Zwonitzer_etal_2010_Phytopathology_100_72

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72 PHYTOPATHOLOGY

Genetics and Resistance

Mapping Resistance Quantitative Trait Loci for Three Foliar Diseases

in a Maize Recombinant Inbred Line PopulationEvidence

for Multiple Disease Resistance?

John C. Zwonitzer, Nathan D. Coles, Matthew D. Krakowsky, Consuelo Arellano, James B. Holland,Michael D. McMullen, Richard C. Pratt, and Peter J. Balint-Kurti

First author: Department of Plant Pathology, and second author: Department of Crop Science, North Carolina State University, Raleigh27695; third author: United States Department of AgricultureAgricultural Research Service (USDA-ARS) and Department of CropScience, North Carolina State University; fourth author: Department of Statistics, North Carolina State University; fifth author: UnitedStates Department of AgricultureAgricultural Research Service (USDA-ARS) and Department of Crop Science, North Carolina StateUniversity; sixth author, University of Missouri, 204 Curtis Hall, Columbia 65211; seventh author: Department of Horticulture and CropScience, The Ohio State UniversityOhio Agricultural Research and Development Center, 1680 Madison Avenue, Wooster 44691; andeighth author: USDA-ARS Plant Science Research Unit and Department of Plant Pathology, North Carolina State University.

Accepted for publication 13 September 2009.

ABSTRACT

Zwonitzer, J. C., Coles, N. D., Krakowsky, M. D., Arellano, C., Holland,J. B., McMullen, M. D., Pratt, R. C., and Balint-Kurti, P. J. 2010.Mapping resistance quantitative trait loci for three foliar diseases in amaize recombinant inbred line populationevidence for multiple diseaseresistance? Phytopathology 100:72-79.

Southern leaf blight (SLB), gray leaf spot (GLS), and northern leafblight (NLB) are all important foliar diseases impacting maizeproduction. The objectives of this study were to identify quantitative traitloci (QTL) for resistance to these diseases in a maize recombinant inbredline (RIL) population derived from a cross between maize lines Ki14 andB73, and to evaluate the evidence for the presence genes or lociconferring multiple disease resistance (MDR). Each disease was scored in

multiple separate trials. Highly significant correlations between theresistances and the three diseases were found. The highest correlation wasidentified between SLB and GLS resistance (r = 0.62). Correlationsbetween resistance to each of the diseases and time to flowering were alsohighly significant. Nine, eight, and six QTL were identified for SLB,GLS, and NLB resistance, respectively. QTL for all three diseasescolocalized in bin 1.06, while QTL colocalizing for two of the threediseases were identified in bins 1.08 to 1.09, 2.02/2.03, 3.04/3.05, 8.05,and 10.05. QTL for time to flowering were also identified at four of thesesix loci (bins 1.06, 3.04/3.05, 8.05, and 10.05). No disease resistanceQTL was identified at the largest-effect QTL for flowering time in bin10.03.

Most maize ( Zea mays L. subsp. mays) disease resistance isquantitative rather than qualitative in nature (83). Qualitativedisease resistance is generally controlled by one gene or a fewgenes with major effects, whereas quantitative disease resistance(QDR) is generally controlled by many minor genes (61,75).Although rapid progress has been made in recent years in thegenetic characterization of qualitative disease resistance (11),progress in the understanding of the genetic and physiologicalprocesses underlying QDR has been limited due to their com-plexity and incomplete and variable expression (29,40,85).

Multiple disease resistance (MDR), in which the same locusconditions resistance to multiple pathogens, is both practicallyand conceptually important and yet is also poorly understood.Limited evidence is available regarding quantitative trait loci

(QTL) conditioning MDR. The detection of clusters of QTL con-ferring resistance to multiple diseases is consistent with but does

not prove the hypothesis that MDR genes are present in plants(83,84). More direct evidence for MDR is the observation ofpleiotropic effects on multiple diseases shown with some inducedgene mutations (17,18,23,54). Mitchell-Olds (53) studied geneticcorrelations among levels of disease resistance ofBrassica rapato three fungal pathogens: Peronospora parasitica, Albugo can-dida, and Leptosphaeria maculans. They reported heritablegenetic variation for resistance to all three pathogens and positive,statistically significant genetic correlations between resistance to

L. maculans and P. parasitica in populations in which selectionwas directed at only one of the pathogens. Recently, Balint-Kurtiet al. (6) identified highly significant correlations between resis-tances to southern leaf blight (SLB), gray leaf spot (GLS), andnorthern leaf blight (NLB) in the maize intermated B73 Mo17

(IBM) population (43), although they did not detect any diseaseresistance QTL associated with resistance to all three diseases.Analysis of complex trait inheritance in single population canonly provide a partial understanding of its genetic architecture,however, because of the potential genetic heterogeneity of suchtraits across diverse germplasm. Therefore, a robust understand-ing of the genetic architecture of MDR requires its analysis inadditional mapping populations.

SLB, causal agent Cochliobolus heterostrophus (Drechsler)Drechsler (anamorph =Bipolaris maydis (Y. Nisik. & C. Miyake)Shoemaker); GLS, caused by Cercospora zeae-maydis (Tehonand E. Y. Daniels); and NLB, causal agent Setosphaeria turcica(Luttr.) K. J. Leonard & Suggs(anamorph Exserohilum turcicum

Corresponding author: P. Balint-Kurti;E-mail address: [email protected]

*The e-Xtra logo stands for electronic extra and indicates that the online versioncontains a figure showing the Ki14 B73 linkage map of the 10 maize chromo-somes and the positions of the QTL identified in this study.

doi:10.1094 / PHYTO-100-1-0072This article is in the public domain and not copyrightable. It may be freely re-

printed with customary crediting of the source. The American Phytopathological

Society, 2010.

e-Xtra*


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(Pass.) K. J. Leonard & Suggs), are foliar, substantially necro-trophic, fungal pathogens of maize. All three pathogens are asco-mycete fungi in the class Dothideomycetes and share somesimilar pathogenesis characteristics (10,38). For all these dis-eases, infection is initiated when spores land on the leaf surface,germinate, and penetrate either directly through the stomata or theleaf cuticle and epidermis. S. turcica grows intracellularly in theleaf while Cochliobolus heterostrophus and Cercospora zeae-maydis grow intercellularly during initial infection. The latentperiods (period of time from infection to sporulation) for the threefungi vary from a few days for Cochliobolus heterostrophus to2 weeks for S. turcica and up to 3 weeks for Cercospora zeae-maydis (10,38). It seems likely that loci associated with regulatingaspects of the parts of the pathogenesis process shared by two ormore of these pathogens may be detected as MDR QTL. Thishypothesis was tested in this study.

SLB is a widespread disease with the potential to cause yieldlosses in hot and humid tropical and subtropical regions, such asthe southeastern United States, parts of India, Africa, LatinAmerica, and Southern Europe (82). Resistance to SLB is quan-titatively inherited and the gene action is primarily additive orpartially dominant (14,3436,46,47,55,71). Under experimentalconditions, yield losses as high as 38 to 46% have been observedin maize inoculated with SLB (15,28). However, losses of thismagnitude are rare, because most hybrids have some level of

quantitative resistance.GLS is one of the most significant yield-limiting diseases of

maize worldwide. It has greatly increased in importance andgeographical distribution in the last 25 years, primarily as a resultof moves toward conservation tillage and no-till practices, whichallows GLS inoculum to overwinter in debris on the soil surface(1,42,56,57). This disease is a serious threat in the Eastern UnitedStates and sub-Saharan Africa as well as in the more temperateand humid regions of Mexico, Central and South America,Europe, and Asia (49,79,82). In most cases, GLS resistance ismoderately to highly heritable and additive in nature (30,37,72).

NLB can cause extensive disease in areas where temperaturesare moderate (15 to 25C) during the growing season (45). SevereNLB infection prior to flowering may cause >50% total yield loss(59,70). Historically both, qualitative and quantitative disease

resistance have been important for controlling NLB. (22,82).The objectives of this study were to map QTL associated with

SLB, GLS, and NLB resistance in a Ki14 B73 (KB) recom-binant inbred line (RIL) population and to identify QTL thatpotentially confer MDR. Disease resistance has been shown in anumber of studies to be correlated with time to maturity (83).Therefore, QTL for time to anthesis were also mapped to seewhether they colocalized with any disease resistance QTL.

MATERIALS AND METHODS

Parents and RIL development. A RIL population wasdeveloped from a cross between two maize inbreds, Ki14 andB73. The parents are of tropical and U.S. Corn Belt origin,

respectively. Ki14 (previously designated KUI14) was released in1975 from Kasetsart University in Thailand. It was developedfrom Suwan-1 (S) C4, a high-yielding cultivar which providedresistance to infection by Peronosclerospora sorghi (casual agentof downy-mildew) (67,69). The Suwan-1 population was initiallydeveloped from a composite pedigree of West Indian, Mexican,and Central and South American lines, U.S. dents, P. sorghi-resistant lines, and other germplasm. Ki14 is more resistant thanB73 to SLB, NLB, and GLS.

B73 was developed at Iowa State University (62,73). AlthoughB73 is susceptible to many diseases and insects, its high yieldpotential (2,26) and excellent seed production attributes led to itswidespread use in the development of public- (32) and private-sector inbred lines (52).

Individual F2 generation plants from a KB cross were selfed forfive generations to produce F5:6 seed. These lines were designatedas RIL lines KB001 to KB135. Then, 117 of the RILs were self-pollinated in Ohio to produce F6:7 seed. The lines were againincreased by self-pollination of several plants within each line andthe seed was bulked to produce F6:8 lines. The F6:8 RILs(henceforth called the KB population) were used for screeningSLB, GLS, and NLB in this study. All 117 lines were assessed foreach disease although, ultimately, only 109 of these lines weresuitable for use in QTL analysis (see below).

Field trials. Disease screening trials for SLB were performedin five environments: at Clayton, NC in 2004, 2005, and 2006(CL04, CL05, and CL06); at Tifton, GA in 2005 (GA05); and oneenvironment in Wooster, OH in 2004 (KING04), with two repli-cations at each environment. Each trial was performed using arandomized complete block design. CL04, CL05, CL06, andGA05 plots were planted as single rows 2 m in length, with0.97 m between rows and a 0.6-m alley at the end of each plot.Twelve seeds per entry were planted in each plot and the rowswere not thinned. KING04 plots were planted as single rows3.04 m in length, with 0.76 m between rows and a 0.46-m alley atthe end of each plot. Fifteen seeds per entry were planted in eachplot and rows were not thinned.

SLB inoculum for the field disease screening experiments wasprepared as previously described (21), and rows were inoculated

at the four- to six-leaf stage by placing 20 grains of Cochlio-bolus heterostrophus race O, isolate 2-16Bm-infested (Sorghumbicolor M.) grain in the leaf whorl (20,21). Immediately afterinoculation in the late afternoon, the field was irrigated by over-head irrigation to provide free moisture to initiate fungal growth.

Disease screening trials for GLS were performed in six envi-ronments: at Andrews, NC in 2004 and 2005 (ANDW04 andANDW05); at Salisbury, NC in 2006 (SBRY06); and twoenvironments in 2004 and one environment in 2005 at Wooster,OH (FRY04, FRY05, and KING04), with two replications at eachenvironment. Each trial conducted in North Carolina wasperformed using a randomized complete block design. Plots wereplanted as single rows 4 m in length, with 0.97 m between rowsand a 0.6-m alley at the end of each plot. Fifteen seeds per entrywere planted in each plot and the rows were not thinned. Ohio

trials were planted as single rows 3.04 m in length, with 0.76 mbetween rows and a 0.46-m alley at the end of each plot. TheFRY04 and KING04 environments were inoculated using isolatescollected in the field in 2003. The inoculum was applied byplacing several infested sorghum kernels in the whorl of themaize plants. Naturally occurring GLS inoculum was used forfield disease screening trials for ANDW04, ANDW05, FRY05,and SBRY06. These four trials were no-till planted into cornresidue from the previous year.

NLB disease screening trials were performed at six environ-ments: at Clayton, NC in 2006 (CL06); and two environments in2004 and one environment in each of 2005, 2006 and 2007 atWooster, OH (FRY04, KING04, SHFTR05, SHFTR06, andOARDC07), with two replications planted at each environment,

except for OARDC07, with one replication. Trials for CL06,FRY04, and KING04 were planted as described above. All otherplots at Ohio were planted as single rows 4.6 m in length, with0.76 m between rows and a 0.61-m alley at the end of each plot.

The CL06 NLB inoculation was conducted using infectedsorghum grain produced in the same way as the SLB inoculum(21). Rows were inoculated at the four- to six-leaf stage byplacing 20 grains of NLB inoculum (sorghum grain culture) inthe leaf whorl. The NLB inoculum contained a mixture of isolateswith various race-specificities (Setosphaeria turcica race 0, race1, race 23, and race 23N). The remaining five environments wereinoculated using the following procedures.E. turcicum inoculumwas produced from an isolate obtained from infected maize leavesfrom Licking County, OH. Aseptic cultures were produced from


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all environments for each disease and were used as the trait valuesfor QTL analysis. In addition, LSM for GDDTA were calculatedacross the four environments in which this trait was measured(see above). LSM were calculated using PROC MIXED in SAS(version 9.1.3; SAS Institute, Cary, NC) with line as a fixed-effectfactor and all other factors (environment, environmentline, andreplication within environment) considered as random effects.The PROC CORR procedure in SAS was used to calculate allphenotypic correlations. Estimates of heritability, environment,replication within environment, line, and environmentline inter-action were considered random in PROC MIXED (33). Signifi-cance of random-effect variation was tested using the type III Ftest (PROC MIXED, method = type3).

QTL detection and estimation. In the current study, detectedQTL are reported for each SLB, GLS, NLB, and GDDTA, withall environments combined for each disease. QTL Cartographerversion 2.5 (78) was used for QTL mapping. Composite intervalmapping (CIM) was used to create an initial model for eachdisease separately. CIM was performed using a 1.0-centimorgan(cM) walk speed, a window size of 10 cM, and the CIM Model 6with forward and backward regression, using a probability of 0.05to include or exclude a QTL from the model.

Multiple interval mapping (MIM) was initiated from the CIMmodels using a logarithm-of-odds (LOD) threshold of 2.6 and aminimum distance of 10 cM between QTL. These values were

chosen to reduce the number of QTL used in the initial model,because the default settings included too many QTL in the initialmodel. The search for the best MIM model was completed in aniterative, stepwise manner, searching for new QTL, testing fortheir significance after each cycle of searching for new QTL, andoptimizing QTL positions when new QTL were added to themodel. New models were accepted if they decreased the Bayesianinformation criterion (BIC) (58). Use of the BIC is the best choiceif the experimental objectives are to deduce genetic parameterssuch as the number of QTL (86). Using the BIC gives preferenceto models with higher likelihoods; however, it also includes apenalty for each additional parameter that is added to the model toprevent overfitting the model (4,60,74). When no additional QTLcould be added to the model while decreasing the BIC, each pairof QTL in the model was tested for epistatic interactions. An

additional approach used to prevent overfitting QTL models wasto exclude models in which the proportion of the total phenotypicvariation accounted for by the QTL exceeded the entry meanheritability. If the model was overfit, the QTL with the smallesteffect was dropped from the model and the effects of the remain-ing QTL were reestimated. Additive effects and QTL interactionswere reported when the LOD score was >2.0. These threshold

values were set to limit the number of QTL reported such thatseveral QTL of very small effect identified in the final MIManalysis were not reported.

RESULTSPhenotypic ratings. The variance components for all random-

effect factors (environment, replication within environment, line,and environmentline interaction) for the three diseases weresignificantly different from zero (Table 1). The high level ofvariation associated with environment likely reflects the fact thateach disease was assessed in relatively diverse environments innorthern Ohio and in central North Carolina. Pairwise Pearsoncorrelations of SLB WMD scores were significant between allpairs of environments of 0.28 to 0.89 (Table 2). The correlationsbetween CL04, CL05, CL06, and GA05 for SLB were highlysignificant (P < 0.0001), with correlation coefficients of 0.80 to0.89. Pearson correlations of GLS WMD scores were highlysignificant between all but one pair of environments, FRY04 andSBRY06 (Table 3). Pairwise NLB WMD Pearson correlationcoefficients ranged from highly significant (SHFTR05 andSHFTR06, r = 0.56 and P < 0.0001) to not significant(OARDC07KING04, r = 0.1, P = 0.1881) (Table 4) but mostpairwise correlations (10 of 15) were significant to at least the P

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