Genetic diversity among acerola accessions collected in different states of Brazil using ISSR markers

doi:10.21203/rs.3.rs-4289993/v1

Download PDF

Research Article

Genetic diversity among acerola accessions collected in different states of Brazil using ISSR markers

https://doi.org/10.21203/rs.3.rs-4289993/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Currently, commercial acerola orchards have been formed using only one or two genotypes, making these crops highly vulnerable to the occurrence of biotic/abiotic stresses. The characterization of available germplasm using molecular primers can identify alleles of interest, useful in the development of new cultivars. Given the above, the aim of this study was to estimate the genetic diversity of a representative sample of acerola germplasm cultivated in Brazil using ISSR primers. Genomic DNA from 96 accessions conserved in Petrolina-PE was extracted and amplified using 15 ISSR primers. The amplifications were annotated for the presence and absence of bands, making it possible to estimate allelic similarity using the Jaccard index and obtaining a dendrogram (UPGMA method). Analysis of molecular variance was used to quantify genetic variability between and within groups. Genes, GenAlex and Cervus software were used. The accessions were grouped into 24 clusters, with ACO01 and MAR12 being the most divergent and Costa Rica and Flor Branca the most similar. The cluster analysis showed that there was greater variation between individuals collected in the same region, a result confirmed by AMOVA and the Shannon-Wiener Index. The ISSR primers were effective in terms of capturing the distribution of the genetic variation present in the studied accessions. Moreover, considering that they are a representative sample of the acerola germplasm in the country, it is understood that this information provides subsidies for decision-making in the management of genetic resources and in the conduction of acerola breeding programs.

Malpighia emarginata

Genetic improvement

Genetic resource management

Genetic divergence

The acerola tree (Malpighia emarginata Sessé & Moc. ex DC.) is a plant of the Malpighiaceae family, native to Central America and northern South America. Its great adaptability and ease of fruit production in tropical and subtropical regions have ensured the spread of cultivation in the Americas, Asia and Africa (Olędzki and Harasym 2024). The fruit of the acerola tree, known as the ‘Barbados Cherry’ or ‘Western Island Cherry’, stands out as a tropical fruit of considerable social and economic importance, due to its high content of ascorbic acid and other bioactive compounds, such as anthocyanins and polyphenols, which give it high antioxidant potential (Miskinis, Nascimento and Colussi 2022; Vilvert et al. 2024).

Brazil is highlighted as the largest producer and exporter of acerola in the world (Santos and Lima, 2020). This production is due to the favorable climatic conditions for the crop, which have kept the country with the largest planted area (Barros et al. 2020, Malegori et al. 2017), reaching 7,724 ha, which were responsible for the production of 143 thousand tons of the fruit in the year 2017 (IBGE 2018).

Currently, commercial acerola production areas are made up of a limited number of genotypes, usually one or two clones. This can cause great vulnerability to orchards due to the narrowing of the genetic base of the crop (Souza et al. 2017). Under these conditions, the emergence of unexpected biotic or abiotic stresses can cause major damage to these production areas. One of the most viable alternatives to overcome this risk is to rationally increase the genetic variability of the areas, by simultaneously cultivating a greater number of genotypes (Souza et al. 2013). In this sense, evaluating the available germplasm can subsidize the development of new acerola cultivars and, consequently, expand the supply of new commercial materials. Accordingly, the study of genetic diversity in plant populations is essential in order to understand the variability present in the species of interest and to apply strategies for the conservation and genetic improvement of these resources (Bueno 2013).

Among the strategies that can be used to study genetic diversity, the use of ISSR (Inter Simple Sequence Repeats) molecular primers has proved to be a viable tool for various plant species, such as: Teucrium stocksianum Boiss. (Kamali et al. 2023), Salvia (Hejazi 2023), Sorghum bicolor (Medraoui et al. 2023) and Euphorbia resinifera O. (Abd-dada et al. 2023). These primers allow the genetic profile to be analyzed effectively, quickly, with high reproducibility, without the environment interfering with the results (Caixeta et al. 2016), providing a detailed view of the genetic structure of the evaluated individuals. Nonetheless, its application to acerola tree or other Malpighiaceae species is incipient.

Embrapa Semiárido has an acerola germplasm collection with 96 accessions. These genotypes were introduced through exchanges with other teaching and research institutions and, mainly, through collections in old commercial orchards, formed from seedlings propagated by seeds and located in the main acerola production areas in Brazil (Souza et al. 2023). Thus, these accessions satisfactorily represent the variability of the species that has existed in the country since the beginning of its cultivation.

In view of the above, this work aimed to estimate the genetic diversity of a representative sample of acerola germplasm cultivated in Brazil using ISSR primers.

Samples of young and healthy leaves were collected from 96 acerola accessions (Chart 1) kept in the largest Active Germplasm Bank (AGB) for the crop in Brazil, which is located in the experimental field of Embrapa Semiárido, established in the irrigated perimeter of the Bebedouro project, in Petrolina-PE. The samples were packed in duly identified paper bags and kept in Styrofoam boxes containing ice, and then taken to the Biotechnology laboratory and stored in a -80°C freezer until the DNA extraction stage.

Chart 1- Acerola accessions from the Embrapa Semiárido AGB, used for DNA extraction and subsequent genotyping.

Acession	Collection location	Acession	Collection location	Acession	Collection location
MAR01	Maranguape - CE	ALHA06	ALHAndra -PB	RECI02	Recife - PE
MAR02	Maranguape - CE	BRS Apodi	Pacajus - CE	LAG01	Lagarto - SE
MAR03	Maranguape - CE	BRS Cereja	Pacajus - CE	LAG03	Lagarto - SE
MAR05	Maranguape - CE	BRS Frutacor	Pacajus - CE	LAG04	Lagarto - SE
MAR06	Maranguape - CE	BRS Jaburu	Pacajus – CE	LAG05	Lagarto - SE
MAR07	Maranguape – CE	BRS Roxinha	Pacajus - CE	LAG06	Lagarto -SE
MAR08	Maranguape - CE	BV01	Pacajus - CE	LAG07	Lagarto -SE
MAR09	Maranguape - CE	BV07	Pacajus - CE	LAG08	Lagarto - SE
MAR10	Maranguape - CE	Clone47/1	Pacajus - CE	LAG09	Lagarto -SE
MAR11	Maranguape- CE	Clone71/2	Pacajus - CE	Olivier	São Paulo -SP
MAR12	Maranguape - CE	FP19	Pacajus - CE	Carolina	Londrina -PR
BRS_Sertaneja	Petrolina - PE	Mineira	Pacajus - CE	Dominga	Londrina -PR
Coopama Nº1	Petrolina - PE	Monami	Pacajus - CE	Lígia	Londrina -PR
Costa Rica	Petrolina - PE	CAMTA	Pacajus - CE	Luisa	Londrina - PR
Flor Branca	Petrolina - PE	Barbados	Pacajus - CE	Manoela	Londrina – PR
Junko	Petrolina - PE	BRS_Cabocla	Cruz das Almas - BA	Natália	Londrina - PR
Nikki	Petrolina - PE	BRS_Rubra	Cruz das Almas - BA	Neusa	Londrina – PR
Okinawa	Petrolina - PE	Morena	Cruz das Almas - BA	Valéria	Londrina - PR
ACO01	Petrolina - PE	Mulata	Cruz das Almas - BA	Samurai	Londrina (PR)
ACO03	Petrolina - PE	Tropicana	Cruz das Almas - BA	UEL01	Londrina – PR
ACO05	Petrolina - PE	Florida Sweet	Cruz das Almas - BA	UEL03	Londrina - PR
ACO07	Petrolina - PE	Florida Sweet x BRS_Cabocla	Cruz das Almas - BA	Eclipse	Londrina - PR
ACO08	Petrolina - PE	ACO30	Petrolina - PE	LOND01	Londrina - PR
ACO09	Petrolina - PE	ACO31	Petrolina - PE
ACO10	Petrolina -PE	ACO32	Petrolina - PE
ACO13	Petrolina - PE	ACO33	Petrolina - PE
ACO14	Petrolina - PE	ACO20	Petrolina - PE
ACO15	Petrolina - PE	CARP01	Carpina - PE
ACO17	Petrolina - PE	CARP02	Carpina -PE
ACO18	Petrolina - PE	CARP03	Carpina -PE
ACO19	Petrolina - PE	CARP04	Carpina -PE
ACO34	Petrolina - PE	CARP05	Carpina -PE
ACO35	Petrolina - PE	CARP06	Carpina -PE
ALHA02	ALHAndra - PB	CARP07	Carpina -PE
ALHA03	ALHAndra - PB	CARP08	Carpina -PE
ALHA04	ALHAndra - PB	CARP09	Carpina -PE
ALHA05	ALHAndra - PB	RECI01	Recife - PE

For DNA extraction, the protocol described by Doyle-Doyle (1990) was used, with some modifications. 1- In the cell and tissue disruptor, model: L-Beader 24, with the programming: 3 cycles of 45s with a 5s interval between one cycle and another with agitation of 6:30m/s, 5g of plant material was macerated, each access, in microtubes of 2 ml, containing 7 spheres of 2mm and 950µL of 2% CTAB + β-Mercaptoethenol solution. 2- Added 950 µL of chloroform-isoamyl alcohol (24:1); 3- Resuspended in 50 µL of ultrapure water. The genomic DNA was quantified on a 1% agarose gel stained with ethidium bromide. The λ phage primer of known weight was used (100, 50 and 25 ng/µL). Electrophoresis was carried out at 100W for 1 hour. After quantification, part of the DNA from the genotypes was diluted into a working solution at 25ng/µl.

Thirteen ISSR primers were used for the PCR reaction (Table 1). The PCR (Polymerase Chain Reaction) amplification reactions were carried out in a Gene Amp 9600 thermal cycler (Applied Biosystems), where each sample had a final volume of 6.25 µL containing: 0.5 µL of genomic DNA at a concentration of 25ng/µL; 0.94 µL of primer (4mM); 1.25 µL of buffer solution (5X); 0.5 µL of dNTPs (2 mM); 0.05 µL of TaqPolymerase (5U); 0.5µL of MgCl₂ (25mM) and 2.51µL of ultrapure water to complete the final volume.

Table 1

ISSR primers used to amplify acerola accessions.
Locus ISSR	Sequence	Annealing temperature (ºC)	Size (pb)
DiGA3´C	5’-GAGAGAGAGAGAGAGAC-3’	50	190–1000
DiGT5´CR	5’-CRGTGTGTGTGTGTGTGT-3’	50	500–1100
DiGT5’CY	5’-CYGTGTGTGTGTGTGTGT-3’	50	300–1100
TriCAC3’YC	5’-CACCACCACCACCACYC-3’	50	190–900
TriCAC’5CR	5’-CRCACCACCACCACCAC-3’	50	400–1200
TriCAC5’CY	5’-CYCACCACCACCACCAC-3’	50	200–1000
TriCAG	5’-CAGCAGCAGCAGCAG-3’	50	500–1200
TriCAG3’RC	5’-CAGCAGCAGCAGCAGRC-3’	50	190–1200
TriCAG3´YC	5’-CAGCAGCAGCAGCAGYC-3’	50	400–1300
TriACA3’RC	5’-ACAACAACAACAACARC-3’	50	250–1000
TriACG3’RC	5’-ACGACGACGACGACGRC-3’	50	300–1000
DiCA5’CY	5’-CYCACACACACACACACA-3’	50	230–700
DiCA5’G	5’-GCACACACACACACACA-3’	50	300–900

The amplification program used consisted of an initial denaturation cycle of 94ºC for 3 minutes, followed by 35 annealing cycles of 94ºC for 1 minute, 50ºC for 30 seconds and 72ºC for 75 seconds; and a final extension at 72ºC for 5 minutes, leaving the reaction products at 4ºC. After the amplifications, the fragments were separated on a 2% agarose gel stained with ethidium bromide and subjected to a constant voltage of 75 V for 3 hours. The results of the amplifications were visualized under ultraviolet light and recorded on a photo documenter (Loccus Biotecnologia). The size of the fragments was determined using a 100 bp molecular weight ladder primer (Cellco).

The data was annotated for the presence (1) versus absence (0) of alleles. Based on the data obtained by the dominant microsatellite primers, Analysis of Molecular Variance (AMOVA) was carried out, with a hierarchical structure level, considering the accessions by collection states, through the GenAlEx software, version 6.2 (Smouse, Peakall and Gonzales, 2008). Subsequently, the diversity between the accessions was estimated using the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) clustering method, based on the similarity matrix obtained with the Jaccard index using the Jaccard genetic similarity coefficient. A graphical representation of the genetic similarity between the accessions was also obtained, and the fit between the distance matrix and the dendrogram, was estimated by means of the cophenetic correlation (CCC) using the Genes software, version 2021.1.9 (Cruz 2016).

In addition, the following parameters were evaluated: Total Number of Amplified Bands (NTB); Number of Different Alleles (Na) estimated by the number of alleles per loci; Effective Number of Alleles (Ne); Shannon-Wiener Index (I); Percentage of Polymorphic Loci (%P), calculated by the ratio between the number of polymorphic loci and the total number of analyzed loci and the Expected Heterozygosity (He), obtained by the ratio of the frequency of heterozygotes for each locus to the total number of loci (Nei 1987; Frankham et al. 2002). Cervus software, version 3.0.7, was used to estimate these parameters (Marshall et al. 1998; Kalinowski 2007).

According to the results of the genetic similarity measures relating to the 13 molecular primers and estimated using the Jaccard index, it was possible to observe that ALHA05 access appears most frequently in relation to the formation of the most dissimilar pairs. The highest dissimilarity values were observed between ACO01 and MAR12 accessions (d = 100%), which were the most divergent pair, followed by ACO01 and ALHA04 (d = 80.70%). These results suggest that crosses between these pairs could produce progenies with greater variability, which could be a useful strategy for the breeding program.

The most similar pairs of accessions were Costa Rica and Flor Branca (d = 7.96%) and FP19 and CARP09 (d = 8.70%), showing that recombination between them could possibly result in progenies with a narrow genetic base. When taking into account the need to identify contrasting individuals as base material for exploitation within species breeding programs, much has been attributed to the recombination of similar accessions as a flawed strategy, but when attributes of these genotypes are observed, such as resistance to a certain stress and commercial characteristics of interest in another, recombination between the two can be advantageous, with the possibility of aggregating these characteristics in a single genotype.

This same scenario was observed between the Flor Branca and Junko accessions, which showed great similarity detected by the ISSR primers. Both are commercial clones, that is, plants with defined characteristics accepted by the market. Nonetheless, Flor Branca has stable flowering throughout the year and produces fruit with fragile flesh; on the other hand, Junko produces fruit with firm and durable flesh, but its production is greatly affected by temperature fluctuations throughout the year (Souza et al. 2013). Accordingly, it can be inferred that the result of crossing the two may be the most suitable resource for producing segregating populations in which genotypes with more stable production and more durable fruit can be selected. Moreover, the fact that both are commercial clones may result in less effort being made to discard individuals with undesirable characteristics (linkage drag).

Analyzing the dendrogram generated by the UPGMA hierarchical clustering method (Fig. 1), it is possible to perceive the existence of genetic divergence between the accessions. When a cut-off point of 50% genetic distance is established, 23 groups are formed, some of which are divided into subgroups. The first group consists of 10 accessions, namely: Costa Rica, Flor Branca, Junko, Clone71/2, LAG03, LAG04, LOND01, Clone47, Dominga and Eclipse. The accessions that make up Group 1 represent 10.42% of the total and comprise accessions that were collected in three regions (Petrolina-PE, Pacajus-CE and Londrina-PR). In Group 2, the accessions named Carolina, CARP07, CARP01, Coopama nº1 and CARP04 (5.21%) were allocated. Group 3 was formed by CARP06, CARP08, BRS Cabocla, CARP02, BRS Sertaneja, CARP09, FP19, Florida Sweet x BRS Cabocla, CARP03, LAG08, CARP05, Florida Sweet, LAG07, BRS Roxinha, BRS Rubra, ALHA06, Barbados, BRS Apodi, ALHA05, ALHA09 and BRS Jaburu (21.88). Group 4 was formed by Luisa, Manoela, LAG09, LAG05 and Lígia (5.21%). Group 6 was formed by the accessions named BV07 and CAMTA (2.08%). Group 7 comprises the accessions named LAG06, MAR10 and MAR08 (3.12%). Group 10 was formed by the accessions named BRS Frutacor, BV01, BRS Cereja, ALHA04 and LAG01 (6.25%). Group 13 formed by the accessions named ACO19, ACO33, ALHA03 and ACO35 (4.17%). Group 17 formed by the accessions named UEL01, Valéria, Samurai, Tropicana and UEL03 (5.21%). Group 19 formed by the accessions Olivier and REC01 (2.08%). Group 20 was made up of the accessions named Monami and Morena (2.08%). Group 21 was made up of the accessions named Natália, Nikki and Mineira (3.12%). In general, the accessions in each of these groups have individuals collected in at least two locations, and these results indicate that there may have been an exchange of genetic material between them at some point, given that they have alleles in common.

Still analyzing the dendrogram (Fig. 1), it was possible to observe individuals from the same location, which should have been grouped together with the others, composing separate groups, such as Group 8, formed by the accessions named MAR06, MAR07, MAR02, MAR03, MAR05 and MAR01 (7.28%). Group 11 was formed by the accessions named ACO31 and ACO34 (2.08%). Group 12 was formed by the accessions named ACO01 and ACO34 (2.08%). Group 14 was formed by the accessions named ACO08, ACO30, ACO09, ACO13, ACO10, ACO15, ACO17, ACO14, ACO05 and ACO07 (10.42%). Finally, Group 18 was made up of the Okinawa and Reci 02 accessions (2.08%). The possible explanation for this behavior can be attributed to the short time that these individuals spent in each location, which did not allow the exchange of alleles between all the individuals to take place, leading them to equilibrium. In addition, these results show that there is possible variation between individuals at each location, leading us to assume that selection within populations would also be an interesting strategy.

There were also groups formed by a single accessory, namely: 5, 9, 15, 16, 22, 23 and 24, which included the accessions named MAR11, MAR09, ACO18, ACO20, Neusa, Mulata and MAR12, respectively, corresponding to 1.04% of the total number of accessions in each group (Fig. 1). These accessions behaved exceptionally well, as individuals collected from the same location were generally grouped together. This behavior indicates that the isolated accessions have different alleles from the others. These alleles may be associated with traits of interest, reinforcing the need to continue collecting and studying the characterization of the AGB.

The dendrogram generated by the UPGMA method is in line with the dissimilarity values generated by the Jaccard index, since the accessions that make up Group 1 are those with the greatest similarity (Costa Rica and Flor Branca; d = 7.96%), with the greatest distance being between accessions from different groups (ACO01 and MAR12; d = 100%). This behavior was expected due to the fact that the cophenetic correlation coefficient of the dendrogram showed a value considered satisfactory (r = 88%), which translates into greater accuracy of the dendrogram in the graphical representation of the data matrix.

In addition to the dendrogram, the Analysis of Molecular Variance (AMOVA) was used to quantify the genetic diversity between and within groups. The results showed that 19% of the contrast between individuals was observed between groups and 81% within groups (Table 2), meaning that selection is more effective within them. These results are in line with those observed in the dendrogram, which suggested greater variation within the regions in which the materials were collected. This result may be associated with the existence of a greater genetic difference, taking into account the mode of reproduction, between individuals that show allogamous reproduction and, especially, if propagated via seed, as is the case with some of the accessions that make up the AGB.

Table 2

Analysis of Molecular Variance (AMOVA) using ISSR primers, considering groups of acerola accessions by collection.
Source of variation	¹DF	SS	VC	% Variation
Between groups	8	338.138	2.956	19
Within groups	86	1079.756	12.555	81
TOTAL	94	1417.895	15.511	-

¹ Degree of Freedom (DF), Sum of Squares (SS), Variance Component (VC)

When observing the parameters obtained using ISSR molecular primers (Table 3), the Total Number of Amplified Bands (NTB) in each population ranged from 101 to 846 in Groups 9 and 1, respectively. Lima et al. (2015), when evaluating the amplification of 20 ISSR primers in 56 acerola accessions, obtained a total of 186 amplified fragments, a much lower number compared to the present study, which shows the existence of polymorphism within the groups. This information is important from the point of view of breeding, as this variability is the raw material for obtaining new cultivars and may contain alleles favorable to tolerance or resistance to biotic and abiotic stresses that are potentially harmful to the commercial cultivation of acerola trees.

Another approach that can be taken in relation to the polymorphism of individuals is that it be attributed to the mode of reproduction, that is, predominantly allogamous. In this case, the exchange of pollen between plants results in great variation in the offspring.

For the number of different alleles (Na) (Table 3), Groups 1 and 8 showed the highest values, with 1.15 and 1.19, respectively. This behavior was confirmed by the dendrogram (Fig. 1), where some individuals were isolated in the clusters. For the effective number of alleles (Ne), all the groups had values above unity, with Groups 4 and 8 standing out, with 1.22 and 1.28, respectively.

For the Shannon-Wiener Index (I), the highest values were expressed in Groups 1, 4 and 8, the first two with 0.21 and the last with 0.28 (Table 3). According to Bolstein et al. (1980), this parameter ranges from 0 to 1 and indicates that the closer it is to 1, the greater the genotypic diversity within the group. Using this definition as a basis, it is possible to observe the presence of low diversity between the groups, which agrees with the results of the dendrogram and AMOVA (Table 2), where greater variation was also observed within the groups.

The expected heterozygosity (He) was low for all populations, with values ranging from 0.05 to 0.18 (Table 3), which can be explained by the nature of the primer being dominant and not being able to distinguish heterozygous from homozygous dominant individuals, which results in a smaller number of alleles compared to the actual amount, revealing low values for this parameter.

The percentage of polymorphic loci (%P) is a parameter used as an aid in the effective detection of polymorphism. In the groups in this study, the average value was 36.09, with a range from 13.63 to 57.40 (Table 3). According to Felix et al. (2010), molecular primers with %P values above 0.5 are considered to be highly informative, those with values between 0.25 and 0.5 are classified as moderately informative, while ladder primers with values below 0.25 are poorly informative. Given this classification, in this study, the primers were effective in terms of capturing high polymorphism in Groups 1 (57.40) and 8 (58.85), moderate polymorphism in Groups 3 (44.97), 4 (47.34), 6 (27.81) and 7 (38.87), and low polymorphism in the others.

Table 3

Genetic diversity parameters obtained using dominant ISSR primers in acerola tree populations.
Groups	¹N	NTB	Na	Ne	I	He	%P
1	26.00	846	1.15	1.21	0.21	0.13	57.40
2	5.00	119	0.39	1.09	0.08	0.05	15.98
3	14.00	391	0.94	1.16	0.16	0.10	44.97
4	7.00	236	0.97	1.22	0.21	0.17	47.34
5	9.00	226	0.49	1.08	0.08	0.05	20.71
6	8.00	238	0.61	1.14	0.13	0.07	27.81
7	11.00	454	0.85	1.20	0.18	0.12	38.46
8	13.00	522	1.19	1.28	0.27	0.18	58.58
9	2.00	101	0.50	1.10	0.08	0.06	13.61

¹N = Number of Individuals Per Population, NTB = Total Number of Amplified Bands, Na = Number of Different Alleles, Ne = Effective Number of Alleles; I = Shannon-Wiener Index, He = Expected Heterozygosity, %P = Percentage of Polymorphic Loci.

The molecular primers used in this study were effective in terms of capturing the genetic variation present in the accessions of the Active Germplasm Bank belonging to Embrapa. Considering that these accessions are a sample of the acerola germplasm that has been cultivated in Brazil since its introduction, it can be inferred that this study minimally represents the distribution of the genetic variability of this species in the country. Moreover, this information provides subsidies for decision-making in the management of acerola genetic resources, as well as in the choice of genitors according to the objectives of breeding programs.

Acknowledgements

The authors would like to thank the Pernambuco Science and Technology Support Foundation (FACEPE), and Embrapa Tropical Semi-Arid for their support, scholarship and grants received through project number: BFP-0087.5.01/22.

Funding

The authors would like to thank the financial support of the Pernambuco Science and Technology Support Foundation (FACEPE) grants received through project number: BFP-0087.5.01/22.

Competing Interests

The authors have not disclosed any competing interests.

Contributions

FFS and TLN conceived and planned the study and wrote the manuscript, FFS Acerola BAG Curator, NFM conceived the markers used in genotyping, TLN, SSS, FFS and NFM genotyped and analyzed the data. All authors read and approved the final manuscript.

Data Availability

The datasets generated during and/or analysed during the current study are not publicly to but are available from the corresponding author on reasonable request.

Abd-dada H, Bouda S, Khachtib Y, Bella YA, Haddioui A (2023) Use of ISSR markers to assess the genetic diversity of an endemic plant of Morocco (Euphorbia resinifera O. Berg). Journal of Genetic Engineering and Biotechnology, 21: 01-12
Barros BRS, Nascimento DKD, Araújo DRC, Baista FRC (2020) Phytochemical analysis, nutritional profile and immune stimulatory activity of aqueous extract from Malpighia emarginata DC leaves. Biocatalysis and Agricultural Biotechnology, 23:101442
Bolstein D, White RL, Skolnick M, Davis RW (1980) Construction of genetic linkage map in man using restriction fragment length polymorphism. American Journal of Human Genetics, 32: 314-331
Bueno LCS (2013) Melhoramento genético de plantas: Princípios e procedimentos. Universidade Federal de Lavras, Lavras
Caixeta ET, Oliveira ACB, Brito GGB, Sakiyama NS (2016) Tipos de marcadores moleculares. In: Borém A, Caixeta ET (eds.) Marcadores moleculares. Universidade Federal de Lavras, Viçosa, pp 11-94
Cruz CD (2016) Genes Software – extended and integrated with the R, Matlab and Selegen. Acta Scientiarum, 38: 547-552, 2016
Felix FC, Chagas KPT, Ferrari CS, Vieria FA, Pacheco MV (2021) APPLICATIONS OF ISSR MARKERS IN STUDIES OF GENETIC DIVERSITY OF Pityrocarpa moniliformis. Revista Caatinga, 33:1017-1024
Frankham R, Briscoe DA, Ballou JD (2002) Introduction to conservation genetics. Cambridge University Press, São Paulo
Hejazi SMH (2023) Evaluation of genetic diversity in different populations of six salvia species using R-ISSR markers analysis. Genetic Resources and Crop Evolution, 71:1-15
IBGE - Instituto Brasileiro de Geografia e Estatística - Sistema IBGE de Recuperação Automática – SIDRA, Censo Agropecuário. 2017. disponível em: <https://sidra.ibge.gov.br/tabela/6616#resultado>. Acesso em: 01 de jan. de 2024.
Kalinowski ST, Taper ML, Marshall TC (2007) Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology, 16: 1099-106
Kamali M, Samsampour D, Bagheri A, Mehrafarin A, Homaei A (2023) Association analysis and evaluation of genetic diversity of Teucrium stocksianum Boiss. populations using ISSR markers. Genetic Resources and Crop Evolution, 70: 691-709
Lima EN, Araújo MEB, Bertini CHCM, Moura CFH, Hawerroth MC (2015) Diversidade genética de clones de aceroleira avaliada por meio de marcadores moleculares ISSR. Comunicata Scientiae, 6: 174-180
Malegori C, Marques EJN, Freitas ST, Pimentel MF, Pasquini C, Casiraghi E (2017) Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms. Talanta, 165: 112-116
Marshall TC, Salte J, Kruuk LE, Pemberton JM (1988) Statistical confidence for likelihood based paternity inference in natural populations. Molecular Ecology, 7: 639-655
Medraoui L, Rabeh K, Ater M, Abdelkarim FM (2023) Genetic diversity analysis of sorghum (Sorghum bicolor L. Moench) landraces from northwestern Morocco using ISSR and AFLP markers. Genetic Resources and Crop Evolution, 71: 1-16
Miskinis RAS, Nascimento LÁ, Colussi R (2023) Bioactive compounds from acerola pomace: A review. Food chemistry, 404: 134-613
Nei, M. (1987) Molecular evolutionary genetics. Columbia University Press, Nova York
Olędzki R, Harasym J (2024) Acerola (Malpighia emarginata) Anti-Inflammatory Activity-A Review. International Journal of Molecular Sciences, 25: 01-17
Santos TSR, Lima RA (2020) Cultivo de Malpighia emarginata L. no Brasil: uma revisão integrativa. Journal of Biotechnology and Biodiversity, 8: 333-338
Smouse PE, Peakall R, Gonzales E (2008) A heterogeneity test for fine-scale genetic structure. Molecular Ecology, 17, 3389-3400
Souza FF, Freitas ST, Castro JMC, Deon MD, Nascimento TL, Silva RS (2023) Caracterização de acessos de aceroleira (Malpighia emarginata Sessé & Moc. ex DC.) do Banco Ativo de Germoplasma da Embrapa Semiárido. Paper presented at the 6°th Simpósio da rede de recursos genéticos vegetais do Nordeste, Associação Brasileira de Recursos Genéticos vegetais, Recife
Souza FF, Deon MD, Castro JMC, Calgaro M (2017) Contribuição das pesquisas realizadas na Embrapa Semiárido para a cultura da aceroleira. Embrapa Semiárido, Petrolina
Souza FF, Deon MD, Castro JMC, Lima MAC, Rybra ACP, Freitas ST (2013) Principais variedades de aceroleiras cultivadas no Submédio do Vale do São Francisco. Embrapa Semiárido, Petrolina
Vilvert JC, Freitas ST, Veloso CM, Amaral CLF (2024) Genetic Diversity on Acerola Quality: A Systematic Review. Brazilian Archives of Biology and Technology, 67:1-12

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Genetic diversity among acerola accessions collected in different states of Brazil using ISSR markers

Status:

Version 1

Abstract

Figures

Introduction

Material and methods

Results and discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1