and the first to be associated with specific wheat THE PL ANT GENOME
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Figure 4. Association mapping of resistance to bacterial leaf streak (BLS), caused by Xanthomonas translucens pv. undulosa, and spot blotch (SB), caused by Cochliobolus sativus. Consensus maps are based on information from Diversity Arrays Technology (DArT) PTL (Canberra, Australia). On the right, values are genetic distance in centimorgans (cM). On the left, DArT markers (bold) are significantly associated with resistance to BLS (blue color) and SB (red color) on the short and long arms of the chromosomes. In the current figures, the genetic linkage maps align directly with the p-value plot. In negative log10 p plots (right), the dotted line indicates the significance threshold without false discovery rate correction and values are marked with * indicating significant markers. Level of significance of p-value for each marker is given in Table 1.
chromosomes based on the consensus map assignment of DArT alleles. The potential sources of resistance to multiple diseases also were identified and mapped in wheat landraces using DArT markers and AM approach (Adhikari et al., 2011a; Gurung et al., 2011). Kumar et al. (2009) identified four QTL (Qsb.bhu-2A, Qsb.bhu-2B, Qsb.bhu-5B, and Qsb.bhu-6D) based on the reaction of Yangmai 6 × Sonalika RILs to the most aggressive C. sativus isolate (isolate no. International Collection of Microorganisms from Plants [ICMP] 13584). These QTL were associated with resistance to SB and explained by 63% of phenotypic variation. Kumar et al. (2010) used the same isolate of C. sativus from New Zealand and recorded disease severity (%) in the field at three different growth stages (GSs) such as GS 63 (beginning of anthesis to half complete), GS 69 (anthesis complete), and GS 77 (late milk). Disease severity values were used to estimate AUDPC in the Ning AD H I K ARI ET AL .: Q UANTITATIVE RESISTAN CE I N SPRI N G WH EAT
8201 × Sonalika and Chirya 3 × Sonalika populations and eight QTL (Qsb.bhu-2A, Qsb.bhu-2B, Qsb.bhu-2D, Qsb.bhu-3B, Qsb.bhu-5B, Qsb.bhu-6D, Qsb.bhu-7B, and Qsb.bhu-7D) were mapped using SSR markers (Kumar et al., 2009, 2010). In the present study, we used highly aggressive isolate (CS45) of C. sativus from Nepal and DArT markers to identify potential genetic regions associated with seedling resistance to SB. Therefore, it is not feasible to directly compare the locations of these QTL with the previous studies (Kumar et al., 2009, 2010) due to different fungal isolates, crop growth stages, and molecular marker systems used. Although the isolate was used in this study was different from the previous studies (Kumar et al., 2009, 2010), the two QTL (1A and 7B) responsible for seedling resistance to SB were identified here and appeared to be novel. Whether or not these two QTL for resistance to SB at seedling stage are also effective to SB at adult plant stage needs to be examined. 9
Figure 4. Continued.
The AM approach relies on LD between pairs of loci in natural populations (Buckler et al., 2001). On an average, LD decay within 5 cM was observed for the A, B, and D genome in this study. Linkage disequilibrium decay ranged from 10 to 40 cM in previous studies (Chao et al., 2007; Crossa et al., 2007; Emebiri et al., 2010). The obtained maps were based on genotyping with DArT markers and do not allow us to correlate the genomic regions found to be involved in resistance in this study with other studies using polymerase chain reaction (PCR)-based SSR or other PCR-based and/or restriction 10
fragment length polymorphism (RFLP) markers. In addition, different population sizes, wheat genotypes, wheat classes, and marker systems were used for estimating LD decay in this study and in previous studies (Chao et al., 2007; Crossa et al., 2007; Emebiri et al., 2010). Therefore, the data are of limited value for a better understanding of the molecular basis of wheat disease resistance. Despite these limitations, we found significant LD extending over 5 cM, indicating the “window of opportunity” for identifying marker–trait association in this study was sufficient based on marker coverage across THE PL ANT GENOME
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Figure 4. Continued.
the genome with the exception of 3D, 4D, 4D, 5A, and 5D (Fig. 2). However, SNP marker-based genotyping is available now in wheat and the findings presented here can later be extended using new markers. In this study, QTL associated with BLS and SB resistances had small phenotypic effects (R2) ranging from 1.4 to 2.6% and from 1.5 to 2.2%, respectively. More recently, we also detected small phenotypic variation effects for P. nodorum (Adhikari et al., 2011a) and P. tritici-repentis (Gurung et al., 2011) in wheat. Similarly, low phenotypic variations in multiple traits have been reported previously. For examples, QTL associated with Fusarium head blight resistance had small phenotypic effect (1 to 3%) due to resistance being complex and polygenic inheritance (Massman et al., 2011). Shi et al. (2011) identified QTL significantly associated with resistance to bacterial blight in common bean (Phaseolus vulgaris L.) accounting for 0.04 to 4.9% of the phenotypic variation. Association mapping of quantitative resistance to Southern leaf blight in maize also identified QTL with small, additive effects (Kump et al., 2011). Additionally, AM of resistance to SB in a barley population identified QTL with a small phenotypic variation ranging from 2.3 to 3.9% (Roy et al., 2010). Our findings are in agreement with the previous reports that resistance to these two emerging diseases is complex and are likely conditioned AD H I K ARI ET AL .: Q UANTITATIVE RESISTAN CE I N SPRI N G WH EAT
by multiple genes with quantitative effects (Duveiller et al., 1993; Kumar et al., 2009, 2010). The occurrence of resistance also varied with geographic origin and differed based on DArT marker haplotype cluster. Therefore, resistant accessions of different geographic origin or haplotype clusters could carry different resistance genes. Landraces often have poor agronomic characteristics and prebreeding will likely necessary to transfer resistance into adapted wheat elite lines for use by breeding programs. Using a set of interrelated standard genetic panels in maize and rice, evidence for the presence clusters of genes or QTL conferring MDR has been well demonstrated (Ramalingam et al., 2003; Wisser et al., 2005, 2011). These findings provide convergent evidence underlying specific chromosomal segments and genes that control of broad-spectrum resistance. Candidate genes involved in MDR are being identified and verified through a range of functional genomics criteria, including microarray experiments identifying differentially expressed genes, clusters of genes with correlated expression, through mutant analysis and gene silencing in rice (Fukuoka et al., 2009; Manosalva et al., 2009), and multivariate test statistic using the AM approach and resequencing analysis of a candidate gene in involved in MDR (Wisser et al., 2011). In wheat, a few DArT markers coincided with 11
Figure 4. Continued.
the putative genomic regions where the other known resistance genes have been mapped, supporting the hypothesis that MDR genes exist in wheat. For example, DArT marker wPt-5769 on 3B identified here significantly 12
associated with resistance to SB was also associated with resistance to Puccinia triticina (Crossa et al., 2007). Diversity Arrays Technology marker wPt-1159 on 3B, which co-segregated with other DArT marker (wPt-4209) THE PL ANT GENOME
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Figure 4. Continued.
on 3B, was significantly associated with resistance to SB in this study. This marker also was associated with powdery mildew (Pm) and yellow rust (Yr) resistance and grain yield (Crossa et al., 2007). Diversity Arrays Technology marker wPt-8292 located at 110.8 cM on chromosome 4B was significantly associated with resistance to BLS. This same marker also was associated with QTL responsible for grain yield (Crossa et al., 2007). Using wheat landraces and DArT marker technology and AM approach in previous studies (Adhikari et al., 2011b; Gurung et al., 2011) and in this study, we have identified the same genetic regions or QTL associated with resistance to multiple pathogens. Further work is needed to test if the same candidate gene or different genes are involved in this MDR. The magnitude of LD and genomewide LD in wheat can be affected by several factors such inbreeding (McNally et al., 2009), selection (Palaisa et al., 2004), domestication (Caicedo et al., 2007), outcrossing (McNally et al., 2009), and admixture (Lexer et al., 2007). Diversity is also reduced in hexaploid wheat as a consequence of the AD H I K ARI ET AL .: Q UANTITATIVE RESISTAN CE I N SPRI N G WH EAT
polyploidy bottleneck (Akhunov et al., 2010). Although gene diversity levels are similar in the A and B genomes, it is greatly reduced in the D genome (Akhunov et al., 2010). The D genome also shows higher levels of LD than the A and B genomes (Akhunov et al., 2010). Low recombination is one of the factors affecting greatly uneven distribution of diversity in the hexaploid wheat D genome compared to the A and B genomes (Akhunov et al., 2010; Zhang et al., 2010). In this study, varied levels of LD were observed. As expected, the D genome had no markers or fewer markers compared to the A and B genomes, suggesting that map resolution can be improved in many regions of chromosomes using the AM approach particularly for chromosomes 3D, 4D, and 5D. Because of the low density DArT markers on the array from the D genome (Diversity Arrays Technology, 2011), it is likely that minor QTL and interactions on the D genome chromosomes remain undetected. Inadequate genome coverage may result in failure to identify critical associations between markers and traits (Flint-Garcia et al., 2003). Genetic distance 13
used for LD estimations in this study was based on the consensus map from several hexaploid wheat mapping populations. A more precise estimation and improved map resolution could be generated using highly abundant SNP markers across wheat chromosomes or genomes. To perform AM in a polyploid wheat genome, a larger panel of genomewide SNP markers may be useful (Akhunov et al., 2010; Chao et al., 2010). In conclusion, the DArT marker system and AM approach were useful for QTL discovery in spring wheat landraces of diverse origin. Five and four genomic regions were found to be associated with resistance to BLS and SB, respectively. To utilize these genetic regions in improved germplasm, the QTL should be validated before deployment in breeding programs. Highthroughput genotyping technology such as genotype-bysequencing or SNP markers would be useful to develop finer genetics maps and to identify more functional diagnostic markers that are tightly linked to disease resistances across the wheat accessions. Alternatively, significant multiple loci can be selected and validated by developing near-isogenic lines in different genetic backgrounds and for testing them in multiple locations or a targeted environment. Taken together, this emerging knowledge would help identify genes involved in MDR and enhance our understanding of molecular basis of disease resistance in wheat and ultimately lead the selection of resistance more likely to prove durable.
Supplemental Information Available Supplemental material is available at http://www.crops. org/publications/tpg. Supplemental Table S1: Resistant wheat accessions to bacterial leaf streak. Supplemental Table S2: Resistant wheat accessions to spot blotch. Supplemental Table S3: Associations of wheat accessions in different clusters. Acknowledgments Th is research was partly supported by the North Dakota Wheat Commission, State Board of Agricultural Research and Education, North Dakota, and USDA-ARS specific cooperative agreement 58-5366-0-133.
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