Open Access

Assessment of genetic diversity in Nordic timothy (Phleum pratense L.)

  • Pirjo Tanhuanpää1Email author,
  • Maria Erkkilä2,
  • Ruslan Kalendar2,
  • Alan Howard Schulman1, 3 and
  • Outi Manninen4
Hereditas2016153:5

DOI: 10.1186/s41065-016-0009-x

Received: 18 December 2015

Accepted: 19 April 2016

Published: 26 April 2016

Abstract

Background

Timothy (Phleum pratense L.), a cool-season hexaploid perennial, is the most important forage grass species in Nordic countries. Earlier analyses of genetic diversity in a collection of 96 genebank accessions of timothy with SSR markers demonstrated high levels of diversity but could not resolve population structure. Therefore, we examined a subset of 51 accessions with REMAP markers, which are based on retrotransposons, and compared the diversity results with those obtained with SSR markers.

Results

Using four primer combinations, 533 REMAP markers were analyzed, compared with 464 polymorphic alleles in the 13 SSR loci previously. The average marker index, which describes information obtained per experiment (per primer combination or locus) was over six times higher with REMAPs. Most of the variation found was within accessions, with somewhat less, 89 %, for REMAPs, than for SSR, with 93 %.

Conclusions

SSRs revealed differences in the level of diversity slightly better than REMAPs but neither marker type could reveal any clear clustering of accessions based on countries, vegetation zones, or different cultivar types. In our study, reliable evaluation of SSR allele dosages was not possible, so each allele had to be handled as a dominant marker. SSR and REMAP, which report from different mechanisms of generating genetic diversity and from different genomic regions, together indicate a lack of population structure. Taken together, this likely reflects the outcrossing and hexaploid nature of timothy rather than failures of either marker system.

Keywords

Genetic diversity Genetic structure Phleum pratense L REMAP Retrotransposon marker SSR Microsatellite Timothy

Background

Timothy (Phleum pratense L.), a cool-season perennial, is the most important forage grass species in Nordic countries. Genetic diversity has been previously assessed [1] in a collection of 96 timothy accessions, of which 88 were of Nordic origin. Simple sequence repeat (SSR) markers revealed Nordic timothy accessions to be very polymorphic, having significant differences in the levels of diversity between countries, vegetation zones, and different cultivar types. However, most of the variation (94 %) existed within accessions, and no clear clustering of accessions based on any grouping was observed. This lack of resolution may either reflect the outcrossing and hexaploid nature of timothy or that SSR markers are not suitable for resolving population structure in timothy.

A wide range of DNA markers are available for diversity studies, which all have their advantages and disadvantages. SSRs are amplified from single loci, but are multiallelic and highly polymorphic. Although they are inherited codominantly, separation of different genotypes may not be possible in a polyploid species such as timothy. Therefore, each allele has to be treated as a dominant marker [1]; consequently, the markers that are amplified from the same SSR locus are not independent of each other, and consequentially information is lost. In the REMAP (retrotransposon-microsatellite amplified polymorphism) markers [2, 3] assay, the diversity is generated by the integration of retrotransposons, which move in the genome by a copy-and-paste mechanism but are fixed in position upon insertion [4]. They are ubiquitous and abundant in plant genomes, where they are dispersed on all chromosomes [5]. REMAP markers are amplified using a primer designed to a conserved retrotransposon region and another anchored to a simple sequence repeat. Products from multiple loci are produced in one PCR reaction, each with only two allele alternatives, a dominant one (amplification) and a recessive one (non-amplification). Because the mechanisms that activate retrotransposons [6] and thereby generate insertional polymorphisms are fully different than that generating SSR allelic variation (polymerase slippage) [7], the two marker systems assay different components of genetic diversity. For potato [8], alfalfa [9], and grapevine [10], retrotransposon and SSR markers in combination were shown to be highly discriminatory and effective.

In the previous study [1], a collection of 96 timothy accessions was analyzed using 13 SSRs, thus describing diversity only at this number of loci. On the other hand, these 13 SSR loci harbored as many as 499 alleles. In the present study, we used REMAP markers for studying diversity in a subset of 51 accessions and compared the results with those obtained with SSR markers. We wanted to determine if another type of marker, which would report from many more locations in the genome and assess different genomic regions where diversity is generated by a different mechanism, could describe diversity more efficiently and also reveal population structure, particularly for a polyploid species. Especially the autonomous nature of retrotransposon diversity generation and display, which is independent of the syntenic organization of polyploids, appeared suited to clonal polyploid species such as timothy. We expected that the retrotransposon markers should thereby be more likely to find genetic structure in timothy, should it exist.

Methods

Plant material

In the previous study [1], SSR markers were analyzed in a collection of 96 timothy accessions. Fifty-one of these were selected for the present study to be screened also with REMAP markers (Table 1). Fifteen to twenty randomly selected individuals per accession were investigated, in total 945 individuals. The number of individuals analyzed from each accession in the two studies was not exactly the same because 20 individuals had to be omitted due to their poor amplification in REMAP analysis.
Table 1

Fifty-one Phleum pratense ssp. pratense accessions analyzed in the study

Number code

Accession no.

Genebank

Name

Country

Cultivar type1

Veg. zone2

3

NGB10830

Nordgen

VA88119

Denmark

W

1

4

NGB10831

Nordgen

HF88266

Denmark

W

1

5

NGB15461

Nordgen

Vildbjerg AC0103

Denmark

W

1

6

NGB16650

Nordgen

Ejsing

Denmark

W

1

7

NGB1672

Nordgen

BILBO

Denmark

CV

 

8

NGB1675

Nordgen

POTA

Denmark

CV

 

9

NGB4053

Nordgen

SR SALTUM MH0202

Denmark

W

1

10

NGB4548

Nordgen

NR FARUP MH0202

Denmark

W

1

11

NGB132

Nordgen

LIPINLAHTI ME0901 SEP A

Finland

L

4

13

NGB14394

Nordgen

KÄRKÖLÄ HM0102

Finland

W

3

16

NGB14404

Nordgen

PAATTINEN MH0201

Finland

L

2

18

NGB14417

Nordgen

MEDVASTÖ MH0101

Finland

W

2

20

NGB747

Nordgen

NUVVUS AK0401

Finland

W

6

24

NGB1095

Nordgen

LAITASAARI ME0201

Finland

L

4

27

NGB1111

Nordgen

MÄLÄSKÄ ME0101

Finland

L

4

30

NGB1119

Nordgen

KATERMA ME0401

Finland

L

4

32

NGB2791

Nordgen

NORRGÅRD AP0101

Finland

L

3

35

NGB4066

Nordgen

TAMMISTO

Finland

CV

 

36

NGB4140

Nordgen

KORPA

Iceland

L

 

37

NGB4141

Nordgen

ADDA

Iceland

CV

 

42

NGB7592

Nordgen

SKJØLSVIK 01-5-46-5

Norway

W

3

45

NGB10785

Nordgen

SANDBU 01-6-49-4

Norway

W

5

47

NGB17194

Nordgen

Ifjord 1-1-2-2

Norway

W

5

48

NGB17198

Nordgen

Karasjok 1-1-3-2

Norway

W

5

49

NGB2169

Nordgen

BODIN

Norway

CV

 

51

NGB2180

Nordgen

GRINDSTAD

Norway

CV

 

53

NGB2918

Nordgen

HUSETER 01-9-70-1

Norway

W

2

57

NGB4226

Nordgen

HATLESTAD 01-7-56-3

Norway

W

5

59

NGB4231

Nordgen

GJERDÅKER 01-7-58-1

Norway

W

5

62

NGB7548

Nordgen

NAMSVATN 01-5-40-1

Norway

W

5

64

NGB722

Nordgen

KUOSSENJARKA JP0404

Sweden

W

5

65

NGB728

Nordgen

PJESKER PH0405

Sweden

W

4

66

NGB11428

Nordgen

JONATHAN

Sweden

CV

 

69

NGB14224

Nordgen

SÖNDRARP IB0101

Sweden

W

2

71

NGB731

Nordgen

RÖRMYRBERG JP0204

Sweden

W

4

73

NGB16975

Nordgen

NORRA KYLSÄTER FO0103

Sweden

W

2

76

NGB16981

Nordgen

BRÄCKETORP FO0501

Sweden

W

2

78

NGB1306

Nordgen

BRATTÅKER GB0101

Sweden

W

4

81

NGB1327

Nordgen

HAMMARN PR0401

Sweden

W

4

83

NGB1331

Nordgen

VÄSTANSJÖ SH0102

Sweden

W

4

85

NGB1537

Nordgen

ESKELHEM TL0104

Sweden

W

2

86

NGB2530

Nordgen

RÄMNE GJ0301

Sweden

W

2

87

NGB4349

Nordgen

BENESTAD JK1506

Sweden

W

1

89

PI381926

GRIN

 

France

P

 

90

PI406317

GRIN

 

Russia

P

 

91

IHAR151908

IHAR

 

Germany

P

 

92

PI210426

GRIN

 

Greece

P

 

93

PI325461

GRIN

 

Russia

P

 

94

PI204480

GRIN

 

Turkey

P

 

95

14G2400116

RICP

 

Czech Republic

P

 

96

RCAT040682

RCAT

 

Hungary

W

 

1 CV advanced cultivar, L traditional cultivar, landrace, P pending, unknown cultivar type, W wild population, weedy

2vegetation zones, according to [21]

The 51 accessions were mostly wild (30, locations in Fig. 1 in [1]); seven each were classified as landraces, cultivars, and of unknown cultivar types. Accessions were derived from all Scandinavian countries (Denmark, 8; Finland, 10; Iceland, 2; Norway, 10; Sweden, 13). In addition, eight gene bank accessions (so-called exotics) originating from non-Scandinavian countries were included in the study.
Fig. 1

Principal coordinate analysis based on Nei’s genetic distances between accessions based on: a 533 REMAP markers; b 464 SSR markers; c 997 REMAP and SSR markers

Marker analyses

DNA was extracted using the method of Tinker et al. [11] with some modifications as described in Tanhuanpää and Manninen [1]. Using the iPBS (inter- primer binding site) method, retrotransposon segments were isolated from the timothy genome, sequenced, and long terminal repeats (LTRs) identified [12]. LTR primers were designed to match conserved motifs at or near their termini, according to the methods of Kalendar et al. [13]. For REMAP marker amplification, four different retrotransposon primers (TIM1 - 4) for grasses were used. These were combined with 19 microsatellite-based primers (ISSR + number) that contain repeat units (composed of two or three bases); the 3′ ends of the primers were anchored by a single nucleotide. Because analyzing markers by gel electrophoresis is very laborious, the retrotransposon primers were labelled with a fluorescent dye, FAM (5-carboxyfluorescein), HEX (hexachloro-6-carboxyfluorescein), or TET (6-carboxytetrachlorofluorescein) to enable resolution and visualization of amplification products with a MegaBACETM 500 Sequencer (GE Healthcare, Buckinghamshire, UK).

Fifty-nine REMAP primer combinations were first tested in a small set of individuals for their functionality and efficiency to produce polymorphic bands. The four best primer combinations were chosen for final analyses (TIM1 with ISSR1, 15 and 20, and TIM2 with ISSR5). These primers, together with their sequences and properties, are shown in Table 2. The REMAP markers were amplified in a reaction volume of 10 μl, using 0.25 U of FIREPol® DNA polymerase I (Solis BioDyne OU, Tartu, Estonia), buffer B with 2.5 mM MgCl2 as supplied by the enzyme manufacturer, 200 μmol/L each dNTP, 10 ng of DNA, and 500 nmol/L each primer. The PCR program was run on a PTC-220 DNA Engine DyadTM Peltier Thermal Cycler (MJ Research, Waltham, MA, USA) and consisted of an initial denaturation step of 2 min at 94 °C; 32 cycles of 30 s at 94 °C, 30 s at 60 °C and 2 min at 72 °C; a final extension step of 10 min at 72 °C. After PCR, the amplified products with different labels were combined for MegaBACE runs. SSRs were developed for timothy [14], and analyses were run as described previously [1].
Table 2

REMAP primers that were used in the analysis of timothy diversity, their sequences and properties

Name

Sequence

nt

Tm (°C)

CG %

Linguistic complexity (%)

TIM1

GGTGCCGGCATCGATCCTTTCA

22

62.4

59.1

88

TIM2

ACGAGTGAGGACAAAGTGCGCAGA

24

61.9

54.2

79

ISSR1

ACCACCACCACCACCACCC

19

63.2

68.4

24

ISSR5

AGCAGCAGCAGCAGCAGCG

19

64.4

68.4

30

ISSR15

GTGGTGGTGGTGGTGGTGGTGA

22

64.2

63.6

28

ISSR20

TGCTGCTGCTGCTGCTGCC

19

64.6

68.4

30

nt nucleotides, Tm melting temperature, CG % percentage of C and G bases

Data analyses

Each REMAP fragment represents a separate locus, and the presence and absence of the fragment was scored in a binary code (1/0). Likewise, each SSR allele was treated as a separate locus and scored in a binary code, even though SSRs are codominant markers. This was because we found the evaluation of allele dosages very unreliable in hexaploid timothy. Diversity indices for markers, including polymorphic information content (PIC), gene diversity, and major allele frequency, were calculated with the program Powermarker v3.0 [15]. A marker index (MI) for each REMAP primer combination and each SSR locus was determined by multiplying the number of polymorphic markers generated (EMF = Effective multiplex ratio) by average PIC value [16]. It illustrates the amount of information obtained per experiment (per primer combination or locus).

Genetic diversity in each accession was described with five different diversity indices: 1) the number of all markers observed (AA), corrected to a sample size of n = 15 with 1000 resamplings without replacement; 2) the mean number of all markers observed in each individual (AI); 3) the mean number of pairwise differences (PWD) (Euclidean distances) between individuals, which was counted with the program ARLEQUIN version 2.000 [17]; 4) Shannon’s diversity index I [18]; 5) the percentage of polymorphic loci. The last two were calculated using the program GenAlex 6.4 [19, 20]. Correlations between diversity indices based on REMAP and SSR markers, and differences in the level of diversity between different groups such as countries, vegetation zones [21], or cultivar types were determined by ANOVA Proc GLM (SAS Enterprise Guide 4.3).

The program GenAlex 6.4 [19, 20] was used to perform analysis of molecular variance (AMOVA) [22] which partitions total genetic variation to within- and among-accession variance components. The significance of the results was tested by permuting the data 999 times. Principal coordinate analyses (PCA) based on Nei’s genetic distances [23] between accessions, and a Mantel test [24], which was used to compare Nei’s distances based on REMAP or SSR data, were carried out with the software GenAlex.

Results

Diversity at marker loci

Four REMAP primer combinations were used for studying diversity of the 51 accessions. Because not all fragments could be read as marker peaks, selections were made on the basis of the size and shape of the peaks. The numbers of scored polymorphic markers produced by different primer combinations were as follows: TIM2 + ISSR5, 91; TIM1 + ISSR20, 84; TIM1 + ISSR1, 209; TIM1 + ISSR15, 149. A total of 533 REMAP markers were analyzed, ranging in size from 80 to 650 bp. A total of 464 polymorphic alleles in the 13 SSR loci were amplified from the 51 accessions, the number varying from 13 to 71 per accession [1]. The average diversity indices of REMAP markers were higher than those of SSR markers (Table 3) leading to a six-fold higher MI for REMAPs.
Table 3

Comparison of diversity measures of REMAP and SSR markers in the analysis of 51 timothy accessions

 

REMAP

SSR

No. of primer combinations or loci

4

13

Total no. of markers

533

464

No. of markers per primer combination or locus = EMF1

133.3

35.7

PIC, average

0.131

0.086

Markers with PIC > 0.1

258 = 48 %

148 = 32 %

Markers with MAF < 0.1

365 = 68 %

371 = 80 %

Average gene diversity

0.152

0.098

Marker index (MI) = EMF x PIC

17.4

3.1

1effective multiplex ratio

Genetic diversity within accessions

The observed number of REMAP markers per accession varied from 195 (PL204480) to 352 (NGB1672) (Table 4), and the number of SSR alleles from 95 (NGB10785) to 194 (NGB1111). There was only one private REMAP marker (in accession PL325461), but 43 private SSR alleles were found [1]. Diversity indices of accessions studied with REMAP or SSR markers, respectively, varied as follows: AI from 47.5 (PL204480) to 84.8 (NGB1672) and from 28.4 (NGB10831) to 35.2 (NGB7592); PWD from 53.1 (PL204480) to 100.2 (NGB1672) and from 28.9 (NGB10785) to 44.9 (NGB7592); I from 0.159 (RCAT040682) to 0.280 (NGB1672), with mean of 0.203 ± 0.029, and from 0.109 (NGB722) to 0.156 (NGB1111), with mean of 0.138 ± 0.014; the percentage of polymorphic loci from 35.8 % (PL204480) to 64.9 % (NGB1672), with a mean of 49.0 ± 7.3 %, and from 19.8 % (NGB10785) to 41.4 % (NGB1095), with a mean of 34.4 ± 5.1 % (Table 4). The AI values based on SSRs changed slightly from the previous results [1] due to exclusion of 20 individuals (see Methods).
Table 4

REMAP and SSR diversity in 51 timothy accessions

  

REMAP (total no. of markers = 533)

  

SSR (total no. of markers1 = 464)

  

Accession

No. of ind.

No. markers

AA 2

AI 3

PWD4

I 5

% polymorphic loci

No. markers

AA 2

AI 3

PWD4

I 5

% polymorphic loci

NGB10830

19

213

200.5

55.8

61.0

0.170

38.6

124

116.7

28.8

30.7

0.114

26.7

NGB10831

18

221

207.3

50.1

56.2

0.161

40.5

148

137.9

28.4

32.5

0.126

31.3

NGB15461

19

323

302.2

73.1

85.7

0.244

59.3

188

171.5

33.3

44.0

0.155

40.5

NGB16650

18

281

264.9

60.6

71.8

0.206

52.0

142

132.9

31.3

36.2

0.127

30.6

NGB1672

19

352

328.7

84.8

100.2

0.280

64.9

162

149.7

31.7

40.1

0.137

34.7

NGB1675

20

297

277.8

78.4

87.2

0.244

54.8

119

110.1

30.0

32.5

0.111

25.6

NGB4053

18

239

226.9

58.8

65.3

0.185

44.7

145

135.2

30.7

36.2

0.135

31.3

NGB4548

19

236

219.2

54.8

61.3

0.177

43.3

154

139.4

30.1

35.1

0.137

33.2

NGB132

19

313

293.0

75.6

84.7

0.242

57.2

186

167.5

31.1

39.1

0.144

39.7

NGB14394

19

276

255.6

59.3

71.2

0.204

50.8

175

162.2

32.7

42.6

0.148

37.7

NGB14404

20

295

266.0

65.7

74.5

0.214

54.6

176

158.4

33.6

40.5

0.144

37.7

NGB14417

18

213

201.4

60.7

59.4

0.166

38.8

121

115.2

28.4

32.5

0.115

25.9

NGB747

20

310

285.9

75.2

81.5

0.235

56.8

144

131.4

30.3

34.7

0.126

30.8

NGB1095

20

330

300.6

72.9

85.0

0.245

61.2

192

170.2

34.2

43.1

0.153

41.4

NGB1111

19

308

289.2

64.3

78.1

0.229

56.7

194

172.7

33.8

44.3

0.156

41.2

NGB1119

19

226

210.1

52.8

58.6

0.170

41.5

189

170.9

31.9

40.5

0.149

40.7

NGB2791

19

315

294.5

67.7

80.7

0.238

58.2

186

169.8

33.3

42.5

0.152

40.1

NGB4066

19

259

239.8

53.6

65.4

0.188

47.5

180

164.5

32.7

38.6

0.149

38.4

NGB4140

18

263

248.3

55.0

70.5

0.201

49.0

193

176.2

33.4

43.9

0.160

40.7

NGB4141

19

260

246.6

66.5

71.6

0.206

48.0

117

111.2

31.3

34.6

0.118

25.0

NGB7592

16

244

239.2

64.5

68.6

0.192

44.7

182

168.4

35.2

44.9

0.155

37.1

NGB10785

18

202

192.0

67.0

59.9

0.165

36.6

95

86.8

32.7

28.9

0.092

19.8

NGB17194

20

286

260.7

62.1

72.7

0.213

52.7

166

149.5

31.4

37.2

0.134

35.8

NGB17198

18

336

313.7

70.1

85.7

0.245

61.9

179

166.4

32.4

42.5

0.150

38.6

NGB2169

19

290

269.0

62.9

76.5

0.219

53.8

167

153.4

30.3

38.8

0.140

35.8

NGB2180

18

304

284.6

67.7

77.4

0.226

56.1

185

169.2

31.2

39.5

0.146

39.7

NGB2918

20

332

305.3

73.4

88.2

0.252

61.7

164

150.2

32.1

39.0

0.140

35.3

NGB4226

17

236

228.4

64.4

67.9

0.191

43.3

158

151.6

34.9

42.2

0.142

34.1

NGB4231

19

230

216.4

56.3

63.0

0.183

42.2

156

143.6

31.3

38.4

0.137

33.6

NGB7548

17

272

260.1

61.4

70.0

0.201

49.7

150

140.8

29.8

35.4

0.131

31.9

NGB722

19

256

241.9

77.4

71.7

0.202

45.8

115

108.8

29.5

32.7

0.109

24.4

NGB728

18

265

246.3

56.7

65.5

0.196

49.5

182

171.2

33.9

43.4

0.155

39.2

NGB11428

16

231

226.2

58.9

64.4

0.180

42.0

139

138

34.2

39.8

0.135

29.7

NGB14224

19

293

273.6

65.7

79.2

0.224

54.0

175

158.4

33.4

42.2

0.146

36.6

NGB731

20

311

288.5

79.8

84.6

0.241

57.0

182

163.2

31.9

41.1

0.150

39.2

NGB16975

19

258

237.0

56.2

66.4

0.189

47.5

176

161.8

33.5

42.2

0.150

37.7

NGB16981

15

245

245.0

59.2

69.6

0.193

44.7

153

149.8

33.6

41.7

0.138

32.1

NGB1306

18

298

280.5

64.0

76.0

0.224

55.0

184

173.6

32.9

41.4

0.152

39.7

NGB1327

19

284

267.1

70.6

79.8

0.225

52.2

162

151.4

33.6

41.2

0.144

34.9

NGB1331

19

223

208.5

57.1

60.7

0.172

40.3

170

158.4

32.8

42.1

0.144

36.4

NGB1537

16

296

290.1

68.2

82.4

0.231

54.6

139

131.7

30.5

36.1

0.125

28.9

NGB2530

19

235

218.0

57.9

61.8

0.177

42.8

165

152.8

31.9

39.6

0.141

35.6

NGB4349

20

267

244.0

62.7

70.7

0.203

49.3

171

153.4

32.4

40.6

0.141

36.9

PL381926

19

240

222.2

67.4

66.5

0.187

43.9

131

122.3

31.1

34.6

0.125

28.0

PL406317

19

243

226.8

56.5

64.1

0.183

44.5

165

151.7

31.8

38.5

0.145

35.6

IHAR151908

17

259

249.0

60.3

66.4

0.192

48.0

150

137.4

31.1

34.8

0.126

30.6

PL210426

17

245

237.0

65.5

69.4

0.194

44.8

146

138.9

31.8

38.3

0.131

30.6

PL325461

17

233

223.4

55.5

61.2

0.175

43.2

170

157.8

30.8

39.8

0.138

34.3

PL204480

19

195

182.4

47.5

53.1

0.151

35.8

158

144.3

31.6

37.6

0.133

33.6

14G2400116

19

234

220.2

57.1

65.8

0.185

42.8

186

170.5

34.2

44.0

0.153

39.9

RCAT040682

20

209

191.5

50.8

55.9

0.159

38.6

157

143.5

30.5

39.1

0.141

33.8

1each SSR allele treated as a separate marker

2corrected number of all markers in each accession

3mean number of all markers observed in each individual

4mean number of pairwise differences (Euclidean distances) between individuals in each accession

5Shannon’s diversity index

The strength of correlation between diversity indices based on REMAP or SSR markers varied depending on the index. No correlation existed in the level of AI. PWD and I correlated weakly at r = 0.27 (P = 0.059) and r = 0.25 (P = 0.073), respectively. The number of markers per accession (AA) correlated moderately at r = 0.37 (P = 0.0075) and the percentage of polymorphic loci strongly with r = 0.44 (P = 0.0012). Nei’s genetic distances between accessions based on REMAP and SSR data correlated strongly (r = 0.67, P < 0.001) with each other.

When studying levels of diversity between countries, vegetation zones, or different cultivar types, we found no significant differences in AA and PWD based on REMAP markers (Table 5). On the other hand, statistically significant (P < 0.05) differences in AA and PWD between different vegetation zones and in AA between different cultivar types were found with SSR markers (Table 5). In the previous study with 96 accessions analyzed with SSR markers, we found significant differences (P < 0.05) in levels of diversity in all groups [1]. When the total number of markers was studied on an individual rather than accession level (AI), significant differences for each grouping and with both marker types were discovered (Table 5). However, these differences explained only a minor fraction of variation between individuals (1 to 5 %).
Table 5

ANOVA tables indicating F-values, significance levels P, and R2 for comparisons between different groups for their levels of REMAP and SSR diversity

REMAP

 

Total no. of markers (AA)

No. of pairwise differences (PWD)

Number of markers per individual

 Diversity index

df

F

P

R2

F

P

R2

F

P

R2

 Grouping

          

  Accession

50

      

6.11

<0.001

0.25

  Country

5

1.73

0.147

0.16

1.49

0.210

0.14

5.08

<0.001

0.03

  Vegetation zone

5

0.74

0.602

0.11

0.74

0.600

0.11

5.08

<0.001

0.04

  Cultivar type

2

2.03

0.144

0.09

1.96

0.153

0.09

3.98

0.019

0.01

SSR

 

Total no. of markers (AA)

No. of pairwise differences (PWD)

Number of markers per individual

 Diversity index

df

F

P

R2

F

P

R2

F

P

R2

 Grouping

          

  Accession

50

      

3.18

<0.001

0.15

  Country

5

1.15

0.348

0.11

1.53

0.200

0.15

5.28

<0.001

0.03

  Vegetation zone

5

3.90

0.008

0.40

3.49

0.014

0.38

6.67

<0.001

0.05

  Cultivar type

2

4.70

0.015

0.19

1.71

0.194

0.08

4.98

0.007

0.01

Genetic divergence between accessions and groups

AMOVA was performed in order to divide the total genetic variation into three components: variation within accessions, among accessions, and among countries. Most of the variation in the studied material was found within accessions: 89 % when based on REMAP markers, 93 % when based on SSR markers, and 91 % when based on both marker types (Table 6).
Table 6

Analysis of molecular variance in 51 timothy accessions based on 533 REMAP markers, 464 SSRs or both

  

REMAP

SSR

REMAP and SSR

Source

df

SS

MS

Variance components

% total

SS

MS

Variance components

% total

SS

MS

Variance components

% total

Among countries

5

896.84

179.37

0.42

1

574.09

114.82

0.21

1

1471.68

294.34

0.63

1

Among accessions/countries

45

5181.97

115.15

4.21

10

3719.53

82.66

2.45

6

8901.50

197.81

6.66

8

Within accessions

894

33312.58

37.26

37.26

89

33271.32

37.22

37.22

93

66583.90

74.48

74.48

91

Total

944

39391.39

 

41.89

100

37564.94

 

39.88

100

76957.08

 

81.77

100

Stat

Value

    

Value

   

Value

   

 PhiRT

0.010

    

0.005

   

0.008

   

 PhiPR

0.101

    

0.062

   

0.082

   

 PhiPT

0.110

    

0.067

   

0.089

   

Probability, P(rand ≥ data), for PhiRT, PhiPR and PhiPT = 0.001, and is based on permutation across the full data set

PhiRT = AC / (WA + AA + AC) = AC / TOT

PhiPR = AA / (WA + AA)

PhiPT = (AA + AC) / (WA + AA + AC) = (AA + AC) / TOT

Key: AC = est. var. among countries, AA = est. var. among accessions, WA = est. var. within accessions

No genetic divergence was observed between vegetation zones or cultivar types either using SSR or REMAP markers or both (AMOVA, P < 0.05), which might be due to the small numbers of members in different classes. However, the same result was obtained with SSR markers when 96 accessions were studied [1]. In PCA analysis as well, no clustering of accessions based on countries, vegetation zones, or cultivar types was seen (Fig. 1). The first two axes respectively explained 44.1 %, 45.8 %, or 41.1 % of the variation when REMAPs, SSRs, or both marker types were used in the analysis.

Discussion

Previously, SSR markers revealed timothy to be very diverse both on the individual and accession level when studied in a collection of 96 accessions. Because it was impossible with SSRs to resolve any population or geographical structure [1], we here have applied a very different kind of neutral marker, REMAPs, which are based on displaying retrotransposon insertions.

Both REMAPs and SSRs were highly polymorphic. Variation was observed mostly within accessions but with slightly smaller proportion for REMAPs (89 % vs. 93 %). This difference may be due to the biology of how SSR and retrotransposon polymorphisms are generated. SSRs are generated by replication slippage [7], a process expected to be independent of the environment. In contrast, retrotransposons are known to be activated by both biotic and abiotic stresses [6], conditions which may well be greater in some populations compared with others. Population-level stress would thereby lower the proportion of polymorphism on the individual level and increase it on population or geographic levels.

Diversity indices in accessions were lower for SSR than for REMAP markers. This is likely because SSR markers (i.e., alleles) are not independent of each other; there is a theoretical maximum number of markers that can exist in one individual. If all SSR loci would amplify from all three genomes of Phleum, the maximum number of markers would be 78 (13 loci, 6 alleles in each). However, there is evidence that timothy is an allopolyploid [25]. Allopolyploidy is consistent with our earlier results [1], with some SSR loci found only in one genome whereas others were present in all three. Therefore, the real maximum number of SSR alleles in any one individual lies somewhere between 26 and 78. In the present study, the observed maximum was 45.

Polyploids represent about 50 % of flowering plants [26]. In polyploids, the problem of lack of independence between SSR loci is particularly a problem, but given a very high number of loci developed from the genome sequences of major crops such as cotton or wheat, chromosome-specific markers can be recovered [27]. For agricultural species without reference genomes such as timothy or for many wild species [28], selection of markers with diploid inheritance can reduce the usable loci to very low numbers.

In contrast to SSRs, no limit exists for the maximum amount of REMAPs in one individual because retrotransposon insertions are independent of each other. Moreover, different retrotransposon families, such as in the hexaploid wheat genomes [29], show different evolutionary histories, enabling discrimination between homeologues. Retrotransposon markers have been deployed effectively for even the highly polyploid sugarcane [30]. Although codominant REMAPs also exist, codominance does not restrict the possibility of co-existence of markers in one individual. The maximum amount for REMAPs observed in one individual in the present study was 121. Correlations between diversity indices based on REMAP or SSR markers were mostly low or moderate because the two marker systems report from different genomic regions where polymorphisms are generated by different processes. On the other hand, even though SSRs could be treated as codominant markers, it has been suggested that large similarities between diversity indices with dominant markers but somewhat lower between dominant markers and SSRs are due to insufficient numbers of analyzed SSR loci [31].

When using markers for measuring distances, PWD between individuals correlated weakly (r = 0.26) but genetic distances between accessions strongly (r = 0.67) between the two marker types. PWD is based on the Euclidean distances between individuals whereas distances between accessions are based on marker frequencies. The same sort of result – poor or nonexistent individual-by-individual correlations but moderate correlation between accessions – was obtained when amplified fragment length polymorphisms (AFLPs), which are comparable to REMAPs by being a multilocus and dominant marker type, and SSRs were compared [32]. In potato, a low correlation of SSR and REMAP markers (r = 0.17) in the Mantel’s matrix correspondence test was found [8].

Comparing the two marker types, REMAP markers were more cost-efficient. The PCRs of four REMAP primer combinations were made separately, and products from two different combinations with different fluorescent labels were combined for MegaBACE runs. As a consequence, for the whole diversity analysis study (945 samples), 40 PCRs on 96-well microtitre plates were made and analyzed in 20 Megabace runs. A total of 533 polymorphic markers was produced. On the other hand, the 13 SSR loci were multiplexed into 5 PCR reactions and analyzed in 5 MegaBACE runs, requiring in total 50 PCR plates and 50 MegaBACE runs. In addition, some planning and optimization was required in order to multiplex the PCR reactions for the various SSR loci. A total of 464 SSR markers (i.e., alleles) was amplified. Accordingly, more REMAP markers (i.e., loci) were produced with less labor, money, and time. The MI was over six-fold higher with REMAPs, which is not due only to the need to interpret SSR alleles as separate markers but is also typical for markers with an effective multiplex ratio, and has been detected also when AFLPs have been compared with SSRs [16].

Knowledge of genetic variation and relationships between individuals and accessions is essential when conserving and using genetic resources. Evaluation of genetic diversity requires analysis of multiple markers as efficiently as possible. Choosing a suitable marker type, several aspects have to be taken into account, not only expected heterozygosity and marker index, but also technical difficulty, ease of genotyping, cost, and availability. Technically, there were no differences between REMAPs and SSRs and we encountered analysis difficulties with both marker types. All SSR peaks contained some degree of stutter, which complicated the identification of alleles. On the other hand, interpretation of REMAP markers was very slow because several markers were amplified in one PCR reaction and there was a wide variation in peak heights. All peaks could not be analyzed and selections had to be made according to peak heights and frequency. Difficulties in scoring hindered the use of automated analysis programs for both marker types. Regarding availability, there are universal retrotransposon primers that can be used in any species, and primers specific for Graminae also have a vast range of use. Moreover, SSRs have not been developed for every species, and transform rates from one species to another depends on the genetic distance of the taxa [33]. These general conclusions regarding the utility of SSR and retrotransposon markers alone and in combination are consistent with those for four diverse dicot species, distant from the monocot timothy [810].

Conclusions

When diversity in a polyploid species is examined, where the codominant nature of SSRs is of no use, dominant REMAP markers, as analyzed by size on a sequencer, were more cost-efficient. REMAPs also described diversity from a larger segment of the genome compared to the same number of SSR alleles. On the other hand, SSRs detected differences in the level of diversity in different groups better than REMAPs. Furthermore, private SSR alleles were found, making SSRs better for accession identification. Private alleles, however, can be developed from retrotransposon markers using the RBIP (retrotransposon-based insertion polymorphism) and ISBP (insertion site-based polymorphism) methods, which are locus-specific [34]. Genetic distances between accessions were similar with REMAP or SSR markers, but neither marker type could reveal any clear divergence between vegetation zones, cultivar types or countries in the polyploid, very polymorphic and heterozygous timothy species. SSR and REMAP polymorphisms derive from very different mechanisms. Variations in SSR numbers at individual loci derive from polymerase slippage during replication. In contrast, retrotransposon insertions, which can be stress-driven, generate the priming sites for retrotransposon-based marker methods. Given the vastly different numbers of microsatellite and retrotransposon loci queried by the marker systems used, which report from very different genomic regions, the fact that they together show a lack of structure likely reflects the outcrossing and hexaploid nature of timothy rather than failures of either marker system. Both retrotransposons and SSRs, however, are neutral markers; patterns of variation in the gene space of timothy, such as through single nucleotide polymorphism (SNP) genotyping, remain to be explored. These would allow the possibility to evaluate allele dosages, thereby increasing the information embodied in each locus. SNP markers have been used in sugarcane, a complex autopolyploid species, to estimate ploidy level and also the dosage of SNPs [35]. The availability of SNP markers has increased with the invention of the genotyping by sequencing strategy (GBS) [36] and a recent study presents its use to evaluate allele frequencies in populations in an outbreeding species, perennial ryegrass [37]. Such techniques could be applied to timothy as well to study the structure of accessions. On the other hand, the importance of using both molecular and phenotypic markers for assessing diversity especially when evaluating adaptive potential has been emphasized in a study where timothy accessions were characterized with SSRs, chloroplast DNA sequences, as well as by morphological and phenological traits [38].

Declarations

Acknowledgements

We want to thank Nordgen, Mika Isolahti and Siri Fjellheim for selecting and providing plant material for the study. We appreciate the excellent technical assistance of Marja-Riitta Arajärvi, Kirsti Mäkelä, Ulla Kojonsaari, Sirpa Moisander, and Anneli Virta. The help of Kristiina Antonius, Marjo Serenius, and Miika Tapio in data analyses is also greatly acknowledged. We are grateful for The Ministry of Agriculture and Forestry in Finland, as well as The Nordic Joint Committee for Agricultural Research for their financial support.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Green Technology, Natural Resources Institute Finland (Luke)
(2)
Internal Expert Services, Natural Resources Institute Finland (Luke)
(3)
Luke/BI Plant Genome Dynamics Laboratory, Institute of Biotechnology, Viikki Biocenter, University of Helsinki
(4)
Boreal Plant Breeding Ltd

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Copyright

© Tanhuanpää et al. 2016

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