Under the traditional mutation load model based on multiplicative fitness effects, the load in a population is 1-e-U , where U is the genomic deleterious mutation rate. Because this load becomes high under large U, synergistic epistasis has been proposed as one possible means of reducing the load. However, experiments on model organisms attempting to detect synergistic epistasis have often focused on a quadratic fitness model, with the resulting general conclusion being that epistasis is neither common nor strong. Here, I present a model of additive fitness effects and show that, unlike multiplicative effects, the equilibrium frequency of an allele under additivity is dependent on the average absolute fitness of the population. The additive model then results in a load of U/(U +1), which is much lower than 1-e-U for large U. Numerical iterations demonstrate that this analytic derivation holds as a good approximation under biologically relevant values of selection coefficients and U. Additionally, regressions onto Drosophila mutation accumulation data suggest that the common method of inferring epistasis by detecting large quadratic terms from regressions is not always necessary, as the additive model fits the data well and results in synergistic epistasis. Furthermore, the additive model gives a much larger reduction in load than the quadratic model when predicted from the same data, indicating that it is important to consider this additive model in addition to the quadratic model when inferring epistasis from mutation accumulation data.

中文翻译：

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加性适应效应下的突变负荷。

在基于乘性适应性效应的传统突变负荷模型下，群体中的负荷为1-eU，其中U为基因组有害突变率。由于此载荷在大的U下会变高，因此提出了协同上位作为降低载荷的一种可能方法。但是，对尝试检测协同上位性的模型生物进行的实验通常集中于二次适应性模型，因此得出的一般结论是上位性既不常见也不强。在这里，我提出了一个加性适应效应的模型，并表明与乘法效应不同，等位基因在加性作用下的平衡频率取决于总体的平均绝对适应度。然后，加性模型会导致U /（U +1）的负载，这比大U的1-eU低得多。数值迭代表明，在选择系数和U的生物学相关值下，这种解析推导具有良好的近似性。此外，对果蝇突变累积数据的回归表明，通过回归检测大的二次项来推断上位的通用方法并非总是必要的，因为加性模型很好地拟合了数据并导致了协同上位。此外，当从同一数据进行预测时，加性模型比二次模型具有更大的负载减少量，这表明从突变累积数据推断上位性时，除了二次模型之外，还必须考虑该加性模型。