Plant Genetic Resources: Diversity of white Guinea yam (Dioscorea
Characterization and
Utilization rotundata Poir.) cultivars from Benin as
revealed by agro-morphological traits and
cambridge.org/pgr SNP markers
Paterne A. Agre1 , Anicet G. Dassou2, Laura E. Y. Loko3, Roger Idossou2,
Research Article Eric Dadonougbo2, Anicet Gbaguidi2, Jean M. Mondo1,4, Yusuf Muyideen5,
Cite this article: Agre PA et al (2021). Diversity Patrick O. Adebola6, Robert Asiedu1, Alexandre A. Dansi2 and Asrat Asfaw1
of white Guinea yam (Dioscorea rotundata
Poir.) cultivars from Benin as revealed by agro- 1International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria; 2Laboratory of Biotechnology, Genetic
morphological traits and SNP markers. Plant Resources and Plant and Animal Breeding (BIORAVE), National University of Sciences, Technologies, Engineering
Genetic Resources: Characterization and
and Mathematics, P.O. Box 14, Dassa-Zoumé, Benin; 3Utilization 1–10. https://doi.org/10.1017/ Laboratory of Applied Entomology, National High School of
S1479262121000526 Biosciences and Applied Biotechnologies (ENSBBA), National University of Sciences, Technologies, Engineering
and Mathematics, P.O. Box 14, Dassa-Zoumé, Benin; 4Department of Crop Production, Université Evangélique en
Received: 15 January 2021 Afrique, P.O. Box 3323, Bukavu, Democratic Republic of Congo; 5Department of Horticultural Sciences, University
Revised: 22 September 2021 of Minnesota, Saint Paul, MN 55108, USA and 6International Institute of Tropical Agriculture (IITA), Abuja, Nigeria
Accepted: 23 September 2021
Key words: Abstract
Benin; genetic structure; joint dissimilarity White Guinea yam (Dioscorea rotundata Poir.) is indigenous to West Africa, a region that har-
matrix; landrace; morphological diversity; SNP bours the crop’s tremendous landrace diversity. The knowledge and understanding of local
marker
cultivars’ genetic diversity are essential for properly managing genetic resources, conservation,
Author for correspondence: sustainable use and their improvement through breeding. This study aimed to dissect pheno-
Paterne A. Agre, typic and molecular diversity of white yam cultivars from Benin using agro-morphological
E-mail: p.agre@cgiar.org and single nucleotide polymorphism (SNP) markers. Eighty-eight Beninese white Guinea
yam cultivars collected through a countrywide ethnobotanical survey were phenotyped with
53 traits and genotyped with 9725 DArT-SNP. Multivariate analysis using phenotypic traits
revealed 30 traits as most discriminative and explained up to 80.78% of cultivars’ phenotypic
variation. Assessment of diversity indices such as Shannon–Wiener (H′), inverse Shannon
(H.B.), Simpson’s (λ) index and Pilou evenness (J ) based molecular and phenotypic data
depicted a moderate genetic diversity in Beninese white Guinea yam cultivars. Genetic differ-
entiation of cultivars among country production zones was low due to the high exchange of
planting materials among farmers of different regions. However, there was high genetic diver-
sity within regions. Hierarchical clusters (HCs) on phenotypic data revealed the presence of
two groups while HCs based on the SNP markers and the combined analysis identified
three genetic groups. Our result provided valuable insights into the Beninese white Guinea
yam diversity for its proper conservation and improvement through breeding.
Introduction
Yam is a popular staple in West Africa (Asiedu and Sartie, 2010; Darkwa et al., 2020a). Its value
chain sustains ∼5 million people’s livelihood, including the rural farming households, traders,
transporters and processors (Mignouna et al., 2020). Yam is an essential source of carbohydrates,
vitamins, essential minerals, fibres, with a low glycemic index (Akinola et al., 2019). Benin is part
of the African yam belt, a region producing more than 90% of the world yam production. It
produces ∼2.9 million tons of yam annually and ranks fourth after Nigeria, Ghana and Côte
d’Ivoire (FAO, 2020). Yam is a primary staple food and symbolic in many key socio-cultural
and religious events (Zannou et al., 2004; Loko et al., 2019). The country produces several
yam species, including Dioscorea cayenensis, D. rotundata, D. alata and D. dumetorum
(Scarcelli et al., 2006). Among these species, D. rotundata is the most popular and preferred
for its economic profitability and suitability for a range of local food recipes.
As part of the crop improvement and genetic conservation efforts, several genetic diversity
studies have been conducted on yam across the globe in general and in the sub-region in par-
© The Author(s), 2021. Published by
Cambridge University Press on behalf of NIAB. ticular. These include an inventory and characterization of local cultivars using phenotypic
This is an Open Access article, distributed traits (Dansi et al., 1997; Loko et al., 2013, 2015; Etchiha et al., 2019), random amplified poly-
under the terms of the Creative Commons morphism DNAs (RAPD) and simple sequence repeat (SSR) molecular markers (Dansi et al.,
Attribution licence (http://creativecommons. 2000; Tostain et al., 2007; Missihoun et al., 2009; Loko et al., 2017). Although a high genetic
org/licenses/by/4.0/), which permits
unrestricted re-use, distribution, and diversity was reported in Benin, only a few cultivars are widely grown for their superior agro-
reproduction in any medium, provided the nomic performance and market value (Dansi et al., 2000; Etchiha et al., 2019). Besides, yam
original work is properly cited. yield in this country is still meagre (13 t/ha) compared to the crop’s yield potential in the
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2 Paterne A. Agre et al.
region (40–50 t/ha) (Frossard et al., 2017). Developing improved 16 markets of Benin’s four largest yam production regions
varieties combining high tuber yield and superior food quality (Atacora, Donga, Borgou, Collines) (online Supplementary
traits is an effective strategy to enhance yam productivity and Fig. S1). The collection targeted the most popular cultivars with
increase farmers’ interest in yam farming (Darkwa et al., 2020a). high market value to be used for yam crop improvement. The
An effective yam improvement programme through breeding, to farmers’ preference criteria for tuber yield potential, tolerance to
develop such varieties, requires proper knowledge of the genetic weed competition, drought and diseases, long shelf life, good
resources available to guide the selection of cross-compatible and taste and aroma of processed food products and slow tuber
suitable parents (with desirable traits) to be involved in hybridiza- flesh oxidation were considered in the sampling process.
tion (Mondo et al., 2020). As above-mentioned, both molecular These varieties were planted at the experimental farm of the
and phenotypic analyses have been routinely used in yam for cul- Laboratory of Biotechnology, Genetic Resources and Plant and
tivar characterization prior to hybridization to achieve breeding Animal Breeding (BIORAVE) located at Massi, Zogbodomey dis-
objectives. However, there are increasing evidences on the limita- trict, during the 2019 growing season (February–November). The
tions of each of these characterization approaches. field was established using an 11 × 8 lattice design with two repli-
Molecular markers previously used for yam genetic diversity cations. The plot consisted of an 11 m column with five plants per
studies include RAPD (Asemota et al., 1995), amplified fragment variety, at a spacing of 1 m × 1m. No fertilizer was applied, while
length polymorphism (AFLP) (Mignouna et al., 1998, 2002a, manual weeding was done regularly to maintain the plot free of
2002b; Terauchi and Kahl, 1999), SSR (Arnau et al., 2009; weed. A total of 53 agro-morphological variables covering differ-
Girma et al., 2017; Loko et al., 2017; Mulualem et al., 2018; ent plant parts (leaves, stem, flowers, fruits, roots, tubers) (online
Babil et al., 2021), inter SSRs (Ousmael et al., 2019) and single Supplementary Table S1) were assessed following the procedures
nucleotide polymorphism (SNP) markers (Agre et al., 2019; described in the yam crop ontology (Asfaw, 2016).
Bhattacharjee et al., 2020; Darkwa et al., 2020b). However, the
reproducibility and reliability ofmarkers like AFLP are limited, time-
consuming and have low distribution across the genome. From the Genotyping
last decade, SNPs generated through the Next Generation About 10 g of young, healthy and fully expanded leaves from each
Sequencing approaches (Diversity Arrays Technology (DArT) and genotype were collected using silica-gel granules with a colour
genotyping-by-sequencing) are themostwidelyused inyamdiversity indicator. Leaves were stored in the silica-gel for 72 h to remove
studies because of their stability and abundance in the genome the moisture. Subsequently, DNA extraction was carried out at
(Girma et al., 2014; Akakpo et al., 2017; Siadjeu et al., 2018; the Bioscience centre, International Institute of Tropical
Scarcelli et al., 2019; Bhattacharjee et al., 2020; Darkwa et al., 2020b). Agriculture (IITA), Ibadan, Nigeria, using the CTAB procedure
According to Andrade et al. (2017); Alves et al. (2017); Agre with slight modification (Dellaporta et al., 1983). The DNA qual-
et al. (2019); Nkhoma et al. (2020); Stanley et al. (2020), genotypic ity was ascertained by running the gDNA in a 1% agarose gel
and phenotypic data display very low or negligible correlations while NanoDrop 2000 spectrophotometer was used to estimate
and produce non-duplicate information. A large portion of vari- its concentration and purity.
ation detected by molecular markers is non-adaptive compared DArT genotyping was performed as described by Sansaloni
withphenotypic characterswhich are influencedby the environment. et al. (2010). For the sequencing-based DArT genotyping, com-
In yam, the effectiveness of combining phenotypic and molecular plexity reduction methods optimized for yam at the DArT Pty
marker information for dissecting genetic diversity has been reported Ltd., Australia, were used. PstI_ad/TaqI/HpaII_ad with TaqI
(Sartie et al., 2012; Agre et al., 2019; Darkwa et al., 2020b). Studies in restriction enzyme was used to eliminate a subset of PstI–HpaII.
yam and other crops suggest that combined analysis for phenotypic The pstI-site-specific adapter was tagged for the 88 accessions
and genotypic information is useful for assessing the functional gen- with different barcodes enabling encoding a plate of DNA sam-
etic diversity in crop plants and for minimizing limitations from ples to run within a single lane on an Illumina GAIIx.
either analysis approach. Genetic diversity assessment studies on
Beninese white Guinea yam cultivars were conducted independently
using either phenotypic or genotypic data. However, a joint analysis Data analysis
has not yet been deployed to explore the diversity of this crop in
Phenotypic analysis
Benin.Hence, this study’s objectivewas to assess the genetic structure
and diversity in a panel of farmers’ cultivars across the country using Prior the Least Squares means (LSmeans) estimation, the data
bothphenotypicandSNPmarkers.Ourstudythereforecomplements were scaled and the LSmeans generated for the 88 yam varieties
previous studies in the country that used either morphological or were used for principal component analysis (PCA) with
molecular data alone. Proper genetic resource characterization pro- FactoMineR (Le et al., 2008) and missMDA (Josse and Husson,
vided by this joint analysis will be instrumental for efficient parental 2016). The optimum number of discriminant factors was deter-
selection for yam breeding programmes in West Africa, including mined through Peres-Neto et al. (2003) principle by considering
Benin. Besides, it will strengthen the crop genetic resource conserva- factors with Eigenvalues >1. On each factor, the variables were
tion efforts which were previously relying on either phenotypic or declared discriminative and retained for subsequent analysis
genotypic information alone. with a correlation above 0.5. Retained variables were then sub-
jected to correlation plot using corrplot function implemented
Materials and methods in R. Considering the discriminant variables, Gower dissimilarity
matrix was generated using cluster package implemented in
Collection of plant materials, field establishment and
R. Count and nominal and or categorical variables (plant vigour,
phenotyping
sprout colour, plant type, stem number per plant, number of
A total of 88 white Guinea yam (D. rotundata) cultivars were internodes, spines on stem above base, flowering degree/intensity,
collected through an ethnobotanical survey in 20 villages and plant sex, inflorescence type, tuber size, number of tubers
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Plant Genetic Resources: Characterization and Utilization 3
harvested per plant, numberofmarketable tubers, numberof tubers genetic groups. The joint matrix was calculated as the algebraic
damaged by rot and disease, tuber shape, tuber skin thickness, tuber sum of the Gower and Jaccard matrix generated through the
flesh oxidation, flesh colour, absence/presence of corms on tubers) phenotypic and the SNP marker data, respectively (Alves et al.,
were listed in the model and transformed using log ratio function 2013; Andrade et al., 2017). Mantel test was performed using
prior generating the dissimilaritymatrix. The final hierarchical clus- the Monte-Carlo method with 9999 permutations to assess the
ter (HC)was then performed based onward.D2method in cluster R correlation among the three matrices (phenotypic, genotypic
package (Maechler et al., 2021).Gower dissimilaritymatrixwas sub- and the combined). Phenotypic data of the different groups
jected to various diversity indexes such as Shannon–Wiener Index obtained through the joint analysis were subjected to k-mean ana-
(H′), Inverse Simpson’s (HB), Simpson’s Index (λ) and Pilou even- lysis and the performance of each cluster was compared by Rstatix
ness (J ) using vegan library (Oksanen et al., 2019). The reason for package (Kassambara, 2020).
using different indices for diversity assessment was solely for
increasing the reliability of conclusions.
Results
SNP analysis Phenotypic diversity
For quality control, DArTseq SNP-derived markers were filtered The first 14 principal components were identified as most dis-
to remove the unwanted SNP markers using the software criminative and accounted for 80.78% of the cultivars’ total
PLINK 1.9 and VCFtools. Markers and genotypes with >20% phenotypic variation. Thirty (30) traits were correlated to the
missing data were eliminated. Rare SNPs with <5% minor allele first 14 principal components (online Supplementary Table S2).
frequencies and low coverage read depth (<5) were also removed. The first principal component explained 13.98% of the total vari-
In the end, only informative 9725 DArT-SNP markers and 88 cul- ation and was significantly associated with tuber size (TBRSZ),
tivars were used for the subsequent analysis. the number of marketable tubers (NMT), marketable tuber weight
The summary statistics such as observed and expected hetero- (MTW), marketable tuber length (MTLS), marketable tuber
zygosity, minor allele frequency (MAF) and polymorphic infor- width (WMT) and tuber yield. The second principal component
mation content (PIC) were estimated using VCFtools (Danecek accounted for 10.16% of the total variation and was significantly
et al., 2011) and PLINK 1.9 (Purcell et al., 2007). Mutation trans- correlated with six variables (online Supplementary Table S1).
version and transition were determined using the SniPlay web The third component explained 7.62% of the total variation and
base (Purcell et al., 2007). Dosage SNP format (0, 1, 2) was gen- was positively associated with four phenotypic traits. Number of
erated using recodeA function implemented in Plink where 0 is rotten tubers (NRDB) and weight of rotten tubers (RDTW) posi-
the homozygote reference, 1 the heterozygote and 2 the homozy- tively contributed to the fourth principal component, which
gote alternative. Dosage format was then subjected to Jaccard dis- explained 7.16% of the total variation.
similarity matrix using phylentropy R package (Drost, 2018). Phenotypic correlation among the discriminating variables
Jaccard dissimilarity matrix was then used to estimate the genetic revealed high and positive correlations among total tuber yield,
diversity indexes such as Shannon–Wiener Index (H′), Inverse number of tubers, tuber weight, plant vigour, tuber length and
Simpson’s (HB), Simpson’s Index (λ) and Pilou evenness (J ) to tuber width while a negative correlation was recorded between
quantify the level of genetic diversity countrywide in Benin as the total tuber yield and tuber flesh oxidation (online
well as within and among surveyed geographical zones using Supplementary Fig. S2). Diversity indexes based on Gower dis-
the vegan library (Oksanen et al., 2019). similarity matrix revealed the presence of moderate genetic diver-
Besides, three multivariate analyses, including admixture for sity across the country (Table 1).
population structure, PCA, and cluster analysis with Ward Cluster analysis based on phenotypic traits differentiated the
method, were employed. Binary file generated from vcf file was 88 varieties into two major clusters (Fig. 1). The first cluster
then subjected to admixture analysis using ‘adegenet’ R package (red) was composed of 62 yam varieties with intermediate to
(Jombart et al., 2010). The optimal number of clusters was late maturity cycle, moderate flowering intensity, moderate
inferred using k-means analysis after varying the number of clus- tuber yield (4.10 kg per plant) and high tuber flesh oxidation.
ters from 2 to 40. Through the admixture analysis, genotypes with The second cluster (green) comprised 26 early maturing yam var-
membership proportions (Q-value) ≥60% were assigned to ieties with low flowering intensity and good agronomic perform-
groups. In comparison, those with membership probabilities ance. Varieties in this cluster had high tuber yield (7.96 kg per
<60% were designated as admixtures (Salazar et al., 2017). For plant) and high marketable tuber weight.
the HC analysis, generated Jaccard dissimilarity matrix was used
and the HC was plotted using Ward.D2 method. Further, PCA
Molecular diversity and population structure
was conducted to reveal the genetic relationships among the
yam varieties using GenAlEx v. 6.503 software (Peakall and A total of 20,000 SNP markers was initially generated, from which
Smouse, 2012) by considering the collection zone as a factor. 9725 were retained after the removal of low-quality SNP markers.
Jaccard dissimilarity matrix was further subjected to the analysis The SNP markers were unequally distributed across the 20 yam
of molecular variance (AMOVA) using GenAlEx v. 6.503 chromosomes with a minimum of 212 SNPs on chromosome 13
(Peakall and Smouse, 2012) to partition components of genetic to a maximum of 1469 SNPs on chromosome 5 (online
variance among and within the populations (k). Supplementary Fig. S3a). The SNPmutation showed that transition
(T.S.) was greater (59%) than the transversion (T.V.) in the yamgen-
ome. Among the transition mutations, A/G had the highest occur-
Joint analysis of phenotypic and molecular data
rence rate in the genome (online Supplementary Fig. S3b). In
The Gower and Jaccard dissimilarity matrices from both pheno- contrast, for the transversionmutations,A/Cappeared at thehighest
typic and molecular data were combined to define potential rate. Values of 0.22 and 0.24 were recorded as averages for the
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4 Paterne A. Agre et al.
Table 1. Genetic diversity indices based on SNP data across collection zones of Benin
Based molecular SNP data
Based phenotypic data Across (88) Atacora (21) Borgou (24) Donga (21) Collines (22)
(HB) 81.54 79.34 18.03 19.04 17.59 19.72
(H′) 4.43 4.39 2.92 2.99 2.88 2.99
(J ) 0.22 0.22 0.31 0.30 0.32 0.31
(λ) 0.98 0.99 0.94 0.95 0.94 0.95
Inverse Simpson’s (HB); Shannon–Wiener Index (H′); Pilou evenness (J ); Simpson’s Index (λ).
Fig. 1. Phenotypic characterization of Beninese white Guinea yam varieties based on Gower dissimilarity matrix using the 30 most discriminating traits. The colours
represent the two clusters: Cluster 1 (red) and cluster 2 (green).
observed and expected heterozygosity, respectively. The PIC varied identified as males except Gnidou, Katala, Eguede and Sotoboua
from 0 to 0.37, with an average of 0.20. The MAF was 0.17. Using which had female flowers. Cluster 3 (blue) was made of 15 culti-
the 9725 SNP markers, all cultivars evaluated were estimated to vars (Fig. 2) and displayed 0.22 as an average genetic distance
be diploid. within the cluster. Members of this cluster were identified as
The Jaccard dissimilarity matrix displayed a high range of females with low to high flowering intensity.
genetic distance from 0 to 0.89. Hierarchical clustering-based Through the Bayesian Information Criteria (BIC) analysis,
DArT-SNP marker grouped the 88 yam varieties into three population structure revealed the presence of deflection at k
major clusters (Fig. 2). Five cultivars were grouped in cluster 1 equal to 3 (online Supplementary Fig. S4, Fig. 3). Using a 60%
(red) (Fig. 2) with the lowest genetic distance (0.10) obtained membership probability threshold, 74 cultivars (84.09%) were
between Taatimanin and Gakatele. In this cluster, the highest successfully assigned to the three clusters. In contrast, 14 cultivars
genetic distance (0.21) was observed between Laboko and with an association probability of <60% were designated as
Taatimanin. Cluster 2 (green) had the highest membership (68), admixtures (Fig. 3).
with its members widely distributed across the different yam Shannon–Wiener, Inverse Shannon, Simpson and Pilou diver-
agro-ecologies. In this cluster, member cultivars were majorly sity indices revealed the presence of moderate yam genetic
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Plant Genetic Resources: Characterization and Utilization 5
Fig. 2. Assessment of the genetic diversity of the 88 Beninese white Guinea yam varieties based on Jaccard dissimilarity matrix using the DArT-SNP markers. The
colours represent the three clusters: Cluster 1 (red), cluster 2 (green) and cluster 3 (blue).
diversity in Benin (Table 1). The level of genetic diversity was vigour and few spines at the stem’s base. The average number
relatively high in Collines, followed by Borgou and Atacora of tubers varied from 2 to 3 per plant with a tuber length between
regions. At the same time, the lowest index was recorded in 15 and 25 cm and an average yield of 2.18 kg/plant. Cultivars from
Donga (Table 2). cluster 2 had vigorous plants, generally no spines on the tuber
The dispersion of the genotypes on the first two principal except Gnidou, and produced long and large tubers (1–2 per
components, which explained 44.2% of the total molecular vari- plant). Many of the cluster 2 members had high tuber flesh oxi-
ation, showed that the 88 yam varieties were clustered irrespective dation intensity such as Gnidou, popularly known for this trait.
of their geographical origins (online Supplementary Fig. S5). The All the cultivars locally known as a ‘Kokoro’ type (intermediate
pairwise fixation index (Fst) value further confirmed the low gen- to late maturity cycle) were grouped into cluster 3. Cultivars in
etic differentiation between geographical origins (Table 2). The cluster 3 were characterized by multiple and small tubers (<15
AMOVA also revealed high genetic variability within populations cm long) per plant that hardly showed the parenchyma’s oxidiza-
(95%), while only 5% of the total variability was among popula- tion at peeling.
tions (online Supplementary Table S3). The distance matrices from the phenotypic and molecular data
were weakly correlated (online Supplementary Fig. S6). However,
such a relationship was relatively improved with the joint matrix
Assessment of genetic diversity based on joint analysis and the respective phenotypic and molecular matrices. The
Clustering based on the joint analysis for phenotypic and molecu- phenotypic distance matrix had a low correlation (0.125) with
lar marker information formed three groups (Fig. 4). Cluster 1 the joint dissimilarity matrix. In contrast, the correlation between
(red) comprised 21 cultivars. Thirty and six cultivars formed the SNP marker-based Jaccard dissimilarity matrix and the joint
the second cluster (green), with 36% of them from the matrix was high and positive (r = 0.98) (online Supplementary
Department of Collines (Central Benin), and the remaining Fig. S6).
from Atacora and Donga Departments (North-West). Cluster 3
(blue) comprised of 31 cultivars majority collected from the
Discussion
Donga department and the rest from the other regions.
Discriminant analysis based on phenotypic variables revealed high Of the 53 phenotypic traits assessed in our study, 30 appeared
phenotypic variation among the clusters (online Supplementary very informative in discriminating the yam cultivars (online
Table S4). Cultivars in cluster 1 were characterized by high plant Supplementary Table S2). To save time and resources during
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6 Paterne A. Agre et al.
Fig. 3. Graphical representation of the 88 Beninese yam varieties’ population structure based on admixture analysis. Subpopulations were set at k = 3. The colours
represent the three clusters: Cluster 1 (red), cluster 2 (green) and cluster 3 (blue) based on a membership coefficient of ≥60%.
Table 2. Pairwise fixation index (Fst) values among the genetic groups ones to production niches and uses has not been reported. As a
result, the development of new cultivars from cross-breed seeds
Population Atacora Borgou Collines Donga
is a less likely phenomenon for the yam germplasm, even if the
Atacora – varietal mixture of different phenotypes is a common farming
practice in many yam growing regions of Africa. Plants of a
Borgou 0.090 – yam clone in a farmer’s field are genetically homogenous with
Collines 0.044 0.080 – negligible recombination rates, as farmers select their planting
Donga 0.054 0.013 0.012 – material from tubers and not from botanical seed. However, the
molecular evidence on ennobled cultivars showed that the tubers
collected by farmers from wild environments are often a mixture
of wild (D. abyssinica and D. praehensilis) and interspecific hybrid
yam germplasm characterization, we would recommend focusing (D. rotundata × wild species) yams (Scarcelli et al., 2006, 2017;
on these informative traits in future studies. Sugihara et al., 2020). This could partly explain the origin of
The level of diversity in yam cultivars from Benin was found the genetic admixture identified among the Beninese white
moderate, especially with SNP markers. Our results identified Guinea yam accessions. Besides, Loko et al. (2013) showed that
three subgroups of yam cultivars with a low level of admixture. farmers often establish yam fields within savannah and forests,
The members of the three sub-groups were generally clustered a practice that favours gene flows between cultivated yams and
based on the genetic distance, although those in clusters 1 and their wild relatives.
2 were also grouped based on their sex information (Fig. 2) The Our results further demonstrated a high level of gene flow
observed genetic divergence with a low admixture level is due between regions of Benin with no apparent pattern of geograph-
to original differences in domestication and subsequent vegetative ical differentiation in farmer cultivars. This result supports Loko
propagation by farmers. In Benin, farmers often collect tubers et al.’s (2017) findings that used microsatellite markers on
from wild yams, plant them in their fields and select suitable Beninese white yam cultivars. Although Tostain et al. (2007)
ones through clonal propagation using tubers (Scarcelli et al., agreed on gene flows among regions, they established a geograph-
2006; Akakpo et al., 2017). This practice is referred to as ‘ennoble- ical pattern between D. rotundata genetic diversity, cropping
ment’ and takes 3–6 years until the suitable tuber morphology is region and farmers’ crop management practices. However, that
achieved. The tradition of raising seedlings from random out- geographic pattern hypothesis has not been supported by other
crossing seeds in a farmer’s field and further selecting the adapted diversity studies on Beninese white Guinea yam accessions
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Plant Genetic Resources: Characterization and Utilization 7
Fig. 4. Assessment of the genetic diversity among Beninese white Guinea yam accessions through the joint dissimilarity matrix. The colours represent the three
clusters: Cluster 1 (red), cluster 2 (green) and cluster 3 (blue).
(Mignouna and Dansi, 2003; Loko et al., 2017). This gene flow water yam and cowpea (Sartie et al., 2012; Alves et al., 2013;
reflects high inter-region seed yam exchange or social and com- Orgogozo et al., 2015; Agre et al., 2019; Darkwa et al., 2020b;
mercial networks for yam in Benin. For instance, farmers often Nkhoma et al., 2020). The low correlation between the phenotypic
exchange seeds of landraces with other farmers within areas or and the genotypic data could have resulted from the natural and
involve outlining localities, and to some extent, with neighbouring artificial selections on phenotypic variables, as these are under
countries such as Nigeria and Togo. They gain access to new land- selection and influence of environmental factors. In contrast,
races, which were adapted to similar environments. Several other the variation detected by molecular markers is commonly
studies on cowpea (Fatokun et al., 2018; Nkhoma et al., 2020), non-adaptive, and hence, not subject to natural and or artificial
maize (Nelimor et al., 2020) and even yam (Loko et al., 2017; selections (Arnau et al., 2017). This highlights the importance
Adewumi et al., 2020, 2021; Bhattacharjee et al., 2020) showed of combining phenotypic and molecular information while select-
the existence of gene flow among regions and the absence of sig- ing parental lines in breeding programmes to account for their
nificant correlations between molecular clustering and geographic agro-morphological performance and genetic background.
origins. Phenotypic traits have the advantage of revealing the agronomic
Based on the diversity indexes, the accessions from the depart- performance of a variety in a particular environment but have
ment of Collines were more diverse. Traditionally, farmers of this limited polymorphism, and they are subjected to changes in
area cultivate multiple yam varieties with different maturity environmental conditions (Darkwa et al., 2020b; Nkhoma et al.,
groups such as extra early (5–6 months), early (6–7 months), 2020). Harnessing the advantages of phenotypic and molecular
intermediate (8 months) and late-maturing (9–10 months) to markers improves the grouping of entries in a germplasm collec-
satisfy the steady demand of the international yam market tion (da Silva et al., 2017), which provides a piece of valuable base
based in Glazoue throughout the year. information for parental selection to realize and sustain genetic
Mantel test revealed a low correlation between the dissimilarity gain. The different genetic groups developed through the joint
matrices originated from the phenotypic and the molecular data. analysis used in this study will be of good use for yam breeding
This finding was supported by the membership inconsistency community in West Africa through germplasm exchange. For
between genomic-based and phenotypic-based clustering. instance, cultivars with desirable traits such as high yield, tuber
Similar results were reported by several authors using the same quality, disease resistance, etc., have been identified and clustered,
approaches on sugar cane, yellow fruit passion, white yam, and the genetic distance and sex information was provided. This
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8 Paterne A. Agre et al.
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et al., 2021) and the ploidy-level influence on gene flow within Scientific Reports 9, 18221–182232.
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B, François O, Alix K and Vigouroux Y (2017) Molecular basis of African
2021b). Therefore, cultivars’ agronomic performance, genetic dis- yam domestication: analyses of selection point to root development, starch
tance and sex information as provided in this study will be a use- biosynthesis, and photosynthesis related genes. BMC Genomics 18, 782–791.
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Genetic diversity parameters such as PIC, MAF, observed and characteristics of White Yam. The West Indian Journal of Engineering 2,
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region to another, which could be due to the presence of natural Alves AA, Bhering LL, Rosado TB, Laviola BG, Formighieri EF and Cruz
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neighbouring countries. new insights on the genetic variability of the Brazilian physic nut germ-
plasm bank. Genetics and Molecular Biology 36, 371–381.
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Conclusion Phenotypic and genotypic characterization and compatibility among geno-
types to select elite clones of cupuassu. Acta Amazonica 3, 175–184.
This study revealed a low to moderate genetic diversity among dif- Andrade LA, Barbosa NA and Pereira J (2017) Extraction and properties of
ferent yam-growing areas of Benin as a result of seed exchange starches from the non-traditional vegetables Yam and Taro. Polímeros 2,
among farmers of different regions. However, high genetic diver- 151–157.
sity was observed within regions due to a range of farmers and Arnau G, Némorin A, Maledon E and Abraham K (2009) Revision of ploidy
other end-users’ preferences within a region. High genetic diver- status of Dioscorea alata L.(Dioscoreaceae) by cytogenetic and microsatellite
sity among plant materials collected within areas as well as infor- segregation analysis. Theoretical and Applied Genetics 7, 1239–1249.
mation related to their genetic distance and sex provide an Arnau G, Bhattacharjee R, Sheela MN, Malapa R, Lebot V, Abraham K and
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Data. VCF file used in this study including the 88 accessions can be found
Babil P, Yamanaka S, Girma G, Matsumoto R, Tamiru-Oli M,
via this public and opendatabase: https://yambase.org/breeders/trial/796?format=
Bhattacharjee R, Abberton M, Muranaka S, Asiedu R, Terauchi R and
under genotyping data.
Takagi H (2021) Simple sequence repeat-based mini-core collection for
Acknowledgements. We acknowledge funding from IITA through a white Guinea yam (Dioscorea rotundata) germplasm. Crop Science 61,
research grant (OPP1052998) received from the Bill and Melinda Gates 1269–1279. https://doi.org/10.1002/csc2.20431.
Foundation and allocated to Benin as a partner to the AfricaYam project. Bhattacharjee R, Agre P, Bauchet G, De Koeyer D, Lopez-Montes A, Kumar
We appreciate the efforts of Mathieu Ayenan and Kwabena Darkwa in critic- P, Abberton M, Adebola P, Asfaw A and Asiedu R (2020)
ally reviewing this manuscript. We appreciate Beninese farmers for providing Genotyping-by-sequencing to unlock genetic diversity and population
studied planting materials and for useful information shared during plant sam- structure in white yam (Dioscorea rotundata Poir.). Agronomy 9, 1437.
ple collection. Genetic materials were collected under the CBD Nagoya Danecek P, Auton A, Abecasis G, Albers CA, Banks E and DePristo MA
Protocol and were only used for research/breeding purposes. (2011) The variant call format and VCFtools. Bioinformatics (Oxford,
England) 15, 2156–2158.
Author contributions. Alexandre Dansi, Asrat Asfaw, Patrick Adebola and Dansi A, Zoundjihekpon J, Mignouna HD and Quin FM (1997) Collecte
Paterne Agre designed the study; Eric Dadonougbo, Roger Idossou, Anicet d’ignames cultivées du Complexe Dioscorea cayenensis – rotundata au
Dassou and Anicet Gbaguidi managed the phenotypic data; Paterne Agre Benin. Plant Genetic Resources Newslett 112, 81–85.
managed the molecular data, performed the analysis and wrote the first Dansi A, Mignouna HD, Zoundjihékpon J, Sangare A, Ahoussou N and
draft with the contribution from Laura Estelle Loko, Anicet Dassou, Jean Asiedu R (2000) Identification of some Benin Republic’s Guinea yam
Mondo, Yusuf Muyideen, Asrat Asfaw, Robert Asiedu and Alexandre Dansi. (Dioscorea cayenensis/Dioscorea rotundata complex) accessions using
Asrat Asfaw critically read and edited the manuscript. All the authors read randomly amplified polymorphic DNA. Genetic Resources and Crop
and approved the content prior to submission. Evolution 6, 619–625.
Darkwa K, Olasanmi B, Asiedu R and Asfaw A (2020a) Review of empirical
Conflict of interest. None. and emerging breeding methods and tools for yam (Dioscorea spp.)
improvement: status and prospects. Plant Breeding 3, 474–497.
Darkwa K, Agre P, Olasanmi B, Kohtaro I, Matsumoto R, Powell A,
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