Smulders et al. BMC Genetics 2010, 11:41
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Open Access
RESEARCH ARTICLE
Characterisation of sugar beet (Beta vulgaris L. ssp.
vulgaris) varieties using microsatellite markers
Research article
Marinus JM Smulders*1, G Danny Esselink1, Isabelle Everaert2, Jan De Riek2 and Ben Vosman1
Abstract
Background: Sugar beet is an obligate outcrossing species. Varieties consist of mixtures of plants from various parental
combinations. As the number of informative morphological characteristics is limited, this leads to some problems in
variety registration research.
Results: We have developed 25 new microsatellite markers for sugar beet. A selection of 12 markers with high quality
patterns was used to characterise 40 diploid and triploid varieties. For each variety 30 individual plants were
genotyped. The markers amplified 3-21 different alleles. Varieties had up to 7 different alleles at one marker locus. All
varieties could be distinguished. For the diploid varieties, the expected heterozygosity ranged from 0.458 to 0.744. The
average inbreeding coefficient Fis was 0.282 ± 0.124, but it varied widely among marker loci, from Fis = +0.876
(heterozygote deficiency) to Fis = -0.350 (excess of heterozygotes). The genetic differentiation among diploid varieties
was relatively constant among markers (Fst = 0.232 ± 0.027). Among triploid varieties the genetic differentiation was
much lower (Fst = 0.100 ± 0.010). The overall genetic differentiation between diploid and triploid varieties was Fst =
0.133 across all loci. Part of this differentiation may coincide with the differentiation among breeders' gene pools,
which was Fst = 0.063.
Conclusions: Based on a combination of scores for individual plants all varieties can be distinguished using the 12
markers developed here. The markers may also be used for mapping and in molecular breeding. In addition, they may
be employed in studying gene flow from crop to wild populations.
Background
Sugar beet (Beta vulgaris L.) is a crop of major importance for sugar production in temperate zones. Varieties
are produced through crosses of diploid male sterile
(CMS) lines with tetraploid, or increasingly, diploid pollinator lines, resulting in triploid or diploid varieties,
respectively [1]. As the parental lines are mixtures of genotypes, the varieties will consist of mixtures of plants
from various parental combinations. This leads to some
problems in variety registration research. Variety registration is based on Distinctiveness, Uniformity, and Stability (DUS) research. Using a visual inspection of
morphological characteristics, distinctiveness from other
varieties is not easy to assess, for several reasons: the crop
has a narrow genetic basis [2,3], which results in varieties
* Correspondence: rene.smulders@wur.nl
1
Plant Research International, Wageningen UR Plant Breeding, PO Box 16, NL6700 AA Wageningen, The Netherlands
that are highly similar in appearance [4], the varieties are
mixtures of genotypes, and breeders change the pollinator line in modern hybrids frequently to produce locally
adapted hybrid varieties. For these reasons, the other two
aspects of the standard DUS research, uniformity and
stability, are not determined, and there are no UPOV
(International Union for the Protection of New Varieties
of Plants) guidelines for this crop.
The number of informative morphological characteristics is limited. Therefore, most often production-related
characteristics as beet yield, sugar content and total sugar
yield are included as descriptors. A preliminary characterisation ("pre-screening") of newly submitted varieties
with molecular markers during the winter before sowing
could be of help in the planning of the field trials and may
give a first indication for distinctiveness, provided that a
sufficient number of markers is used and that overall
marker profile and phenotype correlate well.
Full list of author information is available at the end of the article
© 2010 Smulders et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
BioMed Central Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Smulders et al. BMC Genetics 2010, 11:41
http://www.biomedcentral.com/1471-2156/11/41
Molecular markers have been used successfully for
variety identification in a large number of crops, including selfing species [5,6] and clonally propagated plants
[7,8]. In sugar beet, RFLP, RAPD, and AFLP [9-15] studies
have been reported. Although AFLP markers are reproducible between laboratories [16,17], data base building
can be a problem as different equipment may lead to different profiles. Six co-dominant microsatellites were used
to study genetic diversity in wild, cultivated, and weedy
forms of Beta vulgaris [18,19]. Rae et al. [20] developed a
set of mostly dinucleotide repeats for incorporation into
the linkage map of B. vulgaris, and Richards et al. [21]
characterized eight new polymorphic microsatellite
markers, of which five were based on trinucleotide
repeats. Cureton et al. [22] developed six microsatellite
markers to measure gene flow in sea beet (Beta vulgaris
ssp. maritima). Laurent et al. [23] mapped a large number
of genomic and EST-derived microsatellites on a genetic
map of sugar beet, the majority of which were dinucleotide repeat markers.
To be useful for identification of varieties the markers
should allow determining unequivocally the genotype of
each plant independently. The ease and accuracy of scoring varies among microsatellite markers, with significantly more problems when applying dinucleotide
repeats, due to their tendency to generate more stutter
bands, which may co-migrate with neighbouring alleles.
The experience in those species in which large replication
studies have been set up among laboratories, is that rigorous screening of markers is necessary [5,6]. For that reason we have developed a set of new microsatellite
markers for B. vulgaris with PIG-tailed reverse primers
[24] and stringent quality demands (Quality 1 or 2 of
Smulders et al. [25]). We have applied this set of 12 di-,
tri- and tetranucleotide repeat microsatellite markers to
determine the genetic variation within and between 40
diploid and triploid varieties. Using 30 plants per variety
we have generated a dataset of genotypes of 1200 plants.
We analysed the data with respect to allelic diversity, and
discuss applications of the markers in sugar beet, sea
beets, and ruderal beets.
Results
Microsatellite marker development
For accurate genotyping of varieties and database building, high quality microsatellite markers are needed.
Therefore the isolation of microsatellites was focussed on
tri- and tetranucleotide repeats, although dinucleotide
repeats were isolated as well. In total 3200 clones were
screened for microsatellite-containing inserts. In total 31
clones (1%) were found positive for tetranucleotide
motives, 240 (7.7%) for trinucleotide repeats and 240
(7.7%) for dinucleotide repeats. For 65 unique microsatellite sequences, primer pairs were designed on the flank-
Page 2 of 11
ing regions. For each locus the amplification pattern was
evaluated with respect to pattern quality and degree of
polymorphism on a set of individual plants of 10 varieties
originating from different breeders. Twenty-five primer
pairs (39%) produced polymorphic and simple banding
patterns. These primers were selected for further analysis
with fluorescent primers on an ABI 3700 using the same
set of test varieties. The twelve most robust markers
showing no or moderate stutter bands, a low degree of
differential amplification, and easy scorability, were used
for genotyping the sugar beet varieties (Table 1). These 12
markers consisted of two perfect and four compound
dinucleotide repeat loci, five trinucleotide loci, and one
locus with both a perfect dinucleotide repeat and a perfect tetranucleotide repeat.
Alleles detected
For the evaluation of the markers 30 individual plants per
variety were genotyped. Table 2 shows the number of
alleles detected for each marker, which varied widely
(from 3 to 21), but the effective number of alleles was
quite comparable across loci (1.95-3.74; Table 2). In total
91 different alleles were detected. From the number of
dropouts in amplification and the positive Fis values we
deduced that null alleles may exist. Additional population-genetic parameters of these varieties are listed in
Additional file 1.
Variety characterization based on dominant scoring of
alleles
Using the set of 12 marker loci, we found 25-38 different
alleles (on average 32.3 per variety) in the 30 plants of a
diploid variety and 33-46 (average 39.0) alleles in a triploid variety (Table 2). In general, individual plants from
varieties reported to be diploid had only one or two
alleles per locus. There were only 15 out of 330 plants
from reportedly diploid varieties with three different
alleles at one or two loci (5 plants each of Rebecca and
Brigitta, 3 of Nemil, 1 each of HI0032 and Fortis). On
average, diploid plants had 1.3 alleles per locus. Among
plants of the triploid varieties, the average number of
alleles per locus was 1.6. Depending on the locus,
between 0 (markers bvv17 and bvv21) and 183 (bvv15)
plants contained three different alleles at a single locus.
Overall, 528 of the 870 plants of these varieties had three
different alleles at one or more marker loci, underlining a
considerable amount of genetic variation present within
these plants.
Triploid varieties are produced from tetraploid males
and diploid female plants. While females are always diploid and may be shared between diploid and triploid varieties, the male plants are either diploid or tetraploid and
these may form genetically distinct groups. However, tetraploid lines can also easily be made from diploids. When
Smulders et al. BMC Genetics 2010, 11:41
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Page 3 of 11
Table 1: Characteristics of 25 newly developed sugar beet microsatellite markers.
Micro-satellite
marker1
Bvv01
EMBL Accession
number
Forward primer (5'-3')
Reverse primer2 (5'-3')
[EMBL:AM410749] CCATATGGAGGGGTAGAGCA
Repeat
motif3
Annealing Predicted
Temp
size
Quality4
(GGA)4-1(GGT)7
55
105
1
(CA)56-3
52
212
1
(CAA)34-7
54
298
1
(GAT)12-3
57
128
1
(GCC)3(ACC)3
60
208
1
(GGC)13-5
53
250
1
(CCG)5(CCA)5
52
210
1
(GA)16
50
109
1
(TCA)10
51
121
2
(TCA)13-3,
(TCA)35-10
54
310
1
(GA)31
51
183
1
(CA)14,
(GTAT)69
49
461
2
(CA)14
50
142
2
(GT)27
50
216
1
(GT)96-18
54
257
2
(GT)14-1,
(CGCA)8
55
230
1
(GT)24
50
209
1
(TG)9(AG)32-1
51
272
1
(GT)17, (GA)35
53
226
1
(TC)12(AC)12
52
279
1
(GT)25
50
232
1
(CAT)7, (CAT)7,
(CAT)11-1
52
256
1
(GAA)22
49
200
1
GTTTGCACCATAGGCACCACCACTTG
Bvv10
[EMBL:AM410750] CTTTGAGAATTGAGATACTATG
GTTTGTCTGGACGCAAGCACAC
Bvv155
[EMBL:AM410751] TGCTGACCTTGCAGTTAATAAGTT
GTTTCATGTGATGGCTTGCTTTCTAA
Bvv17
[EMBL:AM410752] CGACGCCTTTTTGAAGGAATAGGAT
GTTTCACCCCTGGGTCCTGATCTACAAC
Bvv186
[EMBL:AM410753] CACCATAACCGCCCCCACCATAAT
GTTTCTTGGCCGTAGGGTAAGGGTCAACTA
Bvv21
[EMBL:AM410754] TTGGAGTCGAAGTAGTAGTGTTAT
GTTTATTCAGGGGTGGTGTTTG
Bvv22
[EMBL:AM410755] CTATGCATCGCCCAATAATTACTTAA
GTTTATATAACACTGCTTATTTAATGTCC
Bvv23
[EMBL:AM410756] TCAACCCAGGACTATCACG
GTTTACTGACAAAGCAAATGACCTACTA
Bvv257
[EMBL:AM410757] GAAACCACATAAAAACCCCTCTTA
GTTTCAAGTAGTCCCGTTAACATCTGA
Bvv27
[EMBL:AM410758] GGGTTCATCATCATCCTTATCATT
Bvv30
[EMBL:AM410759] TGTGCCCAAAATCCTGAA
GTTTACGCTCCTCCATCATCAGACCA
GTTTAATTGGCTGGGTAAAAGAGA
Bvv31
[EMBL:AM410760] AGAAGCCTTTAAAATCCAACT
Bvv32
[EMBL:AM410760] AGAAGCCTTTAAAATCCAACT
GTTTACAGCGTCTCACCATAAGT
GTTTACATATGGAACTTTAATGAACAAGTGATAT
Bvv37
[EMBL:AM410761] TGGACGCCATATTAGAAGAT
GTTTATACAAATGAATATGAGAATACTG
Bvv43
[EMBL:AM410762] TGACACTCTTCTTTGCAACACATAA
GTTTGTAAATGTTGCAAAATATTGGTAT
Bvv45
[EMBL:AM410763] GTATAGCAAAAGTCATTTTGTTTGTGT
Bvv48
[EMBL:AM410764] GGCTTCCCTAGACAACC
GTTTCTCGGCCTTCCCTTTCTAATGTCTAG
GTTTATAGGCAAATGAATGAGG
Bvv51
[EMBL:AM410765] AGCAAAACTTATCTCAAATCTGG
GTTTGTCTACCGTGGCTGTGC
Bvv53
[EMBL:AM410766] CATGTCGAGGAGTGAGTTCAGGAA
GTTTCAACTATAGGTGCATCTTTTAC
Bvv54
[EMBL:AM410767] ATCTGCATGCCGTCACTC
GTTTCACTGTACCTTCGAATGTTAG
Bvv57
[EMBL:AM410768] CATTACCATGGGAACGAA
GTTTAAGGGATACAATGTTAGTTATGAA
Bvv60
[EMBL:AM410769] AAGAATGCTTCAACTTTTTCATGG
Bvv61
[EMBL:AM410770] ATGGGAGAATATTGGTGACA
GTTTAGGGTCGGATATAAGAGGGAGTGG
GTTTGCCACAAATCATCTCTACTAA
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Table 1: Characteristics of 25 newly developed sugar beet microsatellite markers. (Continued)
Bvv62
[EMBL:AM410771] ATGGCAATGCGCAGAATAACC
(CAG)11-2
54
155
1
(CT)18-1(CA)19
51
207
2
GTTTGCTGAGGAGGCTGCATTTGTT
Bvv64
[EMBL:AM410772] TTTTTGGGAGTTTCATCACTACTTT
GTTTCATATAAGGGGAGTCTTCTCACAA
1 In
bold the 12 markers that have been used to genotype 40 cultivars (1200 plants) (see Table 2). They were amplified separately but combined
before analysis on an ABI sequencer, as follows: multiplex 1 consisted of markers Bvv15, Bvv30, and Bvv64; multiplex 2 of Bvv17, Bvv43, and Bvv61;
multiplex 3 of Bvv 51, Bvv 53, and Bvv60; multiplex 4 of Bvv 21, Bvv23, and Bvv32
2 GTTT is a pigtail [24]
3 the number after the minus sign is the number of imperfect repeats. For instance, (CA)
56-3 means that the microsatellite repeat covers of length
of 56 (CA) repeat units, but of these 3 are not (CA).
4 according to Smulders et al. [25]
5 The sequence of the forward primer of Bvv15 was found in cDNA clone EO12340
6 The sequence of the cloned Bvv18 fragment was found in cDNA clone EG551697
7 The sequence of the cloned Bvv25 fragment was found in BAC clone ED032383
we calculated genetic differentiation between diploid and
triploid groups (330 and 870 plants, respectively) Fst =
0.1327 across all loci, ranging from 0.037 for marker
bvv30 to 0.2066 for bvv23. Each of these estimates was
highly significant (p < 0.001, tested by permutation of
individual plants among all varieties).
This differentiation between diploid and triploid varieties could also be the result of the fact that some breeders
specialise in diploid varieties, and others in triploids. If
so, it would reflect differentiation among breeders rather
than between ploidy levels. We therefore also tested the
differentiation among breeding companies. Among
breeders, we found Fst = 0.0628 ± 0.0092, which is roughly
half of the difference between diploid and triploid varieties.
Genetic diversity and differentiation among varieties
A NJ tree was made using the pairwise genetic distances
between varieties to visualise the genetic distances
among varieties (Figure 1). It shows that the genetic distance is, on average, larger among diploid varieties. For
instance, the inner part of the dendrogram contains 17
triploid varieties at relatively small distances from each
other. The same pattern is visible in a PCO plot, with the
triploid varieties central in the plot and the diploid varieties further from each other (Additional file 2). Triploid
varieties have a higher probability of sharing alleles due to
the fact that they have more gene copies, hence on average more alleles, which may explain the pattern observed.
There is no clear structure in the genetic relatedness of
varieties from particular breeding companies in the tree,
except that the top branch consists exclusively of nine
varieties from KWS (Ariana, Aurelia, KWS8123,
KWS9226, Rebecca, Tiara, Brigitta, Lenora, and
Madonna).
Overall, Fst = 0.133, but this value was lower among
triploid varieties (Fst = 0.100) and much higher among
diploid varieties (Fst = 0.232) (Table 3), which is consis-
tent with the pattern observed in the dendrogram. The
correlation between the values of individual marker loci
for triploid and diploid varieties is relatively poor (R2 =
0.54), suggesting that the gene pool differences between
triploid and diploid varieties are not evenly spread across
loci.
The estimate of Fis for the whole dataset was negative
for each of the markers (not shown), which is most likely
an artefact of the dominant scoring of the markers. In
theory, this can influence the Fst estimates as well. For the
diploid varieties we were able to estimate the magnitude
of this effect through a comparison with an analysis using
codominant scoring (assuming two alleles per locus per
plant and no null alleles). Table 3 (middle panel) shows
that the actual Fis value varies widely among marker loci,
from Fis = +0.876 (heterozygote deficiency) to Fis = -0.350
(excess of heterozygotes), with an average of Fis = 0.282 ±
0.124, Table 3). The effect on the estimation of the variation present among varieties (Fst) is limited: Fst averaged
across loci is 0.232 for dominant scores (left panel) and
0.271 for codominant scores (middle panel; 17% more).
The Fst estimates for most loci are close to this systematic
difference of 17%, and the pairwise correlation between
the values per locus is R2 = 0.91. This indicates that differentiation among diploid varieties is being estimated comparably using dominant or codominant scores.
Discussion
We have developed a set of new microsatellite loci for
sugar beet, which amplified 2-21 alleles per locus. This is
comparable to the 2-11 alleles found by Richards et al.
[21] for their microsatellite markers in a set of sugar beet
and sea beet plants. Desplanque et al. [18] and Viard et al.
[19] found up to 10 alleles for a marker in a single variety.
This level of gene diversity does not seem to correspond
with the notion of little genetic variation in the crop sugar
beet due to a bottleneck during its development from
wild beets [1]. The breeding system, which employs sepa-
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Table 2: The number of microsatellite alleles detected in 30 individual plants per variety.
Common, rare alleles found using microsatellite marker
Variety
Seed
Company
Ploidy
level
Bvv15
Bvv17
Bvv21
Bvv23
Bvv30
Bvv32
Bvv43
Bvv51
Bvv53
Bvv60
Bvv61
Bvv64
Total
number
of alleles
A8106
Agrosem
3
5,1
2,0
2,0
3,0
3,0
3,0
5,2
4,0
4,0
2,0
4,1
5,0
42
Ariana
KWS
3
4,1
2,0
3,0
2,0
2,0
3,1
5,0
4,1
3,1
3,0
6,1
5,0
42
Aristo
Novartis
2
4,2
2,0
2,0
4,0
3,1
2,0
2,0
4,0
2,0
2,0
6,1
4,0
37
Assist
SES
3
5,2
2,0
3,0
4,0
3,0
3,0
3,0
4,0
4,2
2,0
5,1
4,1
42
Atlantis
Van der
Have
3
6,0
2,0
1,0
4,1
3,0
2,0
2,0
5,1
5,0
2,0
4,2
6,0
42
Aurelia
KWS
3
3,0
1,0
1,0
4,1
2,0
2,0
3,0
4,0
2,0
3,0
3,1
6,2
34
Blenheim
Van der
Have
3
4,1
2,0
2,0
3,1
3,0
3,0
3,2
5,0
4,0
2,0
5,0
3,1
39
Brigitta
KWS
2
3,0
2,0
2,0
1,0
2,0
1,0
3,0
4,0
4,0
2,0
4,2
2,0
30
Caramel
Kuhn
3
5,0
2,0
1,0
4,0
2,0
2,0
2,0
4,0
4,0
2,0
2,0
3,0
33
Claudia
CFS
3
4,0
2,0
2,0
3,0
3,1
3,1
5,0
5,0
6,3
2,0
6,3
5,2
46
Crestor
Novartis
2
3,0
2,0
3,0
2,0
2,0
2,0
2,0
4,0
2,0
2,0
5,1
3,1
32
Cynthia
KWS
3
4,1
2,0
2,0
4,0
2,0
3,0
3,0
5,0
4,0
2,0
3,0
3,0
37
DS3014
Danisco
3
3,0
2,0
2,0
2,0
2,0
2,0
1,0
4,0
2,0
2,0
6,3
2,0
30
DS3030
Danisco
3
4,0
2,0
2,0
3,1
2,0
3,0
3,0
3,0
4,1
2,0
5,1
3,0
36
Fortis
Hilleshog
2
2,0
2,0
2,0
3,0
3,0
3,0
2,0
4,1
2,0
2,0
4,1
6,0
35
H66377
Van der
Have
3
5,2
2,0
2,0
4,1
3,1
3,0
4,2
4,1
3,0
2,0
5,2
6,2
43
H66411
Van der
Have
3
7,0
2,0
2,0
4,0
2,0
3,0
3,1
5,0
4,0
2,0
3,0
5,1
42
Helsinki
Van der
Have
3
6,1
2,0
2,0
3,0
3,0
3,1
1,0
5,0
4,0
2,0
4,1
3,0
38
HI0032
Novartis
2
3,1
2,0
2,0
3,0
3,1
3,0
4,0
3,0
4,1
2,0
4,0
5,0
38
HM5432
Hilleshog
3
6,1
2,0
2,0
3,0
2,0
3,0
4,0
4,0
3,0
3,0
3,1
5,1
40
KWS8123
KWS
2
3,1
2,0
2,0
2,0
2,0
2,1
1,0
3,0
1,0
2,0
2,1
5,1
27
KWS9226
KWS
3
5,0
1,0
1,0
2,0
2,0
2,0
4,0
3,0
3,0
3,1
4,1
4,1
34
Lenora
KWS
2
4,2
2,1
2,0
2,0
2,0
1,0
1,0
3,0
3,0
2,0
3,0
1,0
26
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Table 2: The number of microsatellite alleles detected in 30 individual plants per variety. (Continued)
Lion9909
Lion
Seeds
3
5,0
2,0
2,0
4,1
2,0
3,0
3,0
4,1
5,1
2,0
6,0
4,1
42
Lion9912
Lion
Seeds
3
6,0
2,0
2,0
3,0
2,0
3,0
2,0
4,0
5,0
2,0
5,1
3,1
39
Madonna
KWS
2
3,1
1,0
2,0
2,0
2,0
2,0
2,0
4,0
3,0
2,0
4,0
2,0
29
MK9907
Kuhn
3
4,0
2,1
2,0
2,0
3,0
3,0
3,1
5,2
4,0
3,0
4,0
4,0
39
Nemil
Novartis
2
6,2
2,0
3,0
3,0
2,0
3,0
1,0
5,2
2,0
2,0
6,2
3,1
38
Opus
Dickman
3
7,0
2,0
2,0
4,0
3,0
3,0
3,0
4,0
5,0
2,0
5,1
5,2
45
Oslo
Van der
Have
3
5,0
2,0
2,0
4,0
3,0
2,0
3,0
4,0
4,0
2,0
5,0
3,0
39
Princesse
Delitzsch
3
3,1
2,0
2,0
4,0
2,0
2,0
4,0
5,0
3,0
2,0
5,1
4,1
38
Ravel
Kuhn
3
4,0
2,0
2,0
4,1
3,0
2,0
1,0
5,0
4,0
2,0
4,1
4,0
37
Rebecca
KWS
2
4,2
2,0
2,0
3,0
2,0
3,0
3,0
5,1
5,2
2,0
3,0
4,0
38
S1901
SES
3
5,0
2,0
2,0
4,0
2,0
2,0
2,1
4,0
4,0
2,0
4,1
4,1
37
Stru2001
Fr Strube
Saatzucht
2
3,0
2,0
1,0
1,0
1,0
2,0
1,0
3,0
3,0
2,0
4,1
2,0
25
Sylvester
Van der
Have
3
7,2
2,0
2,0
3,0
3,0
3,0
2,1
4,0
3,0
2,0
5,2
5,0
41
Tiara
KWS
3
4,0
1,0
2,0
3,1
3,0
2,0
3,0
4,0
5,0
2,0
4,0
3,0
36
Toledo
Novartis
3
4,0
2,0
2,0
4,0
2,0
2,0
3,1
4,0
4,1
2,0
6,0
3,0
38
Winner
Kuhn
3
6,0
2,0
1,0
4,0
3,0
2,0
2,0
4,0
4,0
2,0
4,1
5,1
39
Winsor
Novartis
3
41
4,0
2,0
2,0
3,0
2,0
3,0
4,1
5,0
4,0
2,0
6,0
4,0
201-293
135-138
236-268
092-108
136-143
133-141
257-288
240-281
174-234
252-275
213-370
224-237
Total number of
alleles (in 1200
plants)
11
3
5
5
4
4
9
8
10
6
21
6
Effective number
of alleles (in 1200
plants)
2.7
3.0
2.2
3.5
2.2
3.2
2.6
3.7
3.1
2.0
3.1
3.1
0.525
0.626
0.529
0.686
0.543
0.672
0.700
0.645
0.589
0.458
0.659
0.744
Allele length
range
He (expected
heterozygosity)
(in 316 diploid
individuals)
92
Smulders et al. BMC Genetics 2010, 11:41
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Page 7 of 11
Ariana-3
Aurelia--3
Rebecca-2
KWS9226-3
Tiara-3
Brigitta-2
Madonna-2
Claudia-3
3MW
Princesse-3
Atlantis-3
Winner-3
Opus-3
Sylvester-3
S1901-3
Caramel-3
Oslo-3
H66411-3
Ravel-3
Helsinki-3
MK9907-3
Blenheim-3
Lion9912-3
A8106-3
H66377-3
Assist-3
Lion9909-3
Fortis-2
HI0032-2
DS3014-3
HM5432-3
Cynthia-3
Toledo-3
DS3030-3
Winsor-3
0.00
0.05
KWS8123-2
Lenora-2
Nemil-2
Crestor-2
Aristo-2
Stru2001-2
0.09
0.14
0.19
Genetic Distance
Figure 1 Neighbour-joining tree based on pairwise genetic distances between sugar beet varieties. The genetic distances were calculated using dominant scoring of alleles. The names of the varieties are followed by their ploidy level: 2 = diploid (2n = 2x), 3 = triploid (2n = 3x).
rate gene pools for paternal and maternal parents,
increases the gene diversity within individual plants, and
the habit of working with pools of parental plants, which
contain a large amount of genetic diversity [19], may contribute to the fact that 84-92% [26] of the genetic variation of the crop is present within hybrid varieties.
Ploidy level
We have applied 12 of our markers to analyse 30 plants of
each of 40 sugar beet varieties.
The markers detected only few (15/330) triploid plants
in diploid varieties. The highest frequencies of triploid
plants were found for two varieties (5/30 plants each for
Brigitta and Rebecca). These plants are probably the
result of pollination by tetraploid pollen donors from
production fields for other, triploid, varieties in the neighbourhood of the seed production fields of the diploid
varieties. In Europe seed production of sugar beet variet-
ies takes place in the South-West and South-East of
France, Northern Italy, and the South of Ukraine, and in
these areas the distance between production fields is at
least 1000 m to severely limit cross-pollination, but this
cannot be avoided completely. Accidental cross-fertilization may also take place with ruderal populations in the
vicinity of the seed production fields [27,28], but this
would produce diploid offspring.
Genetic differentiation
The overall genetic differentiation between diploid and
triploid varieties was Fst = 0.1327 across all loci. Part of
this differentiation coincides with the differentiation
among breeders' gene pools, which was Fst = 0.0628. This
suggests that breeders use parental lines that are, to some
extent, genetically different. The latter value can be
expected to gradually decrease in the future, as there have
been mergers between sugar beet breeding companies in
Smulders et al. BMC Genetics 2010, 11:41
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Page 8 of 11
Table 3: F-statistics of 40 varieties genotyped with 12 microsatellite markers.
Dominant scoring
all
triploids
Fst
varieties
Fst
Co-dominant scoring
diploids
Fst
diploids
Fst
Fis
Fit
Ho
Hs
Ht
40
29
11
11
11
11
11
11
11
plants
1200
870
316
316
316
316
316
316
316
bvv15
0,095
0,079
0,140
0,135
-0,113
0,038
0.513
0.460
0.525
bvv17
0,158
0,127
0,257
0,291
0,729
0,808
0.122
0.453
0.623
bvv21
0,181
0,168
0,239
0,257
0,324
0,498
0.272
0.397
0.525
bvv23
0,207
0,145
0,421
0,459
0,820
0,903
0.069
0.383
0.683
bvv30
0,037
0,015
0,119
0,204
-0,011
0,196
0.445
0.442
0.543
bvv32
0,172
0,129
0,288
0,345
0,408
0,613
0.271
0.455
0.670
bvv43
0,168
0,107
0,279
0,291
0,876
0,912
0.065
0.516
0.703
bvv51
0,095
0,082
0,152
0,180
-0,152
0,055
0.615
0.533
0.641
bvv53
0,132
0,106
0,208
0,274
-0,232
0,106
0.542
0.442
0.587
bvv60
0,047
0,034
0,093
0,151
-0,355
-0,150
0.533
0.394
0.457
bvv61
0,119
0,094
0,195
0,198
0,433
0,545
0.260
0.527
0.658
bvv64
0,151
0,096
0,299
0,349
0,582
0,728
0.206
0.504
0.744
0.459
0.613
Jackknifed estimators (over loci)
Overall
Mean
0,133
0,100
0,232
0,271
0,282
0,479
SE
0,014
0,011
0,027
0,028
0,124
0,105
recent years, which may result in merging of the breeding
programs.
When partitioning the genetic variation using F statistics, the estimate of Fis of diploid plants turned out to be
highly variable among microsatellite loci: from Fis = 0.876
(large shortage of heterozygotes) to Fis = -0.35 (excess of
heterozygotes). The excess of heterozygotes is not surprising as the propagation system pairs selected malesterile (CMS) mother lines with selected father lines, with
the aim of assortative mating and hybrid seed production.
The shortage of heterozygotes at some marker loci may
indicate selection. It may also indicate the presence of
null-alleles, i.e. alleles that have gone undetected, or
skewed inheritance [12]. Laurent et al. [23] found 14%
skewed segregation in an F2 population, notably for
markers on linkage group V [29]. Viard et al. [19,30]
found significant heterozygote deficiencies in weed beets.
Fénart et al. [1] observed also significant deviations in Fis,
in both directions, in wild sea beet and weed beet populations. Viard et al. [19] thought it may be related to a low
frequency of self-compatibility alleles commonly used in
breeding programs. This was recently confirmed by
Arnaud et al. [27].
0.326
Nonetheless, Fst values of dominantly scored and
codominantly scored markers (for diploid varieties) were
in good concordance, indicating that regardless of the
statistical analysis of the data, genetically similar and dissimilar varieties can be distinguished reliably. This is in
agreement with the conclusions of De Riek et al. [24],
who compared the power of these microsatellites with
that of a set of AFLP markers. The differentiation among
diploid varieties was quite high: Fst ranged from 0.093 to
0.421 (Table 3). The average of 0.232 is higher than Fénart
et al. [1]'s estimate of Fst = 0.082 among 13 diploid sugar
beet varieties using 5 microsatellite markers, which in
turn was higher than the differentiation among weed
beets and among sea beets. It would be interesting to
determine the level of differentiation assessed with our
markers among these groups of beets.
Applications
Based on a combination of scores for individual plants all
varieties can be distinguished using the 12 markers
employed here. However, as the varieties are mixtures of
genotypes, not all individual plants can always be identified or classified unequivocally. De Riek et al. [26] compared various ways of analysing the data for eight of these
Smulders et al. BMC Genetics 2010, 11:41
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varieties. They concluded that, using the data for 30 individual plants for each variety, assignment methods
accomplished a very good distinction among the genetically diverse varieties. In their assignment-based method,
for each individual plant the 10 most genetically similar
partner plants were identified across the whole data set.
The origin of these highest-ranking plants was then used
to assign the plants to a particular variety. With microsatellite data, between 24 and 30 of the 30 plants analysed
for each variety, were assigned correctly to this variety.
The partitioning of the origin of the highest-ranking partners over all varieties in the dataset was also used to
develop an assignment-based similarity measure for such
sets of mixtures of genotypes, called similarity-by-assignment (Sax, y) [26].
Conclusions
Microsatellite markers may be used for genetic mapping
and breeding purposes [29]. The markers developed here
were polymorphic within all or nearly all varieties, which
indicates that they may be used for mapping in most
crosses in sugar beet. In addition, they may be employed
in studies of crop-to-wild gene flow [1], including those
in the frame of biosafety studies [31].
Methods
Plant material
For the isolation of microsatellites, genomic DNA of Beta
vulgaris L. ssp. vulgaris variety Holly was used. For the
characterization of varieties, 30 individual plants of 40
varieties (listed in Table 2) were analyzed (in total 1200
plants). Young leaves of a single individual were harvested, immediately frozen in liquid nitrogen and stored
at -80°C until use.
DNA extraction
For the construction of a genomic library enriched for
microsatellites, nuclear DNA of high quality was
extracted from leaves of variety Holly according to
Vosman et al. [32]. For microsatellite amplification, DNA
of single individuals was extracted from freeze-dried
leaves either according to Fulton et al. [33] or by a combination of this method with the Qiagen Dneasy Plant Mini
kit (Westburg, The Netherlands). In the combination
extraction protocol, after chloroform extraction the
cleared supernatant was mixed with Qiagen binding buffer (AP3/EtOH) and applied to a DNeasy spin column
(Esselink, unpublished). Subsequently, the column was
washed and DNA eluted. Typical yield of this extraction
protocol was 20 μg DNA per 20 mg dried weight.
Microsatellite isolation
Microsatellites were isolated from enriched small-insert
genomic libraries essentially as described by Van de Wiel
Page 9 of 11
et al. [34] and Esselink et al. [7]. The DNA was digested
with Alu I, Mbo I or Rsa I, and the enrichment was carried out using filter-immobilized synthetic dinucleotide
[(GT)12, (GA)12], trinucleotide [(TCT)10, (TGT)9, (GAG)8,
(GTG)8, (TGA)9, (AGT)10, (CTG)8, (CGT)8], and tetranucleotide [(GATA)8, (TGTT)8, (GTAT)8] repeats, all in
separate reactions. Primers were designed on the
obtained sequences using primer3 http://primer3.sourceforge.net/.
Microsatellite amplification and detection
Microsatellites were amplified in a 20 μl reaction volume
containing 20 ng of genomic DNA, 2-10 pmol of each
primer, 100 μM of each dNTP, 10 mM Tris-HCL pH 9.0,
20 mM (NH4)2SO4, 0.01% Tween 20, 1.5 mM MgCl2 and
0.3 Units Goldstar Taq DNA polymerase (Eurogentec,
Maastricht, The Netherlands). The optimized PCR conditions used for the database construction were 94°C for
3 min. followed by 30 cycles of 94°C for 30 s, at the calculated annealing temperature for 30 s, 72°C for 60 s and a
final extension at 72°C for 3 min. Unlabeled primers were
obtained from Isogen (Maarssen, The Netherlands), fluorescently labelled (HEX, NED, 6-FAM) primers from
Applied Biosystems (Warrington, United Kingdom). The
amplification products were separated on a 6% acrylamide gel and visualized with silver staining according to
Promega Silver sequence DNA sequencing system (Promega, Leiden, The Netherlands) as described [34]. Fluorescent amplification products were combined (see Table
1) and purified using Multiscreen 96-well Sephadex G50
filtration plates (Millipore). One μl of purified sample was
mixed with 10 μl of formamide loading buffer containing
a ROX-labelled internal lane standard. After denaturation
at 95°C for 3 min, followed by quenching on ice, 1 μl samples were loaded in a capillary sequencer (3700 POP6,
ABI) and run for 1.5 h. Fragment sizes were determined
automatically using Genescan 1.1 (ABI). All genotypes
were analyzed using Genotyper 3.5 NT (ABI).
Data analysis
A selection of 12 microsatellite markers with high quality
patterns (see Table 1) was used for the characterization of
the varieties. Screening of varieties in a first round
revealed all existing alleles for each marker and allowed
selection of a set of varieties representing all the alleles.
These varieties were included in each following run and
used as a reference for allele determination. In this way
for each marker the alleles were assigned a name (a, b, c,
etc.) based on an exact match to the length of the corresponding allele present in the reference variety, rather
than as a particular length in base pairs. Only the presence of alleles was scored and recorded as a presence/
absence (1/0) matrix. As a consequence, both AAB and
Smulders et al. BMC Genetics 2010, 11:41
http://www.biomedcentral.com/1471-2156/11/41
ABB genotypes, for example, are scored and entered in
the database as AB. We call this the 'allelic phenotype'
[7,8,24,35] after Becher et al. [36] to distinguish it from
the genotype. An allelic phenotype is not the same as a
genotype, as it only includes information on the presence
of alleles, not on the allele frequency [26]. We report the
number of alleles per locus, the effective number of
alleles, and the number of allelic phenotypes. The effective number of alleles (ne) is estimated as 1/Σpi2, where pi
is the frequency of the ith allele in the variety examined.
We prefer calculating the effective number of alleles to
the expected heterozygosity (which is 1-Σpi2). These two
measures have a non-linear relationship (nE = 1/(1-Hexp)),
and the effective number of alleles scales better when
there are many alleles. More importantly, it is less
affected by our dominant way of scoring alleles, and has a
straightforward interpretation even across ploidy levels.
On the basis of individual allele scores Jaccard distances
were calculated. The Jaccard distance and the related
Dice distance ignore absence-absence pairs, whose number may be inflated by the dominant scoring of a codominant marker. The varieties were clustered using
neighbour-joining in NTSYSpc 2.1.
SpaGeDi 1.0b [37], which can handle plants of different
ploidy levels, was used to calculate genetic differentiation
(Fst) among varieties on the basis of the presence of
alleles. The magnitude of the error in allele frequencies
caused by scoring only presence/absence and ignoring all
presence of more than one copy in diploid and triploid
varieties, was estimated for the diploid plants through a
comparison with the results of an analysis of codominantly scored data.
For the codominantly scored diploid plants also Nei's
heterozygosity, gene diversity, allelic richness, and Fis values were calculated per variety, using SpaGeDi.
Additional material
Additional file 1 A table reporting gene diversity, allelic richness and
Fis values per marker and variety.
Additional file 2 A PCO plot of the sugar beet varieties based on pairwise genetic distances between sugar beet varieties calculated using
dominant scoring of alleles. Triploid varieties: open circles; diploid varieties: filled circles.
Authors' contributions
JdR and BV conceived and designed the study; GDE and IE performed the
experiments; MJMS, IE, JdR, and GDE analyzed the data; MJMS, GDE and BV
wrote the paper. All authors read and approved the final manuscript.
Acknowledgements
This research was funded in part by the Ministry of Agriculture, Nature, and
Food quality of the Netherlands under the Method development scheme for
Plant Breeders Rights research.
Page 10 of 11
Author Details
1Plant Research International, Wageningen UR Plant Breeding, PO Box 16, NL6700 AA Wageningen, The Netherlands and 2Unit Plant, Institute for
Agricultural and Fisheries Research ILVO, Caritasstraat 21, B-9090 Melle, België
Received: 22 December 2009 Accepted: 18 May 2010
Published: 18 May 2010
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doi: 10.1186/1471-2156-11-41
Cite this article as: Smulders et al., Characterisation of sugar beet (Beta vulgaris L. ssp. vulgaris) varieties using microsatellite markers BMC Genetics 2010,
11:41
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