Department of Animal and Dairy Science, University of Georgia, Athens, GA, 30602, USA.
National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, WV, 25430, USA.
Genet Sel Evol. 2023 Feb 9;55(1):11. doi: 10.1186/s12711-023-00782-6.
In aquaculture, the proportion of edible meat (FY = fillet yield) is of major economic importance, and breeding animals of superior genetic merit for this trait can improve efficiency and profitability. Achieving genetic gains for fillet yield is possible using a pedigree-based best linear unbiased prediction (PBLUP) model with direct and indirect selection. To investigate the feasibility of using genomic selection (GS) to improve FY and body weight (BW) in rainbow trout, the prediction accuracy of GS models was compared to that of PBLUP. In addition, a genome-wide association study (GWAS) was conducted to identify quantitative trait loci (QTL) for the traits. All analyses were performed using a two-trait model with FY and BW, and variance components, heritability, and genetic correlations were estimated without genomic information. The data used included 14,165 fish in the pedigree, of which 2742 and 12,890 had FY and BW phenotypic records, respectively, and 2484 had genotypes from the 57K single nucleotide polymorphism (SNP) array.
The heritabilities were moderate, at 0.41 and 0.33 for FY and BW, respectively. Both traits were lowly but positively correlated (genetic correlation; r = 0.24), which suggests potential favourable correlated genetic gains. GS models increased prediction accuracy compared to PBLUP by up to 50% for FY and 44% for BW. Evaluations were found to be biased when validation was performed on future performances but not when it was performed on future genomic estimated breeding values.
The low but positive genetic correlation between fillet yield and body weight indicates that some improvement in fillet yield may be achieved through indirect selection for body weight. Genomic information increases the prediction accuracy of breeding values and is an important tool to accelerate genetic progress for fillet yield and growth in the current rainbow trout population. No significant QTL were found for either trait, indicating that both traits are polygenic, and that marker-assisted selection will not be helpful to improve these traits in this population.
在水产养殖中,可食用肉的比例(FY=鱼片产量)具有重要的经济意义,通过选育具有优良遗传特性的动物,可以提高养殖效率和盈利能力。通过基于系谱的最佳线性无偏预测(PBLUP)模型进行直接和间接选择,可以实现鱼片产量的遗传增益。为了研究基因组选择(GS)在提高虹鳟鱼片产量和体重(BW)方面的可行性,比较了 GS 模型的预测准确性与 PBLUP。此外,还进行了全基因组关联研究(GWAS),以鉴定这些性状的数量性状基因座(QTL)。所有分析均使用 FY 和 BW 的两性状模型进行,在没有基因组信息的情况下估计方差分量、遗传力和遗传相关性。所使用的数据包括系谱中的 14165 条鱼,其中 2742 条和 12890 条分别具有 FY 和 BW 的表型记录,2484 条具有 57K 单核苷酸多态性(SNP)阵列的基因型。
遗传力适中,FY 和 BW 分别为 0.41 和 0.33。两个性状都呈低度正相关(遗传相关;r=0.24),这表明可能存在有利的相关遗传增益。与 PBLUP 相比,GS 模型最多可将 FY 的预测准确性提高 50%,BW 的预测准确性提高 44%。当在未来表现上进行验证时,评估会出现偏差,但当在未来基因组估计育种值上进行验证时则不会。
鱼片产量和体重之间的低但正遗传相关表明,通过间接选择体重,可能会提高鱼片产量。基因组信息提高了育种值的预测准确性,是加速当前虹鳟鱼群体鱼片产量和生长遗传进展的重要工具。未发现任何性状的显著 QTL,表明这两个性状都是多基因的,标记辅助选择对提高该群体的这些性状没有帮助。