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CROP BREEDING, GENETICS & CYTOLOGY

Genetic Analysis and QTL Mapping of Cell Wall Digestibility and Lignification in Silage Maize

^a Unité de Génétique et d'Amélioration des Plantes Fourragères, INRA, 86600 Lusignan, France
^b Station de Génétique Végétale, INRA, 91190 Gif sur Yvette, France

* Corresponding author (barriere{at}lusignan.inra.fr)

	ABSTRACT

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Improving digestibility is a major goal for forage maize (Zea mays L.) breeding programs. Quantitative trait loci (QTL) affecting forage maize digestibility-related and agronomic traits were mapped and characterized in a set of recombinant inbred lines (RIL). Eleven traits were analyzed on whole plant samples: neutral detergent fiber (NDF), starch content (STC), crude protein content (CPC), acid detergent lignin (ADL), in vitro dry matter digestibility (IVDMD), in vitro cell wall digestibility (IVNDFD), in vitro digestibility of non-starch and non-soluble carbohydrate (IVDNSC), dry matter content (DMC), dry matter yield (DMY), mid-silk date (SILK), and plant height (PHT). Evaluation was performed among the RIL populations studied per se (RILps) and in combination with a tester (TC). The genetic variances (²_g) were highly significant and, in most cases, greater than genotype x year interaction variances (²_gxy). Heritabilities ranged from 0.49 to 0.70 in RILps and from 0.12 to 0.58 in TC. Twenty-eight QTL were identified among TC by CIM, which explained individually between 3.3 and 20.2% of the phenotypic variation (R²_p) for traits related to digestibility or agronomic performance. Twenty QTL were identified among RILps, which explained individually between 6.5 and 15.3% of the phenotypic variation (R²_p). Seven of these QTL were common to TC and RILps. Cell wall digestibility estimates (IVNDFD or IVDNSC) were the traits with the highest number of QTL. In contrast, we detected only one QTL for dry matter digestibility (IVDMD). Thus, it may be useful to separate IVDMD into its two component parts, cell wall digestibility, which could be estimated from line per se values, and starch content. Characteristics such as IVDNSC or IVNDFD, coupled with QTL information, would be powerful tools in the search for genes involved in maize lignification or cell wall biogenesis.

Abbreviations: ADL, acid detergent lignin • CIM, composite interval mapping • cM, centimorgan • CPC, crude protein content • DM, dry matter • DMC, dry matter content • DMY, dry matter yield • IVDMD, in vitro dry matter digestibility • IVDNSC, in vitro digestibility of non-starch and non-soluble carbohydrate • IVNDFD, in vitro cell wall digestibility • NDF, neutral detergent fiber • NIRS, near infra-red reflectance spectroscopy • PHT, plant height • QTL, quantitative trait locus/loci • RFLP, restriction fragment length polymorphism • RILs, recombinant inbred lines • RILps, RIL per se • SC, soluble carbohydrate • SILK, date of mid silking • STC, starch content • TC, test cross

	INTRODUCTION

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SILAGE MAIZE is a major forage in dairy cattle feeding because of its high-energy content and good ingestibility. More than 3 500 000 ha are cropped in the European Union for silage making. Forage maize breeding in Europe has been based for a long time on the concept that the best hybrids for grain production were also the most suitable for forage use. However, it is now understood that selection for forage maize has to take into account specific criteria for feeding value (Gallais et al, 1976; Vattikonda and Hunter, 1983). Genetic variation for in vivo or in vitro digestibility has been reported in numerous studies, and improvement in dry matter digestibility should result from an increase in cell wall digestibility (Deinum and Struik, 1985; Dolstra and Medema, 1990; Barrière et al., 1992; Wolf et al., 1993; Coors et al., 1994; Argillier et al., 1995a). Silage digestibility is presently considered a major objective in silage maize breeding programs, but negative associations between digestibility and other agronomic traits (lodging and yield) were highlighted in some studies (Dhillon et al., 1990; Geiger et al., 1992; Argillier et al., 1995b; Barrière and Argillier, 1998). However, the genetic basis of digestibility-related traits and their relationships with other agronomic traits remains poorly documented. To date, only two published reports (Lübberstedt et al., 1997a, 1998) have examined QTL affecting digestibility traits on a whole plant basis. There have been no attempts to map QTL affecting digestibility traits for vegetative components of the plant.

The goal of our study was to determine the genetic basis of traits relating to agronomic and feeding value in silage maize. Genetic variation for these traits was investigated by means of a population of recombinant inbred lines (RIL) studied per se and in hybrid combination with a tester. The relationships between traits were examined, and the colocalization of QTL for different traits was investigated to evaluate the possibility of simultaneously improving quality-related traits and agronomic traits. Finally, we wanted to determine whether quality traits measured for inbred per se values were useful for predicting hybrid performances.

	MATERIALS AND METHODS

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Plant Materials and Field Experiments
One hundred RILs were developed from the cross between the elite inbred lines F2 (derived from the flint population Lacaune) and Io (derived from the dent population Iodent) following a classical single-seed descent procedure to the F_5:6 generation at INRA Le Moulon (France). In previous experiments, F2 and Io had similar values for cell wall digestibility and lignin content, but they had clearly different agronomic characteristics. The two lines belong to different heterotic pools, and the hybrid Io x F2 was registered in France in the mid 1980s. The 100 RILs were evaluated on a per se basis. They were also crossed with the inbred line F252. For simplification, the population of 100 RILs per se is designated "RILps" and the population of testcrosses is designated "TC."

The field experiments were conducted at Lusignan, where the soil is known as "terres rouges à châtaigniers" (450 g kg^-1 sand, 310 g kg^-1 silt, 160 g kg^-1 clay, 2.30 g kg^-1 organic matter) in 1996 and 1997 for TC and 1997 and 1998 for RILps (100 in 1997 and 87 in 1998 because of seed availability). Each year, TC and RILps, together with both parents, were arranged in a 13 by 8 alpha-lattice design with three replicates for the tested families and six replicates for the parents. Each experimental plot was a single row 5.2 m long. Row spacing was 0.80 m and the density was 90 000 plants ha^-1. Irrigation was applied during summer to prevent water stress. At the silage harvest [about 30 to 35% of dry matter (DM)], the plots were machine-harvested with a forage chopper. A representative sample of 1 kg chopped material per plot was collected. Whole plant samples were dried in an oven (70°C) and then ground with a hammer mill to pass through a 1-mm screen for later chemical analyses.

Agronomic Observations and Prediction of Quality-Related Traits
Four agronomic traits were evaluated: SILK, recorded in d after 1 July; PHT, measured as the distance in centimeters from the soil level to the lowest tassel branch; DMC and DMY, recorded at harvest in grams per kilogram and megagrams per hectare, respectively. Six forage quality characteristics were assayed: STC (AFNOR, 1981, Ewers method, EEC ISO 10520.2); SC (Lila, 1977), CPC (Kjeldahl nitrogen x 6.25); NDF (Goering and Van Soest, 1970); ADL (Goering and Van Soest, 1970); IVDMD (Aufrère and Michalet-Doreau, 1983). All forage characteristics except ADL were recorded on a DM basis in grams per kilogram, while ADL was recorded on a cell wall (NDF) basis. All traits related to maize feeding value (biochemical characteristics and IVDMD) were estimated by near infrared reflectance spectroscopy (NIRS). A NIRS system 6500 spectrophotometer was used with wavelengths spaced every 4 nm from 1100 to 2500 nm. Calibration regressions were validated with laboratory analysis of 40 samples. Calibration equations were previously provided by SHB Libramont (Belgium). Coefficients of determination between laboratory analysis and NIRS predictions and standard errors of prediction were, respectively, 0.88 and 0.45 for CPC, 0.97 and 1.75 for STC, 0.94 and 1.23 for SC, 0.93 and 1.87 for NDF, 0.82 and 0.43 for ADL, and 0.91 and 1.79 for IVDMD.

Two different estimations of cell wall digestibility were investigated. Estimates of IVNDFD (in g kg^-1) were computed assuming that the non-NDF part of plant material was completely digestible (Méchin et al., 1998 as adapted from Struik, 1983). Estimates of IVDNSC (in g kg^-1) was computed assuming that starch and SC were completely digestible (Argillier et al., 1995a). The two formula used were

and

Data Analyses
To verify the consistency of data and to assess the genotype effect in comparison with the genotype x year interaction effect, a preliminary analysis of variance was carried out following the standard procedures of a mixed model with a random genetic effect and fixed year, block, and subblock effects. The variance of genetic effects (²_g), the variance of interaction between genotype and year (²_gxy) and the variance of random error (²_r) were estimated according to the Henderson III method (Henderson, 1953) using the Splus statistical package (Venables and Ripley, 1994). In the second step, as genotype x year interaction was always low, the variances of environmental (²_e) and genetic (²_g) effects were estimated on pooled 2-yr data with a restricted maximum likelihood procedure by the Select software package (Gelis et al., 1991). The so-called environmental effects were equivalent to the sum of the genotype x year interaction and random error effects. Broad-sense heritabilities (plot basis) were estimated as h² = ²_g/²_p, where ²_p = ²_g + ²_e. Pearson's phenotypic correlations between RILps and TC values were estimated by means across years and replicates.

A linkage map was constructed with 152 restriction fragment length polymorphism (RFLP) markers as described by Causse et al. (1996) and this map was used for QTL detection. QTL mapping was based on entry means of the TC or RILps over the 2 yr by the method of Composite Interval Mapping [CIM, Zeng (1994)] implemented in the PLABQTL computer package (Utz and Melchinger, 1996). PLABQTL uses the regression method (Haley and Knott, 1992) in combination with selected markers as cofactors. In the first step, QTL mapping was performed without cofactors. In the second step, we used two complementary approaches using cofactors. In the first approach, cofactors were selected by stepwise regression (option cov SELECT) with an "F-to-enter" and an "F-to-delete" value of 7. This F value was chosen to retain markers that were significant at the 1% level to reduce the residual variance. To complete this first approach, all the chromosomes were analyzed again considering as cofactors (i) markers linked to QTL detected in the first step, and (ii) all the markers located on the chromosome being analyzed, except those flanking the position of test (option cov/+). This strategy allows one to isolate the effects of possible QTL linked to the interval of interest and potentially to identify "ghost" QTL. For all analyses, a LOD threshold of 2 was used, yielding an individual type I error rate of 0.31% and an experimentwise error rate of 33% [result obtained by the permutation test method of Churchill and Doerge (1994)] suitable for the biological interpretation of QTL and linkage patterns. QTL positions were estimated where the LOD score reached its maximum in the region under consideration. A one-LOD support interval was constructed for each QTL (Lander and Botstein, 1989). Because the question of the interval support is not fully resolved in the case of CIM, the one-LOD support intervals must be considered as underestimates. QTL with more than 20 centimorgan (cM) separating support intervals were considered to be different. At the end of the process, all the putative QTL found by different methods were simultaneously tested and after a backward elimination, only significant QTL were retained. Their LOD scores, additive effects, and percentages of phenotypic variation explained by the QTL were calculated from the final model. The percentage of phenotypic variation (R²_p) ascribed to an individual QTL was estimated according to Charcosset and Gallais (1996). The total phenotypic variation (R²_pT) explained by all QTL detected for a given trait was also estimated. The additive effects of QTL were estimated as half the difference between the genotypic values of the two homozygotes.

	RESULTS AND DISCUSSION

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Agronomic and Quality-Related Traits Analysis
The ²_g were highly significant for all traits in each population (P 0.01, Table 1). In most cases, ²_g was for the most part greater than the ²_gxy, except for STC among TC where ²_g and ²_gxy were similar. For any particular trait, ²_e was similar among RILps and TC, whereas ²_g was always higher among RILps, as expected (Hallauer and Miranda, 1981). Therefore, heritabilities for RILps were intermediate to high (0.49 < h² < 0.70), whereas heritabilities for TC were low to intermediate (0.12 < h² < 0.58) (Table 1).

View this table: [in this window] [in a new window]   Table 1. Estimated genetic variance components, estimated environmental variances (2e), and broad-sense heritabilities (h2, single plot basis) of neutral detergent fiber (NDF), starch content (STC), crude protein content (CPC), acid detergent lignin (ADL), in vitro dry matter digestibility (IVDMD), in vitro cell wall digestibility (IVNDFD), in vitro digestibility of non-starch and non-soluble carbohydrate parts (IVDNSC), dry matter content (DMC), dry matter yield (DMY), mid-silk date (SILK), plant height (PHT), and their corresponding variances among a set of maize recombinant inbred lines, crossed with a tester (TC) and studied per se (RILps).

QTL Analysis
Twenty-eight QTL were identified among TC that influenced some aspect of agronomic or nutritive value (Table 4). For PHT, IVDNSC, and IVNDFD single QTL accounted for more than 15% of the phenotypic variance. Similarly, 20 QTL were detected among RILps over all 11 traits (Table 5). Among these QTL, only a QTL for IVDNSC explained more than 15% of the phenotypic variance. Seven of these QTL were in common between TC and RILps.

Only one significant QTL among TC and no QTL among RILps were detected for NDF content. Two QTL were detected for STC over both RILps and TC, but none were common to the two populations. One QTL in RILps (chromosome 8) affected both STC and DMC. In accordance with the later flowering date of Io line, the effect of Io alleles for STC was negative in RILps, whereas, in accordance with Io's great heterotic potential, the effect of Io alleles was positive in TC. Four and two QTL were detected for CPC among TC and RILps, respectively. Among these QTL, the QTL on chromosome 4 was located at the same position for both TC and RILps. However, the QTL for CPC detected in this study on chromosomes 1, 4, and 7 were observed in different locations that those observed on the same chromosomes by Lübberstedt et al. (1998). One QTL located on chromosome 4 was detected in both populations for ADL, each explaining approximately 8% of the phenotypic variance. However, the support intervals of these QTL were separated by more than 20 cM, and thus they were considered as different.

Numerous QTL were detected for plant digestibility. However, only one QTL for IVDMD, explaining 13.5% of phenotypic variance was detected among RILps on chromosome 1 and none in TC. This single QTL for IVDMD colocalized with one of the QTL detected for cell wall digestibility. But this QTL for IVDMD did not colocalize with any QTL for IVDOM detected in the study of Lübberstedt et al. (1998). Seven and three QTL for IVNDFD were detected in TC and RILps, respectively. One QTL on chromosome 1 (position 14 cM for TC or 12 cM for RILps) and the QTL on chromosome 4 were common in the two populations. This latter QTL explained more than 20% of phenotypic variance in TC. Two and four QTL for IVDNSC were detected in TC and RILps, respectively. One of them was located on chromosome 4 and was common to both populations. This QTL accounted for 18.2 and 8.8% of phenotypic variance in TC and RILps, respectively, and is the same position as the one found for IVNDFD. The effect of Io alleles was both positive and negative, demonstrating that alleles increasing maize feeding value could be found in both parent lines. Moreover, there were no common QTL between biomass yield and any quality-related traits.

Lignification was often reported as a major factor associated with reduced cell wall digestibility (Jung and Deetz, 1993; Wolf et al., 1993; Lundvall et al., 1994; Méchin et al., 1998 and 2000; Argillier et al., 2000). However, there were no common QTL between ADL and cell wall digestibilities. This may by due to the relatively low number of entries, or to the measurement of ADL rather than Klason lignin (Dence and Lin, 1992). But cell wall digestibility may also vary independently of lignin content. Méchin et al. (2000) showed that maize lines having similar lignin content could differ in cell wall digestibility, perhaps due to variation in lignin composition or linkage of lignin with cell wall carbohydrates. QTL detected for ADL in TC and RILps (chromosome 4) were located in the vicinity of the bm3 locus (map comparison between Maize Data Base id.140266 and Causse et al., 1996). The bm3 allele have a drastic effect on lignin content and cell wall digestibility (Cherney et al., 1991, Barrière et al., 1993). The bm3 mutation is due to a large deletion in exon 2 of the gene encoding the caffeic O-methyltransferase, one enzyme involved in of lignin biosynthesis (Vignols et al., 1995).

The number of RIL observed in this study was relatively low. In a comparison of QTL mapping from two independent studies of small number of RILs derived in the same single cross, Beavis et al. (1994) concluded that the lack of congruency was mainly attributable to sampling of progenies. But they noticed that the comparison was confounded by numerous factors such as a limited data set of only 20 markers, experiments in different environments, and different seed sources and levels of inbreeding. However, Melchinger et al. (1998) and Utz et al. (2000) showed that the power of QTL detection was low, and estimates of QTL effects were biased upwards when QTL effects are estimated from the same data as used for QTL mapping, especially when the number of progeny is fewer than 200, and the trait had only moderate heritability. Moreover, they emphasized that, even with large sample sizes, the power of QTL detection is only moderate for QTL with small effects. These theoretical and experimental results could explain why Lübberstedt et al. (1997a)(1998) detected many more QTL than we did in this study, despite the fact that they investigated maize digestibility only on a whole plant basis, why only few QTL were common to TC and RILps, and why a larger number of QTL was found in TC than in RILs, despite the fact that heritability of all traits was higher for RILps than for TC. The lack of QTL associations between TC and RILps could also be the result of dominance effects and heterotic relationships between the alleles segregating within the population and those of the tester, or higher relative epistatic effects due to inbreeding for RILps. Lastly, the lack of correspondence of QTL may also be related to the lower maturity of RILps compared with TC. However, only one-third of QTL detected in TC had a R²_p value higher than 9.5, in constast to three-quarters of QTL in RILps. The three QTL detected in TC and having the higher R²_p values were also detected in RILps (chromosome 4 for IVDNSC and IVNDFD and chromosome 9 for PHT). The three QTL for IVNDFD in RILps were also detected in TC. Even if the estimates of QTL effects were somewhat biased, QTL found both in RILps and TC, or QTL found for both measures of cell wall digestibility (IVDNSC and IVNDFD) or ADL, are worth further investigation.

	CONCLUSION AND CONSEQUENCES OF BREEDING

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The identification of QTL affecting agronomic and quality-related traits may be important for dissecting genetic variation and, consequently, increasing efficiency of plant breeding. Although further investigations are required to verify the consistency of our results, there are several implications that relate directly to improving silage maize for a higher feeding value. The two cell wall digestibility estimates (IVNDFD and IVDNSC) were the traits with the highest number of detected QTL, whereas only one was detected for the composite trait, IVDMD. Most QTL found in RILps for cell digestibility traits were consistent with the QTL detected in TC. This was not the case for agronomic traits, except maturity related traits. The identification of several QTL having large effects on cell wall digestibility illustrates the need for breeders to evaluate both cell wall digestibility and starch content. The two traits IVNDFD and IVDNSC were shown to be quite equivalent measures of cell wall digestibility, and it is worth noting that these traits are easy and inexpensive to evaluate because they are computed from measurements of whole plant samples. Moreover, and in agreement with Argillier et al. (2000), there was a good correspondence between line per se and top cross values for cell wall digestibility and lignification. Thus, silage breeders could evaluate cell wall digestibility at the per se level, possibly in early generations of selfing, such as the S2 or S3. But breeders should estimate starch content in top crosses because of the heterotic effect on grain yield. Barrière et al. (1997) considered that the optimal value for starch content should be approximately 30% for silage maize fed at 60 to 80% of the diet of dairy cattle (DM basis).

The search for QTL affecting cell wall digestibility may provide new knowledge about the genetics of cell digestibility and cell wall biogenesis. The efficiency of using QTL for marker-assisted selection in improving digestibility value of elite lines remains to be determined (Melchinger et al., 1998; Utz et al., 2000). Currently, measurements of IVDNSC or IVNDFD are easy to obtain in conventional maize breeding programs. QTL colocalizations, EST (expressed sequence tags) mapping, and sequencing will give information on involved mechanisms and genes. But silage maize breeders should also remember that very efficient alleles for maize digestibility improvement should exist in neglected genetic resources which have been used for decades for breeding maize for grain use only.

	ACKNOWLEDGMENTS

 
We are grateful to James G. Coors who gave fruitful advice and suggestions, and contributed mainly to the improvement of the manuscript.

Received for publication January 1, 2000.

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