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Article

Sources of Genetic Variation in Faidherbia albida (Del.) A. Chev. Sub-Saharan African Populations

by
Tchapda Dorothy Tchatchoua
1,2,*,
Iain Mathieson
3,
Tetyana Zhebentyayeva
2,4,
R. Scott Poethig
5 and
John E. Carlson
2,*
1
Department of Agriculture, Animal Husbandry and Derived Products, The National Advanced School of Engineering, University of Maroua, Maroua P.O. Box 46, Cameroon
2
Department of Ecosystem Science and Management, Pennsylvania State University, University Park, PA 16802, USA
3
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4
Department of Forestry and Natural Resources, University of Kentucky, Lexington, KY 40546, USA
5
Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(1), 113; https://doi.org/10.3390/f16010113
Submission received: 23 November 2024 / Revised: 21 December 2024 / Accepted: 8 January 2025 / Published: 9 January 2025
(This article belongs to the Special Issue Genetic Diversity and Gene Analysis in Forest Tree Breeding)
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Abstract

:
The adaptation of Faidherbia albida (Del.) A. Chev. to drought conditions, its reverse phenology, ability to improve soil fertility, importance as source of forage, and its medicinal properties make it an excellent tree for Sub-Saharan African communities. However, there has been little effort to develop improved genotypes of F. albida, in part because there is relatively little information about phenotypic and genetic variation within this species. This study’s aim was to characterize the genetic diversity of F. albida among Sub-Saharan natural populations with the goal of identifying material for the improvement of the species in Cameroon and other African countries. We genotyped seven simple sequence repeat (SSR) loci in 267 individuals, 15 populations, and 8 countries in Sub-Saharan Africa representing the range of distribution of F. albida. A total of 32 alleles were identified. The highest level of polymorphism was found in Senegal and Ethiopia. Structure analysis of allelic distributions differentiated the 15 populations into three clusters representing West, East, and South Africa. However, one population in Ethiopia (Rama) was strikingly similar to the West Africa cluster. Genetic diversity decreased from West and East Africa to South Africa. These results support the hypothesis that F. albida originated in West or East Africa and subsequently spread to South Africa. Further sequence-based genotyping of these and additional populations will provide an accurate picture of the origin and subsequent spread of this species and reveal new sources of genetic variation for its improvement.

1. Introduction

Faidherbia albida (DEL.) A. Chev, commonly known as Acacia albida in Sub-Saharan African countries, is a nitrogen-fixing tree important for agroforestry because it produces leaves and fruits during the dry season, when other trees are leafless and dormant [1,2]. The trees shed their leaves during the rainy season (known as reverse phenology) which, along with its contribution to soil nitrogen, improves soil fertility, and increases the yield of cereals and other crops in multicropping systems [1,2,3,4]. In Malawi, maize yields increased under F. albida trees [5], and similar results were reported in Ethiopia [6]. The species is also appreciated by rural populations as a source of firewood, animal feed, medicine, and wood for construction [1,2].
Mature trees of F. albida are typically 15–20 m in height and live for 70–90 years [1]. They are able to grow in the Sudano–Sahelian region of Africa, where rainfall is between 500 mm and 1000 mm and the temperature commonly rises to about 40 °C during the dry season, because they have a deep root system capable of reaching the water table [7]. The species is also found along water courses or areas where underground water is present [1]. F. albida is diploid (2N = 26) [8] and is mostly self-incompatible, although there is considerable variability in this trait [9]. The species gets its common name (apple-ring acacia) because of its pods that curl into a twist as they mature, resembling an apple peel. Pods typically contain between 10 and 30 seeds [1].
The main area of the natural distribution of F. albida is Africa, but its distribution range has expanded, and it is now cultivated in many parts of the world. However, climate change and land degradation in the past decade have reduced the size of these populations. Many reforestation projects are ongoing in Sub-Saharan African countries, and the use of native species in reforestation projects has been highlighted by many authors [9,10,11,12,13,14]. The impact or benefit of the use of native species in mitigating climate change derives from adaptation to local conditions. The most important factors, among many that can lead to failure, in reforestation projects are the choice of species, seed source, and narrow genetic base of the planting material [15]. The establishment of plantations with restricted seed diversity, i.e., with seeds from one or a few individuals in a population, can lead to genetic inbreeding which may affect the growth, establishment, and long-term viability of the plantation. The advantage of having genetic diversity in planted populations cannot be overemphasized. Within its natural range, F. albida is propagated by farmer-managed natural regeneration: farmers simply protect the plants growing naturally on their farms. In Cameroon, this was encouraged in the 1990s, when farmers were paid based on the number of Faidherbia trees found on their farm. This practice led to the creation of a significant number of agroforestry parklands. Unfortunately, this effort came to a halt at the end of the program, and many of these parklands are now being degraded by human habitation and overexploitation. Developing a new program to characterize, preserve, and improve the genetic diversity of F. albida in its natural range in Africa is therefore critically important if this species is to realize its full potential for agroforestry.
Variation in seed size and seedling morphology has been described for populations of F. albida across Africa [16,17,18]. Field trials designed to characterize the morphological and phenological diversity within F. albida were established in Senegal, Niger, Burkina Faso, and Cameroon [19,20,21,22,23,24]. Results of some of these trials were published a few years later [17,25,26]. Studies of genetic diversity within the Oxford Institute collection of F. albida using isozyme systems [27,28,29,30], Random Amplified Polymorphism Detection (RAPD) [31], or Amplified Fragment Length Polymorphism (AFLP) technologies [32] have also been conducted. Although these technologies are useful for detecting polymorphisms, they have the disadvantage of being unable to provide information about loci in known positions in the genome or accurately measuring the level of heterozygosity of these loci. In contrast, simple sequence repeat (SSR) markers can be mapped to specific sites in the genome and generate a higher level of expected heterozygosity than AFLP or RAPD markers [33,34].
Among the EST-SSRs identified in F. albida by Tchatchoua et al. [35], we identified eight markers useful for studying genetic diversity in F. albida [36]. A comprehensive analysis of populations in Cameroon revealed an overall low genetic diversity, and no genotype or single population stood out as being especially advantageous for initiating breeding [18,36]. In continuation of the efforts to increase the. genetic diversity of F. albida in Cameroon and to find suitable genotypes for breeding, studies were carried out on the adaptation of Sub-Saharan African F. albida provenances in the Sudano–Sahelian climatic conditions of Cameroon [37]. This study suggested that higher yields might be obtained if South Africa populations are grown in Cameroon. However, the limitations of the methods used to measure genetic variation made it difficult to accurately assess the level of genetic variation within and between these populations. To address this problem, we characterized the genetic diversity within provenances of F. albida from across Sub-Saharan Africa using the SSR markers we have previously used to study populations in Cameroon. Our results provide a more detailed picture of genetic diversity within F. albida and insights into genetically diverse sources of this important agroforestry species.

2. Materials and Methods

2.1. Plant Materials and Growth Conditions

Faidherbia trees originating from natural regeneration were selected for seed collection in Cameroon in 2017 and 2018. The method of seed collection and shipping to the USA was reported in [36]. Seeds from other sites in Africa were collected by the US Department of Agriculture and the Oxford Forestry Institute (OFI), and the latter samples were transferred to the World Agroforestry Center, where the seeds were stored, in Nairobi, Kenya, in June 2001 [32]. With the exception of the three samples from Ethiopia, which were collected from individual trees, all of the seed samples were bulk harvested from several trees spaced 100 m apart. See Table S1 for sample sources. Seeds were scarified with a nail clipper and sown in 4-inch plastic plant nursery pots filled with polymix soil. These were placed in a growth chamber at 25 °C and 16 h of fluorescent light. After 4 weeks of growth, fresh leaves were collected for DNA extraction.

2.2. Genetic Analysis

Genotyping was carried out in the Department of Biology at the University of Pennsylvania and the Schatz Center for Tree Molecular Genetics, Pennsylvania State University. Genomic DNA was extracted from approx. 100 mg of leaf tissue from each of the 267 fresh leaf samples. The leaves were ground with a Tissue Ruptor (Qiagen, Inc., Germantown, MD, USA) at a speed of 11.5 rpm for 1 min, and the DNA was extracted using a DNeasy Plant Mini Kit (Qiagen) following manufacturer’s instructions. DNA concentrations were measured using a Qubit DNA kit (Thermofisher Scientific, Waltham, MA, USA), and working solutions with a DNA concentration of 1 ng/µL were prepared and stored in a −80 °C freezer for further use. Genomic DNA (gDNA) of 8 widely separated families from Cameroon populations were used for screening 48 primers previously developed for F. albida [35]. The PCR products were later separated in a 2% agarose gel electrophoresis at 120 V for 2 h. The seven most informative primers were used in genotyping 15 populations from 8 countries of Sub-Saharan Africa (Table S2). The seven primers were polymorphic with band sizes between 100 and 250 base pairs (bp) [36].
The PCR mix reaction consisted of the following: 3 µL of gDNA, 1 µL of fluorescently labeled forward primer (10 μM), 1 µL of reverse primer (10 μM), 21 µL of ultrapure water, and a single PCR EdvoBead (EdvoTek Inc., Washington, DC, USA). The PCR amplification conditions were as follows: 95 °C for 15 min for initial denaturation, followed by 35 cycles of denaturation at 94 °C for 30 s for each cycle, an annealing temperature at 55 °C for 1.5 min, and an extension at 72 °C for 1 min with a final extension at 72 °C for 10 min in an Applied Biosystems Automated Gene amp 9700 thermocycler. (Applied Biosystems, Newport Beach, CA, USA). The PCR products were diluted 1:80 for primer 5, 1:100 for primers 11, 23 and 26, and 1:120 for primers 24 and 30. One µL of each diluted PCR product was transferred to 96 well plates for analysis. Samples were submitted to the Huck Genomics Core of Pennsylvania State University, University Park, USA, for GeneScan analysis using an Applied Biosystems 3730XL Genetic Analyzer (Applied Biosystems, Newport Beach, CA, USA). The fragment sizing data were scored using an Applied Biosystems GeneMapper version 3.7 software.

2.3. Data Analysis

Allelic richness (average number of alleles, Na, and effective number of alleles, Ne), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s information index (I), Nei’s genetic distance among populations, and analysis of molecular variance (AMOVA) were calculated in Excel using the software GenAIEx 6.5 [38]. For each locus, we coded presence/absence of each of the minor alleles as a biallelic marker and used Principal Component Analysis (PCA) and unsupervised ADMIXTURE v1.3.0 [39] to cluster individuals.

3. Results

3.1. Genetic Diversity of Populations

A search of the genome sequence of F. albida [40,41] revealed that at least six of the seven loci analyzed in this study are protein-coding genes and further indicated that all of these loci are located in different sequence scaffolds (Table S2). We genotyped these loci in 267 individuals of F. albida from 15 populations and 8 countries (Figure 1; location data provided in Table S1). The number of alleles per locus ranged from 3 to eight (Table 1). Locus_11 had the largest number of alleles, followed by Locus_5 (Table 2). Four alleles were present in only one individual. Two of these, allele 228 of Locus_11 and allele 206 of Locus_23, varied from a major allele by only one nucleotide and were excluded from our analysis to prevent genotyping errors.
The allelic composition of different populations is provided in Table S3. A summary of the data is provided in Figure 2, and a graphic illustration of the minor alleles is provided in Figure 2. Analysis of variance (AMOVA) showed that 57% of variation was among individuals, 29% was within individuals, and 14% was among populations (Figure 3). West and East African populations (Senegal, Cameroon, Ethiopia, and Kenya) had a larger number of alleles, a larger Shannon diversity index, and a higher amount of observed and expected heterozygosity than countries in South Africa (Tanzania, Malawi, Zimbabwe, and Namibia). Samples from Senegal had the highest level of heterozygosity, followed by those from Kenya, although the sample size for Kenya’s population was relatively small. South African populations (Tanzania, Malawi, Zimbabwe, and Namibia) had very similar levels of allelic diversity and also had similarly low levels of heterozygosity. Populations in West and East Africa were more diverse (Senegal, Cameroon, Ethiopia, and Kenya). Thus, results of genetic diversity analyses suggest that F. albida originated in West or East Africa and then spread to South Africa.

3.2. Population Structure

Principal Component Analysis (PCA) (Figure 4A,B) and Structure Bayesian Analysis (Figure 4C) divided the F. albida populations into three major clusters with cluster 1 consisting of Cameroon and Senegal from West Africa, cluster 2 containing Kenya and Ethiopia from East Africa, and cluster 3 consisting of Tanzania, Malawi, Zimbabwe, and Namibia from South Africa. This corresponds well to the geographic distribution of the populations. The discrete clustering patterns were likely driven by loci 23 and 26, which together distinguished the three populations (Figure 2). Although the PCA divided the populations into three distinct clusters, alleles from Ethiopia (Rama provenance) in East Africa can also be found in the population of Cameroon in West Africa (Figure 4B).
ADMIXTURE analysis for K = 2–4 showed the rate of change was maximum at K = 3 (with CV errors K = 2 0.403, K = 3 0.340, and K = 4 0.383). Three geographic clusters were clearly distinguished (driven by the two loci 23 and 26 as in the PCA plot) (Figure 4C). However, Ethiopian samples from the Rama population looked more similar to the West African cluster than to other samples from Ethiopia. The PCA plot confirmed a similar clustering trend with Cameroon and Senegal clustered together, Ethiopia and Kenya in another cluster, while Tanzania, Namibia, Malawi, and Zimbabwe in a third cluster with Ethiopia individuals scattered among the Cameroon and Senegal cluster (Figure 4B).

3.3. Nei’s Genetic Distance

Nei’s genetic distances calculated between populations were positively correlated with geographical distances between the populations. Nei’s genetic distances ranged from 0.002 between Zimbabwe and Namibia to 0.511 between Cameroon and Namibia, the latter of which showed the highest genetic distance (Table 3). The genetic distances among samples from countries in West, East, and South Africa revealed that populations from Tanzania and Southern African countries were more related than those from East African countries, which is reflected in the cluster analysis results shown in Figure 4B,C.

4. Conclusions

Genetic variation in African populations of F. albida has previously been characterized using isozyme [27,28,29,30], RAPD [31], and AFLP [32] markers. Although these studies have sampled similar, if not identical populations, they have produced somewhat different results. This variation is likely due to the properties of these marker systems. The technologies underlying RAPD and AFLP markers are very good at detecting DNA sequence polymorphisms, but because RAPD and AFLP markers are dominant, the degree of heterozygosity must be inferred rather than directly observed. To obtain a more accurate picture of the population structure of F. albida in Sub-Saharan Africa and identify sources of genetic variation useful for improving the productivity of this species, we therefore decided to re-examine the population structure of F. albida using newly identified genome-mapped SSR markers derived from transcriptome sequences. Our results confirm some of the results from previous studies but also provide new insights into the genetic structure of this species.
We identified a total of 32 alleles for seven loci, with the number of alleles per locus ranging from three to eight. The highest average number of alleles was in Ethiopia which has been proposed as the center of origin of the species [24]. F. albida plants from Senegal and Kenya both had a slightly fewer average number of alleles than that from Ethiopia, but it is unclear if this difference is meaningful because so few loci were sampled in this study. The observed heterozygosity ranged from 0.129 (Tanzania) to 0.466 (Senegal) and was greater than the expected heterozygosity for most populations. This suggests that these populations are not highly inbred and contrasts with other studies, which observed heterozygote deficiency for these populations [27,28]. The level of heterozygosity we observed was also much higher than in studies based on RAPD [31] or AFLP [32] markers. On the other hand, our results are consistent with these studies in showing that West and East African populations of F. albida are genetically more diverse than South African populations.
The most significant result of this study is the evidence that F. albida can be divided into three genetically distinct clusters, representing West, East, and South Africa. The relatively low amount of genetic diversity in South African populations and the genetic distances between these countries and countries in East Africa strongly suggests that South African populations of F. albida are derived from populations in East Africa, supporting Vandenbeldt’s [24] hypothesis. However, the level of heterozygosity in Senegal is actually higher than in East African populations, raising the possibility that F. albida originated in West Africa and spread east and south. This question will only be resolved by additional sampling and genome sequencing.
Faidherbia albida has attracted attention from researchers because of its unusual phenology, value to farmers, and adaptation to a wide range of conditions in Africa. Whether plants adapted to Riparian conditions—which is typical of populations of F. albida in South Africa—are a good source of alleles for the improvement of plants in the Sahel is an open question. The results of this study suggest that populations already adapted to this climate (e.g., from Senegal and Ethiopia) have enough genetic variation to be useful in breeding programs. However, this study was based on a small number of alleles and only sampled one type of genetic variation, simple sequence repeats. It is now possible to obtain genome sequences of F. albida very cheaply using next generation sequencing technologies. Future studies will be able to take advantage of these technologies to answer questions about the origin of this species and the genetic basis of its interesting and useful biology.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16010113/s1, Table S1: seed stocks, Table S2: SSR markers used in genotyping, Table S3: frequency of alleles per country.

Author Contributions

Conceptualization, T.D.T., R.S.P. and J.E.C.; Data Curation, T.D.T.; Formal Analysis, T.D.T., I.M. and R.S.P.; Funding Acquisition, R.S.P. and J.E.C.; Investigation, T.D.T.; Methodology, T.Z.; Project Administration, J.E.C.; Resources, T.D.T. and R.S.P.; Supervision, T.Z., R.S.P. and J.E.C.; Validation, T.Z.; Visualization, T.D.T., I.M. and R.S.P.; Writing—Original Draft, T.D.T.; Writing—Review and Editing, I.M., T.Z., R.S.P. and J.E.C. All authors have read and agreed to the published version of the manuscript.

Funding

D.T. Tchatchoua received a Fulbright Scholarship in support of her position as visiting scholar in the Schatz Center of Tree Molecular Genetics at the Pennsylvania State University. Experiments performed in the Carlson lab were funded by the Schatz Center for Tree Molecular Genetics and by the USDA NIFA Project PEN04532. Research in the Poethig lab was funded by the John H. and Margaret B. Fassitt Professorship.

Data Availability Statement

The data that support the findings of this study are available in the Supplementary Information and available at https://github.com/mathii/Faidherbia (accessed on 1 November 2024).

Acknowledgments

D.T. Tchatchoua thanks the fellowship for the research provided by the Fulbright Program. D.T. Tchatchoua also thanks all of the members of the Schatz Center for Tree Molecular Genetics at Pennsylvania State University for advice and training in molecular genetics techniques. We also thank Oliver Gailing and Markus Mueller at the University of Göttingen in Germany for reviewing the manuscript and providing useful suggestions for improving the manuscript.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of the article.

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Figure 1. Geographic locations of the F. albida populations analyzed in this study.
Figure 1. Geographic locations of the F. albida populations analyzed in this study.
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Figure 2. Distribution of minor alleles in different countries. Each row represents one minor allele for one locus. Major alleles for each locus are not shown. Each vertical line represents one individual. Dark blue = heterozygous; Black = homozygous; White = missing.
Figure 2. Distribution of minor alleles in different countries. Each row represents one minor allele for one locus. Major alleles for each locus are not shown. Each vertical line represents one individual. Dark blue = heterozygous; Black = homozygous; White = missing.
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Figure 3. Analysis of molecular variance (AMOVA). AMOVA shows that the most SSR variation resides among individuals (57%), followed by individuals within populations (29%), while the overall difference between populations is only 14%.
Figure 3. Analysis of molecular variance (AMOVA). AMOVA shows that the most SSR variation resides among individuals (57%), followed by individuals within populations (29%), while the overall difference between populations is only 14%.
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Figure 4. Population structure. (A) Location of samples used for analysis. (B) Principal Component Analysis of genetic variation with sample colors according to (A). Points are jittered to increase visibility—in reality, clusters are largely discrete. PC1 and 2 explain 35% and 32% of the variance in the data, respectively (PC3, not shown, explains 5.4%). (C) Admixture plot from ADMIXTURE analysis K = 3.
Figure 4. Population structure. (A) Location of samples used for analysis. (B) Principal Component Analysis of genetic variation with sample colors according to (A). Points are jittered to increase visibility—in reality, clusters are largely discrete. PC1 and 2 explain 35% and 32% of the variance in the data, respectively (PC3, not shown, explains 5.4%). (C) Admixture plot from ADMIXTURE analysis K = 3.
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Table 1. Frequency of alleles per locus.
Table 1. Frequency of alleles per locus.
Locus_5Locus_8Locus_11Locus_23Locus_24Locus_26Locus_30
Allele (bp)Freq.AlleleFreq.AlleleFreq.AlleleFreq.AlleleFreq.AlleleFreq.AlleleFreq.
1800.0281500.0112090.1982070.3892010.0551820.0231740.002
1830.0241530.0172170.0322090.0192020.8551830.0091770.959
1860.7621650.1412180.0252100.4302060.0901840.5041800.039
1890.0771680.832190.030 1860.426
1920.107 2200.013 1880.038
1950.002 2210.009
2260.019
2270.672
Table 2. Genetic diversity parameters for populations.
Table 2. Genetic diversity parameters for populations.
Population
(# Samples)		NaNeIHoHe
Senegal (38)2.8571.7000.6290.4660.387
Cameroon (54)2.4291.5940.4690.3070.270
Ethiopia (51)3.1431.4500.5100.2270.267
Kenya (16)2.8571.8840.6360.3300.350
Tanzania (20)1.7141.1370.1920.1290.106
Malawi (20)1.7141.2300.2430.1430.147
Zimbabwe (31)2.5711.2310.2990.1470.154
Namibia (37)2.1431.1960.2420.1510.125
Na = Average number of alleles per locus; Ne = effective number of alleles; I = Shannon’s information index; Ho = observed heterozygosity; He = expected heterozygosity.
Table 3. Pairwise population matrix of Nei’s genetic distance.
Table 3. Pairwise population matrix of Nei’s genetic distance.
SenegalCameroonEthiopiaKenyaTanzaniaMalawiZimbabweNamibia
Senegal0.000
Cameroon0.0630.000
Ethiopia0.2080.1830.000
Kenya0.2560.1710.0440.000
Tanzania0.3830.4180.1800.2540.000
Malawi0.4400.4880.1930.2580.0410.000
Zimbabwe0.4240.4710.1610.2390.0210.0140.000
Namibia0.4560.5110.1920.2720.0180.0170.0020.000
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Tchatchoua, T.D.; Mathieson, I.; Zhebentyayeva, T.; Poethig, R.S.; Carlson, J.E. Sources of Genetic Variation in Faidherbia albida (Del.) A. Chev. Sub-Saharan African Populations. Forests 2025, 16, 113. https://doi.org/10.3390/f16010113

AMA Style

Tchatchoua TD, Mathieson I, Zhebentyayeva T, Poethig RS, Carlson JE. Sources of Genetic Variation in Faidherbia albida (Del.) A. Chev. Sub-Saharan African Populations. Forests. 2025; 16(1):113. https://doi.org/10.3390/f16010113

Chicago/Turabian Style

Tchatchoua, Tchapda Dorothy, Iain Mathieson, Tetyana Zhebentyayeva, R. Scott Poethig, and John E. Carlson. 2025. "Sources of Genetic Variation in Faidherbia albida (Del.) A. Chev. Sub-Saharan African Populations" Forests 16, no. 1: 113. https://doi.org/10.3390/f16010113

APA Style

Tchatchoua, T. D., Mathieson, I., Zhebentyayeva, T., Poethig, R. S., & Carlson, J. E. (2025). Sources of Genetic Variation in Faidherbia albida (Del.) A. Chev. Sub-Saharan African Populations. Forests, 16(1), 113. https://doi.org/10.3390/f16010113

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