Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Av. Centenário, 303, CP 96, Piracicaba, SP, 13400970, Brazil
Escola Superior de Agricultura “Luiz de Queiroz”, Universidade de São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418900, Brazil
EMBRAPA Mandioca Fruticultura, R. Embrapa s/n, Cruz das Almas, BA, 44380000, Brazil
Instituto de Ciências Biológicas, Universidade Federal de Juiz de Fora, Campus Martelos, Juiz de Fora, MG, 36016900, Brazil
Abstract
Background
Banana cultivars are mostly derived from hybridization between wild diploid subspecies of
Results
From the 221 accessions analyzed by flow cytometry, the correct ploidy was confirmed or established for 212 (95.9%), whereas digestion of the ITS region confirmed the genomic constitution of 209 (94.6%). Neighborjoining clustering analysis derived from SSR binary data allowed the detection of two major groups, essentially distinguished by the presence or absence of the B genome, while subgroups were formed according to the genomic composition and commercial classification. The codominant nature of SSR was explored to analyze the structure of the population based on a Bayesian approach, detecting 21 subpopulations. Most of the subpopulations were in agreement with the clustering analysis.
Conclusions
The data generated by flow cytometry, ITS and SSR supported the hypothesis about the occurrence of homeologue recombination between A and B genomes, leading to discrepancies in the number of sets or portions from each parental genome. These phenomenons have been largely disregarded in the evolution of banana, as the “singlestep domestication” hypothesis had long predominated. These findings will have an impact in future breeding approaches. Structure analysis enabled the efficient detection of ancestry of recently developed tetraploid hybrids by breeding programs, and for some triploids. However, for the main commercial subgroups, Structure appeared to be less efficient to detect the ancestry in diploid groups, possibly due to sampling restrictions. The possibility of inferring the membership among accessions to correct the effects of genetic structure opens possibilities for its use in markerassisted selection by association mapping.
Background
Cultivated bananas and plantains (
The large majority of banana cultivars are derived from natural crosses between wild seeded diploid subspecies of
There are a limited number of
The precise determination of the ploidy and genomic composition of the accessions are of great interest to define hybridization programs, as the combination of these two genomes (A and B) defines the agronomical attributes (for e.g., yield; resistance to biotic factors) as well as the fruit flavor and quality of the resulting hybrid plants
To determine ploidy in
Despite the multiallelic and highly informative nature of microsatellite (SSR) loci, the allelic information in
Therefore, the objectives of this study were (i) to characterize the accessions of the
Methods
Plant material
A total of 224 accessions of the
Click here for file
No
Acession
Cytometry
Molecular markers
ITS fragments (bp)
Genome ^{ z }
2C pg
Ploidy
530
350
180
Genome
SSR ^{ v }
++: fragment with strong signal; +: fragment with weak signal.
^{z}Information based on the
^{y}NI: No information.
^{x}M: Mixoploidy; C: cultivated; W: wild; H: hybrid.
^{w} ?: undefined name or ploidy, or unresolved genomic constitution.
^{v}Genome composition based on groups formed based on clustering analysis derived from SSR data (Figure
1
ES
1.23
2x
++
++


2
Piraí
BB
1.22
2x
++
++
BB
BB
3
Butuhan
BB
1.23
2x
++
++
BB
BB
4
BB Panama
BB
1.30
2x
++
++
BB
BB
5
Balbisiana França
BB
1.27
2x
++
++
BB
BB
6
Musa Balbisiana
BB
1.25
2x
++
++
BB
BB
7
TIP
ABB
1.98
3x
++
+
+
AAB
AAB
8
Saba Honduras
ABB
1.93
3x
++
++
++
ABB
ABB
9
Saba
ABB
1.95
3x
++
++
++
ABB
ABB
10
Prata Zulu
ABB
M^{x}
?^{W}
++
+
+
ABB/AAB
ABB
11
Poteau Nain
ABB
M
?
++
+
+
ABB/AAB
ABB
12
Pelipita
ABB
1.89
3x
++
++
++
ABB
ABB
13
Namwa Khom
ABB
1.91
3x
++
++
++
ABB
ABB
14
Namwa Daeng
ABB
1.94
3x
++
++
++
ABB
ABB
15
Muisa Tia
ABB
1.90
3x
++
++
++
ABB
ABB
16
Monthan
ABB
1.93
3x
++
++
++
ABB
ABB
17
Ice Cream
ABB
1.94
3x
++
++
++
ABB
ABB
18
Ice Cream
ABB
1.93
3x
++
++
++
ABB
ABB
19
Gia Hui
ABB
1.90
3x
++
++
++
ABB
ABB
20
Figo Cinza
ABB
1.86
3x
++
++
++
ABB
ABB
21
Espermo
ABB
M
?
++
++
++
ABB
ABB
22
Champa Madras
ABB
1.98
3x
++
++
++
ABB
ABB
23
Cachaco
ABB
1.93
3x
++
++
++
ABB
ABB
24
Cacambou Naine
ABB
1.93
3x
++
++
++
ABB
ABB
25
Bendetta
ABB
1.93
3x
++
++
++
ABB
ABB
26
Abuperak
ABB
1.94
3x
++
++
++
ABB
ABB
27
IAC
AB(H)
1.26
2x
++
+
+
AB
?
28
Yangambi nº 2
AAB
1.93
3x
++
AAA
AAB
29
Warik
AAB
1.94
3x
++
+
+
AAB
AAB
30
Walha
AAB
1.95
3x
++
+
+
AAB
AAB
31
Ustrali
AAB
1.93
3x
++
+
+
AAB
AAB
32
Umpako
AAB
1.89
3x
++
+
+
AAB
AAB
33
Thap Maeo
AAB
1.94
3x
++
+
+
AAB
AAB
34
Trois Vert
AAB
1.90
3x
++
+
+
AAB
AAB
35
Tomnam
AAB
1.94
3x
++
+
+
AAB
AAB
36
Tipo Velhaca
AAB
1.92
3x
++
+
+
AAB
AAB
37
Tip Kham
AAB
1.87
3x
++
+
+
AAB
AAB
38
Thong Ruong
AAB
1.98
3x
++
+
+
AAB
AAB
39
Terrinha
AAB
1.94
3x
++
+
+
AAB
AAB
40
Terra S/ Nome
AAB
1.94
3x
++
+
+
AAB
AAB
41
Tai
ABB
1.99
3x
++
++
++
ABB
ABB
42
Sempre Verde
AAB
1.94
3x
++
+
+
AAB
AAB
43
Saney
AAB
1.94
3x
++
+
+
AAB
AAB
44
Samurá B
AAB
1.89
3x
++
+
+
AAB
AAB
45
Red Yade
AAB
1.95
3x
++
+
+
AAB
AAB
46
Pulut
AAB
1.94
3x
++
+
+
AAB
AAB
47
Pratão
AAB
1.95
3x
++
+
+
AAB
AAB
48
Prata Sta. Maria
AAB
1.94
3x
++
+
+
AAB
AAB
49
Prata P. Aparada
AAB
1.95
3x
++
+
+
AAB
AAB
50
Prata Maceió
AAB
1.93
3x
++
+
+
AAB
AAB
51
Prata Comum
AAB
1.90
3x
++
+
+
AAB
AAB
52
Prata IAC
AAB
1.99
3x
++
+
+
AAB
AAB
53
Prata Anã
AAB
1.94
3x
++
+
+
AAB
AAB
54
Prata Branca
AAB
2.29
4x
++
+
+
AAAB
AAB
55
Poovan
AAB
1.94
3x
++
+
+
AAB
AAB
56
Plantain N. 2
AAB
?
?
++
+
+
AB?
AAB
57
Pinha
AAB
1.94
3x
++
+
+
AAB
AAB
58
Padath
AAB
1.95
3x
++
+
+
AAB
AAB
59
Pacovan
AAB
1.93
3x
++
+
+
AAB
AAB
60
N. 113
AAB
1.89
3x
++
+
+
AAB
AAB
61
Mysore
AAB
1.95
3x
++
+
+
AAB
AAB
62
Muracho
AAB
1.95
3x
++
+
+
AAB
AAB
63
Mongolo
AAB
1.98
3x
++
+
+
AAB
AAB
64
Moenang
AAB
1.99
3x
++
+
+
AAB
AAB
65
Maçã Caule Roxo
AAB
1.90
3x
++
+
+
AAB
AAB
66
Kune
AAB
1.92
3x
++
+
+
AAB
AAB
67
Kingala N.1
AAB
1.95
3x
++
+
+
AAB
AAB
68
Kepok Bung
AAB
1.95
3x
++
+
+
ABB/AAB
ABB/AAB
69
Kelat
AAB
1.87
3x
++
+
+
AAB
AAB
70
Java IAC
AAB
1.99
3x
++
+
+
AAB
AAB
71
Garoto
AAB
1.93
3x
++
+
+
AAB
AAB
72
Figue Rose Naine
AAB
1.93
3x
++
AAA
AAA
73
Eslesno
AAB
1.95
3x
++
+
+
AAB
AAB
74
Curare Enano
AAB
1.99
3x
++
+
+
AAB
AAB
75
Comprida
AAB
1.98
3x
++
+
+
AAB
AAB
76
Chifre De Vaca
AAB
1.92
3x
++
+
+
AAB
AAB
77
Adimoo
AAB
1.93
3x
++
+
+
AAB
AAB
78
AAB S/Nome
AAB
1.95
3x
++
+
+
AAB
AAB
79
BRS Tropical
AAAB
2.50
4x
++
AAAA
AAAB
80
Preciosa
AAAB
1.94
3x
++
+
+
AAB
AAAB
81
Porp
AAAB
2.43
4x
++
AAAB
AAAB
82
Platina
AAAB
2.56
4x
++
+
+
AAAB
AAAB
83
Pacova Ken
AAAB
2.45
4x
++
+
+
AAAB
AAAB
84
BRS Platina
AAAB
2.45
4x
++
+
+
AAAB
AAAB
85
Ouro Da Mata
AAAB
2.46
4x
++
+
+
AAAB
AAAB
86
Ngern
AAAB
2.55
4x
++
+
+
AAAB
AAAB
87
Langka
AAAB
2.48
4x
++
+
+
AAAB
AAAB
88
Garantida
AAAB
2.48
4x
++
+
+
AAAB
AAAB
89
FHIA21
AAAB
2.49
4x
++
+
+
AAAB
AAAB
90
FHIA18
AAAB
2.48
4x
++
+
+
AAAB
AAAB
91
FHIA02
AAAB/AAAA
2.40
4x
++
+
+
AAAB
AAAB
92
FHIA01
AAAB
2.49
4x
++
+
+
AAAB
AAAB
93
IC  2
AAAA
2.49
4x
++
AAAA
AAAA
94
Calypso
AAAA
2.43
4x
++
AAAA
AAAA
95
Buccaneer
AAAA
2.45
4x
++
AAAA
AAAA
96
Ambrosia
AAAA
2.47
4x
++
AAAA
AAAA
97
Yangambi Km 5
AAA
1.92
3x
++
AAA
AAA
98
Wasolay
AAA
1.92
3x
++
AAA
AAA
99
Walebo
AAA
1.98
3x
++
AAA
AAA
100
Valery
AAA
1.98
3x
++
AAA
AAA
101
Umbuk
AAA
1.95
3x
++
AAA
AAA
102
Tugoomomboo
AAA
?
?
++
++
++
ABB
AAB
103
Caipira
AAA
1.98
3x
++
AAA
AAA
104
Towoolee
AAA
1.90
3x
++
AAA
AAA
105
Torp
AAA
1.90
3x
++
AAA
AAA
106
Sri
AAA
1.93
3x
++
AAA
AAA
107
Sapon
AAA
1.93
3x
++
AAA
AAA
108
São Tomé
AAA
1.92
3x
++
AAA
AAA
109
Roombum
AAA
1.92
3x
++
AAA
AAA
110
Poyo
AAA
1.91
3x
++
AAA
AAA
111
Pirua
AAA
1.91
3x
++
AAA
AAA
112
Pagatow
AAA
1.91
3x
++
AAA
AAA
113
Ouro Mel
AAA
1.92
3x
++
AAA
AAA
114
Orotawa
AAA
1.93
3x
++
AAA
AAA
115
Nanicão
AAA
1.94
3x
++
AAA
AAA
116
Nam
AAA
1.94
3x
++
AAA
AAA
117
Muga
AAA
M
?
++
A?
AAA
118
Morong
AAA
1.92
3x
++
AAA
AAA
119
Markatooa
AAA
1.91
3x
++
AAA
AAA
120
Maida
AAA
1.94
3x
++
AAA
AAA
121
Leite
AAA
1.93
3x
++
AAA
AAA
122
Lacatan
AAA
1.90
3x
++
AAA
AAA
123
Azedinha
AAB
2.36
4x
++
+
+
AAAB
AAAB
124
Imperial
AAA
1.93
3x
++
AAA
AAA
125
Highgate
AAA
1.91
3x
++
AAA
AAA
126
Gros Michel
AAA
1.94
3x
++
AAA
AAA
127
Grande Naine
AAA
1.92
3x
++
AAA
AAA
128
Dois Cachos
AAA
1.91
3x
++
AAA
AAA
129
Dodoga
AAA
1.94
3x
++
AAA
AAA
130
Cocos
AAA
1.95
3x
++
AAA
AAA
131
Caru Verde
AAA
1.96
3x
++
AAA
AAA
132
Caru Roxa
AAA
1.95
3x
++
AAA
AAA
133
Canela
AAA
1.94
3x
++
AAA
AAA
134
Bakar
AAA
1.91
3x
++
AAA
AAA
135
Bagul
AAA
1.93
3x
++
AAA
AAA
136
Amritsagar
AAA
1.93
3x
++
AAA
AAA
137
Ambei
AAA
1.94
3x
++
AAA
AAA
138
AAA Desconhecida
AAA
1.93
3x
++
AAA
AAA/AAB
139
Zebrinha
AA(W)
1.23
2x
++
AA
AA
140
Selangor
AA(W)
1.25
2x
++
AA
AA
141
Perak
AA(W)
1.26
2x
++
AA
AA
142
Pa Songkla
AA(W)
1.23
2x
++
AA
AA
143
Pa Rayong
AA(W)
1.26
2x
++
AA
AA
144
Pa Phatthalung
AA(W)
1.23
2x
++
AA
AA
145
Pa Musore 3
AA(W)
1.25
2x
++
AA
AA
146
Pa Musore 2
AA(W)
1.27
2x
++
AA
AA
147
Pa Abissinea
AA(W)
1.28
2x
++
AA
AA
148
N.118
AA(W)
1.29
2x
++
AA
AA
149
Monyet
AA(W)
1.23
2x
++
AA
AA
150
Modok Gier
AA(W)
1.25
2x
++
AA
AA
151
Microcarpa
AA(W)
1.26
2x
++
AA
AA
152
Malaccensis
AA(W)
1.23
2x
++
AA
AA
153
Krasan Saichon
AA(W)
1.22
2x
++
AA
AA
154
Khae
AA(W)
1.28
2x
++
AA
AA
155
Jambi
AA(W)
1.27
2x
++
AA
AA
156
Cici
AA(W)
1.24
2x
++
AA
AA
157
Calcutta 4
AA(W)
1.23
2x
++
AA
AA
158
Burmannica
AA(W)
1.23
2x
++
AA
AA
159
Buintenzorg
AA(W)
1.27
2x
++
AA
AA
160
Birmanie
AA(W)
1.28
2x
++
AA
AA
161
M 61
AA(H)
1.23
2x
++
AA
AA
162
M 53
AA(H)
1.27
2x
++
AA
AA
163
M 48
AA(H)
1.28
2x
++
AA
AA
164
F3P4
AA(H)
1.23
2x
++
AA
AA
165
F2P2
AA(H)
1.25
2x
++
AA
AA
166
Tuugia
AA(C)
1.28
2x
++
AA
AA
167
Tongat
AA(C)
1.23
2x
++
AA
AA
168
Giral
AAB
1.90
3x
++
+
+
AAB
AAB
169
Tjau Lagada
AA(C)
1.23
2x
++
AA
AA
170
Thong Dok Mak
AA(C)
1.24
2x
++
AA
AA
171
TA
AA(C)
1.24
2x
++
AA
AA
172
Sowmuk
AA(C)
1.26
2x
++
AA
AA
173
SA
AA(C)
1.23
2x
++
AA
AA
174
S/N. 2
AA(C)
1.25
2x
++
AA
AA
175
Raja Uter
AA(C)
1.25
2x
++
AA
AA
176
Pipit
AA(C)
1.28
2x
++
AA
AA
177
Ouro
AA(C)
1.23
2x
++
AA
AA
178
Niyarma Yik
AA(C)
1.28
2x
++
AA
AA
179
NBF 9
AA(C)
1.27
2x
++
AA
AA
180
NBA 14
AA(C)
1.23
2x
++
AA
AA
181
Mangana
AA(C)
1.23
2x
++
AA
AA
182
Mambee Thu
AA(C)
1.27
2x
++
AA
AA
183
Malbut
AA(C)
M
?
++
AA?
AA
184
Lidi
AA(C)
1.27
2x
++
AA
AA
185
Khi Maeo
AA(C)
1.27
2x
++
AA
AA
186
Khai Nai On
AA(C)
1.27
2x
++
AA
AA
187
Khai
AA(C)
1.23
2x
++
AA
AA
188
Jari Buaya
AA(C)
1.25
2x
++
AA
AA
189
Jaran
AA(C)
1.26
2x
++
AA
AA
190
Fako Fako
AA(C)
1.28
2x
++
AA
AA
191
Berlin
AA(C)
1.23
2x
++
AA
AA
192
Babi Yadefana
AA(C)
1.26
2x
++
AA
AA
193
Prata Manteiga
AAB
1.92
3x
++
AAB
AAB
194
Borneo
AA (W)
1.25
2x
++
AA
AA
195
Madu
AA
1.28
2x
++
++
+
AB?
AA
196
Prata Maçã
AAAB
2.46
4x
++
+
+
AAAB
AAAB
197
Verde
AAB
1.90
3x
++
+
+
AAB
AAB
198
Prata Anã 2
AAB
1.90
3x
++
+
+
AAB
AAB
199
Prata Anã 3
AAB
1.91
3x
++
+
+
AAB
AAB
200
Pacovan Ken?
AAAB
2.46
4x
++
+
+
AAAB
AAAB
201
Pitogo
ABB
1.24
2x
++
++
++
AB?
ABB
202
Pacha Nadan
AB
1.97
3x
++
+
+
AAB
AAB
203
Njok Kon
AAB
1.94
3x
++
++
++
ABB
ABB
204
Marmelo
^{y}NI
1.25
2x
++
++
++
AB?
ABB
205
Lareina BT100
^{y}NI
1.30
2x
++
AA
AAA/AAAA
206
Pisang Ceylan
AAB
?
?
++
+
+
AAB
AAB
207
Pisang Nangka
AA
1.28
2x
++
AA
AAB
208
Willians
AAA
1.92
3x
++
AAA
AAA
209
PV42114
AAAB
2.28
4x
++
AAAB
AAAB
210
PV0376
AAAB
2.29
4x
++
AAAB
AAAB
211
Khae Prae
AA
1.23
2x
++
AA
AA
212
Pitu
AA
1.23
2x
++
AA
AA
213
Paka IV
AA
1.26
2x
++
AA
AA
214
Ido 110
AA
1.28
2x
++
AA
AA
215
P.Kermain
NI
1.23
2x
++
AA
AA
216
P.Serum
AA
1.24
2x
++
AA
AA
217
Pisang Mas
AA
1.25
2x
++
AA
AA
218
Uw Ati
AA
?
?
++
AA?
AA
219
Diplóide Bélgica
AA
1.24
2x
++
++
BB
BB
220
BB França
BB
1.27
2x
++
++
BB
BB
221
BB IAC
BB
1.28
2x
++
++
BB
BB
222
1.28
2x
++


223
Tambi
AAA
1.92
3x
++
AAA
AAA
224
1.27
2x
++
++


Minimal CV (%)
1.23
Maximum CV (%)
4.56
Mean CV (%)
3.31
Flow cytometry analyses
To determine the ploidy, approximately 20 to 30 mg of fresh young healthy leaf tissue from each sample, in addition to the same amount of internal standard
Amplification of the internal transcribed spacers (ITS) for PCRRFLP
The ITS15.8SITS2 regions of the nuclear ribosomal gene were amplified using the primers
To discriminate mixtures of genomes at various dosages, the profiles of fragments and band intensities were initially established by sequential mixtures of DNA samples from the
Restriction profiles of the amplified ITS regions (negative picture)
Restriction profiles of the amplified ITS regions (negative picture). Assays to verify competition between doses of the A and/or B genomes for amplification and digestion of a rDNA region in
Analyses of SSR loci
A total of 21 SSR loci were tested (Additional file
Statistical analysis of the SSR data
For all accessions (2
Two approaches were adopted to investigate the genetic structure and diversity among the accessions. In the first case, polymorphisms were treated as binary data (presence or absence). The binary data were then used to obtain a dissimilarity matrix using the Jaccard index employing the software Genes
The origin of the modern banana cultivars involved intra and interspecific hybridizations, and the mixture model and allelic frequency correlated was adopted. A burnin of 150,000, followed by 70,000 Monte Carlo Markov Chain, was used for each k, varying from 2 to 30, with ten runs for each k. The choice of the likely number of populations was performed based on the highest log value of the likelihood (LnP(K)) and using the method developed by Evanno et al.
Results
Ploidy determination by flow cytometry
Leaf samples from each accession were analyzed by flow cytometry to determine ploidy, and the 2C values were estimated in pg (Table
From the 224 accessions evaluated, 221 were from section
Curiously, accessions 201 (‘Pitogo’) and 204 (‘Marmelo’), classified as diploid by flow cytometry, presented a typical ABB profile by ITS PCRRFLP (compare lanes 7 and 8, top panel Figure
Restriction profile of the amplified ITS regions from
Restriction profile of the amplified ITS regions from
Phenogram demonstrating the genetic relationships among 224 accessions from the
Phenogram demonstrating the genetic relationships among 224 accessions from the
Diversity structure of the 224
Diversity structure of the 224
Characterization of the genomic constitution based on ITSPCRRFLP
To evaluate whether the method proposed by Nwakanma et al.
Amplification of the ITS regions produced a fragment of ~ 700 bp from all 224 accessions and disclosed the expected fragments that characterized the presence of genome A and/or B after digestion with
For the
SSR and genetic diversity analyses
Of the 21 loci tested, only five (
Overall, regardless of ploidy, there was a predominance of accessions with two alleles (35.2 to 55.8%), followed by those with one (14.1% to 60.7%); three (3.5 to 32.8%); and only a small fraction with four alleles (0.3 to 15.6%) (Table
No.alleles
Genomic group ^{ Z }
BB
AA (W)
AA (C)
AAA
AAB
ABB
AAAB
AAAA
(%)
^{Z}W: Wild accessions; C: Cultivated; n: number of samples evaluated; and n_{m}: mean number of samples for the evaluated loci.
1
60.7
41.3
39.7
22.7
31.7
41.9
21.8
14.1
2
35.2
54.3
55.8
52.6
48.7
39.4
47.0
37.5
3
4.1
3.5
4.2
24.3
18.3
18.4
28.2
32.8
4
0.0
0.9
0.3
0.5
1.3
0.3
3.0
15.6
n
8
23
26
43
56
21
16
4
n _{ m }
7.6
21.6
24.1
42.8
51.0
20
14.6
4
The relationship among the 20 most frequent alleles in the cultivated AA and BB accessions was investigated in relation to the other genomic and ploidy groups. In general, the most frequent alleles in cultivated AA tended to increase in frequency according to the dose of the A genome (
Frequency distribution of the 20 most frequent alleles in cultivated diploid accessions AA(C) (panel A) and BB (panel B) in comparison with other genomic groups
Frequency distribution of the 20 most frequent alleles in cultivated diploid accessions AA(C) (panel A) and BB (panel B) in comparison with other genomic groups. W: wild; C: cultivated. The errors bars refer to the ratio of accessions that did not amplify one or more analyzed loci.
Cultivated diploids displayed higher mean heterozygosity (62.4%) than the wild diploids (overall average 56.4%). The lowest mean heterozygosity (37.4%) was detected among the
Clustering analyses of the collection
Clustering analysis based on Neighborjoining essentially allowed the detection of two major clusters (Figure
Population structure analysis
The codominant nature of the SSR markers was exploited to analyze the structure of the populations using a Bayesian approach. The number of subpopulations (k) tested ranged between 2 and 30 (Figure
Left panel: Selection of the most likely number of subpopulations (k) for the evaluated accessions
Left panel: Selection of the most likely number of subpopulations (k) for the evaluated accessions. A. Mean values of LnP(K) for 10 independent runs for each k. B. Plot of Δk values for each k based on the second order change of the likelihood function. Right panel C. Graph for ancestralities for k = 20 (C1), k = 21 (C2), and k = 22 (C3). Group colors are function of colors observed for k = 21.
The two alternative matrices tested (Analysis I and II) presented little differences for genotype allocation and membership values (
From the 21 groups formed by Structure (Figure
The membership value (
Percent of accessions within intervals of membership (
Percent of accessions within intervals of membership (
Essentially, the triploid/tetraploid groups generated by Structure were identical to the clusters revealed by clustering analysis for the major banana subgroups, such as ‘Pisang awak’ (group III; Figure
Regarding the diploid accessions analyzed by Structure, all eight
Diploid accessions were highly heterogeneous (mixture), and their ancestry remained restricted to other group of diploids, except for accessions 161, 162, 183 and 195, which exhibited ancestry with group XXI of AAA triploids, and BB ‘IAC’ (221) with ancestry to group III of the subgroup ‘Pisang awak’ (ABB) (Figure
Discussion
Characterization of ploidy and genomic constitution
Flow cytometry was used to define the genome size (2C content) and the ploidy level of 224 accessions. From the 221 section
Determination of genomic constitution by molecular markers has long been sought, with attempts to use RAPD
Noteworthy, our study revealed that a few accessions presented unexpected behavior, such as ‘Yangambi no.2’ (28) and ‘BRS Tropical’ (79), recognized as AAB and AAAB, respectively, but they exhibited typical AAA and AAAA digestion profiles. These changes in the restriction profiles for ‘Yangambi nº 2’ and ‘BRS Tropical’ (a tetraploid hybrid from ‘Yangambi nº 2’) might have derived from a variant of the B genome rDNAlocus. Other unusual alleles were identified. For example, ‘Tugoomomboo’ (102), considered as AAA, exhibited an ABB PCRRFLP profile, but it was classified as AAB by clustering analysis, suggesting the occurrence of the B genome allele for the ITS regions in one of the A genomes. The diploid AA ‘Madu’ (195) was indicated to be AB, with a slight change in the restriction fragment size for the B genome. This alteration in size was derived from a change in the
Incomplete concerted evolution of ITS sequences observed in
Therefore, despite fact that small differences in genome size between
Genetic diversity and clustering analysis
Sixteen SSR loci were used, revealing 182 alleles, with an average of 11.5, while Christelová et al.
In our study, it was verified a high proportion (more than 75%) of accessions producing one and two alleles among triploids. Banana triploid cultivars supposedly originated from crosses between nonreduced 2
Despite the fact that there was a trend of the participation of AA(C) in some accessions, only 34% (ABB); 39% (AAB); 57% (AAA); 42% (AAAB); and 70% (AAAA) of the accessions contained such alleles. This fact reinforces the previous observation from PCRRFLP, that the origin of cultivated bananas might have involved recombination events (inter and intraspecific) and backcrosses between species as well as human intervention. Therefore, a cultivar cannot carry the whole allelic complement from a specific genome A or B
The analysis performed by converting SSR genotyping into binary data and using it to estimate dissimilarities among genotypes revealed a broad genetic variability among
In addition to the contribution regarding the identification of duplicated accessions, definition of the ploidy level and genomic constitution of the accessions, the cluster analysis based on SSR also enabled us to infer to which subgroup the natural triploid accessions belong, according to their allocation in the phenogram. This is a key aspect because it enabled us to separate accessions with similar agronomic attributes. This information can be used by breeding programs to develop hybrids, which requires certain agronomic or qualitative requisites of the subgroups. However, two clusters (identified as ‘unknown’; Figure
Population structure and genetic relationships of accessions
To our knowledge, this is the first work to explore the codominant nature of the SSR markers in
Here, we suggested the separation of 224 accessions into 21 subpopulations (groups) based on the method proposed by Evanno et al.
There are emerging evidences that the process of evolution of cultivated bananas might have not derived simply by hybridization followed by selection and clonal propagation (“singlestep domestication”), but, on occasions, episodes of meiosis, recombination and fertilization might have eventually occurred
The subgroup ‘Pome’ (AAB; group XX; Figure
‘Cavendish’ and ‘Gros Michel’ were separated into two close subgroups in the cluster analysis (Figure
Noteworthy, some AAB and AAA triploid accessions demonstrated ancestry to other groups, containing other accessions with similar genomic constitution. It is known that some hybrids showed various degree of residual fertility and it is possible that their evolution involved episodes of sexual reproduction, as suggested by the backcross hypothesis
Our results indicated that Structure was efficient in the detection of ancestry of recently developed tetraploid hybrids by breeding programs in Brazil (‘Pome’) and Jamaica (‘Gros Michel’) with a defined genealogy, and for some triploid cultivars. However, this approach appeared to be less efficient to detect the ancestry of most of the primeval triploid accessions, which make up the main commercial subgroups (‘Pisang awak’; ‘Gros Michel’; ‘Cavendish’; ‘Pome’; ‘Plantain’). This absence of detection of ancestry might be explained by a series of hypotheses.
One possibility is that potential parental diploids for the main commercial subgroups were underrepresented in the collection, such as demonstrated by the absence of ancestry in diploids groups for some recent tetraploid hybrids developed by FHIA evaluated in this study (Figure
The relationship between diploids and AAB could have been affected by the potential occurrence of recombinations between homeologue chromosomes with distinct structural organization, contributiong to large genetic changes in allopolyploids
Concerning diploids, the groups formed by clustering analysis presented distinct behavior as to the one observed for the triploid and tetraploid accessions. In the Structure approach, the groups were defined based on the likelihood probability using allelic frequencies that characterize each population
Despite the limited number of accessions for each subspecies, inferences from previous studies were supported. For instance, the grouping of five ssp.
In general, there was a diversified behavior of diploids with accessions of the same subspecies in different groups and/or with different subspecies, as verified for groups XI and XVIII (Figure
Conclusions
The
DNA content was believed to be a good predictor of genomic constitution in
Structure analysis enabled the efficient detection of ancestry of recently developed tetraploid hybrids by breeding programs, and for some triploid cultivars. However, for the main commercial subgroups, Structure appeared to be less efficient to detect the ancestry in diploid groups, possibly either due to diploid underrepresentation in the collection; limited number of analyzed loci evaluated; or allelic changes during evolution of the subgroups, especially the allopolyploids.
Establishing ancestry and genetic relationships by Structure allowed the identification of diploids from group IX and ‘Lareina BT100’ as potentially related to parentals of the sterile ‘Cavendish’ and ‘Gros Michel’ accessions, which could be used in crossing programs or chromosome manipulations (doubling) to obtain/resynthesize ‘Gros Michel’/‘Cavendish’ hybrids. The possibility of inferring the membership of the accessions using Bayesian analysis opens possibilities for its use in markerassisted selection by association mapping by incorporating the effects of the structure (matrix of the membership;
With the completion of the
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
ONJ, SOS and AF conceived the study, which was the Doctoral project of ONJ. SOS, EPA and CFF maintained and provided material from the
Acknowledgments
This work was funded by FAPESP (2008/034700) and CNPq. Technical assistance by Luis Eduardo Fonseca was greatly appreciated. The authors (ONJ, SSO, EP, AF) are grateful for the fellowships provided by CNPq and GGS to FAPESP (2010/013980).