Evaluating genomic selection for bacterial cold water disease resistance in rainbow trout using RAD-seq genotyping and a 57K SNP chip.. Oncorhynchus mykiss breed:NCCCWA Odd-year

Background: Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture. At the National Center for Cool and Cold Water Aquaculture (NCCCWA), we have pursued selective breeding to increase rainbow trout genetic resistance against BCWD and found that BCWD resistance is moderately heritable and responds to selection. Genomic selection (GS) is a recently developed methodology that is revolutionizing animal breeding. In this pilot study, we used GS models to predict genome-enabled breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the reliability of GEBVs to pedigree-based estimated breeding values (EBVs) and compared the impact of two SNP genotyping methods on the reliability of GEBV predictions.Methods: The BCWD phenotypes survival days (DAYS) and survival status (STATUS) were recorded in training animals (n=583). The animals were genotyped using two methods; restriction-site associated DNA (RAD) sequencing and a 57K SNP chip. The GEBVs were estimated using Bayesian variable selection and single-step GBLUP models. The reliability of GEBVs was assessed using validation animals (n=53) that had progeny testing-based EBV records. The reliability was assessed through predictive ability defined as the correlation between progeny testing EBVs and GEBVs.Results: Overall, the reliability of GEBV estimated with GS models was similar to the pedigree-based EBVs. The RAD genotyping platform (~10K informative SNPs) was as efficient as the SNP Chip (~42K SNPs).Conclusions: This study demonstrated the potential advantages of implementing GS for BCWD resistance in rainbow trout sib-testing selection breeding programs. However, the training sample size in this pilot study was small, and hence we expect higher reliability of GEBV predictions in larger commercial rainbow trout breeding populations. This study provides the basis for further investigation on the use of GS in commercial rainbow trout populations, including the potential for its implementation. Although RAD SNP genotyping is a viable method for predicting GEBVs, we find that the SNP chip is more robust and practical for real time breeding in rainbow trout aquaculture.

背景：细菌性冷水病（Bacterial Cold Water Disease, BCWD）会给鲑科水产养殖造成显著经济损失。美国国家冷水水产养殖中心（National Center for Cool and Cold Water Aquaculture, NCCCWA）团队长期开展虹鳟抗BCWD的选择性育种研究，证实BCWD抗性具有中等遗传力，且可通过选育获得遗传进展。基因组选择（Genomic Selection, GS）是近年来兴起的革新性动物育种技术。本预试验针对NCCCWA抗BCWD育种品系初代的10个家系，采用GS模型预测虹鳟抗BCWD性状的基因组育种值（Genome-enabled Breeding Values, GEBVs），对比了GEBVs与基于系谱的估计育种值（Pedigree-based Estimated Breeding Values, EBVs）的预测可靠性，并评估了两种SNP基因分型方法对GEBV预测可靠性的影响。
方法：本试验对583头训练群体个体记录了BCWD相关表型：存活天数（Survival Days, DAYS）与存活状态（Survival Status, STATUS）。采用两种方法对试验个体进行SNP基因分型：酶切位点关联DNA测序（Restriction-site Associated DNA Sequencing, RAD-seq）与57K SNP芯片。分别通过贝叶斯变量选择模型和单步GBLUP模型估算GEBVs。以53头拥有后裔测定EBV记录的验证群体个体评估GEBV的预测可靠性，可靠性以后裔测定EBV与GEBV的相关系数作为预测能力指标进行计算。
结果：总体而言，GS模型估算的GEBV可靠性与基于系谱的EBVs相当。约包含1万个有效SNP的RAD基因分型平台，其预测效率与包含约4.2万个有效SNP的SNP芯片相当。
结论：本研究证实，在虹鳟家系选择育种项目中应用GS开展抗BCWD性状选育具备潜在优势。但本预试验的训练群体规模较小，因此我们预期在更大规模的商业虹鳟育种群体中，GEBV的预测可靠性将得到提升。本研究为后续在商业虹鳟群体中应用GS技术（包括其产业化落地潜力）提供了研究基础。尽管RAD SNP基因分型可用于GEBV预测，但我们发现SNP芯片在虹鳟水产养殖的实时育种场景中具备更强的稳定性与实用性。