School of Biological Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK.
Bioinformatics Research Centre Aarhus University, University City 81, building 1872, 3rd floor. DK-8000 Aarhus C, Denmark.
G3 (Bethesda). 2023 Aug 30;13(9). doi: 10.1093/g3journal/jkad140.
The distribution of fitness effects is a key property in evolutionary genetics as it has implications for several evolutionary phenomena including the evolution of sex and mating systems, the rate of adaptive evolution, and the prevalence of deleterious mutations. Despite the distribution of fitness effects being extensively studied, the effects of strongly deleterious mutations are difficult to infer since such mutations are unlikely to be present in a sample of haplotypes, so genetic data may contain very little information about them. Recent work has attempted to correct for this issue by expanding the classic gamma-distributed model to explicitly account for strongly deleterious mutations. Here, we use simulations to investigate one such method, adding a parameter (plth) to capture the proportion of strongly deleterious mutations. We show that plth can improve the model fit when applied to individual species but underestimates the true proportion of strongly deleterious mutations. The parameter can also artificially maximize the likelihood when used to jointly infer a distribution of fitness effects from multiple species. As plth and related parameters are used in current inference algorithms, our results are relevant with respect to avoiding model artifacts and improving future tools for inferring the distribution of fitness effects.
适应度效应的分布是进化遗传学中的一个关键特性，因为它对几种进化现象都有影响，包括性和交配系统的进化、适应进化的速度以及有害突变的流行程度。尽管适应度效应的分布已经得到了广泛的研究，但强有害突变的影响很难推断，因为这种突变不太可能存在于单倍型样本中，因此遗传数据可能包含关于它们的很少信息。最近的研究试图通过扩展经典的伽马分布模型来解决这个问题，该模型明确考虑了强有害突变。在这里，我们使用模拟来研究这样一种方法，通过添加一个参数 (plth) 来捕获强有害突变的比例。我们表明，当应用于单个物种时，plth 可以提高模型拟合度，但会低估强有害突变的真实比例。当用于从多个物种共同推断适应度效应分布时，该参数也可以人为地最大化似然。由于 plth 和相关参数被用于当前的推断算法，因此我们的结果与避免模型伪影和改进推断适应度效应分布的未来工具有关。
