School of Agricultural and Veterinarian Sciences, Sao Paulo State University (UNESP), Jaboticabal, SP, 14884-900, Brazil.
School of Veterinary and Animal Sciences, Universidade Federal da Bahia (UFBA), Salvador, BA, 40170-110, Brazil.
Trop Anim Health Prod. 2021 Jun 8;53(3):349. doi: 10.1007/s11250-021-02785-1.
The objective of this study was to evaluate the accuracy of genomic predictions of growth traits in Nellore cattle. Data from 5064 animals belonging to farms that participate in the Conexão DeltaGen and PAINT breeding programs were used. Genotyping was performed with the Illumina BovineHD BeadChip (777,962 SNPs). After quality control of the genomic data, 412,993 SNPs were used. Deregressed EBVs (DEBVs) were calculated using the estimated breeding values (EBVs) and accuracies of birth weight (BW), weight gain from birth to weaning (GBW), postweaning weight gain (PWG), yearling height (YH), and cow weight (CW) provided by GenSys. Three models were used to estimate marker effects: genomic best linear unbiased prediction (GBLUP), BayesCπ, and improved Bayesian least absolute shrinkage and selection operator (IBLASSO). The prediction ability of genomic estimated breeding value (GEBVs) was estimated by the average Pearson correlation between DEBVs and GEBVs, predicted with the different methodologies in the validation populations. The regression coefficients of DEBVs on GEBVs in the validation population were calculated and used as indicators of prediction bias of GEBV. In general, the Bayesian methods provided slightly more accurate predictions of genomic breeding values than GBLUP. The BayesCπ and IBLASSO were similar for all traits (BW, GBW, PWG, and YH), except for CW. Thus, there does not seem to be a more suitable method for the estimation of SNP effects and genomic breeding values. Bayesian regression models are of interest for future applications of genomic selection in this population, but further improvements are needed to reduce deflation of their predictions.
本研究旨在评估基因组预测内罗尔牛生长性状的准确性。使用了来自参加 Conexão DeltaGen 和 PAINT 育种计划的农场的 5064 头动物的数据。基因分型采用 Illumina BovineHD BeadChip（777962 个 SNP）进行。对基因组数据进行质量控制后，使用了 412993 个 SNP。使用 GenSys 提供的估计育种值（EBV）和初生重（BW）、断奶重（GBW）、断奶后体重增加（PWG）、一岁身高（YH）和母牛体重（CW）的回归 EBV（DEBV）进行计算。使用了三种模型来估计标记效应：基因组最佳线性无偏预测（GBLUP）、BayesCπ和改进的贝叶斯最小绝对收缩和选择算子（IBLASSO）。通过在验证群体中使用不同方法预测 GEBV 的平均 Pearson 相关系数来评估 GEBV 的预测能力。在验证群体中，计算了 DEBV 对 GEBV 的回归系数，并将其用作 GEBV 预测偏差的指标。一般来说，贝叶斯方法比 GBLUP 提供了稍微更准确的基因组育种值预测。除了 CW 外，BayesCπ 和 IBLASSO 对于所有性状（BW、GBW、PWG 和 YH）的预测都相似。因此，似乎没有更适合估计 SNP 效应和基因组育种值的方法。贝叶斯回归模型是该群体中基因组选择未来应用的一个研究方向，但需要进一步改进以减少其预测的紧缩。
