Genomic prediction has emerged as a powerful tool in aquaculture breeding, but its effectiveness depends on the careful selection of informative single nucleotide polymorphisms (SNPs) and the application of appropriate prediction models. This study aimed to enhance genomic prediction accuracy in Russian sturgeon (Acipenser gueldenstaedtii) by optimizing SNP selection strategies and exploring the performance of linear and machine learning models. Three economically important traits—caviar yield, caviar color, and body weight—were selected due to their direct relevance to breeding goals and market value. Whole-genome sequencing (WGS) data were obtained from 971 individuals with an average sequencing depth of 13.52×. To reduce marker density and eliminate redundancy, three SNP selection strategies were applied: (1) genome-wide association study (GWAS)-based prioritization to select trait-associated SNPs; (2) linkage disequilibrium (LD) pruning to retain independent markers; and (3) random sampling as a control. Genomic prediction was conducted using both linear (e.g., GBLUP) and machine learning models (e.g., random forest) across varying SNP densities (1 K to 50 K). Results showed that GWAS-based SNP selection consistently outperformed other strategies, especially at moderate densities (≥10 K), improving prediction accuracy by up to 3.4% compared to the full WGS dataset. LD-based selection at higher densities (30 K and 50 K) achieved comparable performance to full WGS. Notably, machine learning models, particularly random forest, exceeded the performance of linear models, yielding an additional 2.0% increase in accuracy when combined with GWAS-selected SNPs. In conclusion, integrating WGS data with GWAS-informed SNP selection and advanced machine learning models offers a promising framework for improving genomic prediction in sturgeon and holds promise for broader applications in aquaculture breeding programs.

Caviar yield, caviar color, and body weight are crucial economic traits in sturgeon breeding. Understanding the molecular mechanisms behind these traits is essential for their genetic improvement. In this study, we performed whole-genome sequencing on 673 Russian sturgeons, renowned for their high-quality caviar. With an average sequencing depth of 13.69×, we obtained approximately 10.41 million high-quality single nucleotide polymorphisms (SNPs). Using a genome-wide association study (GWAS) with a single-marker regression model, we identified SNPs and genes associated with these traits. Our findings revealed several candidate genes for each trait: caviar yield: TFAP2A, RPS6KA3, CRB3, TUBB, H2AFX, morc3, BAG1, RANBP2, PLA2G1B, and NYAP1; caviar color: NFX1, OTULIN, SRFBP1, PLEK, INHBA, and NARS; body weight: ACVR1, HTR4, fmnl2, INSIG2, GPD2, ACVR1C, TANC1, KCNH7, SLC16A13, XKR4, GALR2, RPL39, ACVR2A, ADCY10, and ZEB2. Additionally, using the genomic feature BLUP (GFBLUP) method, which combines linkage disequilibrium (LD) pruning markers with GWAS prior information, we improved genomic prediction accuracy by 2%, 1.9%, and 3.1% for caviar yield, caviar color, and body weight traits, respectively, compared to the GBLUP method. In conclusion, this study enhances our understanding of the genetic mechanisms underlying caviar yield, caviar color, and body weight traits in sturgeons, providing opportunities for genetic improvement of these traits through genomic selection.

The increasing frequency and intensity of heatwaves due to climate change pose significant challenges to sturgeon aquaculture. This study investigated the effects of gradual heat stress (1 °C every 8 h) on two reciprocal hybrid sturgeon strains (Acipenser baerii ♀ × A. schrenckii ♂, (BS hybrid); A. schrenckii ♀ × A. baerii ♂, (SB hybrid)), focusing on their antioxidant defense mechanisms, heat shock protein (HSP) expression, and liver and gill tissue histology. When water temperature raised to 34.3 °C (about 104 h), LOE (loss of equilibrium) individuals appeared. Twenty-four hours after sampling, fifteen BS hybrid sturgeon remained alive, whereas no SB hybrid sturgeon survived. In this study, the slow heat stress significantly elevated the expression of HSP-related genes (hsc70, hsp70, hsp90) in both the liver of BS hybrid sturgeon and the gills of SB hybrid sturgeon. However, in the gills of BS hybrid sturgeon and the liver of SB hybrid sturgeon, the expression of hsp family genes in the experimental groups was either lower than or comparable to the control group. Significant liver damage, including cellular vacuolization and necrosis, was observed in BS hybrids, while SB hybrid sturgeon exhibited more pronounced gill tissue damage. Among the four antioxidant enzymes—superoxide dismutase (SOD), lactate dehydrogenase (LDH), catalase (CAT) glutathione peroxidase (GPx)—only LDH activity was elevated in the hepatic tissue of BS hybrid sturgeon, corresponding to increased serum lactate levels, while gill LDH activity was higher in SB hybrid sturgeon. In both hybrids, LDH activity exhibited an increasing trend in the kidney. However, total antioxidant capacity (T-AOC) remained unchanged across all three tissues. Both plasma cortisol and lactate were substantially affected by thermal stress. MDA remained at a relatively stable level after heat stress and recovery. These results demonstrate differential tissue-specific responses to heat stress in the reciprocal hybrids. More importantly, the BS hybrid sturgeon exhibited significantly higher thermal tolerance and post-stress survival compared to the SB hybrid sturgeon. These findings reveal that the choice of maternal parent is a critical factor influencing heat resistance in these hybrids, providing a key basis for selective breeding programs and optimizing aquaculture management.

Genomic prediction has been increasingly applied in aquaculture selective breeding; however, systematic evaluations of prediction accuracy across multiple aquaculture species and analytical methods under a unified and comparable framework remain limited. In this study, we conducted a comprehensive comparative assessment of genomic prediction performance across four representative aquaculture species, including Atlantic salmon (Salmo salar), gilthead sea bream (Sparus aurata), common carp (Cyprinus carpio), and rainbow trout (Oncorhynchus mykiss), using ten genomic prediction models including GBLUP, Bayesian and machine learning methods. Prediction accuracy varied widely among species and models, ranging from 0.49 to 0.85, and was strongly associated with trait heritability. High-heritability traits consistently achieved higher prediction accuracies, with rainbow trout and common carp exhibiting the best overall performance (0.75–0.83 and 0.73–0.85, respectively), whereas Atlantic salmon and gilthead sea bream showed lower and more variable accuracies (0.49–0.61 and 0.49–0.66). No single model performed optimally across all species. Machine learning-based approaches achieved the highest prediction accuracy in specific cases but exhibited pronounced species-dependent variability, while GBLUP provided stable and well-calibrated predictions with consistently low bias. Incremental SNP feature selection further improved prediction accuracy by 2.8–4.2% in three species using only 0.54–9.64% of the available markers, whereas no improvement was observed for a low-heritability trait. These results show that genomic prediction performance is highly context-dependent and underscores the importance of jointly considering trait genetic architecture, population characteristics, model choice, and marker selection when optimizing genomic selection strategies in aquaculture breeding programs.

Masu salmon (Oncorhynchus masou) is an ecologically and economically important cold-water salmonid in East Asia that exhibits diverse life-history forms. To compare population-level genomic variation and patterns of homozygosity among fish from different sources, we analyzed whole-genome resequencing data from 465 individuals representing one field-collected Tumen River population (TM) and two landlocked cultured populations from Chicheng (CC) and Yanji (YJ). After quality control, 6,220,980 high-quality SNPs were retained. Population-specific filtering identified 5,589,828, 3,545,209, and 4,975,751 polymorphic SNPs in CC, TM, and YJ, respectively; although SNP numbers differed, approximately 91% of variants in each population were located in intronic or intergenic regions. Principal component analysis, ADMIXTURE, and distance-based neighbor-joining analysis clearly distinguished the three populations, with CC and YJ showing the closest genetic relationship. Pairwise $F_{\mathrm{ST}}$ was lowest between CC and YJ and highest between TM and YJ. CC exhibited the highest linkage disequilibrium, whereas TM showed the fastest LD decay and the lowest nucleotide diversity and heterozygosity. Runs of homozygosity (ROH) burden was highest in TM, intermediate in CC, and lowest in YJ. TM had the highest number of ROHs, cumulative ROH length, and $F_{\mathrm{ROH}}$, and ROHs longer than 5 Mb were detected only in this population. CC had an intermediate ROH burden dominated by short segments, whereas YJ had the lowest ROH-based genomic inbreeding. The high and heterogeneous ROH burden in TM indicates elevated genome-wide homozygosity among the sampled fish but does not, by itself, demonstrate recent inbreeding throughout the population. Candidate ROH islands and their annotated genes showed limited overlap among populations. Candidate genes in TM were primarily associated with ion regulation, neural processes, and energy metabolism, whereas those in CC and YJ shared broad functional categories involving development, muscle organization, nutrient transport, and neural regulation but differed in most specific genes. These regions and genes should be regarded as exploratory, hypothesis-generating candidates rather than evidence of selection or causality. Overall, this study reveals distinct population genomic characteristics and ROH patterns among masu salmon populations of different origins and provides a basis for future germplasm conservation and genetic management.

11-ketotestosterone (11-KT) has previously been shown to promote previtellogenic ovarian development and vitellogenin-related hepatic responses in sterlet (Acipenser ruthenus), but the endocrine genes involved in this process remain unclear. In this study, previtellogenic female sterlets were implanted with slow-release 11-KT strips at 5 or 25 mg/kg for 30 days, and transcriptional responses of HPG-axis-related genes, including GnRH paralogs, gonadotropins, and gonadotropin receptors, were examined in the brain-pituitary complex, liver, and ovary. In the brain-pituitary complex, 11-KT increased fsh expression in the 25 mg/kg group and upregulated fshr in both treatment groups, whereas gnrh paralogs showed limited responses. In the liver, most GnRH- and gonadotropin-related genes were not significantly altered, suggesting that hepatic expression of upstream reproductive-axis genes was not a major in vivo target of 11-KT feedback. The ovary exhibited the strongest response, with increased gnrh2 and fsh expression and reduced gnrh3, fshr, and lhr expression, especially at the higher dose. To further examine receptor-related regulation of hepatic vtg expression, liver tissue was incubated in vitro with 11-KT alone or in combination with the androgen receptor antagonist flutamide or the estrogen receptor antagonist fulvestrant. Flutamide did not abolish 11-KT-induced vtg expression, whereas high-dose fulvestrant reduced both era and vtg expression relative to 11-KT treatment alone. Overall, 11-KT elicited tissue-specific transcriptional responses in HPG-axis-related genes, particularly involving ovarian gnrh2, fsh, fshr, and lhr. The differential antagonist responses observed in liver explants provide in vitro evidence consistent with the involvement of ER-associated signaling in hepatic vtg expression, but do not establish direct receptor-mediated regulation.