Use of genomic information has resulted in an undeniable improvement in prediction accuracies and an increase in genetic gain in animal and plant genetic selection programs in spite of oversimplified assumptions about the true biological processes. Even for complex traits, a large portion of markers do not segregate with or effectively track genomic regions contributing to trait variation; yet it is not clear how genomic prediction accuracies are impacted by such potentially nonrelevant markers. In this study, a simulation was carried out to evaluate genomic predictions in the presence of markers unlinked with trait-relevant QTL. Further, we compared the ability of the population statistic FST and absolute estimated marker effect as preselection statistics to discriminate between linked and unlinked markers and the corresponding impact on accuracy. We found that the accuracy of genomic predictions decreased as the proportion of unlinked markers used to calculate the genomic relationships increased. Using all, only linked, and only unlinked marker sets yielded prediction accuracies of 0.62, 0.89, and 0.22, respectively. Furthermore, it was found that prediction accuracies are severely impacted by unlinked markers with large spurious associations. FST-preselected marker sets of 10 k and larger yielded accuracies 8.97 to 17.91% higher than those achieved using preselection by absolute estimated marker effects, despite selecting 5.1 to 37.7% more unlinked markers and explaining 2.4 to 5.0% less of the genetic variance. This was attributed to false positives selected by absolute estimated marker effects having a larger spurious association with the trait of interest and more negative impact on predictions. The Pearson correlation between FST scores and absolute estimated marker effects was 0.77 and 0.27 among only linked and only unlinked markers, respectively. The sensitivity of FST scores to detect truly linked markers is comparable to absolute estimated marker effects but the consistency between the two statistics regarding false positives is weak. Identification and exclusion of markers that have little to no relevance to the trait of interest may significantly increase genomic prediction accuracies. The population statistic FST presents an efficient and effective tool for preselection of trait-relevant markers.

中文翻译：

---

剖析优先 QTL 连锁和非连锁 SNP 标记对基因组选择准确性的影响1

尽管对真实生物过程的假设过于简单，但基因组信息的使用已导致不可否认的预测准确性的提高和动植物遗传选择程序中遗传增益的增加。即使对于复杂的性状，大部分标记也不会与导致性状变异的基因组区域分离或有效追踪；然而，尚不清楚基因组预测的准确性如何受到这些潜在的不相关标记的影响。在本研究中，进行了模拟以评估存在与性状相关 QTL 无关的标记时的基因组预测。此外，我们比较了作为预选统计的总体统计 FST 和绝对估计标记效应的能力，以区分链接和未链接的标记以及对准确性的相应影响。我们发现，随着用于计算基因组关系的非连锁标记比例增加，基因组预测的准确性降低。使用全部、仅链接和仅未链接的标记集分别产生 0.62、0.89 和 0.22 的预测精度。此外，发现预测准确性受到具有大量虚假关联的未链接标记的严重影响。FST 预选标记集的 10 k 和更大产生的准确度比使用绝对估计标记效应的预选高 8.97% 到 17.91%，尽管选择的未连锁标记多 5.1% 到 37.7%，并且解释的遗传方差减少了 2.4% 到 5.0%。这归因于由绝对估计标记效应选择的假阳性与感兴趣的特征具有更大的虚假关联并对预测产生更大的负面影响。FST 分数与绝对估计标记效应之间的 Pearson 相关性分别为 0.77 和 0.27。FST 分数检测真正连锁标记的敏感性与绝对估计的标记效应相当，但关于假阳性的两个统计数据之间的一致性很弱。识别和排除与感兴趣的性状几乎没有相关性的标记可能会显着提高基因组预测的准确性。人口统计 FST 为预选性状相关标记提供了一种有效且有效的工具。FST 分数与绝对估计标记效应之间的 Pearson 相关性分别为 0.77 和 0.27。FST 分数检测真正连锁标记的敏感性与绝对估计的标记效应相当，但关于假阳性的两个统计数据之间的一致性很弱。识别和排除与感兴趣的性状几乎没有相关性的标记可能会显着提高基因组预测的准确性。人口统计 FST 为预选性状相关标记提供了一种有效且有效的工具。FST 分数与绝对估计标记效应之间的 Pearson 相关性分别为 0.77 和 0.27。FST 分数检测真正连锁标记的敏感性与绝对估计的标记效应相当，但关于假阳性的两个统计数据之间的一致性很弱。识别和排除与感兴趣的性状几乎没有相关性的标记可能会显着提高基因组预测的准确性。人口统计 FST 为预选性状相关标记提供了一种有效且有效的工具。FST 分数检测真正连锁标记的敏感性与绝对估计的标记效应相当，但关于假阳性的两个统计数据之间的一致性很弱。识别和排除与感兴趣的性状几乎没有相关性的标记可能会显着提高基因组预测的准确性。人口统计 FST 为预选性状相关标记提供了一种有效且有效的工具。FST 分数检测真正连锁标记的敏感性与绝对估计的标记效应相当，但关于假阳性的两个统计数据之间的一致性很弱。识别和排除与感兴趣的性状几乎没有相关性的标记可能会显着提高基因组预测的准确性。人口统计 FST 为预选性状相关标记提供了一种有效且有效的工具。