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Genetic diversity and population structure of superior shea trees (Vitellaria paradoxa subsp. paradoxa) using SNP markers for the establishment of a core collection in Côte d’Ivoire

BMC Plant Biology volume 24, Article number: 913 (2024) Cite this article

Abstract

Background

The shea tree is a well-known carbon sink in Africa that requires a sustainable conservation of its gene pool. However, the genetic structure of its population is not well studied, especially in Côte d’Ivoire. In this study, 333 superior shea tree genotypes conserved in situ in Côte d’Ivoire were collected and genotyped with the aim of investigating its genetic diversity and population structure to facilitate suitable conservation and support future breeding efforts to adapt to climate change effects.

Results

A total of 7,559 filtered high-quality single nucleotide polymorphisms (SNPs) were identified using the genotyping by sequencing technology. The gene diversity (HE) ranged between 0.1 to 0.5 with an average of 0.26, while the polymorphism information content (PIC) value ranged between 0.1 to 0.5 with an average of 0.24, indicating a moderate genetic diversity among the studied genotypes. The population structure model classified the 333 genotypes into three genetic groups (GP1, GP2, and GP3). GP1 contained shea trees that mainly originated from the Poro, Tchologo, and Hambol districts, while GP2 and GP3 contained shea trees collected from the Bagoué district. Analysis of molecular variance (AMOVA) identified 55% variance within populations and 45% variance within individuals, indicating a very low genetic differentiation (or very high gene exchange) between these three groups (FST = 0.004, gene flow Nm = 59.02). Morphologically, GP1 displayed spreading tree growth habit, oval nut shape, higher mean nut weight (10.62 g), wide leaf (limb width = 4.63 cm), and small trunk size (trunk circumference = 133.4 cm). Meanwhile, GP2 and GP3 showed similar morphological characteristics: erect and spreading tree growth habit, ovoid nut shape, lower mean nut weight (GP2: 8.89 g; GP3: 8.36 g), thin leaf (limb width = 4.45 cm), and large trunk size ( GP2: 160.5 cm, GP3: 149.1 cm). A core set of 100 superior shea trees, representing 30% of the original population size and including individuals from all four study districts, was proposed using the “maximum length sub-tree function” in DARwin v. 6.0.21.

Conclusion

These findings provide new knowledge of the genetic diversity and population structure of Ivorian shea tree genetic resources for the design of effective collection and conservation strategies for the efficient use of inbreeding.

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Background

The shea tree (Vitellaria paradoxa, C. F. Gaertn) is an African native plant species. It has been recorded in 21 countries across semi-arid Sub-Saharan Africa within a wide belt of more than 3.4 million km [1]. It supports an estimated 16.2 million of shea nut collectors [1]. V. paradoxa is distributed from Senegal in the West to Uganda in the East [2]. The genus Vitellaria comprises a single species belonging to the Sapotaceae family, and it includes two subspecies: subsp. nilotica, primarily found in East Africa, and subsp. paradoxa, which occurs in West Africa from Senegal to the Central African Republic [3, 4].

In Côte d’Ivoire, shea trees are predominantly found in the northern region. This species exhibits monoecious characteristics and possesses a bisexual reproduction pattern that makes it rely primarily on insect-mediated cross-pollination. However, the plant has also hermaphroditic flowers capable of self-pollination [2]. The dissemination of the shea fruits is primarily achieved through barochory, with secondary dispersal by various animals such as birds, monkeys, rodents, and even humans [5].

Shea tree fruits have been considered as a significant source of economic benefit in a number of countries within the semi-arid savanna regions of West Africa. In fact, dating back to medieval times, their kernels have been used in the production of an important primary derivative: shea butter [6, 7]. Additionally, due to the presence of edible fatty acids that are used in the food, cosmetic, and pharmaceutical industries, shea butter holds a prominent position as a multimillion-dollar export commodity [8,9,10].

Despite its importance, the International Union for Conservation of Nature [11] has classified the shea tree as one of the plant species facing significant threats and experiencing vulnerability. It is confronted with extensive degradation, which is primarily driven by activities such as charcoal production and frequent uncontrolled bushfires. Furthermore, factors like population growth, which reduces the duration of fallow periods, and the systematic collection of fruit from beneath shea trees by local communities, pose significant obstacles to the natural regeneration of the species [3]. In addition, the overexploitation of shea trees and the expansion of new, more profitable agricultural crops such as cashew plantations in northern Côte d’Ivoire are contributing to the decline in shea tree densities.

To safeguard the genetic resources of Vitellaria paradoxa in West Africa, national and regional strategies [12] for the identification and preservation of local shea tree varieties have been promoted [5]. Several additional initiatives aimed at identifying and conserving shea tree resources have also been launched to address these concerns [13, 14].

In Côte d’Ivoire, superior shea trees have been identified and rereferred in several districts based on a participatory survey. These trees constitute the in situ collection of shea trees in the country [13]. The genetic diversity and structure of this population have been not studied. In addition, a recent study on the morphological traits and sustainability of part of these trees demonstrated that the superior shea trees conserved on farmers’ lands are threatened because of biotic and abiotic pressures [15]. Consequently, establishing a core collection based on the genetic diversity of superior shea trees will be helpful for suitable conservation and management [15].

Studying the genetic diversity and population structure is important for designing effective conservation and breeding programs [16], as well as for characterizing the natural selection history and genetic relationships of V. paradoxa [17].

Several authors have mentioned molecular markers such as random amplified polymorphic DNA (RAPDs) [18,19,20] and single sequence repeats (SSRs) [5, 20,21,22,23,24,25,26,27] in studies of shea tree species. Most of these studies have used these molecular markers to access the genetic diversity and population structure of shea tree species. Recently, single nucleotide polymorphism (SNP) markers were applied to study the genetic diversity and population structure of the Ugandan nilotica subspecies of the shea tree [28].

The approach of establishing core collections has emerged to increase the efficiency of the conservation and use of plant genetic resources while preserving the genetic diversity of the entire collection as much as possible [29, 30]. For plant species with recalcitrant seeds, the establishment of a core collection is the most suitable and cost-effective alternative method for in situ conservation of their genetic resources [31].

In the present study, the genotyping by sequency technology was used to genotype a panel of 333 superior V. paradoxa trees from the four key shea production districts in Côte d’Ivoire. The objectives were: (1) to characterize the genetic diversity and population structure of the shea tree using SNP data; (2) to characterize the genetic differentiation among and within Ivoirian shea populations; and (3) to establish a core collection for suitable conservation and management. This study is the first to use SNP markers to assess the genetic diversity and population structure in a V. paradoxa subspecies of Côte d’Ivoire. It lays a foundation for the effective conservation of shea trees and future genome wide association studies (GWAS) in shea tree breeding programs.

Methods

Plant material

From an initial collection of 1,200 superior shea trees previously identified by a shea breeding program from Côte d’Ivoire now known as the Centre Africain de Recherches et d’Applications sur le Karité (CRAK, the African Center for Shea Research and Application), 333 were randomly selected based on geographical distribution and population density. Superior shea trees were identified as described in a previous study [15]. A participatory survey with farmers allowed for the selection of superior trees based on criteria such as high fruit yield, sweet taste of the fruit pulp, large fruit size, early flowering every year, and periodicity of fruit production [15]. These genotypes are being conserved in situ in four northern districts of Côte d’Ivoire: Bagoué, Hambol, Poro, and Tchologo (Fig. 1). To obtain samples, two mature leaves from the lower part of the tree were collected from each individual shea tree between May and July 2020 and 2021. The collected leaves were immediately dried using silica gel and then stored at 4 °C, awaiting DNA extraction.

Fig. 1
figure 1

Spatial distribution of the 333 superior shea trees (163 in Bagoué, 53 in Poro, 97 in Tchologo and 20 in Hambol) from northern Côte d’Ivoire; the colors represent the different districts (red for Bagoué, blue for Poro, green for Tchologo, and yellow for Hambol); the dots represent the samples

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The savannas of northern Côte d’Ivoire, where shea trees grow, are divided into two main zones: the Sudanese and sub-Sudanese savannas, based on climatic factors and differences in vegetation [15]. The Sudanese savanna (Bagoué, Poro, and Tchologo districts) corresponds to the main production zone of the shea tree with a monomodal rainfall pattern (1,200 mm/year) [32, 33]; the sub-Sudanese savanna (Hambol district) corresponds to a transitional production zone with a bimodal rainfall pattern (1,050 mm/year) [32, 34]. The average annual temperature is around 27 °C. The vegetation is Sudano-Guinean and consists of wooded savanna and grassy savanna with scattered gallery forests, particularly along waterways [35]. The pedology of this zone is characterized by three subclasses of ferralitic soils: soils on basic rocks, tropical ferruginous soils and hydromorphic soils [32]. The main crops grown in these districts are cotton, cashew nuts and mangoes. The tree and shrub species commonly found in the study area are Vitellaria paradoxa, Paria biglobosa, Pilliostigma thonningii, Parinari curratellifolia, Terminalia avicennioides and Ficus sciarophylla [36].

DNA extraction and sequencing

The collected samples were sent to SEQART AFRICA, located at the International Livestock Research Institute (ILRI) in Nairobi, for genotyping. DNA extraction was conducted using the NucleoMag plant DNA extraction kit (Takara Bio USA), following the manufacturer recommendations. The concentration of extracted genomic DNA varied between 50 and 100 ng/µl. DNA integrity was checked on 0.8% agarose gel loaded. Libraries were constructed according to the DArTseq complexity reduction method through digestion of the genomic DNA using a combination of PstI and MseI restriction enzymes and ligation of barcoded adapters and the common adapter, followed by PCR amplification of adapter-ligated fragments [37]. Libraries were sequenced using single read sequencing runs for 77 bases. The sequencing was carried out using the Illumina HiSeq 2500 system.

SEQART AFRICA uses genotyping by sequencing DArTseq technology, which provides rapid, high-quality, and affordable genome profiling, even from the most complex polyploid genomes. DArTseq markers scoring was achieved using DArTsoft14, an in-house marker scoring pipeline based on algorithms. DArTseq SNP markers were scored as binary for the presence or absence (1 and 0, respectively) of the restriction fragment with the marker sequence in the genomic region of the corresponding sample.

SNP markers were aligned to the Vitpa_HiCP0_Assembly reference genome freely accessible online at https://bioinformatics.psb.ugent.be/orcae/overview/Vitpa to locate their corresponding chromosome positions.

SNP marker filtering

Two criteria were used to discard low-quality SNP markers and ensure data integrity. First, regarding the proportion of missing data (> 20%), SNP markers with missing data exceeding 20% were excluded from the dataset. Second, regarding the minor allele frequency (MAF), SNPs with a minor allele frequency of less than 5% were considered rare and were therefore discarded.

The next step consisted of selecting the SNPs with substantial information content for further analysis. Specifically, a threshold based on the polymorphism information content (PIC) value equal to or greater than 0.1 was established. In addition, only biallelic SNP markers were kept for this study.

After this rigorous quality control process, a dataset consisting of 7,559 SNP markers and the 333 superior shea tree genotypes were considered for further analyses. This stringent filtering ensured that our dataset was of high quality and suitable for robust genetic analysis.

Genetic properties of markers

A custom Perl script was used to compute the allele counts and allele frequencies from the selected SNPs. Furthermore, to estimate markers-associated statistics such as observed heterozygosity (HO), expected heterozygosity (HE) and minor allele frequency (MAF), the GenAlEx version 6.503 software was used [38]. The PIC values were computed using the formula proposed by Botstein et al. [39] using the Excel software [39]. The web-based SNiPlay software was used to determine transversion and transition mutations [40]. The Plink software “recordeA” function [41] was used for the generation of the SNP dosage format 0, 1, and 2, respectively representing the homozygote, the homozygote alternative and the heterozygote.

Population structure analysis

To assess the population structure of superior shea trees, three complementary methods were employed: Bayesian model-based clustering using Structure version 2.3.4 software [42], principal coordinates analysis (PCoA) was performed using GenAlex version 6.503 software, and discriminant analysis of principal components (DAPC) was performed using the R software version 4.3.0.

For the Bayesian model-based clustering analysis, the Markov chain Monte Carlo (MCMC) method with the admixture model excluding the LOCPRIOR option was used. This analysis was iteratively run 10 times for each K value ranging from 1 to 10. A burn-in period of 50,000 iterations followed by 100,000 MCMC iterations was used. Additionally, we assumed an admixture model with correlated allele frequencies. The most probable K value for each test was determined using the delta K (ΔK) [43] method based on the rate of change in [LnP(D)] between successive K-values. Genotypes with membership probabilities greater than 0.7 were considered as belonging to the same group.

DAPC was used to complement the model-based population structure results obtained from Structure. DAPC is a multivariate method designed to identify and describe clusters of genetically related individuals [44]. It was performed in R software version 4.3.0 using the package “adegenet” with the function “find.clusters”. In the absence of a predefined grouping pattern, DAPC employs sequential K-means and model selection to establish genetic clusters based on genetic data. The bayesian information criterion (BIC) guided the determination of the optimal number of genetic clusters (K) to best describe the data. The calculation of the α-score was instrumental in retaining the optimal number of principal components. DAPC also furnished membership probabilities for each individual with respect to each identified group, which is comparable to the admixture proportions obtained from Structure.

A neighbor-joining (NJ) phylogenetic tree was reconstructed using R software (version 4.3.0) based on Nei’s genetic distance with 1,000 bootstrap replicates. The tree was customized using the online tree annotation platform iTOL (Interactive Tree of Life) [45].

The number of clusters determined by Bayesian model-based clustering was subsequently used in the analysis of molecular variance (AMOVA) to assess the genetic differentiation of the genotypes. This comprehensive AMOVA was performed using the GenAlEx software version 6.503, and it allowed for the estimation of the fixation index (FST) and the gene flow per haploid number of migrants (Nm). FST values range from 0 (no differentiation between groups) to 1 (complete differentiation). Moreover, genetic diversity indices such as the number of different alleles (Na), the number of effective alleles (Ne), the number of loci with private alleles, Shannon’s information index (I), observed heterozygosity (HO), and expected heterozygosity (HE) were also computed using the above-mentioned software [38].

Morphological characteristics of the groups obtained with bayesian model-based clustering from structure

To evaluate the morphological characteristics of 160 genotyped shea trees (availability of their morphological traits), 11 quantitative and qualitative morphological traits (Table S1), described in previous studies [15, 46] were used. A comparison of the morphological traits between the groups obtained using SNP markers with Structure was also accessed. Principal component analysis (PCA) and a heat map of the correlation matrix were performed to structure the quantitative traits. Finally, a Mantel test with 10,000 permutations and Spearman correlation method was performed for matrices comparison between morphological traits and SNP markers to access the relationship between both markers.

Design of the superior shea tree core collection

The design of the core collection followed the methodology proposed in a previous study [47]. DARwin software version 6.0.21 was used for the reconstruction of the diversity of trees using SNP dataset [48]. Dissimilarities were computed and transformed into Euclidean distances. The un-weighted neighbor-joining method was applied to the Euclidean distances to build a tree with all genotypes. The “maximum length sub tree” function was then used to identify the individuals of the core collection: The “maximum length sub-tree” is a step-by-step process that successively eliminates redundant individuals. We then selected the last 100 individuals that retain the largest diversity. The size of the core collection was fixed a priori [49], and the efficiency of the strategy was assessed by comparing and keeping the total number of alleles captured for each run using the same software. The size of the core collection was expressed as the ratio of individuals kept in the core collection to the number of individuals in the entire collection. Principal component analysis (PCA) was plotted using R software (version 4.3.0) to see the distribution of the core sample relative to the entire sample.

Results

Distribution of SNPs, genetic diversity, and polymorphism information content in the Vitellaria paradoxa genome

After the filtering process, 7,559 SNPs, representing 17.7% of the 42,705 SNPs initially yielded, were retained. These 7,559 SNPs were unequally distributed across the 12 chromosomes with, an average marker density of 1 marker per 87.38 kb.

A genome-wide SNP marker analysis revealed that chromosome 2 had the highest number of SNPs, with 12.9% (978 SNPs) of the filtered SNPs, while chromosome 12 had the lowest number, with 6.2% (469 SNPs). In terms of marker density, chromosome 6 had the highest marker density with 1 marker per 74.67 kb, while chromosome 4 had the lowest density with 1 marker per 105.77 kb (Table 1).

Table 1 Genomic distributions of the 7,559 filtered SNPs physically mapped on the 12 chromosomes of Vitellaria paradoxa and the corresponding SNPs densities
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Within the collected shea genome, transition-type SNPs (4,647, or 61.48% of SNP markers) were more common than transversion-type SNPs (2,992, or 38.52% of SNP markers). This resulted in a ratio of transitions to conversion SNPs of 1.6 (4,647/2,912). Specifically, transition A/G and T/C types were more common than G/A and C/T types. For transversion, the types T/G, A/T, A/C, and G/C were more common than G/T, T/A, C/A, and C/G (Table 2).

Table 2 Percentage of transition and transversion SNPs across the Vitellaria paradoxa genome
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The average observed heterozygosity (HO) in this study was 0.17, while the expected heterozygosity (HE) values varied from 0.1 (for 152 SNPs) to 0.5 (for 1,438 SNPs), with an average of 0.26. In parallel, the PIC values ranged from 0.1 (for 237 SNPs) to 0.5 (for 879 SNPs), with an average of 0.24 (Fig S1.a and Fig. S1.b). A consistent number of SNPs (5,104 or 67.52% of the filtered SNPs) had a MAF value less than 0.2 (Fig. S1.c).

Population structure and genetic relationships

The population structure and the genetic relationships among the studied population were investigated using the Structure version 2.3.4 software. The K value was used to estimate the number of clusters in the shea tree population based on genotypic data across the whole genome. The optimal K value was determined by plotting the number of clusters (K) against ∆K. It showed a sharp peak at K = 3 (Fig. 2a), suggesting that the studied population can be clustered into three groups with different genetic backgrounds (GP1, GP2, GP3). The number of genotypes in each group was 168 in GP1, 93 in GP2, and 72 in GP3. Among the population, 154 genotypes were considered as admixed (Table S2). Consequently, these results were considered in the subsequent population genetics analyses.

Fig. 2
figure 2

a Delta K for various number of clusters (K); b Values of the BIC (1,920.82) versus the number of clusters; c Bayesian model-based analysis (K = 3) of 333 Vitellaria paradoxa individuals; accessions in red are clustered into GP1, accessions in green are clustered in GP2, and accessions in blue are clustered into GP3

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The net nucleotide distance between the groups is shown in Table S2, and the maximum distance was recorded between GP1 and GP2 (0.003). The genetic distance between GP1 and GP3 (0.0026) was closely related to the distance between GP2 and GP3 (0.0025). The results from Structure estimated the fixation index (FST) for each group and suggested a significant divergence within the three groups (Table S2). GP3 displayed the highest FST value (0.0182), while GP1 had the lowest FST value (0.0177). In addition, the heterozygosity values were 0.259, 0.261 and 0.262 for GP1, GP2, and GP3, respectively (Table S2).

Consistent with the findings from Bayesian model-based clustering in Structure, the discriminant analysis of principal components also suggested three distinct clusters based on the value of BIC (1,920.82) (Fig. S2.a). In the DAPC results, group1, group2, and group3 have, respectively, 93, 72, and 168 individuals. The probability of each individual belonging to a single cluster was 100%. Therefore, no individual was considered mixed (Fig. S2.a and Fig. S3). The principal coordinates analysis (PCoA) showed that GP2 is an intermediate group between GP1 and GP3 (Fig. S4). In fact, the Structure software triangular plot showed that most of the shea trees from the Hambol, Poro, and Tchologo districts were clustered in GP1, whereas genotypes included in GP2 and GP3 are mainly from the Bagoué district (Fig. S2.b). The same results were observed with the PCoA plot (Fig. S5).

In addition to the three methods above, a neighbor-joining phylogenetic tree also clustered the shea trees into three groups (Fig. 3). Based on the true clades confirmed with the bootstrap values, the samples should be clustered by origin (Fig. 3). This confirm the true structure of our shea tree samples using Structure.

Fig. 3
figure 3

Neighbor-joining phylogenetic tree with bootstrap values (black squares represent the bootstrap values; 1,000 replicates) showing relationships between the 333 superior shea trees based on Nei’s genetic distance matrix using 7,559 SNP markers; branch colors represent genetic groups (red for GP1, light green for GP2, and blue for GP3; label colors represent the origin of the genotypes (red for Hambol, black for Bagoué, green for Tchologo, and blue for Poro)

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Genetic differentiation of populations

The three groups from the Bayesian model-based clustering in Structure were then used to calculate various genetic parameters such as AMOVA, Nei’s genetic distance, and genetic diversity indices using GenAlEx 6.503 software. The results of these analyses are shown in Table 3. Overall, the results showed a low level of genetic differentiation between groups but a high level of genetic differentiation within groups. In addition, Nei’s genetic distance analysis revealed a very low fixation index value (FST: 0.004) and a significant number of migrants (Nm: 59.02), confirming the low level of genetic differentiation between the three genetic groups. The pairwise FST values were found to be 0.005, 0.004, and 0.003 for GP1-GP2, GP1-GP3, and GP2-GP3, respectively (Table 3).

Table 3 Analysis of molecular variance (AMOVA) among the 333 superior shea trees based on genetic variation among and within the identified groups
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Allelic pattern across populations

The three groups showed a grand mean of 2 for the number of different alleles (Na) and 1.395 for the number of effective alleles (Ne) (Table 4). Within the whole population, the means for Shannon’s index (I), gene diversity (HE), and unbiased gene diversity (uHE) were 0.414, 0.258, and 0.260, respectively. The three genetic groups were closely related in terms of diversity metrics (Table 4). The percentage of polymorphic loci per population (PPL) was 100% for GP1 and GP2, while GP3 had a PPL of 99.93%.

Table 4 The means of different genetic parameters in each of the three groups
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Comparison of our results with previous studies

Several studies assessed the genetic diversity in shea tree during the last two decades (Table S3). SSR markers were widely used to assess the genetic diversity of shea tree, representing 54.55% of the studies. SNP markers were the most recent markers used, representing 27.27% including our study. Only two studies mentioned RAPD markers in shea genetic diversity study (18.18%). Moderate genetic diversity was observed in our study and a study conducted in Uganda using SNP markers. However, the HE obtained in the present study (HE: 0.26) was higher than the HE obtained in Uganda (HE: 21). Using the same type of marker, a study conducted in Ghana showed low genetic diversity, with an HE value of 0.041. However, the population genetic differentiation parameter such as the fixation index (FST) was higher in the Ugandan population than in our study and in the population of Ghana (Table S3).

For other studies using SSR markers, low HE value (0.32) to moderate HE value (0.73) was observed. The HE values of SSR markers were higher than HE in our study because SSRs are co-dominant and the HE value varies from 0 to 1, whereas for SNP and RAPD markers, HE is calculated as dominant and the value ranges from 0 to 0.5. Overall, studies conducted in a single country or subspecies showed low genetic differentiation. In contrast, studies conducted in the natural range of V. paradoxa showed high genetic differentiation, and the authors attributed this differentiation to the subspecies nilotica and paradoxa (Table S3).

Design of the core germplasm collection

A core germplasm collection is a critical step in creating a manageable and representative sample that can reflect the diversity within the larger germplasm collection. This process is also relevant to modern plant breeding efforts. Maintaining genetic diversity within the core collection is essential, and the primary criterion for selecting its members is the average genetic distance in the population. Using the “maximum length sub-tree” function in DARwin 6.0.21, we successfully designed a core germplasm set of 100 individuals, representing 30% of the entire population (Fig. S6). Of the core germplasm, 49% of the shea trees are from the district of Bagoué. These individuals belong to GP2 (30 individuals) and GP3 (19 individuals). The remaining 51% of the core germplasm, are from the other three districts (Hambol, Poro, and Tchologo) and belong mainly to GP1 (Table 5). The grand mean diversity metrics of this core collection set were similar to those of the entire population (Table S4).

Table 5 Percentage of individuals from each district and genetic group in the core set
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A phylogenetic tree of the 100 individuals in the core set was reconstructed with the neighbor joining method based on Nei’s genetic distance. The obtained dendrogram is similar to that obtained with the whole population (Fig. S6).

A good spatial representation of the core set within the entire sample was observed in the PCA plot of the entire panel of 333 shea trees (Fig. 4).

Fig. 4
figure 4

Principal component analysis plot showing the distribution of the core sample within the full panel of 333 shea trees. Blue dot represents the discarded genotypes and red dot represents the core sample

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Morphological characteristics of the genotyped shea trees

The analysis of the morphological characteristics of 160 genotyped shea trees revealed significant variations (Table S5). Principal component analysis (PCA) identified three principal components (PCs) that explained 73.03% of the total variance observed among shea trees population (Table S6). The PC1 (x-axis) explained 34.55% of the total variance. The leaf traits (PL, LL, and LW) and nut traits (NL, NWD and NWG) are correlated positively towards PC1 (Fig. 5). However, trunk circumference (TC) correlated negatively towards PC1. Similarly, PC2 (y-axis) accounted for 26.03% of the total variation. The nut traits (NL, NWD, and NWG) and TC positively correlated towards PC2 while the leaf traits (PL, LL, and LW) were negatively correlated (Fig. 5). PC3 captured 12.45% of the total variance, and TC and NWG were positively correlated. In contrast, NWD and NGW were negatively correlated towards PC3 (Table S6).

Fig. 5
figure 5

Estimated PC1 and PC2 for quantitative morphological traits of 160 genotyped shea trees. TC: trunk circumference, PL: petiole length, LL: limb length, LW: limb width, NL: nut length, NWD: nut width, NWG: nut weight

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The correlation heatmap analysis revealed three classes of studied traits: (i) trunk circumference, (ii) nut size (nut length, nut width, and nut weight) and (iii) leaf size (limb width, limb length, and petiole length). Positive correlations were observed within the descriptors of each identified class (see color key in Fig. S7).

Using clustering from Structure as a priori groups, significant differences were found between the three groups in nut weight (F: 14.11; p < 0.001), trunk circumference (F: 5.04; p < 0.01), and limb width (F: 4.16; p < 0.05). GP1 showed a weak mean of trunk circumference (133.4 cm), large leaf size (limb width = 4.63 cm), and a high mean of nut weight (10.62 g), whereas GP2 showed a high mean of trunk circumference (160.5 cm), a thin leaf (limb width = 4.46 cm), and a weak mean of nut weight (8.89 g). GP3 had the lowest value for significant traits such as nut weight (8.36 g), and limb width (4.44 cm) and, it was intermediate for trunk circumference (149.1 cm). A detailed examination of the results shows that GP1 differs from GP2 and GP3, while no significant differences are observed between GP2 and GP3 (Table S7). There were no statistical differences between the groups for the other quantitative traits. The means of these quantitative traits are presented in Table S7.

Meanwhile, all the qualitative traits used to characterize the three groups showed statistically significant differences (Table S7).

While the seed coat colors of “creamy” and “dull brown” were present only in GP1, the spheroid seed shape was found only in GP2 and GP3 (Table S7). In terms of group differentiation, the same trend observed for quantitative traits was also observed for qualitative traits. Therefore, the three groups obtained with SNP data can only be clustered into two groups based on morphological characteristics: (I): GP1 and (II): GP2 and GP3 as a single group.

The Mantel test results revealed that there is no relationship between morphological traits and SNP markers (Mantel r = 0.0176, p = 0.3324).

Morphological characteristics of the core collection

Of the 160 genotyped shea trees, 52 were captured in the established core collection. The analysis of the morphological characteristics of these core individuals showed important variations for the quantitative traits (Table S8). For example, trunk circumference varied from 65.5 to 242 cm with a mean of 19.7 cm. Concerning qualitative traits, observations demonstrated that almost all the modalities of traits were found in the core collection. Only “spheroid shape”, a modality of seed shape (SEES) was not captured in the core collection (Table 6). However, that modality had a very low proportion (2.5%) in the entire collection. In addition, multivariate analyses showed that there is no significant difference between the core collection morphological characteristics compared to those of the entire collection (Table 6).

Table 6 Quantitative and qualitative traits associated with the core and entire collection of Vitellaria paradoxa
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Discussion

In order to assess the genetic diversity within Vitellaria paradoxa, a collection of 333 genotypes was gathered from the in situ superior shea collection in Côte d’Ivoire. The genotypic information from these individuals was used to investigate genetic diversity and population genetics, which could provide valuable insights for future breeding efforts, such as genome-wide association studies (GWAS). To the best of our knowledge, this study is the first one that uses single nucleotide polymorphisms (SNPs) to investigate the genetic diversity and population structure of Vitellaria paradoxa subspecies in Côte d’Ivoire.

Single nucleotide polymorphism markers and mutation types

In this study, 7,559 high-quality SNP markers were retained after data filtering. The analysis of SNPs distribution and mutation types in the genome was conducted. A higher frequency of transitions than transversions was observed (transitions/transversions = 1.6). A ratio of 1.3 in transition SNPs and transversion SNPs was obtained in shea trees in Uganda [28]. Similar transition/transversion results have been reported in other plant species, such as such as Hevea brasiliensis [50], Camellia sativa [16], Vigna unguiculata [47], Colocasia esculenta [51], and Oryza sativa [52], and in living organisms in general [53]. This suggests that during the natural selection of V. paradoxa, transition mutations tend to be more tolerated than transversion mutations.

This may be due to the presence of higher frequencies of synonymous mutations in the protein-coding sequences [53]. However, the non-synonymous SNPs are of interest for this study because they generate new variants that are important in breeding programs [28]. In natural selection, non-synonymous SNPs are important for the conservation of shea tree species because the variants become more adapted to environmental changes [28]. Transition mutation types are also important in evaluating the distribution, extent, and amount of genetic variation among and within shea tree populations [28]. The frequencies of A/G and C/T transitions were similar (A/G: 30.6% and T/C: 30.8%). This result is consistent with those obtained for shea tree and rice species [28, 52].

Gene diversity

The genetic diversity and the population structure of a species are good indicators of its management and conservation status [28].

The findings in this study revealed moderate level of genetic diversity (HE: 0.26) and moderate polymorphism information content (PIC: 0.24) in V. paradoxa. This is consistent with what has been reported for shea trees in East Africa using SNP [28] and SSR markers [25], in West Africa with SSR markers [24, 26] and in the natural range of the species using RAPD markers [18, 19]. This suggests that the SNP markers used in our study were reasonably informative markers. In contrast, a V. paradoxa diversity study in Ghana using SNP markers revealed very low genetic diversity [54]. Other studies showed low genetic diversity in V. paradoxa using SSR markers [21, 55]. These results suggest that the genetic diversity in shea tree species is low to moderate in its natural range.

The moderate genetic diversity may be the result of selection based on farmer-preferred traits such as large fruit size, tasty pulp, and high oil content [56, 57]. Furthermore, this moderate genetic diversity provides an opportunity to generate shea varieties that can be successfully grown in shea belts in different geographical districts [28]. Studies involving other plant species such as Camelina sativa [16], Ziziphus jujuba [58], and winter wheat [59] observed the same trend and attributed these findings to the bi-allelic nature and low mutation rates of SNPs. Our marker density is sufficient to perform genome-wide association studies, as a genotype panel with minor allele frequency (MAF) > 0.1 is desirable for genome-wide association mapping [17, 52].

Population structure and relationships

Population structure analyses are essential for understanding genetic diversity and facilitating subsequent association mapping studies [59]. In our study, the cluster pattern in Structure grouped the shea trees into three groups, which concurs with the DAPC, PCoA, and the neighbor-joining tree reports. These results were also consistent with a previous study using SNP markers in V. paradoxa [54] in Ghana. However, our results were different from those obtained in Uganda using SNP markers. The authors clustered their accessions into two groups. These results can confirm that West African shea trees exhibit higher genetic diversity than East African shea trees using SSR markers [5]. The structure of the Ivorian shea population collection suggests a geographical effect. In fact, the genotypes clustered in GP1 are from the districts of Poro, Tchologo, and Hambol, while the genotypes collected in GP2 and GP3 come from the district of Bagoué. There are thus two geographical groups of shea trees in our collection: one group in the western part of the Poro district, which can also be divided into two subgroups representing GP2 and GP3, and the second geographical group located in the eastern art of the Poro district (see Fig. 1). Similar results were observed in the Ugandan shea tree population structure study [28]. These findings suggest that these geographical groups can serve as genetic resources for hybridization programs to create improved varieties and align with the objectives of the annotated genome [17].

However, a certain number of individuals showed a mixed genetic profile across the three groups (Fig. 2c). This observation is consistent with findings on V. paradoxa using DArTseq SNP markers [28, 54]. Gene flow between individuals in the neighborhood or individuals overlapping the study areas may be responsible for the presence of these admixed individuals. As a result, a limited number of accessions may show clear membership to one group, while the majority may show some degree of membership to the three groups. This indicates that common allelic/gene combinations continue among the collection of shea trees.

Genetic differentiation of populations

The FST value is the most relevant F-statistic used to study the degree of genetic differentiation between and within populations [52]. In this study, a fixation index (FST) value of 0.004 was found for the whole population and the low pairwise FST values found between the three groups (Table 3) indicate low genetic differentiation between these groups [52, 60]. Similar results were found in the genetic differentiation in V. paradoxa populations implying SNP markers [28, 54] and SSR markers [21]. The neighbor-joining tree based on genetic distances confirmed the low differentiation between groups, with few bootstrap values higher than 50.45, with the main branches having bootstrap values lower than 50.45 (Fig. 3). This trend has been reported in shea tree populations using nuclear SSR markers [24]. These results suggest the presence of extensive and anthropogenic gene flow, outcrossing and admixture in the study area. Comparison of our findings with previous studies revealed that low genetic differentiation is observed in shea tree populations when the study is carried out in a small area, limited to one or two countries [21, 25, 54, 55, 61] or within a subspecies [24]. This trend is expected in the shea tree due to its status as a long-lived woody perennial, insect-pollinated outcross, and widespread in a continuous range [24]. However, high genetic differentiation is observed when the study is conducted in the natural range of the subspecies [5, 18, 19]. This high differentiation can be explained by the fact that these studies included both subspecies of V. paradoxa, and differentiation is observed between the nilotica and paradoxa subspecies [5, 18, 19]. These findings suggest that many individual shea trees should be considered for efficient sampling of genetic diversity within a population. This is consistent with the proposal to develop a breeding population of Vitellaria paradoxa [18].

The findings from Structure are consistent with the outcomes from the AMOVA, where the total variation was primarily attributed to variations within the groups. Furthermore, it has been suggested that a high Nm value, indicative of substantial gene flow, can lead to low differentiation between populations [16]. Our study aligned with this observation, as a very high Nm value of 59.02 was obtained. This suggests that in terms of gene flow, the Nm value obtained in our study was higher than that reported in shea trees using SSR markers [55]. Therefore, our results suggest that population genetic differentiation using SNP markers is more informative than other markers.

Allelic pattern and genetic diversity indices

The allelic patterns and genetic diversity indices offered valuable insights into the genetic diversity present within each of the three genetic groups. While the three groups exhibited relatively close levels of expected heterozygosity (HE), GP1 showed a slightly higher HE compared to GP2 and GP3. This indicates that GP1 has a slightly higher level of diversity compared to the other groups, as HE considers both the number of alleles (referred to as richness) and the distribution (or evenness) of those alleles within a population. This could be explained by the origin of the genotypes clustered in GP1. In fact, the genotypes in GP1 are from three different districts (Hambol, Poro, and Tchologo), while the genotypes in GP2 and GP3 are mainly from the Bagoué district.

Genetic diversity ranged from low to high. This result is consistent with recent studies using SNP markers [28] and previous population genetic studies using SSR markers [5, 21, 24, 26, 55]. Understanding the genetic diversity within V. paradoxa populations is essential, this is fundamental element for robust shea tree improvement programs and for future studies using genomic screening methods such as marker-assisted screening (MAS) and genome-wide association studies (GWAS) [17].

Core collection

Sustaining living collections, a common practice for perennial tree crops, can be a costly and labor-intensive endeavor. Creating core collections is an efficient strategy for managing germplasm, effectively reducing costs while retaining the highest possible genetic diversity within the germplasm pool while minimizing redundancy [29, 30]. Developing core collections has been the focus of various approaches [62,63,64], and the choice of the most appropriate evaluation methods depends on the specific objectives associated with these core collections [65].

In this study, we used the “maximum length sub-tree function” of DARwin version 6.0.21 to carefully curate a core set of 100 superior shea trees (30% of the initial population). The analysis indicates that the core collection successfully captures the full genetic diversity of the entire population. This is evidenced by the PCA plot, which showed good coverage of the core sample across the whole panel (Fig. 4), and the representation of each genetic group within the core, which includes 48% from GP1, 30% from GP2 and 22% from GP3. These proportions reflect the overall genetic structure of the entire population, ensuring that no significant genetic diversity is lost in the core. In addition, several genetic parameters in the core closely mirror those of the total population: heterozygosity, allele frequency, and polymorphism rates. This similarity underscores the effectiveness of the core collection in maintaining the genetic variation present in the larger population, making it a robust subset for further genetic studies and conservation efforts. Thus, the core collection can safely be used for genetic research, breeding programs, and conservation strategies without compromising the genetic integrity of the original population [66].

The proportion of this core set is similar to the proportion established for other crops, such as palm oil (31.2%) [67]. However, it differs from the core set proportions observed for E. oleifera (6.4%) [68], S. superba (19.87%) [69], and P. massoniana (19.46%) [66]. A core collection sample size of 5–10% of the original germplasm resources can be sufficient to represent over 70% of the genetic variation present in the entire germplasm [29]. For effective conservation of the genetic diversity of the entire population, a size of 20–30% of the population is required for the core set [70].

However, there is no universal approach to selecting a core size. It depends on factors such as the extent of variability and redundancy within the collection, the resources available for core set management, and the frequency of species regeneration [70, 71].

Field genebanks provide convenient and immediate access to germplasm resources. However, relying solely on in situ conservation is not the most reliable long-term conservation strategy. Shea trees can be uprooted by adverse weather conditions, such as tropical cyclones [15]. To address this, a shift in focus for germplasm conservation of shea trees can be directed towards the identified core set. A viable approach to germplasm conservation is to clone the identified core set through grafting for ex situ conservation. This method ensures the precise conservation of maximum genetic diversity while reducing redundancy and avoiding genetic erosion. Furthermore, the number of accessions to be planted in the field can be reduced, resulting in a more manageable and cost-effective approach compared to progeny trials.

The use of molecular markers to establish a core collection offers distinct advantages because they can accurately capture genetic diversity regardless of plant growth status, developmental stage, and environmental conditions [72]. Importantly, genetic diversity is often positively associated with population persistence and resilience to environmental changes [73].

Morphological characteristics of the genotyped shea trees

Variability in superior shea trees has been widely demonstrated using morphological traits [15, 46]. A similar trend was observed in this study. Using genotype clustering from the Structure software showed significant differences between the groups for trunk circumference, nut weight, and leaf width. These differences are essentially between GP1 and GP2 or GP3. No significant differences were observed between GP2 and GP3. These results suggest that the three groups obtained with molecular markers reflected two morphological groups. This can be explained by the significant influence of the environment on the expression of morphological traits. A savanna gradient has been reported in the study area [46]. The effect of climate on the expression of shea tree morphological traits has been reported in several countries [46]. In addition, we suggest that the SNP markers that structured Bagoué superior shea trees into two groups are synonymous SNPs, while those that separated GP1 and GP2/GP3 could be designed as nonsynonymous SNPs. SNPs may change the encoded amino acids (nonsynonymous SNP) and change the amino acid sequence or be silent (synonymous SNP), thereby maintaining the amino acid sequence or simply occurring in the noncoding regions [28]. A Mantel test realized between morphological traits and SNP markers did not show significant differences (Mantel r = 0.0176; p = 0.3324). This suggests that the morphological characteristics observed in the groups are not the direct effects of the SNP markers. In addition to environmental conditions, the varying mineral composition of the soil from one ecological zone to another could also have an effect on the morphological trait expression. Studies have reported soil mineral composition effects on shea tree morphology in Mali [74], Eastern Ghana [75], and West Africa [76].

The morphological characteristics of the core collection showed a similar trend to those of the entire collection. A similar trend was found in a core establishment in Synsepalum dulcificum [31]. These results suggest that the core collection captured the full morphological characteristics of the entire collection. It is also important to ensure that the core collection is well representative of the whole collection in all genetic aspects, confirming the quality of the established core based on morphological traits as well as molecular markers (SNPs). Therefore, morphological traits could be confidently used in the establishment of core collections of V. paradoxa species without loss of genetic diversity.

Conclusion

In this study, we used high-throughput DArTseq technology to investigate the genetic diversity and population structure of Vitellaria paradoxa in Côte d’Ivoire. The primary objective was to explore the potential utility of SNP markers for various genomic analyses in the context of genetic improvement efforts. Our data revealed that the collection under investigation exhibited a moderate degree of genetic diversity.

This rich genetic diversity serves as a promising foundation upon which to develop novel Vitellaria cultivars boasting desirable traits, including high yield potential, high oil production, and resilience to biotic and abiotic stresses, all while being well-suited for adaptation to diverse environmental conditions.

Furthermore, our research unveiled the presence of three genetic groups within the study population. The differentiation between these groups can be attributed to a combination of factors and natural selection pressures. Notably, the three groups demonstrated very close diversity across multiple parameters, including Shannon’s information index (I), expected heterozygosity (HE), and unbiased expected heterozygosity (uHE).

A core collection of 100 superior shea trees, representing 30% of the entire population was captured. This study marks the inaugural endeavor to molecularly characterize and validate the creation of a core set for shea tree germplasm resources. The core collection successfully captured all the variation in morphological traits and the alleles present within the accessions, while preserving the genetic diversity and structure of the original population.

These findings provide important information for suitable conservation and future allelic/gene identification using genome-wide association studies (GWAS) and marker-assisted selection (MAS) to enhance genetic gain in V. paradoxa breeding programs.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in the Science Data Bank (ScienceDB) repository, https://doi.org/10.57760/sciencedb.17559.

Abbreviations

AMOVA:

Analysis of molecular variance

DAPC:

Discriminant Analysis of Principal Components

FST :

Genetic differentiation

PCoA:

Principal Coordinate Analysis

PCA:

Principal Component Analysis

HE :

Expected heterozygosity

HO :

Observed heterozygosity

I :

Shannon’s information index of diversity

GBS:

Genotyping by-sequencing

GWAS:

Genome-wide association study

PIC:

Polymorphism Information Content

SNP:

Single nucleotide polymorphism

MAS:

Marker-assisted selection

TC:

Trunk Circumference

PL:

Petiole length

LL:

Limb length

LW:

Limb width

NL:

Nut length

NWD:

Nut width

NWG:

Nut weight

TGH:

Tree Growth Habit

LAS:

Leaf Apex Shape

ALC:

Adult Leaf Color

SEES:

Seed Coat Color

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Acknowledgements

We acknowledge the support of the plant Genetics laboratory at Gembloux Agro Bio-Tech and the Department of Biochemistry-Genetics, Educational and Research Unit of Genetics at the Péléforo Gon Coulibaly University in Korhogo, which made this study possible. As a person, we would like to thanks Mr Blé Kpagni Antoine (Senior Technician at University Peleforo Gon Coulibaly of Korhogo) is acknowledged for putting us in contact with the farmers. We also thank Ekra Jean-Yves (Ph.D student at Sokoine University of Agriculture, Tanzania), Soro Nangalourou Adama and the ANADER agents who accompanied us in the field.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the University of Liege Scientific Research mobility (2019/MOB/02924 and 2021/MOB/00089) and the ULiege-PACODEL "Valorization / Reinforcement” Grant.

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Authors and Affiliations

  1. Plant Genetics and Rhizosphere Processes Lab, University of Liege, Gembloux Agro Bio-Tech, Terra Research Center, Passage Des Déportés 2, Gembloux, 5030, Belgium

    Affi Jean Paul Attikora & Ludivine Lassois

  2. Faculty of Biological Sciences, Department of Biochemistry-Genetics, Educational and Research Unit of Genetic, University of Peleforo Gon Coulibaly (UPGC), Korhogo, BP 1328, Côte d’Ivoire

    Saraka Didier Martial Yao, Dougba Noel Dago, Souleymane Silué, Nafan Diarrassouba & Taofic Alabi

  3. African Center for Shea Research and Application (CRAK), Korhogo, Côte d’Ivoire

    Saraka Didier Martial Yao & Nafan Diarrassouba

  4. AgricultureIsLife, University of Liege, Gembloux Agro Bio-Tech, Passage Des Déportés 2, Gembloux, 5030, Belgium

    Caroline De Clerck

  5. Faculté Des Sciences Agronomiques, Département de Production Végétale, Université Catholique de Bukavu (UCB), Bukavu, Democratic Republic of the Congo

    Yves Kwibuka

  6. Functional and Evolutive Entomology, University of Liege, Gembloux Agro Bio-Tech, Passage Des Déportés 2, 5030, Gembloux, Belgium

    Taofic Alabi

  7. Genetics, Biotechnology, and Seed Science Unit (GBioS), Department of Plant Sciences, Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526, Abomey‑Calavi, Benin

    Enoch G. Achigan-Dako

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  1. Affi Jean Paul Attikora
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Contributions

A.J.P.A. E.A.D and L.L. conceptualized the topic. A.J.P.A., D.N.D, and E.A.D analyzed the data. A.J.P.A., Y.K., D.N.D, L.S., E.A.D and L.L. set up the methodology. A.J.P.A., S.S., S.D.M.Y., D.N.D., E.A.D., and L.L. provided resources. N.D., T.A. and L.L. supervised the study. A.J.P.A., S.D.M.Y., D.N.D., T.A., E.A.D., C.D.C., N.D., and L.L. validated the work. A.J.P.A. T.A., and L.L. wrote the main manuscript text. All authors reviewed approved the final manuscript.

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Correspondence to Affi Jean Paul Attikora.

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Attikora, A.J.P., Yao, S.D.M., Dago, D.N. et al. Genetic diversity and population structure of superior shea trees (Vitellaria paradoxa subsp. paradoxa) using SNP markers for the establishment of a core collection in Côte d’Ivoire. BMC Plant Biol 24, 913 (2024). https://doi.org/10.1186/s12870-024-05617-0

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BMC Plant Biology

ISSN: 1471-2229

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