Genomic selection uses whole-genome marker models to predict phenotypes or genetic values for complex traits. Some of these models fit interaction terms between markers, and are therefore called epistatic. The biological interpretation of the corresponding fitted effects is not straightforward and there is the threat of overinterpreting their functional meaning. Here we show that the predictive ability of epistatic models relative to additive models can change with the density of the marker panel. In more detail, we show that for publicly available Arabidopsis and rice datasets, an initial superiority of epistatic models over additive models, which can be observed at a lower marker density, vanishes when the number of markers increases. We relate these observations to earlier results reported in the context of association studies which showed that detecting statistical epistatic effects may not only be related to interactions in the underlying genetic architecture, but also to incomplete linkage disequilibrium at low marker density ("Phantom Epistasis"). Finally, we illustrate in a simulation study that due to phantom epistasis, epistatic models may also predict the genetic value of an underlying purely additive genetic architecture better than additive models, when the marker density is low. Our observations can encourage the use of genomic epistatic models with low density panels, and discourage their biological over-interpretation.

基因组选择使用全基因组标记模型来预测复杂性状的表型或遗传值。这些模型中的一些适合标记之间的相互作用项，因此称为上位性。相应拟合效果的生物学解释并不直接，存在过度解释其功能含义的威胁。在这里，我们表明上位模型相对于加性模型的预测能力会随着标记面板的密度而变化。更详细地，我们表明，对于公开可获得的拟南芥和水稻数据集，当标记数增加时，上位性模型相对于加性模型的最初优势（可以在较低的标记密度下观察到）消失。我们将这些观察结果与关联研究中报道的较早结果相关联，这些结果表明，检测统计上位效应可能不仅与基础遗传结构中的相互作用有关，而且还与低标记密度下的不完全连锁不平衡有关（“幻影上位”） 。最后，我们在模拟研究中说明，由于幻影上位，当标记物密度较低时，上位模型也可能比加成模型更好地预测潜在的纯加性遗传结构的遗传价值。我们的观察结果可以鼓励在低密度面板上使用基因组上位模型，并阻止其生物学上的过度解释。而且在低标记密度下也会导致不完全的连锁不平衡（“幻影上位”）。最后，我们在模拟研究中说明，由于幻影上位，当标记物密度较低时，上位模型也可能比加成模型更好地预测潜在的纯加性遗传结构的遗传价值。我们的观察结果可以鼓励在低密度面板上使用基因组上位模型，并阻止其生物学上的过度解释。而且还会在低标记密度下导致不完全的连锁不平衡（“幻影上位”）。最后，我们在模拟研究中说明，由于幻影上位，当标记物密度较低时，上位模型也可能比加成模型更好地预测潜在的纯加性遗传结构的遗传价值。我们的观察结果可以鼓励在低密度面板上使用基因组上位模型，并阻止其生物学上的过度解释。