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. 2023 May 31;18(5):e0286480. doi: 10.1371/journal.pone.0286480

Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers

Joseph Innocent Massawe 1,2,*, Gladness Elibariki Temu 2
Editor: Mehdi Rahimi3
PMCID: PMC10231824  PMID: 37256869

Abstract

Water yam (Dioscorea alata L.) is among the most cultivated species used as a source of food and income for small-scale farmers in Tanzania. However, little is documented about Dioscorea species available in Tanzania, including their genetic diversity. This study used ten polymorphic microsatellite markers to determine the genetic diversity and relationship of 63 D. alata accessions from six major producing regions. Results revealed a polymorphic information content (PIC) of 0.63, while the number of alleles per locus ranged from 4 to 12 with a mean of 7.60. The expected heterozygosity ranged from 0.20to 0.76, with a mean of 0.53, which suggests moderate genetic diversity of D. alata accessions. Kagera region had the highest mean number of (1.5) private alleles. Analysis of molecular variance revealed that 54% of the variation was attributed to within individual, 39% was attributed to among individual while among population contributed 7% of the total variation. The highest Nei’s genetic distance (0.43) was for accessions sampled from Kilimanjaro and Mtwara regions. Principal coordinate analysis and cluster analysis using Unweighted Paired Group Method using Arithmetic (UPGMA) grouped D. alata accessions into two major clusters regardless of geographical origin and local names. The Bayesian structure analysis confirmed the two clusters obtained in UPGMA and revealed an admixture of D. alata accessions in all six regions suggesting farmers’ extensive exchange of planting materials. These results are helpful in the selection of D. alata accessions for breeding programs in Tanzania.

Introduction

Yam (Dioscorea spp.) is an important food security tuber crop supporting more than 300 million people worldwide [1]. The crop is mainly grown in the tropical and subtropical regions of the world, especially in the yam belt of West African countries [2, 3]. In countries where yam is highly cultivated, it is a staple food due to its high carbohydrate (about 77.5% of dry matter) and other mineral contents [4]. The genus Dioscorea contains more than 650 species whereby 10 being cultivated while many others are wild species [5].

Water yam (Dioscorea alata), also known as greater or winged yam, is a dioecious and polyploid Dioscorea species with a ploidy level (2n = 40 to 80) [6]. The primary chromosome number of D. alata is 20 [6, 7]. D. alata ranks second in production after Guinea yam Cormier et al. [8], although it is the most widely distributed yam globally [9]. D alata has high importance in food security than other cultivated yam species due to its high yield potential, tuber storability and ease of propagation due to the production of bulbils [10].

Assessing yam genetic diversity is essential for germplasm management, conservation and planning for the development of new varieties [11]. The level of genetic diversity in D. alata can be assessed using various molecular markers. Using Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR) markers, Rao et al. [12] reported high variability among D. alata genotypes. The genetic diversity of D. alata accessions from West and Central Africa and Puerto Rico was assessed using Amplified Fragment Length Polymorphism (AFLP) markers and the accessions clustered into three groups, irrespective of the geographical pattern as reported by Egesi et al. [13]. Using Single Nucleotide Polymorphism (SNP) markers, Agre et al. [14] established the genetic diversity of 100 D. alata accessions and reported three genetic groups. Simple Sequence Repeat (SSR) markers are also among the most commonly used markers in the diversity analysis of crops, including yams. Despite being a relatively old method, SSR is less expensive than advanced techniques such as Diversity Array Technology (DarT) and Next-generation Sequencing (NGS). SSR markers are also highly preferred due to their reproducibility, transferability among related species and co-dominance nature of inheritance [15]. Using 24 SSR markers, Arnau et al. [16] established the genetic diversity and population structure of 384 D. alata accessions from the South Pacific, Asia, Africa and the Caribbean and revealed wide genetic diversity in six populations. Chen et al. [17] estimated the genetic diversity of 26 D. alata landraces from China using 9 SSR markers and reported four clusters among yam landraces.

In Tanzania, small-scale farmers mainly produce yam for food, income generation and medicinal purposes. The major yam-producing areas in Tanzania are considered to be Mtwara, Lindi, Morogoro, Arusha, Kilimanjaro, Kagera, Coastal, Zanzibar Islands Mbeya and Ruvuma regions. However, despite the importance of yam to especially farmers, there is limited research on this crop. The genetic diversity of yam accessions grown in the country has never been estimated and documented. Since water yam is the most widely cultivated species in the country, the genetic diversity of 63 D. alata accessions from major yam-growing regions was estimated using ten polymorphic SSR markers. This information is crucial to understanding the genetic structure of D. alata accessions, which will enhance management, conservation and planning for use in breeding programs in Tanzania.

Materials and methods

Ethical statement

This study involved collection of plant materials (yam tubers) from five regions in Tanzania. Research work, including field work and the collection of yam accessions, was permitted by the University of Dar es Salaam, Tanzania, with the informed written permit number AB3/12(B). The respective administrative departments in each region approved collection of yam accessions.

Plant materials

Research work, including field work and the collection of yam accessions, was permitted by the University of Dar es Salaam, Tanzania, with the informed written permit number AB3/12(B). The 63 D. alata tuber accessions were collected from farmer’s fields in five major growing regions in Tanzania during harvesting season between August and October 2019 (Fig 1 and Table 1). Tubers were maintained for 2 months to break the dormancy and planted to collect leaves for DNA extraction. Large tubers were sliced into minisett of about 60 grams and planted on ridges at Tanzania Agricultural Research Institute (TARI-Kibaha) experimental plots. Standard cultural agronomic practices, including hand weeding and stacking were employed. Plants were manually irrigated once per day in three times a week. Three young fresh leaves were collected (from the replicates of each accession) two months post planting in a zip bag and transported to the International Institute of Tropical Agriculture—Tanzania for DNA extraction.

Fig 1. Map of Tanzania showing regions and study sites where yam samples were collected.

Fig 1

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Table 1. Description of 63 D. alata accessions used in this study.

S/n Accession number Local name Distinct morphological character Region
1 YM1 Kiraira Winged stem Kagera
2 YM2 Kanyanyinyi Winged stem Kagera
3 YM3 Kiraira Winged stem Kagera
4 YM4 Kiraira Winged stem Kagera
5 YM5 Kanyanyinyi Winged stem Kagera
6 YM6 Kiraira Winged stem Kagera
7 YM7 Kiraira Winged stem Kagera
8 YM8 Kiraira Winged stem Kagera
9 YM9 Kanyanyinyi Winged stem Kagera
10 YM10 Kanyanyinyi Winged stem Kagera
11 YM11 Kanyanyinyi Winged stem Kagera
12 YM12 Kanyanyinyi Winged stem Kagera
13 YM13 Kanyanyinyi Winged stem Kagera
14 YM14 Ifure Winged stem Kilimanjaro
15 YM15 Ifure Winged stem Kilimanjaro
16 YM16 Ifure Winged stem Kilimanjaro
17 YM17 Ifure Winged stem Kilimanjaro
18 YM18 Ifure Winged stem Kilimanjaro
19 YM19 Ifure Winged stem Kilimanjaro
20 YM20 Kijabo Winged stem Lindi
21 YM21 Mapeta Winged stem Lindi
22 YM22 Nkwande Winged stem Lindi
23 YM23 Mapeta Winged stem Lindi
24 YM24 Katuri Winged stem Lindi
25 YM25 Vijabo Winged stem Lindi
26 YM26 Vijabo Winged stem Lindi
27 YM27 Vijabo Winged stem Lindi
28 YM28 Mapeta Winged stem Lindi
29 YM29 Mikirachi Winged stem Lindi
30 YM30 Mapeta Winged stem Lindi
31 YM31 Vigonzo Winged stem Morogoro
32 YM32 Msagala Winged stem Morogoro
33 YM33 Vigonzo Winged stem Morogoro
34 YM34 Msagala Winged stem Morogoro
35 YM35 Mgendagenda Winged stem Morogoro
36 YM36 Mgendagenda Winged stem Morogoro
37 YM37 Mgendagenda Winged stem Morogoro
38 YM38 Mgendagenda Winged stem Morogoro
39 YM39 Mgendagenda Winged stem Morogoro
40 YM40 Mnangilangi Winged stem Mtwara
41 YM41 Mnangilangi Winged stem Mtwara
42 YM42 Vitungula Winged stem Mtwara
43 YM43 Mnangilangi Winged stem Mtwara
44 YM44 Mnangilangi Winged stem Mtwara
45 YM45 Hamandeke Winged stem Mtwara
46 YM46 Hamandeke Winged stem Mtwara
47 YM47 Hamandeke Winged stem Mtwara
48 YM48 Mnangilangi Winged stem Mtwara
49 YM49 Hangadi shamba Winged stem Mtwara
50 YM50 Mnangilangi Winged stem Mtwara
51 YM51 Hangadi shamba Winged stem Mtwara
52 YM52 Nyuvele Winged stem Mtwara
53 YM53 Nyuvele Winged stem Mtwara
54 YM54 Nyuvele Winged stem Mtwara
55 YM55 Ihumihumi Winged stem Mtwara
56 YM56 Nyuvele Winged stem Mtwara
57 YM57 Ihumihumi Winged stem Mtwara
58 YM58 Mkonga Winged stem Mtwara
59 YM59 Mikirachi Winged stem Mtwara
60 YM60 Mkonga Winged stem Mtwara
61 YM61 Vitungula Winged stem Mtwara
62 YM62 Mapeta Winged stem Mtwara
63 YM63 Mapeta Winged stem Mtwara
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DNA extraction and quantification

Genomic DNA was extracted from 300 mg of fresh leaves following the cetyltrimethylammonium bromide (CTAB) protocol described by Allen et al. [18]. The quality of DNA was visualized by electrophoresis on a 1% agarose gel. The concentration of DNA was estimated using a spectrophotometer at 260 nm wavelength, and DNA was diluted to obtain a working concentration of 25 ng/μL.

Polymerase chain reaction (PCR) and fragment analysis

A total of 15 SSR markers were screened using all 63 D. alata samples used in this study (Table 2). However, only 10 SSR markers were polymorphic and therefore were included in the analysis. PCR was carried out in a total volume of 15 μL containing 25 ng/μL of genomic DNA, 0.2 μM of forward and reverse primers and 7.5 μL of OneTaq Quick-Load 2X Master Mix with Standard Buffer (New England Biolabs, Massachusetts, USA). The PCR program was as follows; denaturation at 94°C for 4 min followed by 35 cycles of 94°C for 30 sec, annealing temperature (various as shown in Table 2) and 72°C for 1 min. The final extension was held at 72°C for 7 min. To prove amplification, 3 μL of the PCR products were run on 1.5% agarose gel (aMReSCO, Solon, Ohio, USA). The PCR products were then sent to Bioscience eastern and central Africa (BecA) Hub in Nairobi, Kenya and were separated on the ABI-3730 capillary electrophoresis (Applied Biosystems). Data was captured and the resulting fragments were scored using GeneMapper V6 software.

Table 2. Description of SSR markers used to genotype 63 D. alata accessions collected from Tanzania.

Markers marked with an asterisk (*) were not used in the analysis.

SN Marker Forward primer (5’–3’) Reverse primer (5’–3’) Motif Ta
1 Da1A01 TATAATCGGCCAGAGG TGTTGGAAGCATAGAGAA (GT)8 51
2 Dab2C05 CCCATGCTTGTAGTTGT TGCTCACCTCTTTACTTG (GA)19 51
3 Dab2D08 ACAAGAGAACCGACATAGT GATTTGCTTTGAGTCCTT (AG)16 51
4 Dab2E07 TTGAACCTTGACTTTGGT GAGTTCCTGTCCTTGGT (CT)23 51
5 Dpr3D06 ATAGGAAGGCAATCAGG ACCCATCGTCTTACCC (GA)15 51
6 Dpr3F12 TCCCCATAGAAACAAAGT TCAAGCAAGAGAAGGTG (GA)16 51
7 Da1F08 AATGCTTCGTAATCCAAC CTATAAGGAATTGGTGCC (TG)13 51
8 H2 AAACCAAACAGGCAAAGCAT TGCCCTGCTTGTAAGATTGA (CA)9 56
9 F1 ATGGCTCAAGAGCACACG GGGCCTCATAAACATGCAAT (TA)5 60
10 YM80 CCGCCCAATCACATCACATC TCCCAAGAAGTCTGAGCCG (CTT)13 60
11 A4* TTCGTTCTCGATAGCGGACT CCAGTTCCCAGCCTCTTGT (CT)2(GAA)3GA(GAA)3 60
12 YM13* CCAATCACATCACGTCTAGTCT GACAATAGAAACTTCGAGACCC (CTT)8 60
13 H12* TTGTAATTGGGTGTTGTATTTGC CGGCCAAAACATTTTCTGAT (AT)6 56
14 Dpr3F04* AGACTCTTGCTCATGT GCCTTGTTACTTTATTC (AG)15 51
15 Da1D08* GATGCTATGAACACAACTAA TTTGACAGTGAGAATGGA (CA)8 51
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Source: [19–21]

Data analysis

Genetic diversity analysis

Genetic diversity parameters including the number of alleles per locus, number of polymorphic alleles, number of effective alleles, Shannon’s Information Index, observed heterozygosity, gene diversity, inbreeding coefficient and principal coordinate analysis were determined using GenAlEx software version 6.503 described by Peakall et al. [22]. Polymorphic Information Content (PIC) was estimated using PowerMarker software V3.25 by Liu [23].

Analysis of molecular variance and cluster analysis

To assess the diversity level and genetic relationship among the D. alata population, Analysis of Molecular Variance (AMOVA) was estimated using GenAlEx software version 6.503 described by Peakall et al. [22]. The Unweighted Paired Group Method using arithmetic Average (UPGMA) was used to construct the dendrogram in PowerMarker software V3.25 by Liu [23]. The dendrogram was then generated in Molecular Evolutionary Genetics Analysis (MEGA-X) V10.1.8 described by Kumar et al. [24].

Structure analysis

Bayesian analysis using Structure software V2.3.4 described by Pritchard et al. [25] was used to estimate the population’s genetic structure. The admixture model for K values from 1 to 10 with a burn-in period of 100 000 steps and 100 000 interactions MCMC (Markov Chain Monte Carlo) was used in the analysis. The most likely optimal number of K clusters was estimated using the ad hoc parameter (ΔK) method described by Evanno [26] in a Structure Harvester [27].

Results

Characteristics of the SSR markers

The Polymorphic Information Content (PIC) values of the ten SSR markers used ranged from 0.33 for the marker F1 to 0.85 for the marker Da1A01, with a mean of 0.63 (Table 3). The high mean PIC obtained implies that the SSR markers used in our study were very informative with high discriminating power. Hence, these markers can be used in genetic diversity analysis.

Table 3. Summary of genetic parameters of ten SSR markers used in assessing the genetic diversity of 63 D. alata accessions.

Locus No. of Alleles Ne Ho He FIS PIC
Da1A01 9 4.11 0.78 0.74 -0.05 0.85
Da1F08 6 3.58 0.70 0.70 0.01 0.70
Dab2C05 7 1.40 0.19 0.23 0.19 0.44
Dab2D08 7 2.96 0.69 0.63 -0.09 0.71
Dab2E07 8 4.20 0.96 0.76 -0.27 0.85
Dpr3D06 6 1.86 0.03 0.46 0.93 0.55
Dpr3F12 12 2.74 0.41 0.62 0.34 0.69
F1 4 1.31 0.07 0.20 0.68 0.33
H2 5 1.67 0.05 0.37 0.86 0.48
YM80 12 2.82 0.33 0.53 0.38 0.67
Mean 7.6 2.67 0.42 0.53 0.29 0.63
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Ne = Number of effective alleles, I = Shannon’s diversity index, Ho = Observed heterozygosity, He = Expected heterozygosity, FIS = Inbreeding coefficient and PIC = Polymorphic Information content.

Genetic diversity of D. alata

Genetic diversity parameters are presented in Table 3. The total number of alleles detected per locus was 76, ranging from 4 to 12, with a mean of 7.60. The lowest numbers of alleles per locus were detected in marker F1 while the highest was detected in markers Dpr3F12 and YM80. The effective number of alleles per locus ranged from 1.31 to 4.20, with a mean of 2.67. The F1 and Dab2E07 markers had the lowest and highest number of effective allele locus per locus, respectively. The expected heterozygosity ranged from 0.20 to 0.76 with a mean of 0.53, whereby the lowest and highest expected heterozygosity was detected in markers F1 and Dab2E07, respectively. The inbreeding coefficient ranged from -0.27 to 0.93, with a mean of 0.29. Markers Dab2E07 and Dpr3D06 had the lowest and highest inbreeding coefficient, respectively. Three markers (30%) showed a negative inbreeding coefficient, indicating an excess of heterozygosity.

Genetic diversity of D. alata based on population

The genetic diversity among and within 63 D. alata accessions based on the population is presented in Table 4. The number of effective allele was lowest (2.33) in Morogoro region and highest (3.32) in Kagera region, with a mean of 2.67. Shannon Information Index was lowest (0.84) in the Morogoro region and highest (1.22) in Kagera region, with a mean of 1.00. The mean number of private alleles ranged from 0.20 (Lindi region) to 1.5 (Kagera region). Observed heterozygosity (Ho) ranged from 0.32 to 0.55 in Mtwara and Kagera regions, respectively, with a mean of 0.42. The expected heterozygosity (He) ranged from 0.45 to 0.60 in Morogoro and Kagera regions, respectively, with a mean of 0.53. Our results indicate that D. alata accessions collected from Kagera region had the highest genetic diversity followed by Mtwara region. The mean polymorphic loci were 92.00%.

Table 4. Genetic diversity of 63 D. alata accessions based on populations as generated by 10 SSR markers.

Population Ne I Private allele Ho He % Poly loci
Kagera 3.32 1.22 1.50 0.55 0.60 100.00
Kilimanjaro 2.41 0.90 0.40 0.41 0.50 80.00
Lindi 2.47 0.95 0.20 0.42 0.50 90.00
Morogoro 2.33 0.84 0.30 0.41 0.45 90.00
Mtwara 2.79 1.14 0.70 0.32 0.57 100.00
Mean 2.67 1.00 0.42 0.53 92.00
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Ne = Number of effective alleles, I = Shannon diversity index, Ho = Observed heterozygosity, He = Expected heterozygosity and % Poly Loci = Percentage of polymorphic loci.

Analysis of molecular variance (AMOVA)

Analysis of molecular variance showed a highly significant difference (p < 0.001) in D. alata accessions within regions. The analysis showed that 54% of the variation was due to within individual, while 39% was due to among individual while only 7% of the total variation was among the population (Table 5). The genetic differentiation index (FST = 0.06) indicates low genetic differentiation among regions.

Table 5. Analysis of Molecular Variance (AMOVA) of the SSR markers among and within 63 D. alata accessions.

Source DF SS MS Estimated Variance Percentage variation p
Among pops 4 38.98 9.74 0.22 7% 0.001
Among individual 58 259.50 4.47 1.32 39%
Within individual 63 116.00 1.84 1.84 54
Total 125 414.48 3.38 100%
F—Statistics
FST = 0.07
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DF = Degree of freedom, SS = Sum of squares and MS = Mean sum of square, FST = Genetic differentiation index

Principal coordinate analysis (PCoA)

The first three principal coordinate axes accounted for 41.19% of the total variation. The PCoA of the correlation between the genetic relationship of D. alata accessions and geographical distribution showed that D. alata accessions were divided into two groups (Fig 2). Group A contained D. alata accessions collected from all six regions, while group B had accessions from Kagera, Morogoro and Lindi regions. The scatter plot grouped D. alata accessions regardless of their geographical origin and local names.

Fig 2. Principal coordinate analysis (PCoA) showing the relationship among 63 D. alata accessions using 10 SSR markers.

Fig 2

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Group A contained accessions collected from all five regions while group B had accessions from Kagera, Lindi and Morogoro. Coord. 1 and Coord. 2 represent the first and second coordinates respectively.

Genetic identity and genetic distance

The Nei’s unbiased genetic identity and genetic distances are presented in Table 6. The genetic distance among accessions and regions varied from 0.01 to 0.43. The highest genetic distance was for accessions collected from Kilimanjaro and Mtwara (0.43), followed by Kagera and Kilimanjaro (0.34), while the lowest genetic distance was for accessions from Lindi and Morogoro (0.01). Genetic identity between accessions and regions varied from 0.65 to 0.99. The highest genetic identity (0.99) was for accession from Morogoro and Lindi regions, while the lowest (0.52) was for accessions collected from Mtwara and Kilimanjaro.

Table 6. Nei’s Unbiased Genetic identity (above diagonal) and genetic distance (lower diagonal) of 63 D. alata accessions using ten SRR markers.

Location Kagera Kilimanjaro Lindi Morogoro Mtwara
Kagera 0.71 0.73 0.72 0.72
Kilimanjaro 0.34 0.74 0.77 0.65
Lindi 0.31 0.30 0.99 0.90
Morogoro 0.32 0.26 0.01 0.83
Mtwara 0.32 0.43 0.10 0.19
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Cluster analysis

UPGMA grouped the 63 D. alata accessions into two major clusters, A and B (Fig 3). Generally, the accessions clustered regardless of their geographical origin. Clusters A and B had 49 and 14 accessions, respectively. Cluster A was further subdivided into two sub-clusters (A1 and A2). Sub-cluster A1 had 28 accessions collected from all regions surveyed, while sub-cluster A2 had 21 accessions collected from Kagera, Morogoro, Lindi and Mtwara regions. Cluster B had two sub-clusters (B1 and B2). Sub-cluster B1 contained 5 accessions from the Kagera region, while sub-cluster B2 had 9 accessions collected from Morogoro, Lindi and Mtwara regions. In group B1, accessions collected from Kagera region clustered together, suggesting a distant relationship from the rest. Results from cluster analysis confirmed the results obtained in PCoA analysis. Two clusters with sub-clusters were observed in both analyses.

Fig 3. UPGMA cluster analysis of 63 D. alata accessions based on 10 SSR markers, using MEGA-X software.

Fig 3

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A1 and A2 are sub-clusters of cluster A while B1 and B2 represent sub-clusters of cluster B.

Structure analysis

The Bayesian analysis performed in STRUCTURE software for the SSR data confirmed the two clusters obtained in the UPGMA. Evanno’s method showed a ΔK peak value of K = 2, suggesting the presence of two distinct clusters, which confirmed that the D. alata accessions collected from the six regions are genetically structured in two groups (Fig 4). The STRUCTURE results are consistent with the UPGMA cluster in that all regions are admixed, and only Kilimanjaro region had a nearly homogeneous population.

Fig 4. Structure of the genetic diversity of 63 D. alata accessions based on Bayesian analysis of 10 SSR markers at K = 2.

Fig 4

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The vertical bar represents yam accessions assigned into two clusters.

Discussion

Estimating the genetic diversity of yam is very important in understanding the extent of genetic variation available for proper germplasm management and planning for breeding programs [11, 28].

SSR markers used in our study were highly polymorphic, suggesting that these markers were able to reveal variations within the accessions. PIC is an essential feature in evaluating the informativeness of a molecular marker [29]. Generally, the PIC values that range between 0.5 and 1.0 implies high informative markers, whereas values less than 0.5 indicate narrow informative marker [30]. Siqueira et al. [31], reported high PIC (0.92) using 11 SSR markers in 89 D. alata accessions collected from Brazil. Otoo et al. [32] reported high PIC (0.91) in 14 SSR markers used to assess 49 D. alata accessions collected from Ghana. Girma et al. [33] reported a lower mean PIC (0.43) using 8 SRR markers in 127 D. alata accessions from the International Institute of Tropical Agriculture (IITA) geneBank. Compared to this study, the different PIC levels observed in other studies could be attributed to different SSR markers employed, which target different loci and also the composition of the yam genotypes.

The low number of alleles observed could suggest the moderate genetic diversity of D. alata accessions reported in this study. Obidiegwu et al. [34] reported 97 number of alleles in 89 D. alata accessions collected from nine West African countries, while Otoo et al. [32] recorded 273 number of allele in 49 D. alata accessions in Ghana. The different number of alleles observed between our study and others, may be due to each using different D.alata genotypes and different SSR markers which assess different loci.

The mean effective number of alleles per locus obtained in our study could indicate moderate genetic diversity of D. alata accessions due to spartial dispersion of accessions in the study sites. Different number of effective alleles were reported in previous studies. Chen et al. [17] reported 2.26 effective number of alleles using nine SSR markers in 26 D. alata germplasm from Southern China while Mulualem et al. [35] reported 1.71 effective number of alleles using 10 SSR markers in 33 yam accessions collected from Ethiopia. Generally, the differences in the number of effective alleles can reflect the genetic diversity of the population. The greater the value of the number of effective alleles the higher the genetic diversity of the population [36].

The mean Shannon diversity index observed in this study indicates moderate genetic diversity among D. alata populations collected from different regions. The Shannon diversity value from this study is higher compared to 0.78 reported by Cao et al. [37] in D. alata accessions from China and lower than 1.29 observed by Siqueira et al. [38] in D. alata cultivars from Brazil. As a vegetatively propagated crop, yam tends to maintain a high level of heterozygosity [39, 40]. However, due to the presence of few flowers and dioecism, limited genetic recombination may occur in yam as reported by Wu-Wenqiang et al. [41], which may explain the moderate genetic diversity observed in this study.

The mean expected heterozygosity (the genetic diversity) observed in our study suggests moderate genetic diversity of D. alata accessions. The sporadic and narrow distribution of D. alata accessions from the study sites could also account for the observed results. The genetic diversity from study is low compared to the 0.66 reported by Arnau et al. [16] in D. alata accessions collected from South Pacific, Asia, Africa and the Caribbean. Similarly, Otoo et al. [32] revealed higher (0.77) genetic diversity in D. alata accessions collected from Ghana than the genetic diversity reported in this study. The limited and inconsistence flowering in yam could explain different levels of genetic diversity reported in previous studies compared to our study. In our study, few accessions flowered which may affect the level of genetic diversity observed. Several factors such as planting materials used (tuber or seed) and environmental factors, such as photoperiod have been reported to affet the rate and pattern of flowering [42]. Furthermore, the different levels of genetic diversity could results from geographical patterns of species and life-history [43]. In our study, accessions were collected from regions with sporadic geographical distribution with possibility of both outcrossing and selfing mode of reproduction.

The inbreeding coefficient (FIS) showed that three markers (30%) had a negative inbreeding coefficient, indicating an excess of heterozygosity within the population. This suggests the possible hybridization of these yam accessions. Similar results by Mulualem et al. [35] stated an excess of heterozygotes in 3 loci out of 10 SSR markers used in yam accession collected from Ethiopia. Similarly, Mengesha et al. [44] observed that 3 of 7 SSR markers had an excess of the heterozygotes in Guinea yam accessions collected from Ethiopia. The observed excess heterozygosity implies the presence of multiple demes within the population [44]. In our study, the private alleles were higher in Kagera region and lowest in Lindi region. Private alleles indicate the existence of unique genes that have evolutionary significance. D. alata accession from the Kagera region showed the highest number of private alleles, higher genetic diversity and higher Shannon diversity index. Therefore, Kagera region is rich in yam diversity compared to the rest of the studied regions.

The AMOVA results in this study revealed that the highest variation was attributed to the within individual variation than among population. The low variation observed among the population in our study suggests a high rate of germplasm exchange between neighbor regions. It also indicates that there is no significant geographical differentiation within D. alata accessions from the study sites. High variation within individuals could be due to recombination, mutation, cross-pollination, or other processes that can produce new genes and alleles [45]. A similar study by Siqueira et al. [31] revealed higher variation within the population (95.91%), while among the population, was only 4.09%. Similarly, Loko et al. [46] reported that 96% of the variation was within population while 4% was attributed to among population. The high within population variation observed implies a lack of genetic structure.

The principal coordinate analysis showed the dispersion of D. alata accessions into two groups regardless of their geographical origin. Most accessions from different geographical distributions were clustered within the same groups. The clustering pattern suggests that these D. alata accessions are genetically similar despite different local names and geographical origins. The clustering observed in our study could be due to the lack of improved yam varieties which causes farmers to grow similar varieties over the years. Similar results were reported by Wu-Wenqiang et al. [41] while assessing the genetic diversity of 142 D. alata cultivars from South China. The authors reported only two groups despite collecting D. alata cultivars from eight provinces in China. Asfaw et al. [47] revealed that grouping of accessions with different names into same group could be due to similar accessions that might be known by different names in different regions.

The genetic distance observed in this study was highest for accessions sampled from Kilimanjaro and Mtwara. Nei [48] reported that genetic distance is mostly related to geographic distance. The highest genetic distance observed between Kilimanjaro and Mtwara can be due to less possibility of germplasm exchange between the two regions. Mtwara is in the Southern part of Tanzania, while Kilimanjaro is in the North, and the distance apart is about 1,100 km. However, the high genetic distance observed in this study between the two regions may enhance the possibilities of yam improvement through breeding [49].

The UPGMA cluster analysis grouped 63 D. alata accessions into two distinct clusters (A and B) and four sub-clusters. Despite different local names, the dendrogram grouped D. alata accessions irrespective of their geographical origin, except for sub-cluster B1, which contained D. alata accessions from Kagera region. The grouping observed in our study suggests these accessions are genetically similar. In contrast, grouping in sub-cluster B1 could suggest that these accessions might have some unique genetic characteristics compared to the rest of the groups. Despite yam accessions being collected from five regions, the two groups observed in this study suggest lack of wide genetic diversity. The exchange of planting materials between farmers over the years also explains the pattern. Similar studies in Brazil, Benin and Ethiopia reported similar results, in which no geographical pattern was observed among yam accessions [31, 35, 50].

The structure analysis confirmed the two clusters generated by UPGMA and showed no clear structure of D. alata accessions. The analysis revealed a high admixture rate of D. alata accessions between the five sampling regions. The high proportion of admixture observed could be due to the high frequency of exchange of yam genetic resources among farmers from neighboring regions and gene flow among D. alata accessions [14, 32]. A similar study by Siqueira et al. [31] reported admixture between D. alata accessions collected from Brazil and attributed the presence of admixture accessions to germplasm exchange between different regions.

Conclusion

This study revealed moderate genetic diversity of D. alata accessions from six major growing regions of Tanzania. The study showed that D. alata accessions are grouped into two clusters, despite they are originated from different geographical regions, which suggests their genetic similarities. The lack of a yam breeding program in the country and extensive farmers’ exchange of planting materials may have contributed to the moderate genetic diversity. Information obtained from this study is crucial for selecting D. alata accessions for breeding programs and conservation strategies.

Acknowledgments

We thank Ms. Magreth Lupembe of Tanzania Agricultural Research Institute and other staff from Tanzania Agricultural Research Institute at Maruku, Selian and Naliendele for their technical support during the field survey. The authors acknowledge the International Institute of Tropical Agriculture (Tanzania), especially the Molecular lab unit, for their support during laboratory work. We also appreciate the assistance of Ms. Lucy Muthui of the International Livestock Research Institute—SegoliP unit and Dr. Inosters Nzuki of Africa Biosystems Limited, Nairobi, Kenya for the SSR data scoring.

Data Availability

All SSR data are available from the Dryad database. URL: https://datadryad.org/stash/dataset/doi:10.5061/dryad.pg4f4qrv4 DOI: https://doi.org/10.5061/dryad.pg4f4qrv4.

Funding Statement

JIM received the research fund under the MoEST 2018 sponsorship. This study was funded by Ministry of Education, Science and Technology (Tanzania). The funder can be accessed via https://www.moe.go.tz/sw. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Mehdi Rahimi

10 Jan 2023

PONE-D-22-32759Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markersPLOS ONE

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Reviewer #1: The present study has been done to estimate the genetic diversity present in the Water yam (Dioscorea alata L.). 63 accessions from six different regions of Tanzania were collected and analyzed using 10 SSR markers. In this study no geographical isolation was found and the accessions shared genetic similarity with each other. The genetic diversity study of Water yam (Dioscorea alata L.) has been done with different research groups using different molecular markers such as RAPD, ISSR, AFLP. SSR and SNP markers. Since this is the first report on genetic diversity of Water yam (Dioscorea alata L.) from Tanzania therefore this study may be interesting for researchers working on this crop.

Reviewer #2: Line 36 add “cluster analysis” before UPGMA

In Table 1 accession on #54 & 55 are repetitions

Line 130 what do you mean by number of different alleles? Do you mean number of polymorphic alleles?

AMOVA only assess the genetic diversity and genetic relationship among populations not accessions

Why do you use a burn-in –period of 10 000? Some researchers use large burn-in-period.

Line 158-160: Shannon information index is a population parameter not locus parameters. It does not has any biological meaning in the case of markers i.e. Shannon information index value for Dab2E07 was 1.47, what does this implies?

Line 173: Some of the regions were not represented by sufficient samples such as Arusha (2) and Kilimanjaro (4), hence the genetic parameter values were under estimated. I suggest to either merging them together or the values should be adjusted based on number of samples per population.

Table 5: why among accession variation was not included? GenAlex can give the results even though the percentage variation is 0%. But I doubt that it won't be the case?

Figure 2: the classification of the accession is confusing. Based on PCoA 1 & 2 the accessions are classified into three potential groups. If we consider PCoA1, then the A & B classification should be completely different. So please recheck it.

Line 222: It is also ideal to report on the genetic distance between accessions since there may be a chance that the same accession given different name in different regions.

Table 7: The table legend is not informative. Specify the values as genetic distance and identity based on above and below diagonal for ease of reading

Line 135-136: How is the relationship in terms of number and patterns of clustering between the two clustering approaches i.e. PCA and the dendrogram generated using UPGMA

Figure 4: The quality of Figure 4 should be improve using Excel.

Citation style when references added at the beginning, in the middle and at the end of the sentence should be rechecked throughout the paper

Line 276: Is not clear? was the low number of alleles due to low or high allelic variants?

Line 276-278: the 97 and 273 values indicated are not the number of alleles per locus, but they are the total number of allele observed overall population.

Line 303-304: Is flowering time and pattern affected by environmental factors in Yam? If so, explain the results obtained in this study and previous studies based on environmental suitability for flowering.

Line 316-318: This might be attributed by the large number of samples collected from Kagera regions unless the values are adjusted based on number of sample per region. This is misleading.

Line 342-343: The implication is not correct since Arusha is represented by only two accessions.

Line 353-354: The results revealed the lack of wide genetic diversity but not lack of improved variety. The molecular data is all about variation at the DNA sequence level.

Reviewer #3: The MS entitled “Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers” have been critically reviewed and comments are as follows:

In the present investigation, the authors evaluated genetic diversity among 63 accessions of the water yam using 10 SSR markers and reported moderate level of genetic diversity. The genetic diversity in this crop has been widely examined at various locations and using large number of accessions and more advance marker systems such as SNP. The present investigation does not provide any new information’s and the number of accessions and number of markers are very less. Large number of markers evenly distributed across the genome are recommend to be used to assessment of genetic diversity. Therefore, the present study could not be recommended for publication in PLOS One.

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Reviewer #1: Yes: Dr Rakesh Singh

Reviewer #2: Yes: Amelework B Assefa

Reviewer #3: Yes: Hemant Kumar Yadav

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Attachment

Submitted filename: PONE-D-22-32759_reviewer with reviewers comment.pdf

Click here for additional data file. (1.4MB, pdf)
PLoS One. 2023 May 31;18(5):e0286480. doi: 10.1371/journal.pone.0286480.r002

Author response to Decision Letter 0

20 Mar 2023

Editor comment: All editor comments are well addressed in the attached manuscript.

Reviewer 1: We appreciate your comments and appreciation to our manuscript.

Reviewer 2: All comments and suggestions raised by the reviewer were addressed point by point and incorporated in the revised clean manuscript.

Reviewer 3: Thank you very much for the time devoted in reviewing our manuscript and your comment. Currently, SSR markers are still markers of choice in genetic diversity studies of different crops. However, given the availability of funding, more advanced markers will be employed. On the other hand, this study provided information about the genetic diversity of D. alata accessions that are grown in Tanzania. This information is currently lacking in Tanzania, however, the information is very important in breeding and conservation strategies.

Attachment

Submitted filename: Responses to Reviewers.doc

Click here for additional data file. (95KB, doc)

Decision Letter 1

Mehdi Rahimi

3 Apr 2023

PONE-D-22-32759R1Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markersPLOS ONE

Dear Dr. Massawe,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

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Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

Dear Author

The reviewer(s) have recommended minor revisions to your manuscript. Therefore, I invite you to respond to the reviewer(s)' comments and revise your manuscript.

With Thanks

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The reply to reviewers comments point-wise has not been submitted by authors. This needs to be submitted. The corrections in the manuscript needs to be done in track change mode which is missing. Therefore, authors are requested to submit the revised manuscript in the track change mode for further evaluation.

Reviewer #2: The abstract needs to be revisited. They have changed the analysis and the results were different from the original analysis, for example, the AMOVA results have been changed as among population, among individual and within individual. I have also seen few editorial issues to be addressed.

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Reviewer #1: Yes: Rakesh Singh

Reviewer #2: Yes: Amelework B. Assefa

**********

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 May 31;18(5):e0286480. doi: 10.1371/journal.pone.0286480.r004

Author response to Decision Letter 1

2 May 2023

Response to editor comment:

Reference list has been thoroughly checked, the list is complete and correct. No retracted paper has been cited in this manuscript.

Response to reviewer 1:

Point-wise reviewer’s comments were submitted in matrix form during previous revision. Further, the revised manuscript with track changes was also submitted in the previous revision. Both attachments are still available in the Editorial manager.

Response to reviewer 2:

The abstract has been updated accordingly. AMOVA and other parameters have been corrected.

Attachment

Submitted filename: Response to Reviewers.doc

Click here for additional data file. (69.5KB, doc)

Decision Letter 2

Mehdi Rahimi

17 May 2023

Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers

PONE-D-22-32759R2

Dear Dr. Massawe,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Mehdi Rahimi, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Mehdi Rahimi

22 May 2023

PONE-D-22-32759R2

Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers

Dear Dr. Massawe:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Associate Prof. Mehdi Rahimi

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: PONE-D-22-32759_reviewer with reviewers comment.pdf

    Click here for additional data file. (1.4MB, pdf)
    Attachment

    Submitted filename: Responses to Reviewers.doc

    Click here for additional data file. (95KB, doc)
    Attachment

    Submitted filename: Response to Reviewers.doc

    Click here for additional data file. (69.5KB, doc)

    Data Availability Statement

    All SSR data are available from the Dryad database. URL: https://datadryad.org/stash/dataset/doi:10.5061/dryad.pg4f4qrv4 DOI: https://doi.org/10.5061/dryad.pg4f4qrv4.

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