Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing

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 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. 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. •First genomic prediction for caviar yield, caviar color and body weight in sturgeons.•BaseVar+STITCH was the best pipeline for low-coverage whole-genome sequencing (LCS) imputation.•A sequencing depth of 2× and a sample size exceeding 300 could generate reliable genotyping for LCS.•A sequencing depth of 0.5× and SNP density of 50 K were sufficient to achieve accurate genomic prediction.•Incremental feature selection is an effective strategy for improving genomic prediction.