Epistatic Interactions between Opaque2 Transcriptional ...Epistatic Interactions between Opaque2 Transcriptional Activator and Its Target Gene CyPPDK1 Control Kernel Trait Variation

Epistatic Interactions between Opaque2 TranscriptionalActivator and Its Target Gene CyPPDK1 Control KernelTrait Variation in Maize1[C][W][OA]

Domenica Manicacci*, Letizia Camus-Kulandaivelu, Marie Fourmann, Chantal Arar, Stephanie Barrault,Agnes Rousselet, Noel Feminias, Luciano Consoli, Lisa Frances, Valerie Mechin, Alain Murigneux,Jean-Louis Prioul, Alain Charcosset, and Catherine Damerval

University Paris-Sud, UMR 0320/UMR 8120 Genetique Vegetale, F–91190 Gif sur Yvette, France (D.M.);INRA, UMR 0320/UMR 8120 Genetique Vegetale, F–91190 Gif sur Yvette, France (L.C.-K., S.B., A.R., A.C.);Groupe Biogemma, ZI du Brezet, F–63028 Clermont-Ferrand cedex 2, France (M.F., C.A., L.C., L.F., A.M.);Groupe Limagrain, Domaine de Mons, F–63200 Aubiat, France (N.F.); INRA, UMR 0206 Chimie Biologique,F–78850 Thiverval-Grignon, France (V.M.); University Paris-Sud, UMR 8618 Institut de Biotechnologie desPlantes, F–91405 Orsay, France (J.-L.P.); and CNRS, UMR 0320/UMR 8120 Genetique Vegetale, F–91190 Gifsur Yvette, France (C.D.)

Association genetics is a powerful method to track gene polymorphisms responsible for phenotypic variation, since it takesadvantage of existing collections and historical recombination to study the correlation between large genetic diversity andphenotypic variation. We used a collection of 375 maize (Zea mays ssp. mays) inbred lines representative of tropical, American,and European diversity, previously characterized for genome-wide neutral markers and population structure, to investigatethe roles of two functionally related candidate genes, Opaque2 and CyPPDK1, on kernel quality traits. Opaque2 encodes a basicleucine zipper transcriptional activator specifically expressed during endosperm development that controls the transcription ofmany target genes, including CyPPDK1, which encodes a cytosolic pyruvate orthophosphate dikinase. Using statistical modelsthat correct for population structure and individual kinship, Opaque2 polymorphism was found to be strongly associated withvariation of the essential amino acid lysine. This effect could be due to the direct role of Opaque2 on either zein transcription,zeins being major storage proteins devoid of lysine, or lysine degradation through the activation of lysine ketoglutaratereductase. Moreover, we found that a polymorphism in the Opaque2 coding sequence and several polymorphisms in theCyPPDK1 promoter nonadditively interact to modify both lysine content and the protein-versus-starch balance, thus revealingthe role in quantitative variation in plants of epistatic interactions between a transcriptional activator and one of its targetgenes.

A major concern in molecular population and evo-lutionary genetics is the dissection of the genetic basisof natural variation of complex traits involved infitness, adaptation to local environments, and evolv-ability. Similarly, plant breeders are interested inscreening large collections of genetic resources inorder to identify haplotypes of interest involvedin agronomic trait variation (i.e. chromosome regions,genes, or even causative polymorphisms that could beused in crop genetic improvement programs). During

the last decade, a great amount of effort has beendevoted to improving molecular marker genotypingand statistical analyses in order to provide both fun-damental and applied researchers with efficient toolsto address their common interest in genes and net-works underlying complex traits.

Since the origin of agriculture, human populationgrowth has generated an increasing demand on plantproduction. Cereals represent a major part of humanand cattle diet in terms of starch and proteins, al-though they provide unbalanced protein intake, sincetheir most abundant proteins are generally poor inessential amino acids (Young et al., 1998). Maize (Zeamays), one of the main crops in the world, accumulatesa large amount of starch (75%280% of endosperm drymatter at maturity) and storage proteins (12%215%,mainly zeins) in its kernel endosperm. Such resourcesare accumulated to support seed germination andinitial seedling growth, which are major traits in bothagronomical and natural conditions. Many kernelmutants are available in maize, indicating that endo-sperm development is driven by the coordinatedexpression of several hundred genes (Neuffer and

1 This work was supported by Genoplante programs.* Corresponding author; e-mail [email protected] author responsible for distribution of materials integral to the

findings presented in this article in accordance with the policydescribed in the Instructions for Authors (www.plantphysiol.org) is:Domenica Manicacci ([email protected]).

[C] Some figures in this article are displayed in color online but inblack and white in the print edition.

[W] The online version of this article contains Web-only data.[OA] Open Access articles can be viewed online without a sub-

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Sheridan, 1980; Scanlon et al., 1994; Verza et al., 2005;Liu et al., 2008). Among nonmutant maize, phenotypicvariation has largely been observed for traits such asyield, endosperm content in lipids, proteins, andstarch, and protein quality. So far, few genes andalleles that control such natural variation among cul-tivated maize have been identified.

Physiological characterization of maize kernel de-velopment showed that starch accumulates from 12 to35 d after pollination, while accumulation of storageproteins, mainly zeins, begins at 10 to 15 d afterpollination and continues until maturity. Recently,the first proteomic analysis of maize kernel develop-ment (Mechin et al., 2007) led to the hypothesis thatpyruvate orthophosphate dikinase (PPDK) plays acrucial role in protein-versus-starch balance in thekernel. In C4 plants such as maize, PPDK is abundantin leaves and has been shown to play a major role inphotosynthesis (Burnell and Hatch, 1986). High PPDKactivity has also been detected in maize endosperm aswell as in non-C4 cereal seeds, where its function hasnot yet been clearly established. In nonphotosyntheticorgans, PPDK catalyzes the reversible conversion ofpyruvate, inorganic phosphate, and ATP into phos-phoenolpyruvate (PEP), inorganic pyrophosphate(PPi), and AMP. Mechin et al. (2007) observed thatthis protein drastically increases in quantity duringgrain development from 21 d after pollination onward(i.e. at the time when starch accumulation is slow-ing but protein accumulation is still ongoing). Inthe endosperm cytosol, PPDK may be involved bothin amino acid synthesis through PEP, a precursor ofaromatic amino acids, and in starch metabolismthrough PPi, which inhibits ADP-Glc synthesis, thuslimiting the first step of starch synthesis. An increasein PPDK activity in the endosperm cytosol could thusinduce a reduction in starch accumulation as well asan increase in protein content.

In the maize kernel, the great majority of PPDKenzyme is cytosolic and encoded by a specific gene(hereafter designated CyPPDK1; Sheen, 1991) whosetranscription has been shown to be up-regulatedby Opaque2 (Gallusci et al., 1996) through bindingto specific sequences in the CyPPDK1 promoter(Maddaloni et al., 1996). Opaque2, which encodes abasic Leu zipper transcriptional activator (Hartingset al., 1989; Schmidt et al., 1990), is specifically expressedduring endosperm development (Gallusci et al., 1994)and controls the transcription of many target genes,including the 14- and 22-kD zein genes, zeins consti-tuting the most abundant storage proteins in thekernel (Schmidt et al., 1992; Cord Neto et al., 1995),the Lys-ketoglutarate reductase/saccharopine dehy-drogenase that catalyzes Lys catabolism (Kemper et al.,1999), and CyPPDK1. The opaque2 mutant possesses asoft, starchy endosperm and a significant increase inLys and Trp content, these essential amino acids beingabsent from zeins (Landry et al., 2002). As a result,Opaque2 may play a complex role in the accumulationof storage compounds during kernel development

through direct (zein synthesis activation, Lys catabo-lism) and indirect (CyPPDK1 activation) effects.

While physiological studies are aimed at understand-ing the role of Opaque2 and CyPPDK1 maize mutations,nothing is known about the role of nonmutant allelediversity in maize phenotypic variation. To date, molec-ular approaches in genetically dissecting kernel qualitytraits in maize have focused on quantitative trait locus(QTL) mapping. Some of these studies highlighted thechromosome bin 6.05, where CyPPDK1 is located(Veldboom and Lee, 1994; Austin and Lee, 1998;Melchinger et al., 1998; Hirel et al., 2001; Ho et al.,2002; Blanc et al., 2006). By contrast, we found no reportof QTLs for yield, kernel number, or weight on chromo-some bin 7:01 that encompasses the Opaque2 gene. Veryfew QTL studies addressed phenotypic traits less com-plex than yield, such as kernel quality or composition,likely because these traits are more difficult to charac-terize in large populations. QTLs for total protein con-tent have been reported on chromosome bins 6.05(Melchinger et al., 1998) and 7.02 (C. Damerval, personalcommunication), and QTLs for carbohydrate contenthave been found by Thevenot et al. (2005) on bins 6.05(Suc content) and 7.01 (Fru and Glc contents). In a partialdiallele design, Lou et al. (2005) found a strong effect ofOpaque2 allelic variation on Lys and protein contentsamong F1 hybrids derived from 15 parents. In the closelyrelated species Sorghum bicolor, Rami et al. (1998) reportedQTLs for amylose content, a component of starch, andkernel hardness in a region syntenic to the Opaque2region, but no QTL was found close to CyPPDK1.Overall, these physiological and genetic studies makeboth CyPPDK1 and Opaque2 interesting candidate genesfor kernel phenotypic variation. Indeed, whether theirnatural sequence variation among diverse maize linesaffects kernel quality traits remains an open question.

The limits of QTL mapping approaches in identify-ing genes involved in natural phenotypic variation are2-fold. First, confidence intervals of QTL location arevery large, usually encompassing thousands of genes,because of the accumulation of few recombinationevents during the production of QTL mapping popu-lations. Second, genetic diversity of these populationsis low, since they derive from a limited number ofparents. Recent decreases in sequencing and genotyp-ing costs, as well as theoretical and statistical devel-opments, led to the emergence of association geneticsas an alternative method to identify gene polymor-phisms responsible for phenotypic variation. Associa-tion genetics takes advantage of existing collectionsand historical recombination to study the correlationbetween large genetic diversity and phenotypic vari-ation. Since the pioneering work of Thornsberry et al.(2001), this method has received increasing interestfrom plant geneticists (Flint-Garcia et al., 2003, 2005;Gupta et al., 2005; Yu and Buckler, 2006) and hasproved powerful in associating allelic variation ofcandidate genes to flowering time in various speciessuch as Arabidopsis (Arabidopsis thaliana; Hagenbladet al., 2004; Olsen et al., 2004; Aranzana et al., 2005),

Epistatic Control of Maize Kernel Quality

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Brassica nigra (Osterberg et al., 2002), pine (Pinus taeda;Gonzalez-Martinez et al., 2007), wheat (Triticum aesti-vum; Crossa et al., 2007), and maize (Thornsberry et al.,2001; Andersen et al., 2005; Camus-Kulandaivelu et al.,2006; Salvi et al., 2007; Ducrocq et al., 2008). In additionin maize, polymorphisms from genes involved in bio-synthetic pathways were found to be associated withkey agronomic traits such as digestibility (Guillet-Claude et al., 2004a, 2004b), kernel composition (Wilsonet al., 2004; Harjes et al., 2008), and phenylpropanoidand flavonoid contents (Szalma et al., 2005). A majorconcern in association studies is that genetic structurewithin the sample can generate linkage disequilibrium(LD) between genetically unlinked loci, leading tofalse-positive associations. Such false-positive associa-tions could be limited by considering both populationstructure and relatedness among individuals in amixed-model analysis (Yu et al., 2006).

Maize is a particularly suitable species for associationgenetic studies, since it contains a substantial amount ofgenetic diversity, local LD rapidly decreases with phys-ical distance, remaining low at about 2 kb on averagefor a diverse population (Remington et al., 2001), andpopulation structure may easily be characterizedthrough neutral markers (Thornsberry et al., 2001; Liuet al., 2003). The aim of this study was to test forassociations between CyPPDK1 and Opaque2 gene poly-

morphisms and kernel quality traits. We first charac-terized the molecular diversity of both candidate genesamong a large collection representative of cultivatedmaize. Second, we characterized this plant collectionfor kernel traits such as kernel, endosperm, and embryoweights, vitreousness, and kernel composition instarch, lipids, total proteins, individual amino acids,and soluble carbohydrates (Table I), taking advantageof the high-throughput method of near infrared re-flectance spectroscopy (NIRS). The maize collectionwe used was previously genotyped for neutral simplestrand repeat (SSR) markers in order to evaluate pop-ulation structure (Camus-Kulandaivelu et al., 2006). Weadditionally estimated pairwise kinship coefficientsbetween inbred lines from the same set of markers.We finally tested for associations between these pheno-typic traits and either individual gene polymorphismsor combined polymorphisms from CyPPDK1 andOpaque2 in order to assess possible epistatic interactionbetween the transcriptional activator and its target gene.

RESULTS

Variation in Kernel Traits

Kernel quality traits (Table I) were highly variableamong inbred lines and showed very high heritability

Table I. Phenotypic traits measured in the collection of 375 maize inbred lines

Abbreviation Method Definition of the Trait

FT Direct Flowering time for male inflorescence, in degree-daysTKW Direct Kernel weight (based on 1,000 grains)KDM NIRS Percentage of kernel dry matterASH NIRS Ashes, in percentage of dry matterSTAR NIRS Starch weight, in percentage of dry matterPROT NIRS Total protein weight, in percentage of dry matterLIP NIRS Total lipid weight, in percentage of dry matterSGL NIRS Soluble carbohydrate, in percentage of dry matterWALL NIRS Wall, in percentage of dry matterAMAMI NIRS Amylose-to-amylopectine weight ratioSATUR NIRS Saturated fatty acid, in percentage of dry matterSACOSE NIRS Suc to total sugars, in percentage of dry matterGLCFRU NIRS Glc 1 Fru to kernel, in percentage of dry matterTHREO NIRS Thr to kernel, in percentage of dry matterMETHI NIRS Met to kernel, in percentage of dry matterISOLEU NIRS Ile to kernel, in percentage of dry matterLEU NIRS Leu to kernel, in percentage of dry matterPHENYL NIRS Phe to kernel, in percentage of dry matterLYSIN NIRS Lys to kernel, in percentage of dry matterSAA NIRS Total soluble amino acids to kernel, in percentage of dry matterKW NIRS Kernel weightEW NIRS Endosperm weightEMB NIRS Embryo to kernel, in percentage of dry matterEND NIRS Endosperm to kernel, in percentage of dry matterAVITRO NIRS Vitreous endosperm to kernel, in percentage of dry matterVITRO NIRS Vitreous endosperm to total endosperm, in percentage of dry matterASP Calculated Asp-derived amino acids (THREO 1 METHI 1 ISOLEU 1 LYSIN)P/S Calculated Protein to starch (PROT/STAR)P/L Calculated Protein to lipids (PROT/LIP)L/S Calculated Lipids to starch (LIP/STAR)PCA1 to PCA8 PCA Coordinates on the eight first PCA axes from NIRS traits

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(Table II). Several of the kernel quality traits wephenotyped were strongly correlated. For instance,we found positive correlation among traits linked toprotein content (r . 0.91 among PROT, THREO, METHI,ISOLEU, LEU, PHENYL, SAA, ASP; Table I) except LYSIN,which correlates positively but to a lower extent thanprotein traits (0.43 , r , 0.66), a negative correlationbetween starch and protein content (r 5 20.74 be-tween STAR and PROT), a positive correlation betweenembryo size and saturated fatty acid content (r 50.82 between EMB and SATUR), and a positive correlationbetween kernel and endosperm weights (r 5 0.95between KW and EW). These traits were synthesizedinto eight principal component analysis (PCA) axesaccounting for more than 95% of the global phenotypicvariance (Table II). The first PCA axis explains as muchas 39.0% of the total variation and correlates positivelyto all traits related to protein content (PROT, SAA, THREO,METHI, ISOLEU, LEU, PHENYL; Table I) and negatively tostarch content (STAR). The second PCA axis (r2 5 16.3%)is correlated to endosperm vitreousness (VITRO, AVITRO),which is known to affect kernel maturation, as indi-cated by the correlation to soluble sugar content (SGL).

The third PCA axis (r2 5 14.1%) is positively correlatedto embryo weight (EMB) and lipid content (LIP, SATUR)and negatively correlated to endosperm weight (EW).

Most phenotypic traits show a significant variationamong groups defined by Structure software (see r2

group in Table II). Many kernel traits (i.e. TKW, ASH, SGL,AMAMI, SATUR, SACOSE, GLCFRU, KW, EW EMB, END, AVITRO

VITRO, PCA1, PCA3, PCA5, PCA6, and L/S) show a strongvariation among groups (P , 0.0001), population struc-ture accounting for 5.51% for L/S to more than 20% oftheir variance for SATUR, KW, VITRO, and PCA3. Otherphenotypic traits are either less variable among groups(such as PROT, LIP, STAR, THREO, METHI, LYSIN, ASP, PCA2, PCA4,PCA8, P/S; r2 group varying from 1.66% to 4.98%) or showno significant structure (KDM, WALL, ISOLEU, LEU, PHENYL,SAA, PCA7, P/L). For most structured traits, the intragroupaverage phenotypic values and SD are given in Table III.

Single Nucleotide Polymorphism and

Insertion/Deletion Polymorphism Genotyping

Both CyPPDK1 and Opaque2 genes show a sizeablenumber of polymorphisms, with an average propor-

Table II. Descriptive statistics for phenotypic traits and correlations between NIRS traits and the eight first PCA axes

Asterisks indicate P values as follows: * P , 0.05, ** P , 0.01, *** P , 0.001.

Group Mean SD h2a r2 GroupbPCA1c

PCA2cPCA3c

PCA4cPCA5c

PCA6cPCA7c

PCA8c

r2d: 39.0 16.3 14.1 7.2 6.8 4.5 4.3 3.5r2 groupb: 6.5*** 1.0* 34.8*** 4.8** 17.8*** 11.2*** 0.5 4.2***

TKW 226.06 59.1 14.6***KDM 89.64 0.24 77.9 0.9 0.268 0.045 0.121 20.301 0.002 0.656 0.496 0.331ASH 1.59 0.1 85.1 11.0*** 0.703 20.437 0.050 20.416 0.102 20.195 0.001 20.108PROT 13.16 1.52 92.1 3.2*** 0.964 0.222 20.083 0.019 0.011 0.026 20.086 20.048LIP 3.51 0.79 89.8 5.0*** 0.228 20.474 0.510 0.562 0.022 0.200 0.199 20.061STAR 68.95 2.59 88.2 3.2** 20.860 0.361 0.189 20.096 0.095 0.017 20.210 20.046SGL 1.23 0.38 92.8 7.2 0.334 20.743 20.353 0.164 0.254 20.012 20.115 0.163WALL 11.5 1.21 84.4 0.9 0.376 20.512 20.438 20.286 20.236 20.157 0.371 0.164AMAMI 31.57 3.73 91.3 6.9*** 0.176 0.237 0.251 0.197 20.454 0.119 20.518 0.552SATUR 0.11 0.02 94.4 23.1*** 0.228 20.357 0.739 0.193 0.353 20.007 0.081 0.115SACOSE 87.33 2.71 90.6 12.2*** 20.329 0.492 0.300 0.167 20.528 0.149 0.186 20.380GLCFRU 0.96 0.1 83.1 13.6*** 0.233 0.001 0.126 0.327 20.478 20.623 0.338 0.202THREO 0.5 0.05 92.0 3.5** 0.960 0.108 20.126 0.089 20.064 0.007 20.059 0.050METHI 0.24 0.03 92.0 2.1* 0.913 0.224 20.152 0.179 20.149 0.023 0.083 20.017ISOLEU 0.53 0.06 92.0 1.4* 0.946 0.233 20.136 0.061 20.010 0.062 20.034 20.126LEU 1.83 0.23 91.9 1.1 0.882 0.378 20.136 0.191 0.010 0.081 0.009 20.079PHENYL 0.76 0.1 91.9 0.9 0.922 0.277 20.154 0.091 0.035 0.072 20.056 20.135LYSIN 0.34 0.03 86.9 4.8*** 0.725 20.516 20.055 0.016 0.067 0.047 20.241 0.007SAA 4.22 0.5 91.9 1.6* 0.939 0.254 20.144 0.136 20.003 0.066 20.030 20.076KW 337.62 23.35 85.0 25.9*** 20.496 0.395 20.531 0.426 0.194 0.053 0.127 0.189EW 282.26 19.72 83.8 18.8*** 20.469 0.509 20.510 0.309 0.272 20.067 0.146 0.181EMB 9.71 0.98 91.0 18.4*** 0.020 20.339 0.828 0.390 0.053 0.003 0.022 20.045END 83.64 1.44 87.2 14.0*** 0.307 0.513 0.161 0.131 0.703 20.222 0.116 0.026AVITRO 55.33 6.6 93.1 27.4*** 0.387 0.572 0.553 20.367 0.109 20.154 0.055 0.144VITRO 65.23 7.65 93.8 29.9*** 0.416 0.537 0.581 20.364 0.046 20.133 0.027 0.147ASP 3.46 0.4 92.1 1.9*P/S 0.19 0.02 90.5 3.5***P/C 3.93 0.99 88.2 1.4L/S 0.05 0.01 86.4 5.4***

aHeritability. bDetermination coefficient (%; i.e. part of phenotypic variance explained by population structure). cEight first PCA axesobtained from kernel traits. dPercentage of kernel trait variance explained by PCA axes.



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tion of pairwise nucleotide differences among se-quences varying from 0.006 to 0.015 (Table IV). Alongthe sequences, one polymorphism was found every 23to 37 bp on average, with about half of them beingsingletons. LD among informative polymorphisms, of-ten high among closely related sites, decreased signif-icantly with physical distance (data not shown), leadingto an important number of haplotypes and high hap-lotypic diversity. No evidence of selective events duringthe history of these genes was found on the basis ofallelic frequency distribution among single nucleotidepolymorphisms (SNPs; nonsignificant Tajima’s D; TableIV). However, the high number of haplotypes given thelevel of nucleotide diversity led to significant Strobeck’sstatistics and Fu’s Fs for the middle region of CyPPDK1and close to significant Strobeck’s statistics for theOpaque2 coding region (Table IV).

Fourteen polymorphisms from CyPPDK1 and 10from Opaque2 were chosen from these sequence data,based on their position, frequency, LD, and potentialfunction, and were then genotyped on the 375 inbredlines (Fig. 1). Polymorphism C4879 in CyPPDK1 leadsto a Leu/Phe amino acid replacement in the proteinsequence, and polymorphism O1606 in Opaque2 leadsto a Pro/Ala replacement. Other polymorphisms inboth genes are either synonymous or located in non-coding regions. Most polymorphisms show balancedallele frequency, except two polymorphisms in Opaque2,

where one allele is present in fewer than 30 inbredlines (OP979 with 26 G alleles and O3243 with 12 Talleles; Fig. 1), suggesting that statistical power may bereduced for association studies involving those sites.

Although significant LD is observed for almost allpairs of polymorphisms within CyPPDK1 (P , 0.0001)and many pairs of polymorphisms in Opaque2,r2 values higher than 0.50 are observed only for SNPsin the CyPPDK1 promoter and some SNPs in theOpaque2 promoter (Fig. 2). On the contrary, LD be-tween genes is very low (r2 , 0.10, P . 0.01) and, giventhe large number of pairs of polymorphisms that weretested, could be considered as nonsignificant. For bothgenes, numerous haplotypes are observed among the375 inbred lines. For CyPPDK1, 57 different haplotypesare observed out of 334 inbred lines genotyped forthe 14 chosen polymorphisms, 30 of these haplo-types being observed for at least two inbred lines.For Opaque2, 19 different haplotypes are observed outof 202 inbred lines genotyped for the 10 chosen poly-morphisms, 12 of these haplotypes being observed forat least two inbred lines. The number of minimumrecombination events necessary to obtain these hap-lotypes considering no recurrent mutation is 11 andtwo for CyPPDK1 and Opaque2, respectively.

All polymorphisms in CyPPDK1 show a high varia-tion among groups (P , 0.0001), population structureexplaining pseudo-r2 5 12.9% to 40.8% of allele frequency

Table III. Average phenotypic value (SD) per genetic group as defined using neutral markers (Camus-Kulandaivelu et al., 2006) for phenotypictraits that show strong effects of genetic structure (P , 0.01)

Groupa NbFT TKW PROT LIP STAR KW EMB END EW AVITRO VITRO PCA1 P/S L/S

Trop 76.5 1,201.8(184.3)

247.4(63.4)

13.29(1.64)

3.45(0.76)

68.54(2.74)

342.5(21.7)

9.56(0.95)

84.4(1.4)

287.7(18.8)

55.18(7.00)

64.59(8.11)

0.14(1.07)

0.195(0.031)

0.051(0.012)

EF 66.6 950.4(106.5)

207.6(54.7)

13.14(1.57)

3.60(0.61)

69.29(2.81)

325.7(201)

10.12(0.80)

83.9(1.6)

275.0(18.0)

59.60(5.69)

70.33(6.45)

0.07(1.02)

0.191(0.029)

0.052(0.010)

NF 54.8 931.6(89.7)

195.9(53.1)

13.71(1.36)

3.83(1.04)

68.18(2.70)

321.4(24.6)

10.30(1.16)

83.7(1.5)

269.5(20.2)

58.90(6.17)

69.83(7.01)

0.49(0.92)

0.202(0.026)

0.057(0.017)

CBD 148.0 1,034.0(118.9)

233.0(54.7)

12.96(1.44)

3.42(0.76)

69.20(2.31)

344.6(20.2)

9.46(0.87)

83.2(1.2)

286.1(18.0)

52.51(5.39)

61.95(6.18)

20.16(0.94)

0.188(0.026)

0.050(0.012)

SS 23.1 1,065.7(110.7)

254.4(46.2)

13.04(1.51)

3.25(0.66)

69.44(2.21)

350.9(17.7)

9.17(0.75)

83.2(1.0)

292.1(15.1)

53.30(5.03)

62.82(5.84)

20.17(0.96)

0.188(0.027)

0.047(0.010)

aGenetic groups defined by Camus-Kulandaivelu et al. (2006) as follows: Trop, tropical origins; EF, European Flints; NF, Northern Flints; CBD, CornBelt Dent; SS, Stiff Stalk. bSize of each group, as the sum of individual memberships across the 375 inbred lines.

Table IV. CyPPDK1 and Opaque2 nucleotide diversity and neutrality tests

Asterisks indicate P values as follows: * P , 0.10, ** P , 0.05.

Gene Region Sizea Nb SNPic SNPsd SNPre bp/SNPf pg uwh Di Hj divHk S l Fsm

CyPPDK1 Promoter to intron 2 2,024 18 42 27 3 29.3 0.0129 0.0138 20.384 15 0.961 0.933 21.479Exon 3 to exon 9 1,638 24 21 23 14 37.2 0.0062 0.0075 20.717 18 0.942 0.997** 24.370Exon 9 to 3# UTR 2,264 16 37 46 9 27.3 0.0090 0.0115 20.923 13 0.950 0.827 20.490

Opaque2 Promoter 887 19 23 6 26.2 0.0154 0.0109 1.632 10 0.854 0.286 1.7915# UTR to 3# UTRn 1,941 18 43 28 25 23.4 0.0114 0.0124 20.353 16 0.987 0.984* 22.608

aSize of the sequenced region in base pairs. bSample size. cNumber of informative substitution polymorphisms. dNumber ofsingleton substitution polymorphisms. eNumber of replacement substitution polymorphisms. fAverage number of base pairs betweenSNPs. gAverage number of nucleotide differences per base pair between sequences. hNumber of polymorphic sites per basepair. iTajima’s neutrality index. jHaplotype number. kHaplotype diversity. lStrobeck’s statistic (Strobeck, 1987). mFu’s haplotype statistic(Fu, 1997). nData from Henry et al. (2005).

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variation. In Opaque2, the effect of genotypic group issignificant for all SNPs in the promoter (P , 0.0003,pseudo-r2 5 10.3%–32.0%). In contrast, only one SNPin the coding region shows a strong association withpopulation structure (O3243; P 5 0.0026, pseudo-r2 517.5%), while the two other SNPs show low (O3988; P 50.0205, pseudo-r2 5 6.9%) or no (O1866; P 5 0.2336;pseudo-r2 5 2.7%) allelic variation among groups.

Phenotype-Genotype Associations

Models of association mapping that control fordifferent levels of population structure and individualkinship were compared (Supplemental Fig. S1), andtwo models were retained: model Q that controls forpopulation structure and model Q1KL that controlsfor both population structure and individual kinshipestimated following Loiselle et al. (1995).

Associations between Kernel Phenotypes andCandidate Genes

Significant associations between phenotypic traitsand polymorphisms in CyPPDK1 and Opaque2 arereported in Table V. In CyPPDK1, the major associa-

tions concern SNP C817, with many kernel traitsrelated to protein and amino acid content (i.e. PROT,THREO, METHI, ISOLEU, LEU, PHENYL, ASP, and SAA contents)and protein-starch (P/S) ratio as well as PCA1 butexcluding Lys content (LYSIN). In Opaque2, SNP O3988is associated with many kernel traits, such as kerneland endosperm weight (KW, EW), Lys (LYSIN), Thr(THREO), and starch (STAR) contents, protein-starch ratio(P/S), and PCA1. OP1496, OP1539-2, and OP1600, whichshow strong LD among themselves, associate withEND and PCA5. These results indicate that associa-tions between phenotypic traits and polymorphismsat CyPPDK1 and Opaque2 are significant, independentlyfrom population structure and individual kinship.

Epistatic Interactions between CyPPDK1 and Opaque2

Since Opaque2 and CyPPDK1 were shown to befunctionally related (Maddaloni et al., 1996), we in-vestigated whether pairs of SNPs from CyPPDK1 andOpaque2 have complementary or synergistic effects onkernel phenotypes. In order to do so, we tested for theeffect of each CyPPDK1-Opaque2 combination eitherunder an additive model or with interaction on each

Figure 1. Allelic frequency at CyPPDK1 and Opaque2polymorphisms among 375 inbred lines. Black, gray,and white bars indicate different alleles. Polymor-phism positions are indicated along genes (solidarrows, polymorphisms in exons; dashed arrows,polymorphisms in introns; SNP names starting withOP or CP are located upstream the ATG start codon).For each gene, one SNP leads to a change in anamino acid.



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phenotypic trait. Combinations of polymorphismsthat are significantly associated with phenotypic traitsare presented in Table VI. While O3988 SNP individ-ually explains 1.2% of protein-starch ratio (either P/S

or PCA1) variation, Table VI shows that this SNP incombination with a SNP in the CyPPDK1 promoter(CP125, CP161, CP509, or CP515) explains up to 7.9%of this variation. For instance, allele CP509-C com-bined with allele O3988-T induces a strong decreasein starch content and correlatively a strong increasein protein content and protein-starch ratio, as com-pared with any other allelic combination (Fig. 3A).The interaction between these SNPs also affects manyprotein- and amino acid-related traits (PROT, METHI,PHENYL, ISOLEU, LEU, SAA) and starch content (STAR). To alower extent, the same phenotypic traits are associatedwith an epistatic combination of the promoterCyPPDK1 SNPs with O1866, the latter being in veryslight LD with O3988 (r2 , 0.1, P , 0.01; Fig. 2).

Combined effects of Opaque2 and CyPPDK1 SNPs arealso found on Lys content. The additive combination ofO3988 and C2252, a CyPPDK1 SNP located in intron 5,explains a significant part of Lys content variation (Fig.3B). Finally, LYSIN is associated with a nonadditive com-bination of SNPs O1866 in the Opaque2 coding sequenceand CP125 in the CyPPDK1 promoter (Fig. 3C).

DISCUSSION

Phenotypic Variation of Kernel Quality among MaizeInbred Lines

We report phenotypic variation in kernel qualitytraits among an extended collection of maize inbredlines encompassing material from tropical, North

American, and European origins. Most traits we mea-sured can be summarized into three main PCA axesthat together explain a major part (70%) of kernelphenotypic variation among our collection. The mainPCA axis (39%) accounts for protein-versus-starchbalance, consistent with the well-known negative cor-relation between protein and starch content in maizeendosperm (Goldman et al., 1993). The second PCAaxis (16%) accounts for endosperm texture, or vitre-ousness, and correlates negatively to Lys, solublecarbohydrate, and wall contents. The negative corre-lation between Lys content and vitreousness likelyresults from the impact of zeins, storage proteinsdevoid of Lys, on starch granule cohesion and thusendosperm texture (Landry et al., 2004). Finally, thethird PCA axis (14%) accounts for lipid content. Over-all, this allows us to describe three major and inde-pendent sources of kernel quality variation, whichcharacterize our collection of 375 maize inbred lines.

Using genome-wide neutral markers, Camus-Kulandaivelu et al. (2006) showed that this collectionis structured into five genotypic groups that may beassigned, through independent knowledge of inbredorigins and pedigrees, to Tropical, Northern Flint,European Flint, Corn Belt Dent, and Stiff Stalk origins.Most kernel quality traits we measured show signifi-cant variation among these groups. Based on both thevariation in kernel traits and neutral marker-derivedgenetic structure, we can characterize three groups ofcontrasting kernel phenotypes: (1) materials of tropicalorigin with heavy grains, large endosperm, and nospecific kernel composition; (2) early Northern Flintand European Flint materials with the smallest grains,small endosperm and relatively large embryo, highprotein and lipid contents and low starch content, as

Figure 2. Half-matrix of LD amongOpaque2 and CyPPDK1 poly-morphisms. Multiallelic polymor-phisms shown in Figure 1 wereconverted into biallelic data (see‘‘Materials and Methods’’). [See on-line article for color version of thisfigure.]

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well as high vitreousness consistent with the classicaldesignation of this group (flint meaning vitreous); and(3) materials from the large maize-producing NorthAmerican regions (i.e. Corn Belt Dent and Stiff Stalk)with grains of very large weight and high starchcontent, low protein and oil content, and low vitreous-ness. Because grains typical of cultivated forms havebeen found in archaeological sites dating as far back as6,250 years ago (Benz and Long, 2000; Benz, 2001;Piperno and Flannery, 2001; Piperno et al., 2004), andbecause the cultivated form of domestication geneswas found to be fixed among fossil maize soon afterdomestication (Jaenicke-Despres et al., 2003), kernelsize and starch content are considered to have under-gone human selection since the early stages of domes-tication. Later, selection may have diverged duringlocal adaptation to diverse climates and grain usage.Northern Flint materials have been cultivated innortheastern America by Native Americans since ap-proximately the 10th century (Smith, 1989) and gaverise to European Flint after being introduced to Europein the early 16th century (Rebourg et al., 2003). Thesematerials thus resulted from several centuries of se-lection for their ability to yield under temperate con-ditions (i.e. with short cycles). Although such selectionmay have mainly concerned the length of the biological

cycle and flowering date, it may have had side effectson plant height and yield, leading to smaller grains andlower starch accumulation. Additionally, their specifi-cally high vitreousness may be due to either a specificgenetic origin or specific selection processes for adap-tation to culinary practices and possibly preventionof postharvest losses. Finally, the high average kernelweight, endosperm weight, and starch content ob-served for the Corn Belt Dent and Stiff Stalk materialsare consistent with the well known intense selection foryield and starch content that was applied to thesematerials by plant breeders during the 20th century(Duvick and Cassman, 1999). The inbred lines derivedfrom the Iowa Stiff Stalk synthetic group clusterseparately from the Corn Belt Dent materials basedon neutral markers and display particularly highkernel weight and starch content, which is consistentwith the major role played by these materials, inparticular through B14, B73, and their derivatives, inthe genetic gain that has been achieved in the U.S.Corn Belt. The trend toward higher kernel size of StiffStalk materials may have been enhanced by theirgeneral use as females in hybrid combinations withmale ‘‘non-Stiff Stalk’’ materials, such as Mo17, asillustrated by Pioneer breeding programs (Tracy andChandler, 2006).

Table V. Significant associations between kernel quality traits and CyPPDK1 or Opaque2 polymorphisms in a collection of 375 maize inbred lines

Asterisks indicate FDR values as follows: * FDR , 0.05, ** FDR , 0.01, *** FDR , 0.001. n.a., Nonavailable value due to the absence ofconvergence of the mixed model.

Gene Trait SNP PadjustQa PQ1K

b r2 (%)c EffNd EffQ

e EffQ1Kf

CyPPDK1 PROT C817 0.0007** 0.0008* 1.94 0.7665 0.7333 0.9332THREO C817 0.0033** 0.0011* 1.66 0.0273 0.0264 0.0328

CIDP33 0.0452* 0.0031* 1.36 0.0185 0.0187 0.0220METHI C817 0.0041** 0.0008* 2.19 0.0190 0.0158 0.0208ISOLEU C817 0.0005** 0.0006** 2.14 0.0366 0.0339 0.0422LEU C817 0.0066** 0.0014* 2.04 0.1194 0.1137 0.1369PHENYL C817 0.0013** 0.0008* 2.15 0.0564 0.0526 0.0647ASP C817 0.0037** 0.0010* 1.97 0.2119 0.1999 0.2443SAA C817 0.0022** 0.0008* 2.08 0.2696 0.2540 0.3103PC1 C817 0.0155* 0.0036 1.13 0.3862 0.3752 0.5311PC2 CP161 0.0358* 0.0016* 2.01 20.3450 20.3318 20.3416PC6 C1060 0.0396* 0.0071 0.63 20.3601 20.2288 20.2777P/S C817 0.0016** 0.0009* 1.75 0.0135 0.0127 0.0170

Opaque2 ASH O3988 0.0001*** ,1024*** 1.48 20.0725 20.0265 20.0671STAR O3988 0.0013** 0.0006** 1.43 1.5226 0.4294 n.a.THREO O3988 0.0381* 0.0024* 1.17 20.0261 20.0062 20.0272LYSIN O3988 0.0001*** ,1024*** 1.48 20.0289 20.0111 20.0293KW O3988 0.0110* n.a. n.a. 8.559 5.257 10.91EW O3988 0.0057* n.a. n.a. 7.138 4.559 9.922END OP1496 0.0007** n.a. n.a. 1.0054 0.6026 0.8726

OP1539-2 0.0037** 0.0006** 2.90 1.0012 0.7072 0.8225OP1600 0.0144** n.a. n.a. 0.9328 0.5153 0.7699

PC1 O3988 0.0026** 0.0004** 1.22 20.5434 20.1550 20.5662PC5 OP1496 0.0100** 0.0007** 2.04 0.7899 0.3672 0.5513

OP1539-2 0.0131** 0.0005** 1.78 0.7946 0.4862 0.5427P/S O3988 0.0238* 0.0018* 1.23 20.0142 20.0035 20.0143

aP for the Q model that controls for population structure, estimated after 10,000 permutations using TASSEL software. bP for the Q1K modelthat controls for population structure and kinship. cPart of the phenotypic variance explained by the SNP in the Q1K model. dAllelic effect(i.e. difference between average phenotypic values for both allelic forms). eAllelic effect corrected for population structure. fAllelic effectcorrected for population structure and individual kinship.



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Genetic Structure of Polymorphism at CyPPDK1 andOpaque2 Candidate Genes

Both CyPPDK1 and Opaque2 genes show a high level

of polymorphism among the collection of inbred lines,

as compared with other genes studied in the same spe-

cies (Tenaillon et al., 2001), with one SNP every 30 and24 bp for each gene, respectively. For both the wholeregion studied in CyPPDK1 and the coding regionin Opaque2, the data show a significant excess ofhaplotypes (Strobeck statistic; Table IV) with negativeand significant (for CyPPDK1) or close to significant

Table VI. Significant associations between kernel phenotypic traits and combined Opaque2 and CyPPDK1 SNPs using the mixed Q1K model

Boldface entries are P values for the genetic part of the model with FDR lower than 0.001, FDR being lower than 0.01 in all reported cases.

Opaque2 SNP: O1866 O3988

CyPPDK1 SNP: CP125 CP161 CP509 C2042 C4879 C5098 CP125 CP161 CP509 CP515 C2252

PROT P-Oa 0.02199 0.12472 0.04227 0.02104 0.00030 0.00404 0.00029 0.00025P-Cb 0.00798 0.02548 0.80667 0.20828 0.00130 0.01985 0.00314 0.00079P-Ic 0.00023 0.00040 0.00739 0.00013 0.00032 0.00076 0.00011 0.00091P gend 0.00054 0.00096 0.00048 0.00016 0.00010 0.00037 0.00006 0.00010r2 gene 4.9 4.7 4.9 5.5 6.8 5.9 7.1 6.7

STAR P-O 0.29950 0.00002 0.00020 0.00003 0.00001P-C 0.11632 0.03623 0.33573 0.03250 0.02982P-I 0.00012 0.00195 0.00474 0.00109 0.00543P gen 0.00098 0.00007 0.00005 0.00005 0.00006r2 gen 4.5 7.0 7.0 7.1 7.0

THREO P-O 0.03409 0.04128 0.00011 0.00192 0.00012 0.00007P-C 0.00825 0.18436 0.00118 0.01997 0.00141 0.00049P-I 0.00010 0.00020 0.00030 0.00178 0.00021 0.00140P gen 0.00039 0.00043 0.00005 0.00041 0.00004 0.00004r2 gen 5.0 5.0 7.2 5.8 7.2 7.2

ISOLEU P-O 0.03110 0.16577 0.05945 0.12247 0.02875 0.00112 0.01074 0.00114 0.00103P-C 0.01717 0.03850 0.86261 0.74040 0.24445 0.00241 0.03998 0.00594 0.00138P-I 0.00038 0.00057 0.00731 0.00328 0.00016 0.00046 0.00140 0.00014 0.00118P gen 0.00113 0.00173 0.00074 0.00206 0.00021 0.00035 0.00125 0.00017 0.00039r2 gen 4.6 4.4 4.8 4.3 5.4 6.1 5.2 6.5 6.0

ASP P-O 0.08277 0.04059 0.00102 0.00101 0.00089P-C 0.84162 0.30403 0.00357 0.00729 0.00187P-I 0.00648 0.00015 0.00060 0.00023 0.00178P gen 0.00078 0.00019 0.00041 0.00022 0.00047r2 gen 4.5 5.5 7.0 7.3 7.0

SAA P-O 0.07375 0.03453 0.00118 0.00116 0.00103P-C 0.88764 0.27023 0.00310 0.00714 0.00174P-I 0.00608 0.00011 0.00061 0.00020 0.00164P gen 0.00072 0.00014 0.00044 0.00022 0.00048r2 gen 4.8 5.6 5.9 6.3 5.8

LYSIN P-O 0.19722 ,1025

P-C 0.14640 0.00564P-I 0.00001 n.s.P gen 0.00004 ,1025

r2 gen 6.6 9.5P/S P-O 0.03448 0.19067 0.03678 0.00006 0.00108 0.00006 0.00004

P-C 0.00748 0.05768 0.15728 0.00109 0.02932 0.00223 0.00068P-I 0.00006 0.00037 0.00009 0.00019 0.00058 0.00006 0.00058P gen 0.00024 0.00124 0.00019 0.00002 0.00010 0.00001 0.00002r2 gen 5.3 4.5 5.3 7.6 6.7 7.9 7.5

PCA1 P-O 0.03200 0.19590 0.03492 0.00002 0.00028 0.00001 0.00001P-C 0.00807 0.07553 0.12793 0.00292 0.05955 0.00578 0.00147P-I 0.00002 0.00035 0.00006 0.00040 0.00081 0.00015 0.00108P gen 0.00011 0.00113 0.00013 0.00002 0.00004 0.00001 0.00002r2 gen 5.4 4.3 5.2 7.5 7.0 7.8 7.4

ASH P-O 0.04503 0.07068 ,1025 0.00002 ,1025

P-C 0.01827 0.02650 0.02753 0.25891 0.03305P-I 0.00002 0.00014 0.00817 0.00173 0.00511P gen 0.00012 0.00068 0.00003 ,1025 0.00003r2 gen 5.5 4.4 7.4 8.7 7.4

aP for the Opaque2 SNP. bP for the CyPPDK1 SNP. cP for the interaction between SNPs. dP for the genetic part of the model (all threeprevious sources of variation, excluding the interaction if not significant). eCoefficient of determination for the genetic part of the model.

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(for Opaque2) F values, indicating an excess of rarehaplotypes as compared with the expectation underneutrality (Fu, 1997). This suggests that both genesmay have evolved under purifying selection, probablydue to their important functions in maize.

For all SNPs we genotyped in the 375 inbred lines,we observed a strong differentiation in allele fre-quency among genetic groups, except for two SNPsin the Opaque2 coding region. Since population struc-ture determined by neutral markers is clearly linkedto the geographical origin of inbred lines (Camus-Kulandaivelu et al., 2006), variation in allelic frequencyin candidate genes could result from either localadaptation, due to natural or human selection of traitsdetermined by these genes, or genetic drift, due toreproductive isolation among groups for several tensor hundreds of generations. We found no correlationbetween allelic frequency and average phenotypicvalues among groups (data not shown), suggestingthat no CyPPDK1 or Opaque2 SNPs are involved inlocal adaptation for kernel quality. Because the twocauses of correlation between genetic and phenotypicvariations may not be easily distinguished, the popu-lation structure should be taken into account in asso-ciation analyses on the whole collection in order toavoid spurious associations (i.e. those due to geneticdrift).

Epistatic Interaction between Opaque2 and CyPPDK1Promoters Modifies Kernel Protein-Starch Balance

We have shown that CyPPDK1 is significantly asso-ciated with diverse phenotypic traits linked to aminoacid and protein contents as well as with the protein-starch ratio. These associations all involve SNP C817, apolymorphism in the CyPPDK1 coding region, sug-gesting that this gene has a direct effect on amino acidsynthesis. In the endosperm, CyPPDK1 reversibly cat-alyzes the conversion of pyruvate into PEP. CytosolicPEP is directly involved in the synthesis of aromaticamino acids, including Phe, and is indirectly involvedvia oxaloacetate in the synthesis of Asp-derived aminoacids, such as Thr, Ile, Met, and Lys. Interestingly, SNPC817 is associated with contents of all of these aminoacids, except Lys. Consistently, all of these amino acidcontents are strongly correlated with each other (r .0.9) among our 375 inbred lines, while LYSIN shows a

significant although much lower correlation (0.43 ,r , 0.66) with all of them. Together, these results indi-cate that CyPPDK1 plays an important role in aromaticand Asp-derived amino acid synthesis, except for Lysaccumulation, which is probably subject to a morecomplex regulation. Finally, C817 is a synonymousSNP in exon 2 that shows low (r , 0.5) LD with theother SNPs in the CyPPDK1 coding region and no LDamong the 17 fully sequenced inbred lines with non-synonymous SNPs. Thus, although the associationsbetween C817 and amino acid contents are stronglysignificant, the causative polymorphism is difficult topinpoint and may not be C817 itself.

Additionally, we found very strong associationsbetween many phenotypic traits related to kernelstarch and protein content and the combination ofOpaque2 SNP O3988 and one of the SNPs in theCyPPDK1 promoter, CP125, CP161, CP509, or CP515.All of these phenotypic traits are strongly correlatedwith each other, and all four SNPs in the CyPPDK1promoter are in strong LD, suggesting that all of theseassociations are due to the same cause. Opaque2 SNPO3988 alone is slightly associated with some of thesetraits, while none of the CyPPDK1 promoter SNPs isindividually associated with kernel quality. In addi-tion, the interactions between these SNP pairs are allsignificant (Table VI), indicating that kernel quality ismodified by specific combinations of Opaque2 andCyPPDK1 alleles. More specifically, low starch content,high protein content, and high protein-starch ratio areonly obtained for the simultaneous change of allele Ato T at O3988 or allele T to C at CP509 (or G to A atCP125, A to G at CP161, or TG to a 2-bp deletion atCP515).

These observations substantiate the hypothesisthat the CyPPDK protein has a critical role in protein-starch balance in the kernel, as suggested by the recentproteomic study of maize endosperm develop-ment (Mechin et al., 2007). It was proposed that thepyruvate-to-PEP1PPi conversion by CyPPDK1 couldboth favor aromatic and Asp-derived amino acidsynthesis, since PEP is a precursor in these pathways,and reduce starch synthesis through a PPi-induceddecrease in ADP Glc, the starch precursor. This studyshows that the natural polymorphism of the CyPPDK1gene itself only slightly affects endosperm proteincontent and is not associated with starch content,

Figure 3. Average protein-starch ratio (P/S)and Lys (LYSIN) content depending oncombined genotypes at CyPPDK1 andOpaque2 SNPs. The letters a and b indi-cate group classification of the four phe-notypic means through Duncan’s test at a0.05 threshold.



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whereas we found that protein and starch contents aswell as protein-starch balance are affected by epistaticinteraction between the Opaque2 coding sequence andthe CyPPDK1 promoter. This suggests that an increasein the protein-starch ratio is unlikely to be achievedthrough selection of more efficient CyPPDK1 allelesbut rather through specific combinations of compati-ble alleles that allow an increased activation ofCyPPDK1 by its transcriptional activator Opaque2.From the study of the opaque2 mutant, it was shownthat Opaque2 up-regulates CyPPDK1 transcriptionthrough DNA binding of the OPAQUE2 protein ontwo specific domains located at positions 163 to 172and 295 to 304, following the same notation as thatused to name CyPPDK1 SNPs in this study (Maddaloniet al., 1996). These two domains show no polymor-phism among the 30 inbred lines initially sequenced;thus, no SNP has been defined in these regions. TheCyPPDK1 promoter SNPs that we studied are incomplete or very strong LD both between each otherand with many other polymorphisms observed in theinitial sequencing all along the promoter. We may thusassume that sequences other than the two domainsdescribed by Maddaloni et al. (1996) are also involvedin the Opaque2-CyPPDK1 interaction, modifying theability of Opaque2 to regulate CyPPDK1 transcription.These unknown sequences may be either close to thedescribed domains (such as CP161, located 2 bp up-stream of the most 5# domain), indicating that thedomains may be a little longer than has been assumed,or at a different position, supporting the hypothesis ofan additional Opaque2 binding domain within theCyPPDK1 promoter. O3988, the SNP involved in theOpaque2 interaction with CyPPDK1, is located in the 3#untranslated region (UTR). This suggests that eitherthe 3# UTR is involved in a regulatory function thataffects transcription or translation of Opaque2 or thatthe causal site lies upstream in the coding sequence orthe promoter and shows strong LD with O3988.

Complex Control of Lys Content by Opaque2and CyPPDK1

The strongest association involving a single SNPthat we found in this study is between Opaque2 poly-morphism O3988 and Lys content (false discovery rate[FDR] , 1024 with Q and Q1K models). The recessiveallele in the opaque2 mutant induces an increase in Lyscontent through a 50% to 70% reduction in Lys-freezeins, the main endosperm storage proteins (Landryet al., 2002), and an increase in Lys-rich proteins(Habben et al., 1993). Because of agronomical interestin essential amino acid contents for human and animalnutrition, this mutant allele has been extensively stud-ied. However, so far no evidence has been reportedthat natural diversity in Opaque2 plays a role in Lysand storage protein content, since no QTL for anyrelated phenotypic trait was found in the 7:01 region.This study shows that, within a large collection ofmaize inbred lines, Opaque2 natural polymorphism is

strongly related to Lys content. The potential role ofCyPPDK1 in Lys content is probably too weak to bedetected through single SNP associations but is re-vealed here in a more complete model that involvesboth C2252 and O3988 polymorphisms (Table VI). Thestrong association of O3988 with LYSIN and the absenceof interaction with C2252 suggest an effect of Opaque2on Lys content independent from CyPPDK1. This maybe achieved through Opaque2 transcriptional control ofLys degradation by the LKR/SDH enzyme (Arrudaet al., 2000). Indeed, in maize kernel, very little Lys isrequired for protein synthesis, since the main storageproteins (zeins) do not contain this amino acid. As aresult, the kernel accumulates more Lys than is re-quired, from both in situ synthesis and translocationfrom vegetative tissues, and Lys is thus continuouslycatabolized through the saccharopine pathway thatinvolves both LKR and SDH activities. Carbon skele-tons from Lys can then be directed toward zein syn-thesis. Our study suggests that natural diversity inOpaque2 affects Lys content through its degradation inthe endosperm, thus playing a central role in storageprotein synthesis and grain nutritional value.

Another relevant association of Lys content withcombined polymorphisms from both genes showed avery different pattern. Although no O1866 and CP125SNPs are individually associated with Lys content,their interaction strongly correlates with its variation.Two combinations of alleles show high LYSIN (i.e. alleleA at CP125 and allele T at O1866 or allele T at CP125and allele A at O1866), while the two remainingcombinations lead to low LYSIN. As for the protein-starch balance association discussed above, this inter-action involves a SNP in the coding region of Opaque2and one in the CyPPDK1 promoter, suggesting thatLYSIN may be increased by specific and efficient allelicinteraction between the transcriptional activatorOpaque2 and its target gene CyPPDK1. The two differ-ent SNP combinations found to be associated with Lyscontent (i.e. the additive O3988-C2252 and the epistaticO1866-CP125 combinations) suggest a complex regu-lation of this essential amino acid in the maize kernel.

This paper reveals that natural variation in severalkernel quality traits, such as Lys content and protein-versus-starch balance, depends on epistatic interac-tions between Opaque2 and the CyPPDK1 promoter.Epistatic interactions between loci have been shown tomake a substantial contribution to complex trait var-iation in human and animals (Carlborg and Haley,2004; Marchini et al., 2005) as well as in plants (Doebleyet al., 1995; Mackay, 2001; Rowe et al., 2008; Janninket al., 2009). In maize, many epistatic interactionsbetween loci involved in yield components, throughear development or resistance to diseases, have beenrevealed from biparental mapping populations ofcontrasting parents (Doebley et al., 1995; McMullenet al., 2001; Ding et al., 2008) or from more complexpopulations, such as connected mapping populations,built to increase the power to detect QTLs and epistaticinteractions (Blanc et al., 2006). Metabolic pathways

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that underlie most complex traits are expected toinvolve multiple enzymatic and regulatory genes aswell as interactions between them that could generateepistasis (McMullen et al., 1998). A clear example is theepistatic control of the pericarp color (p) locus, whichencodes a transcription factor, on genes a1 and whp1,which determine maysin and chlorogenic acid accu-mulation in silks (Szalma et al., 2005). A priori knowl-edge of the epistatic effect of the p locus allowed theseauthors to control for the p genotype (functional versusnonfunctional) and detect a significant associationbetween a1 or whp1 loci and silk maysin content.Kernel quality traits measured in this study, such askernel weight, endosperm weight, starch, protein, andlipid contents, are important yield components thatpresumably rely on complex metabolic pathways.Although it is not surprising that epistatic interactionsunderlie the variation of such phenotypic traits, thiswork emphasizes the power of association genetics todetect the epistatic interaction between a transcrip-tional activator and one of its target genes in a largeplant diversity collection.

MATERIALS AND METHODS

Plant Material

The association population we used consists of 375 inbred lines represen-

tative of American, European, and tropical maize (Zea mays), including both

first generation lines (obtained by selfing from landraces) and recent elite

lines. This collection includes the 102 inbred lines studied by Remington et al.

(2001) and Thornsberry et al. (2001) and is fully described by Camus-

Kulandaivelu et al. (2006, 2007). The complete list is available in Supplemental

Table S1.

Phenotypic Data

Kernel weight (TKW) and kernel composition traits (see abbreviations from

KDM to VITRO; Table I) were evaluated from an experimental trial including two

locations and two replicates per location for each genotype. Because of large

differences in flowering date within the collection, early materials were

evaluated at Le Moulon and St. Martin de Hinx, while late materials were

evaluated at Montpellier and St. Martin de Hinx. At each location, two main

blocks were subdivided into four subblocks of comparable flowering time.

Subblocks were organized into lines of 15 plants and sown at a density of six

plants per square meter. Plants were self-pollinated in order to avoid xenia

effects. Approximately eight ears were harvested per line when the subblock

reached maturity and submitted to complementary mild drying with pulsed

air at ambient temperature. For each line, kernel traits were predicted based

on NIRS calibrated on entire kernels (Limagrain Society). Although the

experimental protocol, using maturity blocks and ear drying, aimed at

reducing variation in the percentage of dry matter among samples, thus

avoiding bias in NIRS phenotypic estimations, KDM was introduced as a

covariate in association studies in order to remove any potential residual effect

of genetic differences in percentage dry matter.

Population Structure and Individual Kinship

Population structure, which generates genome-wide LD, is a major bias

leading to false-positive associations (Flint-Garcia et al., 2003), especially

in world-wide collections of structured species such as maize (Thornsberry

et al., 2001). To control for population structure, all 375 inbred lines were

genotyped for 55 genome-wide microsatellites (SSR) with motifs of three or

more nucleotides, and five groups were defined (Camus-Kulandaivelu et al.,

2006) using Structure version 2 software (Pritchard et al., 2000a). This

provided us with four independent group memberships that were used as

covariates in the genotype-phenotype association analyses.

Recent studies suggested that such measurements of population structure

may be insufficient to limit false-positive associations and that individual

kinship coefficients should also be taken into account (Yu et al., 2006). We thus

used the same 55 SSR markers to build kinship coefficient matrices following

three different estimates referred to as KL (Loiselle et al., 1995), KR (Ritland,

1996), and KZ (Zhao et al., 2007). We estimated KL and KR using SPAGeDi

version 1.2 software (Hardy and Vekemans, 2002) and KZ as the proportion of

SSR alleles common to each pair of inbred lines.

SNP Genotyping

In order to discover polymorphisms, we sequenced the entire CyPPDK1

gene (a total of 5.9 kb, including a 500-bp promoter region, all 18 exons, 17

introns, and 167 bp of the 3# UTR) on 30 inbred lines and the Opaque2 partial

promoter on 18 inbred lines (884 bp). Sequences of 17 inbred lines for an

Opaque2 2.7-kb coding fragment were available from Henry et al. (2005). We

scored SNPs and insertion/deletion polymorphisms (IDPs) including single-

tons, informative, and nonsynonymous sites. We calculated the nucleotide

diversity as the average number of pairwise differences among sequences per

nucleotide site (Tajima, 1983) and the number of polymorphic sites (Watterson,

1975). We calculated haplotype diversity for each gene region (Nei, 1987). We

tested for selective neutrality both from polymorphism frequency distribution

(Tajima’s D; Tajima, 1989) and from haplotype number conditional to nucle-

otide diversity (S [Strobeck, 1987] and Fs [Fu, 1997]).

We then characterized a subset of these polymorphisms for the 375 inbred

lines described above, based on their position (favoring those in exons rather

than introns), potential functional role (favoring nonsynonymous rather than

synonymous changes), frequency (favoring balanced allele frequencies rather

than rare alleles), and complementarity (avoiding redundancy among poly-

morphisms and favoring those that allow characterization of the highest

number of haplotypes observed among the sequenced inbred lines). We

genotyped 10 SNPs and four IDPs in CyPPDK1 and four SNPs and five IDPs in

Opaque2 among the 375 inbred lines (Fig. 1). Large IDPs in CyPPDK1 (377-bp-

long IDP377 and 33-bp-long IDP33) were characterized by PCR/agarose gel

electrophoresis and scoring of the fragment size at UMR le Moulon. SNPs and

short IDPs were scored using the single-base primer-extension method at

either Biogemma or UMR le Moulon. Primer sequences and complementary

information on genotyping are available upon request. For LD and association

studies, IDPs in Opaque2 that show three (OP1539) or four (OP904) alleles were

coded as biallelic polymorphisms (i.e. presence/absence of each allele),

leading to polymorphisms OP1539-0 to OP1539-2 and OP904-0 to OP904-3.

Statistical Analyses

Determination of Individual Phenotypic Values

We evaluated the effects of genotype, location, replicate within location,

and genotype-location interaction on kernel quality traits through ANOVA

using the GLM procedure in SAS (SAS, 1989). We estimated the heritability

(part of the genetic variance among phenotypic variance) of each trait as 1 to

1/F, with F being the Fisher value of the genotype effect in the ANOVA model.

Since the genotype-location interaction showed a much lower effect (10222 ,

P , 0.01) than the genotype (102245 , P , 102117), we calculated adjusted mean

values for each trait and each inbred line (genotype) using the LSMEAN

option in a model considering genotype, location, and replicate within

location effects. Since many of the NIRS traits were highly correlated, we

performed PCA analysis on the correlation matrix from adjusted means

(FACTOR procedure in SAS). We analyzed the eight first PCA axes (more than

95% of the phenotypic variance) as summary phenotypic traits in association

studies. We calculated four additional phenotypes from the NIRS adjusted

means: total Asp-derived amino acids (ASP), protein-starch ratio (P/S), protein-

lipid ratio (P/L), and lipid-starch ratio (L/S).

LD among SNPs

For the groups of sequenced inbred lines, we tested for LD using Fisher’s

exact tests among informative (i.e. nonsingleton) polymorphisms. Although

polymorphisms characterized in the 375 inbred lines were chosen as not fully

redundant among the sequenced lines, some significant LD may occur among



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the 375 inbred lines. In the large collection, we estimated LD either within or

between genes as correlations (r2) among biallelic loci using the CORR

procedure in SAS (SAS, 1989) and using TASSEL version 2.0 software

(Bradbury et al., 2007) for graphical representation. In order to determine

whether LDs among gene polymorphisms were mainly due to population

structure or not, we estimated LD after removing the effect of population

structure using logistic regression of SNPs against each other, including group

memberships as covariates.

Among-Group Divergence for SNP Frequencyand Phenotypes

In order to determine whether population structure is correlated to

phenotypic values or to SNP or IDP allelic frequencies, we tested the effect

of group membership (four independent variables) on (1) phenotypic indi-

vidual values using the GLM procedure and (2) polymorphisms (SNPs or

IDPs) using the LOGISTIC procedure (SAS, 1989). We estimated the average

phenotypic (or allelic frequency) value for each group as the mean of

individual phenotypic (or genotypic) values weighted by individual group

membership. We quantified the effect of population structure on phenotypic and

genotypic variation using r2 (linear regression) and Max-rescaled pseudo-r2

(logistic regression), respectively.

In cases where phenotypic value and polymorphism allele frequency

showed significant variation among groups, we tested whether they were

significantly correlated among groups, using the REG procedure in SAS and

weighting by group size (estimated as the sum of group membership over all

inbred lines). We performed correction for multiple testing by estimating FDR

(Storey and Tibshirani, 2003) over all correlation tests.

Association Genetics

Evaluation of Different Statistical Models in Terms of False-Positive Detection.

Population structure and/or relatedness among individuals may generate

numerous false-positive associations between phenotypic variation and geno-

typic diversity. Different models have been proposed in order to reduce such

false positives, taking into account population structure (Pritchard et al.,

2000b) and/or individual kinship (Yu et al., 2006). Although population

structure and kinship are generally evaluated from the same genome-wide

neutral markers, it has been shown that they may not capture the same part of

the phenotype-genotype associations (Aranzana et al., 2005). The statistical

models we used all include kernel dry matter (KDM) as a covariate and

consider (1) neither population structure nor individual kinship (N), (2)

population structure only (Q), (3) relatedness only (KL, Loiselle’s kinship

coefficient; KR, Ritland’s coefficient; KZ, proportion of shared SSR alleles), or

(4) both population structure and relatedness (Q1KL, Q1KR, and Q1KZ). We

performed these analyses through ANOVA (GLM) for the N and Q models

and mixed linear model (MLM) for models including kinship matrices (Yu

et al., 2006) using TASSEL version 2 software (Bradbury et al., 2007). In order

to evaluate the number of false positives detected with each statistical model

in our data, we tested associations between phenotypes and SSR alleles (coded

as presence/absence in order to be comparable to biallelic SNPs or IDPs). The

null hypothesis assumes that SSRs are not involved in phenotypic variation

and thus that P values over all SSR-phenotype associations should be

uniformly distributed (i.e. the cumulative distribution of P values should

follow the diagonal; Supplemental Fig. S1). We showed that the naive model

(ignoring genetic control, N curve) fails to correct for false-positive associa-

tions for almost all phenotypic traits, particularly for highly structured

phenotypes (where population structure explains more than 10% of the

phenotypic variance) such as TKW, KW, EW, EMB, or VITRO. For all of these traits,

taking either population structure or individual kinship into account strongly

reduces the excess of low P values, and controlling for both leads to an even

flatter distribution. Supplemental Figure S1 shows that all three kinship

coefficient estimates led to similar results for all phenotypes, although the KR

and Q1KR models sometimes gave a better control of type I error rate than

models involving KL or KZ (e.g. FT, LYSIN, EMB, LIP, TKW, KW, and EW). Finally,

note that the KZ and Q1KZ models show many more cases of no convergence

than other mixed models. The mixed model had been extensively used for the

highly structured phenotype of flowering date (Yu et al., 2006; Gonzalez-

Martinez et al., 2007; Zhao et al., 2007; Camus-Kulandaivelu et al., 2008). Our

study confirms its efficiency in excluding false positives for kernel quality

traits, and we consider that both population structure and individual kinship

should be included in association studies of all phenotypic traits with

candidate genes. Consequently, association studies presented here are based

on both the Q model, which considers population structure only, and the

Q1KR model, which is more complex but performs better for some pheno-

typic traits.

Associations between Kernel Quality Traits and Polymorphisms at CyPPDK1 and

Opaque2 Genes. We first performed association analyses on the raw data,

correcting for KDM and population structure, in order to test for SNP-location

interaction and SNP-replicate interaction within each location. Since these

analyses showed nonsignificant interactions and gave very similar SNP-

phenotype associations to the ones performed per inbred line adjusted means,

we do not present them here but rather present the results from Q and Q1KL

models applied on adjusted means. For each gene and each phenotypic trait,

we controlled for multiple testing using FDR (Benjamini and Hochberg, 1995).

In these analyses, we could not use the improved FDR estimation method of

Storey and Tibshirani (2003) since, for each phenotypic trait, only 14 and 15

associations were tested for CyPPDK1 and Opaque2, respectively. Finally, for

each phenotypic trait separately, we tested the epistatic interactions of all 210

CyPPDK1-Opaque2 SNP combinations using both Q and Q1KL models and

correcting for multiple testing (Storey and Tibshirani, 2003). In the mixed

Q1KR model, we estimated genetic r2 as the difference between (1) the

squared correlation coefficient between observed and predicted values under

the total model including covariates and SNPs and (2) the squared correlation

coefficient between observed and predicted values under a reduced model

including covariates only.

Sequence data from this article can be found in the GenBank/EMBL data

libraries under accession numbers FJ935730 to FJ935747 for Opaque2 and

FJ935748 to FJ935778 for CyPPDK1.

Supplemental Data

The following materials are available in the online version of this article.

Supplemental Figure S1. Cumulative distributions of P values for asso-

ciation tests between neutral SSR markers and maize phenotypes.

Supplemental Table S1. Adjusted mean values for phenotypic traits (FT–

WALL; see Table I for definitions) and group memberships (G1–G5) used

in association mapping on 375 maize inbred lines.

ACKNOWLEDGMENTS

We are grateful to J. Laborde, M. Dupin, P. Bertin, B. Gouesnard, D.

Coubriche, S. Jouane, and P. Jamin for their contribution to seed management

and field experiments and to L. Moreau and C. Dillmann for their advice for

statistical analyses. We thank three anonymous reviewers for their relevant

suggestions. We thank Prof. G. Noctor for his contribution to the English

editing.

Received October 30, 2008; accepted March 23, 2009; published March 27,

2009.

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