# Genomic Evaluation of Harvest Weight Uniformity in Penaeus vannamei Under a 3FAM Design Incorporating Indirect Genetic Effect

## TL;DR

This study improves the genetic evaluation of shrimp harvest weight uniformity by incorporating social interactions and genomic data.

## Contribution

The study introduces a novel method combining indirect genetic effects and genomic information to enhance breeding accuracy for shrimp harvest weight uniformity.

## Key findings

- Incorporating indirect genetic effects increased heritability estimates for uniformity by 150% to 240%.
- Genomic data improved prediction accuracy for harvest weight and uniformity by 6.35% and 10.53%, respectively.
- The genetic correlation between harvest weight and uniformity shifted from strongly negative to weakly positive when IGE was included.

## Abstract

The Pacific white shrimp (Penaeus vannamei) is a key species in global aquaculture. However, significant variation in harvest weight—sometimes exceeding a tenfold difference among full-sibling individuals—poses challenges to farm productivity and profitability. A crucial first step in improving this trait through selective breeding is the precise estimation of genetic parameters. While previous studies have reported such variability in various aquaculture species, including shrimp, existing assessment methods require further refinement. It is essential to account for key environmental factors, such as inter-individual interaction effects, which can influence both individual productivity and welfare. Additionally, incorporating genomic information rather than relying solely on pedigree data can provide a clearer representation of genetic relationships among individuals. These studies have aimed to enhance the accuracy of genetic variance estimation by improving models for harvest weight uniformity, ultimately assessing the feasibility of including this trait in selective breeding programs. This study employed a grouping design with three families per group involving 40 families of shrimps containing 36 shrimps per family to estimate the contribution of direct and indirect genetic effects on harvest weight uniformity. This was estimated using a hierarchical generalized linear model, integrating genomic and indirect genetic effect data. Our findings indicate that integrating social interaction effects and genomic information has the potential to improve the precision of genetic evaluations and advancing breeding strategies for enhanced productivity in P. vannamei.

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.