利用 80K、650K 和全基因组序列变异对杜洛克猪饲料效率组成性状进行基因组评估。
Genomic evaluation of feed efficiency component traits in Duroc pigs using 80K, 650K and whole-genome sequence variants.
机构信息
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
Genesus Inc., Oakville, MB, R0H 0Y0, Canada.
出版信息
Genet Sel Evol. 2018 Apr 6;50(1):14. doi: 10.1186/s12711-018-0387-9.
BACKGROUND
Increasing marker density was proposed to have potential to improve the accuracy of genomic prediction for quantitative traits; whole-sequence data is expected to give the best accuracy of prediction, since all causal mutations that underlie a trait are expected to be included. However, in cattle and chicken, this assumption is not supported by empirical studies. Our objective was to compare the accuracy of genomic prediction of feed efficiency component traits in Duroc pigs using single nucleotide polymorphism (SNP) panels of 80K, imputed 650K, and whole-genome sequence variants using GBLUP, BayesB and BayesRC methods, with the ultimate purpose to determine the optimal method to increase genetic gain for feed efficiency in pigs.
RESULTS
Phenotypes of average daily feed intake (ADFI), average daily gain (ADG), ultrasound backfat depth (FAT), and loin muscle depth (LMD) were available for 1363 Duroc boars from a commercial breeding program. Genotype imputation accuracies reached 92.1% from 80K to 650K and 85.6% from 650K to whole-genome sequence variants. Average accuracies across methods and marker densities of genomic prediction of ADFI, FAT, LMD and ADG were 0.40, 0.65, 0.30 and 0.15, respectively. For ADFI and FAT, BayesB outperformed GBLUP, but increasing marker density had little advantage for genomic prediction. For ADG and LMD, GBLUP outperformed BayesB, while BayesRC based on whole-genome sequence data gave the best accuracies and reached up to 0.35 for LMD and 0.25 for ADG.
CONCLUSIONS
Use of genomic information was beneficial for prediction of ADFI and FAT but not for that of ADG and LMD compared to pedigree-based estimates. BayesB based on 80K SNPs gave the best genomic prediction accuracy for ADFI and FAT, while BayesRC based on whole-genome sequence data performed best for ADG and LMD. We suggest that these differences between traits in the effect of marker density and method on accuracy of genomic prediction are mainly due to the underlying genetic architecture of the traits.
背景
增加标记密度被认为有潜力提高数量性状的基因组预测准确性；全序列数据有望提供最佳的预测准确性，因为所有构成性状的因果突变都有望被包含在内。然而，在牛和鸡中，这一假设并没有得到经验研究的支持。我们的目的是比较使用 GBLUP、BayesB 和 BayesRC 方法，使用 80K、推断的 650K 和全基因组序列变异的单核苷酸多态性 (SNP) 面板，预测杜洛克猪饲料效率组成性状的基因组预测准确性，最终目的是确定提高猪饲料效率遗传增益的最佳方法。
结果
来自商业育种计划的 1363 头杜洛克公猪的平均日采食量（ADFI）、平均日增重（ADG）、超声背膘深度（FAT）和腰肌肉深度（LMD）表型可用。从 80K 到 650K 的基因型推断准确性达到 92.1%，从 650K 到全基因组序列变异的推断准确性达到 85.6%。跨方法和标记密度的 ADFI、FAT、LMD 和 ADG 的基因组预测平均准确性分别为 0.40、0.65、0.30 和 0.15。对于 ADFI 和 FAT，BayesB 优于 GBLUP，但增加标记密度对基因组预测几乎没有优势。对于 ADG 和 LMD，GBLUP 优于 BayesB，而基于全基因组序列数据的 BayesRC 给出了最佳的准确性，最高可达 0.35 的 LMD 和 0.25 的 ADG。
结论
与基于系谱的估计相比，使用基因组信息有利于预测 ADFI 和 FAT，但不利于 ADG 和 LMD。基于 80K SNP 的 BayesB 对 ADFI 和 FAT 的基因组预测准确性最高，而基于全基因组序列数据的 BayesRC 对 ADG 和 LMD 的预测准确性最高。我们建议，性状之间在标记密度和方法对基因组预测准确性的影响方面的这些差异主要归因于性状的潜在遗传结构。
Zotero 插件