Genome-wide eQTL Mapping Identifies Regulatory Variants Underlying Growth Traits in Rainbow Trout

Background: Growth traits in rainbow trout are complex and influenced by numerous genetic variants, yet the regulatory mechanisms underlying phenotypic variation remain poorly characterized. Results: We integrated low-coverage whole-genome sequencing, RNA sequencing, differential gene expression analysis, and genome-wide expression quantitative trait locus (eQTL) mapping to identify regulatory variation associated with body weight, muscle yield, and condition factor. Genome-wide eQTL mapping identified 234,630 significant cis-SNP-gene associations involving 2,308 genes. We subsequently focused on 6,275 significant TSS-proximal cis-eQTL associations located within 10 kb of transcription start sites and integrated these with white-muscle differential expression, genotype, and phenotypic data. Candidate genes were prioritized using convergent evidence from complementary analyses of genotype frequencies, genotype-dependent muscle gene expression, differential expression, and quantitative phenotypes. This integrative approach identified candidate regulatory variants associated with body weight, muscle yield, and condition factor and revealed substantial cis-regulatory differentiation between two selectively bred genetic lines. Several candidates showed concordant associations among genotype, muscle gene expression, and phenotype. Candidate SNP association analysis using genomic mixed models in the broader population provided additional support for variants near LOC118940393 and within neurofilament medium polypeptide-like associated with condition factor. Conclusions: This study provides the first comprehensive eQTL resource in rainbow trout and demonstrates the utility of combining genomic and transcriptomic data to elucidate regulatory variation influencing growth. These findings offer valuable targets for functional validation and potential incorporation into genomics-assisted selective breeding programs.