Genomic prediction for tick resistance in Braford and Hereford cattle
Genomic prediction for tick resistance in Braford and Hereford cattle
复制标题
DOI：
10.2527/jas.2014-8832
复制
发表时间：
2015-06-01
影响因子：
3.3
通讯作者：
Aguilar, I.
中科院分区：
文献类型：
--
作者：
Cardoso, F. F.;Gomes, C. C. G.;Aguilar, I.
热带和亚热带牛生产中的主要动物健康问题之一是牛蜱，它会导致生产性能下降、生皮贬值、杀螨剂治疗的生产成本增加以及传染病的传播。本研究调查了基因组预测作为选择对蜱具有抵抗力的布拉福德 (BO) 和赫里福德 (HH) 牛的工具的效用。使用从属于 Delta G Connection 育种计划的 3,435 头 BO 和 928 头 HH 牛获得的 10,673 只蜱计数来评估直接和混合基因组预测的不同方法的准确性和偏差。对 2,803 个 BO 和 652 个 HH 样本的子集进行了基因分型，质量控制后保留了 41,045 个标记。对数转换记录通过谱系重复性模型进行调整，以估计方差分量、遗传参数和育种值 (EBV)，随后用于获得回归的 EBV。蜱计数的估计遗传力和重复性分别为 0.19 +/- 0.03 和 0.29 +/- 0.01。使用 k 均值和随机聚类将数据分为 5 个子集，以交叉验证基因组预测。根据方法的不同，直接基因组值 (DGV) 预测精度的范围从使用贝叶斯最小绝对收缩和选择算子 (LASSO) 的 0.35 到使用 BayesB 进行 k 均值聚类的 0.39，以及使用 BayesLASSO 的 0.42 和使用 BayesC 进行随机聚类的 0.45。所有基因组方法均优于谱系 BLUP (PBLUP)，k 均值准确度为 0.26，随机组准确度为 0.29，其中 k 均值 BayesB (39%) 和随机组 BayesC (55%) 获得的准确度增益最高。通过不同方法混合历史表型和谱系信息，进一步将直接预测方法的 DGV 准确度提高了 0.03 至 0.05 之间。然而，单步基因组 BLUP 的准确度最高，k 均值分别为 0.48 和 0.56，分别比 PBLUP 提高了 84% 和 93%。根据混合方法，观察到的 HH 随机聚类交叉验证品种特异性准确度范围在 0.29 到 0.36 之间，BO 范围在 0.55 到 0.61 之间。 BO 的这些较高值表明，基因组预测可以用作提高蜱遗传抗性和开发该品种抗性品系的实用工具。对于 HH，准确度仍处于中低水平，需要增加该品种训练群体，然后才能可靠地应用基因组选择来提高蜱虫抗性。
One of the main animal health problems in tropical and subtropical cattle production is the bovine tick, which causes decreased performance, hide devaluation, increased production costs with acaricide treatments, and transmission of infectious diseases. This study investigated the utility of genomic prediction as a tool to select Braford (BO) and Hereford (HH) cattle resistant to ticks. The accuracy and bias of different methods for direct and blended genomic prediction was assessed using 10,673 tick counts obtained from 3,435 BO and 928 HH cattle belonging to the Delta G Connection breeding program. A subset of 2,803 BO and 652 HH samples were genotyped and 41,045 markers remained after quality control. Log transformed records were adjusted by a pedigree repeatability model to estimate variance components, genetic parameters, and breeding values (EBV) and subsequently used to obtain deregressed EBV. Estimated heritability and repeatability for tick counts were 0.19 +/- 0.03 and 0.29 +/- 0.01, respectively. Data were split into 5 subsets using k-means and random clustering for cross-validation of genomic predictions. Depending on the method, direct genomic value (DGV) prediction accuracies ranged from 0.35 with Bayes least absolute shrinkage and selection operator (LASSO) to 0.39 with BayesB for k-means clustering and between 0.42 with BayesLASSO and 0.45 with BayesC for random clustering. All genomic methods were superior to pedigree BLUP (PBLUP) accuracies of 0.26 for k-means and 0.29 for random groups, with highest accuracy gains obtained with BayesB (39%) for k-means and BayesC (55%) for random groups. Blending of historical phenotypic and pedigree information by different methods further increased DGV accuracies by values between 0.03 and 0.05 for direct prediction methods. However, highest accuracy was observed with single-step genomic BLUP with values of 0.48 for k-means and 0.56, which represent, respectively, 84 and 93% improvement over PBLUP. Observed random clustering cross-validation breedspecific accuracies ranged between 0.29 and 0.36 for HH and between 0.55 and 0.61 for BO, depending on the blending method. These moderately high values for BO demonstrate that genomic predictions could be used as a practical tool to improve genetic resistance to ticks and in the development of resistant lines of this breed. For HH, accuracies are still in the low to moderate side and this breed training population needs to be increased before genomic selection could be reliably applied to improve tick resistance.