The genus Anacardium contains eight species native to tropical America, of which Anacardium occidentale L, the cashew tree, is the most economically important. The cashew tree (2n = 42), is cultivated in many countries (FAO, 2020), with[MOU1] [MK2]  Western Africa contributing about 45% of  global   production   (Audouin,  2014;  Monteiro  et  al., 2017). Cashew tree is an ecologically flexible plant growing in a wide range of rainfall (500 - 3700 mm/year) and soils (Gupta, 1993). Cashew is grown for diverse reasons, including but not limited to land protection against erosion (Kouakou et al., 2020) and nutritional and health   benefits.  Many   reviews   highlighted  the  health outcomes of regular cashew nut consumption such as the beneficial impacts on memory, obesity, diabetes mellitus, hypertension and cardiovascular diseases (Ros, 2010; Rico et al., 2016; de Souza et al., 2017; Rusu et al., 2019; Arsian et al., 2020). Furthermore, the same authors showed that nut-rich diets prevent oxidative stress, inflammation, visceral adiposity, hyperglycemia, insulin resistance, endothelial dysfunction, and metabolic syndrome, which are precursors of chronic diseases. Such health benefits of cashew nuts conferred prime importance to the crop worldwide.

In Burkina Faso, more than 250,000 ha are cultivated in cashew, primarily in the Western part of the country, with an annual production averaging 100,000 tonnes (Gecit, 2020; INSD, 2020). Cashew nuts represent the third largest export product, after gold mining and cotton, accounting for about 200 million USD, say near 5% of the total value of exports (INSD, 2020). In recent years, many efforts were deployed to enhance the economic value of the cashew industry in Burkina Faso by increasing domestic production and modernising cashew processing. The increasing commercial importance of cashew nut (Savadi et al., 2020; Tarpaga et al., 2020) makes us expect a significant expansion of the production to new regions. Consequently, the high demand for seed plants will go with the need for market desirable traits to meet domestic and international demands.

Despite its broad adaptation to even marginal environmental conditions (Gupta, 1993), cashew tree's productivity is currently low (Tarpaga et al., 2020), far below the cashew crop’s potential (>1000 kg/ha) (Kouakou et al., 2020). Plant fruit setting and overall yield can be affected by many factors such as maintenance conditions, genetics, pollinators and environmental conditions (Yaman and Uzun, 2020, 2021). Low productivity could be also attributable to the use of genetically heterogeneous cultivars by farmers, the weakness of breeding activity to improve yields and nut quality (Tarpaga et al., 2020) and the presence of diseases (Wonni et al., 2017), which are exacerbated by increased climatic instability (Ben Zaied and Ben Cheikh, 2015). Therefore, the selection and breeding of high performing varieties is both important and urgent, to meet market demand for cashew nut. Currently, little is known about agro-morphological and genetic characteristics of locally produced cashews (Tarpaga et al., 2020).

One prerequisite to propose good performing varieties to farmers, and thus increase the quality and quantity of the production, is a comprehensive knowledge of agronomic potentials of planted cultivars (Tarpaga et al., 2020). However, the breeding process is tedious due to the perennial nature of the plant and its prolonged juvenile phase (Syed et al., 2005; Savadi et al., 2020). Additionally, genetic improvement of cashew by traditional approaches is slow and unpredictable due to high degree of   heterozygosity   and   genetic    complexity    of   most agronomic traits (Syed et al., 2005).

Therefore, the application of molecular markers to assist the pre-breeding and breeding processes is highly desirable to accelerate varietal selection and genetic gain.DNA markers are known to be highly efficient in diversity assessment of plant species (Yildiz et al., 2021). DNA analysis can be performed with only small amounts of material from any part of the plant, and markers are independent of environmental conditions and plant phenological stages (Lande and Thompson, 1990; He et al., 2014). Many marker systems have been applied in cashew, including microsatellites, Random Amplified Polymorphic DNA (RAPD) (Santhosh et al., 2009; Jena et al., 2016), single nucleotide polymorphisms (SNPs) (Savadi et al., 2020), Amplified fragment length polymorphism (AFLP) (Archak et al., 2003), or sequence-specific amplification polymorphism (SSAP) (Syed et al., 2005). These marker systems could provide sufficient polymorphism to discriminate within and between populations (Archak et al., 2003) and even construct genetic maps (Syed et al., 2005). Of these, microsatellites have been very popular due to their codominance and high polymorphism (Croxford et al., 2006; Guichoux et al., 2011; Makueti et al., 2015; Ngure et al., 2021) for fingerprinting and diversity estimation. The aim of this study was to assess the genetic relationship among 18 cashew genotypes collected in western Burkina Faso, based on microsatellite markers associated with a few phenotypic attributes.

Plant material

A collection of 18 cashew trees selected from orchards in Western Burkina Faso (Table 1) was used to conduct phenotypic and genotypic investigations. We previously determined agro morphological characteristics of these trees (Tarpaga et al., 2020), which were selected among 820 candidate trees, based on specific performance criteria to make a collection of elite cultivars.

Genomic DNA extraction and amplification

Leaf samples were collected from each of the germplasm accessions for genotyping. Each sample was obtained from three to five young leaves, and were used for DNA extraction using the CTAB method (cetyltrimethylammonium bromide) as described previously by Doyle and Doyle (1990). Extracted DNA was quantified through the Nanodrop model N1000 spectrophotometer (Thermo Fisher Scientific) and then checked for genomic DNA integrity using 1% agarose gel electrophoresis.

Four microsatellite markers selected among those developed by Croxford et al. (2006) were used to estimate the genetic diversity of cashew cultivars (Table 2). The screening of accessions was performed using marker specific primers through Polymerase chain reaction (PCR) amplification. The PCR was carried out in a Bioreba thermocycler, using a reaction volume of 25 µl containing 5 µl of 5X Promega buffer, 0.5 µl of each primer, 0.5 µl of dNTPs, 0.05 µl of Taq  polymerase  and 2 ng of DNA. The PCR programme consisted of an initial denaturation at 94°C for 5 min, followed by 35 cycles of denaturation at 94°C for 1 min, hybridization for 1 min at primer-specific temperatures and extension at 72°C for 1 min, and final extension at 72°C for 7 min. Next, PCR products were run along with 100 bp ladder, through 1% agarose gel to check the presence of a unique amplicon for each marker. The PCR products were then sent to GenWIZ for sequencing.

Generation of marker profiles

The software MUSCLE (https://www.ebi.ac.uk/Tools/msa/muscle/) was used to align amplified DNA sequences with VNTR (variable number tandem repeat) loci as well as their flanking regions.

For greater accuracy, nucleotide variations between repeats were evaluated and the tandems were counted (Supplemental file S1). The number of repeats at each VNTR locus included in the MLVA scheme (Multiple Locus Variable-Number Tandem Repeat Analysis) was converted into binary marker (present or absent). Furthermore, sequence sizes and base composition of insertions were also evaluated across accessions to take into consideration indels that may occur within VNTRs.

In this way, sequence size, repeats, and inserted bases were used in combination to generate a binary matrix of markers to define allelic profile of individuals.

Assessment of genotypic diversity

The matrix generated with binary markers was used to compute genetic diversity indices using the software GenAlex 6.5 (Peakall and Smouse, 2012). These indices included (i) the number of alleles in the population (Na); (ii) allele frequencies per locus, assuming marker dominance and Hardy-Weinberg equilibrium; (iii) Shannon diversity index; and (iv) an estimate of genetic diversity based on percentage of polymorphic loci. Additionally, the polymorphic information content (PIC) was computed using the formula: PIC = 1-∑fi², where ‘fi’ = frequency of i^th allele (Botstein et al., 1980). Principal Coordinate Analysis (PCoA) of marker data was performed using the software GenAlex 6.5 (Peakall and Smouse, 2012) and the first two coordinates were used to present a scatter plot. Then, the binary marker data were used to generate a pairwise-distance matrix based on Nei’s genetic distance (Nei, 1973) with the package poppr in R. Then, using the package cluster, a dendrogram was constructed based on Nei’s distance and UPGMA as by Sneath and Sokal (1973).

Phenotypic diversity in the cashew population

The   diversity    of    cashew    accessions    was   evaluated   using quantitative and qualitative traits, such as inflorescence type (Inflow), type of fruiting time (FruT), nut yield per tree (NutY), nut weight (NutW), shelling percentage (ShellP), apple size (ApSi), shape (ApSh) and colour (Col). Furthermore, the age of each subject tree was recorded, and then classified in three age groups: below 10 years; between 10 and 14, and 15 years and above.

Using the package pastecs in R, summary statistics of the data were computed. Quantitative data were summarised in a table. Then the Shapiro-Wilks test was applied to test for normality of the collection for quantitative traits, whereas qualitative data (ApSi, ApSh, Col, age range and geographic origin) were visualised using pie charts drawn in Microsoft Excel.

To produce pairwise-dissimilarity matrices based on phenotypic variables, we computed Gower’s distance (Gower, 1971), which handles well data with different units in cluster analysis (Hummel et al., 2017). The overall estimate of genetic dissimilarity was performed based on both qualitative and quantitative phenotypic data to generate a single data matrix. To sketch the relationship between cultivars, we performed a factor analysis for mixed data (FAMD) in R software, which output was used to construct a dendrogram.

Differentiation indices

Genetic distances between phenotypic and/or geographic groups in the cashew population were computed by performing a pairwise Phi statistic (Phi-St) (Michalakis and Excoffier, 1996) using msap package in R (Perez-Figueroa, 2013).

The significance of the Phi-St values was evaluated by the analysis of molecular variance (AMOVA),  and  the  probability  of  a null hypothesis (Phi-St = 0) was estimated over 9999 permutations.

Phenotypic variability

The assessment of phenotypic diversity among 18 cashew tree genotypes revealed that this collection of elite cultivars presents noticeable variability in both qualitative (Figure  1) and quantitative traits. Although the coefficient of variation was low for nut weight (9%) and shelling percentage (6.5%), it was high (50.5%) for nut yield (Table 3). The Shapiro-Wilks test suggested that data were normally distributed for nut weight (P = 0.639) and shelling percentage (P = 0.123); whereas, data of nut yield were not (P = 0.019). The cashew tree ET06 had the poorest nut yield (23.5), but the best shelling percentage (33.9). The sample under investigation had three age groups equally shared between young, middle and old trees (Figure 1). Four apple shapes were present at proportions between 17% conical and 33% round, and half of the trees had semi-elongate or elongate apples. Most of trees presented yellow apples (50%) with predominant medium sizes (83%). While most individuals have normal fruiting time (50%), 39 and 11% of them have early and  late  fruiting  schemes,  respectively. Two inflorescence types were detected, of which 72% were clustered (NG) and 28% were sparce (GP) (Figure 1).

Multivariate analyses

FAMD-based  scatter  plots   of   sample    cashew   trees revealed that clusters correlate with age groups and provinces of origin (Figure 2). This grouping was mainly influenced by such traits as nut weight, tree age group and nut yield on the first dimension (Figure 2), and apple shape, fruiting time and apple colour on the second dimension (Figure 2).

Phenotypic variability of cashew accession from western Burkina Faso

Assessment of agro-morphological variations, including qualitative and quantitative traits is commonly used toestimate crop genetic diversity (Sunita et al., 2021; Sousa et al., 2019; Tarpaga et al., 2020). Such traits are important in the breeding process, as they are easy to evaluate (Ngure et al., 2021). The most dominant qualitative traits were medium size of apples (83%), the NG  inflorescence  type  (72%)  and  the  yellow  colour of apple (50%). However, at the western Burkina Faso scale, these proportions may be distorted by sampling bias (Panzeri et al., 2008; Michelangeli et al., 2015) due to the initial conditional selection of elite trees for this study. Nevertheless, the presence of diverse variants of apple characteristics (shape, size and colour), fruiting times, and inflorescence types, indicate that the diversity within this population is substantial. However,  apart  from nut yield which presented a significant variability between individual trees (cv >50%), there was low variability for quantitative traits such as nut weight and shelling percentage. The lack of sufficient variability for these traits was a direct consequence of the trees’ selection process, which only retained individuals with nut weight and shelling percentage above 6 g and 25%, respectively (Tarpaga et  al., 2020).  Furthermore,  nut yield showed a rough correlation with geographic origin of cultivars. This could be attributed to space-specific performance; however, as trees from the same province were nearly in the same age group, such a conclusion may appear erroneous, because old trees usually produce more than their younger counterparts (Aliyaman and Indradewa, 2019; Tarpaga et al., 2020). This is probably explanatory of the lack of normal distribution for nut yield. Furthermore, multivariate analysis of phenotypic data presented significant genetic diversity in the population under investigation. As variability is essential to sustainable crop improvement (Cobb et al., 2019), genetic improvement can be undertaken from these local cultivars, relying on phenotypic selection. However, although phenotype-based selection usually prevails during orchard implantation (Tarpaga et al., 2020), this may be subject to environmental biases which can be circumvented (Lande and Thompson, 1990; He et al., 2014) by the use of molecular markers.

Marker scoring method and polymorphism

The marker system used in this study exploited sequence data from only forward strands of DNA. Though this is not common, in the absence of reverse strand sequences, codominant SSR markers were conveniently converted into binary markers. This approach had the advantage of detecting many alleles, which increase markers’ efficiency in revealing genetic variability (Croxford et al., 2006). However, the lack of codominance appeared as a major drawback of this method. Viz., dominant markers often provide theoretical measures of heterozygosity, which has limited usefulness (Peakall and Smouse, 2012). Nonetheless, the utility of molecular markers in genetic diversity assessment is dependent on their polymorphism and power of discrimination among the genotypes under investigation (Croxford et al., 2006; Santhosh et al., 2009). In this study, the generated binary markers were not only polymorphic but were sufficiently informative (mean PIC = 0.535) to discriminate cashew individual trees. This suggests that the SSRs developed in cashew (Croxford et al., 2006) were of great quality, as testified by many other studies using all or part of the original set of these markers (Sika et al., 2015; Kouakou et al., 2020). This result corroborates with a previous diversity assessment of cashew trees in Benin, in which a set of eight markers presented a polymorphism of 100% (Sika et al., 2015). Microsatellite markers usually present high PIC values, due, at least partially, to higher mutation rates in SSRs compared with other marker types such as SNPs (Chen et al., 2017). However, the low PIC value of the polymorphic SSR maoR55 (0.003) shows that this locus presented many, but rare, alleles (up to 29). It is important to recognise that allelic frequencies and related metrics are sensitive to sample size (Müller et al., 1996). This, therefore, must be kept in mind when evaluating allele frequencies in a population.

Genotypic diversity and population structure

The level of genotypic diversity present among the studied cultivars was substantial (I = 0.217) for an introduced species. This diversity index was higher than that reported (I = 0.04) from the analysis of sixty accessions collected in central Benin (Sika et al., 2015). As a crop only recently introduced in West Africa (Aliyu, 2012; Kouakou et al., 2020), a high genetic variability was not expected in the studied cashew accessions, compared to that present in regions of the species origin. Thus, most of the studies conducted in this sub-region using the SSR markers showed a low genetic differentiation among local germplasms, such as that in Nigeria (Aliyu and Awopetu, 2007; Aliyu, 2012), Benin (Sika et al., 2015) and Cote d’Ivoire (Kouakou et al., 2020). Except Brazil where cashew originates, the crop’s diversity is often low to moderate in other parts of the world (Kouakou et al., 2020). However, the detection of such a high diversity in the population under this study may be attributable to the polymorphism obtained from the marker system applied. Therefore, diversity metrics need to be taken with caution as they are influenced by many factors, including sample size, marker system, and number of alleles (Müller et al., 1996; Powell et al., 1996).

There was no geographic structure of cultivars as shown by the AMOVA and Phi-St on genotypic data. The lack of correlation between clusters and geographic distribution of samples could be attributable to the sampling of the trees, which was based solely on their performance. Thus, the selection of elite individuals across provinces cannot capture neither the entire diversity of the crop, nor the spatial distribution of such a diversity. Nevertheless, the grouping revealed by the PCA shows that there is substantial genetic variability among the selected cultivars independently of geographic origins, as supported by the AMOVA among the three PCA-based groups (overall Phi_ST = 0.224; P < 0.0001). This result suggests that several introductions occurred over the years in Burkina Faso, and then, cultivars were probably disseminated through the same route around producing areas, without any cultivar confinement to a particular producing region. This hypothesis is consistent with conclusions drawn from previous studies in Cote d’Ivoire and India, where the authors thought that orchards in the respective countries were planted with material from a single-entry source (Archak et al., 2009; Kouakou et al., 2020). The detected diversity might also arise from the predominance of entomophilous crosspollination (Vanitha and Raviprasad, 2019) and self-incompatibility (Eradasappa and Mohana, 2019) in cashew.

This is a preliminary investigation to the study of cashew tree diversity in Burkina Faso, and can be useful evidence to  subsequently engage selection and breeding efforts. Characterization of the crop genetic diversity is key to the conservation of superior lines and the development of desirable cultivars by means of genetic improvement. This study provided a picture of genetic diversity among elite lines, and revealed that there is no geographic structure in the genotypes’ distribution, which is consistent with the crop introduction history. The analysis of SSR target sequences including indels and their conversion into binary data provided sufficient markers to perform a conclusive study. This marker system revealed high polymorphism among cultivars. However, the present study could be improved by the use of a higher number of markers, to increase the accuracy of genetic diversity metrics. Since cashew production is correlated with the tree’s age, comparison of yield between trees of different age groups holds inherent bias, which should be addressed in future studies. Future research should identify a set of markers with high discriminatory power to be used for quality control of cultivars.

The authors have not declared any conflict of interests.

The authors appreciate the financial support of the Matching Fund initiative and the West African cashew value chain capacity building project.