International Journal of Marine Science, 2025, Vol.15, No.5, 268-276 http://www.aquapublisher.com/index.php/ijms 271 and competent management of genetic variation. Family and population structure can, however, affect considerably the accuracy of selection, which could lead to prediction accuracy values being spuriously high if they are not appropriately corrected for. This calls for understanding the genetic connections between breeding populations in order to yield reliable selection outcomes and permanent genetic progress (Werner et al., 2020). 4.2 Application of hybrid breeding to enhance growth performance In hybrid breeding in abalone, genetic variation among different species, strains, or geographic populations is utilized to utilize heterosis to produce hybrid offspring with superior growth and overall production performance compared to parents. Methods of production and research have shown that, particularly under situations of large genetic disparity between parents, hybrid generations possess significant improvement in the major growth traits such as shell length, shell width, and body weight and even higher environmental adaptability and survival capability. During the recent years, the integration of hybrid breeding schemes with marker-assisted selection or genomics-based prediction methods has enabled early prediction of offspring performance, thereby accelerating superior combination identification and improvement in breeding program precision and effectiveness (Xu et al., 2018). 4.3 Limitations of traditional breeding Although traditional mass and family selection programs have achieved remarkable progress in the growth performance of abalone, they are also beset with some serious drawbacks. For the first place, the extensive growth period and late sexual maturity of abalone hinder the slowing down of the process of selection and improvement for each generation and thus limit genetic progress. Secondly, traditional selection relies considerably on big and accurate phenotypic recording, whose validity can be greatly masked by extraneous factors such as farming conditions, quality of diet, and management practices. This is particularly a consideration when approximating breeding value of multifactorial and polygenic traits such as growth rate. In addition, traditional methods are unable to fully reflect the additive effect of small-effect genes and the complex interactions among genes, the scope of which optimal improvement is limited. In the recent past, the advent of new molecular breeding technologies such as genomic selection has the possibility of circumventing such limitations by allowing better genetic assessment and efficient breeding strategies for rapid improvement in elite abalone breeds (Xu et al., 2018). 5 Genomic Selection (GS) for Improving Growth Rate in Abalone 5.1 Principles and workflow of genomic selection Genomic selection (GS) is a breeding technique that predicts the genetic value of individuals based on genome-wide molecular markers to facilitate early and accurate selection of polygenic traits such as abalone growth rate. GS models compute all loci effects, major- and minor-effect genes at once, resolving the absence of traditional marker-assisted selection. The standard process involves collecting phenotypic and genotypic data from a reference group, constructing statistical prediction models, and later predicting selection candidate breeding values from genotypes only. The genetic gain may be boosted and breeding cycles minimized significantly through the incorporation of GS in abalone breeding programs (Liu et al., 2022; Su et al., 2025). 5.2 Phenotypic and genotypic data acquisition Accurate phenotyping of growth traits (such as body weight, shell length, and shell width) is the foundation for GS application. QTL mapping and LOD profile analysis of growth traits on 18 linkage groups of abalone revealed the genetic localization characteristics of total weight (A), shell length (B), and shell width (C) and also provided a reference for gene screening and marker development subsequently. Common genotyping technology that may be employed includes SNP arrays, GBS (genotyping-by-sequencing), and WGS (whole-genome sequencing) (Kho et al., 2021) (Figure 2). SNP arrays are of moderate cost and high throughput; GBS is of high marker density, can be used to identify new variants, and is suitable for non-model animals such as abalone, with missing data imputed to further improve data quality; WGS can precisely map QTLs and identify causal variants, enabling support of data in fine mapping and gene function analyses (Munyengwa et al., 2021; Ćeran et al., 2024).
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