Corresponding author: Kar-Hoe Loh (
Academic editor: Nina Bogutskaya
A background study is important for the conservation and stock management of a species.
Chanthran SSD, Lim P-E, Li Y, Liao T-Y, Poong S-W, Du J, Hussein MAS, Sade A, Rumpet R, Loh KH (2020) Genetic diversity and population structure of
A population’s genetic structure describes the total genetic diversity in the population, which is shaped by several factors, including the life history, geographical barriers, gene flow, selection and bottlenecks (
Existing reports on
The main focus of the current study is on the Malaysian populations: Peninsular (West) Malaysia and East Malaysia (Sabah and Sarawak) which are located in the tropical Indo-west Pacific region (Fig.
Sampling localities from East (Sandakan and Tawau, Sabah & Mukah, Sarawak) and West (Peninsula) Malaysia (Kuala Selangor, Selangor and Kuantan, Pahang).
Sampling around major landing sites and local markets was conducted in both East and Peninsular Malaysia where 134 samples of various sizes were collected randomly from five wild populations of
Genomic DNA was extracted using 10% Chelex Resin following the protocol of
Multiple sequence alignment was first performed separately for each gene region using the CLUSTAL X (
Unique haplotypes were quantified and the genetic diversity, nucleotide diversity, and pairwise distance were calculated using DNASP v. 4.0 (
In addition, a neutrality test of the pairwise differences among all populations was performed to infer historical demographic and deviation of sequence variation from evolutionary neutrality. Deviations from neutrality were evaluated using Fu’s Fs (
The 1446 bp concatenated
A total of 83 putative haplotypes were derived from the 134 individuals sequenced with 79 of them being unique haplotypes (95.18%) and four were shared haplotypes (4.82%). The dominant haplotype of Malaysian populations is Hap5 (KS, KN, TW, SN, MH, TAI) while other shared haplotypes are Hap3 (KS, KN, TW, MH, IND), Hap8 (KS and KN) and Hap45 (TW and SN). The population from KS recorded the highest total number of haplotypes (22) of which 19 were unique haplotypes, while Tawau recorded the lowest number of haplotypes (15) with 12 unique haplotypes. The nucleotide diversity (
Information and molecular indices of
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KS | Kuala Selangor, Selangor | 31 | 22 | 19 | 0.9828 ±0.0135 | 0.1817 ±0.0904 | 36.5161 ±16.3285 |
KN | Kuantan, Pahang | 30 | 19 | 16 | 0.9678 ±0.0208 | 0.0288 ±0.0158 | 5.7885 ±2.8485 |
MH | Mukah, Sarawak | 21 | 16 | 14 | 0.9952 ±0.0165 | 0.3434 ±0.1722 | 69.0238 ±31.0005 |
SN | Sandakan, Sabah | 28 | 20 | 18 | 0.9577 ±0.0262 | 0.0487 ±0.0256 | 9.7810 ±4.6212 |
TW | Tawau, Sabah | 24 | 15 | 12 | 0.9167 ±0.0482 | 0.0514 ±0.0271 | 10.3333 ±4.8857 |
Total | 134 | 83 | 79 | 0.9820 ±0.0050 | 0.0248 ±0.0031 | 35.8653 ±12.2638 |
A ML tree was reconstructed based on the 83 haplotypes of this study and four
Maximum likelihood haplotype tree reconstructed based on the concatenated mtDNA dataset. The bootstrap values higher than 50% are shown near the nodes.
The general topology of the median-joining network (Fig.
Haplotypes median-joining network corresponding to the ML tree with three observed clusters. The star-like profile observed in cluster III indicates the presence of sudden expansion.
Pairwise FST comparisons between populations in Malaysia were significant at the 95% confidence level except for the comparison between TW and SN (Table
The genetic structure of the
Pairwise FST (below diagonal) and exact
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KS | - | 0.0000* | 0.0000* | 0.0000* | 0.0000* |
KN | 0.2965 | - | 0.0000* | 0.0270* | 0.0090* |
MH | 0.3310 |
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- | 0.0000* | 0.0000* |
SN | 0.2681 | 0.0702 | 0.5038 | - | 0.0541 |
TW | 0.2633 | 0.1773 | 0.4844 |
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Net between-group mean distances using Kimura-2-parameter (
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KS | - | ||||
KN | 0.003 | - | ||||
SN | 0.003 | 0.000 | - | |||
MH | 0.015 | 0.019 | 0.019 | - | ||
TW | 0.004 | 0.001 | 0.000 | 0.019 | - | |
Cyt |
KS | - | ||||
KN | 0.008 | - | ||||
SN | 0.008 | 0.001 | - | |||
MH | 0.018 | 0.029 | 0.029 | - | ||
TW | 0.008 | 0.001 | 0.000 | 0.029 | - |
AMOVA of
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Among region (FCT) | 800.958 | 39.52 | 0.3952 | 0.1896 ± 0.0134 |
Among populations within region (FSC) | 11.1610 | -0.14 | -0.0033 | 0.0831 ± 0.0082 |
Within populations (FST) | 1476.92 | 62.13 | 0.3952 | 0.0000 ± 0.0000 |
The overall Tajima’s
In the present study, all populations demonstrated bimodal and ragged shaped patterns which points to the population having remained largely constant in size and that the lineage was widespread (
Pairwise number of difference (mismatch distribution) analysis was conducted using the constant population size model to observe the population size changes. The observed frequencies were represented by red dotted line. The frequency expected under the hypothesis of population expansion model was depicted by continuous green line.
Parameter estimates of neutrality tests (Tajima’s
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KS | 1.4817 | -1.252 | 0.0296 | 0.0106 | Bimodal |
KN | -1.2595 | -11.560 | 0.0113 | 0.0333 | Bimodal |
SN | -0.4265 | -6.153 | 0.0301 | 0.0160 | Bimodal |
TW | 1.0793 | -2.075 | 0.0236 | 0.0266 | Bimodal |
MH | 2.1863* | -1.093* | 0.0288 | 0.0142 | Bimodal |
Total | -0.3132 | -24.885* | 0.0247 | 0.0201 | Bimodal |
Species identification was confirmed by morphological observation and DNA sequence data in which the intraspecific
A population’s genetic structure is affected by genetic drift, local adaptation, and gene flow. In a marine environment, the development of population structure is greatly influenced by factors that affect dispersal, such as ocean currents, historical variance, and geographic distance coupled with differences in dispersal ability and habitat discontinuity (
The haplotype tree (Fig.
FST values are often used to infer gene flow, in which a lower FST value indicates low genetic divergence and higher gene flow. FST values below 0.05, as observed between SN and TW populations, indicate negligible genetic divergence, probably due to active exchange of genetic material between populations through breeding. Furthermore, the pairwise divergence between these populations is not statistically significant. According to
Populations from the same region, i.e., TW and SN of Sabah, were the least genetically variable (Tables
Another interesting finding of this study is the occurrence of shared haplotype between the populations from Peninsular and East Malaysia, India, Hainan, Philippines and Taiwan. Common haplotypes between localities and mixed haplotypes of different lineages in some populations in the current study can be explained by the biogeographical history of Southeast Asia (historically known as the Sundaland). Southeast Asia is believed to have experienced simultaneous glaciation and consequent deglaciation along with its associated decrease and increase of seawater levels during the Pleistocene period, which greatly influenced continental and oceanic configuration (
The MH population is the most genetically distinct with the highest between-group mean distances, haplotype and nucleotide diversity among the five populations. Geographical isolation of allopatric populations restricts gene flow between two populations, which in turn allows the evolution of a genome adapted to local condition (
Among the four populations, MH is genetically closest to KS. Geological evidence suggests that the river systems of Sarawak were historically interconnected with most major river systems of Peninsular Malaysia via the Sunda River during Pleistocene glaciation (about 10000 years ago), thus allowing gene flow among these drainages (
Historical demographic expansions were determined by analysing the frequency distributions of pairwise differences between sequences (
The mismatch distribution is generally displayed as a multimodal pattern for populations showing demographic equilibrium. In contrast, a unimodal pattern depicts populations which have experienced recent demographic expansion (
To summarize, we found 1) high haplotype diversity but low nucleotide diversity among
The fish species that was employed in this study is not categorized as endangered species under the IUCN list and all the samples were collected from fish markets and landing sites.
This study was supported by the University of Malaya, Research University Grant (RU009E-2018), Top 100 Universities in The World Fund (TU001-2018), IF030B-2017; Ministry of Science and Technology (108-2119-M-110-005) and the China-ASEAN Maritime Cooperation Fund project “Monitoring and conservation of the coastal ecosystem in the South China Sea”. We would also like to thank Surajwaran Mangaleswaran, an English professional for checking on the language used in this paper.
Percentage of nucleotide composition based on populations.
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KS | 22.4 | 28.8 | 30.9 | 17.9 | 51.2 | 48.8 | 23.5 | 28.7 | 32.9 | 14.9 | 52.2 | 47.8 | 22.9 | 28.9 | 32.0 | 16.2 | 51.8 | 48.2 |
KN | 22.6 | 28.9 | 30.7 | 17.8 | 51.5 | 48.5 | 23.5 | 28.7 | 32.8 | 14.9 | 52.2 | 47.7 | 23.0 | 29.0 | 31.9 | 16.1 | 52.0 | 48.0 |
MH | 22.5 | 28.4 | 31.4 | 17.8 | 50.9 | 49.2 | 23.7 | 28.4 | 32.9 | 15.0 | 52.1 | 47.9 | 23.0 | 28.6 | 32.2 | 16.2 | 51.6 | 48.4 |
SN | 22.6 | 28.9 | 30.7 | 17.8 | 51.5 | 48.5 | 23.5 | 28.7 | 32.8 | 14.9 | 52.2 | 47.7 | 23.0 | 29.0 | 31.9 | 16.1 | 52.0 | 48.0 |
TW | 22.6 | 28.9 | 30.8 | 17.8 | 51.5 | 48.6 | 23.5 | 28.7 | 32.9 | 14.9 | 52.2 | 47.8 | 23.0 | 28.9 | 32.0 | 16.1 | 51.9 | 48.1 |
Total | 22.2 | 29.2 | 30.9 | 17.7 | 51.4 | 48.6 | 23.6 | 28.7 | 32.9 | 14.9 | 52.3 | 47.8 | 23.0 | 28.9 | 32.0 | 16.1 | 51.9 | 48.1 |
HAI | 22.5 | 28.9 | 30.7 | 17.8 | 51.4 | 48.5 | 23.4 | 28.8 | 32.9 | 14.8 | 52.2 | 47.7 | 23.1 | 28.9 | 32.0 | 16.1 | 52.0 | 48.1 |
IND | 22.3 | 29.5 | 30.5 | 17.7 | 51.8 | 48.2 | 23.6 | 28.8 | 32.8 | 14.8 | 52.4 | 47.6 | 22.9 | 29.7 | 31.6 | 15.8 | 52.6 | 47.4 |
PHI | 22.9 | 28.9 | 30.7 | 17.5 | 51.8 | 48.2 | 23.4 | 28.6 | 32.9 | 15.1 | 52.0 | 48.0 | 23.2 | 28.7 | 32.0 | 16.1 | 51.9 | 48.1 |
TAI | 22.5 | 28.9 | 30.7 | 17.8 | 51.4 | 48.5 | 23.6 | 28.7 | 32.9 | 14.8 | 52.3 | 47.7 | 23.1 | 28.8 | 32.0 | 16.1 | 51.9 | 48.1 |
Polymorphic site analysis based on
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KS | 602 | 29 | 20 | 9 | 742 | 73 | 53 | 20 | 1344 | 102 | 73 | 29 |
KN | 618 | 13 | 6 | 7 | 793 | 22 | 14 | 8 | 1411 | 35 | 20 | 15 |
MH | 575 | 56 | 54 | 2 | 716 | 99 | 95 | 4 | 1291 | 155 | 149 | 6 |
SN | 617 | 14 | 7 | 7 | 787 | 28 | 20 | 8 | 1404 | 42 | 27 | 15 |
TW | 619 | 12 | 9 | 3 | 798 | 17 | 14 | 3 | 1417 | 29 | 23 | 6 |
Total | 557 | 74 | 60 | 14 | 687 | 128 | 111 | 17 | 1244 | 202 | 171 | 31 |
Sequence data used in this study. All data were downloaded from
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Hainan |
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Taiwan |
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Kochi, India |
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Lenka et al. 2014 (Unpublished) |
Philippines |
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Malaysia | This study |
Frequency distribution of haplotypes according to localities. Highlighted columns indicate shared haplotypes.
Haplotype | Total | KS | KN | TW | SN | MH | PHI | TAI | HAI | IND |
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Hap_1 | 3 | 3 | ||||||||
Hap_2 | 1 | 1 | ||||||||
Hap_3 | 17 | 4 | 4 | 7 | 1 | 1 | ||||
Hap_4 | 1 | 1 | ||||||||
Hap_5 | 21 | 5 | 6 | 1 | 5 | 3 | 1 | |||
Hap_6 | 1 | 1 | ||||||||
Hap_7 | 1 | 1 | ||||||||
Hap_8 | 2 | 1 | 1 | |||||||
Hap_9 | 1 | 1 | ||||||||
Hap_10 | 1 | 1 | ||||||||
Hap_11 | 1 | 1 | ||||||||
Hap_12 | 1 | 1 | ||||||||
Hap_13 | 1 | 1 | ||||||||
Hap_14 | 1 | 1 | ||||||||
Hap_15 | 1 | 1 | ||||||||
Hap_16 | 1 | 1 | ||||||||
Hap_17 | 1 | 1 | ||||||||
Hap_18 | 1 | 1 | ||||||||
Hap_19 | 1 | 1 | ||||||||
Hap_20 | 1 | 1 | ||||||||
Hap_21 | 1 | 1 | ||||||||
Hap_22 | 1 | 1 | ||||||||
Hap_23 | 1 | 1 | ||||||||
Hap_24 | 4 | 4 | ||||||||
Hap_25 | 1 | 1 | ||||||||
Hap_26 | 1 | 1 | ||||||||
Hap_27 | 1 | 1 | ||||||||
Hap_28 | 1 | 1 | ||||||||
Hap_29 | 1 | 1 | ||||||||
Hap_30 | 1 | 1 | ||||||||
Hap_31 | 1 | 1 | ||||||||
Hap_32 | 1 | 1 | ||||||||
Hap_33 | 1 | 1 | ||||||||
Hap_34 | 1 | 1 | ||||||||
Hap_35 | 1 | 1 | ||||||||
Hap_36 | 1 | 1 | ||||||||
Hap_37 | 1 | 1 | ||||||||
Hap_38 | 1 | 1 | ||||||||
Hap_39 | 1 | 1 | ||||||||
Hap_40 | 1 | 1 | ||||||||
Hap_41 | 5 | 5 | ||||||||
Hap_42 | 1 | 1 | ||||||||
Hap_43 | 1 | 1 | ||||||||
Hap_44 | 1 | 1 | ||||||||
Hap_45 | 2 | 1 | 1 | |||||||
Hap_46 | 1 | 1 | ||||||||
Hap_47 | 1 | 1 | ||||||||
Hap_48 | 1 | 1 | ||||||||
Hap_49 | 1 | 1 | ||||||||
Hap_50 | 1 | 1 | ||||||||
Hap_51 | 1 | 1 | ||||||||
Hap_52 | 1 | 1 | ||||||||
Hap_53 | 1 | 1 | ||||||||
Hap_54 | 1 | 1 | ||||||||
Hap_55 | 1 | 1 | ||||||||
Hap_56 | 1 | 1 | ||||||||
Hap_57 | 1 | 1 | ||||||||
Hap_58 | 2 | 2 | ||||||||
Hap_59 | 1 | 1 | ||||||||
Hap_60 | 1 | 1 | ||||||||
Hap_61 | 1 | 1 | ||||||||
Hap_62 | 1 | 1 | ||||||||
Hap_63 | 1 | 1 | ||||||||
Hap_64 | 2 | 2 | ||||||||
Hap_65 | 1 | 1 | ||||||||
Hap_66 | 1 | 1 | ||||||||
Hap_67 | 1 | 1 | ||||||||
Hap_68 | 1 | 1 | ||||||||
Hap_69 | 2 | 2 | ||||||||
Hap_70 | 1 | 1 | ||||||||
Hap_71 | 1 | 1 | ||||||||
Hap_72 | 1 | 1 | ||||||||
Hap_73 | 2 | 2 | ||||||||
Hap_74 | 1 | 1 | ||||||||
Hap_75 | 1 | 1 | ||||||||
Hap_76 | 3 | 3 | ||||||||
Hap_77 | 1 | 1 | ||||||||
Hap_78 | 1 | 1 | ||||||||
Hap_79 | 1 | 1 | ||||||||
Hap_80 | 1 | 1 | ||||||||
Hap_81 | 1 | 1 | ||||||||
Hap_82 | 1 | 1 | ||||||||
Hap_83 | 1 | 1 | ||||||||
Hap_84 | 1 | 1 | ||||||||
Hap_85 | 1 | 1 |