Penaeid shrimp, with its genomes enriched in short tandem repeats (STRs), presents an ideal model for studying the distribution and biological functions of STRs. In this study, we systematically identified and compared STRs across multiple species, confirming a significantly higher prevalence of STRs (26–32%) in penaeid shrimp, which is markedly higher than that observed in other species, such as mammals (1.3–2.1%) and plants (0.21–0.73%). Further analysis utilizing a cohort of 326 Pacific white shrimp revealed a total of 672,507 high-quality STRs evenly distributed across the genome, with a notably lower frequency of SNPs within these STR regions. Focusing on growth traits as a case study, we conducted a genome-wide association study (GWAS) and correlation analyses to identify the regulatory relationship of STRs on complex traits. We discovered 84 STRs that showed a significant association with body weight. Interestingly, eleven of these STRs, with 81% being composed of an A/T base, showed a significant linear correlation with body weight, revealing the key role of A/T-related STRs in shrimp weight regulation. For instance, a significant association and a negative correlation were found between the copy number of the STR [(A)n] at NW_020872788.1:580574 and body weight. The cytokinesis protein 7-like (LOC113800912) gene, which contains this STR in its splice region, exhibits differential expression associated with body weight variation. These findings introduce a model for STR copy number regulation in non-human species, illuminating the influence of STRs on growth traits. It offers a valuable framework for investigating complex traits and the biological functions of STRs in agricultural animals.

Harvest weight uniformity is a critical economic trait in the production of Pacific white shrimp (Penaeus vannamei). Social interactions among individuals can significantly influence both uniformity and productivity in aquaculture. To improve harvest weight uniformity through selective breeding, it is essential to accurately partition the genetic component of social effects, known as an indirect genetic effect (IGE), from purely environmental factors. Since IGEs cannot be estimated when all individuals are kept in a single group, a specialized experimental design, such as the grouping design with three families per group (3FAM), is required. With this experimental design, the shrimp population is divided into multiple groups (cages), each containing three families. Individuals from each family are then evenly subdivided and placed in three cages, thereby enabling the estimation of both direct and social genetic effects. Additionally, integrating genomic information instead of relying solely on pedigree data improves the accuracy of genetic relatedness among individuals, leading to more precise genetic evaluation. This study employed a 3FAM experimental design involving 40 families (36 individuals per family) to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. The genotypes of all tested individuals obtained using the 55K SNP panel were incorporated into a hierarchical generalized linear model to predict direct genetic effects and indirect genetic effects (IGE) separately. The results revealed that the heritability of harvest weight uniformity was low (0.005 to 0.017). However, the genetic coefficient of variation (0.340 to 0.528) indicates that using the residual variance in harvest weight as a selection criterion for improving uniformity is feasible. Incorporating IGE into the model increased heritability estimates for uniformity by 150% to 240% and genetic coefficient of variation for uniformity by 32.11% to 55.29%, compared to the model without IGE. Moreover, the genetic correlation between harvest weight and its uniformity shifted from a strongly negative value (−0.862 to −0.683) to a weakly positive value (0.203 to 0.117), suggesting an improvement in the genetic relationship between the traits and better separation of genetic and environmental effects. The inclusion of genomic data enhanced the prediction ability of single-step best linear unbiased prediction for both harvest weight and uniformity by 6.35% and 10.53%, respectively, compared to the pedigree-based best linear unbiased prediction. These findings highlight the importance of incorporating IGE and utilizing genomic selection methods to enhance selection accuracy for obtaining harvest weight uniformity. This approach provides a theoretical foundation for guiding uniformity improvements in shrimp breeding programs and offers potential applications in other food production systems.

The Pacific white shrimp (Penaeus vannamei) exhibits pronounced sexual dimorphism in growth, making early and accurate sex identification crucial for targeted monitoring of sexual development and in-depth research into monosex aquaculture. However, morphological sex identification is unreliable at early developmental stages, and single-marker molecular assays are prone to diagnostic failures caused by genotyping errors or missing data. In this study, we developed a multi-SNP framework for sex prediction by integrating genome-wide association studies (GWAS) with Random Forest (RF) modeling. Analyzing 1244 individuals with a 55K SNP array, we identified 18 genome-wide significant SNPs and 10 candidate genes, including SRRM1 and SPSB3, that may be associated with sexual differentiation. Subsequent multi-marker comparative analyses demonstrated that although the top-performing single marker yielded high accuracy, simulated genotyping errors and missing data caused significant drops in accuracy and elevated no-call rates, whereas multi-SNP panels exhibited substantially improved robustness. A stable performance plateau was reached when expanding the panel to 5 SNPs. The model achieved 100% classification accuracy in the two mixed-sex independent populations, ZJ and HN, and correctly identified all females in the all-female MK cohort. Overall, this study provides a robust multi-SNP tool for sex identification in P. vannamei and highlights the practical utility of machine learning in aquaculture.

Salinity fluctuations pose a severe threat to the survival and physiological homeostasis of freshwater crustaceans. Here, an integrated transcriptomic and metabolomic approach was utilised to characterise the dynamic regulatory responses in the hepatopancreas of adult Macrobrachium rosenbergii subjected to acute high-salinity stress (20‰). The number of differentially expressed genes (DEGs) and metabolites (DMs) increased from 24 to 48 h, signalling a transition from an initial acute stress reaction to systemic homeostatic remodelling. At the molecular level, M. rosenbergii activated autophagy (Atg8a) and p53 (TP53INP) signalling, alongside the reprogramming of lipid and nucleotide metabolism. Pathway analysis identified “alanine, aspartate, and glutamate metabolism” as a central regulatory node. Notably, the downregulation of the glutamate dehydrogenase gene (Gdh) alongside the accumulation of L-glutamate suggests a potential transcriptional-metabolic decoupling phenomenon, wherein the organism appears to prioritise the conservation of critical osmoregulatory effectors over their oxidative degradation. These findings detail the systemic adaptive responses of M. rosenbergii to salinity fluctuations, providing candidate targets for the selective breeding of stress-resilient crustacean strains.