International Journal of Aquaculture, 2025, Vol.15, No.5, 229-239 http://www.aquapublisher.com/index.php/ija 232 4 Application of GWAS in Shrimp 4.1 Theoretical framework and technical platform of GWAS Genome-wide association analysis (GWAS) uses natural variation at the population level to discover trait-related genomic loci through statistical associations. Unlike QTL mapping-dependent families, GWAS is usually performed in groups with diverse genetic backgrounds (such as breeding groups or wild populations), each body is genotyped at high density and then the degree of association between each genotype and phenotype is detected. The theoretical basis of GWAS is that if alleles at a certain position in the genome affect traits, individuals carrying different alleles will have significant differences in the traits. By scanning the whole genome markers, these significant differential sites can be localized to identify potential functional genes. The implementation of shrimp GWAS is due to the advancement of classification technology. In the early days, due to the lack of a high-throughput classification platform, it was difficult to carry out GWAS in shrimp. The simplified genome sequencing methods that have emerged in recent years have greatly reduced the cost, and tens of thousands or even hundreds of thousands of SNP markers can be obtained in one sequencing, providing the possibility for GWAS. Yu et al. (2019) used RAD sequencing to obtain approximately 23 000 SNPs and performed GWAS analysis on 200 vannabinoid shrimps, demonstrating the feasibility of this method in crustaceans (Yu et al., 2019). 4.2 Common typing and statistical methods for correlation analysis In the practice of shrimp GWAS, different studies have adopted a variety of classification strategies and statistical methods. In terms of typing technology, the most widely used SNP typing based on simplified genome sequencing. Some studies have used 2b-RAD data for GWAS. As genomic work advances, customized SNP chips for Vannebacteria prawns have also been released, such as the 55K SNP chip with more than 72 000 markers developed by the Chinese scientific research team. In terms of statistical methods, classic single-label tests (such as linear regression per SNP) are prone to false associations when considering population structure, so the common MLM model introduces inter-individual relationship matrix and structural matrix (Medrano-Mendoza et al., 2022). 4.3 Current status and results of GWAS research on shrimp growth traits Although it started late, several GWAS studies on the growth traits of shrimp have been published in recent years, revealing several key genes and molecular mechanisms. In vannabinoid shrimp, genome-wide association analysis was performed on 200 individuals in the breeding population, and four SNP sites significantly associated with body weight were identified, located in the linkage groups 19 and 39, respectively (Lyu et al., 2021). Further, they locked in two candidate genes: the deoxycytidyl deaminase gene (DCD) and the non-receptor type tyrosine kinase gene (NRTK), and found that specific SNPs of these genes were significantly associated with shrimp body weight. Using the screened SNPs, they predicted breeding values using a label-assisted BLUP method, with an accuracy of 9.4% higher than that of traditional BLUPs, indicating that these GWAS findings are of practical value. In another study, experts found that genes such as protein kinase C-δ (PKC-δ) and Rap2a may be involved in shrimp growth regulation through GWAS of a family offspring (Yu et al., 2019). 5 Molecular Markers and Candidate Gene Recognition 5.1 Application of molecular markers such as SSR and SNP in shrimp Molecular markers are the bridge connecting genotypes to trait phenotypes and the basis for QTL and GWAS research. In early genetic studies of shrimp, simple repeat sequences (SSR) are a mainstream marker. SSR, also known as microsatellites, is widely used in the construction of genetic maps and population diversity analysis of shrimps due to its multi-alloid and highly polymorphic characteristics (Chen et al., 2024). The first genetic linkage map of Chinese shrimp is mainly composed of hundreds of SSR markers, which is limited in density but sets the starting point for QTL positioning. SSR labeling technology is mature and has low cost, but automated detection and throughput are relatively limited. At the same time, the genome distribution of microsatellites is uneven, which restricts fine localization. With the development of sequencing technology, single nucleotide polymorphism (SNP) markers have gradually become the preferred marker for shrimp molecular breeding. SNPs are abundant and widely distributed in the genome, and thousands of loci can be discovered at one time through
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