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1480 V.M. Pereira et al.
Genetic diversity between improved banana diploids
using canonical variables and the Ward‑MLM method
Valquíria Martins Pereira (1)^ , Cristine Vanz Borges (2)^ , Lívia Pinto Brandão (2)^ , Larissa Santos Oliveira (1)^ ,
Cintia Paula Feitosa Souza (2)^ , Zalmar Santana Gonçalves (2)^ , Sebastião de Oliveira e Silva (2)^ ,
Janay Almeida dos Santos-Serejo (3)^ , Claudia Fortes Ferreira (3)^ , Edson Perito Amorim (3)
and Carlos Alberto da Silva Ledo (3)
(1) (^) Universidade Federal de Lavras, Departamento de Agricultura, Caixa Postal 3037, CEP 37200 - 000 Lavras, MG, Brazil. E-mail: [email protected], [email protected] (2)^ Universidade do Recôncavo da Bahia, CEP 44380 - 000 Cruz das Almas, BA, Brazil. E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] (3)^ Embrapa Mandioca e Fruticultura, Rua Embrapa, s/n o^ , CEP 44380 - 000 Cruz das Almas, BA, Brazil. E-mail: [email protected], [email protected], [email protected], [email protected]
Abstract – The objective of this work was to estimate the genetic diversity of improved banana diploids using
quantitative and simple sequence repeats (SSR) marker data simultaneously. The experiment was carried out
with 33 diploids, in an augmented block design with 30 regular treatments and three common ones. Eighteen
agronomic characteristics and 20 SSR primers were used. The agronomic characteristics and the SSR were analyzed
simultaneously by the Ward-MLM, cluster, and IML procedures. The Ward clustering method considered the
combined matrix obtained by the Gower algorithm. The Ward‑MLM procedure identified three ideal groups (G1,
G2, and G3) based on pseudo-F and pseudo-t^2 statistics. The dendrogram showed relative similarity between the
G1 genotypes, justified by genealogy. In G2, ‘Calcutta 4’ appears in 62% of the genealogies. Similar behavior was
observed in G3, in which the 028003‑01 diploid is the male parent of the 086079‑10 and 042079‑06 genotypes. The
method which used canonical variables had greater discriminatory power than Ward-MLM. Although reduced,
the genetic variability available is sufficient to be used in the development of new hybrids.
Index terms: Musa , hybrids, microsatellites, morphological characteristics.
Diversidade genética entre diploides melhorados de bananeira
por meio de variáveis canônicas e do método Ward‑MLM
Resumo – O objetivo deste trabalho foi estimar a diversidade genética entre diploides melhorados de bananeira
por meio de dados quantitativos e de marcadores de sequências simples repetidas (SSR), simultaneamente.
O experimento foi conduzido com 33 diploides, em blocos aumentados com 30 tratamentos regulares e
três comuns. Dezoito características agronômicas e 20 iniciadores SSR foram usados. Os dados agronômicos
e de SSR foram analisados, simultaneamente, via os procedimentos Ward-MLM, de agrupamento e IML.
O método de agrupamento de Ward considerou matriz híbrida obtida pelo algoritmo de Gower. O procedimento
Ward‑MLM identificou três grupos (G1, G2 e G3) baseados nas estatísticas de pseudo‑F and pseudo‑t^2.
O dendrograma mostrou similaridade relativa entre os genótipos do G1, justificada pela genealogia. No G2,
‘Calcutta 4’ aparece em 62% das genealogias. Comportamento similar foi observado no grupo G3, em que
o diploide 028003‑01 é o parental masculino dos genótipos 086079‑10 e 042079‑06. O método com uso de
variáveis canônicas teve maior poder discriminatório que o Ward-MLM. Embora reduzida, a variabilidade
genética disponível é suficiente para ser usada no desenvolvimento de novos híbridos.
Termos para indexação: Musa , híbridos, microssatélites, características morfológicas.
Introduction
Bananas are derived from the wild species Musa
acuminata Colla (AA) and Musa balbisiana Colla (BB).
The fruit is produced by smallholders, and millions of
people in developing countries use it as staple food.
According to the Food and Agriculture Organization of
the United Nations (2012), the total area harvested in
2009 was approximately five million hectares, which
produced 95 million tons.
Banana genetic breeding programs use diploids
as the basis for developing new commercial hybrids,
since triploids present sterility as a major constraint
(Amorim et al., 2011). In addition, diploids have traits
of agronomic importance, such as disease resistance
and high fruit yield (Lessa et al., 2010). The agronomic
Genetic diversity between improved banana diploids 1481
characterization of these diploids – including the
estimation of genetic variability using molecular
markers – is important when it comes to choosing
the progenitors for the crosses between divergent
genotypes, aiming to explore heterosis and to develop
new, improved diploids, which can be used in crosses
with triploid and tetraploid genotypes in order to
develop new commercial banana hybrids (Amorim
et al., 2008).
Simple sequence repeats (SSR) or microsatellite
markers have been widely used for characterizing
species, especially due to their co-dominant nature,
repeatability, and easy data interpretation (Creste
et al., 2003). Genetic diversity is usually estimated
considering, separately, quantitative data – such as
plant height and pseudostem girth – and qualitative
data, including anthocyanin content, leaf position, pulp
color, and molecular marker data (Cabral et al., 2010).
Moreover, strategies to rank genotypes considering
combined data have also been proposed using clustering
methods, such as the Ward method and the unweighted
pair group method with arithmetic mean (UPGMA)
(Gonçalves et al., 2009).
The modified location model (MLM) procedure,
proposed by Franco et al. (1998), is another interesting
strategy for quantifying genetic variability using
quantitative and qualitative variables simultaneously.
The MLM has two stages. In the first one, the Ward
clustering method is used to define the groups using
the Gower dissimilarity matrix. In the second one, the
average of the quantitative variable vector is estimated
by the MLM, regardless of the value of the qualitative
variables. This procedure has been used for a variety
of crops, such as common beans (Cabral et al., 2010),
tomatoes (Gonçalves et al., 2009), and bananas
(Pestanana et al., 2011).
The objective of this work was to estimate the
genetic diversity of improved banana diploids using
quantitative and simple sequence repeats (SSR) marker
data simultaneously.
Materials and Methods
Thirty-two improved diploids, developed by the
banana genetic breeding program at Embrapa Mandioca
e Fruticultura, and the SH3263 diploid, developed by
the Fundación Hondureña de Investigación Agrícola,
were used (Table 1).
The experiment was carried out in Cruz das Almas, BA, Brazil (12º40'19"S and 39º06'22'W', 220 m above sea level). The climate of the region is humid tropical, Aw to Am, according to Köppen, with an annual average temperature of 24.5ºC, relative humidity of 80%, and average annual precipitation of 1,249.7 mm (Agritempo, 2008). Federer’s augmented block experimental design was used (Federer, 1956) with 31 regular treatments – diploids 1 to 28, 32 and 33 repeated once in each block with replicates only in the plots – and three common treatments – diploids 29 to 31, considered as controls, repeated in the five blocks. Each plot consisted of six plants with spacing of 2.5x2.5 m. Evaluations were made in the first production cycle and considered the following 18 agronomic traits: plant height (PH, cm); pseudostem girth (PSG, cm); number of tillers during flowering (NTF); number of leaves during flowering (NLF); number of days from bunch emission to harvest (NBH); presence of pollen grains (PPG), based on a scale in which 1 represents the absence of pollen grains, 2 a small amount of pollen grains, 3 the average amount of pollen grains, and 4 the abundance of pollen grains; number of hands per bunch (NHB); number of fruits per hand (NFH); yellow Sigatoka at emergence of flowering (SEF) and at harvest (SH), using the scale proposed by Stover (1972); number of leaves at harvest (NLH); weight of second hand (WSH, kg); length and fruit diameter (LF and FD, respectively, cm); length and diameter of pedicel (LP and DP, respectively, mm); presence of seeds (PS), evaluated with the following scale: 1 is the absence of seeds, 2 represents 1 to 10 seeds, 3 represents 11 to 20 seeds, and 4 is more than 21 seeds; and length and diameter of stalk (LS and DS, respectively, cm). Twenty pairs of SSR primers were used for molecular characterization, with five microsatellite markers from the Ma series (Crouch et al., 1998), 12 from the AGMI series developed by Lagoda et al. (1998), and three from the MaOCEN series (Creste et al., 2006) (Table 2). DNA was extracted from young leaves using the CTAB method (Doyle & Doyle, 1990). Amplification reactions were done in a final volume of 13 μL-^1 , containing the following reagents: KCl 50 mmol L-^1 , Tris‑HCl 10 mmol L-^1 (pH 8.3), MgCl 2 2.5 mmol L-^1 , 100 μmol L-^1 of each of the dNTPs (dATP, dTTP, dGTP, and dCTP), 0,2 μmol L-^1 of each primer, 50 ng of genomic DNA, and one unit of Taq DNA polymerase.
Genetic diversity between improved banana diploids 1483
obtained admitting a fixed model by considering the
effects as constants, except for the experimental error.
The analysis was carried out with the SAS software
package using the procedure for general linear models
(Proc GLM). The average of the treatments was
adjusted by the minimum squares using the lsmeans
SAS module. Standard computational procedures were
carried out as proposed by Duarte (2000).
The quantitative (agronomic) and qualitative
(molecular marker) data were analyzed simultaneously
using the Ward‑MLM procedure (Franco et al., 1998)
and the cluster and interactive matrix programming
(IML) command of the SAS program for cluster
formation. The Ward cluster method considered
the combined data matrix obtained by the Gower
algorithm.
The correlation between the agronomic variables
and the canonical variables was obtained graphically
using the Candisc command in the SAS software.
In order to define the ideal number of groups, the
procedure indicated for the MLM model, based on
pseudo-F and pseudo-t 2 statistics, was used. Taking
into account the definition of the optimal number of
groups, a hierarchical classification was obtained by
the Ward method, which makes available the initial
value needed to program the final step of the MLM (Franco et al., 1998).
Results and Discussion
The SH3263 (Cod. 32) and 013004‑04 (Cod.
- diploids were not evaluated due to their bad development in the field, mainly caused by the attack of the banana weevil ( Cosmopolites sordidus ). Significant differences was detected between diploids and controls for plant height, days from emission to harvest, and number of fruits, which are key characteristics for the selection of improved diploids (Table 3 and 4). Genotypes significantly affected the agronomic traits, except for fruit diameter. This result indicates the existence of genetic variability between the diploids and justifies the use of molecular markers and multivariate methodologies to quantify it. The analysis using microsatellites showed 133 alleles (102 polymorphic), with an average of 6. alleles per primer. The highest number of alleles was identified for the AGMI33/34 and Ma1/27 primers (ten alleles), and the lowest for the AGMI35/36, Ma3/103, and MaOCEN13R/13F primers (four alleles) (Table 2).
Table 2. Microsatellite primers used in the study.
Name Forward and reverse (5’‑ 3’) sequences No. of alleles PIC Reference AGMI103/104 acagaatcgctaaccctaatcctca/ccctttgcgtgcccctaa 8 0.97 Lagoda et al. 1998 AGMI187/188 gcaactttggcagcatttt/tgatggactcatgtgtacctactat 6 0.97 Lagoda et al. 1998 AGMI25/26 ttaaaggtgggttagcattagg/tttgatgtcacaatggtgttcc 7 0.53 Lagoda et al. 1998 AGMI93/94 aacaactaggatggtaatgtgtggaa/gatctgaggatggttctgttggagtg 6 0.74 Lagoda et al. 1998 AGMI101/102 tgcagttgacaaaccccacaca/ttgggaaggaaaataagaagataga 5 0.83 Lagoda et al. 1998 AGMI33/34 agtttcaccgattggttcat/taacaaggactaatcatgggt 10 0.76 Lagoda et al. 1998 AGMI105/108 tcccaacccctgcaaccact/atgacctgtcgaacatccttt 5 0.66 Lagoda et al. 1998 AGMI35/36 tgacccacgagaaaagaagc/ctcctccatagcctgacttgc 4 0.87 Lagoda et al. 1998 AGMI125/126 tcccataagtgtaatcctcagtt/ctccatcccccaagtcataaag 8 0.74 Lagoda et al. 1998 AGMI129/130 ggaggcccaacataggaagaggaat/cataaacgacagtagaaatagcaac 6 0.57 Lagoda et al. 1998 AGMI95/96 acttattcccccgcactcaa/actctcgcccatcttcatcc 8 0.83 Lagoda et al. 1998 AGMI99/100 atttctttcttttcataccttta/taatgagacgctatggagcac 9 0.66 Lagoda et al. 1998 Ma2/7 tgaatcccaagtttggtcaaga/caactcttgtccctcacttca 6 0.87 Crouch et al. 1998 Ma1/17 aggcggggaatcggtaga/ggcgggagacagatggagt 7 0.86 Crouch et al. 1998 Ma1/24 gagcccattaagctgaaca/ccgacagtcaacatacaataca 5 0.74 Crouch et al. 1998 Ma1/27 tgaatcccaagtttggtcaag/caaaacactgtccccatctc 10 0.80 Crouch et al. 1998 Ma3/103 tcgcctctctttagctctg/tgttggaggatctgagattg 4 0.57 Crouch et al. 1998 MaOCEN1F/1R tctcaggaagggcaacaatc/ggaccaaagggaaagaaacc 9 0.80 Creste et al. 2006 MaOCEN3F/3R ggaggaaatggaggtcaaca/ttcgggataggaggaggag 6 0.64 Creste et al. 2006 MaOCEN13R/13F gctgctattttgtccttggtg/cttgatgctgggattctgg 4 0.84 Creste et al. 2006 Total 133 - Average 6.65 0.
1484 V.M. Pereira et al.
Table 3. Analysis of variance for the agronomic characteristics(1)^ of the 31 improved banana diploids in the first production cycle.
Source of variation df Mean square
PH PSG NTF NLF NLH PFEH SL SD NFB
Blocks 4 146.65 1.32 0.43 3.82 0.25 71.86 28.90 0.64 103.
Treatments (diploids vs. control) 1 2,364.60** 1.58** 0.59ns^ 2.34ns^ 0.05ns^ 1,380.71* 62.16* 5.45** 750.78*
Genotypes within treatments 29 1,618.70** 11.88** 1.18* 6.88* 3.00* 645.30* 94.69** 3.57** 975.48**
Diploids 27 1,694.51** 12.46** 0.99* 6.01* 2.63* 625.82* 80.48** 3.81** 768.72** Controls 2 595.19* 4.08* 3.72** 18.53** 7.98** 908.26* 286.45** 0.29ns^ 3,766.67**
Error 8 126.41 0.69 0.35 1.33 0.65 135.35 9.79 0.26 69.
Coefficient of variation (%) 5.80 5.79 14.88 9.21 27.05 8.30 9.27 11.49 8.
General average 194.00 14.00 4.00 13.00 3.00 140.00 34.00 4.00 98.
Regulars Average 188.08 14.54 4.08 12.39 3.02 135.84 32.78 4.75 94. Maximum 320.97 23.76 6.30 18.45 6.49 179.25 51.89 13.12 206. Minimum 133.04 9.56 2.25 7.45 0.36 49.76 12.53 2.99 51.
Commons
Average 203.68 14.14 3.83 12.88 2.94 147.76 35.31 4.00 103. Maximum 216.11 15.17 4.82 14.99 3.78 159.66 43.84 4.17 130. Minimum 195.69 13.51 3.20 11.22 1.49 133.13 29.41 3.73 75.
(1) (^) PH, plant height (cm); PSG, peudostem girth (cm); NTF, number of tillers during flowering; NLF, number of leaves during flowering; NLH, number of leaves during harvest; PFEH, period between flower emission and harvest (days); SL, stalk length (cm); SD, stalk diameter (cm); NFB, number of fruits per bunch. nsNot significant. ** and *Significant at 1 and 5% probability by the F test, respectively.
Table 4. Analysis of variance for the agronomic characteristics(1)^ of the 31 improved banana diploids in the first production cycle.
Source of variation df Mean square
NHB WSH NFSH PL PD FL FD PS PPG
Blocks 4 0.10 0.02 0.89 0.01 0.01 2.27 1.15 0.04 0.
Treatments (diploids vs. controls) 1 0.12 ns^ 0.00ns^ 14.17** 0.64** 0.03 ns^ 3.64ns^ 1.15ns^ 9.71** 0.00ns
Genotypes within treatments 29 2.57** 0.17** 4.25** 0.70** 0.07* 9.05** 0.26ns^ 1.87** 0.24*
Diploids 27 1.60** 0.16** 4.14** 0.73** 0.07* 8.47** 0.27 ns^ 1.27** 0.18* Controls 2 15.68** 0.29** 5.74** 0.40* 0.00 ns^ 16.78** 0.21 ns^ 9.92** 1.06**
Error 8 0.13 0.01 0.47 0.04 0.02 1.60 1.35 0.04 0.
Coefficient of variation (%) 5.78 18.67 4.38 18.15 17.92 12.71 54.49 8.62 6.
General average 6.00 0.57 16.00 1.22 0.81 10.00 2.00 3.00 4. Regulars
Average 6.39 0.57 15.23 1.32 0.83 9.74 2.00 2.19 3. Maximum 10.64 2.04 19.73 5.21 2.02 18.99 3.27 4.19 4. Minimum 4.29 0.12 12.22 0.48 0.41 6.53 0.44 0.85 3.
Commons
Average 6.50 0.58 16.44 1.06 0.78 10.35 2.35 3.19 3. Maximum 8.42 0.79 17.67 1.26 0.81 12.23 2.56 4.00 4. Minimum 4.92 0.31 15.74 0.73 0.73 8.57 2.14 1.56 3.
(1)NHB, number of hands per bunch; WSH, weight of second hand (kg); NFSH, number of fruits of second hand; PL, pedicel length (mm); PD, pedicel diameter (mm); FL, fruit length (cm); FD, fruit diameter (cm); PS, presence of seeds; PPG, presence of pollen grains. nsNot significant. ** and *Significant at 1 and 5% probability by the F test, respectively.
1486 V.M. Pereira et al.
Therefore, the group formed may be associated with the
small number of genitors involved in obtaining these
hybrids. Similar behavior was observed in group 3,
since the 028003-01 diploid is the male parent of the
086079‑10 and 042079‑06 genotypes.
Amorim et al. (2008) used SSR markers to quantify
the genetic variability between cultivated, improved,
and wild banana diploids. Cluster analysis based on
the SSR polymorphism was not able to completely
separate the improved, cultivated, and wild hybrids.
Some diploids were grouped based on their geographic
origin, such as Musa ornata Roxb. and IAC‑1, and 'Tjau
Lagada' and 'Lidi', whereas for others, no relationship
was established. There was a tendency for clustering
the improved diploids based on their genealogy.
The first two canonical variables explained 81.59%
of the variability between the six groups formed
(Figure 3). This value indicates that the graphic representation of the first two canonical variables was appropriate for visualizing the genetic relationship between the groups and between the accessions within the same group. The length of stalk had the greatest correlation with the first canonical variable, followed by the number of tillers during flowering and the number of fruits in the second hand, with values of 0.70, 0.51, and
- 0,27, respectively. However, for the second canonical variable, the estimates of the highest correlations were found for the characteristics number of hands per bunch (r=0.56) and number of fruits per bunch (r=0.46). When comparing the groups formed by the Ward-MLM method (Figure 2) and the ones obtained by the canonical variables (Figure 3), the latter had a higher discriminatory power, since six groups were
Figure 2. Dendrogram constructed by the Ward MLM method using the genetic distances from 18 morphoagronomic characters from 31 improved banana diploids in the second production cycle. The genotype code is as shown in Table 1.
Genetic diversity between improved banana diploids 1487
formed in comparison to only three by the Ward-MLM
method. The criteria used for separating the groups,
considering the canonical variables, were associated to
the genealogy of the diploids.
Currently, the banana genetic breeding program at
Embrapa Mandioca e Fruticultura has 43 improved
diploids with genetic resistance to most pests and
diseases, including yellow and black Sigatoka and
Fusarium wilt, which also present desirable agronomic
characteristics, such as short stature, high yield, and
drought tolerance.
Conclusions
- There is genetic variability between the 31
banana diploids developed by Embrapa Mandioca e
Fruticultura, enabling crosses for the development of
new cultivars.
- Microsatellite markers are efficient in quantifying
genetic variability.
- Canonical variable analysis provides better
clustering of the banana genotypes than the Ward-MLM
method.
Acknowledgements
To Conselho Nacional de Desenvolvimento
Científico e Tecnológico, for financial support; and
to the Fundação de Amparo à Pesquisa do Estado da
Bahia, for scholarship granted.
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Figure 3. Dispersion of the first two canonical variables (CAN) with the formation of six groups (G1‑G6) by the Ward-MLM strategy, considering 31 improved diploids in the first production cycle. Codes of the genotypes are as shown in Table 1.
