Certain agronomic crop traits are complex and thus governed by many small‐effect loci. Statistical models typically used in a genome‐wide association study (GWAS) and genomic selection (GS) quantify these signals by assessing genomic marker contributions in linkage disequilibrium (LD) with these loci to trait variation. These models have been used in separate quantitative genetics contexts until recently, when, in published studies, the predictive ability of GS models that include peak associated markers from a GWAS as fixed‐effect covariates was assessed. Previous work suggests that such models could be useful for predicting traits controlled by several large‐effect and many small‐effect genes. We expand this work by evaluating simulated traits from diversity panels in maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] using ridge‐regression best linear unbiased prediction (RR‐BLUP) models that include fixed‐effect covariates tagging peak GWAS signals. The ability of such covariates to increase GS prediction accuracy in the RR‐BLUP model under a wide variety of genetic architectures and genomic backgrounds was quantified. Of the 216 genetic architectures that we simulated, we identified 60 where the addition of fixed‐effect covariates boosted prediction accuracy. However, for the majority of the simulated data, no increase or a decrease in prediction accuracy was observed. We also noted several instances where the inclusion of fixed‐effect covariates increased both the variability of prediction accuracies and the bias of the genomic estimated breeding values. We therefore recommend that the performance of such a GS model be explored on a trait‐by‐trait basis prior to its implementation into a breeding program.

中文翻译：

---

结合了峰值基因组-全关联研究信号的玉米和高粱的RR-BLUP基因组选择模型的评估。

某些农艺作物的性状很复杂，因此受许多小效应基因座的支配。通常在全基因组关联研究（GWAS）和基因组选择（GS）中使用的统计模型通过评估具有这些基因座对性状变异的连锁不平衡（LD）中的基因组标记贡献来量化这些信号。直到最近，在已发表的研究中，评估了包括来自GWAS的峰相关标记作为固定效应协变量的GS模型的预测能力之前，这些模型一直用于单独的定量遗传学背景中。先前的工作表明，这样的模型对于预测由几个大效应和许多小效应基因控制的性状可能是有用的。我们通过评估玉米（玉米（Zea mays L.）和高粱[双色高粱[L.] Moench]使用包括最佳效果协变量标记峰值GWAS信号的固定参数协方差的岭回归最佳线性无偏预测（RR-BLUP）模型。在多种遗传结构和基因组背景下，对此类协变量在RR‐BLUP模型中提高GS预测准确性的能力进行了量化。在我们模拟的216种遗传结构中，我们确定了60种固定效应协变量的加入可提高预测准确性。但是，对于大多数模拟数据，未观察到预测准确性的增加或减少。我们还注意到了一些实例，其中包括固定效应协变量，这既增加了预测准确性的可变性，又增加了基因组估计育种值的偏差。