Printer Friendly

Identification of essentially derived varieties obtained from biparental crosses of homozygous lines: II. Morphological distances and heterosis in comparison with simple sequence repeat and amplified fragment length polymorphism data in maize.

PLANT VARIETY PROTECTION (PVP) systems and their laws and regulations should balance commercial interests against sustainable development of new cultivars. On the one hand, registered plant varieties need to be protected against plagiarism and misuse. On the other, protected germplasm should be accessible to secure future breeding progress. The concept of breeder's exemption was, therefore, introduced into the International Union for the Protection of New Varieties of Plants (UPOV) convention to solve the obvious conflict between the different stakeholders within the PVP system (UPOV, 1978). Accordingly, plant breeders have access to protected germplasm for the development of new varieties.

Tools like doubled haploids, marker-assisted backcrossing, and genetic engineering allow to add a small number of genes to a protected variety and apply for PVP for this new variety. In addition, it is possible to select intentionally for lines that are similar to their parents. The efforts invested in breeding the original variety can thus be exploited by the breeder of the plagiarized variety without indemnification. The concept of EDVs was, therefore, implemented into the revised UPOV convention (UPOV, 1991) and several national PVP acts.

Regarding the EDV concept, a variety is deemed essentially derived from an IV if it is clearly distinguishable but conforms genetically to the IV. If the extent of conformity exceeds a certain threshold, the concept of EDVs indicates that the breeder of the EDV has to reach an agreement with the breeder of the IV. However, no consensus has currently been reached on the methods for determining the genetic conformity to distinguish between EDVs and independently derived varieties (IDVs). In addition, accepted or nonaccepted breeding procedures have not yet been defined.

Molecular markers, especially SSRs and AFLPs, have been recommended as appropriate tools for determining EDVs in various crops including maize (Dillmann et al., 1997; Roldan-Ruiz et al., 2000a; Borgo et al., 2002). By contrast, the use of morphological traits or heterosis is still under debate (International Association of Plant Breeders for the Protection of Plant Varieties, 1999). Until now, accurate morphological and agronomic descriptions of cultivars and varieties have been the basis of tests for distinctness, uniformity, and stability (DUS) within worldwide PVP systems and have assured farmers and breeders that they are using clearly identifiable varieties to high standards of purity and quality (Smith and Smith, 1989a). In addition, numerous studies showed significant correlations between MPH and the coefficient of parentage (f) (Melchinger, 1999; Smith et al., 1991). For these reasons, proponents of the use of morphological traits or heterosis for identifications of EDVs state that phenotypic information provides the basis for PVP and should also be used for identification of EDVs. Studies on the ability of morphological traits to estimate the genetic conformity between related ryegrass (Lolium perenne L.) varieties revealed that they had only a limited power to distinguish between IDVs and EDVs (Gilliland et al., 2000; Roldan-Ruiz et al., 2000b).

In maize, a triangular instead of a linear relationship was observed between MDs and GDs or the coancestry coefficient (f) (Dillmann and Guerin, 1998), which indicates that low GDs correspond necessarily with low MDs, whereas the reverse does not necessarily hold true because high GDs can correspond with both high and low MDs. In addition, genetic relationships among maize inbred lines based on morphology were essentially random compared with any relation derived from heterosis or pedigree data (Smith and Smith, 1989b). However, data on the usefulness of heterosis or morphological traits that reflect the degree of relatedness between maize inbred lines in terms of essential derivation is scanty.

The main goal of this study was to investigate the relationship of homozygous progeny lines in maize derived from [F.sub.2], [BC.sub.1], or [BC.sub.2] populations to their parental inbreds based on heterosis and MDs in comparison with SSR- and AFLP-based GDs. In detail, our objectives were to (i) evaluate the power of heterosis and MDs to discriminate between progenies derived from [F.sub.2], [BC.sub.1], and [BC.sub.2] populations, (ii) compare the findings to published data based on SSRs and AFLPs, and (iii) draw conclusions about the usefulness of the various distance measures for the identification of EDVs.

MATERIALS AND METHODS

Plant Materials

A total of 58 elite maize inbred lines was analyzed comprising 24 European flint and 34 European dent lines. These lines originated from the maize breeding programs at the University of Hohenheim (Stuttgart, Germany) and two commercial breeding companies in Germany. The 58 lines comprised 38 triplets. A triplet consisted of one progeny line O and both parental lines P1 and P2. The materials consisted of 26 intrapool triplets of European dent and 12 intrapool triplets of European flint lines. European flint and dent lines, as well as the pooled U.S. lines, were regarded as separate germplasm pools. Progenies were either derived from [F.sub.2], [BC.sub.1], or [BC.sub.2] populations. This is a subset of the material analyzed in our companion study (Heckenberger et al., 2005).

For each combination of lines within a triplet (P1 x P2, P1 x O, and P2 x O), seeds from the corresponding [F.sub.1] hybrid were generated. In addition, if more than one progeny line ([O.sub.1], [O.sub.2], ...., [O.sub.j]) was derived from a cross of the same two parental lines, each possible [F.sub.1] hybrid ([O.sub.1] x [O.sub.2], ...., [O.sub.j-1] x [O.sub.j]) was generated. In total, 114 intrapool [F.sub.1] hybrids were tested in this study. Detailed information on all 38 triplets, the 58 maize inbreds, and the hybrids included in this study is available as supplemental data on the internet at http://crop.scijournals.org/.

Experimental Design

Field experiments were conducted in 2000 and 2001 at three locations in South Germany, with two replications per location. All sites (Bad Krozingen, Eckartsweier, and Scherzheim) are located in the Upper Rhine Valley, a major area of grain-maize production in Germany. All inbred lines and hybrids of a triplet were grown together in one block. Within each triplet block, [F.sub.1] hybrids were grown side-by-side with their parental lines to guarantee heterosis estimates with high accuracy. All trials received standard cultural practices of fertilization as well as control of insects and weeds.

The experimental unit was a three-row plot with a row spacing of 0.75 m and a plot length of 4.0 m. Trials were overplanted and later thinned manually to 26 plants per row, with a final plant density of 8.7 plants [m.sup.-2]. Each row was harvested separately. To reduce neighbor effects between adjacent plots with different vigor (inbreds vs. hybrids), only data of the middle row of each plot were used for further analyses. The experiment was performed using a randomized complete block design. Parameter values were observed for 23 morphological traits according to the UPOV guidelines (UPOV, 1978), and for six additional agronomic traits (Table 1) by measuring a minimum of five individual plants of a particular plot or by visual observation of the whole plot.

Molecular Analyses

All lines were genotyped with a set of 100 SSR markers uniformly covering the entire maize genome, as described in detail by Heckenberger et al. (2002). The 100 SSRs were selected on the basis of reliable single-locus amplification, absence of null alleles, high degree of polymorphism, and high reproducibility of the bands. The SSR analyses were performed on a commercial basis. In addition, all lines were genotyped using commercial AFLPs. A total of 20 AFLP primer combinations (PCs) was used as described in detail by Heckenberger et al. (2003). The AFLP markers were mapped according to a proprietary integrated map of maize, as described by Peleman et al. (2000).

Statistical Analyses

Grain yield for each single row was adjusted to 84.5% dry matter content. Heterosis was determined as MPH = ([F.sub.1] - MP)/MP, where [F.sub.1] is the [F.sub.1] hybrid performance, and MP = ([P.sub.1] + [P.sub.2])/2, the midparent value in which [P.sub.1] and [P.sub.2] are the performances of the inbred parents. Analyses of variance were performed for morphological traits of the inbreds using a mixed linear model considering genotypes as fixed effects, and environments as well as genotype x environment interactions as random effects. Genotypic variance among lines ([[theta].sup.2.sub.g]) and the variance of genotype x environment interaction ([[delta].sup.2.sub.ge]) were defined and estimated according to Scheffe (1959). Similarly, genetic ratios (GR) analogous to broad-sense heritability were calculated as [GR.sub.g] = [[delta].sup.2.sub.g]er/[MS.sub.g], where e is the number of environments, r is the number of replications, and [MS.sub.g] is the mean square due to genotypes. Dhillon et al. (1990) may be consulted for further details. Likewise, ANOVAs were calculated for MPH values of each triplet to estimate the genotypic variance for midparent values among triplets ([[theta].sup.2.sub.t]) and corresponding genetic ratio ([GR.sub.t]), using the same procedures.

For calculation of MDs, observations for each trait were standardized by dividing by the phenotypic standard deviation of the particular trait. Euclidean ([MD.sub.Euc]) and Mahalanobis (1936) ([MD.sub.MAH]) distances were calculated based on standardized observations for each pairwise comparison of inbred lines. Malecot's (1948) coancestry coefficient (f) was calculated between all pairwise line combinations. Polymorphic information content (PLC) values were estimated as suggested by Anderson et al. (1993). Genetic distances between lines based on SSR ([GD.sub.SSR]) or AFLP ([GD.sub.AFLP]) data were estimated using Rogers' distance (Rogers, 1972). The linear relationships between 1 - f, GDs, MDs, and heterosis estimates were evaluated with a lack-of-fit test (Snedecor and Cochran, 1980). Empirical and approximated frequency distributions of MD values were compared with a Kolmogorov-Smirnov test (Lehmann, 1986) to check for significant deviations. Furthermore, simple correlations (r) were calculated between 1 - f, GDs, MDs, and heterosis estimates. Homogeneity of variance components of data from flint and dent germplasm was evaluated with Levene's test (Levene, 1960). Variance components and correlations were not significantly different between flint and dent lines. Consequently, only results from pooled data were reported.

To evaluate potential EDV thresholds, the cumulative frequency distributions for GDs were approximated by [beta] distributions (Johnson et al., 1995), as described in detail in our companion study (Heckenberger et al., 2005). Frequency distributions for MDs and MPH for [F.sub.2]-, B[C.sub.1], or B[C.sub.2]-derived progeny lines were approximated by normal distributions with parameters chosen such that the mean and variance of the original distribution were conserved. On the basis of these distributions, we calculated Type I ([alpha]) and Type II ([beta]) errors for various EDV thresholds and various types of populations, as suggested by Heckenberger et al. (2005) for molecular marker data. Here, [alpha] corresponds to the probability that a true IDV will be wrongly judged as EDV, whereas [3 corresponds to the probability that a true EDV will not be recognized as such and judged as IDV. We first investigated the situation that a [F.sub.2]-derived progeny would be considered as IDV, and a B[C.sub.1]-derived progeny as EDV. Alternatively, we regarded a B[C.sub.1]-derived progeny as IDV, and a B[C.sub.2]-derived progeny as EDV.

Statistical analyses of marker data and f values were performed as described by Heckenberger et al. (2005) using the PLABSIM software package (Frisch et al., 2000). The ANOVAs for field experiments were calculated with the PLABSTAT software (Utz, 2001). All other statistical calculations were performed with the R software package (Ihaka and Gentleman, 1996).

RESULTS

Morphological Traits and Heterosis Data

Estimates of genotypic variances ([[theta].sup.2.sub.g]) pooled across flint and dent inbred lines were significant (P < 0.01) for all traits (Table 1). In addition, significant (P < 0.01) genotype x environment interactions ([[sigma].sup.2.sub.ge]) were observed for most traits due to cool and wet weather conditions in 2000 and hot and dry weather conditions in 2001 (data not shown). In most cases, [[sigma].sup.2.sub.ge] was considerably smaller than [[sigma].sup.2.sub.g].

Significant (P < 0.01) estimates of [[sigma].sup.2.sub.t] among triplets for MPH were observed for most traits (Table 2). However, considerable differences were found between traits depending on the relative amount of MPH with highest values for grain yield (GYD), grain yield of hand harvested ears (GYE), number of kernels per ear (NKE), and plant length (PLG). The GR for MPH of heterotic traits ranged from 0.66 to 0.97 and were slightly smaller than for line per se performance.

Simple Sequence Repeat and Amplified Fragment Length Polymorphism Marker Data

A total of 580 SSR alleles and 1053 polymorphic AFLP bands was identified for the set of 58 maize lines. The number of alleles per SSR ranged from 3 to 12, with a mean of 5.9. The PIC values for SSRs varied between 0.08 and 0.86, and averaged 0.64. The number of polymorphic bands per AFLP primer combination varied from 40 to 70, with an average of 54. The PIC values for individual AFLP bands ranged from 0.03 to 0.50, with a mean of 0.33.

Relationships among Distance Measures, Heterosis, and Coancestry

Correlations (r) between 1 - f and GDs based on SSRs (G[D.sub.SSR]) and AFLPs (G[D.sub.AFLP]) were highly significant (P < 0.01) and exceeded 0.85 in both flint and dent lines, with a single exception (Table 3). By comparison, r values between GDs and MDs were moderate (0.40 [less than or equal to] r [less than or equal to] 0.68). Likewise, r values between M[D.sub.EUC] and M[D.sub.MAH] were only of moderate sizes. Correlations were consistently higher for flint lines than for dent lines. Coancestry was moderately correlated with M[D.sub.EUC], but poorly correlated with M[D.sub.MAH]; both relationships showed a triangular form (Fig. 1).

[FIGURE 1 OMITTED]

In contrast, the relationships between G[D.sub.SSR], G[D.sub.AFLP], and 1 - f on the one hand, and MPH on the other, were linear for most heterotic traits (Fig. 2). Corresponding r values were highly significant (P < 0.01) and moderate to high, depending on the trait (Table 2). In general, these correlations were considerably higher than those of M[D.sub.EUC] or M[D.sub.MAH] with G[D.sub.SSR], G[D.sub.AFLP], or 1 - f.

[FIGURE 2 OMITTED]

Threshold Scenarios for Identification of EDVs

Estimates of M[D.sub.EUC] and M[D.sub.MAH] for [F.sub.2]-, B[C.sub.1]-, and B[C.sub.2]-derived progenies were approximately normally distributed in the joint analysis of flint and dent lines. Considerable overlaps between the frequency distributions of M[D.sub.EUC] and M[D.sub.MAH] were observed for [F.sub.2]- vs. B[C.sub.1]-, as well as for B[C.sub.1]- vs. B[C.sub.2]-derived progenies (Fig. 3).

[FIGURE 3 OMITTED]

For thresholds based on MDs, the power 1 - [beta] to classify a B[C.sub.1]-derived progeny line as EDV amounted to 18% for M[D.sub.EUC] and 3% for M[D.sub.MAH], when choosing [alpha] = 0.05 for [F.sub.2]-derived lines (Table 4). Assuming [alpha] = 0.05 for B[C.sub.1]-derived lines, corresponding values of 1 - [beta] for B[C.sub.2]-derived lines were considerably higher for M[D.sub.EUC] and M[D.sub.MAH]. The power 1 - [beta] for thresholds determined by [alpha] = [beta] to classify B[C.sub.1] - or B[C.sub.2]-derived progenies as EDVs increased considerably compared with the values of [alpha] = 0.05. This increase in 1 - [beta] was associated with higher values of [alpha].

When potential thresholds were based on MPH, the power 1 - [beta] to classify a B[C.sub.1]-derived progeny line as EDV ranged from 2 to 30%, assuming [alpha] = 0.05 between [F.sub.2]-derived lines (Table 4). Choosing [alpha] = 0.05 for B[C.sub.1]-derived lines, the values of [beta] increased for B[C.sub.2]-derived lines. For [alpha] = [beta], the power to classify B[C.sub.1]- or B[C.sub.2]-derived progenies as EDVs increased substantially; however, this was again associated with higher values of [alpha]. In general, values of [alpha] and [beta] were of similar magnitude for MDs and MPH.

DISCUSSION

The maize inbred lines examined in our study represent a cross-section of present-day elite flint and dent inbred lines from commercial and public maize breeding programs in Germany. Morphological traits were chosen according to the UPOV guidelines of DUS. Heterosis and morphological traits were determined in extensive field trials across 2 yr and three locations, thus exceeding by far the number of environments employed for DUS testing within PVP systems. Furthermore, SSRs were selected as a suitable marker system due to their known map positions and high degree of polymorphism. The AFLPs were chosen due to the greater number of markers per assay unit and their high reproducibility (Heckenberger et al., 2003). Thus, the present study is the first larger investigation after a series of pioneering studiess based on isozymes and restriction fragment length polymorphisms (RFLPs) (Smith and Smith, 1989a, 1989b; Smith et al., 1991) to provide critical data on the ability of MDs and heterosis for the identification of EDVs in maize in direct comparison with SSR and AFLP data. For this reason, our results provide a well-founded comparison of different distance measures for the identification of EDVs in maize and may serve as an example for other crops.

Data Quality and Relatedness between Different Measures for Genetic Conformity

Despite the contrasting climatic conditions during the vegetation seasons in 2000 and 2001, high GR values were observed for morphological traits and MPH, the former being considerably higher than those reported by Rebourg et al. (2001). In addition, UPGMA cluster analysis based on M[D.sub.EUC] showed a clear grouping of flint and dent lines, which further corroborates the high quality of morphological data (available as supplemental data to the online version of this paper; see http://crop.scijournals.org/). However, the dendrogram based on M[D.sub.MAH] deviated considerably from the expectations based on pedigree data or GDs, and only moderate correlations between M[D.sub.EUC] and MDMAH were observed. This can be explained by the different statistical properties of M[D.sub.EUC] and M[D.sub.MAH], because M[D.sub.MAH], adjusts for the correlations of traits.

The graphs between MDs and GDs (Fig. 1) confirmed the triangular relationship between morphological and GDs reported in previous studies (Dillmann et al., 1997; Rebourg et al., 2001). This indicates that low GDs correspond necessarily with low MDs, whereas the reverse does not necessarily hold true because high GDs can correspond with both high and low MDs (van Eeuwijk and Baril, 2001). In addition, the triangular shape has several biological explanations (Nuel et al., 2001) and is also expected if only molecular markers tightly linked with the genes controlling the phenotypic trait(s) were used (Burstin and Charcosset, 1997).

In the present investigation, flint and dent lines showed similar estimates of 0[alpha] and correlations among the various criteria, and the same applied to flint and dent triplets. This is in harmony with a previous study of BarHen et al. (1995), who examined 974 maize inbred lines with morphological traits and RFLPs. Correlations of 1 - f, G[D.sub.SSR], or G[D.sub.AFLP], with MPH were higher than reported by Ajmone Marsan et al. (1998) for AFLPs but similar to correlations of MPH with 1 - f and GDs based on RFLPs in intrapool crosses (Boppenmaier et al., 1993; Smith et al., 1990). In addition, our study confirms the findings of Smith and Smith (1989b) that correlations of molecular markers or 1 - f are considerably higher with MPH than with M[D.sub.EUC] or M[D.sub.MAH].

Distinctness versus Conformity

To confirm an essential derivation in the sense of the UPOV convention (UPOV, 1991), three separate criteria must be fulfilled. An EDV must (i) be distinct from the IV, (ii) be predominantly derived from the IV, and (iii) conform to it in the expression of its essential characteristics. Distinctness can be determined based on morphological traits by established procedures for DUS testing. Establishing a predominant derivation will either require a directly documented evidence, for example, by breeding books, or could be determined with molecular evidence similar to forensic approaches in the human sector (Gill et al., 1995). However, the question whether conformity in the expression of essential characteristics should be assessed by phenotypic rather than molecular data is still unsolved. While differences in the expression of one single trait are sufficient to prove distinctness between two varieties, assessment of conformity should be based on a large number of morphological traits and still could not give a definite answer due to the triangular relationship mentioned above.

Proponents of phenotypic data state that the term "conform in the expression of its essential characteristics that result from the genotype" (UPOV, 1991) implies the use of phenotypic and agronomic data rather than molecular data (Troyer and Rocheford, 2002). In contrast, opponents state that even highly heritable phenotypic traits can only offer an indirect measure of the relatedness between two cultivars (Roldan-Ruiz et al., 2000b). Molecular data provide a direct estimate of the true relatedness between two genotypes because they are unbiased by environmental effects and reflect the percentage of the genome in common between the IV and a putative EDV. On the basis of our results, GDs based on molecular markers have clear advantages for identification of EDVs.

Power of Morphological Distances and Heterosis for Identification of EDVs

For M[D.sub.EUC] as well as for M[D.sub.MAH], extensive overlaps of the frequency distributions of [F.sub.2]-, B[C.sub.1]-, and B[C.sub.2]-derived progenies were found in spite of the significant correlations with 1 - f. Type I (a) and Type II ([beta]) errors observed for MDs were thus considerably higher than observed for GDs based on molecular markers (Table 4). Consequently, MDs provide only a rough estimate of the true relatedness between two lines and can only poorly discriminate [F.sub.2]-, B[C.sub.1]-, and B[C.sub.2]-derived progenies. These results confirm data from ryegrass (Gilliland et al., 2000) and maize (Smith and Smith, 1989b), showing that morphological conformity could give an initial indication of the relatedness between two cultivars, particularly for highly conforming pairs of inbreds. Nevertheless, a small MD between two varieties cannot be taken as a definitive proof that they are in fact closely related because of the triangular relationship between 1 - f and MDs.

In contrast to the triangular relationship between GDs and MDs, a linear relationship of MPH with GDs or 1 - f was observed, as expected by quantitative genetic theory (Melchinger, 1999). However, in spite of the higher correlation between MPH and 1 - f or GDs, MPH was not markedly superior to MDs regarding the power to discriminate between [F.sub.2]-, B[C.sub.1]- or B[C.sub.2]-derived progeny. This is attributable to the larger experimental error and genotype x environment interactions of MPH in comparison with line per se performance (data not shown), as reflected by the comparison of G[R.sub.1] vs. G[R.sub.g].

For nearly all of the examined scenarios, GDs based on SSRs or AFLPs were superior to MDs or MPH for any trait or combination of traits in their power 1 - [beta] to discriminate among [F.sub.2]-, B[C.sub.1]-, and B[C.sub.2]-derived progenies for given values of [alpha]. However, different from MDs and MPH, the use of SSR or AFLP markers would require thresholds specific for a given germplasm pool. This is necessary because flint and dent lines differ significantly in their mean GD between unrelated lines due to the different levels of polymorphism within each germplasm pool (Heckenberger et al., 2005). The lower T values for AFLPs compared with SSRs are due to their lower degree of polymorphism, but this has no influence on the ability to discriminate different types of progeny.

CONCLUSIONS

On the basis of our results, MDs and MPH can provide only a vague indication for putative EDVs, but reliable identification of EDVs by MPH or MDs is not possible due to the large overlaps in the frequency distributions of MDs and MPH of [F.sub.2]-, B[C.sub.1]-, and B[C.sub.2]-derived progenies. In addition, MDs and MPH have several disadvantages compared with molecular markers. First, assessment of morphological traits and MPH requires extensive field trials across several years and locations to minimize environmental effects. These measurements are therefore more expensive and time consuming than molecular marker analyses. Second, heterosis estimates requires production and testing of hybrids. In addition, reciprocal crosses should be evaluated to minimize the risk of maternal effects (Melchinger et al., 1986). Third, the scoring of morphological traits is subjective to a certain extent. Therefore, a number of check inbreds must be included in the study to warrant a high quality of morphological traits across different years and scoring persons.

In conclusion, we strongly recommend the use of molecular markers for the identification of suspected EDVs by fingerprinting with at least 100 SSR markers or 20 AFLP PCs. If doubts still remain whether a variety is IDV or EDV, the corresponding genotypes should be fingerprinted with an additional set of SSRs or AFLPs. Use of MPH for the identification of EDVs is problematic because the rationale for using MPH is merely its linear relationship with 1--f, and the biological mechanisms underlying heterosis are not yet fully understood.

Abbreviations: AFLP, amplified fragment length polymorphism; BC, backcross; DUS, distinctness, uniformity, and stability; EDV, essentially derived variety; GD, genetic distance; GR, genetic ratio; IDV, independently derived variety; IV, initial variety; MD, morphological distance; MPH, midparent heterosis; PIC, polymorphic information content; PVP, plant variety protection; RFLP, restriction fragment length polymorphism; SSR, simple sequence repeat; UPOV, International Union for the Protection of New Varieties of Plants.
Table 1. Morphological and agronomic traits, their genotypic variance
([[theta].sup.2.sub.g]) and genetic ratios (G[R.sub.g]) observed for 58
flint and dent maize lines in four or six environments in South
Germany.

                          Trait                   Code

Ear        diameter, mm                           EDI
           number of kernels                      NKE
           type of grain (1-9 scale)              TGR
           anthocyanin coloration of glumes of
             cob                                  AGC
           anthocyanin coloration of silks
             (1-9 scale)                          ACS
           color of dorsal side of grain (1-9
             scale)                               CDG
           color of tip of grain (1-9 scale)      CTG
           length, mm                             ELG
           number of rows of grain                NGR
           days to silk emergence                 TSE
Kernels    thousand kernel weight, g              TKW
           grain yield, Mg [ha.sup.-1]            GYD
           grain yield of four manually
             harvested ears, g                    GYE
Leaf       angle between blade and stem (1-9
             scale)                               LAN
           attitude of blade (1-9 scale)          LAT
           width of blade, mm                     WBL
Plant      ear height, cm                         EHT
           length, cm                             PLG
Tassel     anthocyanin coloration of base of
             glume (1-9)                          ABG
           anthocyanin coloration of glume
             excluding base (1-9)                 AEB
           length of side branches, cm            LSB
           angle between main axis and lateral
             branches (1-9)                       TAN
           anthocyanin coloration of anthers
             (1-9)                                AAH
           attitude of lateral branches (1-9)     ALB
           length of main axis above lowest
             side branch, cm                      TLL
           length of main axis above upper
             side branch, cm                      TLU
           number of primary lateral branches     NLB
           days to anthesis                       TAH

                                                          UPOV
                                                       ([dagger])
                          Trait                           code

Ear        diameter, mm                                    27
           number of kernels                               --
           type of grain (1-9 scale)                       30
           anthocyanin coloration of glumes of
             cob                                           34
           anthocyanin coloration of silks
             (1-9 scale)                                   17
           color of dorsal side of grain (1-9
             scale)                                        32
           color of tip of grain (1-9 scale)               31
           length, mm                                      26
           number of rows of grain                         29
           days to silk emergence                          15
Kernels    thousand kernel weight, g                       --
           grain yield, Mg [ha.sup.-1]                     --
           grain yield of four manually
             harvested ears, g                             --
Leaf       angle between blade and stem (1-9
             scale)                                         3
           attitude of blade (1-9 scale)                    4
           width of blade, mm                              24
Plant      ear height, cm                                  --
           length, cm                                      22
Tassel     anthocyanin coloration of base of
             glume (1-9)                                    8
           anthocyanin coloration of glume
             excluding base (1-9)                           9
           length of side branches, cm                     21
           angle between main axis and lateral
             branches (1-9)                                12
           anthocyanin coloration of anthers
             (1-9)                                         10
           attitude of lateral branches (1-9)              13
           length of main axis above lowest
             side branch, cm                               19
           length of main axis above upper
             side branch, cm                               20
           number of primary lateral branches              14
           days to anthesis                                 7

                                                         No. of
                          Trait                        test sites

Ear        diameter, mm                                     6
           number of kernels                                6
           type of grain (1-9 scale)                        4
           anthocyanin coloration of glumes of
             cob                                            4
           anthocyanin coloration of silks
             (1-9 scale)                                    4
           color of dorsal side of grain (1-9
             scale)                                         4
           color of tip of grain (1-9 scale)                4
           length, mm                                       6
           number of rows of grain                          6
           days to silk emergence                           4
Kernels    thousand kernel weight, g                        6
           grain yield, Mg [ha.sup.-1]                      6
           grain yield of four manually
             harvested ears, g                              6
Leaf       angle between blade and stem (1-9
             scale)                                         4
           attitude of blade (1-9 scale)                    4
           width of blade, mm                               4
Plant      ear height, cm                                   4
           length, cm                                       6
Tassel     anthocyanin coloration of base of
             glume (1-9)                                    4
           anthocyanin coloration of glume
             excluding base (1-9)                           4
           length of side branches, cm                      4
           angle between main axis and lateral
             branches (1-9)                                 4
           anthocyanin coloration of anthers
             (1-9)                                          4
           attitude of lateral branches (1-9)               4
           length of main axis above lowest
             side branch, cm                                4
           length of main axis above upper
             side branch, cm                                4
           number of primary lateral branches               4
           days to anthesis                                 4

                          Trait                   [[theta].sup.2.sub.g]

Ear        diameter, mm                                   9.28 **
           number of kernels                           3108 **
           type of grain (1-9 scale)                      2.04 **
           anthocyanin coloration of glumes of
             cob                                          4.48 **
           anthocyanin coloration of silks
             (1-9 scale)                                  1.27 **
           color of dorsal side of grain (1-9
             scale)                                       0.37 **
           color of tip of grain (1-9 scale)              1.13 **
           length, mm                                   188.9 **
           number of rows of grain                        1.19 **
           days to silk emergence                        33.1 **
Kernels    thousand kernel weight, g                   1059 **
           grain yield, Mg [ha.sup.-1]                  107.9 **
           grain yield of four manually
             harvested ears, g                            3.72 **
Leaf       angle between blade and stem (1-9
             scale)                                       1.04 **
           attitude of blade (1-9 scale)                  0.72 **
           width of blade, mm                             0.44 **
Plant      ear height, cm                               164.8 **
           length, cm                                   484.0 **
Tassel     anthocyanin coloration of base of
             glume (1-9)                                  2.90 **
           anthocyanin coloration of glume
             excluding base (1-9)                         1.41 **
           length of side branches, cm                    3.90 **
           angle between main axis and lateral
             branches (1-9)                               1.67 **
           anthocyanin coloration of anthers
             (1-9)                                        1.18 **
           attitude of lateral branches (1-9)             1.59 **
           length of main axis above lowest
             side branch, cm                              8.16 **
           length of main axis above upper
             side branch, cm                             12.5 **
           number of primary lateral branches             1.23 **
           days to anthesis                              21.7 **

                                                       G[R.sub.g]
           Trait                                    ([double dagger])

Ear        diameter, mm                                   96.2
           number of kernels                              88.6
           type of grain (1-9 scale)                      97.4
           anthocyanin coloration of glumes of
             cob                                          98.4
           anthocyanin coloration of silks
             (1-9 scale)                                  87.7
           color of dorsal side of grain (1-9
             scale)                                       86.6
           color of tip of grain (1-9 scale)              96.5
           length, mm                                     92.6
           number of rows of grain                        92.8
           days to silk emergence                         96.4
Kernels    thousand kernel weight, g                      95.4
           grain yield, Mg [ha.sup.-1]                    79.4
           grain yield of four manually
             harvested ears, g                            90.4
Leaf       angle between blade and stem (1-9
             scale)                                       88.4
           attitude of blade (1-9 scale)                  90.1
           width of blade, mm                             81.3
Plant      ear height, cm                                 95.4
           length, cm                                     94.7
Tassel     anthocyanin coloration of base of
             glume (1-9)                                  94.7
           anthocyanin coloration of glume
             excluding base (1-9)                         88.9
           length of side branches, cm                    86.6
           angle between main axis and lateral
             branches (1-9)                               88.7
           anthocyanin coloration of anthers
             (1-9)                                        85.0
           attitude of lateral branches (1-9)             80.6
           length of main axis above lowest
             side branch, cm                              84.7
           length of main axis above upper
             side branch, cm                              83.2
           number of primary lateral branches             91.2
           days to anthesis                               96.7

** Significant at the 0.01 probability level.

([dagger]) UPOV = International Union for the Protection of New
Varieties of Plants.

([double dagger]) G[R.sub.g] = genetic ratio on a line-mean basis
pooled across flint and dent lines.

Table 2. Estimates of mean, range, genotypic variance
([[theta].sup.2.sub.1]), and genetic ratio of midparent heterosis
(G[R.sub.t] among triplets observed for different morphological and
agronomic traits of 114 flint and dent hybrids and their parental lines
tested in four or six environments in South Germany as well as
correlations (r) of MPH with coancestry (1 - f), genetic distance based
on 100 simple sequence repeats (G[D.sub.SSR]) or 20 amplified fragment
length polymorphism primer combinations (G[D.sub.AFLP]), and Euclidean
(M[D.sub.EUC]) or Mahalanobis (M[D.sub.MAH]) morphological distances.

                                           MPH

Trait ([dagger])    Test sites    Mean     Min.     Max.

EDI                     6          0.09     0.01    0.18
NKE                     6          0.59     0.03    1.34
ELG                     6          0.25     0.02    0.53
NGR                     6          0.06    -0.02    0.16
TSE                     4         -0.06    -0.17    0.03
TKW                     6          0.10    -0.04    0.42
GYD                     6          0.79     0.14    2.14
GYE                     6          0.75     0.06    1.84
EHT                     4          0.27     0.01    0.63
PLG                     4          0.17     0.01    0.36
TAH                     4         -0.05    -0.17    0.04

                                                G[R.sub.t]
Trait ([dagger])    [[theta].sup.2.sub.t]    ([double dagger])

EDI                       <0.001                   91.3
NKE                        0.070 **                96.3
ELG                        0.010 **                96.9
NGR                        0.010 **                73.2
TSE                        0.001 **                65.9
TKW                        0.004 **                90.1
GYD                        0.150 **                92.1
GYE                        0.130 **                96.9
EHT                        0.010 **                83.8
PLG                        0.010 **                95.2
TAH                       <0.001                   72.1

                                        r

Trait ([dagger])     1 - f      G[D.sub.SSR]    G[D.sub.AFLP]

EDI                  0.68 **       0.78 **         0.76 **
NKE                  0.76 **       0.86 **         0.87 **
ELG                  0.78 **       0.85 **         0.89 **
NGR                  0.38 **       0.56 **         0.50 **
TSE                 -0.70 **      -0.66 **        -0.70 **
TKW                  0.66 **       0.71 **         0.76 **
GYD                  0.73 **       0.84 **         0.86 **
GYE                  0.80 **       0.90 **         0.92 **
EHT                  0.75 **       0.78 **         0.80 **
PLG                  0.75 **       0.85 **         0.87 **
TAH                 -0.59 **      -0.76 **        -0.74 **

                                 r

Trait ([dagger])    M[D.sub.EUC]    M[Dsub.MAH]

EDI                    0.65 **        0.55 **
NKE                    0.69 **        0.60 **
ELG                    0.68 **        0.63 **
NGR                    0.43 **        0.39 **
TSE                   -0.58 **       -0.51 **
TKW                    0.55 **        0.53 **
GYD                    0.72 **        0.61 **
GYE                    0.66 **        0.63 **
EHT                    0.58 **        0.57 **
PLG                    0.63 **        0.55 **
TAH                   -0.49 **       -0.42 **

** Significant at the 0.01 probability level.

([dagger]) For abbreviations, see Table 1.

([double dagger]) G[R.sub.t] = Genetic ratio on a triplet-mean basis
for midparent heterosis pooled across flint and dent lines.

Table 3. Simple correlations between coancestry coefficient (1 - f),
genetic distances based on 100 simple sequence repeats (G[D.sub.SSR])
and 20 amplified fragment length polymorphism primer combinations
(G[D.sub.AFLP]), as well as Euclidean (M[D.sub.EUC]) and Mahalanobis
(M[D.sub.MAH]) morphological distances based on 25 traits (see
Table 1) for 24 flint (below diagonal) and 34 dent inbreds
(above diagonal).

                  1 - f     G[D.sub.SSR]    G[D.sub.AFLP]

1 - f                         0.75 **          0.85 **
G[D.sub.SSR]     0.88 **                       0.92 **
G[D.sub.AFLP]    0.88 **      0.97 **
M[D.sub.EUC]     0.55 **      0.65 **          0.65 **
M[D.sub.MAH]     0.44 **      0.49 **          0.59 **

                 M[D.sub.EUC]    M[D.sub.MAH]

1 - f              0.58 **          0.31 **
G[D.sub.SSR]       0.57 **          0.40 **
G[D.sub.AFLP]      0.68 **          0.40 **
M[D.sub.EUC]                        0.62 **
M[D.sub.MAH]       0.76 **

** Significant at the 0.01 probability level.

Table 4. Determination of EDV thresholds (T, measured in the scale of
the particular trait) and power (1 - [beta]) for various conceivable
EDV scenarios ([F.sub.2] vs. B[C.sub.1]; B[C.sub.1] vs. B[C.sub.2];
[alpha] = 0.05; [alpha] = [beta]) based on morphological distances,
midparent heterosis, and genetic distances (GDs) calculated from simple
sequence repeats (SSRs) and amplified fragment length polymorphisms
(AFLPs).

                                          [F.sub.2] vs. B[C.sub.1]

                                      [alpha] = 0.05   [alpha] = [beta]

                                                1 -            [alpha]
Parameter                               T     [beta]     T     = [beta]

Morphological distances
  Euclidean (M[D.sub.EUC])             4.0     0.18     5.8      0.32
  Mahalanobis (M[D.sub.MAH])          22.5     0.03    38.5     11.39
Heterosis
  Grain yield (GYE), Mg [ha.sup.-1]    0.24    0.05     0.58     0.39
  Plant length (PLG), m                0.08    0.29     0.13     0.31
  Number of kernels per ear (NKE)      0.17    0.02     0.48     0.47
  Cumulative ([dagger])                0.14    0.07     0.29     0.38
Genetic distances
  100 SSRs (G[D.sub.SSR])              0.21    0.68    11.25    11.14
  20 AFLP PCs (G[D.sub.AFLP])          0.12    0.65     0.14     0.21

                                          B[C.sub.1] vs. B[C.sub.2]

                                      [alpha] = 0.05   [alpha] = [beta]

                                                1 -            [alpha]
Parameter                               T     [beta]     T     = [beta]

Morphological distances
  Euclidean (M[D.sub.EUC])             3.1     0.40     4.1      0.21
  Mahalanobis (M[D.sub.MAH])          24.5     0.60    31.0      0.28
Heterosis
  Grain yield (GYE), Mg [ha.sup.-1]    0.24    0.47     0.41     0.25
  Plant length (PLG), m                0.03    0.30     0.08     0.29
  Number of kernels per ear (NKE)      0.22    0.52     0.36     0.24
  Cumulative ([dagger])                0.13    0.49     0.21     0.25
Genetic distances
  100 SSRs (G[D.sub.SSR])              0.08    0.38     0.12     0.18
  20 AFLP PCs (G[D.sub.AFLP])          0.04    0.37     0.05     0.10

([dagger]) Average relative heterosis of five traits (GYE, ELG, NKE,
PLG, EHT) showing highest correlation with 1 - f; for abbreviations,
see Table 1.

Fig. 3 Cumulative histograms (columns) and approximated normal
distributions (curves for (A) Euclidean or (B) Mahalanobis
morphological distances based  on 25 morphological traits for
[F.sub.2]-, B[C.sub.1]-, B[C.sub.2]-derived progency lines. Variables
n, [mu], and SD refer to the number of values, the mean, and the
standard deviation of Mahalanobis distance values for the particular
distribution, repectively.

Euclidean Distance

                n     [mu]     SD

[F.sub.2]       64    6.50    1.51
B[C.sub.1-]     16    5.14    1.27
B[C.sub.2-]      9    3.26    1.11

Mahalanobis Distance

                n     [mu]     SD

[F.sub.2]       64    42.1    12.1
B[C.sub.1-]     16    36.3     7.4
B[C.sub.2-]      9    21.4    12.5


ACKNOWLEDGMENTS

We are indebted to the 'Gesellschaft zur Forderung der privaten Pflanzenzuchtung' (GFP), Germany, for a grant to support Dr. Bohn and the molecular analyses of the presented study. Financial support for M. Heckenberger and the field trials was provided by the European Union Grant No. QLKCT-1999-01499 (MMEDV).

REFERENCES

Ajmone Marsan, P., P. Castiglioni, F. Fusari, M. Kuiper, and M. Motto. 1998. Genetic diversity and its relationship to hybrid performance in maize as revealed by RFLP and AFLP markers. Theor. Appl. Genet. 96:219-227.

Anderson, J.A., G.A. Churchill, J.E. Autrique, S.D. Tanksley, and M.E. Sorrells. 1993. Optimizing parental selection for genetic linkage maps. Genome 36:181-186.

Bar-Hen, A., A. Charcosset, M. Bougoin, and J. Guiard. 1995. Relationship between genetic markers and morphological traits in a maize inbred lines collection. Euphytica 84:145-154.

Boppenmaier, J., A.E. Melchinger, G. Seitz, H.H. Geiger, and R.G. Herrmann. 1993. Genetic diversity for RFLPs in European maize inbreds. III. Performance of crosses within versus between heterotic groups for grain traits. Plant Breed. 111:217-226.

Borgo, L., B. Serani, and S. Conti. 2002. Molecular profiling of genetically modified maize lines as related to the issue of essential derivation. Poster presented at the Annual Congr. of the Italian Society of Agricultural Genetics, Giardini Naxos, Italy. 18-21 Sept. 2002. Italian Society of Agricultural Genetics, Portici.

Burstin, J., and A. Charcosset. 1997. Relationship between phenotypic and marker distances: Theoretical and experimental investigations. Heredity 79:477-483.

Dhillon, B.S., C. Paul, E. Zimmer, P.A. Gurrath, D. Klein, and W.G. Pollmer. 1990. Variation and covariation in stover digestibility traits in diallel crosses of maize. Crop Sci. 30:931-936.

Dillmann, C., A. Bar-Hen, D. Guerin, A. Charcosset, and A. Murigneux. 1997. Comparison of RFLP and morphological distances between maize (Zea mays L.) inbred lines--Consequences for germplasm protection purposes. Theor. Appl. Genet. 95:92-102.

Dillmann, D., and D. Gutrin. 1998. Comparison between maize inbred lines: Genetic distances in the expert's eye. Agronomie 18:659-667.

Frisch, M., M. Bohn, and A.E. Melchinger. 2000. Plabsim: Software for simulation of marker-assisted backcrossing. J. Hered. 91:86-87.

Gill, P., C.P. Kimpton, A. Urquhart, N. Oldroyd, E.S. Millican, S.K. Watson, and T.J. Downes. 1995. Automated short tandem repeat (STR) analysis in forensic casework: A strategy for the future. Electrophoresis 16:1543-1552.

Gilliland, T.J., R. Coll, E. Calsyn, M. de Loose, M.J.T. Van Eijk, and I. Roldan-Ruiz. 2000. Estimating genetic conformity between related ryegrass (Lolium) varieties. 1. Morphology and biochemical characterisation. Mol. Breed. 6:569-580.

Heckenberger, M., M. Bohn, and A.E. Melchinger. 2005. Identification of essentially derived varieties obtained from biparental crosses of homozygous lines. I. Simple sequence repeat data from maize inbreds. Crop Sci. 45:1120-1131, this issue.

Heckenberger, M., M. Bohn, J.S. Ziegle, L.K. Joe, J.D. Hauser, M. Hutton, and A.E. Melchinger. 2002. Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties. I. Genetic and technical sources of variation in SSR data. Mol. Breed. 10:181-191.

Heckenberger, M., J. Rouppe van der Voort, A.E. Melchinger, J. Peleman, and M. Bohn. 2003. Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties. II. Genetic and technical sources of variation in AFLP data and comparison to SSR data. Mol. Breed. 12:97-106.

Ihaka, R., and R. Gentleman. 1996. R: A language for data analysis and graphics. J. Comput. Graphical Stat. 5:299-314.

International Association of Plant Breeders for the Protection of Plant Varieties. 1999. Essential derivation and dependence--Practical information [Online]. Available at http://www.worldseed.org/Position_papers/derive.htm [verified 30 Nov. 2004]. ASSINSEL, Nyon, Switzerland.

Johnson, N.L., S. Kotz, and N. Balakrishnan. (ed.) 1995. Continuous univariate distributions. Vol. 2. J. Wiley & Sons, New York.

Lehmann, E.L. (ed.) 1986. Testing statistical hypotheses. John Wiley & Sons, New York.

Levene, H. 1960. Robust tests for equality of variances, p. 278-292. In Contributions to probability and statistics: Essays in honor of Harold Hotelling. Stanford Univ. Press, Stanford, CA.

Mahalanobis, P.C. 1936. On the generalized distance in statistics. Proc. Natl. Acad. Sci. India 2:49-45.

Malecot, G. 1948. Les mathematiques de l'hereditt. Masson & Cies, Paris.

Melchinger, A.E. 1999. Genetic diversity and heterosis. Int. Syrup. on Genetics and Exploitation of Heterosis in Crop Plants. p. 99-118. In J.G. Coors and S. Pandey (ed.) The genetics and exploitation of heterosis in crops. ASA, CSSA, and SSSA, Madison, WI.

Melchinger, A.E., H.H. Geiger, and F.W. Schnell. 1986. Epistasis in maize (Zea mays L.). 2. Genetic effects in crosses among early flint and dent inbred lines determined by three methods. Theor. Appl. Genet. 72:231-239.

Nuel, G., S. Robin, and C.P. Baril. 2001. Predicting distance using a linear model: The case of varietal distinctness. J. Appl. Stat. 28: 607-621.

Peleman, J., R. van Wijk, J. van Oeveren, and R. van Schaik. 2000. Linkage map integration: An integrated genetic map of Zea mays L. Poster P472. Plant & Animal Genome Conf. VIII, San Diego, CA. 9-12 Jan. 2000.

Rebourg, C., B. Gouesnard, and A. Charcosset. 2001. Large scale molecular analysis of traditional European maize populations. Relationships with morphological variation. Heredity 86:574-587.

Rogers, J.S. 1972. Measures of genetic similarity and genetic distance. p. 145-153. In Studies in Genetics VII. Publ. 7213. Univ. of Texas, Austin.

Roldan-Ruiz, I., E. Calsyn, T.J. Gilliland, R. Coll, M.J.T. van Eijk, and M. de Loose. 2000a. Estimating genetic conformity between related ryegrass (Lolium) varieties. 2. AFLP characterization. Mol. Breed. 6:593-02.

Roldan-Ruiz, I., F.A. van Eeuwijk, T.J. Gilliland, P. Dubreuil, C. Dillmann, J. Lallemand, M. de Loose, and C.P. Baril. 2000b. A comparative study of molecular and morphological methods of describing relationships between perennial ryegrass (Lolium perenne L.) varieties. Theor. Appl. Genet. 103:1138-1150.

Scheffe, H. 1959. The analysis of variance. John Wiley & Sons, New York.

Smith, J.S.C., and O.S. Smith. 1989a. The description and assessment of distance between inbred lines of maize: I. The use of morphological traits as descriptors. Maydica 34:141-150.

Smith, J.S.C., and O.S. Smith. 1989b. The description and assessment of distances between inbred lines of maize: II. The utility of morphological, biochemical, and genetic descriptors and a scheme for testing of distinctiveness between inbred lines. Maydica 34:151-161.

Smith, J.S.C., O.S. Smith, S.L. Bowen, R.A. Tenborg, and S.J. Wall. 1991. The description and assessment of distances between inbred lines of maize. III. A revised scheme for the testing of distinctiveness between inbred lines utilizing DNA RFLPs. Maydica 36: 213-226.

Smith, O.S., J.S.C. Smith, S.L. Bowen, R.A. Tenborg, and S.J. Wall. 1990. Similarities among a group of elite maize inbreds as measured by pedigree, Fl grain yield, grain yield, heterosis, and RFLPs. Theor. Appl. Genet. 80:833-,40.

Snedecor, G., and W. Cochran. 1980. Statistical methods. Iowa State Univ. Press, Ames.

Troyer, A.F., and T.R. Rocheford. 2002. Germplasm ownership: Related corn inbreds. Crop Sci. 42:3-11.

UPOV. 1978. International convention for the protection of new varieties of plants http://www.upov.int/en/publications/conventions/1978/ content.htm [verified 30 Nov. 2004]. International Union for the Protection of New Varieties of Plants, Geneva.

UPOV. 1991. International convention for the protection of new varieties of plants [Online]. Available at http://www.upov.int/en/publications/ conventions/1991/content.htm [verified 30 Nov. 2004]. International Union for the Protection of New Varieties of Plants, Geneva.

Utz, H.F. 2001. Plabstat, ein Computerprogramm zur statistischen Analyse von pflanzenzuchterischen Experimenten (in German). Version 2P vom 14. Univ. Hohenheim, Germany.

van Eeuwijk, F.A., and C.P. Baril. 2001. Conceptual and statistical issues related to the use of molecular markers for distinctness and essential derivation. Acta Hortic. 546:35-53.

M. Heckenberger, M. Bohn, D. Klein, and A. E. Melchinger *

M. Heckenberger, D. Klein, and A.E. Melchinger, Institute of Plant Breeding, Seed Science, and Population Genetics, Univ. of Hohenheim, 70593 Stuttgart, Germany; M. Bohn, Crop Science Dep., Univ. of Illinois, S-110 Turner Hall, 1102 South Goodwin Avenue, Urbana, IL 61801. Received 23 Feb. 2004. Crop Breeding, Genetics & Cytology. * Corresponding author (melchinger@uni-hohenheim.de).
COPYRIGHT 2005 Crop Science Society of America
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2005 Gale, Cengage Learning. All rights reserved.

 
Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:CROP BREEDING, GENETICS & CYTOLOGY
Author:Heckenberger, M.; Bohn, M.; Klein, D.; Melchinger, A.E.
Publication:Crop Science
Geographic Code:1USA
Date:May 1, 2005
Words:7944
Previous Article:Identification of essentially derived varieties obtained from biparental crosses of homozygous lines: I. Simple sequence repeat data from maize...
Next Article:Nitrogen remobilization during grain filling in wheat: genotypic and environmental effects.
Topics:

Terms of use | Privacy policy | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters