Data from: Comparison of non-Gaussian quantitative genetic models for migration and stabilizing selection

The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate non-normality and a non-constant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model which approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does however generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.

此前，学界已采用多种复杂度逐步提升的数量遗传模型（quantitative genetic models）对稳定选择与不适应个体迁移之间的平衡关系开展建模，其中涵盖基于恒定表达遗传方差的模型以及基于正态分布的模型。无穷小模型（infinitesimal model）可容纳由连锁不平衡（linkage disequilibrium）引发的非正态分布与非恒定遗传方差，该模型可被视为一种简约单参数模型，在涉及大量基因位点时，能够较好地近似真实的遗传细节。本研究中，我们将该模型的表现与多种更具现实意义的显式多位点模型进行对比，这些模型的每个基因位点分别拥有2个、多个或大量效应量不等的等位基因。不同模型对种群均值与最优表型偏差的预测结果高度相似，因此非高斯无穷小模型是一种优良的近似模型。不过，该模型通常会比多位点模型估算出更高的遗传方差，且两者的差值随基因位点数的增加而逐渐缩小。多位点模型之间的差异，更多取决于考量了各基因位点对方差相对贡献度的有效位点数，而非单个位点的等位基因数量。