Next Article in Journal
Substituting Fish Meal with a Bacteria Protein (Clostridium autoethanogenum Protein) Derived from Industrial-Scale Gas Fermentation: Effects on Growth and Gut Health of Juvenile Large Yellow Croakers (Larimichthys crocea)
Previous Article in Journal
Effects of Macleaya cordata Extract on Growth Performance, Serum Biochemical Parameters, and Intestinal Health of Juvenile American Eel (Anguilla rostrata)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Fishes
Volume 7
Issue 5
10.3390/fishes7050230
fishes-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 thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Novel SNPs in the 3′UTR Region of GHRb Gene Associated with Growth Traits in Striped Catfish (Pangasianodon hypophthalmus), a Valuable Aquaculture Species

by
Liang-Sen Jiang
1,
Zhuo-Hao Ruan
1,2,3,
Zhi-Qiang Lu
1,
Yi-Fu Li
1,
Yuan-Yuan Luo
1,
Xi-Quan Zhang
4 and
Wen-Sheng Liu
1,3,5,*
1
College of Marine Sciences, South China Agricultural University, Guangzhou 510642, China
2
Laboratory of Aquatic Sciences, Key Laboratory of Animal Nutrition and Feed Science in South China of Ministry of Agriculture and Rural Affairs, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
3
Guangdong Province Engineering Research Centre of Aquatic Immunization and Aquaculture Health Techniques, South China Agricultural University, Guangzhou 510642, China
4
Guangdong Provincial Key Laboratory of Agro-animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
5
Hong Kong and Macao Region on Marine Bioresource Conservation and Exploitation, University Joint Laboratory of Guangdong Province, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Fishes 2022, 7(5), 230; https://doi.org/10.3390/fishes7050230
Submission received: 8 August 2022 / Revised: 25 August 2022 / Accepted: 25 August 2022 / Published: 1 September 2022
(This article belongs to the Section Genetics and Biotechnology)
Browse Figures
Versions Notes

Abstract

:
The striped catfish, Pangasianodon hypophthalmus is an important freshwater aquaculture species in Southeast Asian countries due to its rapid growth. The growth hormone receptor (GHR) is a significant regulatory factor for the growth axis and has great potential applications in animal genetic breeding. This study aims to characterize the GHRb cDNA of the striped catfish and analyze the distribution of its mRNA. Screening of single nucleotide polymorphisms’ (SNPs) loci and diplotypes was performed to provide basic information for the assisted selection of molecular markers in genetic breeding. The results showed that the GHRb cDNA of striped catfish had 2791 bp, which encoded for 569 amino acids. In a phylogenyic study, the ghrb of the striped catfish was clustered with those of other catfish and they were highly homologous. Quantitative real-time PCR (qRT-PCR) experiments showed that GHRb mRNA was expressed in ten different organs of the striped catfish, with the highest expression level in the liver. Five SNP and a haplotype block were identified in the 3′UTR of the GHRb gene using the direct sequencing of 307 individuals. Three haplotypes were found and four diplotypes were constructed. The association analysis revealed that these polymorphisms were significantly associated with growth traits in the striped catfish (p < 0.05). These polymorphisms will provide a valuable reference for future molecular genetic marker-assisted breeding of striped catfish.

1. Introduction

The striped catfish, Pangasianodon hypophthalmus, is an important freshwater aquaculture species in Southeast Asian countries due to its rapid growth [1]. In 2020, the global aquaculture production quantity and economic value of striped catfish were around 2.52 million ton and USD 3 billion, respectively [2]. The body weight is the main growth trait of striped catfish, which is popular for its tender meat and delicious fillets. The striped catfish is highly adaptable to a hypoxic aquatic environment due to its ability to tolerate hypoxia [3]. The production of striped catfish has increased substantially by 200% in the past decade and the striped catfish has become the fourth largest commercially farmed inland fish in the world [4,5]. It is currently an increasingly emerging aquaculture species in the aquatic product market.
Growth is a crucial economic trait for fish that is influenced by multiple genes. The growth axis is composed of growth hormone, growth hormone receptor, insulin-like growth factor, and its binding protein [6]. Like other vertebrates, the growth hormone receptor (GHR) plays a pivotal role in enhancing the growth rate of fish. GHR, a member of the cytokine receptor superfamily, regulates the expression of related genes in the growth and development signal network. Two isoforms of the GHR gene have been found in teleosts in contrast to mammals, of which the GHRb gene has been identified in striped catfish [7]. The expression of the GHR gene in fish is influenced by age, nutrient condition, growth environment, and hormone level. It has been found that GHR gene expression is widely distributed in various organs of individual fish and is expressed primarily in the liver [8]. The genetic polymorphism of GHR can influence the normal function of GH, thereby affecting growth traits such as body weight and body length [9]. Therefore, a base mutation in the GHR gene can impact the GH gene in terms of its expression level and connectivity. Additionally, this causes some changes in the growth characteristics of animals.
Previous studies have shown that the genomes of many aquaculture animals have been sequenced and that SNP is the most common genetic marker used for assisted breeding [10]. SNPs are strongly linked to biological heredity with the advantages of high density and genetic diversity [11]. Currently, an association between polymorphisms in the GHR gene and growth traits has been discovered in Cyprinus carpio [12], genetically improved strain tilapia [13], and Cynoglossus semilaevis [14], which has a direct effect on fish breeding. Therefore, it would be pragmatic to use GHRb as a gene to find SNPs and their association with the growth traits of striped catfish to be able to implement the results to improve the aquaculture performance of this species.
The purposes of the present study are to (1) clone the GHRb cDNA of striped catfish and conduct an organ distribution analysis; (2) screen SNP loci in the 3′UTR region by direct sequencing and perform linkage disequilibrium analysis; (3) analyze the association between polymorphisms in the GHRb gene and growth traits. The results of this study can provide a theoretical basis for the development of the molecular marker to assist with the breeding of striped catfish.

2. Materials and Methods

2.1. Experimental Animals

In this study, striped catfish (average body weight: 96.06 ± 12.72 g) for screening SNP loci and analyzing the associations between growth traits were taken from a commercial farm (Foshan, Guangdong, China). A total of 307 individuals were randomly selected from the same population of striped catfish. Experimental fish were kept in a circulating water system at 28–30 °C for two weeks to adapt to the experimental environment in accordance with fishery management regulations and were fed twice a day [4]. All experiments were performed according to the Experimental Animal Management Law of China with approval from the Animal Care Committee of South China Agriculture University (Guangzhou, China) (approval ID: 2021-C021).
After the fish were anaesthetized with 3-aminobenzoic acid ethyl ester methanesulfonate (MS222, Keyida Biotechnology Co. Ltd., Guangzhou, China), the body weight (BW), body height (BH), total length (TL), body length (BL), body thickness (BT), head length (HL), caudal peduncle height (CPH), and caudal peduncle length (CPL) of the striped catfish were measured. Their caudal fins were sampled and stored at −20 °C in anhydrous ethanol for genomic DNA extraction. Three individuals were randomly selected for dissection, and ten organs were collected, including heart, muscle, head-kidney, stomach, kidney, intestine, brain, spleen, liver, and gill. Then, they were immediately frozen in liquid nitrogen and stored at −80 °C for RNA extraction.

2.2. Isolation of Total RNA and cDNA Cloning

Total RNA was isolated from the organs of striped catfish using the TRIzol reagent (Invitrogen, Waltham, MA, USA) following the manufacturer’s recommendations. Their concentration and quality were measured using a NanoDrop spectrophotometer (Thermo Scientific, Waltham, MA, USA) and 1% agarose gel electrophoresis, respectively. Then, an Evo M-MLV RT Kit with gDNA Clean was used for qPCR (Accurate Biotechnology Co., Ltd., Hunan, China) with a reaction volume of 20 μL for reverse transcription.
Three pairs of primers for polymerase chain reaction (PCR) amplification of partial GHRb (XM_026941231.2) cDNA fragment in striped catfish (Table 1) were designed using Primer Premier 5.0 software [15]. The reaction volume of 30 μL for PCR containing 0.5 μL reverse transcription product, 15 μL of GoTaq® Green Master Mix (Promega, Madison, WI, USA), 1 μL for each forward and reverse primers (10μM), and 12.5 μL of ddH2O. The cycle conditions were as follows: initial denaturation at 94 °C for 3 min, followed by 35 cycles of 94 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min; and then final extension at 72 °C for 5 min. Amplified products were detected by 1% agarose gel electrophoresis and purified by a gel extraction kit (OMEGA, USA). Then, they were cloned into the pMDTM19-T vector (TaKaRa, Dalian, China) and transformed into Escherichia coli DH5α competent cells. Randomly selected positive clones were sent to Tianyihuiyuan (Guangzhou, China) for sequencing.

2.3. Sequence Analysis

The sequencing results were assembled using DNAStar software (vision 5.0, Wisconsin, USA), and the open reading frame (ORF) of the GHRb gene was predicted using the NCBI ORFfinder program (https://www.ncbi.nlm.nih.gov/orffinder/) (accessed on 26 July 2021). Alignments of homologous sequences were performed using BLAST online tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi) (accessed on 27 July 2021). The molecular weight and isoelectric point of GHRb protein were analyzed using ExPASy (https://web.expasy.org/protparam/) (accessed on 27 July 2021).

2.4. Phylogenetic Analysis

The homology analysis of the amino acid sequence was carried out by DNAMAN 9.0 software. Multiple amino acid sequence alignment was obtained using Clustal W [16]. Additionally, the molecular phylogenetic tree was constructed by MEGA-X software according to the neighbor-joining (N-J) method with 1000 bootstrap replications [17,18].

2.5. Organ Distribution

Quantitative real-time PCR (qRT-PCR) was conducted to quantitatively study the GHRb mRNA expression in striped catfish using primers GHRb-F and GHRb-R (Table 1). The qRT-PCR reaction was performed on a CFX ConnectTM Real-Time System (BIO-RAD, Hercules, CA, USA) using SYBR qRT-PCR Kit (Cowin Biotechnology Co., Ltd., Jiangsu, China), where 18S rRNA was employed as the internal control. The reaction volume of qRT-PCR was 10 μL containing 5 μL of SYBR Mixture, 0.2 μL of each primer (10 μM), 1 μL template cDNA, and 3.6 μL of ddH2O. Two-step PCR was used for amplification: initial denaturation at 95 °C for 10 min; followed by 40 cycles of 95 °C for 15 s and 60 °C for 35 s. After cycles, melting curve analysis was added to the reaction. Three replicates were set for each sample and the expression level of the GHRb gene was estimated with the 2−ΔΔCt method. The data were analyzed in terms of GHRb mRNA expression levels normalized to 18S rRNA gene expression levels [19,20].

2.6. Genomic DNA Extraction

According to the manufacturer’s instructions, the Genomic DNA Extraction Kit (Tsingke Biotechnology Co., Ltd., Beijing, China) was used to extract genomic DNA from the caudal fins of the striped catfish. Additionally, the concentration and quality of genomic DNA was measured using a NanoDrop spectrophotometer and 1% agarose gel electrophoresis. Finally, DNA was diluted to 100 ng/μL for preservation.

2.7. Screening and Genotyping of SNPs

Using extracted genomic DNA as the template, a pair of primers (Table 1) was designed to amplify the 3′ untranslated region (Geno_PHYP_1.0) of GHRb in striped catfish. The reaction volume of PCR was performed as follows: 15 μL of GoTaq® Green Master Mix (Promega, USA), 1 μL of each primer (10 μM), 0.5 μL of genomic DNA template (100 ng/μL), and ddH2O up to 30 μL. The cycling protocol was performed with an initial denaturation at 94 °C for 3 min; followed by 35 cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 1 min; and a final extension at 72 °C for 5 min. PCR products were detected by 1% agarose gel electrophoresis. The purified PCR products were sent for sequencing. The sequencing results were identified by BLAST online tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi) (accessed on 16 August 2021).
Direct sequencing of PCR products was employed using Sanger sequencing technology and ABI 3730 (SynBio Technologies, Suzhou, China) to screen the SNP loci of 307 individuals in this study. Multiple sequence alignment was performed using DNAStar software to obtain potential preliminary mutations. Then, HaploView 4.2 software was used to calculate the minor allele frequency (MAF) for each SNP, and the value of MAF was determined to filter SNP (less than 0.05 will be ignored) [21]. The bases of SNP loci in each individual were counted, and SNP loci were genotyped using Sequencher software (vision 5.4.5, Beijing, China) [22].

2.8. Data Analysis

According to genotyping results, Popgene32 Version 1.32 software was used to conduct the Hardy–Weinberg equilibrium (HWE) test and calculate the genotype frequency, allele frequency, heterozygosity (He), and effective number of alleles (Ne) for each SNP loci [23]. Additionally, the polymorphic information content (PIC) was computed using PIC-CALC software [24].
The construction of haplotype blocks and linkage disequilibrium (LD) analysis of SNP loci were completed using HaploView 4.2 software [25]. Linkage relationships between alleles based on each SNP locus were analyzed and the frequency of haplotypes was estimated (those less than 0.05 were ignored). In general, D’ and r2 were used to indicate the degree of LD among SNP loci. The larger the value of r2, the higher the degree of LD between SNP loci [26]. When values of both D’ and r2 = 1, the two SNP loci are in complete LD. The diplotype was formed by the combination of haplotypes at each SNP locus within the gene [27].
Association analysis between polymorphisms in the GHRb gene with growth traits in striped catfish was executed by the general linear model (GLM) program of SPSS 25.0 software. Then, all the data were analyzed using one-way analysis of variance (p < 0.05 means the difference was statistically significant, p < 0.01 means the difference was extremely significant) followed by Duncan’s multiple comparisons test [28]. The data of the association analysis are presented as mean ± SD.

3. Results

3.1. Sequence Analysis

The cDNA of GHRb (2791bp) in the present study was deposited in GenBank (Accession: OK093414), which contains an ORF of 1710 bp (369–2078bp). The ORF was predicted to encode a protein of 569 amino acids with an estimated molecular mass of 63.42 kDa and a theoretical isoelectric point (PI) of 4.69.
The amino acid protein encoded by striped catfish GHRb cDNA shared 84.71%, 82.95%, 82.78%, and 65.94% amino acid identity with Ictalurus punctatus, Tachysurus vachellii, Silurus meridionalis, and Colossoma macropomum, respectively (Figure 1). As is shown in Figure 2, the molecular phylogenetic analysis classified the GHR into four clusters: fish, amphibians, birds, and mammals. Furthermore, our results showed that the fish cluster was divided into GHRa and GHRb. The results illustrate that the striped catfish GHRb protein shares the highest homology with other catfish such as Ictalurus punctatus and Tachysurus vachellii when compared with other species.

3.2. Organ Distribution

Although GHRb mRNA was widely expressed in all organs examined, the expression level of GHRb varied between different organs. Our results reveal that GHRb mRNA was detected in the heart, muscle, head-kidney, stomach, kidney, liver, gill, spleen, intestine, and brain (Figure 3). The level of GHRb mRNA was predominantly expressed in the liver followed by the heart, spleen, intestine, and muscle, and the lowest level was observed in the brain.

3.3. Filtered SNPs and Diplotypes

In total, 307 individuals striped catfish were screened for SNPs in this study. Five SNP loci were detected in the 3′UTR region of the GHRb gene, which were named SNP 1 A>G, SNP 2 T>G, SNP 3 G>C, SNP 4 A>G, and SNP 5 G>C (Table 2).
The results of the LD analysis are shown in Figure 4 using the Haploview software. According to the condition of the linkage disequilibrium analysis (D’ > 0.75, r2 > 0.33), a high degree of linkage was observed between SNP 1 A>G and SNP 2 T>G (r2 > 0.9). Furthermore, a haplotype block (Block 1) was identified in the 3′UTR, which consisted of SNP 1 A>G, SNP 2 T>G, and SNP 5 G>C. Haplotypes and diplotypes (the combination of haplotypes) with their frequencies are shown in Figure 5. Three haplotypes were found in the striped catfish population. H_1 was the most common haplotype with an estimated frequency of 65%. Excluding the individuals whose diplotype frequency was less than 3% and individuals whose diplotype is not present in this striped catfish population, a total of four diplotypes were observed and named D_1, D_2, D_3, and D_4 [27].

3.4. Genetic Diversity of GHRb Gene

The genotype frequency and allele frequency for five SNPloci of the GHRb gene in the striped catfish population are shown in Figure 6. Among the five SNP loci, AG (43.97%), TG (44.30%), GG (72.96%), AA (68.73%), and GG (63.52%) had the highest genotype frequency, respectively. The highest allele frequency in the five SNP loci were A (64.98%), T (65.15%), G (86.48%), A (83.39%), and G (81.76%), respectively. The analysis data of the HWE and the genetic parameters (He, Ne, PIC) of the SNP loci are also shown in Table 2. The results indicate that the heterozygosity (He) in 3′UTR ranged from 0.2338 to 0.4551. SNP 3 G>C and SNP 4 A>G exhibited low polymorphisms (PIC < 0.25), and the other loci showed moderate genetic diversity (0.25 < PIC < 0.5). Three SNP loci were in Hardy–Weinberg equilibrium (p > 0.05) except SNP 3 G>C and SNP 5 G>C.

3.5. Association Analysis of Polymorphisms in the GHRb Gene with Growth Traits

The association between five SNP loci and eight growth traits of striped catfish was investigated by SPSS software and the results are shown in Table 3. For the SNP 1 A>G, the GG genotype had a significant effect on body length (p < 0.05) than the AA genotype, and the GG genotype significantly affected body height (p < 0.05) and caudal peduncle height (p < 0.01) than genotypes AA and AG. Notably, the GG genotype was superior to the TT genotype at the SNP 2 T>G, which contained body length and body height (p < 0.05). Additionally, the caudal peduncle height of the GG genotype was significantly higher than that of genotypes TT and TG (p < 0.05). It is interesting to note that the total length of the GC genotype at SNP 3 G>C was significantly lower than that of GG (p < 0.05). Therefore, the GC genotype may belong to an inferior genotype in the third SNP loci. Likewise, it was observed that the GG genotype at SNP 4 A>G had significantly greater superiority for body height than the AA genotype (p < 0.05). Finally, the caudal peduncle length of the CG genotype at SNP 5 G>C was significantly longer than that of the GG genotype (p < 0.05).
To further verify the reliability of the association between genetic variation in GHRb and the growth traits of striped catfish, an association analysis between the diplotypes and growth traits of striped catfish was carried out. The results of the diplotype association analysis are shown in Table 4. There were no significant differences in body weight between the different diplotypes. We found an extremely significant effect of D_4 on caudal peduncle height (p < 0.01) compared to D_2.

4. Discussion

The growth rate is a significant commercial characteristic of aquaculture animals that genotypes and environmental factors can affect [29]. GHR is one of the vital factors for the growth axis of GH/IGF and plays a prominent role in the growth of animals. In the current study, many species of GHR cDNA were amplified, which encode amino acid sequences and reveal the conservation of GHR in the evolutionary process. The homology between species reflects the genetic relationship; that is, the closer the relationship between species, the higher the homology [30]. In this study, GHRb in striped catfish was clustered with other catfish, including Ictalurus punctatus, Pelteobagrus vachellii, and Silurus meridionalis. The phylogenetic analysis of the GHRb gene indicates that their genetic relationship is very close.
GHR was expressed in many organs of fish and mediates the function of the growth hormone [31]. The results demonstrated that GHRb was expressed in ten organs of striped catfish in this study. Notably, it was observed that the highest expression level was found in the liver and high mRNA levels were also detected in the heart, spleen, intestine, and muscle, but the level of GHRb mRNA was rarely expressed in the brain. This is consistent with the expression profile of GHRb in Ictalurus punctatus [32], Cynoglossus semilaevis [14], and Squaliobarbus curriculus [33], which were primarily expressed in the liver.
To promote the growth traits of fish, selective breeding may be an effective method under certain conditions [34]. Molecular markers were supposed to promote the accuracy of selection and increase the genetic gain of significant traits [35]. SNP, the third-generation molecular marker, has been widely reported in the genetic breeding of aquaculture animals in recent years [10]. Studies on the association between GHR gene polymorphisms and the growth traits of bony fish have been carried out recently [36]. SNP loci screened in the GHR gene were significantly associated with weight gain in Oreochromis niloticus (p < 0.05) [13]. Two SNP loci were identified in the GHR1 of Cynoglossus semilaevis. The difference in body weight between the three genotypes of SNP1 was significant (p < 0.01), indicating that SNP1 could be used as a potential genetic marker [14]. Furthermore, genotypes of Cyprinus carpio var. Jian at five SNP loci in the GHR gene were detected. The analysis indicates that all SNPs were extremely significantly associated with body weight (p < 0.01), which reflects a considerable effect of GHR on growth and development [12]. These play a guiding role in aquaculture animal breeding and lay the foundation for molecular-assisted breeding.
We screened five SNP loci in this study in the 3′UTR region of striped catfish. The 3′UTR region plays a dominant role in the regulation of gene expression [37]. It has significant regulatory functions in the post-transcriptional modification of mRNA, intracellular localization and transportation, maintaining mRNA stability, and ensuring the efficiency of translation [38]. The 3′UTR region contains miRNA binding sites, and previous studies have shown that variants of 3′UTR can affect the binding of target genes to miRNA and thus affect individual phenotypes [39]. Szewczuk found that one SNP in the 3’UTR region of cattle IGF1R directly affects the stability of mRNA and may inhibit the expression of IGF1R [40]. One novel SNP was observed in the 3’UTR region of the IGF1 gene in goat, which may be located in or near the miRNA binding site to interfere with the function of miRNA, resulting in differential gene expression. Moreover, this SNP was associated with meat quality traits, which provides a theoretical basis for further research on the function of this SNP [41]. One SNP in the 3’UTR region of the IGFBP2 gene in chicken was significantly associated with abdominal fat weight and abdominal fat percentage in chickens, suggesting that the IGFBP2 gene plays a role in gene function and the regulation of fat development in chicken [42]. In this study, genetic variation detection in 3’UTR region in striped catfish was conducted to provide a basis for further research on the association between GHRb variation and growth traits and the regulation of gene expression in striped catfish.
The polymorphisms in the GHRb gene were analyzed for association with growth traits. The results showed that polymorphisms in the GHRb gene could have effects on the growth traits of striped catfish. Nevertheless, the mutation of SNP loci was non-directional, so not all SNP loci were supposed to optimize growth traits of striped catfish. Interestingly, body weight and total length of the GC genotype of SNP 3 G>C were significantly lower than the GG genotype (p < 0.05), which may have a negative effect on the growth of striped catfish. Moreover, SNP 3 G>C and SNP 4 A>G had low polymorphism, indicating that the genetic variation at these loci was small. Additionally, three SNP loci had medium polymorphism, illustrating that they could provide reasonable genetic information. Additionally, two SNP loci that deviated from the Hardy–Weinberg equilibrium (p < 0.05) may be related to the artificial selection or genetic drift of genes [43]. It also indicates that these SNP loci had higher genetic variation and selection pressure in the population.
As a traditional method of LD analysis and association analysis, single SNP analysis has some problems such as ambiguous information of loci [44]. There may be interactions between different SNP loci. The haplotype block was composed of highly linked SNP loci, indicating that the gene was fixed in long-term breeding and forming a new closely linked region [45]. In this study, it was obvious that SNP 1 A>G, SNP 2 T>G, and SNP 5 G>C of striped catfish were distributed in a block region (Block 1) using HaploView software. Compared with the analysis of single SNPs, diplotypes can provide a stronger ability to detect associations and obtain more genotype frequency information [46]. The caudal peduncle height has a significant direct effect on the body weight of striped catfish (p < 0.01), which indicates that caudal peduncle height is one of the vital traits affecting the body weight [47]. In the present study, diplotypes, derived from three haplotypes, were extremely significantly associated with the caudal peduncle height of striped catfish (p < 0.01). It may also be related to the body weight of striped catfish.
Accordingly, the SNPs and diplotypes discovered in this study may provide a scientific basis for the breeding of striped catfish through molecular marker-assisted selection (MAS).

5. Conclusions

Five SNP loci and four diplotypes have been identified in the 3’UTR region of the GHRb gene in striped catfish. The results of this study increase the number of candidate molecular markers and provide a theoretical basis for the further use of molecular marker-assisted selection in the breeding of striped catfish. Furthermore, the development of molecular markers for other regions in the GHRb gene of striped catfish can be carried out in the future.

Author Contributions

L.-S.J.: conceptualization, data curation, formal analysis, methodology, and writing—original draft. Z.-H.R.: validation and visualization. Z.-Q.L.: investigation. Y.-F.L.: resources. Y.-Y.L.: software and conceptualization. X.-Q.Z.: supervision and writing—review and editing. W.-S.L.: project administration, funding acquisition, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding (grant 2020B1212060060) and the Administration of Ocean and Fisheries of Guangdong Province (grant A201501B06).

Institutional Review Board Statement

All experiments were performed according to the Experimental Animal Management Law of China with approval from the Animal Care Committee of South China Agriculture University (Guangzhou, China) (approval ID: 2021-C021).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We are grateful for all of Pinhong Li’s (South China Agricultural University, Guangzhou, China) help with collecting the samples. Our thanks also go to the team at the Guangdong Province Key Laboratory for Animal Genomics and Molecular Breeding for their recommendations regarding data analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rasal, A.; Roy, S.; Rana, R.S.; Murali, S.; Krishna, G.; Gupta, S. Molecular cloning and nutritional regulation of putative ∆6 desaturase mRNA from striped catfish (Pangasianodon hypophthalmus). Aquaculture 2016, 451, 413–420. [Google Scholar] [CrossRef]
  2. Food and Agriculture Organization of the United Nations. Available online: https://www.fao.org/fishery/statistics-query/en/aquaculture (accessed on 22 May 2022).
  3. Tartila, S.S.Q.; Jusadi, D.; Setiawati, M.; Fauzi, I.A. Evaluation of dietary supplementation with cinnamon products on growth, blood composition, liver structure, and meat quality of striped catfish (Pangasianodon hypophthalmus). Aquac. Int. 2021, 29, 2243–2257. [Google Scholar] [CrossRef]
  4. Islam, M.M.; Ferdous, Z.; Mamun, M.M.U.; Akhter, F.; Zahangir, M.M. Amelioration of growth, blood physiology and water quality by exogenous dietary supplementation of pepsin in striped catfish, Pangasianodon hypophthalmus. Aquaculture 2021, 530, 735840. [Google Scholar] [CrossRef]
  5. Mansour, A.T.; Allam, B.W.; Srour, T.M.; Omar, E.A.; Nour, A.A.M.; Khalil, H.S. The feasibility of monoculture and polyculture of striped catfish and nile tilapia in different proportions and their effects on growth performance, productivity, and financial revenue. J. Mar. Sci. Eng. 2021, 9, 586. [Google Scholar] [CrossRef]
  6. Feng, X.; Yu, X.; Tong, J. Novel single nucleotide polymorphisms of the insulin-like growth factor-I gene and their associations with growth traits in common carp (Cyprinus carpio L.). Int. J. Mol. Sci. 2014, 15, 22471–22482. [Google Scholar] [CrossRef] [PubMed]
  7. Liang, Y.; Guo, H.; Liu, B.; Zhu, K.; Jiang, S.; Zhang, D. Genomic structure and characterization of growth hormone receptors from golden pompano Trachinotus ovatus and their expression regulation by feed types. Fish Physiol. Biochem. 2019, 45, 1845–1865. [Google Scholar] [CrossRef]
  8. Akinci, A.; Rosenfeld, R.G.; Hwa, V. A novel exonic GHR splicing mutation (c.784G > C) in a patient with classical growth hormone insensitivity syndrome. Horm. Res. Paediatr. 2013, 79, 32–38. [Google Scholar] [CrossRef]
  9. Yuan, X.; Lin, Y.; Qin, J.; Zhang, Y.; Yang, G.; Cai, R.; Liao, Z.; Sun, C.; Li, W. Molecular identification, tissue distribution and in vitro functional analysis of growth hormone and its receptors in red-spotted grouper (Epinephelus akaara). Comp. Biochem. Physiology. B Biochem. Mol. Biol. 2020, 250, 110488. [Google Scholar] [CrossRef]
  10. You, X.; Shan, X.; Shi, Q. Research advances in the genomics and applications for molecular breeding of aquaculture animals. Aquaculture 2020, 526, 735357. [Google Scholar] [CrossRef]
  11. Mong, J.L.; Ng, M.C.; Guldan, G.S.; Tam, C.H.; Lee, H.M.; Ma, R.C.; So, W.Y.; Wong, G.W.K.; Kong, A.P.S.; Chan, J. Associations of the growth hormone receptor (GHR) gene polymorphisms with adiposity and IGF-I activity in adolescents. Clin. Endocrinol. 2010, 73, 313–322. [Google Scholar] [CrossRef]
  12. Tao, W.J.; Long-Jun, M.A.; Rui-Xia, R.; Ju-Hua, Y.U. SNP loci associated with weight gain on growth hormone receptor genes in cyprinus Carpio var. Jian. Acta Hydrobiol. Sin. 2011, 35, 622–629. [Google Scholar] [CrossRef]
  13. Ruan, R.X.; Yu, J.H.; Li, H.X.; Li, J.L.; Tang, Y.K.; Wei, K.P. Isolation of two growth hormone receptor genes and SNPs associated with body weight in GIFT strain Tilapia Oreochromis niloticus. Chin. J. Zool. 2011, 46, 37–46. [Google Scholar] [CrossRef]
  14. Zhao, J.L.; Si, Y.F.; He, F.; Wen, H.S.; Li, J.F.; Ren, Y.Y.; Huang, Z.J.; Chen, S.L. Polymorphisms and DNA methylation level in the CpG site of the GHR1 gene associated with mRNA expression, growth traits and hormone level of half-smooth tongue sole (Cynoglossus semilaevis). Fish Physiol. Biochem. 2015, 41, 853–865. [Google Scholar] [CrossRef]
  15. Singh, V.K.; Mangalam, A.K.; Dwivedi, S.; Naik, S. Primer premier: Program for design of degenerate primers from a protein sequence. Biotechniques 1998, 24, 318–319. [Google Scholar] [CrossRef] [PubMed]
  16. Thompson, J.; Higgins, D.G.; Gibson, I.J. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22, 1673–1680. [Google Scholar] [CrossRef] [PubMed]
  17. Kumar, S.; Nei, M.; Dudley, J.; Tamura, K. MEGA: A biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief. Bioinform. 2008, 9, 299–306. [Google Scholar] [CrossRef]
  18. Saitou, N.; Nei, M. The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 1987, 4, 406–425. [Google Scholar] [CrossRef]
  19. Lu, Z.; Tang, M.; Li, Y.; Shi, F.; Zhan, F.; Zhang, M.; Zhao, L.; Li, J.; Lin, L.; Qin, Z. Molecular cloning and characterization of FADD from the grass carp (Ctenopharyngodon idellus) in response to bacterial infection. Aquaculture 2021, 542, 736829. [Google Scholar] [CrossRef]
  20. Livak, K.J.; Schmittgen, T. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCt method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  21. Guerrero-Cózar, I.; Jimenez-Fernandez, E.; Berbel, C.; Córdoba-Caballero, J.; Claros, M.G.; Zerolo, R.; Manchado, M. Genetic parameter estimates and identification of SNPs associated with growth traits in Senegalese sole. Aquaculture 2021, 539, 736665. [Google Scholar] [CrossRef]
  22. Niu, D.H.; Wang, L.; Bai, Z.Y.; Xie, S.M.; Zhao, H.G.; Li, J.L. Identification and expression characterization of the myostatin (MSTN) gene and association analysis with growth traits in the razor clam Sinonovacula constricta. Gene 2015, 555, 297–304. [Google Scholar] [CrossRef] [PubMed]
  23. Francis, C.Y.; Rong, C.Y.; Boyle, T. POPGENE, Microsoft Window-Based Freeware for Population Genetic Analysis, Version 1.31; University of Alberta and Center for International Forestry Research: Edmonton, AB, Canada, 1999; pp. 1–31. [Google Scholar]
  24. Nagy, S.; Poczai, P.; Cernak, I.; Gorji, A.M.; Hegedus, G.; Taller, J. PICcalc: An online program to calculate polymorphic information content for molecular genetic studies. Biochem. Genet. 2012, 50, 670–672. [Google Scholar] [CrossRef]
  25. Barrett, J.C.; Fry, B.; Maller, J.; Daly, M.J. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 2005, 21, 263–265. [Google Scholar] [CrossRef] [PubMed]
  26. Sun, C.F.; Sun, H.L.; Dong, J.J.; Tian, Y.Y.; Hu, J.; Ye, X. Correlation analysis of mandarin fish (Siniperca chuatsi) growth hormone gene polymorphisms and growth traits. J. Genet. 2019, 98, 58. [Google Scholar] [CrossRef]
  27. Li, X.; Bai, J.; Hu, Y.; Ye, X.; Li, S.; Yu, L. Genotypes, haplotypes and diplotypes of IGF-II SNPs and their association with growth traits in largemouth bass (Micropterus salmoides). Mol. Biol. Rep. 2012, 39, 4359–4365. [Google Scholar] [CrossRef] [PubMed]
  28. Lyu, D.; Hu, Y.L.; Wu, H.H.; Sun, S.; Wang, W.J. Estimating breeding values for juvenile body weight using trait associated SNP assisted BLUP in turbot (Scophthalmus maximus). Aquaculture 2019, 508, 46–51. [Google Scholar] [CrossRef]
  29. Teng, T.; Zhao, X.Q.; Li, C.J.; Guo, J.Q.; Wang, Y.F.; Pan, C.L. Cloning and expression of IGF-I, IGF-II, and GHR genes and the role of their single-nucleotide polymorphisms in the growth of pikeperch (Sander lucioperca). Aquac. Int. 2020, 28, 1547–1561. [Google Scholar] [CrossRef]
  30. Zhu, W.; He, Y.; Ruan, Z.; Zhang, X.; Liao, L.; Gao, Y.; Lin, N.; Chen, X.; Liang, R.; Liu, W.-S. Identification of the cDNA encoding the growth hormone receptor (GHR) and the regulation of GHR and IGF-I Gene expression by nutritional status in Reeves’ Turtle (Chinemys reevesii). Front. Genet. 2020, 11, 587. [Google Scholar] [CrossRef]
  31. Tian, Y.G.; Yue, M.; Gu, Y.; Gu, W.W.; Wang, Y.J. Single-nucleotide polymorphism analysis of GH, GHR, and IGF-1 genes in minipigs. Braz. J. Med. Biol. Res. 2014, 47, 753–758. [Google Scholar] [CrossRef]
  32. Small, B.C.; Murdock, C.A.; Waldbieser, G.C.; Peterson, B.C. Reduction in channel catfish hepatic growth hormone receptor expression in response to food deprivation and exogenous cortisol. Domest. Anim. Endocrinol. 2006, 31, 340–356. [Google Scholar] [CrossRef]
  33. Wang, J.; Li, D.; Zhao, X.; Sun, T.; Jin, S.; Wang, H.; Xiao, T.; Li, Y. GH and GHR gene cloning, expression and their associations with growth-related traits of the barbel chub (Squaliobarbus curriculus). Comp. Biochem. Physiology B Biochem. Mol. Biol. 2020, 243–244, 110429. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, H.; Zhao, C.; Yin, S.; Li, Z.; Cao, Q.; Li, X.; Xie, W.; Zhang, J.; Zhu, W.; Wang, D. Characterization and identification of single nucleotide polymorphism within the IGF-1R gene associated with growth traits of Odontobutis potamophila. J. World Aquac. Soc. 2018, 49, 366–379. [Google Scholar] [CrossRef]
  35. Migdal, L.; Palka, S.; Kmiecik, M.; Derewicka, O. Association of polymorphisms in the GH and GHR genes with growth and carcass traits in rabbits (Oryctolagus cuniculus). Czech J. Anim. Sci. 2019, 64, 255–264. [Google Scholar] [CrossRef]
  36. Yu, Y.P.; Yang, Z.T.; Sun, F.; Wang, L.; Lee, M.; Yue, G.H. Two SNPs in SNX2 are associated with SGIV resistance in Asian seabass. Aquaculture 2021, 540, 736695. [Google Scholar] [CrossRef]
  37. Andreassen, R.; Lunner, S.; Hoyheim, B. Targeted SNP discovery in Atlantic salmon (Salmo salar) genes using a 3’UTR-primed SNP detection approach. BMC Genom. 2010, 11, 706. [Google Scholar] [CrossRef]
  38. Dairoh; Jakaria; Ulum, M.F.; Sumantri, C. A single nucleotide polymorphism of CAPN1 gene region 3’UTR in Bali cattle. IOP Conf. Ser. Earth Environ. Sci. 2021, 788, 012017. [Google Scholar] [CrossRef]
  39. Baev, V.; Daskalova, E.; Minkov, I. Computational identification of novel microRNA homologs in the chimpanzee genome. Comput. Biol. Chem. 2009, 33, 62–70. [Google Scholar] [CrossRef]
  40. Szewczuk, M.; Zych, S.; Wojcik, J.; Czerniawska-Piatkowska, E. Association of two SNPs in the coding region of the insulin-like growth factor 1 receptor (IGF1R) gene with growth-related traits in Angus cattle. J. Appl. Genet. 2013, 54, 305–308. [Google Scholar] [CrossRef]
  41. Naicy, T.; Venkatachalapathy, T.; Aravindakshan, T.; Raghavan, K.C.; Mini, M.; Shyama, K. cDNA cloning, structural analysis, SNP detection and tissue expression profile of the IGF1 gene in Malabari and Attappady Black goats of India. J. Genet. 2017, 96, 307–312. [Google Scholar] [CrossRef]
  42. Yu, Y.Y.; Qiao, S.P.; Sun, Y.N.; Song, H.; Zhang, X.F.; Yan, X.H.; Li, H. Identification and analysis of a functional SNP 1196C>A in 3’UTR of chicken IGFBP2 gene. Prog. Biochem. Biophys. 2014, 41, 1163–1172. [Google Scholar] [CrossRef]
  43. Lopez, M.E.; Benestan, L.; Moore, J.S.; Perrier, C.; Gilbey, J.; Di Genova, A.; Maass, A.; Diaz, D.; Lhorente, J.; Correa, K.; et al. Comparing genomic signatures of domestication in two Atlantic salmon (Salmo salar L.) populations with different geographical origins. Evol. Appl. 2019, 12, 137–156. [Google Scholar] [CrossRef] [PubMed]
  44. Liu, F.-p.; Bai, J.-j.; Song, H.-m.; Ye, X.; Li, S.-j.; Yu, L.-y. Correlation analysis of microsatellite DNA markers with major growth traits of tilapia (Oreochromis niloticus). J. Fish. China 2010, 34, 169–177. [Google Scholar] [CrossRef]
  45. Sharma, A.; Dutt, G.; Sivalingam, J.; Singh, M.K.; Pathodiya, O.P.; Khadda, B.S.; Dixit, S. Novel SNPs in IGF1, GHR and IGFBP-3 genes reveal significant association with growth traits in Indian goat breeds. Small Rumin. Res. 2013, 115, 7–14. [Google Scholar] [CrossRef]
  46. Liu, X.X.; Bai, J.J.; Ling-Yun, Y.U.; Sheng-Jie, L.I. SNPs screening of 3’UTR in Apoprotein A-I-1 gene and its association with growth traits in grass carp. J. Dalian Ocean. Univ. 2012, 27, 12–17. [Google Scholar] [CrossRef]
  47. Li, A.N.; Zhu, Y.A.; Pei-Sheng, F.U.; Zhang, L.G.; Xian, L.I.; Dong, X.S.; Yang, P.F.; Meng, Q.L. Mathematical analysis of effects of morphometric attributes on body weight in sutchi catfish Pangasius sutchi. Chin. J. Fish. 2013, 26, 5–9. [Google Scholar] [CrossRef]
Fishes 07 00230 g001 550
Figure 1. Multiple amino acid sequence alignment of GHR in Pangasianodon hypophthalmus with Ictalurus punctatus, Tachysurus vachellii, Silurus meridionalis, and Colossoma macropomum. The red indicates the same amino acid and the number on the right indicates the amino acid position of GHR in the different species. Different colors represent the degree of homology level. Red indicates 100% identity between species, dark blue indicates ≥75% identity, and light blue indicates ≥50% identity.
Figure 1. Multiple amino acid sequence alignment of GHR in Pangasianodon hypophthalmus with Ictalurus punctatus, Tachysurus vachellii, Silurus meridionalis, and Colossoma macropomum. The red indicates the same amino acid and the number on the right indicates the amino acid position of GHR in the different species. Different colors represent the degree of homology level. Red indicates 100% identity between species, dark blue indicates ≥75% identity, and light blue indicates ≥50% identity.
Fishes 07 00230 g001
Fishes 07 00230 g002 550
Figure 2. The phylogenetic tree of GHRb in Pangasianodon hypophthalmus with other homologous species was constructed using MEGA-X software by the N-J method.
Figure 2. The phylogenetic tree of GHRb in Pangasianodon hypophthalmus with other homologous species was constructed using MEGA-X software by the N-J method.
Fishes 07 00230 g002
Fishes 07 00230 g003 550
Figure 3. The expression of GHRb mRNA in different organs was detected by qRT-PCR. The expression levels of the ten organs relative to the head-kidney. The data are presented as mean ± SD.
Figure 3. The expression of GHRb mRNA in different organs was detected by qRT-PCR. The expression levels of the ten organs relative to the head-kidney. The data are presented as mean ± SD.
Fishes 07 00230 g003
Fishes 07 00230 g004 550
Figure 4. Different colors in the figure represent the degree of linkage between the loci. The color of the square gets darker with the degree of linkage from low to high. The number inside each square in the figure indicates the value of r2 multiplied by 100.
Figure 4. Different colors in the figure represent the degree of linkage between the loci. The color of the square gets darker with the degree of linkage from low to high. The number inside each square in the figure indicates the value of r2 multiplied by 100.
Fishes 07 00230 g004
Fishes 07 00230 g005 550
Figure 5. Frequency distribution of GHRb gene haplotypes and diplotypes in the striped catfish population. (A). The construction and frequency distribution of three haplotypes. (B). The composition and frequency distribution of four diplotypes.
Figure 5. Frequency distribution of GHRb gene haplotypes and diplotypes in the striped catfish population. (A). The construction and frequency distribution of three haplotypes. (B). The composition and frequency distribution of four diplotypes.
Fishes 07 00230 g005
Fishes 07 00230 g006 550
Figure 6. The genotype and allele frequency distribution of GHRb gene SNP loci in the striped catfish population. (A). Genotype frequency of five SNP loci, where the blank space indicates that we did not observe the third genotype at this SNP locus. (B). Allele frequency of five SNP loci.
Figure 6. The genotype and allele frequency distribution of GHRb gene SNP loci in the striped catfish population. (A). Genotype frequency of five SNP loci, where the blank space indicates that we did not observe the third genotype at this SNP locus. (B). Allele frequency of five SNP loci.
Fishes 07 00230 g006
Table
Table 1. Primers used in this study.
Table 1. Primers used in this study.
Primer NameSequence (5′–3′)Used in
GHRb-cF1CTCGGTCCTGGGTGCTCcDNA-PCR
GHRb-cR1TCTTGTTTGGCGTTGGCcDNA-PCR
GHRb-cF2AAAGCCAAACTGATAGGGGcDNA-PCR
GHRb-cR2TTTACATTGTCTGCCGCCGcDNA-PCR
GHRb-cF3ATTGTATTTCCTGACCCACCcDNA-PCR
GHRb-cR3TCCAAGCCGTCCATCATCTCcDNA-PCR
GHRb-PFCAAGCCAGGCAGAGAGTTATATCASNP detection
GHRb-PRCTCCCGACCTACTACACAGCATTASNP detection
GHRb-FGTTCCGCTGTCACTGGGGqPCR
GHRb-RCGGTGCTGTAACTTGGGCqPCR
18S rRNA -FCGTGATTGGGACTGGGGATTGqPCR
18S rRNA -RTAGTAGCGACGGGCGGTGTGTqPCR
Table
Table 2. Detection of SNPs in the 3′UTR region of GHRb gene (Chromosome 15, Genbank assembly accession: GCA_009078355.1) and calculation of population genetic parameters in striped catfish. In the table headings, “Ref” represents the base of the reference sequence. “Alt” represents the mutated base. “MAF” refers to the minor allele frequency. “He” refers to the degree of heterozygosity obtained theoretically. “Ne” refers to the reciprocal homozygosity in a population, which is one of the parameters used to measure a population’s genetic variation. “PIC” is one of the parameters used to measure the variation degree of a genetic marker. “p-value (HWE)” refers to the value of the Hardy–Weinberg equilibrium calculated by the Chi-square test.
Table 2. Detection of SNPs in the 3′UTR region of GHRb gene (Chromosome 15, Genbank assembly accession: GCA_009078355.1) and calculation of population genetic parameters in striped catfish. In the table headings, “Ref” represents the base of the reference sequence. “Alt” represents the mutated base. “MAF” refers to the minor allele frequency. “He” refers to the degree of heterozygosity obtained theoretically. “Ne” refers to the reciprocal homozygosity in a population, which is one of the parameters used to measure a population’s genetic variation. “PIC” is one of the parameters used to measure the variation degree of a genetic marker. “p-value (HWE)” refers to the value of the Hardy–Weinberg equilibrium calculated by the Chi-square test.
SNP NameLocusRefAltMAFHeNePICp-Value (HWE)
SNP 1 A>G12,573,351AG0.350.45511.83520.35150.5352
SNP 2 T>G12,573,360TG0.3490.45411.83190.35100.6471
SNP 3 G>C12,573,367GC0.1350.23381.30520.20650.0065
SNP 4 A>G12,573,504AG0.1660.27711.38320.23870.3209
SNP 5 G>C12,573,610GC0.1820.29831.42510.25380.0001
Table
Table 3. Association analysis of five SNP loci and growth traits in striped catfish. Note: The data are presented as mean ± SD. For each growth trait of the same SNP locus, different lowercase letters indicate significant differences (p < 0.05) and different capital letters indicate extremely significant differences (p < 0.01).
Table 3. Association analysis of five SNP loci and growth traits in striped catfish. Note: The data are presented as mean ± SD. For each growth trait of the same SNP locus, different lowercase letters indicate significant differences (p < 0.05) and different capital letters indicate extremely significant differences (p < 0.01).
SNP NameGenotypeBW
(g)		TL
(cm)		BL
(cm)		BH
(cm)		BT
(cm)		HL
(cm)		CPH
(cm)		CPL
(cm)
SNP 1 A>GAA (132)94.970 ± 11.85022.910 ± 0.99718.882 ± 0.867 b4.522 ± 0.307 b2.317 ± 0.3354.440 ± 0.2301.637 ± 0.154 bB2.536 ± 0.285
AG (135)96.442 ± 11.59523.133 ± 1.00919.101 ± 0.917 ab4.539 ± 0.317 b2.349 ± 0.3404.458 ± 0.2271.642 ± 0.195 bB2.621 ± 0.275
GG (40)98.388 ± 18.05823.183 ± 1.35819.208 ± 1.188 a4.658 ± 0.367 a2.373 ± 0.3844.460 ± 0.2921.720 ± 0.222 aA2.622 ± 0.314
SNP 2 T>GTT (132)94.970 ± 11.85022.910 ± 0.99718.882 ± 0.867 b4.522 ± 0.307 b2.317 ± 0.3354.440 ± 0.2301.637 ± 0.154 b2.536 ± 0.285
TG (136)96.499 ± 11.57023.132 ± 1.00519.096 ± 0.916 ab4.543 ± 0.319 ab2.350 ± 0.3404.457 ± 0.2261.643 ± 0.195 b2.621 ± 0.274
GG (39)98.241 ± 18.27023.187 ± 1.37519.231 ± 1.194 a4.646 ± 0.365 a2.367 ± 0.3884.462 ± 0.2951.718 ± 0.225 a2.623 ± 0.318
SNP 3 G>CGG (224)96.990 ± 12.862 a23.138 ± 1.049 a19.058 ± 0.9414.554 ± 0.3252.342 ± 0.3484.455 ± 0.2441.662 ± 0.1782.590 ± 0.285
GC (83)93.561 ± 12.055 b22.789 ± 1.047 b18.922 ± 0.9404.529 ± 0.3152.327 ± 0.3344.439 ± 0.2181.619 ± 0.1982.572 ± 0.294
SNP 4 A>GAA (211)95.733 ± 11.70823.009 ± 1.00718.961 ± 0.8934.530 ± 0.230 b2.324 ± 0.3304.444 ± 0.2301.632 ± 0.1692.573 ± 0.269
AG (90)96.250 ± 14.19423.083 ± 1.13019.121 ± 1.0244.573 ± 0.354 ab2.366 ± 0.3434.464 ± 0.2401.688 ± 0.2052.599 ± 0.312
GG (6)104.867 ± 21.49023.667 ± 1.63919.633 ± 1.1154.767 ± 0.501 a2.433 ± 0.7204.483 ± 0.4311.717 ± 0.2862.767 ± 0.468
SNP 5 G>CGG (195)95.516 ± 12.24922.981 ± 1.02618.970 ± 0.8964.536 ± 0.3312.337 ± 0.3584.450 ± 0.2341.649 ± 0.1732.562 ± 0.306 b
CG (112)97.015 ± 13.50823.152 ± 1.10819.112 ± 1.0124.567 ± 0.3052.340 ± 0.3184.452 ± 0.2431.652 ± 0.2032.625 ± 0.246 a
Table
Table 4. Association analysis of diplotypes and growth traits in striped catfish. Note: The data are presented as mean ± SD. For each growth trait of the same diplotype, different lowercase letters indicate significant differences (p < 0.05) and different capital letters indicate extremely significant differences (p < 0.01).
Table 4. Association analysis of diplotypes and growth traits in striped catfish. Note: The data are presented as mean ± SD. For each growth trait of the same diplotype, different lowercase letters indicate significant differences (p < 0.05) and different capital letters indicate extremely significant differences (p < 0.01).
NameBW
(g)		TL
(cm)		BL
(cm)		BH
(cm)		BT
(cm)		HL
(cm)		CPH
(cm)		CPL
(cm)
D_1 (132)94.970 ± 11.85022.910 ± 0.99718.882 ± 0.8674.522 ± 0.3072.317 ± 0.3354.440 ± 0.2301.637 ± 0.154 bAB2.536 ± 0.285
D_2 (78)96.915 ± 11.47423.176 ± 1.01519.103 ± 0.9284.536 ± 0.2832.331 ± 0.3234.450 ± 0.2321.622 ± 0.192 bB2.637 ± 0.229
D_3 (57)95.795 ± 11.82823.074 ± 1.00719.100 ± 0.9114.544 ± 0.3612.374 ± 0.3644.468 ± 0.2211.670 ± 0.198 abAB2.600 ± 0.329
D_4 (33)97.036 ± 17.73423.100 ± 1.33219.158 ± 1.2094.624 ± 0.3402.355 ± 0.3104.458 ± 0.2731.718 ± 0.217 aA2.597 ± 0.286
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Jiang, L.-S.; Ruan, Z.-H.; Lu, Z.-Q.; Li, Y.-F.; Luo, Y.-Y.; Zhang, X.-Q.; Liu, W.-S. Novel SNPs in the 3′UTR Region of GHRb Gene Associated with Growth Traits in Striped Catfish (Pangasianodon hypophthalmus), a Valuable Aquaculture Species. Fishes 2022, 7, 230. https://doi.org/10.3390/fishes7050230

AMA Style

Jiang L-S, Ruan Z-H, Lu Z-Q, Li Y-F, Luo Y-Y, Zhang X-Q, Liu W-S. Novel SNPs in the 3′UTR Region of GHRb Gene Associated with Growth Traits in Striped Catfish (Pangasianodon hypophthalmus), a Valuable Aquaculture Species. Fishes. 2022; 7(5):230. https://doi.org/10.3390/fishes7050230

Chicago/Turabian Style

Jiang, Liang-Sen, Zhuo-Hao Ruan, Zhi-Qiang Lu, Yi-Fu Li, Yuan-Yuan Luo, Xi-Quan Zhang, and Wen-Sheng Liu. 2022. "Novel SNPs in the 3′UTR Region of GHRb Gene Associated with Growth Traits in Striped Catfish (Pangasianodon hypophthalmus), a Valuable Aquaculture Species" Fishes 7, no. 5: 230. https://doi.org/10.3390/fishes7050230

Article Metrics

Back to TopTop