Estimation of Genetic Parameters for Growth Traits and Genomic Selection Study in Siniperca Scherzeri

Abstract

Siniperca scherzeri is an economically important freshwater species in East Asia, characterized by high market value and considerable breeding potential. To elucidate the genetic underpinnings of growth traits and evaluate the feasibility of genomic selection (GS) in this species, we employed whole-genome resequencing data to estimate genetic parameters and conduct genomic prediction analyses for five growth traits: body weight, body length, extended length, body height, and body thickness. We compared six GS models-GBLUP, BayesA, BayesB, BayesC, BayesL, and BayesRR-and assessed the effects of marker density and SNP selection strategies on prediction ability. The growth traits exhibited generally low-to-moderate heritability; notably, body length (BL) and extended length (EL) showed higher estimates (0.224 and 0.208, respectively), suggesting priority targets for genetic improvement. Across the six models, prediction ability was moderate overall (0.16-0.31), with GBLUP delivering relatively high and stable performance. Further analyses demonstrated that GWAS-informed SNP selection markedly outperformed random selection, enabling high prediction ability at reduced marker densities. In particular, for BL and EL, panels with only 500 SNPs achieved high prediction ability (0.70), while random selection reached prediction levels comparable to the whole-genome set at 1 K SNPs. Thus, low-density marker panels can substantially reduce genotyping costs while maintaining prediction ability. Collectively, these findings delineate the genetic characteristics of growth in S. scherzeri, validate the feasibility of GWAS-guided marker selection for GS, and provide a sound theoretical basis and practical reference for molecular breeding and genetic improvement in aquaculture species.

Keywords: Genomic selection; Growth traits; Heritability; Marker density; Prediction ability; Siniperca scherzeri.