Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (~13.68×) . The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of high-depth sequencing. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.
To leverage the advantages of sequencing data, we investigated the potential of incremental feature selection, which involved ranking the SNPs based on the strength of their association with the phenotype as determined by a GWAS, to improve the accuracy of genomic prediction. As shown in Fig. 1 , the genomic prediction accuracy increased by 4.9%, 1.1% and 9.2% for caviar yield, caviar color and body weight, respectively.
In this study, SNP panels with different densities were generated by linkage disequilibrium pruning of sequencing data at different depths, and the genomic prediction performance under different densities of SNP was compared. The results showed that a density of 50K for SNP was sufficient to obtain accuracy similar to that of high-density SNP with less bias.