AQUA 2024

August 26 - 30, 2024

Copenhagen, Denmark

GENOMIC PREDICTION FOR GENETIC IMPROVEMENT OF LOW-FISHMEAL DIET ADAPTABILITY IN OLIVE FLOUNDER Paralichthys olivaceus

Ji Hun Lee* , D.S. Liyanage, W.K.M. Omeka, H.M.V. Udayantha, Jeongeun Kim, Gaeun Kim, Y.K . Kodagoda, H.A.C.R. Hanchapola, M.A.H. Dilshan, D.C.G. Rodrigo, G.A.N.P. Ganepola,  Mun-kwan Kim,  Taehyug Jeong,  Sukkyoung Lee, and Jehee Lee

 

Department of Marine Life Sciences

Jeju National University

Jeju Self-Governing Province 63243

wlgns5306@naver.com

 



Olive flounder (Paralichthys olivaceus) is a carnivorous marine flatfish one of the in-demand aquaculture species in East Asian countries. In most of the olive flounder farms in Korea, moisture pellets (MP) are used as feed. However, MP can easily deteriorate and catalyze microbial proliferation due to its high moisture content, which possesses negative impacts on the nutritional supply and augments the likelihood of disease transmission. Although numerous studies have been conducted on extruded pellets (EP) and fishmeal alternatives to reduce fishmeal content in EP, Korean farmers are hesitant to use EP due to lower growth rates compared to MP. Therefore, developing fast-growing olive flounder strains under low-fishmeal EP regimes is required. In this study, we conducted a genome-wide association study (GWAS) to elucidate the genetic architecture related to low-fishmeal diet adaptability and constructed a genomic prediction model to generate a fast-growing population under low-fishmeal EP regimes.

 We used low-fishmeal EP with a 50% reduction in fishmeal content compared to commercial EP and fed from June to October 2023.  We measured the  growth phenotype every two months and collected caudal fin samples for genotyping using  a  70K SNP chip designed by our laboratory. The phenotypic data used for analysis included final weight (WT), weight gain rate (WGR), and specific growth rate (SGR).

 GWAS was performed using  a linear mixed model with the gaston package in R. As a result, we identified 1, 3, and 1 significant SNPs for WT, WGR,  and SGR, respectively, based on the Bonferroni cutoff (The result for WT is shown in Figure 1 as a representative example).

The genomic prediction models were built using 10 different algorithms, and the prediction ability was estimated through 3 repeats of  3-fold cross-validation. As a result, the prediction ability for WT was relatively high compared with WGR and SGR (Table 1) . Especially, the Bayes B algorithm exhibited the highest value (Table 1).  The findings of this study can be utilized in the development of new olive flounder strains with high low-fishmeal diet adaptability.