The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference.

中文翻译：

---

Nelore牛牛肉脂肪酸谱的基于单倍型和基于个体snp的基因组预测的比较

本研究的目的是使用单步基因组最佳线性无偏预测器 (ssGBLUP) 方法评估基因组预测，该方法基于与 Nelore 牛的牛肉脂肪酸 (FA) 谱相关的 SNP 和单倍型标记。该数据集包含 963 头在饲养场（±90 天）完成并在大约 24 个月大时被屠宰的 Nelore 公牛的记录。取来自背最长肌的肉样品用于 FA 分布测量。FA 使用 SP-2560 毛细管柱通过气相色谱法定量。使用包含 777,962 个标记的高密度 SNP 面板（BovineHD BeadChip 检测）对动物进行基因分型。从数据集中删除了具有次要等位基因频率和调用率分别低于 0.05 和 0.90、单态、位于性染色体上且位置未知的 SNP。经过基因组质量控制后，共有 469,981 个 SNP 和 892 个样本可用于后续分析。使用 FImpute 软件对缺失的基因型进行估算和定相。使用 Haploview 软件基于连锁不平衡定义单倍型块。估计方差分量和遗传参数以及预测基因组值的模型包括随机遗传加性效应、当代群体的固定效应和屠宰年龄作为线性协变量。使用基于单倍型的方法的准确度范围为 0.07 到 0.31，而基于 SNP 的方法的准确度范围为 0.06 到 0.33。使用基于单倍型和 SNP 的方法，回归系数的范围分别为 0.07 到 0.74 和 0.08 到 1.45。尽管基因组值的准确度低到中等，可以使用基于 SNP 或单倍型标记的基因组信息通过选择获得遗传进展。基于 SNP 的方法允许较少有偏见的基因组评估，并且在考虑单倍型推断的计算和操作成本时更可行。