Genetic
diversity of arabica coffee (Coffea arabica L.) using 20 microsatellite
markers in the germplasm bank of UNESUM, Ecuador.
Diversidad genética del café arábigo (Coffea
arabica L.) aplicando 20 marcadores microsatélite en el banco de germoplasma de
la UNESUM, Ecuador
Carlos Castro Piguave 1
María González Vega 2
Juan García Cabrera 3
Jessica Morán Morán 4
Julio Gabriel-Ortega 5
Published Edwards Deming Higher
Technological Institute. Quito
- Ecuador Periodicity April - June Vol. 1, Num. 25, 2025 pp.
30-53 http://centrosuragraria.com/index.php/revista Dates of receipt Received: January, 2024 Approved: March 3, 2025 Correspondence author Creative Commons License Creative Commons License,
Attribution-NonCommercial-ShareAlike 4.0
International.https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
[1] Mg. Universidad Estatal del
Sur de Manabí, Jipijapa, Ecuador. carlos.castro@unesum.edu.ec, https://orcid.org/ https://orcid.org/ 2 Phd. University of Havana, Havana, Cuba. maria.gonzalez@unesum.edu.ec,
https://orcid.org/0000-0001-5841-8272 3 Mg. Universidad
Estatal del Sur de Manabí, Jipijapa, Ecuador. juan.garcia@unesum.edu.ec, https://orcid.org/0000-0002-2026-3751 4 Mg. Universidad Estatal del Sur de Manabí,
Jipijapa, Ecuador. jessica.moran@unesum.e du.ec, https://orcid.org/0000-0002-6487-1038 5 Phd.
Universidad Estatal del Sur de Manabí, Jipijapa, Ecuador. julio.gabriel@unesum.edu.ec, http://orcid.org/0000-0001-9776-9235
Keywords: Accession,
in vivo, DNA, genotyping, gels, markers.
Resumen: Con el objetivo
de determinar la diversidad genética del café arábigo (Coffea
arabica L.) aplicando 20 marcadores microsatélite en 20 accesiones el banco
de germoplasma de la Universidad Estatal del Sur de Manabí (UNESUM), se colectaron foliolos
jóvenes del tercio medio superior de cada planta de las 20 accesiones de café
conservados in vivo en la Finca Andil, en sobres pequeños con cierre y ubicados en una caja con 500 g de sílica gel para el secado y conservación
de las muestras de foliolos, para luego enviarlas al laboratorio de Biología
Molecular de la Estación Experimental Santa Catalina, del Instituto Nacional de
Investigaciones Agropecuarias y forestales (INIAF). El análisis de QTA –
genotyping fue realizado con la tecnología M13 Tailing para 20 marcadores
microsatélite. Los geles fueron analizados de forma visual, determinando
el peso de los fragmentos en pares de bases (pb) amplificado para el
QTA-genotyping en referencia a un marcador de peso conocido del fragmento. Los pb de cada marcador para cada una de las accesiones de café
(ausencia/presencia), fueron anotados en una hoja de cálculo en Excel para su
posterior análisis estadístico. Para cada marcador ensayado se
aplicó un test de Chi cuadrada para comparar las medias de niveles de presencia
en las accesiones que pertenecen a cada clase de marcador (ausencia/presencia) determinado,
respecto de los pb de los alelos reportados por otros investigadores. Los resultados
mostraron los marcadores SSR utilizados tienen una baja o
limitada detección de la diversidad genética, y que se pudo observar
limitaciones en los niveles de heterocigosis y homocigosis en los locus
específicos por la incapacidad de estos SSR para distinguir alelos de
cromosomas homólogos, así como la probabilidad de hallar alelos nulos en
poliploides. Por otra parte, se determinó que el marcador Cam22 fue monomórfico.
Palabras clave: Accesión,
in vivo, ADN, genotipado, geles, marcadores.
Introduction
Sánchez
Barrantes (2017) mentions that coffee is the most popular non-alcoholic
beverage in the world and the second most important product in world trade
after petroleum products (Dessalegn et al., 2009). The International Coffee
Organization (ICO) for the year 2016, mentions that the average world
production was 153 869 million 60 kg bags. Brazil was the main producing
country contributing 35.7% of world production. The Coffea genus
presents great economic importance in the Rubiaceae family (Montoya et al.,
2006), it consists of more than 100 known species (Missio et al., 2009a) of
which only Coffea arabica (coffee arabica) and C. canephora (robusta
coffee) are commercially cultivated (Tornincasa et al., 2010). Currently, C.
arabica is the most important species in trade as it produces high quality
coffee compared to C. canephora, and contributes around 70% of the
world's total coffee production (Anthony et al., 2002, Tornincasa et al.,
2010). In Ecuador coffee is of great economic importance and is grown on
approximately 199 215 hectares, of which 68% are of the species C. arabica,
and 32% of the species C. canephora, which are distributed in 23 of the
24 provinces of the country, so it has a wide social range (Valverde et al.,
2020). Arabica coffee, unlike the other
species, possesses conditions such as good fruit yield, bean size and is known
for its excellent cup quality (Pérez, 1977). However, it has a major drawback,
as it suffers from low genetic diversity due to its origin and domestication
process, this low diversity is reflected in a higher susceptibility to diseases
and pests (Pereira et al., 2015, Prakash et al., 2002). Therefore, coffee
requires adaptation to the different environments in which it is grown to
maintain market requirements. To achieve this requires the genetic improvement
of coffee, which is mainly carried out through the use of conventional methods,
which are generally slow and laborious (Hendre &
Aggarwal, 2007). This situation calls for the development of tools that
accelerate and provide reliability in the characterization of the gene pool,
which will lead to a more efficient utilization of the germplasm available in
coffee breeding programs (Hendre & Aggarwal, 2014, Brandão Motta et al.,
2014). In this context, genetic markers based on DNA (deoxyribonucleic acid)
polymorphism become an important tool that proved to be of great value in the
characterization and genetic improvement of plant genetic resources. Among the
main molecular markers used in commercially important cultivars are
alloenzymes, RFLP (Restriction fragment length polymorphic), RAPD (Random
amplified polymorphic DNA), AFLP (Amplified fragment length polymorphism), SNP
(Single nucleotide polymorphism) and SSR (Simple sequence repeats)
microsatellites (Azofeifa, 2006, Gabriel, 2009, Brandão Motta et al., 2014 ). Alloenzymes were the first molecular markers used in
plant genetics. They are co-dominant markers and have been of great value in
breeding studies in both natural populations and plantations, however, it is a
very laborious technique, one must know the enzymes very well and it is poorly
reproducible between laboratories (Azofeifa, 2006). RFLPs are also codominant
markers that can be used with both nuclear DNA and organelle DNA. This
technique requires large amounts of good quality DNA for the detection of
single copy loci; specific probes are needed, it requires many manipulations
and only a fraction of the variability of sequences existing in the genome is
detected, i.e., its information is limited (Alcántara, 2007). RAPDs amplify
both coding and non-coding regions of DNA and reveal higher levels of variation
than RFLPs and isoenzymes (Parker et al., 1998). It is a technique that does
not require prior knowledge of the DNA sequence and does not require
species-specific probes (Azofeifa, 2006), however, it
has low reproducibility and cannot differentiate between homozygous and
heterozygous individuals as they are dominant markers (Zhivotovsky, 1999).
AFLPs are highly polymorphic, do not require any prior sequence information for
analysis and can be dominant or codominant markers. This technique is useful
for generating genetic fingerprints and mapping; it has also been used for
germplasm characterization, phylogenetic studies in plants, bacteria, fungi and
in population genetics studies (Alcántara, 2007). However, it requires a high
amount of DNA and the technique is more complicated to perform than RAPD
(Azofeifa, 2006). SNPs are markers that have been widely used in genetic
diversity studies; however, they have a rather low
information content, so many have to be used to reach a good level of
information (Rischkowsky & Pilling, 2010). Finally, there are simple
sequence repeats (SSR) or microsatellite markers that are DNA sequences
composed of short tandemly repeated motifs (Cristacho & Gaitán, 2008,
Poncet et al., 2004). These markers have a wide site-specific length
polymorphism due to different amounts of repeat units; they are also robust,
transferable, codominant, chromosomally located at a
single locus and can be developed as markers based on the polymerase chain
reaction (PCR) technique, with amplification of complementary repetitive
regions of the genome. The flanking regions of microsatellite sequences are conserved, this allows high reproducibility of the technique
because the primers are designed for these desired regions (Motta et al.,
2015). In addition, microsatellites are multiallelic and have numerous
advantages compared to other types of markers because they are highly
informative, require little genetic sample material and have the possibility of
automation (Rovelli et al., 2000). Initially, the identification of
microsatellite markers is expensive, as they need to be isolated de novo in
most of the species being analyzed for the first time and the development of
the primers requires a very long and laborious process (Zane et al., 2002).
Currently, there are different strategies for the development of these primers,
4 however the most widely used method is the genomic library enriched with
selective hybridization (Zane et al. 2002). Microsatellites are found in coding
and non-coding regions of the genome (Missio et al., 2011), so the strategy of
designing universal primers does not work very well for this type of markers.
However, some microsatellites have been reported to have highly conserved
flanking regions, which allows amplification of these microsatellites in
divergent species (Zane et al., 2002). In varieties with a narrow genetic base
such as arabicas, the use of molecular markers has been very useful for several
studies. Berthou and Trouslot (1977) conducted an isoenzyme study to determine
the enzyme polymorphism of Coffea, and later Lashermes et al. (1999)
conducted an RFLP analysis to characterize the characterization and origin of Coffea
arabica; however, both studies found that polymorphism is extremely low
among Coffea accessions and these markers were not sufficiently
informative. Another molecular marker that has been important in Coffea studies
was RAPD, which has been used to construct dendrograms that were found to be
consistent with the known history and evolution of Coffea arabica (,
Orozco et al., 1994, Lashermes et al., 1999). In addition, Orozco et al. (1994)
report that RAPD markers have also been good markers for differentiating
between C. arabica var. typica and C. arabica var. bourbon,
as well as for detecting natural and interspecific introgression between
diploid C. canephora and C. arabica (Rume Sudan RS-510
accession). A few years later, Steiger et al. (2002) used AFLP markers to
determine the diversity between different cultivars of Coffea arabica.
They mention that the AFLP technique used in this study is as reliable as RFLP
and SSR at a lower cost, and is more reliable than RAPD markers. They found
polymorphisms among some cultivars that allowed for cultivar differentiation;
however, the genetic variation among arabica cultivars was similar to the
variation within cultivars, and no cultivar-specific DNA markers were detected.
The main disadvantage they mention with the use of the technique is that erroneous
marker data may arise due to partial digestion of genomic DNA. On the other
hand, SSR or microsatellite markers are potentially useful, especially for
exploring highly variable regions of the genome between individuals or
populations of the same species (Hurtado & Herrera, 2013, Missio et al.,
2009a). These markers are ideal for the study of genetic diversity, population
structure, phylogenetic relationships, construction of linkage maps, QTL
interval assignment, among others (Hendre & Aggarwal, 2014). In previous
studies of Coffea arabica the degree of allelic diversity revealed in
SSR loci in most of these organisms indicates that SSRs are ideal for linkage
analysis, agronomic trait selection, germplasm evaluation, and cultivar
identification (Powell et al., 1996). However, despite all the advantages
provided by these markers, the availability of polymorphic SSR markers remains
a constraint in C. arabica (Ferrao et al, 2015, Missio et al., 2011),
this due to the low abundance of SSRs present in its genome, which has caused
great difficulty in the development of specific markers (Hendre & Aggarwal,
2007). For the reasons mentioned above, the present research aimed to determine
the genetic diversity of arabica coffee (Coffea arabica L.) by applying
20 microsatellite markers in 20 accessions of the germplasm bank of the
Universidad Estatal del Sur de Manabí.
Methodology
The research was carried out at the Andil Farm,
belonging to the State University of Southern Manabi (UNESUM), located at
kilometer 5 of the road leading to the Noboa Parish of Canton 24 de Mayo. The canton Jipijapa, is bounded to the north
by the cantons Montecristi, Portoviejo and Santa Ana, to the south by the
province of Santa Elena and Puerto Lopez, to the east by the cantons Paján and
24 de mayo; and, to the west by the Pacific Ocean [Decentralized Autonomous
Government (GAD, 2015)]. Jipijapa's predominant climate is hot dry in the
western zone and hot humid with dry seasons in the eastern zone, with an
average temperature of 24°C and a relative humidity of 85%, affected by the
presence of two seasons, dry (between May and October) and rainy (between
November and April) (GAD, 2015)
Plant
material
The plant material used for this research
consisted of 20 accessions from the coffee germplasm bank of the Universidad
Estatal del Sur de Manabí, which is
conserved in vivo on the grounds of the Granja Andíl, in Jipijapa. The
characteristics of each of the accessions are described in Table 1.
Table
1. Accessions of the germplasm bank of Coffea arabica L. of the Universidad
Estatal del Sur de Manabí.
|
No. |
Accession |
Origin |
Progenitors |
Feature |
Source |
|
1 |
Catimor 8666 (4-3). |
Portugal |
Timor x Caturra |
Tolerant to rust and high grain production. |
Villacreses (2017). Lucas-Suarez (2018), Parrales
Marcillo (2018). |
|
2 |
Red Catuai UFV |
Brazil |
(Sumatra x Bourbon |
Susceptible to rust |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
3 |
Gheisha. |
Ethiopia |
C.
arabica |
Cup quality |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
4 |
Yellow bourbon |
Africa |
C.
arabica |
Susceptible to coffee rust. It has a tall growth habit and is of high
quality. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
5 |
Yellow Caturra T-3386 |
Brazil |
Bourbon mutants |
High grain production. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
6 |
Catimor CIFC-P2. |
Portugal |
Caturra x Timor |
Resistant to rust. High
grain yield. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
7 |
Catimor CIFC-P1 |
Portugal |
Caturra x Timor |
Resistant to rust. High
grain production. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
8 |
Castle |
It is located in Africa |
Caturra x de
Timor |
High grain production.
Resistance to rust and cherry disease (Colletotrichum
coffeanum varvirulans). |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
9 |
Arara |
Brazil |
Catuaí x Sarchimor |
Rust resistance |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
10 |
Pache |
Guatemala |
mutation of the Típica
variety |
Susceptible to rust |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
11 |
Acawa |
Brazil |
Mundo Novo IAC 388-17 x Sarchimor IAC 1668 |
Drought and rust
resistance. Tolerant to nematodes. Cup quality and late cycle. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
12 |
Catimor CIFC-P3 |
Portugal |
Caturra x de
Timor |
Low bearing and rust
resistant. High yield and more production. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
13 |
Catucai Yellow - SL |
Brazil |
Icatu and Catuai |
Moderate rust resistance |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
14 |
Catimor UFV-5607 |
|
Timor # 832 x Caturra |
|
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
15 |
Red Caturra- Pichilingue |
Brazil |
Bourbon mutation |
Low growth and high
productivity. Sun tolerant. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
16 |
Catimor 8664 (2-3) |
Portugal |
s Timor and Caturra |
It is tolerant to rust and
has a high grain yield. Cup quality. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
17 |
Catucai Yellow |
Brazil |
Mundo Novo and Caturra |
|
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
18 |
Sarchimor 4260 |
Portugal |
Villa Sarchi CIFC 971/10 x Timor
hybrid CIFC 832/2 |
Rust resistance |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
19 |
Tipica |
Ethiopia |
C.
arabica |
Susceptible to coffee rust. |
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
|
20 |
Catucai Red 785-15 |
|
|
|
Villacreses (2017). Lucas-Suarez
(2018), Parrales Marcillo (2018). |
Source. Gabriel et al. (2023)
Microsatellite
marker analysis (SSR) for
QTA-genotyping
QTA genotyping was carried out using M13
Tailing technology on 20 coffee accessions with 20 microsatellite markers (SSR)
in the Molecular Biology laboratory of the Santa Catalina Experimental Station
of the National Institute of Agricultural and Forestry Research (INIAF). The molecular markers used are described in
Table 2
Based on the defined model and after analysis
of normality and homogeneity of variance for each case, analysis of variance
(ANOVA) was performed to test hypotheses of fixed effects, as well as
comparisons of treatment means using Tukey's test at P<0.05 probability.
ANOVA of the data was also used to estimate variance components for random
effects. The indicated analyses were performed using Proc GLM of SAS (SAS, 2020).
The economic analysis was performed to determine the benefit/cost of
each treatment applied. This analysis allowed defining the profitability or not
of the treatments (Boardman et al., 2018).
Table 2. Microsatellite
markers (SSR) used for QTA-genotyping of 20 coffee accessions from the UNESUM
Germplasm Bank.
|
Name |
Direct
primer |
Reverse
primer |
Repetition |
Ta (°C) |
Amplicon (bp) |
Reference |
|
CaM42 |
TGGGTCAAGGATCCGTGTGTAAGAAAGA |
CCCTCACCACCAGTTCCCGATGTCAG |
(CT)8 |
55 |
190 |
Hendre et al.
(2008) |
|
CaM41 |
ATGGGGGGGGGGTGTCGGTCTATGTGA |
CGCAATTCGCTGTCACCTCCG |
(GA)4(G)4 (A)27 |
50 |
183 |
Hendre et al.
(2008) |
|
CaM38 |
GAAGCTGAAGCGGGGGAGGGTAGTAATT |
CCCATCCACCCAACCAACCTTCATTTC |
|
55 |
228 |
Hendre et al.
(2008) |
|
SSRCa091 |
CGTCTCGTATCACGCTCTCTC |
TGTTCCTCGTTCCTCTCTCTCTCTCTCT |
(G T) 8(GA )1 0 |
56 |
110 |
Missio et al.
(2011) |
|
CaM17 |
GGATTCGACAAGGTTGGCAGAGC |
TGCCGAGAAGAAGAGGGAGAGATAGTGATG |
(CCT)5-87
bp-(CTG)6 |
57 |
193 |
Hendre et al.
(2008) |
|
CaM26 |
CGAGCTAGAATGGATGGATGACTTGGTTGG |
GTTGCTCGCACCCCCGCTTCC |
(TGGAAG)5 |
55 |
203 |
Hendre et al.
(2008) |
|
CaM32 |
GGGTCAAGGATCCGTGTGTAAGAAAGA |
CCCTCACCACCAGTTCCCGATGTCAG |
(CT)8 |
55 |
191 |
Hendre et al.
(2008) |
|
CaM55 |
ATGGGGGGGGGGTGTCGGTCTATGTGA |
CGCAATTCGCTGTCACCTCC |
(GA)4(G)4 (A)27 |
50 |
183 |
Hendre et al.
(2008) |
|
CaM24 |
GGATTCGACAAGGTTGGCAGAGC |
TGCCGAGAAGAAGAGGGAGAGATAGTGATG |
(CCT)5-87
bp-(CTG)6 |
57 |
193 |
Hendre et al.
(2008) |
|
CaM22 |
CCCCTCCTCCTCCTCCTACTACTAGATGGTGGTGG |
AACCACCCCCACGCCCACCAATTAAAT |
(AT)9 (AC)12 |
55 |
222 |
Hendre et al.
(2008) |
|
CaM33 |
GCGCATTAGGCGTGGGAGAA GCGCATTAGGCGTGGGAGAA |
CAGAGAGGTTGTCGGTCGGTCAGGTGGAGAA |
(A)13-5 bp-(AG)18 |
55 |
240 |
Hendre et al.
(2008) |
|
CaM46 |
GGTGCGGTGTTTTTTTCAGTTTGGAGA |
AACCACCCCCACGCCCACCAATTAAAT |
(AT)9 (AC)12 |
55 |
222 |
Hendre et al.
(2008) |
|
CaM40 |
TTGACACACGAAACAGGAAATAAATATAG |
CCCTTCCCCTCATAGCCCTTT |
(CGA)8 |
55 |
238 |
Hendre et al.
(2008) |
|
SSRCa018 |
GTCTCGTTTCACGCTCTCTCTCTC |
ATTTTTTTGGCACGGTATGTTC |
(GT)18 (GA)10 |
57 |
115 |
Brandao et al.
(2014, 2015), Missio et al. (2009, 2011), |
|
CaM03 |
CGCGCTTGCTCCCTCTCTCTGTCTCTCT |
TGGGGGGGAGGGGGCGGTGTT |
(AC)11 |
57 |
173 |
Hendre et al.
(2008), Geleta et al. (2012). |
|
CaM18 |
CCGACTTGGACTGATGCGAAATTGA |
AAAGCAAAAAAAACCAGAAAACCAGAAAACGAAGA |
(TC)9 |
57 |
181 |
Hendre et al.
(2008) |
|
CaM20 |
AAGAGAGTGTTTGGGATTGCATTTTTAT |
CCGCGCGTAGGCTTTGTTTGG |
(TA)7(GT)14 |
55 |
178 |
Hendre et al.
(2008) |
|
CaM49 |
CCGGGGTTAATACATTGGTCTTT |
ATGACATTGTTGTTGACTTTGCTATAA |
(A)33 |
55 |
200 |
Hendre et al.
(2008) |
|
CaM36 |
TGGTTTTTTAGTTTGTTTATTTTTATTTTGATGTGAT |
CGAGCCCTCCCCTTGCA |
(TTA)7 |
55 |
185 |
Hendre et al.
(2008) |
|
CaM02 |
CGCCAGCCACAGCCACTTGC |
GCGGGGGGGTAAGAAAGAGGCGAG |
(AGG)7 |
50 |
224 |
Hendre et al.
(2008) |
SSR: Simple Sequence Repeats
Young, healthy, fully expanded leaves of the 20 genotypes from the
germplasm bank of the Southern Manabi State University (Table 1) were collected
in the field and used for DNA extraction using the protocol proposed by Doyle
and Doyle (1990) as modified by Diniz et al. (2005) for C. arabica with
a slight modification in which the leaves were macerated in liquid nitrogen,
instead of lyophilized. The quality and quantity of extracted DNA were tested
on 0.8% agarose gel in TBE buffer 1X (pH 8.3) (Tris-Borate-EDTA), stained with
ethidium bromide (0.25/mL) and visualized under UV light.
To analyze the genetic diversity of C. arabica genotypes,
heterologous amplification of 20 pairs of SSR primers for C. arabica was
developed. To perform the analyses on all genetic materials, SSR primers
available in the literature were used (Hendre et al., 2008, Missio et al. 2011)
(Tables 2).
PCR reactions for the SSR markers were performed in 20 µL containing 2.0
µL of 10x buffer, 150 mM/L dNTP, 0.1 mM/L of each primer, 50 ng of DNA, 1 mM/L
MgCl2, 0.6 U of Taq DNA polymerase and the remaining volume was made up with
water (Missio et al. 2009b). SSR primer amplification reactions were performed
using a Veriti Applied Biosystems 96 thermal cycler using a primer detection
PCR procedure modified by Missio et al. (2009b). This consisted of an initial
denaturation at 94 °C/2 min, followed by 10 cycles of denaturation at 94 °C/30
s, hybridization from 66 °C to 57/30 s, decreasing by 1 °C each cycle, and
extension at 72 °C/30 s. The last 30 cycles were 94 °C/30 s 57 °C/30 s and 72
°C/30 s, followed by a final extension at 72 °C/8 min. Samples were run on
polyacrylamide gels (6 %) in the presence of 1X TBE buffer. Electrophoretic
separation was performed for 2 h 30 min at 100 volts. At the end of the run,
the gels were stained in ethidium bromide solution (0.25 mg/mL). All images of
the fragments obtained on the gels were photodocumented (Locus Biotechnology)
using the LPIx Image program.
Allele analysis
The gels were analyzed visually, determining the weight of the fragments
in base pairs (bp) amplified for QTA-genotyping in reference to a marker of
known fragment weight. The bp of each marker for each of the coffee accessions
(absence/presence) were annotated in an Excel
spreadsheet for subsequent statistical analysis. For each marker assayed, a Chi-square test (Gabriel et al., 2022)
was applied to compare the means of presence levels in the accessions belonging
to each marker class (absence/presence) determined, with respect to the bp of
the alleles reported by other researchers (Hendre et al. 2008, Missio et.al,
2011).
Results
Allele analysis
Table 3 shows the QTA-genotyping for the 20 SSR
molecular markers evaluated, determining that in this set of 20 markers were
not effective to differentiate between the accessions of Pache, Acawa, Catimor
CIFC-P3, Catucai Amarillo - SL, Catimor UFV-5607, Caturra rojo- Pichilingue,
Catimor 8664 (2-3), Catucai Amarillo, Sarchimor 4260, Tipica, Catimor 8666
(4-3), Catucai Rojo 785-15, Catuai rojo UFV, Gheisha, Bourbon amarillo, Caturra
amarillo T-3386, Catimor CIFC-P2, Catimor CIFC-P1, Castillo and Arara.
It was also noted that microsatellites CaM42,
CaM41, CaM38, SSRCa091, CaM17, CaM26, CaM32, CaM55, CaM24, CaM22, CaM33, CaM46,
CaM40, SSRCa018, CaM03, CaM18, CaM20, CaM49 (Table 3 and Figure 1) showed the
expected allele fragments in all coffee accessions evaluated. However, two SSRs
such as CaM36 and CaM02 did not react with any of the accessions.
Likewise, it is noteworthy to observe that
Yellow Caturra T-3386 showed no reaction to the CaM41 marker. Yellow Bourbon
did not react to markers SSRCa091, CaM17, CaM26, CaM55 and CaM49. Catuai red
UFV did not react to markers CaM32 and CaM55. Pache did not react to Cam24
markers CaM20 and CaM49. Acawa did not react to marker CaM33. Catucai Amarillo
- SL did not react to marker CaM49; and, finally Catucai Amarillo did not react
to marker CaM49.
Table 3. Height of base pair fragments of each microsatellite
marker or SSR in each coffee accession evaluated.
|
Accession |
CaM42 |
CaM41 |
CaM38 |
SSRCa091 |
CaM17 |
CaM26 |
CaM32 |
CaM55 |
CaM24 |
CaM22 |
CaM33 |
CaM46 |
CaM40 |
SSRCa018 |
CaM03 |
CaM18 |
CaM20 |
CaM49 |
CaM36 |
CaM02 |
|
Pache |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
-1 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
-1 |
-1 |
-1 |
-1 |
|
Acawa |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
-1 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catimor CIFC-P3 |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catucai Yellow - SL |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
-1 |
-1 |
-1 |
|
Catimor UFV-5607 |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Red Caturra- Pichilingue |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catimor 8664 (2-3) |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catucai Yellow |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
-1 |
-1 |
-1 |
|
Sarchimor 4260 |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Tipica |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catimor 8666 (4-3). |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catucai Red 785-15 |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Red Catuai UFV |
193 |
223 |
222 |
104 |
176 |
254 |
-1 |
-1 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Gheisha. |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Yellow bourbon |
193 |
223 |
222 |
-1 |
-1 |
-1 |
207 |
-1 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
-1 |
-1 |
-1 |
|
Yellow Caturra T-3386 |
193 |
-1 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catimor CIFC-P2. |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Catimor CIFC-P1 |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Castle |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
|
Arara |
193 |
223 |
222 |
104 |
176 |
254 |
207 |
156 |
199 |
104 |
240 |
224 |
229 |
119 |
167 |
183 |
205 |
200 |
-1 |
-1 |
Figure 1.
Reading frame of the SSR
markers CaM42 and CaM41, for the coffee accessions evaluated.
Figure 2. Reading
frame of the SSR markers CaM38, CaM26, SSRCa091 and CaM17, for the coffee
accessions evaluated.
A statistical analysis was performed using the
Chi-square test (X2) in order to check whether the fragments
determined in the QTA-genotyping analysis of this research correspond to the
expected fragments found by other researchers (Table 4).
Table 4. Chi-square analysis for QTA-genotyping fragments (bp) determined.
|
Microsatellite |
0i |
ei |
oi-ei |
(oi-ei)2 |
(oi-ei)2/ei |
|
CaM42 |
193 |
190 |
3,00 |
9,00 |
0.05ns |
|
CaM41 |
223 |
183 |
40,00 |
1600,00 |
8,74** |
|
CaM38 |
222 |
228 |
-6,00 |
36,00 |
0.16ns |
|
SSRCa091 |
104 |
110 |
-6,00 |
36,00 |
0.33ns |
|
CaM17 |
176 |
193 |
-17,00 |
289,00 |
1.50ns |
|
CaM26 |
254 |
203 |
51,00 |
2601,00 |
12,81** |
|
CaM32 |
207 |
191 |
16,00 |
256,00 |
1.34ns |
|
CaM55 |
159 |
183 |
-24,74 |
704,11 |
3,84* |
|
CaM24 |
199 |
193 |
6,00 |
36,00 |
0.19ns |
|
CaM22 |
104 |
222 |
-118,00 |
13924,00 |
62,72 |
|
CaM33 |
240 |
240 |
0,00 |
0,00 |
0.00ns |
|
CaM46 |
224 |
222 |
2,00 |
4,00 |
0.02ns |
|
CaM40 |
229 |
238 |
-9,00 |
81,00 |
0.34ns |
|
SSRCa018 |
119 |
115 |
4,00 |
16,00 |
0.14ns |
|
CaM03 |
167 |
173 |
-6,00 |
36,00 |
0.21ns |
|
CaM18 |
183 |
181 |
2,00 |
4,00 |
0.02ns |
|
CaM20 |
205 |
178 |
27,00 |
729,00 |
4,10* |
|
CaM49 |
201 |
197 |
4,50 |
121,50 |
0.65ns |
X2 analysis
(Table 3) showed that overall all fragments (bp) determined were not
significant, which would indicate that these fragments are as expected;
however, markers CaM41 and CaM26 were highly significant (P<0.01) and
markers CaM55 and CaM20 were significant (P<0.05). This would suggest that
these markers had different expression than expected.
The co-dominance
characteristic of SSR markers was not considered in this genetic diversity
study. There are difficulties in using the SSR marker as codominant in species
with a polyploid genome such as Coffea arabica, which is an
allotetraploid species. This could be explained by limitations in establishing
heterozygosity or homozygosity levels at specific loci, given the inability of
SSR markers to distinguish alleles from homologous chromosomes, as well as the
possibility of finding null alleles in polyploids (Missio et al., 2009,
Cordeiro et al., 2003).
To determine the
useful attributes of the genetic markers evaluated, the 20 microsatellite
markers were tested in a panel of 20 C.
arabica genotypes. Allelic amplification
was obtained for all markers in all genotypes tested, except CaM36 and CaM02
which did not amplify. Contrary to Hendre et al. (2007), who determined that
these markers did amplify and CaM54 did not amplify in arabicas, this marker
was not used in our research. In general, the markers revealed low to medium
allelic diversity, and in particular 18 of them (CaM42, CaM41, CaM38, SSRCa091,
CaM17, CaM26, CaM32, CaM55, CaM24, CaM22, CaM33, CaM46, CaM40, SSRCa018, CaM03,
CaM18, CaM20 and CaM49) resulted in double alleles in the case of all C.
arabica genotypes tested. In contrast, Hendre et al. (2007) found that
markers CaM03, CaM15, CaM18, CaM21, CaM31, CaM34, CaM35, CaM39, CaM43, CaM55,
CaM57 and CaM58) resulted in low to medium allelic diversity. Comparing both
studies, we observed that different markers were validated and only two of the
markers used (CaM03 and CaM18) coincided with this characteristic of allelic
diversity detection.
We determined that of
the 20 validated molecular markers, one (CaM22) was monomorphic. In contrast,
Hendre et al. (2007) also determined that seven markers (CaM08, CaM09, CaM11,
Cam12, CaM23 and CaM53) were monomorphic.
Regarding the
SSRCa091 markers, the fragment at 104 bp was determined. In this regard, this
fragment was reported at 110 bp (Missio et al., 2011), with a moderate
significant correlation (0.40). In our analysis using the Chi-square test we
observed that there was no significant difference, which would indicate that it
is the same fragment.
Conclusions
The SSR markers
used have low or limited detection of genetic diversity, and limitations in the
levels of heterozygosity and homozygosity at the specific locus could be
observed due to the inability of these SSRs to distinguish alleles from
homologous chromosomes, as well as the probability of finding null alleles in
polyploids. The Cam22 marker was monomorphic.
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