Transferability, development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

doi:10.21203/rs.3.rs-37023/v1

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Transferability, development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

https://doi.org/10.21203/rs.3.rs-37023/v1

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published 03 Dec, 2020

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Background: In Sub-Saharan Africa, Borassus aethiopum Mart. (African fan palm) is an important non-timber forest product-providing palm that faces multiple anthropogenic threats to its genetic diversity. However, this species is so far under-studied, which prevents its sustainable development as a resource. The present work is a first attempt at characterizing the genetic diversity and population structure of B. aethiopum across nine collection sites spanning the three climatic regions of Benin, West Africa, through the use of microsatellite markers.

Results: During a first phase we relied on the reported transferability of primers developed in other palm species. We find that, in disagreement with previously published results, only 22.5% of the markers tested enable amplification of B. aethiopum DNA and polymorphism detection is very low.

We thus generated a B. aethiopum-specific genomic dataset through high-throughput sequencing and used it in a second phase for the de novo detection of microsatellite loci. Among the primer pairs designed to target these, 11 enabled polymorphism detection and were further used for analyzing genetic diversity. Across the nine collection sites, expected heterozygosity (He) ranges from 0.263 to 0.451 with an overall average value of 0.354, showing a low genetic diversity. Analysis of molecular variance (AMOVA) shows that within-site variation accounts for 53% of the genetic variation, and accordingly the low number of migrants and the positive values of the fixation index (F) in sites from both the Central (Sudano-Guinean) and the Southern (Guinean) climatic regions suggest limited gene flow between sites. While we globally observe a weak correlation between genetic and geographic distances, our clustering analyses indicate that B. aethiopum palms from Savè (Center) are genetically more similar to those from the Northern sites than to samples from the other Central sites.

Conclusions: In the light of our results, we discuss the use of inter-species transfer vs. de novo development of microsatellite markers in genetic diversity analyses targeting under-studied species. We also suggest future applications for the molecular resources generated through the present study.

Epigenetics & Genomics

Borassus aethiopum

genetic diversity

microsatellite

marker transferability

high-throughput sequencing

simple sequence repeat

under-studied species

Many plant species remain under-studied due to their low economic importance, complicated biology and/or the absence of available genome sequence information. Upon initiating a research project aimed at characterizing the genetic diversity of such a species, researchers may be confronted with the situation that some resources can be found in related taxa. In such cases, the first step is often to assess whether some of these resources, such as molecular markers, can be used to study the new species. Provided that the "source" species display enough genetic similarities to the "target" species and that marker transferability has been previously assessed, this first step may lead to quick progress in a cost-effective manner. Often, transferring markers between species is seen as a smarter investment than developing and testing new markers, especially if limited funding is available [1, 2].

Over the last three decades, molecular markers have been widely used to study genetic variation among and within populations of various plant species [3–7]. Among the different types of markers that are available, microsatellites or simple sequence repeats (SSRs) are often selected due to their high mutation frequency, which ranges from 10^− 2 to 10^− 6 nucleotides per locus per generation [2, 8] and generates multiple allelic forms, and their co-dominant nature. The combination of both characteristics make them sensitive tools for the assessment of genetic diversity among species, determination of population structure, phylogenetic reconstruction, genetic mapping, evolutionary analyses, and molecular breeding [9–12]. From a practical perspective, the popularity of SSRs is also related to their low resource requirements (i.e. technical skills, laboratory equipments and consumables) that enable their easy implementation and the reproducibility of results in most research environments [2, 8]. However, the steps leading to the development of functional SSR markers, namely the initial identification of microsatellite loci, primer selection and assessment of amplification/polymorphism detection, require some prior knowledge of the genome of the target species and may prove to be expensive and time-consuming [11, 13]. In order to overcome this difficulty, approaches relying on the transfer of SSR markers between species or genera have therefore been implemented. They have been successful in many instances, as documented across Prunus species and among members of the Rosaceae family [14, 15]; between species of the Hevea genus and to other Euphorbiaceae [16]; among Lamiaceae [17]; among Legumes belonging to the Vicia genus [18] and from the Phaseolus genus to Vigna [19]. In other cases, the ever-increasing affordability of high-throughput sequencing technologies and the development of dedicated bioinformatics data mining tools have enabled the identification of microsatellite loci and the development of SSR markers, including in non-model plant species with limited or no background genetic information [20–23].

Borassus aethiopum Mart., also known as ron palm, toddy palm or African fan palm, is a dioecious species belonging to the Arecaceae family. It is widely distributed across West and Central Africa, where it is present as wild populations [24]. The species is classified as a non-timber forest products (NTFPs)-providing plant, since different parts of the plant are used for various purposes by local populations [24, 25]. In Benin (West Africa) for instance, 121 different uses distributed in seven categories (medicinal, handicrafts, food, construction, firewood, ceremonies and rituals) have been reported for the species [26]. Among these, the consumption of ripe fruits (fresh or roasted) and hypocotyls as food, the use of the weather- and pest-resistant stipe as construction wood and that of leaves and petioles in handicrafts, are the most widespread in local populations [26–28]. These different products are also sold in markets, mostly by women, to whom they provide additional income: it is indeed estimated that in Benin, sales of hypocotyls alone may represent 50% to nearly three times the minimum wage of 40,000 CFA Francs (ca. 61 euros) a month [27].

These multiple uses of products derived from B. aethiopum have put a strong anthropogenic pressure on the species, thus contributing to both fragmentations of its populations and their poor natural regeneration [27, 29–32]. Further fragmentation of the species' habitat has been observed as a result of land clearing for agriculture or urban development [32–34]. As illustrated through similar examples in the literature [35, 36], such phenomena may lead to restricted gene flow and ultimately, to loss of genetic diversity among B. aethiopum populations. A sustainable management policy for B. aethiopum populations is therefore urgently needed and acquiring information on the genetic diversity of the species and population structure is a major step towards defining sustainable management actions. At the time of writing the present article, only a few chloroplast sequences are publicly available for B. aethiopum through NCBI (https://www.ncbi.nlm.nih.gov/search/all/?term=borassus%20aethiopum). By contrast, abundant molecular resources, including genome assemblies or drafts, are available for model palm species such as Elaeis guineensis Jacq. [37], Phoenix dactylifera L. [38–40] and Cocos nucifera L. [41, 42]. In each of these three palm species, large numbers of SSR markers have been identified and for a fraction of them, cross-species and cross-genera transferability tests among species belonging to the Palmaceae family have been performed [43–49]. In several instances [44–47, 49] these tests included samples from B. flabellifer, the Asian relative of B. aethiopum.

Here we first describe attempts to use SSR markers identified in these other palm species, for the analysis of genetic diversity in B. aethiopum. Then, we describe the low-coverage sequencing of the B. aethiopum genome with the aim of developing the first set of specific SSR markers targeting this species. Finally, we used the novel SSR markers to assess the genetic diversity and population structure of B. aethiopum samples collected across the three different climatic regions of Benin, as an important first step towards more comprehensive studies.

Assessment of palm SSR marker transferability to B. aethiopum and evaluation of their capacity for characterizing genetic diversity

Of the 80 microsatellite markers selected from the three model palm species E. guineensis, P. dactylifera and C. nucifera and tested for amplification on B. aethiopum DNA, 18 (22.5%) generate amplification products (Table 1). No amplification is observed using the 11 C. nucifera markers, whereas 7 (15.9%) and 11 (44%) of the P. dactylifera and E. guineensis markers, respectively, show a successful amplification. None of the amplification products generated with P. dactylifera primers display genetic polymorphism in our B. aethiopum test panel. Among E. guineensis-derived SSR markers however, two, namely ESSR566 and ESSR652, display polymorphism. However, it must be noted that depending on the DNA sample the ESSR566 primer pair generates a variable number of amplicons with distinct sizes, which may be an indication that more than one locus is targeted.

Table 1

Summary of SSR markers transferability assessment
Species of origin	Number of SSR markers tested	Number of successful amplifications (% of markers)	Number of polymorphic amplicons (% of amplifications)
Cocos nucifera	11	0 (0)	0 (0)
Phoenix dactylifera	44	7 (15.9)	0 (0)
Elaeis guineensis	25	11 (44.0)	2 (18.2)
Total	80	18 (22.5)	2 (11.1)

Overall, during this phase of the study we detect polymorphism in our B. aethiopum test panel with only 2 (11.1% of successfully amplified markers, 2.5% of total) of the palm SSR primer pairs assayed. Only one of these markers, namely ESSR652, enables unambiguous detection of microsatellite locus polymorphism in B. aethiopum, and might therefore be used for studying genetic diversity in this species.

De novo identification of microsatellite sequences in the B. aethiopum genome and assessment of potential SSR markers

In order to enable a more precise evaluation of genetic diversity in B. aethiopum, we developed specific B. aethiopum markers from de novo sequencing data. A total of 23,281,354 raw reads with an average length of 250 bp have been generated from one MiSeq run. Raw sequence reads have been trimmed resulting in 21,636,172 cleaned-up reads, yielding 493,636 high-quality reads after filtering (Q > 30) from which 216,475 contigs have been assembled.

From the contigs, the QDD software identifies a total of 1,618 microsatellite loci (Additional file 1), of which 1,327 (82.01%) are perfect (i.e. repeat size 4 bp or smaller and repeat number 10–20). Among the perfect microsatellite loci, 83.86% are composed of di-nucleotidic repeat units, 13.06% of tri-nucleotidic units, 2.39% of tetra-nucleotidic repeats and 0.67% of repeats with five nucleotides and over. From these, we selected SSR markers composed of di- (AG) or tri- nucleotide repeats, using the following criteria for specific amplification of easily scorable bands: primer lengths ranging from 18 to 22 bp, annealing temperatures 55–60 °C, and predicted amplicon sizes 90–200 bp.

The characteristics of the 57 selected primer pairs and the results of the test amplifications are presented in Table 2. Successful amplification of B. aethiopum DNA is obtained for 54 (94.7%) primer pairs and of these, 34 (60.0% of amplifying couples) show no polymorphism. The remaining 20 primer pairs enable the amplification of polymorphic products, however nine of them yield complex, ambiguous amplification profiles that prevent their use for reliable detection of genetic variation. As a result, 11 putative B. aethiopum SSR markers (representing 20.4% of primer pairs associated with successful amplification and 55.0% of those detecting polymorphic products in our study) are both polymorphic and unambiguously mono-locus in our amplification test panel and may therefore be used for further analyses.

Table 2

List of selected primer pairs targeting putative *B. aethiopum* microsatellite loci and assessment of their polymorphism detection ability.
Locus name	Repeat motif	Primer sequences (5'-3' orientation)	Expected amplicon size (bp)	Amplification product
MBo01	[AGG]₇	CCTATCCTTCCATCCCGATCG	90	complex, polymorphic
MBo01	[AGG]₇	TTGCCGTGAATCAGCCTCAA	90	complex, polymorphic
MBo02	[ATC]₇	GGGAGAACAAGGATAACAGCAG	115	single locus, monomorphic
MBo02	[ATC]₇	TCCATTTCATCACTAGCTCGGT	115	single locus, monomorphic
MBo03	[AGG]₇	CTCCGAGCCCTAGCAACTTT	131	single locus, monomorphic
MBo03	[AGG]₇	TCTGGATGACGAAACCTTCACA	131	single locus, monomorphic
MBo04	[ACC]₇	GATGTGGCCGCTCTGATCTC	192	single locus, monomorphic
MBo04	[ACC]₇	ACATGCTGGCAAGGTATTCT	192	single locus, monomorphic
MBo05	[AAG]₇	GTCCTAGCACGCTGGCATTA	202	single locus, monomorphic
MBo05	[AAG]₇	TGGGTTGCCAATGAACCCTT	202	single locus, monomorphic
MBo06	[ATC]₇	TGGCCATTCAACTGCTTCAC	202	single locus, monomorphic
MBo06	[ATC]₇	GAATCTAGCACCAGCAAACCC	202	single locus, monomorphic
MBo07	[AAG]₇	GGCACTGGAGTCCACATCAA	239	single locus, monomorphic
MBo07	[AAG]₇	TCCTTCTGTACTGGCATCTCT	239	single locus, monomorphic
MBo08	[AGG]₈	TGATTGTTTCCTCTTCCCTCCT	90	single locus, monomorphic
MBo08	[AGG]₈	TTAATGAGCCGAAGAGGAGCC	90	single locus, monomorphic
MBo09	[AGG]₈	TCCCTCACTCCCATCCTCTC	163	single locus, monomorphic
MBo09	[AGG]₈	ACTCCACTCCTTCCCTCATACA	163	single locus, monomorphic
MBo10	[AAC]₈	GTTAAAGACGCAGGGCTGGA	166	single locus, monomorphic
MBo10	[AAC]₈	CCCACTTAGTGAGATAAGACTTGA	166	single locus, monomorphic
MBo11	[ATC]₈	GCATCACATGGTTTCAGGCT	219	single locus, monomorphic
MBo11	[ATC]₈	GCTCAACCATCGGCAGTGTA	219	single locus, monomorphic
MBo12	[ATC]₉	GGAGGAAAGGTTGCCCTAGAA	102	single locus, monomorphic
MBo12	[ATC]₉	TCTCAACCTGATGTCATTGCA	102	single locus, monomorphic
MBo13	[AAG]₉	CAGGTTGCATCGGCCCATT	103	complex, polymorphic
MBo13	[AAG]₉	GGAGCCTAATGCACCCAGAG	103	complex, polymorphic
MBo14	[AAC]₉	ATGGCCGATCCCACTTAGTG	117	single locus, monomorphic
MBo14	[AAC]₉	GAGAGAACGGCAATAATTTATGCA	117	single locus, monomorphic
MBo15	[AAG]₁₀	GCTGAAGAGGATGAAGAAGAAGC	92	complex, monomorphic
MBo15	[AAG]₁₀	TCATCATCTCCCTCTCCTTCT	92	complex, monomorphic
MBo16	[AGG]₁₀	CAGCACTGGCCTCACAGC	118	single locus, monomorphic
MBo16	[AGG]₁₀	CCGTCGATCAGTTGTTGGAGA	118	single locus, monomorphic
MBo17	[ATC]₁₀	ACACAATGACCTTTCGCTGA	124	single locus, monomorphic
MBo17	[ATC]₁₀	CCAAACAGGACCTTATGCCA	124	single locus, monomorphic
MBo18	[AAG]₁₀	ACATCCTCTCCTTCATCTCCTT	187	complex, polymorphic
MBo18	[AAG]₁₀	GTTCCTACAATGCTTGGCGC	187	complex, polymorphic
MBo19	[AAG]₁₀	TGCTATCACCCAATATCTAGGCT	202	single locus, monomorphic
MBo19	[AAG]₁₀	ACAGTCAACAACTACCATACTGC	202	single locus, monomorphic
MBo20	[AAG]₁₀	TGTGGTTAAAGCAATGGAAGCA	229	single locus, monomorphic
MBo20	[AAG]₁₀	GCCGAACTCCTACTCTCATACG	229	single locus, monomorphic
MBo21	[AAG]₁₁	ACAACAGAAGATCAGTATACGTTCT	171	single locus, monomorphic
MBo21	[AAG]₁₁	TTGAGGAATCATGCTTGTCAGT	171	single locus, monomorphic
MBo22	[AAG]₁₄	AGAAGAATTCGGTTAGGTCACAA	108	single locus, monomorphic
MBo22	[AAG]₁₄	AGATAACATGGGTAAGAATTGCCT	108	single locus, monomorphic
MBo23	[AAT]₅	TGAGTTCTTGTCTTGTCTTCGT	100	single locus, monomorphic
MBo23	[AAT]₅	GGTTTGGGACACCCTTCAGG	100	single locus, monomorphic
MBo24	[AAT]₉	AAAGTCATGTCTGGGTGATGAA	90	single locus, monomorphic
MBo24	[AAT]₉	ATGATGAGCACAGCTACAACTCT	90	single locus, monomorphic
MBo25	[AAT]₆	TCTTCAGGTGACAAGCAACA	96	single locus, monomorphic
MBo25	[AAT]₆	CCTGGGCATGGAGATAGCAT	96	single locus, monomorphic
MBo26	[AAT]₇	CCATAGGCCAGCCCACTATA	134	single locus, monomorphic
MBo26	[AAT]₇	ACCCTTTCTTCTTCCTCATTTGT	134	single locus, monomorphic
MBo27	[AAT]₇	TCTCTATTGCTTGGTGATCCC	103	single locus, monomorphic
MBo27	[AAT]₇	TCCAACAAGGGATGGTTATCATG	103	single locus, monomorphic
MBo28	[AAT]₈	GCCTTGAGAGTGGAAGAGGC	205	single locus, monomorphic
MBo28	[AAT]₈	TCTCTTCTTTGCGCCCTCAT	205	single locus, monomorphic
MBo29	[AAT]₁₆	AGACATGTAGAGGTGGGACT	211	single locus, monomorphic
MBo29	[AAT]₁₆	TCTGTATGAGAGACGTGTTACAGT	211	single locus, monomorphic
MBo30	[AAT]₈	TGACCATAACAAGCTACCAGGT	146	single locus, monomorphic
MBo30	[AAT]₈	GGTGGAAGCTATTGATATTGCATGT	146	single locus, monomorphic
MBo31	[AAT]₁₀	TGACAATGATGCATGCGATAACA	187	single locus, monomorphic
MBo31	[AAT]₁₀	GCATCACCCATGTCCTTTAGC	187	single locus, monomorphic
MBo32	[AAT]₁₀	TCCGAGGGCAGTATTTGTCG	117	single locus, monomorphic
MBo32	[AAT]₁₀	CACTATTTCGGAAACCTAAGCCC	117	single locus, monomorphic
MBo33	[AAT]₁₇	GCACACTTTGTATCCGACGC	147	single locus, monomorphic
MBo33	[AAT]₁₇	CAGGGATAGTAACCGTCAGGG	147	single locus, monomorphic
MBo34*	[AG]₂₈	GTGGCACCTCTGCGGTTT	192	single locus, polymorphic
MBo34*	[AG]₂₈	CGAGATGGAAGCACCTGGAG	192	single locus, polymorphic
MBo35*	[AG]₂₄	AGCATGCTTTCTGCTTCATGTG	137	single locus, polymorphic
MBo35*	[AG]₂₄	CCTTTCCCTGACTGCATTGC	137	single locus, polymorphic
MBo36	[AG]₂₃	TCGGAAGTCGAATGTGGCAG	180	no amplification
MBo36	[AG]₂₃	TCGGAAGAGTGGTCAATCATGG	180	no amplification
MBo37	[AG]₂₃	GCTCTACTCCCAGAGACGGA	142	complex, polymorphic
MBo37	[AG]₂₃	AACAGTCGACGGAATGCTCA	142	complex, polymorphic
MBo38*	[AG]₂₀	AGTCCTCACTGCTGGTGGTA	130	single locus, polymorphic
MBo38*	[AG]₂₀	TCCTTGAATAGTCCATCTTGCA	130	single locus, polymorphic
MBo39	[AG]₁₉	AACGCAGGTTAAGAGGCTCC	168	complex, monomorphic
MBo39	[AG]₁₉	CCTCCTGGTGCAACCCTTAC	168	complex, monomorphic
MBo40	[AG]₁₉	TGTGGAGTGTGAGTCGATGG	193	complex, polymorphic
MBo40	[AG]₁₉	GGCTGCATAATCTCATCACGC	193	complex, polymorphic
MBo41*	[AG]₁₈	TTCTCCACCAGCCTCACAAC	184	single locus, polymorphic
MBo41*	[AG]₁₈	ATACGGCCCATCAACCCTTC	184	single locus, polymorphic
MBo42	[AG]₁₈	CCTGGTGGTACATGTGGTCA	136	complex, polymorphic
MBo42	[AG]₁₈	TGTGGCACATTCATTTCTGAAGG	136	complex, polymorphic
MBo43	[AG]₁₈	AGTTTGTTCTGTGTGTTGTCAC	137	no amplification
MBo43	[AG]₁₈	GCACACATCTTGCTTTGAAGAC	137	no amplification
MBo44	[AG]₁₇	AACACACTTTAAATCGACTTCTTCA	193	complex, polymorphic
MBo44	[AG]₁₇	CACGGCTGCCATGTGAGG	193	complex, polymorphic
MBo45	[AG]₁₇	TAGATCGGAAGTCAGGCCC	193	no amplification
MBo45	[AG]₁₇	AGAGAAGTGGGAGGAGAGGTC	193	no amplification
MBo46	[AG]₁₇	GCCGATATTAGCTTCTTCTTGGC	154	single locus, monomorphic
MBo46	[AG]₁₇	GCCTTGTTGATCCCGTTTCAC	154	single locus, monomorphic
MBo47	[AG]₁₆	GGCACCTGACGCCTCTTT	188	single locus, monomorphic
MBo47	[AG]₁₆	TCACTTCGACTCAATTGTATCCAT	188	single locus, monomorphic
MBo48	[AG]₁₆	AGGACAAAGAGATGAGAAGCCT	92	complex, polymorphic
MBo48	[AG]₁₆	ACCAATTCCCAGTTAGTTGACCA	92	complex, polymorphic
MBo49*	[AG]₁₆	CATCACCCATTCTCTCTGCCT	141	single locus, polymorphic
MBo49*	[AG]₁₆	GAGAAACCATCCGCACCTCA	141	single locus, polymorphic
MBo50*	[AG]₁₅	AGAAGTCATCTTGAGGGCCC	150	single locus, polymorphic
MBo50*	[AG]₁₅	TTGCTAGAATGATACACAAATTGCT	150	single locus, polymorphic
MBo51*	[AG]₁₅	TGTGCTATTTGTTGGGAATGCA	191	single locus, polymorphic
MBo51*	[AG]₁₅	GCAAGCTCATGTTCTAGTTTCAAGT	191	single locus, polymorphic
MBo52*	[AG]₁₅	ACACATCCTACATGAATAGACCTCC	122	single locus, polymorphic
MBo52*	[AG]₁₅	TCTTGTCATAGCCTAGATTCCCT	122	single locus, polymorphic
MBo53	[AG]₁₅	AGGTTTAAGGGTTTGGGTTAGGG	131	single locus, monomorphic
MBo53	[AG]₁₅	GGTGGAGTAAGTTTGAGGGTCA	131	single locus, monomorphic
MBo54*	[AG]₁₁NNN[AG]₁₅	CATATGCTGATACAAGAGAGAGGG	124	single locus, polymorphic
MBo54*	[AG]₁₁NNN[AG]₁₅	ACCTTATAAGCAGGATCCAGACA	124	single locus, polymorphic
MBo55	[AG]₁₅	TGGAATCAACCTTGGGTCTACA	198	complex, polymorphic
MBo55	[AG]₁₅	TCGTCGGTCTTCTAGCCACT	198	complex, polymorphic
MBo56*	[AG]₁₅	ACCAAGATCAAGCACGAGGA	103	single locus, polymorphic
MBo56*	[AG]₁₅	AGGATCACCCTTTCTTTCTTTCT	103	single locus, polymorphic
MBo57*	[AG]₁₅	GGGTTCAATCCTGATGAGAGCA	136	single locus, polymorphic
MBo57*	[AG]₁₅	ACCGTTCGATCAACCATGGT	136	single locus, polymorphic
Loci for which single-locus SSR polymorphism has been detected within our test panel of seven B. aethiopum individuals are signaled by an asterisk (*).
Conventionally, microsatellite motifs are displayed under the form [N₁N₂]_x or [N₁N₂N₃]_x for dinucleotide and trinucleotide loci, respectively, where N₁, N₂ and N₃ represent nucleotides included in the elementary unit of the motif and x is the number of unit repetitions. Expected amplicon size is as predicted by QDD.

Microsatellite-based characterization of genetic variation of B. aethiopum in Benin

The previously defined set of 11 B. aethiopum-specific SSR markers has been used for the characterization of genetic diversity in our full panel of 180 individual samples from nine locations distributed across Benin. As shown in Table 3, among our sample set the number of alleles per microsatellite locus ranges from 2 for marker Mbo41 to 6 for markers Mbo34, Mbo35, and Mbo50, with an average value of 4.27, whereas expected heterozygosity (He) values range from 0.031 (marker Mbo56) to 0.571 (marker Mbo35). Using these markers, the analysis of genetic diversity (Table 4) shows that the number of polymorphic markers detected at the microsatellite loci investigated ranges from 8 (sites of Togbin and Malanville) to 10 (Savè, Agoua, Pendjari, Pingou and Trois Rivières), with a mean value of 9 ± 0.865. With the exception of Savè, Hounviatouin and Malanville, 1 to 3 private alleles of the targeted microsatellite loci are observed in most sampling locations. Regarding the genetic parameters, the number of effective alleles (Ne) ranges from 1.447 to 2.069 with an average number of 1.761. He values range from 0.263 (Hounviatouin) to 0.451 (Savè) with an average value of 0.354 whereas the observed heterozygosity (Ho) varied from 0.234 (Togbin) to 0.405 (Pingou) with an average value of 0.335. Negative values of Fixation index (F) are obtained for Pingou, Malanville and Trois rivières whereas positive F values, indicating a deficit of heterozygosity, are observed in all other sites investigated.

Table 3

Characteristics of 11 polymorphic microsatellites markers used for genetic diversity analysis of *B. aethiopum*
Locus name	Number of alleles scored/locus	Expected Heterozygosity (He)	Observed Heterozygosity (Ho)
Mbo34	6	0.520	0.383
Mbo35	6	0.571	0.522
Mbo38	5	0.458	0.513
Mbo41	2	0.343	0.356
Mbo49	4	0.167	0.146
Mbo50	6	0.548	0.542
Mbo51	3	0.320	0.304
Mbo52	3	0.201	0.232
Mbo54	4	0.26	0.435
Mbo56	3	0.031	0.034
Mbo57	5	0.296	0.263

Table 4

Mean diversity parameters for each of the nine *B. aethiopum* sampling sites.
Geo-climatic region	Site	Number of polymorphic markers	Na	Ne	Number of private alleles	Ho	He	F
Guineo-Congolian (South)	Togbin	8	2.273	1.584	3	0.234	0.288	0.145
Guineo-Congolian (South)	Hounviatouin	9	2.182	1.447	0	0.272	0.263	0.007
Sudano-Guinean (Center)	Savè	10	2.909	2.069	0	0.384	0.451	0.134
	Biguina	9	2.364	1.770	2	0.345	0.374	0.064
	Agoua	10	2.273	1.722	1	0.329	0.358	0.059
Sudanian (North)	Pendjari	10	2.818	1.900	3	0.368	0.396	0.055
	Pingou	10	2.364	1.906	1	0.405	0.390	-0.063
	Malanville	8	2.455	1.627	0	0.302	0.303	-0.020
	Trois rivières	10	2.545	1.822	2	0.373	0.360	-0.055
	Overall mean	9 ± 0.865	2.465 ± 0.103	1.761 ± 0.065		0.335 ± 0.023	0.354 ± 0.023	0.035 ± 0.022

Na: average number of different alleles; Ne: effective number of alleles; Ho = Observed Heterozygosity; He: Expected Heterozygosity; F: Fixation index

Population structure of B. aethiopum in Benin

Nei's genetic distance among locations (Table 5) ranges from 0.073, as observed between Togbin and Hounviatouin (Guineo-Congolian region), to 0.577 between Togbin (Guineo-Congolian region) and Trois Rivières (Sudanian region). Overall, genetic distances between B. aethiopum sampling locations are lowest within the same region, with the lowest genetic distances among the sites of Pendjari, Pingou, and Trois Rivières which are all located in the Northern part of the country. One interesting exception is the Central (Guineo-Sudanian) region of Benin, where we find that the most genetically distant location from Savè is the one from the Agoua forest reserve (0.339). Surprisingly, Savè displays its highest genetic identity value when compared to the other two collection sites located within protected areas, namely Pendjari (0.870) and Trois Rivières (0.882) which are both located in the Sudanian region. This is an unexpected finding considering the geographic distances involved.

Table 5

Pairwise location matrix of Nei’s genetic distance and genetic identity values
	Togbin	Hounviatouin	Savè	Biguina	Agoua	Pendjari	Pingou	Malanville	Trois Rivières
Togbin	-	0.073	0.477	0.253	0.337	0.517	0.494	0.487	0.577
Hounviatouin	0.929	-	0.419	0.110	0.215	0.435	0.317	0.375	0.535
Savè	0.621	0.658	-	0.270	0.339	0.140	0.265	0.238	0.126
Biguina	0.776	0.896	0.763	-	0.152	0.241	0.161	0.186	0.316
Agoua	0.714	0.806	0.713	0.859	-	0.408	0.304	0.359	0.490
Pendjari	0.596	0.647	0.870	0.786	0.665	-	0.167	0.108	0.103
Pingou	0.610	0.728	0.767	0.851	0.738	0.846	-	0.174	0.175
Malanville	0.614	0.688	0.788	0.831	0.699	0.898	0.841	-	0.145
Trois Rivières	0.561	0.585	0.882	0.729	0.613	0.902	0.840	0.865	-

Above the diagonal: Nei's genetic distance; below: genetic identity.

A similar structure of genetic distances emerges from the analysis of pairwise location genetic differentiation (Fst) (Table 6), suggesting genetic differentiation according to geographic distances between collection sites, with the notable exception of the lower genetic differentiation between samples from Savè and those from either one of the forest reserves in the Northern region, namely Pendjari and Trois Rivières.

Table 6

Pairwise sampling locations Fst value
	Togbin	Hounviatouin	Savè	Biguina	Agoua	Pendjari	Pingou	Malanville	Trois Rivières
Togbin	0.000
Hounviatouin	0.072	0.000
Savè	0.233	0.221	0.000
Biguina	0.168	0.086	0.145	0.000
Agoua	0.215	0.153	0.157	0.105	0.000
Pendjari	0.247	0.212	0.077	0.120	0.188	0.000
Pingou	0.252	0.181	0.138	0.103	0.169	0.100	0.000
Malanville	0.301	0.246	0.149	0.121	0.197	0.072	0.119	0.000
Trois Rivières	0.285	0.279	0.076	0.178	0.224	0.073	0.104	0.107	0.000

In order to assess the strength of the relationship between genetic and geographic distances, we plotted them as a linear regression and performed the Mantel permutation test. As shown in Fig. 1, the positive correlation between both variables is weak, but significant (R² = 0.1139, P = 0.040).

AMOVA (Table 7) shows that within-site variation underlies the major part (53%) of total variance, whereas among-site and among-regions variations explain genetic variance to a similar extent (23 and 24%, respectively). Accordingly, the average Number of migrants between collection sites (Nm = 1.019) is low, indicating very limited gene flow.

Table 7

AMOVA results.
Source	df	SS	MS	Est. var.	% total variance	P value
Among Regions	2	309.407	154.704	1.944	24%	< 0.001
Among Locations	6	254.302	42.384	1.903	23%	< 0.001
Within Locations	171	739.100	4.322	4.322	53%	< 0.001
Total	179	1302.809		8.169	100%

df = degree of freedom, SS = sum of squares, MS mean squares, Est. var. = estimated variance

The Principal Coordinates Analysis (PCoA) of 180 B. aethiopum samples (Fig. 2A) shows that the first axis (accounting for 24% of total variation out of a sum of 33.90 for axes 1 and 2) roughly separates individual samples in two main groups, a result that is in agreement with the analysis of genetic distances. The sampling locations-based PCoA (Fig. 2B) confirms the genetic separation along the first axis (accounting for 44.08% of total variation over a total of 61.06% for the sum for axes 1 and 2) between sites from the Guineo-Congolian (Southern) region, plus the sites of Agoua and Biguina (Center) vs. sites from the Sudanian (Northern) region, plus the site of Savè (Center). Although the distinction is not as clearly marked, the second axis (accounting for 16.98% of total variation) further allows to distinguish two subgroups within the first group, corresponding to sites belonging to the Southern region and to those from the Central one, respectively.

Likewise, the Bayesian analysis of our data indicates an optimal value of K = 2 for the clustering of the samples into two groups (Fig. 3A and Fig. 3B): one group that includes samples from Togbin and Hounviatouin in the Southern part of the country, as well as most samples from Biguina and Agoua at the Western (Togolese) border of the Centre region; and one group composed of the majority of samples collected in Savè (Eastern part of the Centre region) and from the Northern locations of Pendjari, Pingou, Malanville, and Trois Rivières. Since there is a possibility that the ΔK method used for estimating K leads to over- or under-estimated values, clustering with a value of K = 3 has also been tested (Fig. 3C). As previously observed with the location-based PCoA, under this hypothesis further clustering emerges within the first group, involving samples from Togbin and Hounviatouin (South) and those from Biguina and Agoua (Center), respectively.

The Unweighted pair-group method with arithmetic mean (UPGMA) tree constructed from our data (Fig. 4) distinguishes two main groups matching the ones defined through the Bayesian analysis with K = 2, and which are supported by bootstrap values above 50. Within each of these groups, subgroups corresponding to those observed with K = 3 clustering and that globally match geo-climatic regions (Savè excepted) can further be defined. However, in this case most bootstrap values attached to these secondary branches are not significant.

In flowering plant, the efficiency of cross-species transfer of SSR markers is highly variable among taxa, especially when important differences in genome complexity exist between the marker source and the target [50]. Nevertheless, this method has been used successfully for accelerating the analysis of genetic diversity in many plant species, including palms [11, 51–53]. In the present study, we find that the transferability rate of microsatellite markers developed in other palms genera to B. aethiopum, i.e. their ability to successfully amplify genomic DNA from the latter species, is very low. Indeed, among the 80 primer pairs designed on either E. guineensis, P. dactylifera or C. nucifera, we observe that only 22.5% produce amplicons from B. aethiopum. This percentage is very low when compared to both the inter-species and inter-genera transferability rates that have been found in similar studies targeting other palm species: from 17 to 93% in a panel of 32 palm species [49], 75% from E. oleifera to E. guineensis [53], 86% between the wooly jelly palm (Butia eriospatha Mart.) and related species B. catarinensis [54] and up to 100% in the licuri palm (Syagrus coronate Mart) [55]. When considering other plant families, our transferability rate is also markedly lower than both the average rate of 50% found by Peakall et al. [56] within the Glycine genus and among Legumes genera, and the overall rate of 35.2% calculated by Rossetto [57] for within-family transferability among Gymnosperms and Angiosperms. The low transferability rate in our study might be explained in part by the fact that we used markers originating from genomic sequences. Indeed, as pointed out by Fan et al. [1], such markers have a lower transferability rate when compared to Expressed Sequence Tags (ESTs)-derived microsatellites due to the higher inter-species sequence variability within non-coding vs. coding sequences. Similarly, it is plausible that differences in genome size and complexity among palm species and genera account for our difficulty to identify palm SSR markers that successfully amplify in B. aethiopum. As a matter of fact, the size of the B. aethiopum genome, as determined by flow cytometry (1C = 7.73 Gb; Jaume Pellicer, unpublished data), is 3.2 to 11.5 times larger than those of the microsatellite source species used in the present study: P. dactylifera genome is estimated to be 671 Mb [39] whereas the E. guineensis genome is 1.8–1.9 Gb [37, 58] and C. nucifera genome is 2.42 Gb [42]. It is possible that these differences in genome sizes among related diploid plant species rely on differences in transposable element (TE) content, which in turn might have induced structural alterations throughout the genome through indels, copy number variations and recombinations [59, 60]. The illustration of such a mechanism working at the intra-genus level has been provided by cultivated rice species Oryza sativa L. and its wild relative O. australiensis [59]. Ultimately, TE-induced structural variations may have a negative effect on the cross-species amplification ability of some of the SSR primers. Indeed, in a recent study Xiao et al. [49] showed that over 70% of the conserved microsatellite loci between E. guineensis and P. dactylifera are located within genic regions of the genome with low TE content, and which are therefore less likely to be submitted to TE-dependent structural variations. More generally, gaining a better understanding of genome structures within the Borassus genus could also help reconcile our results with previous published reports of successful transfer of SSR markers developed from other palm sources to B. flabellifer (see references cited in Table 8, Methods section). Indeed, since the genome size of B. flabellifer (7.58 Gb; Jaume Pellicer, unpublished data) is only marginally smaller than that of B. aethiopum, significant differences in genome composition may be underlying the lack of SSR transferability between both species.

Table 8

Characteristics of the palm SSR markers tested for transferability to *B. aethiopum*.
Marker name	Primer sequences (5'-3' orientation)	T_a (°C)	Source palm species	Successful transfer to other palm species	References
mEgCIR0230	CCCTGGCCCCGTTTTTC	57.0	E. guineensis	E. oleifera Syragus sp. C. nucifera P. roebelinii P. canariensis P. reclinata	[73]
mEgCIR0230	AGCGCTATATGTGATTCTAA	57.0
mEgCIR0326	GCTAACCACAGGCAAAAACA	59.0
mEgCIR0326	AAGCCGCACTAACATACACATC	59.0
mEgCIR0465	TCCCCCACGACCCATTC	63.1
mEgCIR0465	GGCAGGAGAGGCAGCATTC	63.1
mEgCIR0476	TTCCTCGGCCCCTTCTC	61.6
mEgCIR0476	TCGCCGACCTTCCACTG	61.6
EgCSSR-5781	TTCACGCTACTGATGGTTGG	59.4	E. guineensis	B. flabellifer	[49]
EgCSSR-5781	TCGATCCCTTCTCTGGAAAC	59.4
EgCSSR-1461	GTCCTCTCCTACGCCTCCTC	60.3
EgCSSR-1461	ATGCGATCCGAGTTCAGAAG	60.3
mEgCIR2332	GAAGAAGAGCAAAAGAGAAG	55.0	E. guineensis.	B. flabellifer	[44, 45]
mEgCIR2332	GCTAGGTGAAAAATAAAGTT	55.0
mEgCIR3295	TGCCTCCAGACAATCAC	55.0
mEgCIR3295	GTAAGGCTTAACCAGATAAC	55.0
mEgCIR3311	AATCCAAGTGGCCTACAG	55.0
mEgCIR3311	CATGGCTTTGCTCAGTCA	55.0
mEgCIR3413	AAAGCTATGGGGTGAAAGAT	55.0
mEgCIR3413	TGGATAAGGGCGAGAAGAGA	55.0
mEgCIR3477	CCTTCAAGCAAAGATACC	55.0
mEgCIR3477	GGCACCAAACACAGTAA	55.0
mEgCIR3592	GAGCCAAAACAGACTTCAA	55.0
mEgCIR3592	ACCGTATATGACCCCTCTC	55.0
mEgCIR3755	GCTCACCAAAAAGTGTTAAGTC	55.0
mEgCIR3755	AGTTTCAACGGCAGGTATAT	55.0
mEgCIR3788	TTGTATGACCAAAGACAGC	55.0
mEgCIR3788	AGCGCAACATCAGACTA	55.0
ESSR75	AGATGGTTGGAGATTTCATGGT	60.0	E. guineensis	B. flabellifer	[44, 45, 47]
ESSR75	AACTTGAGGGTGCCATTACAAG	60.0
ESSR76	CCATACCAGCAGAAGAGGATGT	60.0
ESSR76	CTGAAGGTCATAGGGGTCTCTG	60.0
ESSR82R	CCCTCGACACCCATAGTTATTT	60.0
ESSR82R	CTCGATTTCTGGCCTCTCATAC	60.0
ESSR332	AGTTAATGTGTCAGGGCCAGTT	60.0
ESSR332	CTTGGTTCACTTGGGTGTGTC	60.0
ESSR553	ATAAATTGTGCGAGGGGAAAAC	60.0
ESSR553	AGATCCGCGACAGGTCTTAAC	60.0
ESSR566	GTGTCATCAAATTCGGTCCTTT	60.0
ESSR566	CGGTTCTTCTGCTGCTCTACTT	60.0
ESSR609	AGGCGGTGATGAAGATGAAG	59.0
ESSR609	CTCCTCTCAAACAGAGTGGGAT	59.0
ESSR650	GCCTTTTCTGGTTAATGGACTG	59.0
ESSR650	GTTTGTCTATGGATGATTGTGAGG	59.0
ESSR652	CATACCGTCACCACTCAGAAAC	60.0
ESSR652	GCCGTCATTCTACCAGTTGAG	60.0
ESSR673	TTCTGGCTACGAGCATAAGGA	59.0
ESSR673	TCAATAACCCTGGCTAAACACA	59.0
ESSR681	TCTGAATTGTCGGAGTGGC	59.0
ESSR681	CATCCTTGCGTAAACAAAAGAG	59.0
CNZ34	CATGTCGATAATTATACCCAA	55.0	C. nucifera	B. flabellifer K.laciniosa Z. zalacca D.kurzianus C.simplicifolia C. mannan C. thwaitesii C. erectus C. palustris	[46, 75]
CNZ34	TGCAAATATGAATGCAAACAC	55.0
CN2A5	AAGGTGAAATCTATGAACACA	53.2
CN2A5	GGCAGTAACACATTACACATG	53.2
CNZ 12	TAGCTTCCTGAGATAAGATGC	54.6	C. nucifera	B. flabellifer P. dactylifera E. guineensis	[46, 76]
CNZ 12	GATCATGGAACGAAAACATTA	54.6
CNZ 24	TCCTAAGCTCAATACTCACCA	55.0
CNZ 24	CGCATTGATAAATACAAGCTT	55.0
CNZ 18	ATGGTTCAGCCCTTAATAAAC	60.3
CNZ 18	GAACTTTGAAGCTCCCATCAT	60.3
CNZ 42	TGATACTCCTCTGTGATGCTT	55.5
CNZ 42	GTAGATTGTGGGAGAGGAATG	55.5
CN2A4	CAGGATGGTTCAAGCCCTTAA	61.0
CN2A4	GGTGGAAGAGGGAGAGATTGA	61.0
CAC 21	AATTGTGTGACACGTAGCC	54.1	C. nucifera	B. flabellifer	[74, 77]
CAC 21	GCATAACTCTTTCATAAGGGA	54.1
CAC 71	ATAGCTCAAGTTGTTGCTAGG	54.2
CAC 71	ATATTGTCATGATTGAGCCTC	54.2
CAC 84	TTGGTTTTTGTATGGAACTCT	54.4
CAC 84	AAATGCTAACATCTCAACAGC	54.4
CN1H2	TTGATAGGAGAGCTTCATAAC	53.2	C. nucifera	B. flabellifer P. dactylifera	[74]
CN1H2	ATCTTCTTTAATGCTCGGAGT	53.2	C. nucifera	B. flabellifer P. dactylifera	[74]
PdAG-SSR	TCTGATTTCGTTTACTTCTTAGGA	58.0	P. dactylifera		[44]
PdAG-SSR	TTCATATTCAGTTGTCGGGTGTA	58.0
mPdCIR015	AGCTGGCTCCTCCCTTCTTA	59.1
mPdCIR015	GCTCGGTTGGACTTGTTCT	59.1
mPdCIR063	CTTTTATGTGGTCTGAGAGA	52.5
mPdCIR063	TCTCTGATCTTGGGTTCTGT	52.5
mPdIRD1	CTCGGAAGGGTATGGACAAA	59.6	P. dactylifera	P. reclinata P. roebelenii P. rupicola P. theophrasti H. thebaica L. carinensis C. humilis	[72]
mPdIRD1	TTGCCTTCGACGTGGTAGTA	59.6
mPdIRD3	CATTGATCCAACACCACCAC	60.3
mPdIRD3	GCCAAAACCAGCTCTGGTAAC	60.3
mPdIRD4	TTGGTGGCCTTTCTCAGAGT	59.8
mPdIRD4	TGGGATCAAAGTAGGGTTGG	59.8
mPdIRD5	CTATCAGGATGGGGGTGATG	60.2
mPdIRD5	ACCCATCTGCATAGCTCCAG	60.2
mPdIRD7	TGCAATACGATGGCAGAGTC	60.2
mPdIRD7	CCTTGCAAGTTTTCCACACC	60.2
mPdIRD8	CTATTGGGTCCCTTGGTGAG	59.7
mPdIRD8	TGACTGCTCGTCATCAGGTC	59.7
mPdIRD10	ATGCGTTCATCTCCCTTGAG	59.7
mPdIRD10	GCTGCAAACATCATCCTCAC	59.7
mPdIRD11	GAGTTGGAGGCAAAACCAGA	59.8
mPdIRD11	CCACAAAACCCTTGTCTTCC	59.8
mPdIRD14	GAGGGGTTCACGTTTGTGTC	60.9
mPdIRD14	GCACCAAGCACAAGAGCAAT	60.9
mPdIRD15	CCGAGTCTGGCGAAGTAAAC	60.0
mPdIRD15	CTCCCCTTCCTCATCCTCTC	60.0
mPdIRD16	CTGTCCGATCGAATTCTGC	50.7
mPdIRD16	GGACATCTCTTTGCGGTCAT	50.7
mPdIRD17	GTGGGAGAAACCCGAAGAAT	60.2
mPdIRD17	CTGCTGCCTCATCTGCATT	60.2
mPdIRD20	TTGAATGGTCCCCTGTAGGT	59.5
mPdIRD20	GTCCCAGCATGATTGCAGTA	59.5
mPdIRD22	GGCTGTATGGGAAAGACCTG	59.5
mPdIRD22	CCTGCTGCATATTCTTCGTG	59.5
mPdIRD24	GCTCCTGCAGAACCTGAAAC	59.9
mPdIRD24	GGACATCACCGTCCAATTCT	59.9
mPdIRD25	CACTGGAAATTCAGGGCCTA	59.9
mPdIRD25	CCCAATTTCTCAGCCAAGAC	59.9
mPdIRD26	CCTCCAGTTCATGCTTCTCC	60.0
mPdIRD26	GAGCAGACCCGACAGACAAT	60.0
mPdIRD28	GAAACGGTATCGGGATGATG	59.7
mPdIRD28	TTAACGACGCCGTTTCCT	59.7
mPdIRD29	GGCTCCACCATCATTGACA	60.3
mPdIRD29	AACAGCATCGACTGCCTTCT	60.3
mPdIRD30	GCAGATGGTTGAAAGCTCCT	59.8
mPdIRD30	CCCCATTAACAGGATCAACG	59.8
mPdIRD31	GCAGGTGGACTGCAAAATCT	60.0
mPdIRD31	CTATTGGGGTGCTGATCCAT	60.0
mPdIRD32	AAGAAGACATTCCGGCTGGT	59.9
mPdIRD32	GCGGGTGTGTGATATTGATG	59.9
mPdIRD33	GGAGCATACAGTGGGTTTGC	60.1
mPdIRD33	CAGCCTGGGAATGAGGATAG	60.1
mPdIRD35	CAGCCCCTTACTCAGACTGG	59.6
mPdIRD35	CCCATAAGCTGATTGTGCTG	59.6
mPdIRD36	GACACGTTGACGATGTGGAA	60.7
mPdIRD36	CCATTGCTGTTGAGGAGGAG	60.7
mPdIRD37	TTTCCTGCTCGAAAGACACC	60.2
mPdIRD37	CTTAGCCAGCCTCCACACTC	60.2
mPdIRD40	GAGAGATGCGTCAGGGAATC	59.2
mPdIRD40	CCAGAATCTTCCAAGCAAGC	59.2
mPdIRD42	GAGGCAAAACTATGGGAAGC	59.5
mPdIRD42	TTCACTGGAGCAAGGGTAGG	59.5
mPdIRD43	GCAGCCATTGCTTACAGTGA	60.2
mPdIRD43	TAAACTGCTGCCTTCCTTGG	60.2
mPdIRD44	CAGATCCGGGAGATGATGAA	60.4
mPdIRD44	AGCAGGAGCAGCTGCATAA	60.4
mPdIRD45	TAGCCTGTGCATGTTCGTTG	60.4
mPdIRD45	AACAGCAGCTGATGGTGATG	60.4
mPdIRD46	ATGGGTCCATTGGAGGAACT	60.2
mPdIRD46	GACGGAGACCTTGACTGCTC	60.2
mPcCIR10	ACCCCGGACGTGAGGTG	62.8	P. dactylifera		Cherif, Castillo and Aberlenc-Bertossi, unpublished data.
mPcCIR10	CGTCGATCTCCTCCTTTGTCTC	62.8
mPcCIR20	GCACGAGAAGGCTTATAGT	51.7
mPcCIR20	CCCCTCATTAGGATTCTAC	51.7
mPcCIR32	CAAATCTTTGCCGTGAG	53.3
mPcCIR32	GGTGTGGAGTAATCATGTAGTAG	53.3
mPcCIR35	ACAAACGGCGATGGGATTAC	60.8
mPcCIR35	CCGCAGCTCACCTCTTCTAT	60.8
mPcCIR50	CTGCCATTTCTTCTGAC	50.6
mPcCIR50	CACCATGCACAAAAATG	50.6
mPcCIR57	AAGCAGCAGCCCTTCCGTAG	62.0
mPcCIR57	GTTCTCACTCGCCCAAAAATAC	62.0
mPcCIR85	GAGAGAGGGTGGTGTTATT	51.8
mPcCIR85	TTCATCCAGAACCACAGTA	51.8
mPdIRD41	ATCTTCCATGCAGCCTCAAG	60.3
mPdIRD41	CAGGTCGTCCCGTCTCTAAA	60.3
mPdIRD47	GTTGGCATCACTTCAGAGCA	60.1
mPdIRD47	GCTCTTTCGGTGCTAGTTGC	60.1
For each marker, forward (top) and reverse primers (bottom) are provided.
T_a: average annealing temperature for each primer pair.
Species names are abbreviated as follows: P. roebelinii: Phoenix roebelinii; P. canariensis: Phoenix canariensis; Phoenix reclinata; H. thebaica : Hyphaene thebaica; L. carinensis : Livistona carinensis; C. humilis : Chamaerops humilis ; K. laciniosa : Korthalsia laciniosa; Z. zalacca : Zalacca zalacca ; D. kurzianus : Daemonorops kurzianus ;C. simplicifolia : Calamus simplicifolia ;C. mannan : Calamus mannan ; C. thwaitesii : Calamus thwaitesii ; C. erectus : Calamus erectus ; C. palustris : Calamus palustris; P. rupicola : Phoenix rupicola; P. theophrasti: Phoenix theophrasti.

In any case, from the low number of successfully transferred microsatellite markers we could only identify one displaying polymorphism in our B. aethiopum test panel, making it impossible to rely on for analysis of genetic diversity. Still, the fact that so little microsatellite polymorphism (2 out of 18 amplifying primer pairs: 11.1%) could be detected in this subset of 20 palms sampled across different locations throughout Benin is somewhat surprising and its reasons remain to be elucidated. In addition to possibly being a symptom of habitat fragmentation, this low diversity might also result from the extremely long juvenile phase that has been attributed to this palm species. Indeed, floral maturity has been reported to occur 30 to 50 years after germination [61]. The manner of seed and pollen dispersal, which have so far not been studied in B. aethiopum, might also play a role. Indeed, in pollen-mediated gene flow species, the distance the pollen travel is of importance in the occurrence of crossing between populations [62, 63].

Regarding the development of novel SSR markers, our results are similar to other studies based on the use of high-throughput sequencing techniques in species where very little information is available [22, 64]. We identified 57 microsatellite loci, from which we selected 11 markers displaying polymorphism that were used to assess the genetic structure of B. aethiopum sampled from different sites in Benin. We find low genetic diversity, with an average He value (0.354) that is substantially below those reported for B. flabellifer (0.417) [45] and for other non-timber forest products such as Khaya senegalensis (0.53) [65] and Phyllanthus sp. (0.607 and 0.582 for P. emblica and P. indofischeri respectively [66]. The positive F value that we observed in the majority (6 out of 9) of locations in the present study indicates an overall deficiency of heterozygotes across sites. This deviation from the Hardy-Weinberg equilibrium (HWE) might reflect low gene flow through pollen and seed dissemination, leading to crosses between related individuals, as supported by the low average number of migrants between sites. Accordingly, our data reveal limited genetic distances among collection sites, with values lower than those reported for others palm species. Indeed for B. flabellifer, genetic distances ranged from 0.716 to 0.957 [67] and among natural E. guineensis accessions an average of 0.769 was observed [68]. Both our Fst values and AMOVA analysis point to intra-site differentiation as being the main source of genetic variation.

As illustrated by the global agreement between our PCoA and Bayesian analyses, Beninese B. aethiopum samples cluster into two main groups that are mostly dependent on geo-climatic regions and geographic distances between collection sites, although the correlation between genetic and geographic distance is poorly significant. There might be further genetic separation between Southern B. aethiopum samples and those from the Central sites of Agoua and Biguina, resulting in the splitting of one group into two subgroups. However, with our current dataset it is not possible to achieve this level of discrimination in our analyses. Additional sampling campaigns from intermediate locations in the Central and Northern regions will be necessary in order to make progress on the subject.

Among the nine locations studied in Benin, samples from Savè appear to be the most diversified (He = 0.451) and constitute the exception to the general distribution according to geographical distances. This site located in the Sudano-Guinean transition zone of Benin is currently the most active for the production of B. aethiopum hypocotyls, and it acts as a supplier for the whole national territory (V.K. Salako, personal communication), suggesting that it might be the largest population of B. aethiopum in the country. Moreover, individuals sampled in Savè appear to be genetically distinct from those sampled in other locations of the Central region and closer to those originating from the Northern region, despite the considerable geographical distances involved in the latter case. We postulate that seed dispersion by elephants might have played a major role in the observed pattern of genetic diversity and explain the singularity observed in Savè. As a matter of fact, Salako et al. [31, 32] detected the presence of B. aethiopum seeds in elephant dungs and hypothesized that elephants may have played important role in the seed dissemination for this species through fruit consumption and long-distance herd migrations. In support to this assumption, Savè is part of a continuous forest corridor connecting with the Northern region that was likely used by elephants in their migrations. Up until 1982, the seasonal occurrence of the animal has been reported in the Wari-Maro forest of Central Benin [69].

The specific microsatellite markers developed in this study from the partial genomic sequencing of B. aethiopum appear to be efficient to assess the genetic diversity and population structure of this species. Additionally, and provided that genome divergence is not too extensive to allow marker transferability, our SSR markers may also been used in a palm species that belongs to the same genus and that is reported to share parts of its distribution area, namely Borassus akeassii B.O.G., which has long been confused with B. aethiopum due to its similar morphology [70]. High-throughput sequencing techniques are an effective way of developing new microsatellite markers in plant species without significant molecular data. The increasing technical performances and financial affordability of these technologies make it feasible to overcome the difficulties arising in case studies such as ours, where marker transfer was proved to be limited or ineffective.

To our knowledge, the data presented in the present article constitute the first sizeable molecular resource available for B. aethiopum, which we have made available to the scientific community at large in order to facilitate the implementation of an increasing number of studies on this palm species. We have also performed the first analysis of the genetic diversity of B. aethiopum in an African country, which we see as a first step towards the elaboration of an evidence-based strategy for sustainable resource management and preservation in Benin. As a complement, the acquisition of agro-morphological data and the characterization of processes regulating the reproductive development of the species are currently under way. Beyond that, we also aim to extend our analysis of B. aethiopum diversity to the West African sub-region, and leverage the data acquired to improve knowledge of other species within the Borassus genus, and of palms diversity as a whole.

Plant material sampling and DNA extraction

Samples of B. aethiopum were collected in nine distinct sites (three located in protected forest areas, six in farmlands) that were distant from each other by at least 50 km and which spanned the three main climatic regions encountered in Benin (Fig. 5). According to White [71], Benin covers three contrasted climatic regions which are the Sudanian region in the North, the Sudano-Guinean region in the Centre and the Guineo-Congolian region in the South. Along a South-North gradient, the rainfall regime switches from bimodal to unimodal, the climate becomes globally drier [29] and the density of B. aethiopum distribution increases [31]. At each location, young leaves from 10 male and 10 female adult trees separated by at least 100 m were collected and stored in plastic bags containing silica gel until further processing. The complete list of samples and their characteristics is available in Additional file 2.

Genomic DNA was extracted from 250 mg of leaves ground to powder under liquid nitrogen using the Chemagic DNA Plant Kit (Perkin Elmer, Germany), according to the manufacturer’s instructions on a KingFisher Flex™ (Thermo Fisher Scientific, USA) automated DNA purification workstation. Final DNA concentration was assessed fluorometrically with the GENios Plus reader (TECAN) using bis-benzimide H 33258 (Sigma-Aldrich) as a fluorochrome.

Transferability of palms microsatellite markers: selection and amplification

A total of 80 SSR markers from previous studies were selected for assessment of their transferability to B. aethiopum: 44 developed for P. dactylifera [72]; 25 developed for E. guineensis [44, 73]; and 11 developed for Cocos nucifera [74]. The respective sequences and origins of these primer sets are displayed in Table 8.

Transferability of the 80 palm SSR markers was assessed on a representative subset of 20 B. aethiopum individuals sampled at the different locations, plus four positive controls from each

source species for these markers (i.e. P. dactylifera, C. nucifera, and E. guineensis). Microsatellite amplification was performed with a modification of the M13-tailed Primers protocol [75] adapted to the use of fluorescent labelling [76]. The PCR reaction was performed on 20 ng of leaf DNA in volume of 20 µL with the following final concentrations or amounts: 1X PCR buffer, 200 µM dNTP, 2 mM MgCl2, 0.4 pmol M13-tailed forward primer, 4 pmol M13 primer, (5’-CACGACGTTGTAAAACGAC-3’) fluorescently labeled at the 5' end with FAM, HEX or TAMR, 4 pmol reverse primer, and 0.5 U of KAPA Taq polymerase (Sigma-Aldrich). The following program was used: 3 min of initial denaturation at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 50 °C and 72 °C for 1 min and a final extension at 72 °C for 5 min. The resulting amplification products were then diluted to 1/10th, mixed with 0.5 µL of an internal size standard (GeneScan 500 ROX, Thermo Fisher Scientific), and denatured for 5 minutes at 94 °C prior to separation through capillary electrophoresis on an Applied Biosystems 3500 Genetic Analyzer (Thermo Fisher Scientific). Amplification products visualization was performed using the GeneMapper software version 3.7 (Applied Biosystems).

De novo identification of microsatellite loci in the B. aethiopum genome, marker selection and diversity analysis

One B. aethiopum leaf sample (originating from the Togbin site) was randomly selected and used for genomic DNA purification according to the protocol of Mariac et al. [77]. The DNA was then used for the construction of an Illumina paired-end library, as described in Mariac et al. [81], before high-throughput sequencing on a MiSeq v3 platform (Illumina; average read size 250 bp). Demultiplexing of the raw data output was performed using the Maillol script (https://github.com/maillol/demultadapt), with a 0-mistmatch threshold. Adapters were eliminated using Cutadapt version 1.10 [82]. (http://code.google.com/p/cutadapt/) with the following parameters: overlap length = 7, minimum length = 35 and quality = 20. High-quality reads (Q > 30) were filtered using the following script: https://github.com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality.pl and the resulting filtered reads were deposited into GenBank under BioProject ID PRJNA576413. Paired-end reads were then merged using FLASH version 1.2.11 (https://github.com/SouthGreenPlatform/arcad-hts/blob/master/scripts/arcad_hts_3_synchronized_paired_fastq.pl). Finally, microsatellite motif detection and specific primer design were carried out after elimination of redundant sequences using the QDD software version 3.1.2 [83] with default settings (Additional file 3: File S1).

Using selected primer pairs, test amplifications were performed with two randomly selected B. aethiopum DNA samples, then primers showing successful amplification were further tested for polymorphism detection among seven randomly selected DNA samples. The M13 Tailed Primers protocol described previously was used, with the following program: 3 min of initial denaturation at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 55 °C and 72 °C for 1 min and a final extension at 72 °C for 5 min. Separation and vizualization of amplification products were performed as described previously. Finally, the primer pairs enabling successful and unambiguous amplification of polymorphic bands were used for the analysis of genetic diversity among the complete set of 180 B. aethiopum individuals under the same PCR conditions.

Data analysis

Amplification products were scored using the GeneMapper software version 3.7 (Applied Biosystems) and only unambiguous amplification products were considered for data analysis. Genetic diversity parameters were calculated for each locus and each sampling location using the GenAlEx software version 6.502[84]. Expected heterozygosity (He) was calculated using the formula:

$$\text{H}\text{e}=1-\sum {\text{p}}_{\dot{\text{i}}}^{2}$$

where p_i is the frequency of each allele. The fixation index (F) was calculated as:

$$\text{F}=1-\frac{{\text{H}}_{0}}{\text{H}\text{e}}$$

where Ho is observed heterozygosity and He is expected heterozygosity [

F-statistics analysis assessing genetic differentiation (Fst), genetic identity, number of migrants (Nm) [

] and analysis of molecular variance (AMOVA) for estimating genetic differentiation within and among locations were performed with the same software. The Mantel permutation test was used for assessing the correlation between genetic and geographic distances between sampling sites [

87,

88]. Two Principal Coordinates Analyses (PCoA) enabling the visualization of genetic variation distribution across individuals and sampling sites, respectively, were performed using GenAlEx. The STRUCTURE software version 2.3.4[

89] was used for the determination of the most probable number of clusters for population structure (K value). Using the admixture model, eight simulations were performed for each inferred K value, with a running length composed of 300,000 burn-in periods and 50,000 Markov chain Monte Carlo (MCMC) replicates. The output from this analysis was then used as input in the Structure HARVESTER online program version 0.6.94 (

http://taylor0.biology.ucla.edu/structureHarvester/)to determine the optimal value of K using the ΔK method of Evanno et al. [

90] and allowing for different estimates of K in accordance with Janes et al [

91]. Based on the resulting values of K, a clustering analysis of the studied sampling sites was performed. In order to further assess genetic clustering, a UPGMA tree based on Fst values using 1,000 bootstrap replications was constructed using the POPTREE2 software [

92].

AMOVA: Analysis of molecular variance; F: Fixation index; F_st: inter-population genetic differentiation coefficient; He: Expected Heterozygosity; Ho: Observed Heterozygosity; HWE: Hardy-Weinberg equilibrium; Na: average number of different alleles; Ne: effective number of alleles; N_m: Number of migrants; PCoA: Principal coordinate analysis; SSR: Simple sequence repeat; UPGMA: Unweighted pair-group method with arithmetic mean

Ethics approval and consent to participate

In accordance with the Nagoya Protocol on Access and Benefit Sharing (ABS), a field permit allowing access and non-commercial use for research purposes of the plant material used in the present study has been submitted to the competent national authority (Direction Générale des Eaux, Forêts et Chasse/Ministère du Cadre de Vie et du Développement Durable, Benin).

Consent for publication

Not applicable

Availability of data and materials

Data generated from genome sequencing (filtered reads) were deposited into GenBank under BioProject ID PRJNA576413. Capillary electrophoresis profiles are available upon reasonable request to the Corresponding Author. All other data generated or analyzed during this study are included in this published article (and its supplementary information files).

Competing interests

The authors declare that they have no competing interests

Funding

The work described in this article was funded through travel grants to MJK and KA under the framework of the MooSciTIC project granted to EJ by Agropolis Fondation (ID 1501-011, "Investissements d’Avenir" Program - Labex Agro: ANR-10-LABX-0001-01). Additional funding was provided by the Sud Expert Plantes - Développement Durable (SEP2D) programme to KA (GenPhyB project, ID AAP3-64). MJK is the recipient of a PhD fellowship from the French Embassy in Benin. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Authors’ contributions

VKS, EJ, TB and KA conceived and designed the experiments and were responsible for funding acquisition. All authors were involved in defining the experimental strategy. MJK, SS, ML, CTC, KA, VKS performed the experiments. MJK, KA, SS, CM, LZ and TB processed and analyzed data. All authors contributed to writing the manuscript.

Acknowledgements

The Authors wish to thank Emira Cherif, Karina Castillo and Frédérique Aberlenc (IRD, UMR DIADE, Montpellier, France) for sharing their unpublished microsatellite markers data for transferability tests, and Dr Jaume Pellicer (Comparative Plant & Fungal Biology, Jodrell Laboratory, Royal Botanical Gardens, Kew, UK), for allowing us to use the palm flow cytometry data.

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Transferability, development of simple sequence repeat (SSR) markers and application to the analysis of genetic diversity and population structure of the African fan palm (Borassus aethiopum Mart.) in Benin

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Abstract

Figures

Background

Results

Discussion

Conclusions

Methods

Plant material sampling and DNA extraction

Transferability of palms microsatellite markers: selection and amplification

Data analysis

Abbreviations

Declarations

References

Supplementary Files

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