Next Article in Journal
Rubiadin Mediates the Upregulation of Hepatic Hepcidin and Alleviates Iron Overload via BMP6/SMAD1/5/9-Signaling Pathway
Previous Article in Journal
Antibiofilm Activities of Halogenated Pyrimidines Against Enterohemorrhagic Escherichia coli O157:H7
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 26
Issue 3
10.3390/ijms26031387
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genetic Diversity and Environmental Adaptation Signatures of the Great Seahorse (Hippocampus kelloggi) in the Coastal Regions of the Indo-Pacific as Revealed by Whole-Genome Re-Sequencing

by
Wen-Xin Hao
1,†,
Ying-Yi Zhang
2,†,
Xin Wang
2,
Meng Qu
2,
Shi-Ming Wan
1,2,* and
Qiang Lin
2,*
1
College of Fisheries, Hubei Hongshan Laboratory/Key Lab of Freshwater Animal Breeding, Ministry of Agriculture and Rural Affairs/Engineering Research Center of Green Development for Conventional Aquatic Biological Industry in the Yangtze River Economic Belt, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China
2
CAS Key Laboratory of Tropical Marine Bio-Resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2025, 26(3), 1387; https://doi.org/10.3390/ijms26031387
Submission received: 16 December 2024 / Revised: 30 January 2025 / Accepted: 4 February 2025 / Published: 6 February 2025
(This article belongs to the Section Molecular Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
The great seahorse (Hippocampus kelloggi) is one of the larger species within the seahorse group and is widely distributed in coastal areas of the Indo-Pacific. However, the natural resources of this species continue to decrease, rendering it a vulnerable species that faces a high risk of extinction. Therefore, there is an urgent need to conduct research on the genetic diversity of this species to protect its genetic resources. In this study, we conducted whole-genome re-sequencing (WGRS) on three H. kelloggi populations from the Red Sea (RS, n = 30), the Andaman Sea (AS, n = 13), and the South China Sea (SCS, n = 13), and a total of 1,398,936 high-quality single-nucleotide polymorphisms (SNPs) were identified. The results indicate that the average observed heterozygosity (Ho) and the average expected heterozygosity (He) for the RS, AS, and SCS populations are 0.2031 and 0.1987, 0.1914 and 0.1822, and 0.2083 and 0.2001, respectively. The three geographic populations exhibit a high degree of genetic differentiation with only a minimal gene flow between them. Consistently, in a population structure analysis, the three groups are also clearly distinguished, which is consistent with the results of the population differentiation coefficient. Demographic analyses revealed that the effective population size (Ne) of the SCS population underwent a dramatic bottleneck during the Last Glacial Maximum (LGM), followed by a substantial recovery, whereas the RS and AS populations maintained stable Ne values throughout this period. To investigate adaptive responses to climate change in the SCS population, we employed selective elimination analysis, which identified 21 candidate genes potentially involved in environmental adaptation. Of particular significance were myo5a, hps4, znf385a, msh3, and pfkfb4, which likely play crucial roles in the adaptive mechanisms of H. kelloggi. This comprehensive study not only illuminates the genetic diversity patterns of H. kelloggi but also provides a valuable foundation for future investigations into the species’ evolutionary adaptations.

1. Introduction

The great seahorse, Hippocampus kelloggi, is one of the larger species within the seahorse group, with a broad distribution across the Indo-Pacific region, encompassing the Red Sea off East Africa, the Indo-Australian Archipelago, and waters near Japan [1]. Unlike other seahorse species, H. kelloggi exhibits a unique trans-oceanic and trans-latitudinal distribution pattern, resulting in significant habitat variations among populations [2]. As a valuable ingredient in traditional Chinese medicine, H. kelloggi possesses considerable economic importance and diverse therapeutic properties, including anti-thrombotic and anti-aging effects [3]. However, due to habitat destruction and overfishing, wild populations of most seahorse species, including H. kelloggi, have experienced significant declines [4]. It is estimated that the population of H. kelloggi decreased by at least 30% between 2006 and 2016, resulting in its inclusion on the IUCN Red List of Endangered Species in 2017 [5]. While habitat destruction and overfishing remain major threats to H. kelloggi populations, climate change also poses a significant challenge to their survival. Decreased water temperature, ocean acidification, and changes in ocean currents can all negatively impact H. kelloggi habitats and their prey, thereby exacerbating population declines [6]. Understanding how H. kelloggi adapts to these climatic pressures is essential for devising effective conservation strategies. Studies have demonstrated that species’ environmental adaptability and evolutionary potential are intrinsically linked to their genetic diversity [7,8]. The assessment of genetic diversity within a species’ germplasm resources is fundamental for sustainable resource utilization and the maintenance of species–ecosystem coexistence [9]. Therefore, investigating the genetic diversity of H. kelloggi is crucial for protecting its genetic resources and developing effective conservation strategies.
Research on H. kelloggi remains limited due to its unique biological characteristics and conservation status. Existing studies primarily focus on morphological features [10,11] and habitat distribution [3,4,12], whereas population genetic studies are notably scarce. Seahorses are particularly intriguing subjects for studying marine organism dispersal patterns and geographical distribution [13,14] due to their restricted dispersal capabilities [15] and distinctive male brood pouch reproductive strategy [16], which limit genetic exchange between populations and create distinct population structures. Consequently, a whole-genome analysis of H. kelloggi promises to provide comprehensive insights into the genetic relationships and biogeographical evolution patterns of seahorse populations across the Indo-Pacific region.
To date, previous population genetics studies on seahorses have predominantly relied on sparse molecular markers or concentrated on a limited number of conserved genetic fragments [17,18,19]. With the rapid advancement of sequencing technologies, researchers can now efficiently and precisely identify genetic variations across the entire genome. The emergence of high-throughput sequencing technologies has particularly revolutionized genomic and molecular biological research, enabling unprecedented insights into genetic architecture [20]. Currently, whole-genome re-sequencing (WGRS) and single-nucleotide polymorphism (SNP) loci have been widely used in the genetic research of aquatic species. WGRS can detect high-density SNPs and Indels across the entire genome, thereby exploring the genetic background, environmental adaptation, and evolutionary process of target populations or identifying candidate genes associated with important traits [21]. For instance, Zhao et al. [22] employed WGRS to investigate the environmental adaptability of Sillago sinica across diverse climatic and geographical regions. Similarly, Li et al. [2] conducted a comprehensive study combining a de novo genome assembly of Hippocampus erectus with 358 re-sequenced genomes from 21 species to examine global seahorse populations. Their research revealed the species’ center of origin, dispersal patterns, and spatiotemporal migration pathways, while also illuminating the evolutionary mechanisms underlying trait diversification during population differentiation, thereby establishing a crucial theoretical framework for seahorse population genetics.
In this study, we aim to gain insight into the genetic background of H. kelloggi by performing high-depth whole-genome re-sequencing on 56 individuals from three different regions in the Indo-Pacific area. While previous research on H. kelloggi has primarily relied on traditional molecular markers, whole-genome-level population genetic analyses of this species remain scarce. Our research specifically focused on examining genetic differentiation patterns, population structures, and adaptive evolutionary characteristics among these three populations. The results of this study will promote resource management and provide valuable genetic markers and a theoretical basis for the conservation and improvement of the germplasm of H. kelloggi.

2. Results

2.1. Variant Discovery and SNPs Annotation

In each sequencing library of three populations in the Red Sea (RS), the Andaman Sea (AS), and the South China Sea (SCS), an average of 84.89 million, 84.7 million, and 97.6 million high-quality clean reads were obtained. The average mapping rate of successful alignments to the reference genome of H. kelloggi was >97%, and the average sequencing depth reached 23× (Table S1). A total of 66,939,708 SNPs were obtained from all the analyzed samples. After annotation, 40.04% (26,657,611) of these SNPs were located in intergenic regions, 46.987% (31,282,950) were located in intronic regions, and only 3.918% (2,608,787) were located in exonic regions. Among all exonic variants, 54.95% (1,433,592) were synonymous mutations and 44.29% (1,155,340) were nonsynonymous mutations (Table 1). After stringent quality control, 1,398,936 high-quality SNPs were retained for a subsequent population genetic diversity analysis (Figure 1). These SNPs are relatively evenly distributed across the 21 chromosomes, although slight deletions are observed at the ends of some chromosomes.

2.2. Population Genetic Diversity Analysis

To further understand the genetic diversity of the natural populations of H. kelloggi in three regions, we calculated several metrics, including the inbreeding coefficient (Fis), average observed heterozygosity (Ho), average expected heterozygosity (He), polymorphism information content (Pic), nucleotide diversity ratios (π-ratio), effective number of alleles (Ae), and observed number of alleles (Ao) (Table 2). Our analysis shows that the Fis values for the three populations range from −0.0251 to −0.0082, all of which are negative, indicating the absence of inbreeding in these populations. Among the three populations, the Ho ranges from 0.1914 (AS) to 0.2083 (SCS), while the He ranges from 0.1822 (AS) to 0.2001 (SCS). In all populations, the Ho is higher than the He. The Pic ranges from 0.1534 (AS) to 0.1673 (RS), and the π-ratio ranges from 0.3269 (RS) to 0.3677 (SCS), indicating a moderate level of genetic diversity. Compared to the genetic diversity indices of the three H. kelloggi populations, it was found that, in all three populations, the Ho, He, and Pic are lowest in the AS, indicating lower genetic diversity, while the SCS has the highest values for the Ho, He, Ae, and π-ratio among the three populations, indicating higher genetic diversity.

2.3. Genetic Divergence and Population Structure Analysis of Three Populations

Based on whole-genome SNPs, the population differentiation of the RS, AS, and SCS populations was studied. The genetic differentiation between the three populations is relatively high, with a moderate differentiation between the RS and AS populations (pairwise fixation index (Fst) = 0.2371) and a high differentiation between the SCS and both the RS and AS populations (Fst = 0.3222 and 0.3183) (Table 3).
The genetic flow between the three populations was measured based on the number of effective migrants (Nm) value. The results show that there is low gene flow between these three populations, with a relatively limited genetic exchange (Nm < 1) (Table 3). The gene flow demonstrates a population differentiation similar to Fst, with the SCS population exhibiting slightly higher genetic differentiation compared to the RS and AS populations, as indicated by the smaller Nm value.
The Principal component analysis (PCA) results show that all analyzed samples are significantly clustered into three distinct clusters on the coordinate axis. Notably, for the SCS population, there is an outlier sample (Figure 2C). The phylogenetic tree analysis also confirms the distribution of similar samples, with the samples from the three populations being distinctly separated in the phylogenetic tree (Figure 2A).
In the population structure analysis, the test values for cross-validation error (CV error) were set from 1 to 10. When the CV error reached the minimum value, the optimal K value was observed (K = 3). This indicates that the 56 analyzed samples are most likely to be divided into three subgroups which clearly separate the populations from the three sampling sites (Figure 2B,E).

2.4. Correlation Analysis of Protein and mRNA

Using the r2 of adjacent pairs of SNPs to represent the linkage disequilibrium (LD) decay of the three populations. The maximum values of r2 in the SCS, AS and RS populations are similar, with the maximum average r2 values after smoothing being calculated as 0.6701, 0.6811 and 0.6633, respectively. The r2 for the AS and RS populations weakened to 0.0809 and 0.0452, respectively, at 300 kb. Within the short distance of 0–100 kb, the RS population shows the fastest rate of r2 decay, followed by the AS and SCS populations (Figure 2D).

2.5. Trends in Historical Effective Population Size

In the historical effective population size variation graphs derived from the SMC++ method, the horizontal lines depict the temporal trends of Ne. Changes in Ne reflect alterations in the number of ancestral lineages within the population, thereby indicating population contraction, expansion, or stability [23]. Overall, the results of the SMC++ analysis revealed that the effective populations of H. kelloggi in the three regions have undergone a continuous decline for millions of years. Specifically, the Ne of the three populations began to diverge around 0.03 Mya and then stabilized around 2 Kya (Figure 3). It is worth noting that the Ne of the SCS population experienced a significant bottleneck effect during the Last Glacial Maximum (LGM, 0.18–0.24 Mya), with a rapid decline in population size, followed by a brief period of stability and then rapid expansion. In contrast, the Ne of the AS and RS populations remained stable during the LGM. This may imply the process of adaptation of the SCS population from intolerance to extreme cold environments to adaptation.

2.6. Candidate Genes Under Selection

In order to study the genetic adaptation selection of H. kelloggi in different geographical regions, we employed a comprehensive method combining the Fst and π-ratio to identify genomic selection regions closely related to environmental adaptivity (Figure 4A–C and Figure 5A–C). Considering the distinct historical effective population dynamics between the SCS population and the AS and RS populations, we used the AS and RS populations as reference groups and identified selected genes in the selected SCS population to explore the genetic traces of the LGM historical event.
Compared to the RS population (πRS/πSCS), only 99 genes displaying the selected features were identified in the SCS population. A Gene Ontology (GO) enrichment analysis showed that these genes were significantly enriched in 105 GO terms (Figure 4D). The major enriched GO terms included the single-organism process (GO: 0044699) and “cellular process” (GO: 0009987) in biological process, and the “membrane part” (GO: 0044425) and “membrane” (GO: 0016020) in cellular component, “binding” (GO: 0005488) and “catalytic activity” (GO: 0003824) in molecular function. A Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis yielded a total of 31 significant biological pathways (Figure 4E); the top five biological pathways were “taste transduction” (ko04742), “ovarian steroidogenesis” (ko04913), “aldosterone synthesis and secretion” (ko04925), “melanogenesis” (ko04916), and “regulation of lipolysis in adipocyte” (ko04923).
Compared to the AS population (πAS/πSCS), 278 genes were identified as being under selection pressure in the SCS population. A GO enrichment analysis indicated that these genes were significantly enriched in 110 GO terms (Figure 5D). The main enriched GO terms include “cellular process” (GO:0009987) and “single-organism process” (GO:0044699) in biological process, “cell” (GO:0005623) and “cell part” (GO:0043226) in cellular component, and “binding” (GO:0005488) and “catalytic activity” (GO:0003824) in molecular function. In the KEGG enrichment analysis, a total of 10 biological pathways were significantly enriched (Figure 5E), with the top five biological pathways being “Estrogen signaling pathway” (ko04915), “Glycosaminoglycan biosynthesis—heparan sulfate/heparin” (ko00534), “MAPK signaling pathway—fly” (ko04013), “Inflammatory mediator regulation of TRP channels” (ko04750), and “Prolactin signaling pathway” (ko04917).
We identified 21 overlapping genes between the selected regions of the SCS vs. AS and the SCS vs. RS. These genes were viewed as potential genes associated with environmental adaptation for the SCS population (Table 4). This includes some genes that may function in animals’ adaptation to climate change, including myosin VA (myo5a) and HPS4 biogenesis of lysosomal organelles complex 3 subunit 2 (hps4) related to melanin synthesis; zinc finger protein 385A (znf385a) and mutS homolog 3 (msh3) related to genetic information repair and apoptosis; and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4 (pfkfb4) related to hypoxia adaptation. These genes may play a significant role in the process of climate adaptation in the SCS population.

3. Discussion

3.1. Genetic Diversity of H. kelloggi

Genetic diversity is an inherent manifestation of a species’ evolutionary potential and adaptive capacity. As a vulnerable species facing a high risk of extinction, evaluating the genetic diversity of H. kelloggi and comparing it with its closely related species holds significant importance for understanding its evolutionary history and informing conservation strategies. Among the important indicators for assessing genetic diversity are Ho, He, and π-ratio. Our study calculated these parameters for three natural populations of H. kelloggi from different sea areas using WGRS data. The results revealed Ho values ranging from 0.1914 to 0.2083, He values ranging from 0.1822 to 0.2001 and π-ratio values ranging from 0.3269 to 0.3677. Notably, the Ho values exceeded He values in all three populations, indicating an excess of heterozygotes. When compared with other fish populations analyzed using WGRS data, these values represent low to moderate levels of genetic diversity [24,25,26]. Previous studies on seahorse species have reported varying levels of genetic diversity. For instance, Lazic et al. [27] analyzed the genetic diversity of the long-snouted seahorse (Hippocampus guttulatus) in the Mediterranean region using eight microsatellite loci, reporting Ho values ranging from 0.23 to 0.44 and He values from 0.30 to 0.54. The comparatively lower values observed in our study populations might be attributed to either methodological differences in genetic variant detection or could indicate genuinely lower genetic variation in H. kelloggi populations.
The genetic diversity of the SCS population are all at the highest level among the three populations. However, in the linkage disequilibrium (LD) analysis, the decay rate of LD in the SCS population is the slowest among the three populations and the LD coefficient is the highest. The historical analysis of the population dynamics seems to support the results of the LD analysis. After experiencing a bottleneck effect during the LGM, the SCS population rapidly expanded, indicating that this was possibly due to the positive selection of the SCS population to adapt to the cold climate. Research has shown that, if a population is under positive selection, due to the effects of genetic linkage, the frequency of the surrounding loci linked to advantageous loci will rapidly increase, leading to a higher linkage between genetic variations [28]. However, in the context of high LD coefficients, the high genetic diversity of the SCS population may be due to various reasons. Firstly, the sampling points within the SCS population are relatively dispersed, which may result in a more complex genetic background within the population. Additionally, we suppose that there may have been an introgression of genetic information from new populations during the rapid expansion period of the SCS population. However, a strong LD linkage has already been established by the indigenous population, and even if genetic diversity has been restored within a short time, the previously high LD linkage may still be retained for a period.

3.2. Population Structure of H. kelloggi

This study investigated the genetic structure of three H. kelloggi populations through structure, phylogenetic, and principal component analyses. While marine ecosystems generally exhibit lower dispersal barriers compared to terrestrial ecosystems [29,30], and many marine fish species demonstrate long-distance dispersal capabilities [31,32], seahorses present a unique case. Their weak swimming abilities and reliance on drifting while entwined with floating objects result in localized living patterns [33]. This limited dispersal capacity, combined with geographical isolation, has led to a reduced gene flow and high genetic differentiation among H. kelloggi populations, as evidenced by our findings.
Our analyses provide multiple lines of evidence supporting this population structure. The principal component analysis, phylogenetic analysis, and population structure analysis all demonstrate clear differentiation among the three geographical populations, indicating distinct population stratification. Notably, while sampling points within the SCS population are relatively scattered along the coastline, they show no prominent genetic differentiation (Figure S1), suggesting that H. kelloggi can achieve medium to long-distance dispersal along nearshore ocean currents.
A comparative analysis of the genetic structures of the three populations reveals the influence of Chinese coastal ocean currents on H. kelloggi dispersal patterns. Seasonal oceanic circulation maintains genetic connectivity between geographically dispersed communities along the Chinese coastal area [9,34]. The Kuroshio Current and its tributaries create a barrier between the open ocean and coastal waters [9], while the Indo-Pacific Archipelago serves as a geographical barrier affecting oceanic circulation between the Indian and Pacific Oceans [35,36]. This has resulted in a relatively independent and isolated environment along the coast of China. Consequently, although the SCS population is geographically closer to the AS population, it exhibits a relatively high Fst value compared to both the AS and RS populations. In contrast, the genetic differentiation between the AS and RS populations is lower.
These findings suggest that the three natural populations of H. kelloggi have evolved into relatively independent genetic lineages within their respective habitats [27]. Such genetic structural characteristics indicate that long-term geographical isolation may have led to differential adaptation among these populations.

3.3. Historical Demography and Environmental Adaptation

According to our observations, the Ne of H. kelloggi has shown a significant decreasing trend across all three geographical regions over the past million years. Similar dynamic changes have been documented in other coastal species, including Sillago sinica [22] and Mytilus coruscus [9]. This decline in Ne can primarily be attributed to historically extreme environmental events and climate change-related stressors. The oscillation between glacial and interglacial periods has led to continuous fluctuations in ocean temperatures, imposing greater survival pressure on coastal species [37]. Additionally, human activities have emerged as a contributing factor to coastal biological population decline [38]. Notably, our analysis revealed distinct patterns in Ne dynamics across populations. The SCS population experienced a significant decline during the LGM, followed by a rapid rebound after a brief stabilization period, ultimately exceeding its original Ne level. In contrast, the AS and RS populations maintained stable Ne values during the LGM. This pattern suggests an adaptive process in the SCS population in response to severe environmental stress, potentially indicating genome-level genetic selection.
Based on these observations, we employed a combined method utilizing the Fst and π-ratio to screen for genetic selection signals in the SCS population, an approach widely used for analyzing environmental adaptation traits and genetic selection in various marine species [22,39,40]. KEGG enrichment analysis identified several broad biological pathways associated with environmental adaptation, including taste transduction, melanogenesis and gastric acid secretion in the SCS vs. RS population and autophagy in the SCS vs. AS population. Through our screening process, we identified 21 overlapping genes in the selection regions of SCS vs. AS and SCS vs. RS comparisons. These genes represent potential candidates involved in climate adaptation within the SCS population. Functional annotation ultimately revealed five key genes related to environmental adaptation: myo5a, hps4, znf385a, msh3, and pfkfb4.
Melanin plays a crucial role in UV light absorption, reducing damage to skin and tissues, and its adaptive function strongly correlates with climate change impacts [41,42,43]. The myo5a gene interacts with melanosomes through the RAB27A/MLPH receptor in melanocytes [44], and its direct link to heat stress has been confirmed through genome-wide association analyses [45]. Additionally, hps4 activates Rab32/38, key functional factors in melanocytes that participate in crucial steps of melanin synthesis [46]. Climate change-induced temperature fluctuations affect water oxygen levels, implicating hypoxia-associated genes in coastal species’ environmental adaptation. The pfkfb4 gene, a target of hypoxia-inducible factor 1-alpha (HIF1A), is essential for hypoxia-induced glycolysis [47,48] and can modulate autophagy through ROS level regulation [49]. DNA damage repair capabilities also play a crucial role in environmental adaptation. The znf385a gene, situated upstream of the p53 activation pathway, promotes cell cycle arrest induced by DNA damage [50], while certain polymorphisms in msh3 may influence DNA repair capabilities [51].
Our study provides valuable genomic resources regarding H. kelloggi’s environmental adaptation-related selection features across different regions, offering important insights for future research into these genes’ functional roles in adaptive evolution. However, the limited number of populations and sample sizes may introduce some bias in the genetic diversity estimates. Future studies should analyze H. kelloggi genetic diversity across broader geographic areas with larger sample sizes.

4. Materials and Methods

4.1. Ethics Statement

All sampling and procedures involving H. kelloggi were performed in accordance with the Wild Animals Protection Law of the People’s Republic of China and approved by the Animal Ethics Committee of the Chinese Academy of Sciences (approval number: SCSIO-IACUC-2019-000137). All individuals were treated with MS-222 (50 mg/L) before experimental treatment.

4.2. Samples Collection

Fifty-six individuals of H. kelloggi from different seas were collected for use in this study, including 30 individuals from the Red Sea (RS), 13 individuals from the Andaman Sea (AS), and 13 individuals from the South China Sea (SCS). The distribution of sampling points is referenced from the following link: https://www.gbif.org/species/5201145 (accessed on 30 January 2025). From each population, 2–3 individuals were selected in order to amplify and sequence their COI genes. The sequences obtained were used for multiple sequence alignments and visualization using the Weseq tool from the WeMol computing platform (https://wemol.wecomput.com), and all of them were identified as H. kelloggi (Figure S2). The template sequences for comparison were obtained from the Barcode of Life Data System database (https://www.boldsystems.org). Information about the geographic location of all samples in this study and the distribution density of the H. kelloggi is shown in Figure 6.

4.3. DNA Extraction and Whole-Genome Re-Sequencing

The fin tissue from each collected sample was used to extract high-quality genomic DNA using the CTAB method. DNA purification was performed using the QIAGEN® kit (Cat#13343; QIAGEN, Hilden, Germany), and DNA integrity was checked through agarose gel electrophoresis. The DNA concentration was accurately quantified using the Qubit v4.0 (Invitrogen, Waltham, MA, USA). Subsequently, the whole-genome DNA was sent to Wuhan Zhenyue Biotechnology Co., Ltd. (Wuhan, China) for DNA library construction based on the Illumina NovaSeq platform. The raw data were processed using FASTP v0.12.4 software (http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed on 30 January 2025)) to remove adapter contamination, low-quality sequences, and reads with a high N content (≥10%). After initial quality control, clean reads were aligned to the seahorse reference genome using BWA v0.7.17-r1188 software (https://github.com/lh3/bwa/releases/tag/v0.7.17 (accessed on 30 January 2025)) [52]. Duplicates reads were then removed using Picard software (https://www.psc.edu/index.php/user-resources/software/picard (accessed on 30 January 2025)). The deduplicated data were used for statistics on alignment rate, coverage, and sequencing depth. GATK v4.2.2.0 (https://gatk.broadinstitute.org/hc/en-us/sections/4405443482011-4-2-2-0 (accessed on 30 January 2025)) was employed for variant detection and filtering, resulting in high-confidence SNPs [53]. VCFtools v0.1.16 software (https://vcftools.github.io/man_latest.html (accessed on 30 January 2025)) was utilized for SNP filtering, and the detected SNP loci in the samples were annotated using ANNOVAR software (https://annovar.openbioinformatics.org/en/latest/user-guide/download/ (accessed on 30 January 2025)) [54]. Finally, to ensure the reliability of subsequent population analyses, SNPs were filtered at the population level based on the following criteria: (1) minimum allele frequency (MAF) > 0.05; (2) the ratio of samples containing SNPs to the total number of samples (SNP call rate) > 80%.

4.4. Genetic Diversity Statistics

Genetic diversity indices for the three seahorse populations were calculated using PLINK v1.90 (https://www.cog-genomics.org/plink/ (accessed on 30 January 2025)) with default parameters. The analyzed parameters included the Pic, where Pi and Pj represent the frequencies of the i-th and j-th alleles, respectively; He; Ho, calculated as the ratio of heterozygous genotypes to total individuals; Ao, defined as the count of distinct alleles at each locus; Ae; and Fis. Population differentiation was evaluated using VCFtools with default parameters to calculate the π-ratio and Fst. Following Wright’s criteria [55], population differentiation was classified as low (Fst < 0.05), moderate (0.05 < Fst < 0.15), high (0.15 < Fst < 0.25), or extremely high (Fst > 0.25). Nm was estimated using the formula Nm = [(1/Fst) − 1]/4.

4.5. Population Structure Analysis

Based on the completely filtered SNP data, we used a principal component analysis and the method of constructing a phylogenetic tree to explore the group stratification to confirm the genetic evolutionary relationship between individuals and groups. A principal component analysis (PCA) was conducted using PLINK v1.90. The phylogenetic tree of the sample population was constructed using the maximum likelihood (ML) algorithm of RAxML software (https://github.com/stamatak/standard-RAxML (accessed on 30 January 2025)) [56,57]. Subsequently, the Admixture v1.3.0 software (https://biodockerfiles.github.io/admixture-1-3-0/ (accessed on 30 January 2025)) was used to assess the population structure of all samples [58]. The tested K values (subgroup numbers) were set from 1 to 5, and the optimal K value was determined by the lowest cross-validation error.

4.6. Historical Effective Population Size

SMC++ is a statistical inference method based on coalescent theory. It leverages genomic SNP data from multiple individuals and employs a Markov Chain Monte Carlo (MCMC) approach to analyze the coalescent patterns of gene lineages, thereby inferring a time series of Ne and characterizing the historical effective population size [59]. We employed the SMC++ v1.15.2 (https://github.com/popgenmethods/smcpp/releases (accessed on 30 January 2025)) method to estimate the Ne of three populations [58]. Use the vcf2smc script embedded in SMC++ to convert each VCF file to the required input file format. All simulations were performed with the default mutation rate set at 1.25 ×  10 − 8  as the initial condition.

4.7. Linkage Disequilibrium (LD) Decay Assay

An LD analysis was performed using PopLDdecay v3.41 (https://github.com/BGI-shenzhen/PopLDdecay/releases (accessed on 30 January 2025)) to calculate pair-wise SNP associations and generate LD decay plots. The R-squared correlation coefficient ( r 2 ) was calculated using the formula  r 2  = D2/(PA·Pa·PB·Pb), where D represents the deviation of allelic frequencies at two loci from random association and PA, Pa, PB, and Pb denote the frequencies of alleles A, a, B, and b, respectively. The LD decay plots display the  r 2  values against the physical distance between pair-wise SNPs in the reference genome. We defined the LD decay distance as the genomic physical distance at which the LD value decreases to half of its maximum.

4.8. Screening for Selective Sweeps

In order to compare the genetic differences between seahorse populations from different geographical regions, we conducted joint π-ratio and Fst tests to detect potential selection signatures on the genome using a window size of 5 kb and a step size of 5 kb. The AS and RS populations were designated as control groups, while the SCS population was designated as the experimental group. Based on the results of the genetic parameters, the top 5% of windows for the Fst values and π-ratio were selected as signal regions for strong selection to be used for subsequent candidate gene localization and functional enrichment analyses.

4.9. Identification of the Candidate Genes Associated with Selection Signatures

The identified genomic selection signals were mapped to the annotation file of the seahorse genome to determine gene function [60]. Subsequently, the genes were subjected to GO and KEGG enrichment analyses to identify the biological processes and pathways in which candidate genes may be involved.

5. Conclusions

This study conducted WGRS on three populations of H. kelloggi from the RS, the AS, and the SCS, identifying 1,398,936 high-quality SNPs. A genetic diversity analysis shows that, among the three populations, the SCS population has higher genetic diversity, while the AS population has lower genetic diversity. A genetic differentiation analysis revealed significant differentiation among these geographic populations, with limited gene flow. A population structure analysis clearly distinguished these groups, consistent with the results of the population differentiation coefficient. A demographic analysis indicated that the SCS population experienced a significant bottleneck during the LGM, followed by recovery, while the Ne of the RS and AS populations remained stable. Through a selective sweep analysis, 21 candidate genes potentially involved in the environmental adaptation of the SCS population were identified, notably myo5a, hps4, znf385a, msh3, and pfkfb4, which may play key roles in the adaptive mechanisms of H. kelloggi. This comprehensive study not only elucidates the genetic diversity patterns of H. kelloggi but also provides a crucial foundation for future research on the species’ evolutionary adaptation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms26031387/s1.

Author Contributions

W.-X.H.: Methodology, Validation, Formal Analysis, Investigation, Writing—original draft. Y.-Y.Z.: Methodology, Validation, Formal Analysis, Investigation, Writing—Original Draft. X.W.: Methodology, Validation, Formal Analysis. M.Q.: Methodology, Investigation. S.-M.W.: Methodology, Data curation, Formal analysis, Writing—Review and Editing. Q.L.: Conceptualization, Funding Acquisition, Writing—Review and Editing, Supervision, Project Administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China (42425004, 42106120) and the Guangdong Basic and Applied Basic Research Foundation (2023A1515012165).

Institutional Review Board Statement

All sampling and procedures involving H. kelloggi were performed in accordance with the Wild Animals Protection Law of the People’s Republic of China and approved by the Animal Ethics Committee of the Chinese Academy of Sciences (approval number: SCSIO-IACUC-2019-000137).

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw reads for the H. kelloggi individuals have been deposited in the NCBI Sequence Read Archive under the accession number PRJNA1136163.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Cheng, R.; Liao, G.; Ge, Y.; Yang, B.; Zhang, G. Complete mitochondrial genome of the great seahorse Hippocampus Kelloggi Jordan & Snyder, 1901 (Gasterosteiformes: Syngnathidae). Mitochondrial DNA Part A 2017, 28, 227–228. [Google Scholar] [CrossRef]
  2. Li, C.; Olave, M.; Hou, Y.; Qin, G.; Schneider, R.F.; Gao, Z.; Tu, X.; Wang, X.; Qi, F.; Nater, A.; et al. Genome sequences reveal global dispersal routes and suggest convergent genetic adaptations in seahorse evolution. Nat. Commun. 2021, 12, 1094. [Google Scholar] [CrossRef]
  3. Lourie, S.A.; Green, D.M.; Vincent, A.C.J. Dispersal, habitat differences, and comparative phylogeography of Southeast Asian seahorses (Syngnathidae: Hippocampus). Mol. Ecol. 2005, 14, 1073–1094. [Google Scholar] [CrossRef] [PubMed]
  4. Muhala, V.; Guimarães-Costa, A.; Macate, I.E.; Watanabe, L.; de Sousa, R.P.C.; Rocha, G.; Carneiro, J.; Vallinoto, M.; Sampaio, I. Molecular evidence for the first records and range extension of the great seahorse (Hippocampus Kelloggi, Jordan & Snyder, 1901) in Quelimane, central coast of Mozambique. J. Mar. Biol. Assoc. UK 2023, 103, e77. [Google Scholar] [CrossRef]
  5. Pollom, R. Hippocampus kelloggi. The IUCN Red List of Threatened Species. 2017. Available online: https://www.iucnredlist.org/species/41010/54908593 (accessed on 30 January 2025).
  6. Faleiro, F.; Baptista, M.; Santos, C.; Aurélio, M.L.; Pimentel, M.; Pegado, M.R.; Paula, J.R.; Calado, R.; Repolho, T.; Rosa, R. Seahorses under a changing ocean: The impact of warming and acidification on the behaviour and physiology of a poor-swimming bony-armoured fish. Conserv. Physiol. 2015, 3, cov009. [Google Scholar] [CrossRef] [PubMed]
  7. Lavergne, S.; Molofsky, J. Increased genetic variation and evolutionary potential drive the success of an invasive grass. Proc. Natl. Acad. Sci. USA 2007, 104, 3883–3888. [Google Scholar] [CrossRef] [PubMed]
  8. Sgrò, C.M.; Lowe, A.J.; Hoffmann, A.A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 2010, 4, 326. [Google Scholar] [CrossRef]
  9. Guo, F.; Ye, Y.; Zhu, K.; Lin, S.; Wang, Y.; Dong, Z.; Yao, R.; Li, H.; Wang, W.; Liao, Z.; et al. Genetic diversity, population structure, and environmental adaptation signatures of Chinese coastal hard-shell mussel Mytilus coruscus revealed by whole-genome sequencing. Int. J. Mol. Sci. 2023, 24, 13641. [Google Scholar] [CrossRef]
  10. Thangaraj, M.; Lipton, A.P. Morphological characterization of four selected seahorse species (Genus: Hippocampus) from India. Ann. Biol. Res. 2011, 2, 159–167. [Google Scholar]
  11. Balasubramanian, R.; Murugan, A. Length—Weight relationship of the great seahorse, Hippocampus kelloggi (Jordan and Snyder 1902), inhabiting Coromandel coast, Southeast coast of India. Indian J. Mar. Sci. 2017, 46, 1193–1197. [Google Scholar]
  12. Lipton, A.P.; Thangaraj, M. Distribution pattern of seahorse species (Genus: Hippocampus) in Tamilnadu and Kerala coasts of India. Not. Sci. Biol. 2013, 5, 20–24. [Google Scholar] [CrossRef]
  13. Pinheiro, H.T.; Bernardi, G.; Simon, T.; Joyeux, J.C.; Macieira, R.M.; Gasparini, J.L.; Rocha, C.; Rocha, L.A. Island biogeography of marine organisms. Nature 2017, 549, 82–85. [Google Scholar] [CrossRef]
  14. Teske, P.R.; Cherry, M.I.; Matthee, C.A. The evolutionary history of seahorses (Syngnathidae: Hippocampus): Molecular data suggest a West Pacific origin and two invasions of the Atlantic Ocean. Mol. Phylogenet. Evol. 2004, 30, 273–286. [Google Scholar] [CrossRef] [PubMed]
  15. Foster, S.J.; Vincent, A.C.J. Life history and ecology of seahorses: Implications for conservation and management. J. Fish Biol. 2004, 65, 1–61. [Google Scholar] [CrossRef]
  16. Qin, G.; Zhang, Y.; Ho, A.L.F.C.; Zhang, Y.; Lin, Q. Seasonal distribution and reproductive strategy of seahorses. ICES J. Mar. Sci. 2017, 74, 2170–2179. [Google Scholar] [CrossRef]
  17. Sanna, D.; Addis, A.; Biagi, F.; Motzo, C.; Carcupino, M.; Francalacci, P. mtDNA control region and D-HPLC analysis: A method to evaluate the mating system in Syngnathidae (Teleostei). Mar. Biol. 2008, 153, 269–275. [Google Scholar] [CrossRef]
  18. van de Vliet, M.S.; Diekmann, O.E.; Serrão, E.T.A. Highly polymorphic microsatellite markers for the short-snouted seahorse (Hippocampus hippocampus), including markers from a closely related species the long-snouted seahorse (Hippocampus guttulatus). Conserv. Genet. Resour. 2009, 1, 93–96. [Google Scholar] [CrossRef]
  19. Saarman, N.P.; Louie, K.D.; Hamilton, H. Genetic differentiation across eastern Pacific oceanographic barriers in the threatened seahorse Hippocampus ingens. Conserv. Genet. 2010, 11, 1989–2000. [Google Scholar] [CrossRef]
  20. Yu, Y.; Hilsdorf, A.W.S.; Zhou, L.; Lin, Q.; Gao, Z.X. Editorial: Genetics and molecular breeding in aquaculture animals. Front. Genet. 2022, 13, 1071303. [Google Scholar] [CrossRef]
  21. Stratton, M. Genome resequencing and genetic variation. Nat. Biotechnol. 2008, 26, 65–66. [Google Scholar] [CrossRef]
  22. Zhao, X.; Zheng, T.; Gao, T.; Song, N. Whole-genome resequencing reveals genetic diversity and selection signals in warm temperate and subtropical Sillago sinica populations. BMC Genom. 2023, 24, 547. [Google Scholar] [CrossRef] [PubMed]
  23. Charlesworth, B. Effective population size and patterns of molecular evolution and variation. Nat. Rev. Genet. 2009, 10, 195–205. [Google Scholar] [CrossRef] [PubMed]
  24. Jiang, L.; Chen, Y.; Zhang, J.; Zhu, A.; Wu, C.; Liu, L.; Lin, Z.; Dong, Y. Population structure of large yellow croaker (Larimichthys crocea) revealed by single nucleotide polymorphisms. Biochem. Syst. Ecol. 2015, 63, 136–142. [Google Scholar] [CrossRef]
  25. Ji, D.; Su, X.; Yao, J.; Zhang, W.; Wang, R.; Zhang, S. Genetic diversity and genetic differentiation of populations of golden-backed carp (Cyprinus carpio var. Jinbei) in traditional rice fields in Guizhou, China. Animals 2022, 12, 1377. [Google Scholar] [CrossRef] [PubMed]
  26. Luo, H.; Fang, D.; He, M.; Mao, C.C.; Kuang, J.; Qi, H.F.; Xu, D.P. Genetic diversity and population structure of Gymnocypris przewalskii based on SNP markers. South China Fish. Sci. 2023, 19, 86–96. [Google Scholar]
  27. Lazic, T.; Pierri, C.; Cardone, F.; Cariani, A.; Colangelo, P.; Corriero, G.; Ferrari, A.; Marzano, M.; Messinetti, S.; Pesole, G.; et al. Genetic structure of the long-snouted seahorse, Hippocampus guttulatus, in the Central–Western Mediterranean. Sea Biol. J. Linn. Soc. 2020, 130, 771–782. [Google Scholar] [CrossRef]
  28. Halachev, M.; Meynert, A.; Taylor, M.S.; Vitart, V.; Kerr, S.M.; Klaric, L.; S. G. P. Consortium; Aitman, T.J.; Haley, C.S.; Prendergast, J.G.; et al. Increased ultra-rare variant load in an isolated Scottish population impacts exonic and regulatory regions. PLoS Genet. 2019, 15, e1008480. [Google Scholar] [CrossRef] [PubMed]
  29. Bierne, N.; Bonhomme, F.; David, P. Habitat preference and the marine-speciation paradox. Proc. R. Soc. Lond. B 2003, 270, 1399–1406. [Google Scholar] [CrossRef] [PubMed]
  30. Selmoni, O.; Vajana, E.; Guillaume, A.; Rochat, E.; Joost, S. Sampling strategy optimization to increase statistical power in landscape genomics: A simulation-based approach. Mol. Ecol. Resour. 2020, 20, 154–169. [Google Scholar] [CrossRef]
  31. Riginos, C.; Crandall, E.D.; Liggins, L.; Bongaerts, P.; Treml, E.A. Navigating the currents of seascape genomics: How spatial analyses can augment population genomic studies. Curr. Zool. 2016, 62, 581–601. [Google Scholar] [CrossRef]
  32. Zheng, J.; Zhao, L.; Zhao, X.; Gao, T.; Song, N. High genetic connectivity inferred from whole-genome resequencing provides insight into the phylogeographic pattern of Larimichthys polyactis. Mar. Biotechnol. 2022, 24, 671–680. [Google Scholar] [CrossRef] [PubMed]
  33. Casey, S.P.; Hall, H.J.; Stanley, H.F.; Vincent, A.C. The origin and evolution of seahorses (genus Hippocampus): A phylogenetic study using the cytochrome b gene of mitochondrial DNA. Mol. Phylogenet. Evol. 2004, 30, 261–272. [Google Scholar] [CrossRef] [PubMed]
  34. Palma, E.D.; Matano, R.P.; Piola, A.R. A numerical study of the southwestern Atlantic shelf circulation: Stratified ocean response to local and offshore forcing. J. Geophys. Res. 2008, 113, 2007JC004720. [Google Scholar] [CrossRef]
  35. Gaither, M.R.; Toonen, R.J.; Robertson, D.R.; Planes, S.; Bowen, B.W. Genetic evaluation of marine biogeographical barriers: Perspectives from two widespread Indo-Pacific snappers (Lutjanus kasmira and Lutjanus fulvus). J. Biogeogr. 2010, 37, 133–147. [Google Scholar] [CrossRef]
  36. Treml, E.A.; Roberts, J.; Halpin, P.N.; Possingham, H.P.; Riginos, C. The emergent geography of biophysical dispersal barriers across the Indo-West Pacific. Divers. Distrib. 2015, 21, 465–476. [Google Scholar] [CrossRef]
  37. Imbrie, J.; Boyle, E.A.; Clemens, S.C.; Duffy, A.; Howard, W.R.; Kukla, G.; Kutzbach, J.; Martinson, D.G.; McIntyre, A.; Mix, A.C.; et al. On the structure and origin of major glaciation cycles 1. Linear responses to milankovitch forcing. Paleoceanography 1992, 7, 701–738. [Google Scholar] [CrossRef]
  38. Lv, Y.; Yuan, J.; Li, Q.; Zhang, Y.; Lv, X.; Su, C. Impacts of land-based human activities on coastal and offshore marine ecosystems. Acta Ecol. Sin. 2016, 36, 1183–1191. [Google Scholar]
  39. Liu, C.; Wang, S.; Dong, X.; Zhao, J.; Ye, X.; Gong, R.; Ren, Z. Exploring the genomic resources and analysing the genetic diversity and population structure of chinese indigenous rabbit breeds by RAD-seq. BMC Genom. 2021, 22, 573. [Google Scholar] [CrossRef] [PubMed]
  40. Lv, J.; Cai, Y.; Liu, P.; Huang, X.; Lv, Z.; Zhang, R.; Zhao, L.; Bao, L.; Wang, S.; Hu, J. Genomic differentiation and selection signatures of two elite varieties of Yesso scallop Mizuhopecten yessoensis. Aquaculture 2022, 550, 737842. [Google Scholar] [CrossRef]
  41. Roulin, A.; Burri, R.; Antoniazza, S. Owl melanin-based plumage redness is more frequent near than away from the equator: Implications on the effect of climate change on biodiversity. Biol. J. Linn. Soc. 2011, 102, 573–582. [Google Scholar] [CrossRef]
  42. Roulin, A. Melanin-based colour polymorphism responding to climate change. Glob. Change Biol. 2014, 20, 3344–3350. [Google Scholar] [CrossRef] [PubMed]
  43. Britton, S.; Davidowitz, G. The adaptive role of melanin plasticity in thermally variable environments. J. Evol. Biol. 2023, 36, 1811–1821. [Google Scholar] [CrossRef]
  44. Mermall, V.; Post, P.L.; Mooseker, M.S. Unconventional myosins in cell movement, membrane traffic, and signal transduction. Science 1998, 279, 527–533. [Google Scholar] [CrossRef]
  45. Aboul-Naga, A.M.; Alsamman, A.M.; El Allali, A.; Elshafie, M.H.; Abdelal, E.S.; Abdelkhalek, T.M.; Abdelsabour, T.H.; Mohamed, L.G.; Hamwieh, A. Genome-wide analysis identified candidate variants and genes associated with heat stress adaptation in Egyptian sheep breeds. Front. Genet. 2022, 13, 898522. [Google Scholar] [CrossRef]
  46. Ohishi, Y.; Kinoshita, R.; Marubashi, S.; Ishida, M.; Fukuda, M. The BLOC-3 subunit HPS4 is required for activation of Rab32/38 GTPases in melanogenesis, but its Rab9 activity is dispensable for melanogenesis. J. Biol. Chem. 2019, 294, 6912–6922. [Google Scholar] [CrossRef]
  47. Minchenko, O.; Opentanova, I.; Caro, J. Hypoxic regulation of the 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase gene family (PFKFB-1–4) expression in vivo. FEBS Lett. 2003, 554, 264–270. [Google Scholar] [CrossRef] [PubMed]
  48. Minchenko, O.; Opentanova, I.; Minchenko, D.; Ogura, T.; Esumi, H. Hypoxia induces transcription of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-4 gene via hypoxia-inducible factor-1α activation. FEBS Lett. 2004, 576, 14–20. [Google Scholar] [CrossRef]
  49. Strohecker, A.M.; Joshi, S.; Possemato, R.; Abraham, R.T.; Sabatini, D.M.; White, E. Identification of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase as a novel autophagy regulator by high content shRNA screening. Oncogene 2015, 34, 5662–5676. [Google Scholar] [CrossRef]
  50. Das, S.; Raj, L.; Zhao, B.; Kimura, Y.; Bernstein, A.; Aaronson, S.A.; Lee, S.W. Hzf determines cell survival upon genotoxic stress by modulating p53 transactivation. Cell 2007, 130, 624–637. [Google Scholar] [CrossRef]
  51. O’Reilly, D.; Belgrad, J.; Ferguson, C.; Summers, A.; Sapp, E.; McHugh, C.; Mathews, E.; Boudi, A.; Buchwald, J.; Ly, S.; et al. Di-valent siRNA-mediated silencing of MSH3 blocks somatic repeat expansion in mouse models of Huntington’s disease. Mol. Ther. 2023, 31, 1661–1674. [Google Scholar] [CrossRef] [PubMed]
  52. Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef]
  53. Zhu, P.; He, L.; Li, Y.; Huang, W.; Xi, F.; Lin, L.; Zhi, Q.; Zhang, W.; Tang, Y.T.; Geng, C.; et al. Correction: OTG-snpcaller: An optimized pipeline based on TMAP and GATK for SNP calling from ion torrent data. PLoS ONE 2015, 10, e0138824. [Google Scholar] [CrossRef] [PubMed]
  54. Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010, 38, e164. [Google Scholar] [CrossRef] [PubMed]
  55. Stebbins, G.L.; Wright, S. Evolution and the genetics of populations. volume 3. experimental results and evolutionary deductions. Syst. Bot. 1977, 2, 347. [Google Scholar] [CrossRef]
  56. Stamatakis, A. RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 2006, 22, 2688–2690. [Google Scholar] [CrossRef]
  57. Lin, Q.; Fan, S.H.; Zhang, Y.H.; Xu, M.; Zhang, H.X.; Yang, Y.L.; Lee, A.P.; Woltering, J.M.; Ravi, V.; Gunter, H.M.; et al. The seahorse genome and the evolution of its specialized morphology. Nature 2016, 540, 395–399. [Google Scholar] [CrossRef]
  58. Alexander, D.H.; Novembre, J.; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009, 19, 1655–1664. [Google Scholar] [CrossRef] [PubMed]
  59. Schiffels, S.; Durbin, R. Inferring human population size and separation history from multiple genome sequences. Nat. Genet. 2014, 46, 919–925. [Google Scholar] [CrossRef] [PubMed]
  60. Zhang, Y.H.; Ravi, V.; Qin, G.; Dai, H.; Zhang, H.X.; Han, F.M.; Wang, X.; Liu, Y.H.; Yin, J.P.; Huang, L.M.; et al. Comparative genomics reveal shared genomic changes in syngnathid fishes and signatures of genetic convergence with placental mammals. Natl. Sci. Rev. 2020, 7, 964–977. [Google Scholar] [CrossRef]
Ijms 26 01387 g001
Figure 1. The genetic information of Hippocampus kelloggi. The identification of high-quality SNPs and their distribution across 21 chromosomes of H. kelloggi. The gradient colors from green to red indicate an increase in SNP density within 0.1 Mb interval.
Figure 1. The genetic information of Hippocampus kelloggi. The identification of high-quality SNPs and their distribution across 21 chromosomes of H. kelloggi. The gradient colors from green to red indicate an increase in SNP density within 0.1 Mb interval.
Ijms 26 01387 g001
Ijms 26 01387 g002
Figure 2. Population structure analyses of H. kelloggi. (A) A phylogenetic tree of the three analyzed populations based on genotype data. The blue, red and yellow backgrounds represent individuals in the SCS, AS, RS population, respectively. (B) A population structure map for K = 2~5. (C) Population structure revealed by PCA. The blue, red and yellow dots represent individuals in the SCS, AS and RS populations, respectively. (D) The decay of linkage disequilibrium in the three experimental populations. The X-axis represents physical location. The Y-axis represents the LD value (r2). The blue, red and yellow line represent the SCS, AS and RS populations, respectively. (E) The error rate of the cross validation (CV) for K = 1~10 (K value represents the number of subgroups of the population).
Figure 2. Population structure analyses of H. kelloggi. (A) A phylogenetic tree of the three analyzed populations based on genotype data. The blue, red and yellow backgrounds represent individuals in the SCS, AS, RS population, respectively. (B) A population structure map for K = 2~5. (C) Population structure revealed by PCA. The blue, red and yellow dots represent individuals in the SCS, AS and RS populations, respectively. (D) The decay of linkage disequilibrium in the three experimental populations. The X-axis represents physical location. The Y-axis represents the LD value (r2). The blue, red and yellow line represent the SCS, AS and RS populations, respectively. (E) The error rate of the cross validation (CV) for K = 1~10 (K value represents the number of subgroups of the population).
Ijms 26 01387 g002
Ijms 26 01387 g003
Figure 3. Demographic history of the three H. kelloggi populations in this study. Pink, green and blue dots represent individuals in the SCS, AS, RS populationd, respectively. The orange background represents the period of the Last Glacial Maximum (LGM).
Figure 3. Demographic history of the three H. kelloggi populations in this study. Pink, green and blue dots represent individuals in the SCS, AS, RS populationd, respectively. The orange background represents the period of the Last Glacial Maximum (LGM).
Ijms 26 01387 g003
Ijms 26 01387 g004
Figure 4. Candidate gene exploration and enrichment analysis of H. kelloggi from the SCS population (SCS vs. RS). (A) A plot of the moving average FST (SCS vs. RS) values of the SNPs per chromosome. The blue line indicates the significant threshold for identifying putative selection regions (top 5 FST = 0.479, p-value < 0.05). (B) Distribution of the π-ratio (SCS/RS) on 21 chromosomes. The blue line indicates the significant threshold for identifying putative selection regions (top 5 log2(π-ratio SCS/RS) = 2.265, p-value < 0.05). (C) The distribution of the log2 (π-ratio) and FST. The RS population is the control group and the SCS population is the selection group. (D,E) Results of the GO and KEGG enrichment analysis of selected genes in the SCS population.
Figure 4. Candidate gene exploration and enrichment analysis of H. kelloggi from the SCS population (SCS vs. RS). (A) A plot of the moving average FST (SCS vs. RS) values of the SNPs per chromosome. The blue line indicates the significant threshold for identifying putative selection regions (top 5 FST = 0.479, p-value < 0.05). (B) Distribution of the π-ratio (SCS/RS) on 21 chromosomes. The blue line indicates the significant threshold for identifying putative selection regions (top 5 log2(π-ratio SCS/RS) = 2.265, p-value < 0.05). (C) The distribution of the log2 (π-ratio) and FST. The RS population is the control group and the SCS population is the selection group. (D,E) Results of the GO and KEGG enrichment analysis of selected genes in the SCS population.
Ijms 26 01387 g004
Ijms 26 01387 g005
Figure 5. Candidate gene exploration and enrichment analysis of H. kelloggi from the SCS population (SCS vs. AS). (A) A plot of the moving average FST (SCS vs. AS) values of SNPs per chromosome. The blue line indicates the significant threshold for identifying putative selection regions (top 5 FST = 0.525, p-value < 0.05). (B) Distribution of the π-ratio (SCS/AS) on 21 chromosomes. The blue line indicates the significant threshold for identifying putative selection regions (top 5 log2(π-ratio SCS/AS) = 1.937, p-value < 0.05). (C) Distribution of the log2 (π-ratio) and FST. The AS population is the control group and the SCS population is the selection group. (D,E) Results of the GO and KEGG enrichment analysis of selected genes in the SCS population.
Figure 5. Candidate gene exploration and enrichment analysis of H. kelloggi from the SCS population (SCS vs. AS). (A) A plot of the moving average FST (SCS vs. AS) values of SNPs per chromosome. The blue line indicates the significant threshold for identifying putative selection regions (top 5 FST = 0.525, p-value < 0.05). (B) Distribution of the π-ratio (SCS/AS) on 21 chromosomes. The blue line indicates the significant threshold for identifying putative selection regions (top 5 log2(π-ratio SCS/AS) = 1.937, p-value < 0.05). (C) Distribution of the log2 (π-ratio) and FST. The AS population is the control group and the SCS population is the selection group. (D,E) Results of the GO and KEGG enrichment analysis of selected genes in the SCS population.
Ijms 26 01387 g005
Ijms 26 01387 g006
Figure 6. Information on the natural distribution of H. kelloggi and sampling sites. The H. kelloggi pattern map indicates their natural distribution density, and the blue, red, and yellow triangles represent the population sampling points for the SCS, AS, RS.
Figure 6. Information on the natural distribution of H. kelloggi and sampling sites. The H. kelloggi pattern map indicates their natural distribution density, and the blue, red, and yellow triangles represent the population sampling points for the SCS, AS, RS.
Ijms 26 01387 g006
Table 1. SNP annotation by genomic region and function class.
Table 1. SNP annotation by genomic region and function class.
CategoryType (Alphabetical Order)CountPercentage (%)
Regionupstream (1 kb)2,293,2683.514
exonic2,608,7873.918
intronic31,282,95046.987
intergenic26,657,61140.04
5′UTR328,6900.494
3′UTR1,209,4131.817
downstream (1 kb)2,190,3173.290
upstream; downstream357,4040.527
splicing11,2680.017
Function classsynonymous SNV1,433,5922.153
nonsynonymous SNV1,155,3401.735
stopgain10,6580.016
stoploss22930.003
Table 2. Summary of genetic diversity compared between the Red Sea (RS), the Andaman Sea (AS), and the South China Sea (SCS) populations.
Table 2. Summary of genetic diversity compared between the Red Sea (RS), the Andaman Sea (AS), and the South China Sea (SCS) populations.
PopHoHePicFisAoAeπ-Ratio
RS0.20310.19870.1673−0.00821.61781.36940.3269
AS0.19140.18220.1534−0.02511.56461.33580.3353
SCS0.20830.20010.1654−0.01841.56601.38240.3677
Ho, average observed heterozygosity; He, average expected heterozygosity; Pic, polymorphism information content; Fis, inbreeding coefficient; Ao, observed number of alleles; Ae, effective number of alleles; π-ratio, nucleotide diversity ratios.
Table 3. Genetic differentiation analysis among three H. kelloggi populations.
Table 3. Genetic differentiation analysis among three H. kelloggi populations.
GroupRSASSCS
RS00.23710.3222
AS0.804500.3183
SCS0.52580.53550
Pairwise fixation index (Fst) value (top right) and the number of effective migrants (Nm) value (bottom left).
Table 4. Candidate genes related to regional environmental adaptation in the SCS population.
Table 4. Candidate genes related to regional environmental adaptation in the SCS population.
LGStart (bp)End (bp)Gene IDGene SymbolFst (CHN and THA)Fst (CHN and EGY)π-Ratio (THA/CHN)π-Ratio (EGY/CHN)
451050015110000Hke018906 0.56620.49003.8545.265
453250015330000Hke018922uckl10.70350.54254.88520.383
649150014920000Hke019774cyc10.61070.49287.7829.077
101587500115880000Hke014228prkd30.63710.640815.63212.435
121063000110635000Hke006341adam220.58890.495310.24312.111
15740001745000Hke004708ythdc20.70450.63424.8004.958
151334500113350000Hke005245msh30.55990.678410.2629.447
151933500119340000Hke005537hps40.56760.51207.62910.824
1610550011060000Hke006809pfkfb40.62990.53184.4076.020
161609000116095000Hke007537 0.63630.559127.98234.741
161665500116660000Hke007578rbm120.70050.567826.76024.788
171187000111875000Hke004222myo5a0.58410.50806.8955.935
172266000122665000Hke004577hydin0.56440.690210.7426.254
182436500124370000Hke010048uncharacterized gene0.58930.588015.67513.745
2437000124375000 0.58290.603822.15015.978
2438500124390000 0.57450.57448.1275.582
2440000124405000 0.58010.59019.3798.319
2440500124410000 0.66030.568816.98713.649
182440500124410000Hke010049uncharacterized gene0.66030.568816.98713.649
182454000124545000Hke010050uncharacterized gene0.59940.49706.5007.031
182461000124615000Hke010051mhc0.68030.616423.84225.619
2461500124620000 0.60860.515116.22815.089
1929300012935000Hke012808actr30.57330.527911.11711.360
2096250019630000Hke001750efnb30.53540.500212.89712.440
211956000119565000Hke000631szrd10.64420.50603.9695.247
213368500133690000Hke001234znf385a0.56290.59285.2645.851
LG, linkage group; Start, start physical location of genes on the reference genome; End, end physical location of genes on the reference genome; Fst, pairwise fixation index; π-ratio, nucleotide polymorphisms.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hao, W.-X.; Zhang, Y.-Y.; Wang, X.; Qu, M.; Wan, S.-M.; Lin, Q. Genetic Diversity and Environmental Adaptation Signatures of the Great Seahorse (Hippocampus kelloggi) in the Coastal Regions of the Indo-Pacific as Revealed by Whole-Genome Re-Sequencing. Int. J. Mol. Sci. 2025, 26, 1387. https://doi.org/10.3390/ijms26031387

AMA Style

Hao W-X, Zhang Y-Y, Wang X, Qu M, Wan S-M, Lin Q. Genetic Diversity and Environmental Adaptation Signatures of the Great Seahorse (Hippocampus kelloggi) in the Coastal Regions of the Indo-Pacific as Revealed by Whole-Genome Re-Sequencing. International Journal of Molecular Sciences. 2025; 26(3):1387. https://doi.org/10.3390/ijms26031387

Chicago/Turabian Style

Hao, Wen-Xin, Ying-Yi Zhang, Xin Wang, Meng Qu, Shi-Ming Wan, and Qiang Lin. 2025. "Genetic Diversity and Environmental Adaptation Signatures of the Great Seahorse (Hippocampus kelloggi) in the Coastal Regions of the Indo-Pacific as Revealed by Whole-Genome Re-Sequencing" International Journal of Molecular Sciences 26, no. 3: 1387. https://doi.org/10.3390/ijms26031387

APA Style

Hao, W.-X., Zhang, Y.-Y., Wang, X., Qu, M., Wan, S.-M., & Lin, Q. (2025). Genetic Diversity and Environmental Adaptation Signatures of the Great Seahorse (Hippocampus kelloggi) in the Coastal Regions of the Indo-Pacific as Revealed by Whole-Genome Re-Sequencing. International Journal of Molecular Sciences, 26(3), 1387. https://doi.org/10.3390/ijms26031387

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop