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Single nucleotide polymorphism SNP19140160 A > C is a potential breeding locus for fast-growth largemouth bass (Micropterus salmoides)

BMC Genomics volume 25, Article number: 64 (2024) Cite this article

Abstract

Background

Largemouth bass (Micropterus salmoides) has significant economic value as a high-yielding fish species in China’s freshwater aquaculture industry. Determining the major genes related to growth traits and identifying molecular markers associated with these traits serve as the foundation for breeding strategies involving gene pyramiding. In this study, we screened restriction-site associated DNA sequencing (RAD-seq) data to identify single nucleotide polymorphism (SNP) loci potentially associated with extreme growth differences between fast-growth and slow-growth groups in the F1 generation of a largemouth bass population.

Results

We subsequently identified associations between these loci and specific candidate genes related to four key growth traits (body weight, body length, body height, and body thickness) based on SNP genotyping. In total, 4,196,486 high-quality SNPs were distributed across 23 chromosomes. Using a population-specific genotype frequency threshold of 0.7, we identified 30 potential SNPs associated with growth traits. Among the 30 SNPs, SNP19140160, SNP9639603, SNP9639605, and SNP23355498 showed significant associations; three of them (SNP9639603, SNP9639605, and SNP23355498) were significantly associated with one trait, body length, in the F1 generation, and one (SNP19140160) was significantly linked with four traits (body weight, height, length, and thickness) in the F1 generation. The markers SNP19140160 and SNP23355498 were located near two growth candidate genes, fam174b and ppip5k1b, respectively, and these candidate genes were closely linked with growth, development, and feeding. The average body weight of the group with four dominant genotypes at these SNP loci in the F1 generation population (703.86 g) was 19.63% higher than that of the group without dominant genotypes at these loci (588.36 g).

Conclusions

Thus, these four markers could be used to construct a population with dominant genotypes at loci related to fast growth. These findings demonstrate how markers can be used to identify genes related to fast growth, and will be useful for molecular marker-assisted selection in the breeding of high-quality largemouth bass.

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Background

Largemouth bass (Micropterus salmoides) is an important freshwater aquaculture fish in China because of its high-quality meat and ease of culture [1]. However, the rapid development of the largemouth bass aquaculture industry has led to genetic degradation, which is manifested as individual miniaturization, decreased resistance, and increased disease susceptibility [2, 3]. These phenomena negatively affect the sustainable development of largemouth bass aquaculture [4]. Therefore, the selection and breeding of high-quality lines of largemouth bass is very important. With advancements in biological research and experimental methods, new technologies such as molecular marker-assisted selection (MAS) are now available to supplement traditional selection and hybrid breeding methods [5]. The application of new technologies has shortened the selection process and accelerated the breeding process. Thus, new technologies are important auxiliary measures for breeding work [6].

In the production and culture of aquatic animals, growth traits are key measures of the effectiveness of culture. The goal is to produce fish with higher quality and higher yields at the same culture cost. The growth of aquatic animals is affected by the combination of endogenous and exogenous factors and their interactions [7]. Endogenous factors affect aquatic animals mainly through the expression of growth-related genes. A number of growth-related genes have been identified in aquatic animals, such as mstn (encoding myostatin), igf (encoding insulin-like growth factor, and gh (encoding growth hormone) [8, 9]. Growth-related genes have been identified in economically important aquatic animals such as black carp (Mylopharyngodon piceus), bighead carp (Hypophthalmichthys nobilis), and Chinese mitten crab (Eriocheir sinensis), and the discovery of these genes has provided opportunities to explore the mechanisms of growth and development [10,11,12]. Exogenous factors such as temperature, dissolved oxygen levels, and light also affect the growth and development of aquatic animals [13,14,15].

Single nucleotide polymorphism (SNP) markers are the most widely used molecular markers in genetics research. Their advantages include their genome-wide distribution, high density, and stable inheritance [16]. With the rapid development of next-generation sequencing (NGS) technology, many SNP markers related to the growth of aquatic animals have been discovered. For example, Salem et al. (2012) conducted RNA-sequencing (RNA-Seq) analysis of fast- and slow-growing populations of rainbow trout (Oncorhynchus mykiss) and identified 22 SNP markers associated with growth traits [17]. Zhou et al. (2021) constructed a high-density genetic linkage map of red-tail catfish (Hemibagrus wyckioides) using 2b-restriction site-associated DNA sequencing (RAD-Seq) technology. In total, 2,369 SNP markers were assigned to 29 linkage groups and 17 growth-related quantitative trait loci (QTL) were identified [18]. Omeka et al. (2022) used NGS technology and genome-wide association study (GWAS) to screen eight SNP markers associated with the growth traits of olive flounder (Paralichthys olivaceus) [19].

The relative lag in genomic research on aquatic species, coupled with the heterogeneity and high complexity of their genomes, has limited the application of high-throughput sequencing techniques. However, recent advancements in RAD-seq have introduced novel tools for studying aquatic species [20,21,22]. This technology has gained widespread popularity in breeding programs because of its many advantages, such as consistent size of digested DNA fragments, streamlined library construction procedures, and high accuracy. In mandarin fish (Siniperca chuatsi), RAD-seq data were screened to identify growth-associated SNPs and candidate genes [23]. Ai et al. (2023) identified 11 SNPs related to fast growth in orange-spotted grouper (Epinephelus coioides) using RAD-seq data. Among them, five SNPs were associated with body weight, body length, and body height, and three were associated with body weight and body height [24]. RAD-Seq data can also be used to analyze the genetic structure and genetic diversity of different selection generations to evaluate the selection effect [25, 26].

In this study, we focused on the American northern subspecies of largemouth bass, which was introduced into China from the USA by the Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences (FFRC, Wuxi, China), in 2020. Previously, we used simple sequence repeat (SSR) markers to compare the diversity of the northern subspecies population with several major cultured populations (or strains) in China, and detected the highest level of polymorphism in the northern subspecies population [27]. In 2021 and 2022, we constructed a foundation population and an F1 population of the northern subspecies of largemouth bass using the population selection method. In the present study, we used RAD-seq technology to investigate the degree of genetic differentiation between fast- and slow-growth groups within the F1 population, and screened for SNPs and candidate genes significantly correlated with growth traits. Analyses of the effect of the presence of multiple dominant genotypes at the SNP loci related to fast growth provide a foundation for understanding the mechanism of growth trait regulation in largemouth bass. The markers and genes identified in this study will be useful for molecular marker-assisted breeding and for further research on the growth of largemouth bass.

Materials and methods

Sample collection

For the initial screening of SNP loci, we collected samples from 30 individuals representing the top 1% mean body weight (724.2 ± 119.2 g; fast-growth population, FG) and bottom 1% mean body weight (307.2 ± 107.7 g; slow-growth population, SG) in the F1 generation (3000 individuals) of northern subspecies largemouth bass reared at the experimental base of the FFRC. All experimental fish were anesthetized with MS-222 (200 mg/L) before clipping a 0.5-g sample from the tail fin. All samples were frozen in liquid nitrogen and stored at − 80 °C until use.

DNA extraction

The genomic DNA was extracted from the tail fin samples of largemouth bass using a genomic DNA extraction kit in accordance with the manufacturer’s instructions (Vazyme, Nanjing, China). The concentration and purity of the extracted DNA were assessed by 0.8% agarose gel electrophoresis and using a NanoDrop 2000 Ultra-Micro spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The genomic DNA was diluted to a final concentration of 50–70 ng/µL with sterilized water and stored at − 20 °C.

dd-RAD library construction and sequencing quality analysis

The genomic DNA was fully digested using a combination of HindIII and MseI restriction endonucleases. The recovered fragments were ligated to P1 and P2 adapters. The P1 adapter comprised the amplification primer, sample barcode, and HindIII cleavage site, whereas the P2 adapter contained amplification primers and an MseI site. Subsequently, magnetic beads were employed for PCR product purification. Library fragment selection and purification were accomplished through 2% agarose gel electrophoresis. Generally, the insertion fragments in the library ranged from 220 to 450 bp in length, so they were suitable for double-digest RAD (dd-RAD) library construction and sequencing using NGS technology. The resulting library was subjected to 2 × 150-bp paired-end (PE) sequencing using the Illumina NovaSeq sequencing platform (Personalbio, Shanghai, China). The raw data were filtered using fastp (v0.20.0) with the sliding window method to generate high-quality data for further analyses.

Whole-genome SNP screening

The bwa-mem (0.7.12-r1039) program was used to compare the high-quality data obtained after filtering with the largemouth bass reference genome (https://www.ncbi.nlm.nih.gov/genome/?term=Micropterus+salmoides). This comparison was conducted using bwa-mem with default parameters. The SNP loci were identified using GATK software [28]. To ensure the reliability of SNP loci, the obtained SNP loci were further filtered using the following criteria: Fisher test of strand bias (FS) ≤ 60; HaplotypeScore ≤ 13.0; Mapping Quality (MQ) ≥ 40; Quality Depth (QD) ≥ 2; ReadPosRankSum ≥ − 8.0; MQRankSum > − 12.5. Annotation was conducted using ANNOVAR software [29].

Analysis of population structure and kinship

Before analyzing the association between SNPs and certain growth traits, GCTA (http://www.complextraitgenomics.com/software/gcta/) software was used to analyze the population structure by performing a principal component analysis (PCA) using the SNP data and removing SNPs with minor allele frequency (MAF) less than 0.05. Admixture software (http://dalexander.github.io/admixture) and fastTree software (http://www.microbesonline.org/fasttree/) were used to analyze the genetic structure of the population and to construct a phylogenetic tree to corroborate the results of the population PCA analysis. All analyses of population structure were based on SNP information. Kinship within the population was analyzed by calculating an identity-by-state (IBS) matrix (using plink v1.9) and a genomic relationship matrix (using Gmatrix Ver2).

Population genetic diversity analysis

The genotyping results based on SNP loci were processed using the populations command in Stacks v1.46. We calculated the following indexes of population genetic diversity (i.e., the multiple genetic differences between individuals within a population of organisms that result from variation among genes or genotypes): Mean observed heterozygosity, mean observed homozygosity, mean expected heterozygosity, mean expected homozygosity, mean nucleotide diversity (pi), and population inbreeding factor (Fis).

Screening for SNPs specific to the fast-growth population

Population-specific SNP screening identifies genotypes based on their frequency of distribution in populations. In this study, population-specific screening was conducted for the fast- and slow-growth populations, using a script made by Personal Biotechnology Co., Ltd. (Shanghai, China). The individuals within the fast- and slow-growth populations were categorized into four genotypes at each SNP locus: homozygous reference genotype (0/0), heterozygous reference and alternative genotype (0/1), homozygous alternative genotype (1/1), and deletion genotype (./.). The frequency of individuals with each of these four genotypes within the fast- and slow-growth populations was counted separately. A population-specific genotype frequency threshold was set such that, for each of the above four genotypes, if the frequency of individuals with the genotype in the fast-growth population was higher than the threshold for the population-specific genotype frequency and the frequency of individuals with the genotype in the slow-growth population was lower than (1 − the threshold), then the genotype was genotype-specific for the fast-growth population. In this study, a threshold of 0.7 was set to establish the frequency of fast-growth population-specific genotypes.

Genotyping and growth trait association analysis

To verify the growth-related SNP loci identified in the preliminary screening round, we randomly collected 230 one-year-old F1 individuals of northern subspecies largemouth bass reared under identical breeding conditions from the same lineage. Four growth-related traits, i.e., body weight, length, height, and thickness, were measured for correlation analyses to validate the growth-related SNP markers. Tissue samples were collected from the tail fin as described in DNA extraction. Genotyping of SNP loci was performed by multiplex PCR and high-throughput sequencing, and amplification primers were designed using Primer3.0 software. The first round of amplification was performed with a multiplex primer mix, and one round of PCR amplification of 10-µL reaction mixture consisted of DNA samples (2 µL), H2O (3.2 µL), primers (2 µL), 10× buffer (1 µL), and enzyme (1.8 µL). The PCR reaction procedure was as follows: 95 °C for 15 min; 94 °C for 30 s, 60 °C for 10 min, 72 °C for 30 s, 4 cycles; 94 °C for 30 s, 60 °C for 1 min, 72 °C for 30 s, 20 cycles. The products of one round of PCR were used as a template for the second round of PCR amplification, and the 20-µL reaction mixture for the second round of PCR amplification consisted of DNA samples (10 µL), H2O (3.6 µL), primers (3.6 µL), 10× buffer (2 µL), and enzyme (0.8 µL). The second-round PCR reaction procedure was as follows: 95 °C for 15 min; 94 °C for 30 s, 60 °C for 4 min, 72 °C for 30 s, 5 cycles; 94 °C for 30 s, 60 °C for 1 min, 72 °C for 30 s, 10 cycles. The multiple primers were then appropriately diluted and the amplification products were mixed to form a library by repeating two rounds of PCR. After purification and dilution, the library was sequenced on the Illumina X-10 sequencing platform. On the basis of the sequencing results, samples were able to be distinguished using bioinformatics methods, and the mutation information was obtained for each locus. TASSEL software (http://tassel.bitbucket.org/) was used to import the SNP genotypes and trait data, and the Generalized Linear Model (GLM) was used to conduct an association analysis to detect relationships between growth traits and SNP genotypes. The significance of differences in quantitative trait values among fish with different genotypes at each SNP locus was tested using SPSS 22.0 (IBM Corp, USA). In these analyses, the genotypes of SNP loci were used as the independent variable and different trait indicators were used as the dependent variable. A significance level of P < 0.05 was considered to indicate a significant association between the SNP locus and the trait.

Prediction of candidate genes

On the basis of the association analysis, the SNP markers with significant associations were mapped to the largemouth bass genome (http://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/014/851/395/GCF_014851395.1_ASM1485139v1) using Basic Local Alignment Search Tool (BLAST) software (E-value ≤ 1e-10). Genes located within 200 kb of each significant growth-associated SNP locus were identified as candidate genes related to growth. Gene annotation information and published literature about gene functions were used to predict the basic biological functions of the candidate genes.

Results

Sequencing data statistics and comparisons

In this study, a tagged sequencing library of 60 largemouth bass samples was constructed using dd-RAD sequencing technology. After filtering low-quality data, we obtained 252,315,516 high-quality reads (94.61% of total reads) for the fast-growth population, with an average of 8,410,517 reads per sample and an average GC content of 43.97%. The Q20 and Q30 values were 97.06% and 92.08%, respectively. Similarly, 238,299,380 high-quality reads (95% of total reads) were obtained for the slow-growth population, with an average of 7,943,312 reads per sample and an average GC content of 43.96%. The Q20 and Q30 values were 97.18% and 92.28%, respectively (Table S1). Notably, the average GC content was similar in the fast-growth population and the slow-growth population. More reads were obtained for the fast-growth population than for the slow-growth population, with an average of 8,451,526 reads per sample and a high contrast ratio of 99.82%. The slow-growth population had fewer reads, averaging only 7,998,597 per sample, but the same contrast ratio of 99.82%. The sequencing depth was higher for the fast-growth population (1.18X) than for the slow-growth population (1.12X), but both populations had the same 10X coverage depth of 2.48% (Table S2).

Whole genome SNP statistics

As calculated using GATK software, a summary of SNPs across the whole genome, derived from data from 60 individuals, is listed in Table S3. In total, 1,783,556 homozygous SNPs and 296,915 heterozygous SNPs were detected in the fast-growth population, with average values of 59,452 and 9,897, respectively, per individual. Overall, 1,814,207 homozygous SNPs and 301,808 heterozygous SNPs were detected in the slow-growth population, with average values of 60,474 and 10,060, respectively, per individual. Thus, the SNP detection rate was higher in the slow-growth population than in the fast-growth population. The transition to transversion ratio (Ts/Tv) was 1.27. The distribution of SNPs on each chromosome was analyzed (Fig. 1). Counting the number of SNPs in a slide window of 0.5 Mb on chromosome NW_024040040.1 had the highest number of clustered SNPs.

Fig. 1
figure 1

Distribution of SNPs across 23 chromosomes. Color changes from green to red indicate increasing SNP density over a 0.5 Mb interval

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Population structure and kinship analysis

In the PCA chart, the two populations exhibited similar clustering patterns and belonged to the same subgroup (Fig. 2A). The population structure analysis indicated a K value above 0.6, suggesting that the analyzed samples originated from a common ancestor without distinct subgroups (Fig. 2B). These findings supported the results of the intra-population correlation analyses. Furthermore, the SNP phylogenetic tree results were consistent with the results of both the PCA and the population structure analysis (Fig. 2C). The G values ranged from 0.326 to 1 and the genetic distances ranged from 0 to 0.215 (Fig. 2D and E). There was only a small range of variation in relatedness among individuals.

Fig. 2
figure 2

Population structure and kinship. (A) Principal component analysis of fast- and slow growth populations; (B) Distribution of CV error values corresponding to different values of K; (C) Population phylogenetic tree analysis; (D) G-value matrix indicating kinship results; (E): IBS genetic distance matrix indicating degree of difference among individuals

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Population genetic diversity analysis

The average expected heterozygosity did not differ significantly between the two populations but was higher than the average observed heterozygosity, suggesting that self-interbreeding may have occurred. The pi value of the slow-growth population (0.0173) exceeded that of the fast-growth population, indicating a small average base difference between samples. Additionally, both populations had coefficients of inbreeding below 0.78, implying that no serious inbreeding had occurred. Overall, there was no significant difference in genetic diversity levels between the two populations, making them suitable for exploring SNP markers related to growth (Table 1).

SNPs specific to the fast-growth population

According to the population-specific SNP screening criteria used in this study, 30 fast-growth population-specific SNP markers were screened. The proportions of each genotype for these 30 SNP markers within the population were subjected to statistical analysis (Table S4). Chi-square tests revealed potential correlations between growth traits and the genotypes of four SNPs: SNP19140160, SNP9639603, SNP9639605, and SNP23355498. Analyses of the genotyping ratios of the four SNPs showed that there was a higher percentage of homozygous genotypes at SNP19141060 in the fast-growth population. Similarly, there were higher percentages of homozygous genotypes at SNP9639603, SNP9639605, and SNP23355498 in the fast-growth population than in the slow-growth population (Fig. 3). The sequences of the primers used for genotyping at the four significantly associated SNP loci are shown in Table S5.

Table 1 Genetic diversity analysis data of fast-growth and slow-growth populations
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Population polymorphism analysis based on SNP markers

The genetic diversity of the F1 generation was further analyzed using the four growth-related SNP markers. The results (Table 2) demonstrated a moderate level of polymorphism with Ho ranging from 0.38 to 0.43 (mean = 0.41), He ranging from 0.38 to 0.44 (mean = 0.42), Nei ranging from 0.38 to 0.44 (mean = 0.42), and a mean PIC value of 0.33. The average PIC value was 0.33, indicative of moderate polymorphism. The average Nei was 0.4196 and the average I was 0.6102, which indicated that the selected largemouth bass population had relatively rich genetic diversity. All four SNP markers were consistent with the Hardy-Weinberg Equilibrium.

Table 2 Genetic diversity analysis data for fast-growth and slow-growth populations
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Association analysis of SNP markers with growth traits of largemouth bass

Summary of growth traits

The F1 generation of 230 largemouth bass exhibited a continuous distribution of body weight, body length, body height, and body thickness (Fig. 4). The mean body weight was (624.5 ± 169.8 g) with a coefficient of variation (CV) of 27.2%, the mean body length was (25.6 ± 2.3 cm) with a CV of 8.8%, the mean body height was (8.7 ± 0.8 cm) with a CV of 9.7%, and the mean body thickness was (5.1 ± 0.6 cm) with a CV of 11.4% (Table 3). The four traits exhibited wide phenotypic variation.

Fig. 4
figure 3

Distribution of four growth traits in the F1 generation of 230 largemouth bass. (A) Distribution of body weight; (B) Distribution of body length; (C) Distribution of body height; (D) Distribution of body thickness

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Fig. 3
figure 4

Genotyping ratios of the four SNPs in fast-and slow growth populations. (A) SNP19140160; (B) SNP9639603; (C) SNP9639605; (D) SNP23355498

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Table 3 Summary of growth data for the largemouth bass population (n = 230) used for the association analysis
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Correlation between different genotypes of SNP markers and growth traits of largemouth bass

The results of the marker polymorphism validation analysis showed that SNP19140160, SNP23355498, SNP9639603, and SNP9639605 were all significantly associated with the body length of the F1 generation (P < 0.05); and SNP19140160 was significantly associated with body weight, length, height, and thickness of the F1 generation (P < 0.05), with an R2 contribution of more than 5% for body weight, body height, and body thickness. Therefore, SNP19140160 could be used as a major effect QTL (Table S6). Multiple comparisons of the different genotypes of the four SNP markers with different growth traits (Table 4) showed that the average values of the four growth traits were higher for fish with the AA genotype at SNP19140160 than for those with the CC genotype. Three genotypes at SNP23355498 were significantly correlated with body weight, body length, and body height. The average values of growth traits of fish with the GG genotype at SNP23355498 were higher than those of fish with the AA and AG genotypes, and there were significant differences in growth trait values between fish with the GG genotype and those with the AG genotype. The average value of body length was lower for fish with the GT genotype at SNP9639603 than for those with the other two genotypes, and was significantly different between fish with the GT genotype and those with the GG genotype at this locus. The average value of body length was lower for fish with the AT genotype at SNP9639605 than for those with other two genotypes, and differed significantly between fish with the AT genotype and those with the AA genotype. Multiple comparisons showed that, for most of the traits, the mean values were higher for individuals with homozygous genotypes than for those with heterozygous genotypes, and the mean values of growth traits were higher in individuals with dominant genotypes. Therefore, when breeding the F2 generation of largemouth bass, selecting individuals with dominant genotypes will achieve a better genetic improvement effect.

Table 4 Correlation analysis of different genotypes of the four SNP markers with growth traits
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Distribution of different genotypes of SNP markers with corresponding growth traits

Analyses of the genotypes and individual distributions of growth traits significantly associated with the four markers (Figs. 5 and 6) showed that: The synergistic alleles of SNP19140160 in body weight, body length, body height, and body thickness were all A, with additive effects of 58.88 g, 0.60 cm, 0.29 cm, and 0.20 cm, respectively (Table 5); the dominant genotypes were all AA with the maximum count of individuals having this genotype being 111. Both SNP9639603 and SNP9639605 had an additive effect of 0.19 cm for body length; and their dominant genotypes were GG and AA, respectively. SNP23355498 had the smallest additive effect on body length at 0.06 cm; its dominant genotype was GG with a count of 125 individuals. Figure 4 provides an overview of the effect of each marker’s dominant genotype on the different traits. For example, regarding the body weight trait associated with SNP19140160, the median value for the AA genotype was 652 g whereas that of the AC genotype was 623 g and that of the CC genotype was 534 g. Among these three groups, the AA genotype had the most individuals falling within its range, and was present in the largest proportion of the population. SNP19140160 exhibited a significant additive effect and can serve as a primary genetic marker. By prioritizing individuals with SNP19140160 as parental candidates, the growth traits of the resulting offspring can be enhanced.

Fig. 5
figure 5

Distribution of growth traits corresponding to different genotypes of the four markers. (A–D). Distribution of three genotypes of SNP19140160 in terms of body weight, length, height, and thickness; (E–G). Distribution of three genotypes of SNP9639063, SNP9639065, and SNP23355498 in terms of body length

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Fig. 6
figure 6

Distribution of individuals corresponding to three genotypes of four markers. (A) SNP19140160; (B) SNP9639603; (C) SNP9639605; (D) SNP23355498

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Table 5 Additive effects of SNP markers for different genotypes
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Correlation between number of dominant genotypes and traits

Comparative analyses between growth traits and the number of dominant genotypes at the SNP loci in the F1 generation (Table 6) showed that the group with four dominant genotypes had the highest values of growth traits. The mean body weight of the group with four dominant genotypes (703.86 g) was 19.63% higher than that of the group without these four dominant genotypes (588.36 g). The average body length, body height, and body thickness were also 6.22%, 6.10% and 5.77% higher, respectively, in the group with four dominant genotypes than in the group without dominant genotypes. The higher the number of dominant genotypes at the four loci, the higher the mean values of the growth traits. The statistical analysis of the correlations between the cumulative number of associated dominant genotypes and trait values (Fig. 7A) revealed highly significant differences (P < 0.01) in average body weight and average body length between the group with the four dominant genotypes and the groups with one or two dominant genotypes. The average body height showed a significant difference (P < 0.05) between the group with four dominant genotypes and the groups with one or two dominant genotypes. Additionally, the groups with no dominant genotypes or one or two dominant genotypes at the four SNP loci displayed significantly lower values for the other growth traits than did groups with three or four dominant genotypes. Individuals with more dominant genotypes had better growth traits (Fig. 7B). The phenotypic growth traits can be significantly enhanced by pyramiding dominant genotypes. Consequently, in the subsequent breeding of the F2 generation, selecting individuals with more growth-dominant genotypes as parental candidates will effectively enhance the growth traits of the offspring.

Fig. 7
figure 7

Correlation between number of dominant genotypes at the four growth-related SNP loci and growth traits. (A) Correlation between number of dominant genotypes and growth traits. (B) Growth trends in growth traits of dominant genotype-enriched individuals. Different letters above bars indicate significant differences among genotypes; ** indicates highly significant difference between groups (P < 0.01)

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Table 6 Growth traits of fish with different numbers of dominant genotypes at four SNP loci
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Gene annotation analysis of SNP markers

The flanking sequences of these four SNP markers were compared with the largemouth bass reference genome using BLAST. The results revealed that the locus of marker SNP19140160, which exhibited significant associations with all four traits and displayed the most favorable additive effect, was annotated as fam174b located on chromosome NW_024044570.1. However, no gene annotations were obtained for the loci of the markers SNP9639603 and SNP9639605, which were significantly associated with body length. The locus of marker SNP23355498, which was related to body length, was annotated as kiaa1459Ib located on chromosome NW_024044348.1. Predicting the functions of these two genes will provide a guideline for further fast-growth gene cloning and functional analysis.

Discussion

In view of the rapid development of the largemouth bass aquaculture industry and the requirements for improved breeding varieties, genetic breeding of largemouth bass and breeding new varieties with excellent traits have become important research goals in recent years. The genetic analysis of economic traits is undoubtedly a hot spot in breeding research [30]. Marker-assisted selection is a modern breeding technology that indirectly selects target traits through molecular markers that are closely linked to the target traits. This breeding method is not affected by environmental factors, it allows for strong selection intensity, and it produces reliable results [31]. Largemouth bass is one of the most important economic freshwater aquaculture species in China. The development of SNP markers for this species has mainly focused on sex determination, rather than on growth [32, 33]. SNPs are variants with low redundancy and high coverage in the genome, and they can be used to facilitate high-throughput genotyping [34]. They have been widely used to construct high-density genetic linkage maps and discover QTL and economic trait-related genes [35,36,37]. Li et al. (2017) used RNA-seq data to mine for fast-growth-related SNPs in largemouth bass. In this study, nearly 4 × 106 largemouth bass SNPs were obtained by screening RAD-seq data [38]. When using genome sequence data to search for SNPs, SNP mutations can be identified not only in gene coding regions, but also in non-coding regions such as intra- and intergenic regions, which results in a larger and more accurate collection of SNPs. The SNP statistics showed that, among the six variant types, the C/T variant showed the highest frequency. This may be because methylated cytosine residues on CpG dinucleotides can easily undergo deamination, resulting in the formation of thymine [39].

The accuracy and reliability of correlations between SNP markers and target traits within a population can be affected by factors such as the population’s genetic background, linkage disequilibrium, and sample numbers. In this study, we examined the polymorphism of four SNP markers in an F1 generation of the northern subspecies of largemouth bass. We found that the PIC values of the four markers were higher than 0.3, indicative of moderate polymorphism, showing that these four SNP markers had high validity and reliability when used in genetic diversity analysis [40].

The growth traits of largemouth bass, namely body length, body height, body thickness, and body weight, are economically important traits affecting the body shape and yield. In this study, the four growth traits of largemouth bass used for correlation analysis were quantitative traits with continuous distributions [41]. The association analysis of SNP markers with growth traits showed that SNP19140160 was significantly associated with body height, body thickness, and body weight of largemouth bass, and was highly significantly associated with body length; and that SNP23355498, SNP9639603, and SNP9639605 were significantly associated with body height, body thickness, and body weight of largemouth bass. The association of one trait with more than one SNP suggests that multiple genes affect that trait; i.e., the trait is controlled by multiple genes (loci) that act in sequence, consistent with the theory of quantitative traits, or the trait is controlled by more than one QTL and is regulated by different physiological and biochemical processes [42]. SNP19140160 showed significant associations with four growth traits. The fact that several different growth traits were associated with one locus suggested that those growth traits were associated with each other. In contrast, the loci SNP23355498, SNP9639603, and SNP9639605 were significantly associated with a single trait, body length, and not with any other growth traits. The molecular mechanism bridging the gap between genotype and phenotype is very complicated [43]. By analyzing the correlations between different genotypes and growth traits of largemouth bass, we detected a case in which one factor (gene/locus) had multiple effects; and a case in which multiple factors (genes/loci) had a single effect. These loci could be used as a reference to generate new largemouth bass germplasm with fast growth and/or a good body shape.

Heterozygosity reflects the degree of genetic uniformity, and PIC is a measure of polymorphism. When PIC > 0.5, the locus is highly polymorphic, when 0.25 < PIC < 0.5, it is moderately polymorphic, and when PIC < 0.25, it has low polymorphism. The higher the PIC value, the higher the heterozygosity at the locus, and the richer the genetic information it provides [44]. In this study, all 30 SNP markers were moderately polymorphic, so they were able to provide sufficient information for assessing the genetic diversity of largemouth bass. None of the markers was highly polymorphic, because SNPs are typical two-allele markers. The average PIC value of the SNP markers obtained in this study was 0.33, reflecting that the F1 population of the northern subspecies of largemouth bass had rich genetic diversity. Therefore, further selective breeding can be used to improve the growth performance of largemouth bass. In another study, RNA-seq data were used to identify expressed sequence tag (EST)-SNP markers in the “Youlu No.1” largemouth bass population, and the average PIC value of 10 SNP markers was 0.36 [45]. High-intensity artificial selection will reduce the diversity within the gene pool of a population to some degree. The F1 generation population in this experiment was mainly derived from the original 3000 tails introduced in 2020. Therefore, it is necessary to maintain a larger number of breeding progeny in each generation during the F2 generation selection process to reduce the negative effects of inbreeding.

Association analysis to identify candidate genes has become an important method for determining the genetic basis of correlations between markers or loci and phenotypic traits in some aquaculture species. The primary target of association studies is SNPs in exons. Based on the genotypes of the four markers significantly associated with growth traits, the mean values of the four trait parameters were significantly higher in individuals with the AA genotype at SNP19140160 than in those with the CC genotype at this SNP locus. The BLAST analysis revealed that SNP19140160 was located in the exon 3′-untranslated region (UTR) of the fam174b gene, suggesting that this SNP may be involved in the regulation of mRNA stability, subcellular localization, and/or protein translation [46]. Sarahan et al. (2011) identified a region on Chr7 that affects body fat mass in mice, which included fam174b, suggesting that this gene was related to growth [47]. The SNP locus in the 3′UTR of fam174b may affect miRNA target binding, but further research is required to confirm this. Some researchers consider synonymous and non-synonymous SNPs in exon regions to be equally useful for genetic applications because synonymous SNPs can also lead to phenotypic variability [48]. kiaa1459Ib is a member of the KIAA gene family, whose members encode large proteins. The KIAA genes studied so far play crucial roles in protein synthesis, cell division, and cell metabolism [49, 50]. In this study, we selected an SNP marker associated with growth traits in largemouth bass, and found that the average values for body weight, length, height, and thickness were significantly higher for AA-genotype individuals than for CC-genotype individuals. We hypothesize that fam174b is a functional gene associated with the growth of largemouth bass. Further research is required to explore its mechanism of action in detail.

Pyramiding of several genes with beneficial effects is a useful strategy to breed new strains with excellent growth traits. A study on growth-related markers in leopard coral grouper (Plectropomus leopardus) identified 12 SNPs and two genes significantly associated with growth, and identified dominant genotypes [51]. In abalone (Haliotis discus hannai), seven SNPs were found to be reliable markers for growth and 10 candidate genes related to growth were aggregated [52]. In the present study, analyses of groups of fish with different numbers of dominant genotypes at the four SNPs showed that the number of dominant genotypes was positively correlated with the growth traits of the F1 generation of the northern subspecies of largemouth bass. The average body weight of individuals containing all four dominant genotypes was 19.63% higher than that of individuals without those dominant genotypes, indicating that these phenotypic growth traits could be improved by pyramiding the dominant alleles of the growth-related SNP markers. Therefore, in the future F2 generation breeding process of the northern subspecies of largemouth bass, genetic improvement can be achieved using MAS technology to directly select individuals with high numbers of dominant genotypes at the growth-related loci as parents for breeding.

Conclusions

In this study, four growth-specific SNP markers were developed using RAD-seq technology. Based on the data for four growth traits, accurate additive effects of these traits were detected, with marker SNP19140160 exhibiting the most significant effect on all four growth traits. The 35 individuals of largemouth bass with dominant genotypes at all four SNPs showed superior growth traits, providing evidence that the combination of dominant genotypes leads to enhanced growth characteristics. Additionally, two genes related to growth (fam174b and kiaa1459Ib) were annotated in this study. These findings lay the foundation for identifying candidate genes associated with growth traits, elucidating the regulatory mechanisms underlying such traits, and exploring MAS breeding strategies for largemouth bass.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files. The largemouth bass reference genome is publicly available at NCBI (https://www.ncbi.nlm.nih.gov/genome/?term=Micropterus+salmoides).

Abbreviations

RAD-seq:

Restriction-site Associated DNA Sequencing

SNP:

Single Nucleotide Polymorphism

MAS:

Marker-Assisted Selection

NGS:

Next-Generation Sequencing

2b-RAD-Seq:

2b-Restriction site-Associated DNA Sequencing

QTL:

Quantitative Trait Loci

GWAS:

Genome-Wide Association Study

SSR:

Simple Sequence Repeat

FFRC:

Freshwater Fisheries Research Center

FG:

Fast-Growth

SG:

Slow-Growth

dd-RAD:

double-digest RAD

PE:

Paired-End

FS:

Fisher test of Strand bias

MQ:

Mapping Quality

QD:

Quality Depth

PCA:

Principal Component Analysis

MAF:

Minor Allele Frequency

IBS:

Identity-By-State

GLM:

Generalized Linear Model

BLAST:

Basic Local Alignment Search Tool

HWE:

Hardy-Weinberg Equilibrium

CV:

Coefficient of Variation

EST:

Expressed Sequence Tag

UTR:

Untranslated Region

References

  1. Liu Y, Lei M, Victor H, Wang Z, Yu C, Zhang G, Wang Y. The optimal feeding frequency for largemouth bass (Micropterus salmoides) reared in pond and in-pond-raceway. Aquaculture 2022, 548.

  2. Du J, Li S, Shao J, Song H, Jiang P, Lei C, Bai J, Han L. Genetic diversity analysis and development of molecular markers for the identification of largemouth bass (Micropterus salmoides L) based on whole-genome re-sequencing. Front Genet 2022, 13.

  3. Yi W, Zhang X, Zeng K, Xie D, Song C, Tam K, Liu Z, Zhou T, Li W. Construction of a DNA vaccine and its protective effect on largemouth bass (Micropterus salmoides) challenged with largemouth bass virus (LMBV). Fish Shellfish Immunol. 2020;106:103–9.

    Article  PubMed  CAS  Google Scholar 

  4. Xu W, Zhang Z, Lai F, Yang J, Qin Q, Huang Y, Huang X. Transcriptome analysis reveals the host immune response upon LMBV infection in largemouth bass (Micropterus salmoides). Fish Shellfish Immunol 2023, 137.

  5. Hou X, Liu H, Chen X, Wang J, Wang C. Polymorphisms and association analysis of the myostatin gene of the Chinese mitten crab (Eriocheir sinensis). Aquaculture 2023, 572.

  6. Yue GH, Wang L. Current status of genome sequencing and its applications in aquaculture. Aquaculture. 2017;468:337–47.

    Article  CAS  Google Scholar 

  7. Yue GH. Recent advances of genome mapping and marker-assisted selection in aquaculture. Fish Fish. 2014;15(3):376–96.

    Article  Google Scholar 

  8. Dixit M, Poudel SB, Yakar S. Effects of GH/IGF axis on bone and cartilage. Mol Cell Endocrinol 2021, 519.

  9. Segev-Hadar A, Alupo G, Tal K, Nitzan T, Biran J. Identification and characterization of a non-muscular myostatin in the Nile Tilapia. Front Endocrinol 2020, 11.

  10. Luo W, Zhou Y, Wang J, Yu X, Tong J. Identifying candidate genes involved in the regulation of early growth using full-length transcriptome and RNA-Seq analyses of Frontal and Parietal Bones and Vertebral bones in Bighead Carp (Hypophthalmichthys nobilis). Front Genet 2021, 11.

  11. Tian Z, Jiao C. Molt-dependent transcriptome analysis of claw muscles in Chinese mitten crab Eriocheir sinensis. Genes & Genomics. 2019;41(5):515–28.

    Article  CAS  Google Scholar 

  12. Zhang J, Shen Y, Xu X, Dai Y, Li J. Transcriptome analysis of the liver and Muscle Tissues of Black Carp (Mylopharyngodon piceus) of different growth rates. Mar Biotechnol. 2020;22(5):706–16.

    Article  CAS  Google Scholar 

  13. Song Z, Ye W, Tao Y, Zheng T, Qiang J, Li Y, Liu W, Xu P. Transcriptome and 16S rRNA analyses reveal that hypoxic stress affects the antioxidant capacity of Largemouth Bass (Micropterus salmoides), resulting in intestinal tissue damage and structural changes in Microflora. Antioxid. 2022;12(1).

  14. Takahashi A, Kasagi S, Murakami N, Furufuji S, Kikuchi S, Mizusawa K, Andoh T. Chronic effects of light irradiated from LED on the growth performance and endocrine properties of barfin flounder Verasper moseri. Gen Comp Endocrinol. 2016;232:101–8.

    Article  PubMed  CAS  Google Scholar 

  15. Udayantha HMV, Lee S, Liyanage DS, Lim C, Jeong T, Omeka WKM, Yang H, Kim G, Kim J, Lee J et al. Identification of candidate variants and genes associated with temperature tolerance in olive flounders by Genome-Wide Association Study (GWAS). Aquaculture 2023, 576.

  16. Orliac EJ, Trejo Banos D, Ojavee SE, Läll K, Mägi R, Visscher PM, Robinson MR. Improving GWAS discovery and genomic prediction accuracy in biobank data. Proceedings of the National Academy of Sciences 2022, 119(31).

  17. Salem M, Vallejo RL, Leeds TD, Palti Y, Liu SX, Sabbagh A, Rexroad CE, Yao JB. RNA-Seq identifies SNP markers for growth traits in Rainbow Trout. PLoS ONE 2012, 7(5).

  18. Zhou Y-L, Wang Z-W, Guo X-F, Wu J-J, Lu W-J, Zhou L, Gui J-F. Construction of a high-density genetic linkage map and fine mapping of QTLs for growth and sex-related traits in red-tail catfish (Hemibagrus wyckioides). Aquaculture 2021, 531.

  19. Omeka WKM, Liyanage DS, Lee S, Lim C, Yang H, Sandamalika WMG, Udayantha HMV, Kim G, Ganeshalingam S, Jeong T et al. Genome-wide association study (GWAS) of growth traits in olive flounder (Paralichthys olivaceus). Aquaculture 2022, 555.

  20. Ai CH, Li BJ, Xia JH. Mapping QTL for cold-tolerance trait in a GIFT-derived tilapia line by ddRAD-seq. Aquaculture 2022, 556.

  21. Li S, Xu L, Shi Y, Chen J. Male-specific markers developed by next-generation sequencing confirmed an XX/XY sex-determination system in farmed ayu (Plecoglossus altivelis). Aquaculture 2021, 541.

  22. San-Jose LM, Bestion E, Pellerin F, Richard M, Di Gesu L, Salmona J, Winandy L, Legrand D, Bonneaud C, Guillaume O, et al. Investigating the genetic basis of vertebrate dispersal combining RNA‐seq, RAD‐seq and quantitative genetics. Mol Ecol. 2023;32(12):3060–75.

    Article  PubMed  CAS  Google Scholar 

  23. Zhou Y, Fu H-C, Wang Y-Y, Huang H-Z. Genome-wide association study reveals growth-related SNPs and candidate genes in mandarin fish (Siniperca chuatsi). Aquaculture 2022, 550.

  24. Ai CH, Zhu ZX, Huang DD, Yang G, De Liu T, Bai Y, Liang XY, Xiong YY, Lin YL, Lin HR et al. Identification of SNPs and candidate genes associated with early growth in orange-spotted grouper (Epinephelus coioides) by a genome-wide association study. Aquaculture 2023, 565.

  25. Guppy JL, Jones DB, Kjeldsen SR, Le Port A, Khatkar MS, Wade NM, Sellars MJ, Steinig EJ, Raadsma HW, Jerry DR et al. Development and validation of a RAD-Seq target-capture based genotyping assay for routine application in advanced black tiger shrimp (Penaeus monodon) breeding programs. BMC Genomics 2020, 21(1).

  26. Jing Y, Bian L, Zhang X, Zhao B, Zheng R, Su S, Ye D, Zheng X, El-Kassaby YA, Shi J. Genetic diversity and structure of the 4th cycle breeding population of Chinese fir (Cunninghamia lanceolata (lamb.) Hook). Front Plant Sci 2023, 14.

  27. Zhang D, Qiang J, Fu JJ, Xu GC, Zhou GQ, Dong ZJ, et al. Genetic analysis of five stocks of largemouth bass (Micropterus salmoides) with microsatellite and mitochondrial D-loop sequences. J Fish Sci China. 2021;29(09):1277–89.

    Google Scholar 

  28. Zhu P, He L, Li Y, Huang W, Xi F, Lin L, Zhi Q, Zhang W, Tang YT, 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(9).

  29. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164–4.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Fu G, Yuna Y. Phenotyping and phenomics in aquaculture breeding. Aquaculture and Fisheries. 2022;7(2):140–6.

    Article  Google Scholar 

  31. Wu X, Zhao L, Fan Z, Lu B, Chen J, Tan D, Jiang D, Tao W, Wang D. Screening and characterization of sex-linked DNA markers and marker-assisted selection in blue tilapia (Oreochromis aureus). Aquaculture 2021, 530.

  32. Du J, Zhou J, Li S, Shao J, Jiang P, Dong C, Bai J. A PCR-based method for genetic sex identification and evidence of the XX/XY sex determination system in largemouth bass (Micropterus salmoides L). Aquaculture 2021, 545.

  33. Hua J, Qiang J, Tao Y, Li Y, Lu S, Bing X. Development and validation of a PCR-RFLP / TaqMan MGB probe method for rapid sex identification of largemouth bass (Micropterus salmoides). Aquaculture Rep 2023, 30.

  34. Syvänen A-C. Toward genome-wide SNP genotyping. Nat Genet. 2005;37(S6):5–S10.

    Article  Google Scholar 

  35. Li R, Bekaert M, Lu J, Lu S, Zhang Z, Zhang W, Shi O, Chen C, Mu C, Song W et al. Mapping and validation of sex-linked SNP markers in the swimming crab Portunus trituberculatus. Aquaculture 2020, 524.

  36. Peichel CL, Kamiya T, Kai W, Tasumi S, Oka A, Matsunaga T, Mizuno N, Fujita M, Suetake H, Suzuki S et al. A trans-species missense SNP in Amhr2 is Associated with Sex determination in the Tiger Pufferfish, Takifugu rubripes (Fugu). PLoS Genet 2012, 8(7).

  37. Zhang Z, Lin Z, Wei M, Chen Z, Shen M, Cao G, Wang Y, Zhang Z, Zhang D. Development of Single Nucleotide Polymorphism and Association Analysis with Growth Traits for Black Porgy (Acanthopagrus schlegelii). Genes 2022, 13(11).

  38. Li S, Liu H, Bai J, Zhu X. Transcriptome assembly and identification of genes and SNPs associated with growth traits in largemouth bass (Micropterus salmoides). Genetica. 2017;145(2):175–87.

    Article  PubMed  CAS  Google Scholar 

  39. David G, Wang J-BF, Siao C-J, Berno A, Young P, Sapolsky R et al. Large-Scale Identification, Mapping, and Genotyping of Single-Nucleotide Polymorphisms in the Human Genome. science 1998.

  40. Kong L, Bai J, Li Q. Comparative assessment of genomic SSR, EST–SSR and EST–SNP markers for evaluation of the genetic diversity of wild and cultured Pacific oyster, Crassostrea gigas Thunberg. Aquaculture 2014, 420–421:S85-S91.

  41. Bai X, Wang X, Wang Y, Wei Y, Fu Y, Rao J, Ma Y, Zeng Z, Li F, Wang M et al. Genome-wide Association study of six forage traits in Ramie (Boehmeria nivea L. Gaud). Plants 2022, 11(11).

  42. McConnell B, Leamon S. Microsatellite marker based genetic linkage maps of Oreochromis aureus and O. Niloticus (Cichlidae): extensive linkage group segment homologies revealed. Anim Genet. 2002;31(3):214–8.

    Article  Google Scholar 

  43. Newton-Cheh C, Hirschhorn JN. Genetic association studies of complex traits: design and analysis issues. Mutat Research/Fundamental Mol Mech Mutagen. 2005;573(1–2):54–69.

    Article  CAS  Google Scholar 

  44. Bai J, Lutz-Carrillo DJ, Quan Y, Liang S. Taxonomic status and genetic diversity of cultured largemouth bass Micropterus salmoides in China. Aquaculture. 2008;278(1–4):27–30.

    Article  CAS  Google Scholar 

  45. Li SJ, Bai JJ, Zhao L, Zhu B, Zhu XP et al. Development of EST-SNPs in largemouth bass (Micropterus salmoides) and analysis of their correlation with growth traits. Mar Fisheries 2018, 40(1).

  46. Hoshida H, Kondo M, Kobayashi T, Yarimizu T, Akada R. 5´-UTR introns enhance protein expression in the yeast Saccharomyces cerevisiae. Appl Microbiol Biotechnol. 2016;101(1):241–51.

    Article  PubMed  Google Scholar 

  47. Sarahan KA, Fisler JS, Warden CH. Four out of eight genes in a mouse chromosome 7 congenic donor region are candidate obesity genes. Physiol Genom. 2011;43(18):1049–55.

    Article  CAS  Google Scholar 

  48. Kimchi-Sarfaty C, Oh JM, Kim I-W, Sauna ZE, Calcagno AM, Ambudkar SV, Gottesman MM. A silent polymorphism in the MDR1 gene changes substrate specificity. Science. 2007;315(5811):525–8.

    Article  PubMed  CAS  Google Scholar 

  49. Kikuno R. HUGE: a database for human KIAA proteins, a 2004 update integrating HUGEppi and ROUGE. Nucleic Acids Res. 2004;32(90001):502D–504.

    Article  Google Scholar 

  50. Nakayama M, Iida M, Koseki H, Ohara O, Nakayama M, Iida M, Koseki H, Ohara O. A gene-targeting approach for functional characterization of KIAA genes encoding extremely large proteins. FASEB J. 2006;20(10):1718–20.

    Article  PubMed  CAS  Google Scholar 

  51. Wang T, Wu X, Song L, Yang Y, Gong S, Zeng L, Tao Y, Zhong C, Meng Z, Liu X. Identification of candidate growth-related SNPs and genes using GWAS and transcriptome analyses in leopard coral grouper (Plectropomus leopardus). Aquaculture 2023, 574.

  52. Peng W, Yu F, Wu Y, Zhang Y, Lu C, Wang Y, Huang Z, Lu Y, Chen N, Luo X et al. Identification of growth-related SNPs and genes in the genome of the Pacific abalone (Haliotis discus hannai) using GWAS. Aquaculture 2021, 541.

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Acknowledgements

We thank Jennifer Smith, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.

Funding

This research was funded by the Project of Seed Industry Revitalization in Jiangsu Province, China (Grant No. JBGS [2021] 130), Suzhou Science and Technology Program (Grant No. SNG2021009), Major Technology Collaborative Promotion Plan for Largemouth Bass Industry in Jiangsu Province (2022-ZYXT-07) and the Central Public-Interest Scientific Institution Basal Research Fund, Freshwater Fisheries Research Center, CAFS (NO. 2023JBFR02).

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Authors and Affiliations

  1. Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China

    Jixiang Hua, Chunyi Zhong, Qingchun Wang & Jun Qiang

  2. Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi, 214081, China

    Jixiang Hua, Chunyi Zhong, Jianjun Fu, Yan Li, Yifan Tao, Yalun Dong, Siqi Lu, Wenting Liu & Jun Qiang

  3. Suzhou Aquatic Technology Extension Station, Suzhou, 215004, China

    Wenhua Chen, Geyan Zhu & Maoyou Zhang

  4. Guangxi Xinjian Investment Group Limited Company, Hechi, 530201, China

    Jian Wang

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Contributions

JXH participated in sample collection, data analysis, and drafted the manuscript. CYZ, WHC and MYZ involved in sample collection and measurement of growth traits. GYZ, JW and JJF conducted methodological design and manuscript. QCW, YL and YFT participated analysis, discussion and writing. YLD performed project assistance. SQL and WTL performed administration. JQ conducted project management, experimental design, founding acquisition, and manuscript review. All the authors reviewed and approved the final manuscript.

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Correspondence to Jun Qiang.

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Ethics approval and consent to participate

All experiments were performed according to the Guidelines for the Care and Use of Laboratory Animals in China. The use of experimental fish in this study was approved by the Ethics Committee at the Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences (FFRC, Wuxi, China). All methods of this study also adhere to the ARRIVE Guidelines for reporting animal research. Furthermore, the species Largemouth bass (Micropterus salmoides) is not listed in CITES.

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Additional file 1:

Table S1. The ddRAD-seq summary of 30 fast growth (FG) individuals and 30 slow growth (SG) individuals.

Additional file 2:

Table S2. Comparison of ddRAD-seq sequencing results of fast and slow growth populations.

Additional file 3:

Table S3. Statistics of SNPs genotyping results.

Additional file 4:

Table S4. Genotyping frequencies of the 30 SNP loci in the fast-and slow growth populations.

Additional file 5:

Table S5. Base mutation information and primer sequences for four loci.

Additional file 6:

Table S6. Correlation analysis of the four SNP loci with growth traits.

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Hua, J., Zhong, C., Chen, W. et al. Single nucleotide polymorphism SNP19140160 A > C is a potential breeding locus for fast-growth largemouth bass (Micropterus salmoides). BMC Genomics 25, 64 (2024). https://doi.org/10.1186/s12864-024-09962-0

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