Model based methods for genetic clustering of individuals, such as those implemented in structure or ADMIXTURE, allow the user to infer individual ancestries and study population structure. The underlying model makes several assumptions about the demographic history that shaped the analysed genetic data. One assumption is that all individuals are a result of K homogeneous ancestral populations that are all well represented in the data, while another assumption is that no drift happened after the admixture event. The histories of many real world populations do not conform to that model, and in that case taking the inferred admixture proportions at face value might be misleading. We propose a method to evaluate the fit of admixture models based on estimating the correlation of the residual difference between the true genotypes and the genotypes predicted by the model. When the model assumptions are not violated, the residuals from a pair of individuals are not correlated. In the case of a bad fitting admixture model, individuals with similar demographic histories have a positive correlation of their residuals. Using simulated and real data, we show how the method is able to detect a bad fit of inferred admixture proportions due to using an insufficient number of clusters K or to demographic histories that deviate significantly from the admixture model assumptions, such as admixture from ghost populations, drift after admixture events and nondiscrete ancestral populations. We have implemented the method as an open source software that can be applied to both unphased genotypes and low depth sequencing data.

中文翻译：

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推论混合物比例的模型拟合评估。

基于模型的个体遗传聚类方法（例如在结构或ADMIXTURE中实现的方法）允许用户推断个体祖先并研究种群结构。基本模型对塑造分析的遗传数据的人口历史做出了一些假设。一种假设是所有个体都是K的结果数据中都很好地代表了祖先的均质种群，而另一个假设是混合事件发生后没有漂移。许多现实世界人口的历史不符合该模型，在这种情况下，以推断的面值得出的混合比例可能会产生误导。我们提出了一种基于估计真实基因型与模型预测的基因型之间残留差异相关性的评估混合模型拟合的方法。当不违反模型假设时，一对个体的残差不相关。在不良拟合混合模型的情况下，具有相似人口统计学历史的个人的残差呈正相关。使用模拟和真实数据，K或与混合模型假设显着不同的人口历史记录，例如来自幻影种群的混合，混合事件之后的漂移和祖先非离散种群。我们已经将该方法实现为可同时应用于非定相基因型和低深度测序数据的开源软件。