Data from: How does epistasis influence the response to selection?

Much of quantitative genetics is based on the "infinitesimal model", under which selection has a negligible effect on the genetic variance. This is typically justified by assuming a very large number of loci with additive effects. However, it applies even when genes interact, provided that the number of loci is large enough that selection on each of them is weak relative to random drift. In the long term, directional selection will change allele frequencies, but even then, the effects of epistasis on the ultimate change in trait mean due to selection may be modest. Stabilising selection can maintain many traits close to their optima, even when the underlying alleles are weakly selected. However, the number of traits that can be optimised is apparently limited to ~4Ne by the "drift load", which is hard to reconcile with the apparent complexity of many organisms, which arguably implies that selection maintains a very large number of traits. Just as for the mutation load, this limit can be evaded by a particular form of negative epistasis. A more robust limit is set by the variance in reproductive success. This suggests that selection accumulates information most efficiently in the infinitesimal regime, when selection on individual alleles is weak, and comparable with random drift. A review of evidence on selection strength suggests that while most variance in fitness may be due to alleles with large Subscript[N, e]s, substantial amounts of adaptation may be due to alleles in the infinitesimal regime, in which epistasis has modest effects.数量遗传学（quantitative genetics）的核心研究多基于无穷小模型（infinitesimal model），该模型假定选择对遗传方差的影响可忽略不计。该假定的常规合理性依据为：存在大量具有加性效应的基因位点（loci）。然而，即便基因间存在互作，只要基因位点数量足够多，使得每个位点上的选择作用相较于随机遗传漂变（random drift）而言较弱，该模型依然适用。长期来看，定向选择（directional selection）会改变等位基因频率（allele frequencies），但即便如此，上位性（epistasis）对选择所导致的性状均值最终变化的影响通常较为有限。稳定选择（stabilising selection）可使众多性状维持在其最优值附近，即便其所依赖的等位基因所受选择压力较弱。然而，受漂变负荷（drift load）的限制，可被优化的性状数量上限约为4$N_e$，这一结论与诸多生物所展现出的复杂表型难以兼容，这也间接表明选择实际上维持了大量性状的存在。与突变负荷（mutation load）的情况类似，这种限制可通过特定形式的负上位性（negative epistasis）得以规避。而由繁殖成功率方差所定义的限制则更为稳健。这表明，在无穷小模型框架中，当单个等位基因所受选择作用较弱且与随机遗传漂变的强度相当之时，选择积累信息的效率最高。一项针对选择强度相关证据的综述研究表明：尽管多数适合度（fitness）方差可能源自那些$N_e s$值较大的等位基因，但大量的适应性进化事件或许源于处于无穷小模型框架内的等位基因，此类等位基因所在的场景中，上位性的影响相对有限。