Establishment of an effective set of simple sequence repeat markers ...

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Establishment of an effective set of simple sequence repeat markers for sunflower variety identification and diversity assessment L.S. Zhang, V. Le Clerc, S. Li, and D. Zhang

Abstract: The objective of this study was to identify an efficient set of simple sequence repeat (SSR) markers for sunflower (Helianthus annuus L.) variety fingerprinting, relying on semi-automated analysis conditions. Based on criteria such as quality of amplification products, co-dominant and single locus, 78 SSR markers were selected and used to assess the genetic variability among a large set of 124 sunflower inbred lines, including 67 female maintainers (M lines) and 57 male restorers (R lines). They revealed a total of 276 alleles across the 124 elite inbred lines, with a mean of 3.5 alleles per SSR locus. The polymorphism index content per locus varied from 0.06 to 0.81, with an average of 0.51. Relationships among the inbred lines were studied using estimations of Rogers’ distances. The great majority of the distance estimates ranged between 0.4 and 0.6, but distances between some pairs of lines were less than 0.1. The genetic diversity value was similar within each subset of R and M lines and low, but significant differentiation was found (GST = 0.049) between the two pools. The selected set of SSRs proved to be useful both for sunflower fingerprinting and genetic diversity assessment. Key words: genetic diversity, genotyping, Helianthus annuus, multiplex PCR, simple sequence repeats (SSR). Résumé : Cette étude a consisté à établir un ensemble efficace de marqueurs microsatellites pour caractériser les variétés de tournesol (Helianthus annuus L.), en s’appuyant sur des conditions d’analyse semi automatisées. En se basant sur la qualité d’amplification et le choix de marqueurs mono-locus et codominants, 78 microsatellites ont été retenus. Ces microsatellites ont ensuite été utilisés pour évaluer la variabilité génétique de 124 lignées de tournesol, comprenant 67 lignées mainteneuses de stérilité (lignées M) et 57 lignées restauratrices de fertilité (lignées R). Au total, 276 allèles ont été identifiés au sein des 124 lignées élites, avec une moyenne de 3,5 allèles par locus microsatellite. L’indice de la teneur en polymorphisme par locus variait de 0,06 à 0,81, avec une moyenne de 0,51. Les relations génétiques entre lignées ont été analysées à l’aide de l’estimation de la distance de Rogers. La grande majorité des distances étaient comprises entre 0,4 et 0,6 tandis que les distances entre certaines paires de lignées particulières étaient inférieures à 0,1. La diversité génétique du groupe de lignées R et du groupe de lignées M était très similaire et faible mais une différentiation significative a été trouvée (GST = 0,049) entre les deux groupes. L’ensemble de marqueurs microsatellites sélectionnés a prouvé son efficacité pour l’identification variétale et l’analyse de la diversité génétique chez le tournesol. Mots clés : diversité génétique, identification variétale, Helianthus annuus, multiplexage, marqueurs microsatellites (SSR). Zhang et al.

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Introduction The cultivated sunflower (Helianthus annuus L.) is one of the most important oilseed crops in the world. Its development has been largely owing to the discovery of genes for cytoplasmic male sterility and for the restoration of male fertility (Leclercq 1966), which led to modern cultivars. The modern cultivars used for the production of oilseeds are hyReceived 17 May 2004. Published on the NRC Research Press Web site at http://canjbot.nrc.ca on 18 January 2005. L.S. Zhang,1 V. Le Clerc, and D. Zhang.2 BioGEVES, Le Magneraud, B.P. 52, 17700 Surgères, France. S. Li. Institute of Botany, Academy of Science of China, 100093, Beijing, China. 1

Present address: Horticultural College, China Agricultural University, Beijing, 100094, China. 2 Corresponding author (e-mail: [email protected]). Can. J. Bot. 83: 66–72 (2005)

brids obtained by crossing a male sterile female inbred line (A line) by a restorer male line (R line). The sterility of the A line is maintained by crossing it with its isogenic fertile line (B line or M line). Intensive breeding programs with this plant have led to an increasing number of hybrids. In France, the legal protection of Plant Breeders’ Rights on newly bred cultivars, according to the convention of the Union internationale pour la protection des obtentions végétales (UPOV 1961), is essentially based on the ability of the parent inbred lines to succeed in distinctness, uniformity, and stability (DUS) testing using phenotypic trait description. The number of protected varieties and parent lines increases each year. More than 600 inbred lines are now maintained in the French DUS reference collection. The genetic basis for sunflower breeding is being reduced, owing to the frequent use of same genetic resources for common breeding objectives (e.g., grain yield and resistance). Furthermore, the sunflower is a plant very sensitive to interactions among

doi: 10.1139/B04-155

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Zhang et al.

genotype, place, and year; the phenotype of the same plant material may vary greatly according to the place and the year of growing. All these factors make the accurate description of the new inbred lines undergoing DUS testing more and more difficult. GEVES (Groupe d’étude et de contrôle des variétés et des semences), the official French institute in charge of plant variety and seed testing, has been involved for many years in the development of new descriptors in sunflowers, in particular molecular markers (Gentzbittel et al. 1994; Teulat et al. 1994; Zhang et al. 1995). Among the different marker systems currently available, simple sequence repeat (SSR) is an excellent marker system for plant variety description and identification (Jones et al. 1997; Senior et al. 1998; Bredemeijer et al. 2002; Coburn et al. 2002) because it is simple to use and has a general high level of polymorphism, good coverage of the genome, and quality of information (single locus, co-dominance, reproducibility). Recently, SSR markers have also been developed and used for genotyping inbred lines and genetic mapping in the sunflower (Paniego et al. 2002; Yu et al. 2002; Tang and Knapp 2003; Tang et al. 2002, 2003). Until now, sunflower genotyping studies have only been carried out on a limited number of inbred lines: the highest number analysed by Zhang et al. (1995) was 26. In the present study, we analysed a large set of 124 sunflower inbred lines selected from the French DUS testing reference collection. Our objectives were (i) to screen an efficient set of SSR markers for sunflower variety identification, relying on semi-automated analysis conditions, and (ii) to assess the variability and relationships among a large panel of inbred lines with this selected set.

Materials and methods Plant materials Genotyping performance, as well as the quality of SSR, was tested on 10 public inbred lines: HA89, H52, HA372, HA383, HA821, RHA274, RHA377, RHA801, PAC2, and RHA266. Interline variability was then assessed on the 124 inbred lines selected from the GEVES reference collection. Among these were 67 female maintainer lines coded from M1 to M105 and 57 male restorer lines coded from R1 to R80; the numbers after M or R were not continuous. Among these 124 samples, some of them had special relationships, e.g., the form A and B of a same female inbred line, the normal and the downy mildew resistant version of the same inbred line, or two seed lots of the same inbred line supplied as reference seeds for DUS testing by seed companies in two different time periods. Leaves were harvested from 10 individual plants per inbred line in the fields of GEVES Le Magneraud in western France during the summers of 2000 and 2001 and were pooled for DNA extraction. They were frozen at –20 °C, lyophilized, and ground manually using a pestle and mortar. For each inbred line, about 100 mg of the powder obtained was subjected to DNA isolation, using the DNeasy Plant Mini kit (QIAGEN, Paris, France). SSR primer pairs used SSR primer pairs used in our study were developed under a collaborative research project (named CARTISOL), involving private seed companies and public institutes (Institut

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national de la recherche agronomique and GEVES). This was done by the teams of S.J. Knapp (Oregon State University, Corvallis, Oregon) for the SSRs prefixed with ORS and K. Edwards (University of Bristol, Bristol, UK) for the SSRs prefixed with SSL. All the primers of the SSR markers prefixed with ORS have been published and are publicly available (Tang et al. 2002). On the contrary, those prefixed with SSL are proprietary markers that belong to Groupe d’intérêt économique – CARTISOL. All requests for these SSRs should be addressed to Groupe d’intérêt économique – CARTISOL, 20, rue Bachaumont 75002, Paris, France. SSR analysis conditions SSR analysis conditions using a LI-COR automated DNA analysis system have been previously described by Zhang et al. (2003). Combinations into duplexes or triplexes were tested for all the selected primer pairs. Using one fluorophore, we were able to multiplex five primer pairs but, to avoid irregular amplifications, only 2–3 primer pairs were multiplexed in routine analysis. However, using the dye 700 and the dye 800 for tail labelling in two independent polymerase chain reation (PCR) runs, amplicons can be pooled before gel loading. The same gel can be easily reloaded at least three times and therefore, 12–18 SSR loci can be scored when using duplexes or triplexes. The PCR reactions were performed in 10 µL containing 1× PCR buffer, 0.125 mmol of each dNTPs, 3 mmol MgCl2, 0.25 µmol of each primer, 0.5 U AmpliTaq Gold (Applied Biosystems, Paris, France), and 10 ng template DNA. Primers were labelled with a fluorescent dye detectable at 700 or 800 nm, using a tailed primer strategy (Middendorf et al. 1992). Usually one tail, for example, M13 (5′-CACGACGTTGTAAAACGAC-3′) or 35S1 (5′-GCTCCTACAAATGCCATCA-3′), was added to the 5′-end of one of the SSR primer pair (forward or reverse) during the primer synthesis. Three primers are required for the amplification of each SSR locus: one tailed forward primer, one normal reverse primer, and one labelled tail. To avoid the appearance of spurious amplifications, a “touchdown” PCR program (Don et al. 1991) was used. It consisted of an initial denaturation step at 95 °C for 10 min, followed by 10 cycles of 94 °C for 30 s, 64 °C for 30 s, and 72 °C for 30 s, with the annealing temperature being reduced by 1 °C per cycle, and 30 cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, finishing at 72 °C for 10 min. PCR products were run on a 5% denaturing acryl amide gel. Data analysis Gel analysis and data scoring were performed using the software RFLPscanTM (LI-COR); data were manually validated. The molecular data were analyzed using the software LCDMV developed by the GEVES using SAS tools. For one line and one locus, the allelic frequency was 1 when there was only one band on the gel and 0.5 for each allele when two bands were detected with the same intensity. Exceptionally, when a faint band was found, it was not taken into account and the locus was considered to be homozygous for the strongest band. Variability for each locus was calculated using the polymorphism index content (PIC): © 2005 NRC Canada

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Can. J. Bot. Vol. 83, 2005 n

PIC = 1 − ∑ pi2 i

where pi is the frequency of the ith allele in the germplasm. Genetic relationships among the lines were investigated by calculating Rogers’ (1972) genetic distance. For further analysis, inbred lines were dispatched into two populations comprising M and R lines (hereafter called population M and population R, respectively). For each population, the mean number of alleles and the average gene diversity were calculated using an unbiased estimator (Nei 1987). The total genetic diversity (HT), the genetic diversity within populations (HS), and the proportion of diversity resulting from genetic differentiation among populations (GST) were also estimated according to Nei (1987). All these computations were done using the FSTAT program (Goudet 2001). Analysis of molecular variance (AMOVA; version 2.3.9; Excoffier et al. 1992) was used to statistically assess genetic variation among and within populations. Φststatistics, analogous to Wright’s FST and permutation procedures (10 000 replicates) for significance testing were performed using ARLEQUIN software (version 2000; Schneider et al. 2000).

Table 1. Type of combination, linkage group, number of alleles and PIC value of the 78 SSRs used for genotyping the 124 sunflower (Helianthus annuus) inbred lines. Combination

SSR

Duplex 1

ORS 53 ORS 509 ORS 215 ORS 486 ORS 779 SSL 30 ORS 677 ORS 437 ORS 675 ORS 595 ORS 691 ORS 78 ORS 380 SSL 171 ORS 788 ORS 546 ORS 617 ORS 610 SSL 216 SSL 241 ORS 442 SSL 63 SSL 231 SSL 262* ORS 502 ORS 509 ORS 151 ORS 547 ORS 513 ORS 309 ORS 605 SSL 81 ORS 605 ORS 442 ORS 303 ORS 53 ORS 57 ORS 727 ORS 329 ORS 317 ORS 307 ORS 677 ORS 428 ORS 502 ORS 303 ORS 121 ORS 309 ORS 716 ORS 617 ORS 727 ORS 716 ORS 613 ORS 591

Duplex 2 Duplex 3 Duplex 4 Duplex 5 Duplex 6 Duplex 7 Duplex 8 Duplex 9 Duplex 10

Results Duplex 11

Screening a set of SSR markers for genotyping sunflower inbred lines From the 1111 primer pairs provided by the CARTISOL project, only a subset of 323 best primer pairs was selected for the present study, according to the information supplied by the two developer laboratories. Their quality was evaluated across 10 public inbred lines. Only 103 primer pairs out of the 323 were able to generate SSR markers corresponding to the criteria chosen: single locus, co-dominant, and with clear amplification (no stutter band). Then a validation trial was organized to evaluate the robustness of these 103 SSRs. Four laboratories from four seed companies participated in this trial: Euralis Génétique (Mondonville, France), Monsanto S.A.S. (Peyrehorade, France), Ragt (Rodez, France), and Syngenta (Saint-Sauveur, France). The validation trial consisted of performance analysis of the 103 SSRs on the same 10 sunflower inbred lines with the DNA solutions supplied by our laboratory; each participating laboratory used its own working and analysis conditions. Among the four laboratories, two of them used a LI-COR automated DNA analysis system and the other two used a gel electrophoresis system. After comparison of results, 78 SSRs were retained and proposed for further genotyping use in the sunflower (Table 1). Of those 78 SSRs, 51 were mapped by Tang et al. (2002) on 14 of the 17 linkage groups. All these retained SSR markers had a high reproducibility in each laboratory. Of the 78 SSRs, 65 were combined into 16 duplexes and 20 triplexes, whereas for the remaining thirteen SSRs, the combination before PCR did not give reliable results or provide any clear amplification products. Among these 65 SSR, 22 were tested in more than one combination, for example, ORS 53 was combined with ORS 509 (duplex 1) or with ORS 57 and ORS 727 (triplex 2), respectively to provide reproducibility controls.

Duplex 12 Duplex 13 Duplex 14 Duplex 15 Duplex 16 Triplex 1

Triplex 2

Triplex 3

Triplex 4

Triplex 5

Triplex 6

Triplex 7

Linkage group 1 13 2 10 17 10 1 10 10 10 10 16 5 9 1

9

12 1 15 5 17 4 1 1 9 16

17 8 13 14 17 9 12 16 4 1 9 17 1 10 10

No. of alleles

PIC

3 4 3 3 2 8 3 4 2 4 4 4 3 4 4 3 3 5 4 4 3 4 4

0.61 0.43 0.32 0.63 0.12 0.81 0.21 0.72 0.46 0.71 0.62 0.63 0.62 0.62 0.74 0.57 0.06 0.47 0.46 0.64 0.55 0.68 0.66

3 4 3 4 2 2 4 5 4 3 2 3 2 5 2 4 3 3 2 3 2 3 2 3 3 5 3 5 2

0.38 0.43 0.66 0.68 0.44 0.48 0.66 0.60 0.66 0.55 0.29 0.61 0.48 0.47 0.41 0.62 0.53 0.21 0.13 0.38 0.29 0.60 0.48 0.33 0.06 0.47 0.33 0.62 0.30

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Table 1 (concluded). Combination Triplex 8

Triplex 9

Triplex 10

Triplex 11

Triplex 12

Triplex 13

Triplex 14

Triplex 15

Triplex 16

Triplex 17

Triplex 18

Triplex 19

Triplex 20

Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex Simplex

1 2 3 4 5 6 7 8 9 10 11 12 13

SSR ORS 342 ORS 329 ORS 307 ORS 407 ORS 510 ORS 338 ORS 779 ORS 159 ORS 533 ORS 176 SSL 15 ORS 605 ORS 781 ORS 621 ORS 533 ORS 437 ORS 609 ORS 170 ORS 442 ORS 203 ORS 10 SSL 9 ORS 605 SSL 84 ORS 510 ORS 513 SSL 89 ORS 543 ORS 513 ORS 530* ORS 559 ORS 215 ORS 547 ORS 303 ORS 146 ORS 337 SSL 255 SSL 29 SSL 283 ORS 7 ORS 310 ORS 432 ORS 656 ORS 674 ORS 810 ORS 811 SSL 13 SSL 3 SSL 38 SSL 51 SSL 54 SSL 85

Linkage group 2 8 14 16 9 3 10 5 9 1 13 11 5 10 12 17 9

1 9 17 1 17 12 13 5 16 4

15 16 6 16 4 12 17

No. of alleles 4 2 3 3 3 2 2 2 4 9 5 4 2 5 4 4 2 2 3 4 2 5 4 5 3 2 2 5 2

PIC 0.42 0.41 0.53 0.43 0.37 0.19 0.12 0.44 0.58 0.81 0.65 0.66 0.30 0.63 0.58 0.72 0.16 0.26 0.55 0.69 0.34 0.74 0.66 0.73 0.37 0.44 0.48 0.71 0.44

2 3 4 2 3 2 4 4 5 3 4 3 5 4 2 4 4 4 4 2 7 3

0.11 0.32 0.68 0.29 0.52 0.32 0.74 0.68 0.76 0.51 0.71 0.52 0.51 0.60 0.48 0.62 0.22 0.70 0.72 0.38 0.45 0.57

Note: The SSRs that were locus amplified in different combinations are indicated with boldface type. *SSR was not retained for analysis.

Polymorphism and level of fixation of the selected SSRs The 78 SSRs generated a total of 276 alleles across the 124 sunflower inbred lines. The number of alleles per SSR locus varied from 2 to 9, with an average of 3.5. The expected heterozygosity (PIC value) per locus ranged from 0.06 to 0.81, with a mean of 0.51 (Table 1). Table 2 shows that 50 out of 78 SSR loci (64.1%) were well fixed across all the 124 inbred lines. About 64% of the lines were absolutely homozygous for all their loci whereas less than 5% of the lines presented heterozygoty for 2 to 4 loci (Data not shown). Genetic relationships among the inbred lines The mean Rogers’ distance among the 124 lines was 0.54, with values ranging from 0.01 to 0.79 (Table 3). Similar estimations of genetic distances were obtained separately for M lines and R lines, presenting average values of 0.50 and 0.52, respectively. Five cases were particularly interesting (Table 4): (1) Form A and B of the same line (two pairs were concerned). The distance was 0.03 for the M23–M24 pair and 0.06 for the M89–M90 pair, analysed with 69 and 74 SSR, respectively. (2) Two versions (normal versus modified) of the same line. With regard to the R8–R9 pair, R8 was an original line and R9 was the modified version in which a downy mildew resistance gene was introduced via traditional backcross. The distance for this pair was 0.13, calculated from 39 SSR. (3) Two seed lot supplies of the same line obtained at two different times (seven pairs were concerned). These pairs were analysed with a number of SSR ranging between 29 and 73. The distances varied from 0.02 to 0.08, with a mean of 0.04. (4) Two repetitions of a same line in the DUS reference collection (in the field). For the M62–M63 pair, one difference was observed out of the 78 SSR loci analyzed. (5) Pairs of lines quite phenotypically related. Based on morphological investigations in the field, 16 pairs of lines were identified. For these materials, molecular distances varied from 0.04 to 0.48, with a mean of 0.25. In this case, SSR ranging between 32 and 78 were analysed. Genetic diversity and differentiation between the M and R lines The mean genetic diversity was similar for each population of lines with values ranging from 0.00 to 0.82 for population M and from 0.11 to 0.82 for population R (Table 5). The total genetic diversity (HT = 0.538) mainly originated from the genetic diversity within each population (HS = 0.511). The diversity between the two populations was quite low (FST = 0.049). Genetic structure was also analysed with AMOVA. Similar values for within population (92.4%) and between population variance (7.6%) were obtained, and they compared well with the HS (95.0% of the total diversity) and FST values, respectively. The Φst analogue of FST among populations was 0.076. In other words, 7.6% of the total variance was due to the partition between the M and R lines. Moreover, AMOVA showed that the molecular diversity was © 2005 NRC Canada

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Can. J. Bot. Vol. 83, 2005 Table 2. Level of fixation of SSR loci across the analyzed inbred lines of sunflower (Helianthus annuus). Level of fixation

No. of SSRs

%

Fixed for all lines Not fixed in 1 line

50 13

64.1 16.6

7 3 2 2 1

9.0 3.8 2.6 2.6 1.3

Not Not Not Not Not

fixed fixed fixed fixed fixed

in in in in in

2 3 4 5 9

lines lines lines lines lines

SSR markers ORS 10, ORS 57, ORS 151, ORS 203, ORS 303, ORS 329, ORS 510, ORS 559, ORS 595, ORS 621, ORS 677, ORS 691, SSL 171 ORS 121, ORS 159, ORS 170, ORS 176, ORS 486, ORS 547, ORS 610 ORS 502, SSL 9, SSL 54 SSL 30, SSL 63 SSL 13, SSL 51 ORS 337

Table 3. General information on the estimations of Rogers’ distances for the two groups of lines (M and R) of sunflower (Helianthus annuus). Rogers’ distance Group of lines

No. of lines

Mean

SD

Minimum

Maximum

M lines R lines M+R lines

67 57 124

0.50 0.52 0.54

0.0597 0.0575 0.0587

0.01 0.03 0.01

0.74 0.79 0.79

significantly different between the two populations (p < 0.001).

Discussion The mean number of alleles and the mean PIC values obtained in the present study were very similar to those reported by Paniego et al. (2002), Yu et al. (2002), Tang and Knapp (2003), and Tang et al. (2003) for different sets of sunflower inbred lines. This level of molecular polymorphism was rather comparable with that revealed using RFLPs among sunflower lines (Gentzbittel et al. 1994; Zhang et al. 1995). However, higher SSR polymorphism levels have been reported in wheat (Röder et al. 1995), maize (Smith et al. 1997), and rice (Coburn et al. 2002). According to their PIC values, all 78 SSRs do not have the same efficiency for routine genotyping work and for variety identification in the sunflower. Anyway, all the lines were clearly identified by the present set of markers. Using only 40 out of the 78 SSRs, all the lines were identified except for the two lines of case 4. The global level of heterogeneity observed in the present study was low, suggesting that the cultivated sunflower inbred lines were correctly fixed. Heterogeneity has already been observed by isozyme analyses in our laboratory (data not shown). The existence of heterogeneity at the molecular level for some sunflower inbred lines is easily understandable, since the selection of sunflower inbred lines is principally based on phenotypic traits. Therefore, sunflower genotyping and variety identification should avoid (i) using SSRs showing low PIC values, in particular ORS 617, ORS 559, ORS 779, ORS 428, and ORS 609, which have PIC values less than 0.20 and (ii) using SSRs that detect high levels of heterogeneity among inbred lines. Within the whole sample, as well as within each group of lines, mean genetic distances were high, reflecting that many of the inbred lines used for this study were original lines and

represented the whole range of morphophysiological variability observed in the French DUS reference collection. Similar results were obtained using RFLPs (Zhang et al. 1995). Logically, the lowest genetic distance values were observed between particular pairs of lines, as demonstrated by cases 1–4 (see below). For cases 1 and 2, theoretically, the only difference between each pair of lines is either the gene responsible for male sterility or the gene responsible for downy mildew resistance. However, in practice, residual heterogeneity between the two isogenic lines A and B obtained after the backcross procedure exists and is therefore reflected by the small genetic distance. For the resistant and susceptible version of the same line, the genetic distance was quite important and could probably be explained by the number of backcrosses, possibly too limited to reduce the genetic background of the non-recurrent parent. Low genetic distances were observed for case 3, showing that conservative selection had been achieved with the exception of the M14–M15 pair. In our laboratory, different allele forms have already been observed in sunflower inbred lines by isozyme analysis during the routine check of reference seed lots. This is not surprising because, for the inscription of a new variety, a breeder must supply a reference seed lot, which is used for various official investigations, and the breeder must provide another reference seed sample when the first supply has been used up. As the breeder continues to improve his plant material, small differences may occur between the two seed lots. Some heterogeneity was observed in the two samples corresponding to repetitions of the same line in the DUS reference collection (case 4), but genetic distance was very insignificant. For case 5, the results recalled the triangular relationships among molecular distances and morphological distances already observed and described in maize (Bar-Hen and Charcosset 1995) and in oilseed rape (Lombard 2000): pairs of lines (or genotypes) having small morphological distances may have small or large molecular distances. In terms of allelic richness (i.e., number of alleles per locus) and gene diversity, similar results were obtained within each group of lines. As previously described by Gentzbittel et al. (1994) and Zhang et al. (1995), the distribution of the genetic diversity within and between populations showed that a high proportion of the total diversity was maintained within each group of M and R lines, respectively. Genetic differentiation between the M and R populations was low, around 5% (0.049), but it was significant, according to AMOVA. Conclusions obtained from RFLP analysis on a smaller number of lines were identical. The present study, © 2005 NRC Canada

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Table 4. Genetic distance for the particular pairs of lines (or samples) analyzed with different numbers of SSRs for sunflower (Helianthus annuus). Distance Case

No. of pairs

Mean

Minimum

Maximum

Form A and B of a same line Two versions (normal vs. modified) of the same line Two seed lot supplies of the same line obtained at two different times Two repetitions of a same line in the DUS testing fields Pairs of lines phenotypically related

2 1 7 1 16

0.04 0.13 0.04 0.01 0.25

0.03 — 0.02 — 0.04

0.06 — 0.08 — 0.48

Table 5. Number of alleles and genetic diversity statistics for the two subsets of lines (M and R) of sunflower (Helianthus annuus). No. of alleles

Genetic diversity

Population

Mean

Minimum

Maximum

Mean

Minimum

Maximum

M lines R lines

3.3 3.36

1 2

8 9

0.51 (0.19) 0.52 (0.18)

0 0.11

0.82 0.82

HT = 0.538 HS = 0.511 DST = 0.026 GST = 0.049

carried out on a high number of lines, confirms the clear partition between R and M line germplasms as previously stated, and also confirms that a similar level of genetic diversity is maintained in each pool. As highlighted by Gentzbittel et al. (1994), sunflower breeders may rely on these results to orient parental choice to maximize variability among lines. Introgressions from wild populations or species should also be considered. Moreover, as previously demonstrated by Tang and Knapp (2003), wild populations have been identified as an extraordinary reservoir of novel alleles. In conclusion, the present study allowed the definition of an efficient set of SSR markers in the sunflower, using a LICOR automated DNA analysis system. Our study showed that the great majority of the selected SSRs have a very good polymorphism level among the set of 124 sunflower inbred lines. The SSRs also demonstrated their utility in the study of genetic relationships among sunflower lines. Among these 78 SSR markers, those well fixed and with a high PIC value will be very useful for variety identification in sunflower, for example, variety description, purity testing, hybrid formula verification, and variety identity checking in the process of seed certification.

Acknowledgements This research program was funded by a 3-year grant (1999–2002) to D.Z. from the French Ministry of Agriculture and a post-doctoral fellowship (2001–2002) to L.Z. from the Institut national de la recherche agronomique (INRA). The French interprofessional association AMSOL was our partner in this project. Four seed companies, Euralis Génétique, Monsanto S.A.S., Ragt, and Syngenta, participated and contributed to the validation work of the selected SSR markers. The authors would like to thank V. Cellier for the choice of plant materials used in the present study,

S. Lassalvy for technical assistance in the data analysis, J. Coates for help with English expression, and the PoitouCharentes Region for financing the purchase of scientific equipment.

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