
In recent time, the dwindling economy and drastic fall in petroleum price have made life tougher for low-income Nigerians. Sustainable utilization of
A molecular approach to genetic analysis of plant populations and genotypes is more effective than morphological markers, because it directly accesses the hereditary information for understanding the existing relationships between individuals (Paterson et al. 1991; Williams et al. 1990). The use of molecular markers has proved to be reliable, simple and versatile techniques of genetic diversity profiling. Therefore, they have been used in genetic diversity, identification and genotyping studies of species or natural populations. (Animasaun et al. 2015; Mohapatra and Rout 2005). There little is information on physiological and agronomic aspects of
The sample area was Southwest Nigeria which consists of six states (Fig 2). The states fall within the rain forest zone of the country. Samples were studied on five selected locations in each of the states. The
Geographical locations of the selected towns and villages for genetic diversity study of Thaumatococcus daniellii populations in the southwest Nigeria
Locations | Population code | States | LGA† | Coordinates |
---|---|---|---|---|
Mosabi | TdOs01 | Osun | Iwo | 7o50'0" N - 4o69'6" E |
Oke-oba | TdOs02 | Osun | Iwo | 5o43'15" N - 4o22'0" E |
Ola-teju | TdOs03 | Osun | Iwo | 5o41'18" N - 11o29'15" E |
Okuku | TdOs04 | Osun | Odo-Otin | 8°8'57" N - 4° 24' 59" E |
Erin-Ijesha | TdOs05 | Osun | Oriade | 7°56'52" N - 4°90'22" E |
Iyana-Offa | TdOy01 | Oyo | Lagelu | 7°50'05" N - 4°07'47" E |
Ile igbon | TdOy02 | Oyo | Lagelu | 7°29'0" N - 4°5'0" E |
Lalupon | TdOy03 | Oyo | Lagelu | 7°28'0" N - 4°4'0" E |
Dabiri | TdOy04 | Oyo | Akinyele | 7°31'54" N - 3°56'57" E |
Odo Oba Ejemu | TdOy05 | Oyo | Oyo East | 7°50'59" N - 4°4'0" E |
Emuren | TdOg01 | Ogun | Shagamu | 6°42'0" N - 3°37'0" E |
Odo-owa | TdOg02 | Ogun | Shagamu | 6o27'27" N - 3o28'15" E |
Igbafa | TdOg03 | Ogun | Shagamu | 6 o.23'30" N - 3o27'10" E |
Ijebu Ife | TdOg04 | Ogun | Ijebu East | 6°47'0" N - 4°2'0" E |
Owode Egba | TdOg05 | Ogun | Obafemi Owode | 6°57′42" N - 3°30′ 15" E |
Imota, Ikorodu. | TdLg01 | Lagos | Ikorodu | 6°40'0" N - 3°40'0" E |
Igbodu | TdLg02 | Lagos | Epe | 6° 38' 0" N - 3° 55' 0" E |
Agura-gberigbe | TdLg03 | Lagos | Ikorodu | 6°34'0" N - 3°38'0" E |
Agbowa | TdLg04 | Lagos | Epe | 6°39'0" N - 3°43'0" E |
Ejinrin | TdLg05 | Lagos | Epe | 6°.15'39" N - 3°39'35" E |
Boluwatife | TdOn01 | Ondo | Odigbo | 6o25'15" N - 4o35'30" E |
Illara | TdOn02 | Ondo | Ifedore | 7°20′53″ N - 5°06′52″ E |
Ayegun | TdOn03 | Ondo | Odigbo | 7o20′45″ N - 5o20′ 5″ E |
Ipogun | TdOn04 | Ondo | Ifedore | 7°18′53″ N - 5°04′48″ E |
Oka-Akoko | TdOn05 | Ondo | Akoko-South | 7°27'0" N - 5°48'0" E |
Ayetoro-Ekiti | TdEk01 | Ekiti | Moba | 7°15'59" N - 5°14'56" E |
Ipoti-Ekiti | TdEk02 | Ekiti | Ijero | 7°27'15" N - 5°07'29" E |
Igogo-Ekiti | TdEk03 | Ekiti | Moba | 7°22'90" N - 5°11'15" E |
Oke-Imesi Ekiti | TdEk04 | Ekiti | Ekiti West | 7°49′0″ N - 4°55′0″ E |
Ogotun-Ekiti | TdEk05 | Ekiti | Ekiti South-West | 7°30'0" N - 5°0'0" E |
†Key: LGA = Local Government Area where the site of collections were located.
Twenty stands of
Genomic DNA (gDNA) was extracted from young fully expanded leave of a
PCR amplification of the gDNA was performed with thermocycler (Applied Biosystems; USA) using ISSR primers. Each reaction mixture contained 50 ng gDNA, 1 unit of Taq polymerase (Thermofisher, USA), 1X PCR buffer consisting of 2.5 mM MgCl2 and 200 µM of each dNTP mixture (Thermofisher, USA) and 10 pmol of the ISSR primer (Eurofins, Germany) was made up to 25 µl reaction volume by addition of double distilled water. The cycling condition was as described by Animasaun et al. (2015). The amplicons were resolved on 1.5% agarose gel stained with ethidium bromide in 1X TAE buffer ran at 100 V for 45 min. 1 Kb gene ruler (Fermentas, USA) was used as a standard fragment size for comparison. The electrophoresed gel was viewed under UV transmulator and photographed by a gel documentation system (Biorad, USA).
Fragment analysis was carried out by similarity index, distinct and reproducible band from the electrophoregrams anticipated by the standard DNA ruler (DNA ladder) were scored as ‘1’ for presence and ‘0’ for absence. The data were entered into binary matrix format for diversity analysis. The allele frequency were evaluated from the electrophoregrams and the marker efficiency determined by percentage polymorphism. The Polymorphic information content (PIC) were calculated for each primer adopting the formula of Botstein et al. (1980) and a matrix generated. Principal coordinate (PCO) analysis was performed using (PAST ver 3.5). Cluster analysis was performed by an agglomerative technique using the unweighted pair group method of arithmetic average (UPGMA) algorithm method in NTSYS-pc version 2.1 (Numerical taxonomy and multivariate analysis system) software package. The genetic diversity and relationships within and between the populations were graphically presented as dendrogram.
To explore population structure and genetic diversity and of
Folia morphometric characters of
State | Population code | PL (cm) | LW (cm) | LL (cm) | LT (cm) | PD (cm) |
---|---|---|---|---|---|---|
TdOs01 | 98.30±6.91bc | 29.22±2.65cd | 41.68±3.21bc | 0.04±0.01ab | 0.78±0.08cd | |
Osun | TdOs02 | 65.02±3.98f | 33.02±3.17ab | 44.64±3.99bc | 0.05±0.01a | 0.82±0.06bc |
TdOs03 | 78.46±5.85e | 29.24±2.65cd | 34.00±2.37e | 0.05±0.01a | 0.96±0.07a | |
TdOs04 | 94.02±6.81cd | 27.02±2.17cd | 44.16±3.99bc | 0.06±0.01a | 0.82±0.07bc | |
TdOs05 | 86.16±6.35de | 32.24±3.11bc | 39.00±2.57d | 0.05±0.01a | 0.96±0.08a | |
TdOy01 | 83.36±6.13de | 32.96±3.15bc | 42.56±3.54bc | 0.06±0.01a | 0.81±0.06bc | |
Oyo | TdOy02 | 91.44±6.72cd | 30.08±3.01bc | 42.58±3.21bc | 0.05±0.01b | 0.66±0.07e |
TdOy03 | 61.76±3.60fg | 27.47±2.17cd | 40.14±3.17cd | 0.05±0.01b | 0.78±0.09cd | |
TdOy04 | 94.06±6.81cd | 33.02±3.17ab | 44.14±3.89bc | 0.06±0.01a | 0.82±0.07bc | |
TdOy05 | 69.08±5.08ef | 33.46±3.19ab | 41.64±3.21cd | 0.05±0.01a | 0.82±0.08bc | |
TdOg01 | 71.61±5.16ef | 26.52±2.17d | 39.52±2.21d | 0.04±0.01ab | 0.62±0.06e | |
Ogun | TdOg02 | 89.30±6.60d | 33.90±3.17ab | 45.80±3.65ab | 0.05±0.01a | 0.52±0.05f |
TdOg03 | 78.78±5.88e | 30.82±3.04bc | 42.60±3.54cd | 0.05±0.01a | 0.74±0.04cd | |
TdOg04 | 99.30±6.90bc | 29.90±2.70cd | 41.80±3.21cd | 0.06±0.01a | 0.52±0.05f | |
TdOg05 | 88.12±6.65d | 32.82±3.14bc | 44.60±3.89bc | 0.05±0.01a | 0.74±0.09cd | |
TdLg01 | 72.20±5.23ef | 29.38±2.54cd | 41.00±3.46cd | 0.05±0.01a | 0.71±0.05de | |
Lagos | TdLg02 | 81.26±6.02de | 28.26±2.43cd | 42.82±3.54cd | 0.04±0.01ab | 0.88±0.09ab |
TdLg03 | 97.70±6.98bc | 32.30±2.45bc | 47.86±4.01a | 0.05±0.01a | 0.68±0.07de | |
TdLg04 | 73.20±7.23ef | 31.38±3.14bc | 41.02±3.46cd | 0.05±0.01a | 0.71±0.06d | |
TdLg05 | 98.26±6.91bc | 31.26±2.13bc | 43.82±3.80bc | 0.05±0.01a | 0.78±0.08cd | |
TdOn01 | 98.12±6.90bc | 30.56±3.06bc | 46.18±3.68ab | 0.05±0.01a | 0.88±0.08ab | |
Ondo | TdOn02 | 87.30±6.18d | 34.30±3.27ab | 39.46±2.21d | 0.06±0.01a | 0.93±0.08ab |
TdOn03 | 104.54±8.86b | 36.00±4.19a | 47.28±4.01a | 0.05±0.01a | 0.76±0.08cd | |
TdOn04 | 111.30±9.18ab | 34.30±3.27ab | 45.46±3.62ab | 0.05±0.01a | 0.96±0.12a | |
TdOn05 | 89.54±6.86d | 33.00±3.19ab | 39.28±2.19d | 0.06±0.01a | 0.94±0.01a | |
TdEk01 | 89.24±6.85d | 30.06±3.03bc | 39.24±2.19d | 0.05±0.01a | 0.87±0.08c | |
Ekiti | TdEk02 | 109.06±7.34b | 34.66±3.30ab | 48.86±4.18a | 0.05±0.01a | 0.79±0.10cd |
TdEk03 | 104.32±7.47b | 35.02±4.04a | 44.42±3.58bc | 0.05±0.01a | 0.91±0.09a | |
TdEk04 | 93.06±6.54cd | 32.66±3.49bc | 38.86±2.02d | 0.06±0.01a | 0.79±0.05cd | |
TdEk05 | 118.32±7.47a | 36.46±3.20a | 49.42±4.21a | 0.05±0.01a | 0.90±0.09a |
Values with same alphabet(s) along the column were not significantly different at p < 0.05. Keys: PL: Petiole length; LW: Leaf width; LL: Leaf length; LT: Leaf thickness; PD: Petiole diameter
The length of the leaves differs significantly within and across the states (Table 2). The populations TdEk05, TdEk02, TdOn03 and TdLg03 produced significantly long leaves while the shortest leaves occurred in TdOs03 (Ola-Teju) population in Osun State. While leaf thickness was similar for all the populations, the petiole diameter varied. However, no population recorded petiole diameter greater than 1 cm. At a probability level of 0.05, the folia characters studied cross the states showed Ekiti populations had the highest average petiole length followed by Ondo while the least occurred in Oyo (Supplementary Table 1). The trend was similar for leaf dimensions. Whereas leaf thickness was statistically similar for the populations across the states, the petiole diameter was the same except for Ogun populations that had the most slender petioles. The values obtained for petiole length, leaf length, leaf thickness and petiole diameter for the populations were not skewed, which mean that most values were around the median (Supplementary Fig. 1). However, leaf breadth values skewed towards the upper limit, indicating the majority of the populations had vales less than the median in term of leaf breadth. Nevertheless, leaf thickness and petiole diameters were similar for the states. In general, the values obtained for the folia traits of the populations in each of the states had no outliers, as the values were close to the mean.
The result of the biplot analysis revealed that leaf length, leaf width and petiole length are the major parameters that delimit the populations as most populations were marked by these parameters (Fig 3). Some populations from Osun (TdOs02, TdOs03), Oyo (TdOy03, TdOy05), Ogun (TdOg01, TdOg03) and Lagos (TdLg01, TdLg02, TdLg04) in the quadrant II and III were nor properly delimited by the parameters which remarkably partitioned and marked populations from Ekiti and Ondo. The petiole diameter and leaf thickness are weak parameters for delimiting the populations. There are significant positive correlations between petiole length and leaf width (Table 3), also, leaf length and petiole length had positive associate at P<0.05. Furthermore, at P<0.01, a positive association existed between the leaf length and leaf width of the populations.
Correlation coefficients of quantitative folia traits of
PL | PD | LW | LL | LT | |
---|---|---|---|---|---|
PL | 1 | ||||
PT | 0.2613 | 1 | |||
LW | 0.6995** | 0.2534 | 1 | ||
LL | 0.6704** | 0.3209 | 0.9563* | 1 | |
LT | 0.3950 | 0.1208 | 0.0517 | 0.1179 | 1 |
Key: PL: Petiole length; PD: Petiole diameter; LW: Leaf width; LL: Leaf length; and, LT: Leaf thickness. **values are significant at P<0.05, and * at P<0.01
The amplification information from the ISSR markers revealed that the markers effectiveness in the assessment of the genetic diversity and relationships among the
Loci and allelic polymorphism generated by inter-simple sequence repeat markers used for genetic diversity of
SN | Code | Sequence (5ˈ - 3ˈ) | NAL | NML | NPL | %P | FZ (pb) |
---|---|---|---|---|---|---|---|
1 | ISSR1 | AGAGAGAGAGAGAGAGT | 7 | 3 | 4 | 57.1 | 1300 – 400 |
2 | ISSR2 | TGTGTGTGTGTGTGTG | 8 | 3 | 5 | 62.5 | 2000 – 300 |
3 | ISSR3 | AGCACGAGCAGCAGCGA | 5 | 3 | 2 | 40.0 | 1400 – 300 |
4 | ISSR4 | AGCACGAGCAGCAGCGG | 10 | 4 | 6 | 60.0 | 1400 – 400 |
5 | ISSR5 | AGCACGAGCAGCAGCGT | 8 | 5 | 3 | 37.5 | 1100 – 500 |
6 | ISSR6 | CACACACACACACAAT | 6 | 1 | 5 | 83.3 | 1700 – 400 |
7 | ISSR7 | CACACACACACACAAC | 10 | 4 | 7 | 70.0 | 1800 – 300 |
8 | ISSR8 | CACACACACACACAGT | 7 | 3 | 4 | 57.1 | 2000 – 400 |
9 | ISSR9 | CACACACACACACAGC | 7 | 2 | 5 | 71.4 | 1500 – 500 |
10 | ISSR10 | GTGTGTGTGTGTGTTG | 12 | 4 | 8 | 66.7 | 2800 – 300 |
11 | ISSR11 | GTGTGTGTGTGTGTCA | 6 | 3 | 3 | 50.0 | 1600 – 400 |
12 | ISSR12 | GTGTGTGTGTGTGTCT | 7 | 2 | 5 | 71.4 | 2500 – 300 |
13 | ISSR13 | GTGTGTGTGTGTGTAT | 8 | 2 | 6 | 75.0 | 1000 – 100 |
14 | ISSR14 | GCTGAGAGAGAGAGAGA | 7 | 5 | 2 | 28.6 | 1500 – 300 |
15 | ISSR15 | GCAGAGAGAGAGAGAGA | 5 | 3 | 2 | 40.0 | 1100 – 300 |
16 | ISSR16 | GAGAGAGAGAGACC | 9 | 2 | 7 | 77.8 | 2100 – 300 |
17 | ISSR17 | CACACACACACAAG | 5 | 1 | 4 | 80.0 | 1700 – 100 |
18 | ISSR18 | CAGCACACACACACACA | 5 | 0 | 5 | 100.0 | 1800 – 500 |
19 | ISSR19 | GTGTGTGTGTGTCC | 3 | 0 | 3 | 100.0 | 1400 – 400 |
Keys: NAL: Number of amplified loci; NML: Number of monomorphic loci; NPL: Number of polymorphic loci; %P: Percentage polymorphism; PIC: Polymorphic information content; FZ: Range of amplified fragments
The Principal Coordinate Analysis (PCoA) based on the allelic frequency placed the populations in different quadrants. Seven markers accounted for 81.6% of the observed genetic diversity. A mixture of eleven populations from Ekiti, Oyo and Lagos congregated on quadrant I (Fig. 5). Interestingly, only two populations (TdEk01 and TdEk03) from Ayetoro-Ekiti and Igogo-Ekiti were found in quadrant II wile five populations from Ondo State occupied quadrant III. The coordinate axis assembled 10 heterogeneous populations comprising of five from Osun, two each from Lagos and Oyo and one from Ogun in quadrant IV.
The dendrogram based on hierarchical diversity matrix and genetic similarity among the
Proper identification, documentation, conservation and estimation of variability among populations of a plant species are essential for the development of improved cultivars and sustainable utilization of the available genetic resources. Inadequate information on existing variability and genetic diversity of
In an earlier report, Makinde and Taiwo (2004) elucidated that the general growth of
Earlier, significant relationships between foliar development and soil nutrient elements have been reported among
Characterization and assessment of diversity using morphological and agronomic traits are often restricted and influence by environmental factors (Chen et al. 2014). Therefore, more reliable and precise techniques that rely on molecular markers is imperative. It is important, however, to note that markers are different in their actions and will reflect different aspects of genetic diversity (Tadesse 2017). The degree of loci and allelic polymorphism achieved with the ISSR markers employed in this study indicated the effectiveness of the markers for the diversity study. The populations exhibited considerable heterologous amplification of the alleles, which implies that the selected markers are effective in assessing the genetic variation of the populations. This agreed with earlier studies which demonstrated that microsatellite markers are effective in characterization and genotyping of crop plants (Ajibade et al. 2000; Rana et al. 2014; Animasaun et al. 2015, Olatunji and Afolayan 2019). Since a higher number of alleles and high polymorphism help to accurately estimate the genetic diversity of the population, marker profiles are usually interpreted in terms of allele phenotypes (Esslink et al. 2004). Thus the degree of polymorphism shows the extent of diversity and efficacy of the markers (Pfeifer et al. 2011). Polymorphic information of a molecular marker is related to the expected heterozygosity obtained from allelic frequency. Although the allelic frequency and polymorphic information contents of the ISSR markers used in this study revealed that most of the markers are effective, ISSR6, ISSR17, ISSR18 and ISSR19 are the most informative. Effectiveness of the markers in the study is in tandem with the previous reports on the use of molecular markers on diversity analysis of some members of the family Marentheceae which
The principal coordinate analysis (PCoA) based on the marker information showed that both morphometric and biological markers help delimit the populations. Populations that occupied the same quadrant are genetically related irrespective of their location and the climatic variations that applied. For instance, the occurrence of four Ondo populations (TdOn01, TdOn02, TdOn032, TdOn04) and a population from Oyo (TdOy05) on the same quadrant showed though the populations were from different states, they are related. It has been established that population, accessions or genotypes that clustered into a group based on their similarity, hence, they are genetically related (Tyagi et al. 2014, Animasaun et al. 2017, Olatuji and Afolayan 2019). Thus, populations from different locations and states may be relatives as obtained in the present study.
The dendrogram obtained in this study based on the UPMGA using Ward’s method revealed that all the populations were related at a genetic distance less than 10. Further separation of the populations into more clusters at lesser genetic distance signified the closeness and strong relationship of the members in a cluster. A similar clustering pattern was reported by Chinedu et al. (2018) for some
Low variability and poor gene pool can be of great negative consequences in a given plant species or population. Some of the possible effects include; retards growth, poor vigour, susceptibility to diseases and genetic erosion among others (Rauf et al. 2010). The degree of diversity demonstrated in this study is sufficient to be harnessed for the improvement and sustainable utilization of the plant. Where populations from different states clustered, stronger bonds exist among populations from different locations within a state than those from different states. Such populations could have arisen from a common progenitor, but isolated by ecological factors, domestication and agricultural activities which ultimately resulted in different populations/genotypes that became stabilised over a while. Whichever population concept adopted from this study, whether phonetic (morphometric) or genomics, it is evident that there exist variation in the studied
In crop improvement and conservation for sustainable utilisations, knowledge of existing variability and genetic divergence of available populations and germplasm is important. To achieve this, diversity study using both morphometric and molecular marker is imperative. The morphometric and ISSR markers used in the current study delimited the populations and partitioned them based on their genetic relationships. The study showed the effectiveness of the markers for the genetic diversity of
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