* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Quantitative trait loci affecting amylose, amylopectin and starch
Behavioural genetics wikipedia , lookup
Genetic engineering wikipedia , lookup
Human genetic variation wikipedia , lookup
Polymorphism (biology) wikipedia , lookup
Ridge (biology) wikipedia , lookup
Genome evolution wikipedia , lookup
Site-specific recombinase technology wikipedia , lookup
Genetically modified crops wikipedia , lookup
Biology and consumer behaviour wikipedia , lookup
Minimal genome wikipedia , lookup
Public health genomics wikipedia , lookup
Genetically modified food wikipedia , lookup
Point mutation wikipedia , lookup
Gene expression profiling wikipedia , lookup
Copy-number variation wikipedia , lookup
History of genetic engineering wikipedia , lookup
Dominance (genetics) wikipedia , lookup
Heritability of IQ wikipedia , lookup
Polycomb Group Proteins and Cancer wikipedia , lookup
Population genetics wikipedia , lookup
Genomic imprinting wikipedia , lookup
Gene expression programming wikipedia , lookup
Epigenetics of human development wikipedia , lookup
Artificial gene synthesis wikipedia , lookup
Skewed X-inactivation wikipedia , lookup
Microevolution wikipedia , lookup
Designer baby wikipedia , lookup
Y chromosome wikipedia , lookup
Neocentromere wikipedia , lookup
X-inactivation wikipedia , lookup
Plant Physiol. Biochem., 2000, 38 (6), 459−472 / © 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved S0981942800007622/FLA Quantitative trait loci affecting amylose, amylopectin and starch content in maize recombinant inbred lines Mouhamadou Sénea, Mathilde Causseb§, Catherine Damervalb, Claudine Thévenota, Jean-Louis Prioula* a Laboratoire structure et métabolisme des plantes, Institut de biotechnologie des plantes, bât. 630, université Paris-Sud (CNRS, UMR 8618), 91405 Orsay cedex, France b Station de génétique végétale (Inra, CNRS, UPS), La ferme du Moulon, 91190 Gif-Sur-Yvette, France * Author to whom correspondence should be addressed (fax +33 1 69 33 64 24; e-mail [email protected]) (Received 15 December 1999; accepted 2 March 2000) Abstract – The loci explaining the variability of quantitative traits related to starch content and composition (amylose, amylopectin and water soluble fraction) were searched for in maize kernels. Multifactorial genetic methods were used to detect and locate QTLs (quantitative trait loci) on a genetic map consisting mainly of RFLP markers for genes with known function. The genetic material was recombinant inbred lines originating from parents differing in starch structure (dent vs. flint). Kernels were harvested from field grown plants for two successive years and under two pollination systems. Main effect and epistasis QTLs were detected using two methods, composite interval mapping (MQTL) and ANOVA. Despite large year-to-year differences, physiologically meaningful co-locations were observed between trait QTLs. Moreover, the number of expressed sequences on our map allowed the search for co-locations between QTLs and genes involved in carbohydrate metabolism. The main co-location was between an amylose QTL and Shrunken 2 (SH2) locus, on chromosome 3 (SH2 encoding for the large subunit of ADPglucose pyrophosphorylase). The importance of this locus as a candidate gene for a starch QTL is in agreement with previous studies based either on QTL co-locations or on revertant analysis. Other co-locations were observed between amylose and amylopectin QTLs and the two loci of IVR1 invertase genes on chromosomes 2 and 10. Further comparison with previously detected QTLs for carbohydrate metabolism in maize leaves showed consistent co-location in map regions devoid of candidate genes, such as near chromosome 1S telomere. The possible contribution of regulatory genes in this region is discussed. © 2000 Éditions scientifiques et médicales Elsevier SAS Candidate gene / invertases / kernel / maize / QTL / Shrunken 2 AGPase, ADPglucose-pyrophosphorylase / AM, amylose / AP, amylopectin / cM, centiMorgan / DW, dry weight / INCW1, INCW2, cell wall invertase genes / IVR1, IVR2, soluble invertase genes / QTL, quantitative trait locus / RIL, recombinant inbred line / SH2, Shrunken 2 / SPS, sucrose phosphate synthase / Starch.T, total starch / Starch.I, insoluble starch / WSF, water soluble fraction / WX, waxy 1. INTRODUCTION Most traits of agricultural and biochemical importance, such as kernel starch content, display complex inheritance because they are governed by several genes, each of them contributing to a fraction of the genetic variance. Environmental factors also modulate § Present address: Station de génétique et d’amélioration des fruits et légumes, Domaine St-Maurice, BP 94, 84143 Monfavet, France. gene expression, the final result being a continuous distribution of the phenotypic values. The use of DNA based markers (RFLP, RAPD) has allowed the construction of dense linkage maps in several crops, making possible the localisation of loci controlling quantitative trait variation (QTL) as in the pioneering work of Paterson et al. [23]. Mapping of QTLs has been documented for numerous traits: yield and yield components [2, 10, 35], abiotic stresses [17, 29, 32], insect [16] or pathogen resistance [11]. Crop biochemical composition has also been studied e.g. starch, Plant Physiol. Biochem., 0981-9428/00/6/© 2000 Éditions scientifiques et médicales Elsevier SAS. All rights reserved 460 M. Séne et al. oil, or protein content, in maize kernels [1, 13]. As these traits are more directly related to the biosynthetic pathways, one may expect to identify more easily the genes corresponding to the QTLs [27]. Accordingly, a co-location was pinpointed between one starch content QTL and one Shrunken 2 (SH2) locus on chromosome 3 [13]; furthermore Giroux et al. [12] demonstrated later on, that point mutations in the 3’-end of the Sh2 gene were correlated with changes in kernel starch content. Starch is the major component of cereal kernels; its composition and structure are well documented in maize endosperm [21, 25]. Starch is separable into several components, all of them glucose polymers: amylose (AM) an essentially linear unit, amylopectin (AP) a branched molecule of very high molecular mass (5·106) and phytoglycogen a highly branched and soluble polymer. The AM:AP ratio is rather constant and close to 20/80, in the wild type but it may be strongly affected by mutations. Amylose-free kernels are produced by the waxy mutation. Conversely, the amylose extender mutation leads to a higher amylose proportion than in the wild type. Sugary mutation results in a large starch deficiency and a large increase in water soluble fraction. Progress in mutant characterisation and in biochemical analysis now allow a good understanding of the starch biosynthetic pathway (figure 1). The sucrose unloaded from phloem is cleaved by invertase (EC 3.2.1.26) in the basal endosperm, then re-synthesised and further cleaved by sucrose synthase (EC 2.4.1.13). The reaction products are used by ADPglucosepyrophosphorylase (EC 2.7.7.27, AGPase) synthesising ADP-glucose, the glycosyl donor for starch synthases (EC 2.4.1.21) forming alpha-1,4 glycosyl chains. Then, branching enzymes (EC 2.4.1.18) form ramifications at alpha-1,6 positions on amylopectin and phytoglycogen. The debranching enzymes simplify the phytoglycogen, leading to amylopectin. The pathway was ascertained by mutations in structural genes affecting each step. For example, the shrunken phenotypes in maize grains are impaired in sucrose synthetase, shrunken 1, in AGPase small and large subunits, brittle 2 and shrunken 2, respectively. The waxy and amylose extender mutations are due to deficient granule bound starch synthase (GBSS) and starch branching enzyme (SBE IIa), respectively, whereas the sugary mutation originates from an impairment of a starch debranching enzyme (see Neuffer et al. [22] for review on mutants). Now all the corresponding genes have been mapped [5]. Plant Physiol. Biochem. Figure 1. Present knowledge of the metabolic pathway for starch synthesis in the developing maize kernel. A mutation on any of the presented enzymes induces abnormal starch phenotypes which proved their involvement in the pathway, at some period of kernel filling. Cell wall invertase (1, in figure) seems to be required in the basal endosperm, only in the first 10–15 d. The majority of ADPglucose pyrophosphorylase is now considered to be located in the cytosol of maize endosperm [8]. Enzyme numbering and corresponding mutations: 1, cell wall invertase/miniature 1 (mn1); 2, sucrose-synthase/ shrunken1 (sh1); 3, ADPglucose pyrophosphorylase/shrunken2 (sh2) and brittle2 (bt2); 4, ADPglucose translocator/brittle1 (bt1); 5, GBSS (granule bound starch synthases)/waxy; 6, SSS (soluble starch synthases); 7, branching enzymes/amylose-extender1 (ae1); 8, debranching enzymes/sugary1 (su1). The purpose of this study was to combine the physiological and biochemical knowledge on starch metabolism with a QTL mapping approach in order to get insights into the genetic determinism of starch content and composition (i.e. amylose (AM), amylopectin (AP) and phytoglycogen estimated as water soluble fraction (WSF)) in mature maize kernels, using a recombinant inbred line (RIL) population derived from a cross between a French flint and an American dent genotype. The observed co-locations of QTLs and major genes affecting starch biosynthesis were considered in order to identify potential candidate genes involved in the genetic variation of starch accumulation and composition. QTLs for starch in maize kernel 461 Figure 2. Reproducibility of amylopectin (AP) and amylose (AM) measurements in 100 identical RILs in 1994. The two independent series were measured after several months interval. 2. RESULTS 2.1. Reproducibility of amylose and amylopectin measurements The presently used colorimetric method [31] used here for amylose and amylopectin determination was adapted to maize for this experiment because it provided the possibility of measuring a great number of samples in a reasonable amount of time. However, it is potentially less precise than methods based on size exclusion chromatography, for example. In order to test its reliability, a preliminary analysis was done by measuring independently two kernel samples from each of the 100 identical RILs in the 1994 harvest. The results in figure 2 show strong correlation (r = 0.81 and 0.85 for AP and AM, respectively) between the two independent assays, which validate the method. 2.2. Correlation among traits and year effect The two parental lines had previously been shown to differ significantly for the measured traits [31] (table I): starch and AM contents were lower in F-2 than in Io for both 1994 and 1995, while AP presented large year-to-year differences, since the 1994 content was lower in F-2 than in Io, and the reverse was true in 1995. Water soluble fraction (WSF) was measured only in 1994 and was higher in F-2. The correlations between traits in the RILs are presented in table II for the 2 years. The kernel starch content was correlated to AP (r = 0.81 and 0.92) which is not surprising as starch consists mainly of AP. By contrast, the AM content was poorly but positively correlated to starch in 1994 and not significantly in 1995. AM and AP were negatively correlated both years. A negative correlation was observed between amylopectin and water soluble fraction. Correlations between values for the 2 years were examined for each trait (figure 3). The 1994 and 1995 contents were significantly correlated only for amylose and AM:AP ratio. Comparison of the mean AM and AP values showed that contents were several percents lower in 1995 than in 1994. Trait analysis and QTL detection were thus performed independently for each year. Table I. Total starch, amylose, amylopectin and water soluble fraction (WSF) contents in the two parental lines (Io and F-2) and the recombinant inbred lines. RILs, Recombinant inbred lines; M, mean; SD, standard deviation; CV, coefficient of variation. Values are expressed as % kernel dry weight. The coefficients of variation for amylose were 0.03 in Io and F-2. The corresponding figures were 0.02 and 0.05 for amylopectin in Io and F-2, respectively (from [31]). Years 1994 1995 Traits Amylose Amylopectin Total starch WSF Amylose Amylopectin Total starch Io 15.7 58.1 73.6 9.1 15.1 49.2 68.4 F-2 12.5 55.2 71.7 12.2 10.2 52.3 63.6 RILs M SD CV Range 12.1 50.6 68.3 7.7 13.4 44.0 57.2 2.33 3.84 4.11 3.40 2.66 6.40 5.36 0.19 0.08 0.06 0.45 0.20 0.15 0.09 8–19.2 42.3–62 59–80.2 0–15.1 6.9–19.4 31.0–61.3 45.6–70.2 vol. 38 (6) 2000 462 M. Séne et al. Table II. Correlation coefficients between trait pairs in the RIL population for 1994 and 1995 samples. ** Very significant = P < 1 %; *** highly significant = P < 1 %; ns = non-significant. WSF94 AM94 AP94 WSF94 AM94 AP94 Starch94 1 –0.06 –0.36** 0.37** 1 –0.31** 0.22** 1 0.81*** AM95 AP95 Starch95 AM95 1 –0.52*** –0.151ns AP95 1 0.92*** For all traits, the frequency distribution fitted the normal distribution and the phenotypic range was larger than in the parents, since the highest RIL values were larger than the best parent value and the lowest lines had lower values than the lowest parent value. Such an observation is common with segregant lines and is indicative of transgressive variation which has been well documented for various traits in many crops. It is interpreted as the result of the recombination of favourable or unfavourable alleles in the RILs [9]. 2.3. Quantitative trait loci involved in starch composition and content QTLs detected by the MQTL composite interval method (CIM) with five to nine co-factors depending on the trait are presented in figure 4 for the two successive years. The two measurements from 1994 were averaged. Water soluble fraction was not measured in 1995, thus the traits WSF95 and Starch.T95 were not available. Thirteen QTLs were detected for seven of the ten traits evaluated. No QTL were detected either for total or insoluble starch content, and one (AM94, AP94 and AM95) to three (AP95, WSF94) QTLs were found for the other traits (table III). In 1994, a QTL for AP was detected on the short arm telomere of chromosome 2, an AM QTL was located at the other telomeric end of chromosome 3, near the SH2 locus and two QTLs for AM:AP ratio were detected: a) on the first quarter of chromosome 9, at 8 cM from the WAXY locus (gsy200 WX); b) in the second-third of chromosome 10 close to the soluble invertase locus IVR1(gsy348a). Water soluble fraction content showed three QTLs: a) and b) middle of chromosomes 4 and 5 (gsy419, ATPsynthase locus, and gsy403_PAL, phenylalanyl-ammonialyase locus, Figure 3. Correlation between years 1994 and 1995 for amylose (A), AM/AP ratio (B), amylopectin (C), and starch (D) content in grains from 100 recombinant inbred lines originating from Io × F-2 cross. Plant Physiol. Biochem. QTLs for starch in maize kernel 463 Figure 4. Mapping of QTLs by MQTL for amylose (AM), amylopectin (AP), amylose/amylopectin ratio (AM:AP), insoluble starch (Starch.I), total starch (Starch.T) and water soluble fraction (WSF) in maize grains grown in 1994 and 1995 (94 and 95 suffix, respectively). All values are expressed as % kernel dry weight. The recombinant inbred lines originated from the Io and F-2 parent lines (8th generation). Continued on following page. vol. 38 (6) 2000 464 M. Séne et al. Figure 4. Cont’d. Main effect QTLs are displayed by black bars when effect is from the Io parent, and by open bars for F-2 parent. Interaction QTLs are represented by stippled bars, the position of the other pair member is given in italics by chromosome # and distance in cM from the telomere top. Whatever the QTL type, the bar length is proportional to the R2 value. The full description of marker names placed along each chromosome is reported in Causse et al. [5]. Plant Physiol. Biochem. QTLs for starch in maize kernel 465 Table III. Influence of the detection method on the number and position of QTLs for starch components in a population of 100 RILs for two successive years (1994 and 1995). Marker by marker detection (ANOVA, P = 0.01), compared to composite interval mapping (MQTL). The allele providing the higher value for the trait is coded by a negative sign for Io and a positive one for F-2. Traits AP95 AM95 AP95 AM:AP95 AP95 AP94 AP94 AM95 AP95 AM:AP95 AM:AP95 AP95 AM95 AM94 AM:AP94 WSF94 WSF94 AP95 AM:AP95 AP94 AM:AP94 AM:AP94 AM94 WSF94 ANOVA (P = 0.01) Allele effect R2 (%) –0.76 9.3 –0.03 9.0 1.16 –0.70 1.94 –0.03 –0.04 1.99 –1.01 0.78 0.02 –1.23 9.0 7.9 8.7 10.1 14.1 8.4 15.4 12.2 11.4 10.9 1.77 –0.02 –1.17 7.7 7.7 9.6 0.02 0.70 1.09 11.8 10.1 8.8 Chromosome and loci chr. 1 chr. 1 chr. 1 chr. 2 chr. 3 chr. 3 chr. 3 chr. 3 chr. 4 chr. 5 chr. 6 chr. 9 chr. 10 respectively); c) long arm of chromosome 10 (gsy348a, soluble invertase IVR1 locus). In 1995, a similar number of QTLs were detected but, at the chosen threshold, no QTL was detected at the same position as in 1994 for the same trait. Such a result could be anticipated in view of the low correlation coefficients obtained between years for most of the traits. Two AP95 QTLs were detected in close proximity at the telomeric end of chromosome 1 (gsy297a_EMB and gsy271_P), with opposite allelic effects. The detection curve showed two clear peaks above 1, and the test values were highly significant: P < 0.0007 for the Io allele QTL, and P < 0.0001 for the F-2 allele QTL. Such detection appears consistent with the reported power of CIM methods to detect close QTLs with opposite effects. Another AP95 QTL was found at 175 cM further along the same chromosome 1, at ‘adh1_iso’ locus. The only AM95 QTL mapped near the middle of chromosome 3 (gsy406a_6PGDH) at the same place as an AM:AP95 QTL. A possible co-location of QTLs for related traits between the 2 years was another AM:AP95 QTL in the gsy297a_EMB gsy271_P idem idem ADH1_iso umc6 umc92 umc10 idem idem gsy406a_6PGDH idem idem gsy58_SH2 idem gsy419_AT gsy403_PAL idem idem umc65 gsy89 gsy348a_IVR1 idem idem MQTL (co-factors < 10) R2 (%) Allele effect 8.7 –6.8 12.3 8.8 2.9 5.2 5.6 4.3 8.2 –2.0 8.5 –0.07 10.1 1.5 11.4 7.8 –3.0 –2.9 7.1 –0.06 6.6 7.8 0.03 0.04 6.5 3.3 central part of chromosome 5 (gsy403_PAL) close to the WSF94 QTL. The low number of QTLs detected for each trait (0–3) compared with results frequently reported in the literature may be explained by two factors: a low trait heritability, and/or the choice of a detection method which is more stringent than conventional analyses based on MAPMAKER/QTL with a LOD threshold of 2 or ANOVA with P = 0.01. In order to try to clarify this point, we compared the MQTL results with those from ANOVA using a 1 % threshold (table III). Under these conditions, ANOVA results are theoretically more subject to type I error than those from MQTL. This argument may be challenged by two facts: a) the permutation test may not be robust when co-factors are used; and b) the estimate of a global type I risk is difficult for ANOVA as the markers are not independent [14]. For the seven traits showing main effect QTLs, eighteen QTLs were detected by ANOVA vs. thirteen by MQTL, seven being detected by both methods. However, when examining our data (table III), it is doubtful that all the QTLs detected by ANOVA vol. 38 (6) 2000 466 M. Séne et al. and not by MQTL are false, as some of them were frequently located close to QTLs for similar traits or where likely candidate genes are located. All the genetic distances, derived from recombination rates, are measured in centiMorgan (cM) from the top of each chromosome. The confidence intervals for QTL position cannot be easily determined [18]; however, simulation studies (Agnès Leonardi, pers. comm.) have shown that 10 to 20 cM is a likely range. Taking this range into account, QTL clusters are apparent on chromosomes 1 (gsy297a_EMB – gsy271_P), 3 (umc10 – gsy406a_6PGDH and gsy58_SH2), 5 (gsy403_PAL) and 10 (gsy348a_IVR1) when comparing the two columns of table III. Close examination of the graphical display of the test statistics (TS) output from MQTL showed, in fact, that these additional QTLs detected by ANOVA were frequently just below the threshold value (not shown). These co-locations revealed by lowering the threshold might be meaningful, since the probability of co-location by chance of several QTLs for two traits is very low, except if they rely on common genetic determinants. Examination of the parental allele effects showed that favourable effects over the 2 years are well balanced between Io and F-2, eleven vs. thirteen, respectively, when pooling all traits and both methods (table III). However, large differences appeared when looking at trait by trait. In 1995 for AP, most of the beneficial alleles were from F-2 (5/1) while in 1994 F-2 brought favourable alleles in two cases compared with one in Io. The fact that a greater number of favourable F-2 alleles is present in 1995 may be consistent with a higher AP content in F-2 than in Io in 1995 but not in 1994. Similarly the three AM-favourable alleles originated from Io in 1995, compared with two F-2 favourable alleles in 1994, which is also consistent with the relative contents (table III). For the water soluble fraction (measured only in 1994), the favourable alleles came from both parents (two from Io and one from F-2). 2.4. Epistasis Genetic interaction between loci may play an important role in the determinism of some traits [7, 9]. This can be investigated by testing interactions between all pairs of markers by two-way ANOVAs. Twelve interactions were detected at a P < 0.0005 threshold, compared to thirteen main effect QTLs (figure 4). Either total or insoluble starch content, that had no main effect QTLs, showed four significant interacting QTLs in 1994. The highest number of interaction QTLs (6) was found for AM in both years. Whatever the trait, a Plant Physiol. Biochem. large increase of the variation explained was observed when including interactions in the model, with R2 values ranging from ca. 12 % (Starch) to 38 % (WSF). Part of the interaction QTLs mapped in the same region as main effect QTLs for similar or related traits are: on chromosome 1, AP95 and AM:AP95-inter; on chromosome 2, AP94, Starch.T94-inter, and Starch.I94inter; on chromosome 3, AP95-inter, AM95 and AM:AP95; on chromosome 10, AM94-inter, AM95inter, WSF94 and AM:AP94. However, a large part of the interaction QTLs mapped away from main effect QTLs. They formed clusters on chromosomes 3 and 4, and only interaction QTLs were detected on chromosomes 6, 7 and 8. On chromosome 4, the interaction QTLs for 1995 and 1994 are co-located. 2.5. Relationships between starch components The contents of amylose, amylopectin, the two components of starch, and phytoglycogen (water soluble fraction), a likely intermediate of amylopectin, are not well correlated (table II). This is confirmed by the QTL distribution since AM and AP QTLs were not co-located. However, among the four amylose/ amylopectin QTLs detected by MQTL over the 2 years, three of them were co-located with other starch component QTLs: AM:AP95 with AM95 on chromosome 3, AM:AP95 with WSF94 on chromosome 5 and AM:AP94 with WSF94 on chromosome 10. These co-locations between AM:AP and AM or AP were confirmed by ANOVA (table III) which provided some more associations with AM95 and AP95 on chromosome 1 (gsy271_P at 19–23 cM) and chromosome 3 (umc10 at 36 cM). Taking into account the epistasis QTLs and the main effect QTLs, two clusters appeared on chromosomes 1 (at 23 cM) and 10 (at 90 cM) (figure 4). On chromosome 10, the AM:AP94 QTL was close to AM94-inter, AM95-inter and WSF94. Although no main effect QTL was detected for total starch content either by MQTL or by ANOVA (table III), two epistasis QTLs mapped close to the AP94 QTL at 14 cM on chromosome 2. 2.6. QTL candidate genes from starch biosynthetic pathway Taking advantage of the location of numerous known function genes on our map, we looked for co-locations between starch component QTLs and genes involved in carbohydrate metabolism, in order to detect candidate genes explaining trait variations. The main genes involved in kernel starch biosynthesis are located on chromosome 3 (for AGPase large sub- QTLs for starch in maize kernel unit, gsy58_SH2, Shrunken 2), on chromosome 4 (for AGPase small subunit, gsy59c_BT2, Brittle2) and (for starch debranching enzyme, gsy315_SU1, Sugary 1), on chromosomes 2 and 10 (for the two loci of vacuolar invertase IVR1, gsy348c and gsy348a, respectively), on chromosome 5 (for cell wall invertases, gsy343b_INCW1), and on chromosome 9 (for sucrosesynthase, gsy66_SH1, Shrunken 1 and for granule bound starch synthase, gsy200_WX, waxy mutation) (figure 4). Assuming a 10–20-cM confidence interval for QTL position, we considered that the co-locations between candidate gene and QTL may be significant when distances were lower than 10 cM. One QTL (AM:AP94) detected on chromosome 9 was located between the SH1 and WX loci but closer to WX (9 cM). Thus we considered that this co-location may be significant. Mutations of those two genes are known to affect starch (SH1) and amylose content (WX), respectively. QTLs located in three other regions are also close to genes of interest: a) on chromosome 2, the AP94 QTL and two Starch-inter QTLs at 15 cM from the vacuolar invertase locus IVR1 (gsy348c); b) on chromosome 10, at another locus of the same probe (gsy348a) and QTLs for two similar traits (AM94, AM:AP94 and AM95); c) on chromosome 3, an AM94 QTL located exactly at the SH2 locus (gsy58). In addition, more distant co-locations were noted: a) on chromosome 4, a WSF QTL was rather far (40 cM) from the Sugary1 locus (gsy315_SU1) but closer (26 cM) to the Brittle2 locus (gsy59c_BT2); b) on chromosome 5, another WSF QTL and an AM:AP95 QTL were 18 cM from the cell wall invertase locus INCW1 (gsy343b_INCW1). Less obvious candidates appeared on chromosome 1 since it is not clear whether the SH2 locus located 20 cM downstream of the AP95, AM:AP95 QTLs corresponds to an expressed gene. Similarly, QTLs associated with AM95 in the middle of chromosome 3 are located near 6-phosphogluconate dehydrogenase and SPS loci, but a direct involvement of these enzyme loci in kernel starch biosynthesis has not been established. Examination of the interaction QTLs suggested further candidates. The AM:AP95 QTL-inter on chromosome 1 interacts with the SPS locus on chromosome 8; Starch.I94 QTL-inter on chromosome 2 interacts with a BT2 locus on chromosome 8; AM95 QTL-inter on chromosome 10 interacts with another SPS locus on chromosome 6, although AM94 QTLinter from the same chromosome 10 region interacts with another region of chromosome 6 close to two loci related to starch synthesis: AGP1 and BT1 (figure 4). 467 3. DISCUSSION In maize kernels, the control of genetic variation in starch synthesis can occur at various levels of that complex biosynthesis pathway, making valuable a QTL approach, as previously shown by Goldman et al. [13] using lines divergently selected for low or high kernel protein, for 76 generations. The RIL population used in this study is more likely representative of the natural variability occurring in lines commonly used for breeding. QTLs were sought not only for total starch but also for its components (AM, AP, WSF), from data collected at the same place, in two successive years, and under two pollination systems. Large year-to-year differences in QTL location for a particular trait were observed, which addresses the question of inbreeding effect and of QTL-environment interactions. The comparison of self-pollination vs. allo-pollen fertilisation, by Bulant and Gallais [3], showed that the yield was consistently, ca. 12 %, lower in self-pollinated plants over 3 years, at the same experimental site as in this experiment. Thus, the lower starch content observed in 1995 might be, at least partly explained by a self-pollination effect. However a well known problem, when dealing with yield QTLs, is the great influence of environment, and especially drought which is critical during the grain filling period [28, 35, 36]. In the case of water stress, the evidence of stress specific QTLs is now established [24, 29, 32]. In terms of physiology, this means that a different set of genes was probably expressed under stress conditions. Examination of weather records from July 1st to mid-August showed that the number of days with maximum temperature higher than 30 °C was 13 in 1994 and 17 in 1995. During August, which corresponded to the first phase of grain filling in most RILs, the average temperature was 2 °C higher in 1995 than in 1994 (18.7 vs. 20.7 °C). Thus, the high temperature coupled to a low humidity during the 1995 grain filling period could also explain the difference with 1994. Under this hypothesis, the 1995-QTLs could be associated with stress whereas those from 1994 would represent more normal conditions. The lower starch yield in 1995 is also consistent with this hypothesis. In other cereals, similar problems associated with high temperature have been described: in wheat, above 30 °C, a slower starch deposition rate was associated with reduction of the soluble starch synthase activity [15]. One to three main effect QTLs were detected for all traits except for total starch (% DW). It is worth noting that traits for starch and for its components resulted in vol. 38 (6) 2000 468 M. Séne et al. this study from independent measurements since starch was measured enzymatically whereas AM and AP determinations were colorimetric. As the first method is much more precise than the second, the experimental variance is likely to have been lower. Thus the poor starch QTL detection may be due to a low genetic variability as suggested in figure 2 when comparing starch and AP scattering. Furthermore, in the present study, the parent lines were not very different in starch content, as compared with the Goldman et al. [13] material. Another point to consider is that starch represents the major part of kernel DW (72–75 %), thus introducing a physical constraint for upward variation. The existence of such a constraint could explain why genetic alteration of the SH2 gene in the starch pathway did not increase kernel starch content but rather the starch amount per kernel [12]. However, looking for epistasis in the control of starch content allowed the detection of two pairs of interaction QTLs in 1994. In one pair, the QTLs co-located with an AP94 QTL on chromosome 2 and the Bt2 locus on chromosome 8, respectively. In the other pair, one QTL also mapped close to the AP94 QTL on chromosome 2. Both co-locations may be physiologically meaningful since the common precursor of AM and AP is ADPglucose through the action of the ADPglucose-pyrophosphorylase encoded by the BT2 and SH2 genes. Amylose, amylopectin and water soluble fraction QTLs were not frequently co-located (figure 4) which could be anticipated from the fact that once ADPglucose is synthesised, their synthetic pathways are separated (figure 1). The low correlation between these traits (table II) is consistent with the low frequency of QTL co-locations (figure 4). For instance, the only case of co-location between AM and AP QTLs was for an interaction QTL in the cluster on chromosome 4. However ANOVA results (table III) showed further associations on chromosome 1 (gsy271_P) and chromosome 3 (umc10) for AM95 and AP95. WSF mapped twice on chromosomes 4 and 5 and co-located once on chromosome 5 with AM:AP95. Most of the other co-locations appeared with AM:AP, as expected from a composite trait. Over the eight AM:AP QTLs detected on chromosomes 1, 3, 4, 5, 8, 9, 10 (figure 4), three main effect and three interaction QTLs co-located with other starch component QTLs; association with AM (on chromosome 3 at two locations, on chromosomes 4 and 10) was more frequent than with AP (on chromosomes 1, 3 and 4), suggesting that the AM:AP ratio in mature grain might be controlled more strongly by the Plant Physiol. Biochem. amount of AM rather than by that of AP in the progeny studied here. The identification of candidate genes is based on the widely accepted idea that a QTL corresponds to a slightly altered gene allele and that a major QTL in one genetic background could look like a mutation in another. But, in general, mutations are likely to be more extreme allelic (or null) forms of QTLs. Most genes for starch synthesis were first known from large mutations leading to altered kernel phenotype (shrunken, brittle, waxy, miniature, etc.). Then, they were mapped and their functions uncovered. Our map consists mainly of loci derived from known function probes, and many of them encode for enzymes of carbohydrate metabolism and especially of the starch biosynthetic pathway [5]. Thus, co-locations between starch component QTLs and these probe loci may be fruitful in detecting possible candidate genes explaining the trait variations. The most documented candidate in this experiment is the SH2 gene located on chromosome 3 at the gsy58 locus, at the same position as an AM94 QTL. A similar co-location has been previously described by Goldman et al. [13] for starch QTLs. In a separate experiment, we tried to test the validity of SH2 as a candidate by examining the gene polymorphism in 46 genetically diverse lines, in relation to kernel starch. The significant correlation obtained between one restriction pattern and starch content represents a first step in the candidate validation [26]. The role of SH2 as a candidate gene for starch content variation was further supported by a totally different strategy [12] through the analysis of series of revertants after Ac/Ds transposition/excision at the SH2 locus. The imperfect excision of Ds element produced micro-alterations in the 3’-region of the gene, leading to larger kernels in one revertant without alteration of the starch content relative to dry weight. However, this observation does not fully account for our results since the observed QTL at the SH2 locus was for amylose and not for amylopectin. Actually, data from various starch deficient mutants in Chlamydomonas may provide some explanations since a mutation in the large sub-unit of ADPglucose-pyrophosphorylase reduced ADPglucose supply, and altered AM synthesis more than AP, because the granule bound starch synthase might have less affinity for ADPglucose than the other starch synthases involved in AP synthesis [34]. Another related candidate may be inferred from the close location between the AM:AP94 QTL and the WX locus on chromosome 9, as the corresponding gene QTLs for starch in maize kernel encodes for the granule bound starch synthase whose deficiency leads to an amylose-free phenotype. Other co-locations of interest occurred between QTLs and enzyme structural genes. In the case of soluble acid invertase, two loci of the same gene (IVR1) on chromosomes 2 (gsy348c) and 10 (gsy348a) showed co-location with starch, AM and/or AP QTLs. The fact that similar QTLs were associated with homologous invertase loci is likely to indicate that such associations may be pertinent since IVR1 is expressed in reproductive organs [37]. Also, a cell wall invertase gene (INCW2) is located on chromosome 2 26 cM downstream from the IVR1 locus and close to a AM95-inter QTL. The mutation of INCW2 produces a miniature-1 grain phenotype [20] due to a depression of both soluble and insoluble invertase activities. The mutation was also mapped on chromosome 2 at the INCW2 locus. A large proportion of the interaction QTLs mapped in regions where no main effect QTLs were found, namely on chromosomes 6, 7 and 8. However co-locations with some copies of structural gene may be significant: for example, AM:AP95-inter with a SPS locus on chromosome 8, Starch.I94 QTL-inter with a BT2 locus on chromosome 8, AM95-inter with another SPS locus on chromosome 6, and AM94 with another region of chromosome 6 close to two loci related to starch synthesis: AGP1 and BT1 (figure 4). All these co-locations may be physiologically pertinent since: a) SPS encodes for a sucrose-phosphate synthase which is likely to be involved in the re-synthesis of sucrose in the grain after its initial hydrolysis in the basal endosperm cell layer; b) BT2 and AGP1 correspond to AGPase subunits, expressed in endosperm and embryo respectively; c) BT1 encodes for the ADPglucose transporter in the amyloplast envelope. It remains to be proven however that these loci correspond to genes expressed in kernels. Comparison of the present QTL locations with those for carbohydrate metabolism QTLs in maize mature leaves [4] shows striking similarities on chromosomes 1 and 10. Thus, on chromosome 1 (umc11 to gsy59b_SH2), QTLs for leaf sucrose-synthase activity, hexose and sucrose contents were found in the same region as two main effect QTLs for grain AP content and one interaction QTL for grain AM:AP level. In addition, on chromosome 10 (gsy15_POL to umc44b), QTLs for leaf AGPase and invertase activities, and sucrose content were found in the same region as two main effect QTLs for grain WSF and AM:AP level and two interaction QTLs for grain AM content. One possible explanation for these QTL clusters for related 469 traits could be that the candidate genes would correspond to common regulatory genes for the pathway, as shown by McMullen et al. [19] in the case of anthocyanin biosynthesis, or that they correspond to the expression of the same structural genes transcribed in different organs. 4. METHODS 4.1. Genetic material and sampling The maize (Zea mays L.) recombinant inbred lines, used for mapping, were derived from a cross between an American dent line (Io) and a French early flint line (F-2) followed by eight generations of self-pollination. The two parents and a sample of 100 RILs, taken among the 145 lines from the mapping population, were grown in the field at Gif-sur-Yvette, France (20 km SW Paris) in 1994 and 1995. Plants were planted in a complete randomised two-block design. In each block, each line plot consisted of ten plants in a row. Open-pollinated (1994) or self-pollinated (1995) ears from three plants in each block were collected at maturity. Six grains picked in the central zone of the six ears were mixed and, among these 36 grains, eight grains were sampled for further biochemical measurements. 4.2. Biochemical and chemical analysis of starch Starch samples were prepared from eight kernels, air-dried overnight at 50 °C and ball-milled to a fine powder. A sub-sample (70 mg) was defatted by twice shaking in 2 mL 70 % acetone. After centrifugation the pellet was suspended in 1 mL water and solubilized in 4 mL 5 M NaOH under constant stirring at ambient temperature during 1 h, then neutralised with HCl. Starch amount was determined by an enzymatic coupled assay by hydrolysis with amyloglucosidase (Boehringer 150 units·mL–1) in 0.3 M citrate buffer pH 4.6 for 1 h at 50 °C. The generated glucose was spectrophotometrically determined after hexokinase and glucose 6-phosphate dehydrogenase treatment, in the presence of NAD and ATP, as described in Séne et al. [31]. Water soluble fraction was extracted from a 1-g subsample of maize kernel powder, by deionized water under constant stirring during 1 h at ambient temperature. The iodine spectrum of the supernatant containing soluble starch corresponds to that of phytoglycogen (brown colour), but the presence of some oligosaccharides cannot be excluded. This supernatant was submitted to amyloglucosidase and then to vol. 38 (6) 2000 470 M. Séne et al. hexokinase/glucose 6-phosphate dehydrogenase in the presence of NAD and ATP. Two variables were derived from these measurements: Starch.I94, insoluble starch and Starch.T94, soluble + insoluble starch, so-called total starch. The insoluble starch corresponds to starch granules, it represents at least 90 % of total starch. So-called soluble starch corresponds to the water soluble fraction. Amylose and amylopectin contents were measured simultaneously using the iodine colorimetric method fully described in Séne et al. [31] and which is based on the difference in colour of iodine complex with amylose (blue) and with amylopectin (red). The starch neutral solution was diluted four times and a 30-µL aliquot was added to a spectrophotometric cuvette containing 0.92 mL 0.05 M phosphate buffer (pH 7). Then, 50 µL iodine solution (0.2 g iodine in 100 mL, 85 % v/v DMSO) was added. The colour was allowed to develop for 10 min and absorbance was determined at six wavelengths (530, 545, 560, 600, 630, 680 nm). Standard solutions containing AM (15 µg·mL–1) and AP (75 µg·mL–1) and mixture AM + AP (15 + 75 µg.mL–1) were used. An Excel 4 (Microsoft) spreadsheet allowed the resolution of the six equations derived from the Lambert-Beer’s relation: A = eo共 AM 兲 × C共 AM 兲 + eo共 AP 兲 × C共 AP 兲 The extinction coefficients eo(AM) and eo(AP) were experimentally determined for each wavelength using AM and AP standards [31]. 4.3. Genetic map The genetic map was constructed using 145 RILs and 148 loci were revealed using 130 RFLP probes corresponding to expressed sequences (gsy codes) and anchor markers already mapped in other populations (University of Missouri - Columbia (umc) and Brookhaven National Laboratory (bnl)). In addition, isozyme marker loci were mapped. They were based on four enzymes involved in carbon metabolism: malate dehydrogenase (MDH1 and 2), isocitrate dehydrogenase (IDH2) and alcohol dehydrogenase (ADH1). Twenty-seven other mapped loci were related to carbohydrate metabolism, 26 corresponding to enzyme structural genes of the starch biosynthetic pathway and one to a putative sugar transporter. The enzyme structural genes code for AGPase (SH2, BT2, BTL2, AGP1 loci), GBSS (WX locus), starch branching enzyme (SBE I, SBE II loci), starch debranching enzyme (SU1 locus), sucrose synthase (SH1, SUS1 loci), vacuolar invertase (IVR1, IVR2 loci), cell wall Plant Physiol. Biochem. invertase (INCW1, INCW2 loci), and sucrose phosphate synthase (SPS locus) (see [4] and [25] for complete references on these probes). 4.4. Quantitative trait locus detection Both single-marker analysis (ANOVA) and composite interval mapping (MQTL-CIM) [33] were used for QTL detection. In the case of MQTL, special care was taken for the choice of co-factors by stepwise regression. A cut-off was placed just where a drop in the partial R2 values was evident in the ordered list of co-factors (the minimum R2 was usually more than 0.03), so that the final number of co-factors varied from 5 to 9 according to the trait. Increasing the number of co-factors in MQTL up to 20, which is the limit (2*SQUARE_ROOT(N), N being the number of RILs) proposed by Sakamoto et al. [30] increased the number of detected QTLs by a factor of 2, as 27 QTLs were obtained (data not shown) instead of thirteen. This shows the great sensitivity of the method to the number of co-factors used. Simulation calculations with known QTLs have shown that reducing the number of co-factors strongly reduced the false positive (type I error) without affecting true positive detection (type II error) (A. Leonardi, pers. comm.). For each trait, the TS (test statistics) was calculated as a function of the ratio of the residual sums of squares in the full model to the residual sums of squares of a model without the effect being tested. A permutation test was used for determining a threshold test value corresponding to a 5 % type I error for the whole genome [6]. For each trait, the TS values were then divided by the threshold value obtained from the permutation test, and the ratio was plotted along the chromosomes. QTLs were retained at each peak above the TS ratio value of 1. Confidence intervals for QTL position, evaluated by simulation, were estimated as 10–20 cM (Leonardi, pers. comm.). The actual value is dependent on a great number of parameter: detection method, marker density, etc. [18]. Pearson correlation coefficients were calculated using the PROC CORR procedure. The SAS package (SAS Institute, Cary, NC) was also used for ANOVA and all the other calculations before and after the MQTL procedure. Epistasis QTLs were searched using analyses of variance, checking for significant interaction between all pairs of markers after removal of the part of variation explained by the main effect QTLs. A P < 0.0005 threshold was used, which would yield by chance five false QTLs if the more than 10 000 tests were independent, which is not the case because of the genetic linkage between markers. Although difficult to QTLs for starch in maize kernel estimate, the expected number of false positives may be lower. As a supplementary criterion, interactions having a probability three times that of the less probable main effect QTL were discarded. When several pairs of markers yielded significant interaction but involved genetically linked markers, only the most significant pair was kept. Then, the combination of interactions and main effect QTLs yielding the highest R2 value was chosen as the final model to account for trait variation. Acknowledgments We gratefully acknowledge Dr Agnès Leonardi (Station de génétique végétale) for fruitful discussions on the statistical bases and the genetic meaning of the QTL detection methods, and for sharing results of simulations and providing numerous program/software utilities for using MQTL. REFERENCES [1] Alrefai R., Berke T.G., Rocheford T.R., Quantitative trait locus analysis of fatty acid concentrations in maize, Genome 38 (1995) 894–901. [2] Beavis W.D., Grant D., Albertsen M., Fincher R., Quantitative trait loci for plant height in four maize populations and their associations with quantitative genetic loci, Theor. Appl. Genet. 83 (1991) 141–145. [3] Bulant C., Gallais A., Xenia effects in maize with normal endosperm: I. Importance and stability, Crop Sci. 38 (1998) 1517–1525. [4] Causse M., Rocher J.P., Henry A.M., Charcosset A., Prioul J.L., de Vienne D., Genetic dissection of the relationship between carbon metabolism and early growth in maize, with emphasis on key-enzyme loci, Mol. Breeding 1 (1995) 259–272. [5] Causse M., Santoni S., Damerval C., Maurice A., Charcosset A., Deatrick J., de Vienne D., A composite map of expressed sequences in maize, Genome 39 (1996) 418–432. [6] Churchill G.A., Doerge R.W., Empirical threshold values for quantitative trait mapping, Genetics 138 (1994) 963–971. [7] Damerval C., Maurice A., Josse J.M., de Vienne D., Quantitative trait loci underlying gene product variations - a novel perspective for analyzing regulation of genome expression, Genetics 137 (1994) 289–310. [8] Denyer K., Dunlap F., Thorbjornsen T., Keeling P., Smith A.M., The major form of ADP-glucose pyrophosphorylase in maize endosperm is extraplastidial, Plant Physiol. 112 (1996) 779–785. 471 [9] De Vicente M.C., Tanksley S.D., QTL analysis of transgressive segregation in an interspecific tomato cross, Genetics 134 (1993) 585–596. [10] Edwards M.D., Helentjaris T., Wright S., Stuber C.W., Molecular-marker-facilitated investigations of quantitative-trait loci in maize. 4 Analysis based on genome saturation with isozyme and restriction fragment length polymorphism markers, Theor. Appl. Genet. 83 (1992) 765–774. [11] Geffroy V., Sévignac M., Fouilloux G., Thoquet P., Gepts P., Langin T., Dron M., Inheritance of partial resistance against Colletotrichum lindemuthianum in Phaseolus vulgaris and co-localisaton of QTL with genes involved in specific resistance, Mol. PlantMicrobe Interact. 13 (2000) 287–296. [12] Giroux M.J., Shaw J., Barry G., Cobb B.G., Greene T., Okita T., Hannah L.C., A single gene mutation that increases maize seed weight, Proc. Natl. Acad. Sci. USA 93 (1996) 5824–5829. [13] Goldman I.L., Rocheford T.R., Dudley J.W., Quantitative trait loci influencing protein and starch concentration in the Illinois long term selection maize strains, Theor. Appl. Genet. 87 (1993) 217–224. [14] Hyne V., Kearsey M.J., Pike D.J., Snape J.W., QTL analysis: unreliability and bias in estimation procedures, Mol. Breeding 1 (1995) 273–282. [15] Jenner C.J., Starch synthesis in the kernel of wheat under high temperature conditions, Austr. J. Plant Physiol. 21 (1994) 791–806. [16] Knipple D.C., Doyle K.E., Marsellaherrick P.A., Soderlund D.M., Tight genetic linkage between the kdr insecticide resistance trait and a voltage-sensitive sodium channel gene in the house fly, Proc. Natl. Acad. Sci. USA 91 (1994) 2483–2487. [17] Lebreton C., Lazic Jancic V., Steed A., Pekic S., Quarrie S.A., Identification of QTL for drought responses in maize and their use in testing causal relationships between traits, J. Exp. Bot. 46 (1995) 853–865. [18] Mangin B., Goffinet B., Comparison of several confidence intervals for QTL location, Heredity 78 (1997) 345–353. [19] McMullen M.D., Byrne P.F., Snook M.E., Wiseman B.R., Lee E.A., Widstrom N.W., Coe E.H., Quantitative trait loci and metabolic pathways, Proc. Natl. Acad. Sci. USA 95 (1998) 1996–2000. [20] Miller M.E., Chourey P.S., The maize invertasedeficient miniature-1 seed mutation is associated with aberrant pedicel and endosperm development, Plant Cell 4 (1992) 297–305. [21] Nelson O., Pan D., Starch synthesis in maize endosperm, Annu. Rev. Plant Physiol. Plant Mol. Biol. 46 (1995) 475–496. [22] Neuffer M.G., Coe E.H., Wesseler S.R., Mutants of maize, in: Neuffer M.G., Coe E.H., Wesseler S.R. (Eds.), Cloned Genes, Cold Spring Harbor Laboratory Press, 1997, pp. 325–388. vol. 38 (6) 2000 472 M. Séne et al. [23] Paterson A.H., Lander E.S., Hewitt J.D., Paterson S., Lincoln S.E., Tanksley S.D., Resolution of quantitative traits into mendelian factors by using a complete linkage map of restriction fragment length polymorphisms, Nature 335 (1988) 721–726. [24] Pelleschi S., Recherche de locus à effet quantitatif, liés au métabolisme glucidique au cours d’une contrainte hydrique, chez le maïs (Zea mays L.), Ph.D. thesis, université Paris-Sud, France, 1997. [25] Prioul J.L., Corn, , in: Zamski E., Shaffer A.A. (Eds.), Photoassimilate Distribution in Plants and Crops, Marcel Dekker Publisher, New York, 1996, pp. 549–593. [26] Prioul J.L., Pelleschi S., Séne M., Thévenot C., Causse M., de Vienne D., Leonardi A., From QTLs for enzyme activity to candidate genes in maize, J. Exp. Bot. 50 (1999) 1281–1288. [27] Prioul J.L., Quarrie S., Causse M., de Vienne D., Dissecting complex physiological functions through the use of molecular quantitative genetics, J. Exp. Bot. 48 (1997) 1151–1163. [28] Ragot M., Sisco P.H., Hoisington D.A., Stuber C.W., Molecular-marker mediated characterization of favorable exotic alleles at quantitative trait loci in maize, Crop Sci. 35 (1995) 1306–1315. [29] Ribaut J.M., Jiang C., Gonzales-de-Leon D., Edmeades G.O., Hoisington D.A., Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies, Theor. Appl. Genet. 94 (1997) 887–896. [30] Sakamoto Y., Ishiguro M., Kitagawa G., Akaike Information Criterion Statistics, KTK Scientific Publishers, Tokyo, 1986. Plant Physiol. Biochem. [31] Séne M., Thévenot C., Prioul J.L., Simultaneous spectrophotometric determination of amylose and amylopectin in starch from maize kernel by multiwavelength analysis, J. Cereal Sci. 26 (1997) 211–221. [32] Teulat B., This D., Khairallah M., Borries C., Ragot C., Sourdille P., Leroy P., Monneveux P., Charrier A., Several QTLs involved in osmotic adjustment trait variation in barley (Hordeum vulgare L.), Theor. Appl. Genet. 96 (1998) 688–698. [33] Tinker N.A., Mather D.E., MQTL: software for simplified composite interval mapping of QTL in multiple environmentsJQTL 1:2 (1995) (http:// probe.nalusda.gov:8000/otherdocs/jqtl/). [34] Van den Koornhuyse N., Libessart N., Delrue B., Zabawinski C., Decq A., Iglesias A., Preiss J., Ball S., Control of starch composition and structure through substrate supply in the monocellular alga Chlamydomonas reinhardtii, J. Biol. Chem. 271 (1996) 16281–16288. [35] Veldboom L.R., Lee M., Molecular-marker-facilitated studies of morphological traits in maize. 2. Determination of QTLs for grain yield and yield components, Theor. Appl. Genet. 89 (1994) 451–458. [36] Westgate M.E., Boyer J.S., Reproduction at low silk and pollen water potentials, Crop Sci. 26 (1986) 951–956. [37] Xu J., Avigne W.T., McCarty D.R., Koch K.E., A similar dichotomy of sugar modulation and developmental expression affects both paths of sucrose metabolism: evidence for a maize invertase gene family, Plant Cell 8 (1996) 1209–1220.