In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved. Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy. In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included. While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

中文翻译：

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利用全基因组序列数据和新颖的基因组选择策略来提高适应热带气候的小母牛的青春期年龄选择。

在热带适应的牛小母牛中，由于预测准确性较低，因此在青春期进行基因组预测的应用受到了限制。我们的目的是研究预先选择全基因组序列（WGS）变体的新方法和替代分析方法。包括具有多个基因组关系矩阵（MGRM）和贝叶斯（BayesR）分析的基因组最佳线性无偏预测（GBLUP），以确定是否可以提高青春期的预测准确性。基因型和表型来自两个研究群体。在第一个种群中总共记录了868个婆罗门小母牛和960个热带复合小母牛，在第二个种群中记录了3695个婆罗门，Santa Gertrudis和Droughtmaster小母牛。基因型被估算为2300万个全基因组序列变异体。使用条件或联合（COJO）分析，使用八种策略从全基因组关联研究（GWAS）结果中预先选择变体。预选的变体包含在三个模型中，分别是具有单个基因组关系矩阵（SGRM）的GBLUP，GBLUP MGRM和BayesR。五向交叉验证用于测试标记面板密度（6 K，50 K和800 K），分析模型以及包括预选的WGS变体对预测准确性的影响。在所有测试的场景中，BayesR分析中青春期的预测准确性最高。当使用BayesR时，添加预选的WGS变体对预测的准确性影响很小。包含通过MGRM模型进行荟萃分析并通过染色体COJO分析进行预选的WGS变体，无论标记密度如何，GBLUP分析中的预测准确性最高。当使用低密度（6 K）面板时，当仅包括6个其他序列变体（通过染色体的荟萃分析COJO识别）时，GBLUP的预测准确性等于高密度面板的预测准确性（0.42）。尽管BayesR在预测准确性方面一直优于其他方法，但是当使用GBLUP和低密度面板（包括相对少量的高度相关的WGS变体）时，可以实现准确性的合理提高。42）到高密度面板时，仅包括六个其他序列变体（使用基于染色体的荟萃分析COJO识别）。尽管BayesR在预测准确性方面一直优于其他方法，但是当使用GBLUP和低密度面板（包括相对少量的高度相关的WGS变体）时，可以实现准确性的合理提高。42）到高密度面板时，仅包括六个其他序列变体（通过染色体的荟萃分析COJO识别）。尽管BayesR在预测准确性方面一直优于其他方法，但是当使用GBLUP和低密度面板（包括相对少量的高度相关的WGS变体）时，可以实现准确性的合理提高。