Functional association between malting quality trait components andcDNA array based expression patterns in barley (Hordeum vulgare L.)

E. Potokina1, M. Caspers2, M. Prasad1, R. Kota1, H. Zhang1, N. Sreenivasulu1, M. Wang2

and A. Graner1,*1Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany; 2Center forPhytotechnology RUL/TNO, 2333 AL Leiden, The Netherlands; *Author for correspondence (phone: 49(0)39482 5521; fax: 49 (0)39482 5155; e-mail graner@ipk-gatersleben.de)

Received 8 December 2003; accepted in revised form 6 February 2004

Key words: Candidate gene identification, EST, Functional genomics, Gene expression, QTL mapping

Abstract

We developed an approach for relating differences in gene expression to the phenotypic variation of a trait ofinterest. This allows the identification of candidate genes for traits that display quantitative variation. To validatethe principle, gene expression was monitored on a cDNA array with 1400 ESTs to identify genes involved in thevariation of the complex trait ‘malting quality’ in barley. RNA profiles were monitored during grain germinationin a set of 10 barley genotypes that had been characterized for 6 quality-associated trait components. The selec-tion of the candidate genes was achieved via a correlation of dissimilarity matrices that were based on �i� traitvariation and �ii� gene expression data. As expected, a comparison based on the complete set of differentially-expressed genes did not reveal any correlation between the matrices, because not all genes that show differentialexpression between the 10 cultivars are responsible for the observed differences in malting quality. However, byiteratively taking out one gene �with replacement� and re-computing the correlation, those genes that are posi-tively contributing to the correlation could be identified. Using this procedure between 17 and 30 candidate geneswere identified for each of the six malting parameters analysed. In addition to genes of unknown function, thelist of candidates contains well-known malting-related genes. Five out of eight mapped candidate genes displaylinkage to known QTLs for malting quality traits. The described functional association strategy may provide anefficient link between functional genomics and plant breeding.

Introduction

In plants, DNA arrays have been successfully utilizedto examine a range of biological issues including de-velopmental processes, environmental stress re-sponses, identification and genotyping of mutations�Aharoni and Vorst 2001�. The common goal of mostof these experiments is to investigate the reaction ofa given genotype to a specific environmental effect,to study spatio-temporal patterns of gene expression,or to compare a wild-type plant with a correspondingmutant. An extension of the latter approach would be

the comparison of different genotypes, breeding lines,or cultivars that exhibit differences in quantitativetraits. Based on the hypothesis that the observed dif-ferences at the trait level are due to differences in theexpression of the underlying genes, cDNA arraytechnologies could be deployed to monitor gene ex-pression in different genotypes and to identify genescontributing to traits that display quantitative varia-tion. In this paper, we describe a functional associa-tion approach to relate the variation in geneexpression to the variation in a phenotypic trait of in-terest. We illustrate the procedure by identifying can-

Molecular Breeding 14: 153–170, 2004.© 2004 Kluwer Academic Publishers. Printed in the Netherlands.

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didate genes involved in the variation of the complextrait ‘malting quality’ in barley.

Malt is the major raw material for the productionof beer. During the malting process barley grains ger-minate under strictly-controlled conditions. Withinthe grain, hydrolytic enzymes convert the high mo-lecular weight components �grain carbohydrates andstorage proteins� into partially degraded biopolymers,which are required in the subsequent brewing process.Simply speaking, the final malt is formed by germi-nated barley seeds that provide partly degradedbiopolymers along with the enzymes needed to fur-ther break down polysaccharides, proteins, and lipidsinto the monomers required by yeast to produce alco-hol.

Malting technology includes several phases: aftersteeping and germinating the barley seedlings, whosemetabolic system becomes fully activated, are driedby heating �kilned�. First, air at 40 to 60 °C is forcedto reduce the malt moisture level to about 20%, thenthe air temperature is increased gradually from 60 °Cto 85 °C �Burger and LaBerge 1985�. Kilning is acritical step: it should be performed at a relativelyhigh temperature to arrest modification and rendermalt stable for storage, while at the same time sur-vival of the most heat-sensitive enzymes �endo-�-glucanases, endopeptidase, limit dextinase, �-amy-lase� must be ensured, because these enzymes areessential for the further breakdown of carbohydratesin the subsequent mashing process.

Malting quality is a complex phenotype, combin-ing a large number of interrelated trait components,each of which shows complex inheritance �Hayes andJones 2000�. The genetic improvement of maltingquality is impaired by the quantitative inheritance andthe comparatively low heritability of the trait. With

the availability of molecular markers, a series of ef-forts were initiated to genetically dissect this trait andto localize the individual quantitative trait loci �QTL�on the genetic map of barley �Hayes et al. 1993; Hanet al. 1997; Mather et al. 1997�. Although QTL stud-ies led to a better understanding of the trait, the sizeof QTL intervals usually is too large to �i� identifymolecular markers useful for marker-assisted selec-tion and �ii� to readily identify candidate genes, e.g.in a map-based cloning approach.

To develop a generic strategy for the identificationof QTL-related candidate genes, we monitored RNAprofiles during a micromalting experiment in a set of10 barley genotypes that were in parallel character-ized for 6 malting quality parameters. Candidategenes were identified based on the correlation of ex-pression patterns with the individual trait compo-nents. For further validation a subset of the identifiedcandidate genes was placed on a genetic map andcompared to previously mapped QTLs for brewingquality.

Materials and methods

Plant material

A set of 10 barley lines, encoded as N-005, N-008,N-009, N-134, N-137, N-145, N-147, N-157, N-158,and N-159, was obtained from Cebeco Seeds B.V.�The Netherlands�. For the 10 cultivars, grown at onelocation and representing a relative wide diversity inmalting properties, 6 relevant classical malting-qual-ity parameters were analysed �Table 1�. The maltingquality specifications are discussed in reviews byBurger and LaBerge �1985� and Bamforth and Bar-

Table 1. Malting parameters of the 10 barley lines analysed

Barley Line Extract Yield �%� VZ 45 °C Kolbach Index Friability �%� Final Attenuation �%� �-amylase Activity1 2 3 4 5 6

N-005 81.3 31 38.9 72 81 89N-008 82.7 41.8 44.4 92 83.2 90N-009 82.7 37.9 47.4 93 82.8 76N-134 79.1 32.7 35 65 78.4 67N-137 79.6 32.8 35.6 77 80.2 65N-145 83.2 41.5 44 90 82.4 86N-147 82.4 46.1 39.8 88 83.6 94N-157 82.7 48.7 53.6 95 82.1 79N-158 81.9 44.3 41.8 70 82.2 68N-159 83 41.2 46.5 73 80 80

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clay �1993�. In the subsequent paragraphs, wedescribe briefly the malting quality components un-der study for developing a better understanding of theunderlying biological processes.

Extract. Malt extract is an index of the total solublematerial that can be obtained from a given malt �per-cent of dry matter�. Approximately 90% of thesoluble materials are carbohydrates, the remainderconsists of amino acids and peptides of various sizes.Extract is one of the first factors a brewer considersfor quality assessment of a malt. The theoretical ex-tract maximum is 100%.

Friability. The process of ‘modification’ results indecomposition of cell walls making the grain softerand more friable. For the indirect assessment of thisprocess a ‘Friabilimeter’ is commonly used, in whichmalt is ‘ground’ between a rubber roller and a metalsieve. The percentage of the malt that can be ‘pow-dered’ is an indicator of Friability, ergo modification.

VZ 45 °C represents a further measure of modifi-cation termed 45 °C mash viscosity. Primary contrib-utors to viscosity are �-glucans, that mainly originatefrom cell walls. If these are not sufficiently degradedduring the malting process, the mash becomes viscousand does not filter well, causing serious problems inthe brewery.

Kolbach Index. This parameter is a measure of theextent of protein conversion, as opposed to cell wallmodification. The more extensively proteolysis hastaken place during malting, the more nitrogenoussubstances will be solubilized. Thus, the Kolbach In-dex represents the ratio of soluble to total nitrogen.

Final Attenuation serves as a parameter of ‘fer-mentability’, measuring the extent to which yeasts areable to convert the wort into alcohol. It is necessaryto achieve a consistent degree of ‘Attenuation’ for thewort, i.e., the limit to which fermentation proceeds.

�-amylase measures the amount of ß-amylase ac-tivity extracted from a fixed weight of barley maltpowder. ß-amylase is deposited during seed develop-ment and is involved in the hydrolysis of starch to-gether with �-amylase that is formed during germi-nation. Together they form the ‘diastatic power’ of abarley line resulting in fermentable sugars.

Micromalting

Seed samples �100g� were micro-malted �the small-scale industrial process of barley seed germination� inone batch using a commercially-available micro-malting device �Phoenix, Australia�. The procedure

was performed as follows: 1� rinsing and steepingstep 1 �7.5h, 12 °C, to 36% seed moisture content�;2� air-rest step 1 �19.5h 16 °C�; 3� steeping step 2�10h, 15 °C�; 4� air-rest step 2 �32h, 15 °C�. Wholeseeds were harvested 69 hr after the start of imbibi-tion and placed in liquid nitrogen. Total RNA wasisolated in two replications �from one micromalt seedbatch� from frozen, powder-ground, whole seeds.

Array experiments

Total RNA from each of the 10 barley lines understudy was used for mRNA extraction with oligo�dT�-paramagnetic beads. A solid-phase cDNA library wassynthesized using reverse transcriptase and thesecond strand was labeled by random priming with�33P� dCTP, dNTPs, and DNA-polymerase. The 33PdCTP – labeled cDNA probes were hybridized tocDNA arrays. The synthesis of 33P-labeled cDNA andhybridization procedure were performed as describedby Sreenivasulu et al. �2002�.

The cDNA array used in this study contains 1440cDNA fragments mainly representing unique genes�Michalek et al. 2002�. Among these cDNAs, 86%were derived from clones of a cDNA library of de-veloping caryopsis. Information about the cDNA se-quence, cluster membership, and putative function isavailable from http://pgrc.ipk-gatersleben.de/est/log-in.php. A putative function was assigned to an ESTbased on the best BlastX hit � 1.0 � 1020. A de-tailed description of the development of the cDNAarray as well as an evaluation of the reliability of theexperiments performed with this array is given inSreenivasulu et al. �2002� and Potokina et al. �2002�.

Array data processing

Signal intensities were normalized with the totalamount of radioactivity bound to the array �Potokinaet al. 2002�. Since amplified cDNA fragments werespotted in duplicate and hybridizations were per-formed with two independent RNA preparations, theexpression value for each gene can be considered tobe replicated 4 times. For those 4 raw expression val-ues the mean, standard deviation, and coefficient ofvariation were calculated. The reproducibility of thesignal intensities was estimated via the coefficient ofvariation �standard deviation divided by the mean�.When the coefficient of variation of the signals ex-ceeded 30%, the cDNA was excluded from furtheranalysis. Since the accuracy of a score decreases with

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signal intensity, only signals with an intensity 10times greater than background were taken into con-sideration. After these pre-processing steps, allremaining signals showed a less than two-fold devi-ation when comparing two independent RNA probepreparations of each of the cultivars �data not shown�.Next, those clones that consistently showed a morethan two-fold difference between at least twocultivars were considered as differentially expressed.The final list contains 100 differentially expressedclones.

Correlation of phenotypic and expression data

Before the analysis, both malting and expression datawere standardized to reduce the effects of differentscales of the measurement of the different maltingquality components. To this end a linear transforma-tion was used in the form: Xstand � �X – mean� /stdev. The mean of each variable for the 10 cultivars�mean� was subtracted from the individual data val-ues for each cultivar �X� and the difference was di-vided by the standard deviation �stdev). The samestandardization procedure was performed with the setof differentially expressed genes.

The degree of difference between cultivars basedon a malt quality trait was measured by computingthe average Manhattan distance coefficients �NTSYS-pc, version 1.8�. When the distances were computedbetween all pairs of the 10 barley cultivars, these val-ues were arranged in a distance matrix. In the sameway, a Manhattan distance matrix was developed forthe cultivars based on the patterns of the differentiallyexpressed genes.

A Mantel test �Mantel 1967; Sokal and Rohlf 1995�was used to estimate the association between thesetwo independent distance matrices describing thesame set of cultivars. To determine a significance ofthe matrix correlation, the Mantel Nonparametric TestCalculator for Windows, version 2.00 �Liedloff 1999�was employed. The program performs 100 permuta-tions of rows and columns of one of the matrices,each time computing the Mantel Coefficient �Z� �ref-erence distribution�. The observed Z coefficient iscompared against this reference distribution via thestandard normal variate �g�. When g exceeds a criti-cal value, the null hypothesis that there is no associa-tion between elements in distance matrix 1 anddistance matrix 2 should be rejected. For the mostcommon levels of significance �p value 0.01, 0.025,

and 0.05� the critical g-values are 2.326, 1.960, and1.645, respectively.

Linkage of candidate genes to malting-quality QTLs

To locate the identified candidate genes on chromo-somes we employed the Steptoe �feed-grade� x Morex�malting grade� doubled haploid �DH� barley map-ping population, which previously has been used tomap QTLs affecting malting quality traits �Hayes etal. 1993; Han et al. 1997�. Grain protein content,�-amylase activity, diastatic power, and malt extractwere evaluated and reported by Hayes et al. �1993�for nine environments. The complete description ofthe malting quality phenotypes �parents and 150 DHlines� is available at �http://wheat.pw.usda.gov/ggpag-es/SxM/�.

The candidate genes were placed on the Steptoe/Morex map. If no polymorphism was detected in thispopulation, genes were placed on the Oregon Wolfe�Dom/Rec� map �Costa et al. 2001�. Having markersfor the genes of interest and having phenotypic datafor 150 DH lines derived from the Steptoe/Morexcross, we performed a QTL analysis in order to ex-amine linkage between the identified candidate genesand malting-quality QTLs.

RFLP and SNP markers

Selected candidate genes were mapped as SNP �singlenucleotide polymorphism� and RFLP �restrictionfragment length polymorphism� markers. For RFLPanalysis a set of six restriction enzymes �BamHI,HindIII, EcoRI, EcoRV, XbaI, and DraI� was used todigest genomic DNA. Southern blotting and probelabeling was carried out according to Graner et al.�1991�. Autoradiography was performed by exposureof hybridized blots to imaging plates �Fuji PhotoFilm, Japan� and subsequent signal detection on aphosphoimager �Fuji, Japan�.

For the development of SNP markers, primers werederived from the candidate EST based on sequenceinformation available from the crop EST database�CR-EST; http://pgrc.ipk-gatersleben.de�. Subse-quently, PCR fragments for candidate genes wereamplified from the DNA of the parental genotypes ofthe Steptoe/Morex DH mapping population. The cor-responding sequences were aligned and manually in-spected for polymorphisms. The PCR productshaving SNPs were mapped by heteroduplex analysisaccording to Kota et al. �2001�.

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Linkage map and QTL analysis

The RFLP and SNP markers were mapped using theMAPMAKER v. 2.0 program �Lander et al. 1987�based on Kosambi’s mapping function �1944� and byusing a minimum LOD score of 3.0 and maximumrecombination frequency of 50%. The positions andeffects of QTLs for the malting quality parameterswere determined by simple interval mapping �SIM�using the QGENE v. 3.0 program �Nelson 1997�. ALOD score � 3 was used to detect a QTL. The pro-portion of observed phenotypic variation explained�PVE� due to a particular QTL was estimated by thecoefficient of determination �R2� from the SIM model.The vicinity of the candidate genes to malting qualityQTL peaks was estimated for 9 environments.

Results

Functional association approach

To identify ‘quality related’ candidate genes it is notpossible to simply estimate a difference in gene ex-pression observed between two phenotypically con-trasting lines �for instance high and low maltingquality�, because, except for the case of near-isogeniclines, differential gene expression is not only due tothe trait under consideration but also because of thevariation present in the genetic background. Hence,background effects need to be eliminated in order toestablish a functional association between the level ofgene expression and a given trait. This was achievedby including in the analysis a representative numberof well-described genotypes, which can be relatedbased on their phenotypic parameters. Then, from atotal set of genes, which are differentially expressedbetween the lines, only those genes can be extracted

whose expression profile accounts for the phenotype-based relation between the lines.

To recognize those candidates one can checkwhether two variables �a malting parameter profileand a gene expression pattern� vary together, i.e., arecorrelated. By this simple, bivariate correlationanalysis 7 differentially expressed cDNAs were iden-tified whose expression pattern showed significantassociation with individual malting traits �Table 2�. Acloser inspection of the remaining non-correlatedgenes revealed that in some cases their expressionprofile was very similar to the expression profiles ofthe 7 genes shown in Table 2, although they failed toreach the significance threshold �r2 � 0.63, p �0.05�. As an example, Figure 1 illustrates two almostparallel 10-cultivar-expression-patterns of ESTsHY01N04 and HY06B09. The first EST significantlycorrelates with a profile of one of six malting param-eters under analysis – VZ 45° �r2 � 0.66, p < 0.05�.The second EST, showing a correlation coefficient r2

� 0.59, is not significantly associated with the VZ45° malting parameter �p � 0.05�. To identify thosegenes whose expression patterns might be associatedwith phenotypic variation but which are not detectedas significant in the bivariate correlation analysis, thefollowing alternative procedure has been applied.

The analysis of matrices representing similarities�dissimilarities� among taxonomic units is a basic ap-proach in ‘numerical taxonomy’ �Sokal and Sneath1963�. The procedure consists of computing similar-ity matrices, frequently composed of product-momentcorrelation coefficients, and of clustering of thesematrices into a taxonomical relationship based onthese similarities �Sokal and Rholf 2001�. In ourfunctional association approach we applied this prin-ciple of numerical taxonomy in the following modi-fied way: the relationship between the varieties interms of malting quality was taken as fact, and the

Table 2. Correlation coefficients between cDNA expression patterns and malting quality traits obtained by bivariate correlation analysis �bolddesignates significant correlation n�10, r2 � 0.63, p � 0.05�.

EST ID Putative function Extract VZ45 °C

KolbachIndex

Friability Final Attenuation ß-amylase Activity

HW01F04 d-TDP-glucose dehydratase � 0.55 � 0.21 � 0.31 � 0.62 � 0.70 � 0.55HY05D10 unknown 0.23 0.66 0.55 0.34 0.05 0.03HY07K19 cysteine proteinase, sf42 0.48 0.58 0.65 0.19 0.02 0.18HY03H12 glycine-rich RNA binding protein 0.38 0.71 0.45 0.30 0.19 0.30HY01N04 unknown 0.21 0.66 0.28 0.10 0.20 0.19HY03N21 dehydroascorbate reductase � 0.32 � 0.77 � 0.53 � 0.29 � 0.08 � 0.11HY06K20 initiation factor 4A � 0.29 � 0.23 � 0.09 � 0.50 � 0.68 � 0.26

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goal was to select only those genes whose expressionprofiles supported this relationship.

Identification of candidate genes

The strategy is illustrated below by an example of se-lected candidate genes related to one of the six malt-ing components under analysis: mash viscosity at45 °C �VZ 45°�. For the given sample, the Mantelstatistics was performed to test the associationbetween the dissimilarity matrices �Manhattan dis-tance matrix� that were based on �i� a selected traitparameter �VZ 45°, Table 3a� and on �ii� the hybrid-ization signals of all of the 100 differentiallyexpressed cDNAs �Table 3b�. The Mantel test yieldeda standard normal variate g � � 0.8027 for the ob-served Z-value, indicating that there was no correla-tion between the matrices �g should be greater than1.645, p � 0.05�. Evidently, not all differentially ex-pressed genes are correlated with the malt-quality pa-rameter. Therefore, within the complete set of 100genes that showed differential expression between atleast two of the 10 genotypes that were analysed, cD-NAs with a negative effect on the matrix correlationneeded to be identified and removed. To this end aniterative selection algorithm was applied: cDNA 1was taken out, and the Mantel test was repeated toestimate the association between the VZ 45° distancematrix and the new distance matrix calculated ongene expression data of the remaining 99 cDNAs. There-calculated Mantel test yielded a g-value � 0.7499,

which exceeded the initial value �-0.7499 �� 0.8027�. Consequently, cDNA 1 has a negative ef-fect on the matrix correlation, because in its absencethe correlation increases. In the next step, instead ofcDNA 1, cDNA 2 was removed from the analysis andthe Mantel test was repeated. The resulting g �� 0.8499 indicates that, in contrast to cDNA 1,cDNA 2 has a positive effect, because its omissionreduces the correlation �-0.8499 � � 0.8027�. Thisprocedure was iteratively performed for each of the100 differentially expressed cDNAs.

Finally, 36 cDNAs were identified as positivelycontributing to the correlation with the VZ 45° dis-tance matrix. However, recalculation of Z using onlythese clones still did not result in a significant g-value�g � 1.1297�. Therefore, the iterative procedure wasrepeated for the 10-cultivar 36-gene dataset. Table 4shows how the g value changed in the absence of anyof the 36 pre-selected cDNAs, reflecting the impor-tance of the individual clones for the matrix correla-tion. The list in Table 4 �black box� begins with fourcDNAs �HY03N21, HY05D10, HY03H12,HY01N04�, whose expression patterns show a signif-icant correlation with VZ 45° identified by the bivari-ate correlation analysis �Table 2�. However, regardingthe matrix correlation approach, the first 17 cDNAsfrom the top of Table 4 �dark grey box� provide themaximal contribution to the correlation, because inthe absence of any of these the initial Mantel testvalue �1.1297� decreased. Taken together those 17cDNAs yield a distance matrix, which was signifi-

Figure 1. Association between a single malting parameter profile �VZ 45°� and the expression patterns of two ESTs. The standardized valueof VZ 45° and the expression level of the ESTs �Y-axis� are plotted against ten barley cultivars �X-axis�. The correlation between VZ 45° andthe expression pattern is significant for EST HY01N04 �r2 � 0.66, p � 0.05�. In case of EST HY06B09 the correlation between VZ 45° andthe expression pattern is not significant �r2 � 0.59, p � 0.05� despite of a similar expression pattern.

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cantly correlated to the malting distance matrix at a99% confidence level �g � 2.6615, p � 0.01�. Whenthe 17-gene data set was extended up to 30 genes�Table 4, light grey box�, the correlation was still sig-nificant, but only at the 95% level �g � 1.7006, p �0.05�. Further addition of any of the remaining cD-NAs listed in Table 4 �empty zone� does not result ina significant correlation. Using the same procedure,sets of candidate genes were identified for the otherfive malting components �Table 5�. The numbersranged from 17 �Final Attenuation� to 22 genes �Kol-bach Index� that are significantly correlated at the95% confidence level.

Correlation of gene expression with the complextrait ‘malting quality’

Malting quality is a complex phenotype, resultingfrom the interplay of a large number of interrelatedtrait components, reflecting various aspects of themodification process. Some of the aspects can be di-rectly or indirectly estimated, as was done in thepresent study for the 6 most commonly analyzed

malting parameters. As shown above, the relation be-tween cultivars might be established based on any ofthose quantitative empirical data. However, a Man-hattan distance matrix can be also computed from thecombined data set of all 6 trait components. Techni-cally, this approach allows the recognition of geneexpression patterns associated with not only one, butseveral single quality trait components. The right col-umn of Table 5 shows that this generalized methodmainly identifies genes having an impact on at least 3out of 6 traits evaluated.

Using the combined data set, representing an ‘in-dex’ for the trait ‘malting quality’ 19 cDNAs weresignificantly correlated at the 95% confidence level.Those broad-spectrum candidates were subjected to averification procedure and were further characterizedby genetic mapping, which is described in thefollowing section.

Cultivar re-sampling procedure

To verify the robustness of the matrix association ap-proach outlined above, a re-sampling procedure was

Table 3. Correlation of distance matrices based on trait and expression data. The genetic distance between the barley genotypes �lines� isbased on the malting quality parameter VZ 45° �a� and on the differential expression of 100 genes �b�. The Mantel coefficient Z computed byMantel Nonparametric Test Calculator was 118.63. After a sampled permutation test, the standard normal variate for the observed Z valuewas g � � 0.8027, showing no association between the matrices. The correlation between the matrices is significant when g � 1.645 �p �

0.05�.

Lines 005 008 009 134 137 145 147 157 158 159a

005 0008 0.91 0009 1.42 1.55 0134 1.11 0.64 1.61 0137 0.78 0.78 1.06 1.02 0145 1.01 1.06 2.22 1.23 1.41 0147 1.60 1.58 0.71 1.74 1.20 2.35 0157 1.10 0.73 1.63 0.84 0.89 1.28 1.53 0158 0.92 0.88 1.00 0.88 0.57 1.45 1.18 0.90 0159 0.96 0.76 1.36 0.97 0.78 1.36 1.19 0.67 0.73 0

b

005 0008 1.79 0009 1.14 0.65 0134 0.28 1.51 0.86 0137 0.30 1.49 0.84 0.02 0145 1.74 0.05 0.60 1.46 1.44 0147 2.50 0.71 1.36 2.22 2.20 0.76 0157 2.93 1.14 1.79 2.65 2.63 1.19 0.43 0158 2.20 0.41 1.06 1.92 1.90 0.46 0.30 0.73 0159 1.69 0.10 0.55 1.41 1.39 0.05 0.81 1.24 0.51 0

159

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applied. In addition to the original set of 10 barleycultivars used to identify the 19 cDNAs, we gener-ated data sets comprising only a subset of genotypes�8, 7, or 6�. It was assumed that this random removalof genotypes will reveal the most robust candidategenes. The results of this re-sampling procedure aresummarized in Figure 2. After the removal of twogenotypes from the analysis, the iterative matrix cor-relation procedure was performed for the remaining 8cultivars, and 48 differentially expressed genes wereidentified, out of which 22 genes were correlated�Figure 2, re-sampling II�. In another set of 6randomly selected genotypes, 21 cDNAs were corre-lated to the corresponding malting matrix �Figure 2,re-sampling III�. In total 7 genotype combinationshave been analysed in this way.

This rough re-sampling procedure revealed that notall of the 19 initially identified candidate genes werealways included in a correlated set for the correspond-ing re-sample scheme �Figure 2�. For instance,HW01G04 �cysteine proteinase 1� was identified as acandidate gene in all 7 possible genotype combina-tions �Figure 2, red line�. This gene is characterizedby a ‘Re-sampling Index’ �Rs Index� of 100 �mean-ing that there is a chance of 100% that the gene issignificantly correlated no matter which set of culti-vars was analysed�. Another gene, dTDP-D-glucosedehydratase �HW01F04�, was identified as a candi-date gene in 6 out of 7 combinations resulting in anRs Index of 86 �Figure 2, yellow line�. Any of the 19initially identified candidate genes was assigned anRs Index, providing a measure for the reliability ofthe observed association in the present set of geno-types.

Mapping of the candidate genes

Candidate genes with Rs Indices higher than 40 �Fig-ure 2� were further analysed by genetic mapping. For14 candidates the designed primer combinationsyielded an appropriate PCR product, and the corre-sponding genes were checked for single nucleotidepolymorphisms �SNPs� in the Steptoe x Morex �SM�mapping population, for which extensive QTL dataon malting quality are available. Four markers�HW01N21, HY06A05, HY09E23, and HY07C03�were monomorphic in the SM population, but couldbe placed on the Oregon Wolfe �OWB� genetic map�Table 6�. For those genes the QTL analysis could notbe performed, and their linkage to previously reportedQTLs was roughly estimated by their location on the

barley ‘BIN’-map �Kleinhofs and Graner, 1999�. TwocDNAs, HW01G04 and HY07K19, showing homol-ogy to a cysteine proteinase 1 gene, could not bemapped, since no variation between parent lines ofany of the crosses analysed in this study wasobserved. The remaining 8 cDNAs displayed poly-morphism in the SM-population, where they weremapped on chromosomes 1H, 3H, 4H, and 7H.

For five of these genes, a significant QTL effectwas detected �Table 6, Table 7�. In this case, themarker positions coincide with a QTL peak, or arelocated within the support interval. Three candidategenes �HY01C08, HY03C01, and HY03N21� did notdisplay any linkage to malting QTLs detected in theSM-population. This might be due to the fact that theQTL analysis was restricted to the Steptoe/Morexcross and only 4 malting quality components wereconsidered in this progeny �grain protein, �-amylaseactivity, diastatic power, and malt extract�. Wort clar-ity �Steptoe/Morex� and fermentability �Derkado/B83-12-21-5� QTLs were reported for BIN 13 onchromosome 3H, where HY01C08 �putative seleniumbinding protein� is located �Hayes and Jones 2000;Larson et al. 1997; Swanston et al. 1999�. Similarly,a grain protein QTL, which was initially detected in aDicktoo/Morex cross, has been reported for BIN 12on chromosome 7H �position of HY03C01� �Oziel etal. 1996�.

Discussion

In the present study a functional association approachwas employed using a cDNA array with 1400 ESTsto identify genes putatively involved in the variationof the complex trait ‘malting quality’ in a set of 10barley genotypes. Computing bivariate correlationsbetween gene expression and the individual maltingquality components, 7 genes were identified whoseexpression was significantly correlated to VZ 45 °C,Kolbach Index, and Final Attenuation. In addition tothese seven genes, a considerable number of geneswas observed whose correlation coefficients wereonly slightly below the significance threshold. There-fore, as an alternative procedure, a matrix correlationapproach was devised. Using this approach, the sixmalting quality components were either consideredseparately or were used in combination and correlatedas ‘malting quality’ to the gene expression data.While the individual quality trait components reflectdifferent aspects of the malting process, an index for

163

Figure 2. Scheme of the re-sampling procedure for the verification of candidate genes. I-VII – number of re-sampling combinations. Num-bers on the left side of the pie charts represent the numbers of differentially expressed genes detected for the corresponding re-sampled setof barley lines. Numbers on the right side of the pie chart represent the numbers of genes selected as candidates. The Re-sampling ratio �Rsratio� of a gene is defined as the percentage of all possible re-sampling combinations in which the gene was selected as a candidate �note thatif the gene is no longer differentially expressed due to the omission of one or more critical lines in a re-sampling set, then the maximumnumber of re-sampling combinations for that gene decreases correspondingly�. Red, yellow, and green lines: genes showing Rs I � 100, 86,and 57 respectively. *� Indicates the 19 candidate genes initially identified for the 10-cultivar data set �3rd column�.

164

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165

the trait ‘malting quality’ refers to the averaged amyl-olytic, proteolytic, and modification potential of agenotype.

The list of candidates contains well-known maltingrelated genes

To further confirm the validity of the candidate genes,their putative function was assigned by BlastX analy-sis. As follows from Table 5, well-known malting re-lated genes are present among the identified candi-dates. Glucan-endo � 1,3-beta-glucosidase�HY07C03�, a key hydrolytic enzyme that depoly-merises cell wall components, can enhance the rateof endosperm modification and may affect many as-pects of malting quality �Fincher and Stone 1993�.Based on our results, �1-3�,�1-4�-�-glucanase is asso-ciated with two main indices of endosperm modifica-tion: cell wall modification �VZ 45°� and proteinconversion �Kolbach Index�.

Cysteine proteinases account for about 90% of thetotal endopeptidase activity during malting �Bamforthand Barclay 1993�. The expression of a candidate,HW01G04, showing high homology to cysteine pro-teinase 1 precursor �Q10716 Blast accession number�is correlated with all malting parameters except for�-amylase activity. The activity of genes involved inprotein conversion may determine diverse maltingparameters, because proteolysis is shown to berelevant for starch degradation �Slack et al. 1979�.Our results also show that several indices of modifi-cation might be affected by enzymes indirectlyinvolved in proteolysis, for instance, catalysing theformation �protein disulfide-isomerase, HY02J22� orthe reduction �HY01H09, thioredoxin h� of intra-mo-lecular disulfide bridges of storage proteins. It corre-sponds to the previous suggestion that the thioredoxinreductase �NTR�/thioredoxin h system plays a keyrole in the mobilization of storage compounds fromthe starchy endosperm during wheat grain germina-tion �Kobrehel et al. 1992; Serrato et al. 2001�. Simi-larly, the alpha-amylase/subtilisin inhibitor was foundto be important for protein solubilisation �KolbachIndex� and for the total extractable material �Extract�.

Wainwright �1988� mentioned oxygenated fatty ac-ids formed by lipoxygenases as causes of increasedviscosity and extent of wort run-off. In accordance tothat lipoxygenase 2 �LoxC, HY06B09� was identifiedas a gene related to VZ 45°.

Unexpected genes for malting quality

Other than the well-known malting-quality relatedgenes, additional candidates with known putativefunction were identified, whose expression may affectmalting quality. Most might be associated with dehy-dration and high temperature response:

A dTDP-D-glucose dehydratase �TGD� relatedcDNA was found to be among the candidates with thehighest Rs Index �HW01F04, Figure 2�. Similar tobacteria, plant TGD might be involved in synthesis ofmonosaccharides for their incorporation into cellwalls �Reiter and Vanzin 2001�. Because the productsof sugar metabolism are known to play a role in stresstolerance, TGD may be involved in adaptation todrought stress through an osmotic regulation mecha-nism �Cheng et al. 2001�. The drought inducible genepaf93 �HW01N21� is functionally very similar to de-hydrins that respond rapidly to dehydration stress�Grossi et al. 1995�. Dehydroascorbate reductase�HY03N21� was found to be induced by heat shockin rice seedlings, suggesting a protective function athigh temperatures �Urano et al. 2000�. Similarly, theexpression of a 70 kd heat shock protein �HY09E23�correlates with �-amylase activity, Friability, and Ex-tract �Table 5�.

The protective function of proteins correspondingto dehydration and high-temperature induced genesmay be relevant to the malting process, especially atthe kilning step where the seedlings are heated. In thiscontext Loi et al. �1987� showed that more than 60%of the �1-3�, �1-4�-�-glucanase in the germinatedgrain is inactivated during the kilning process. If thethermostabilities of the enzymes could be improvedby increasing the levels of the corresponding chaper-ones, losses of activity during kilning and mashingcould be reduced, and the starch degrading enzymeswould be able to hydrolyse residual �-glucans in themalt extract during mashing �Bamforth and Barclay1993�. The results indicate that feed and malting-grade barley varieties might differ not only due totheir hydrolytic potential, but also due to activity ofgenes involved in the protection of hydrolases againstdenaturation at high temperatures during the maltingprocess.

Genetic mapping of identified functional candidates

Another approach to validate candidate genes is theircorrelation to known QTLs. Five out of eight mappedcandidate genes were co-located with QTLs for malt-

166

ing-quality traits, indicating that the chromosome re-gions surrounding the location of the markerssignificantly influence the malt phenotype differences.The observed positional coincidence of the candidateESTs and the malting quality QTL may be the resultof cis-acting factors, e.g., differences in promoter re-gions that have a direct impact on gene expression,as opposed to trans-acting factors, for which thestructural gene segregates from the QTL �Jansen andNap 2001�. The combination of QTL analysis andquantitative RNA profiling can be effectively used tonarrow down candidate genes involved in complexgenetic processes �Toma et al. 2002; Wayne andMcIntyre 2002�. However, the proximity of a candi-date gene to a quality QTL provides only circumstan-tial evidence, because the physical size of QTLconfidence intervals on genetic maps is very large�Hayes and Jones 2000�.

The QTL analysis performed in this study was re-stricted to the cross of Steptoe and Morex barleylines. Neither cultivar was included in the set ofgenotypes investigated in this study, and these two

genotypes represent only a fraction of the genetic di-versity present in the genepool of cultivated barley forthe trait ‘malting quality.’ Hence, it is not surprisingthat some of the QTLs detected in this cross are notdistinguished by the candidate genes and, vice versa,that two of the mapped candidate genes do not coin-cide with a QTL. Nevertheless, the detected linkageof the candidates to QTLs on the Steptoe/Morex ge-netic map suggests that the candidate genes identifiedin the functional association approach are involved inpathways that commonly differ between malting-grade and feed-grade barley lines.

It is assumed that malting quality traits are affectedby the level of expression of the candidate genes.Hence, a further step to establish a causal linkbetween a candidate and a phenotype is an investiga-tion of the regulatory factors acting at the transcrip-tional level of the candidates. To this end our mappingapproach can be extended by ‘genetical genomics’�Jansen and Nap 2001�. Here, the expression profileof each gene in a segregating population is treated asa quantitative trait �eQTL�. Combined with a genetic

Table 7. Interval analysis of candidate genes mapped in Steptoe/Morex population segregating for malting quality traits

Chromosome Interval Marker 1Environment Malt extract Diastaticpower

Grain pro-tein

Alpha-amylaseactivity

1SIM SIM SIM SIM

LOD R2 LOD R2 LOD R2 LOD R2

1H MWG938c-HY01D13 HY01D13 2 4.0 0.1910 3.7 0.1712 3.0 0.1414 6.4 0.3215 3.6 0.17

3H HY06C05-ABG065a HY06C05 12 4.2 0.215 3.6 0.17

4H ABG472-HW01F04 HW01F04 10 4.8 0.2411 4.6 0.2215 5.9 0.29

ABG484-HY02J22 HY02J22 10 4.8 0.2411 3.9 0.1915 5.2 0.25

7H ABC255-HY05O13 HY05O13 10 3.7 0.1714 3.1 0.15

9 4.3 0.2110 5.6 0.2811 3.4 0.1612 4.3 0.2113 6.1 0.3114 4.1 0.2

1Environments are coded as in original phenotype data file �http://wheat.pw.usda.gov/ggpages/SxM/phenotypes.html�; 2SIM – simple intervalanalysis. Only significant LOD scores presented

167

map, eQTL analysis will pinpoint the regions of thegenome influential for gene expression. Then, onecould check whether those regions coincide with theQTLs for a phenotypic trait of interest. Regarding theresults obtained from our present analysis, a next stepwould be to apply the ‘genetical genomics’ approachto those candidates where the mapping position of thestructural gene does not coincide with a trait QTL�HY01C08, HY03C01, and HY03N21� �Table 6�. Inthis way it would be possible to test the correspon-dence between eQTL and trait QTLs.

Possible limitations of the functional associationapproach

The functional association approach searches for acorrelation of expression and phenotype profiles in aset of genotypes. The less cultivars are included in theanalysis, the more likely this correlation will be amatter of chance. Multiple significance testing alsoincreases the risk of type-I errors, i.e. to erroneouslyconclude the presence of a significant correlation. Themore analyses are performed on a data set, the morelikely the significance threshold will be exceeded bychance. One solution to the problem could be to ad-just the significance level by using the Bonferronicorrection �Curtin and Schulz 1998�. This simplymeans that the alpha value �i.e., � � 0.05� needs tobe lowered to account for the number of comparisonsbeing performed. However applying a more stringentsignificance threshold also increases the risk toexclude a valuable clone from further analysis,resulting in the basic problem of finding an optimalbalance of sensitivity versus selectivity.

Multiple hypothesis testing may also lead to spuri-ous positives in the matrix association approach.Here, a large number of different expression profilesis tested by identifying small improvements in thecorrelation found for the ‘major’ associated genes. Ifa sufficient number of profiles are tested, some willbe found to improve the correlation just by chance.Re-sampling using 100 and more randomized pheno-type sets allows to evaluate the robustness of the pro-cedure and to estimate p values for associations.Among the re-sampling methods available, the Jack-knife technique �Tukey 1977; Sokal and Rholf 2001�or its modification �delete-half-jackknifing, Felsen-stein 1995� seem most appropriate.

A severe restriction might be caused by the geneticlinkage in germplasm of similar pedigree. Cultivatedbarley is known to display extensive population

structure and extensive linkage disequilibrium �e.g.,Melchinger et al. 1994; Russell et al. 2003; unpub-lished results�. Therefore, it can be expected that dueto background selection a defined chromosome frag-ment containing a candidate gene may harbour addi-tional linked genes which are identical by descent andmay show correlated expression patterns without be-ing functionally involved in the expression of the traitunder consideration. To minimize such spurious cor-relations, it is important to choose genotypes that arenot related by descent.

In practical terms, the identification of candidategenes whose expression profiles correlate with malt-ing quality may be achieved by various statistical ap-proaches. Our data shows that both the matrixassociation approach and the bivariate correlationanalysis �using a ‘relaxed’ stringency of p � 0.1�identify similar candidate genes for the individualmalting quality components �data not shown�. How-ever, after combining all of the malting quality com-ponents to form one complex trait, the matrixcorrelation probably provides a better option to iden-tify those key genes that are of highest importance ina breeding program.

Although the present study was focused on malt-ing quality, the functional association approach can beapplied to any quantitatively inherited trait. Becauseour strategy is based on a random collection of geno-types, no experimental populations �e.g., segregatingpopulations�, near-isogenic lines, or mutants, whichare not available for most agronomic traits, arerequired. It capitalizes on the ‘Darwinian’ diversitypresent in breeding lines, cultivars, landraces, ornatural populations, which typically represent a muchlarger spectrum of allelic diversity than segregatingprogenies. Thus, the functional association approachmay provide an efficient link between functional ge-nomics and plant breeding.

Acknowledgedgements

We are grateful to A.E. Melchinger and N.S. Rostovafor stimulating discussions and U. Wobus, P.M.Hayes, and I. Grosse for critically reading the manu-script. This study was supported by grant 0312282from the plant genome program �GABI� of theBMBF.

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