Non-random mating among individuals can lead to spatial clustering of genetically similar individuals and population stratification. This deviation from panmixia is commonly observed in natural populations. Consequently, individuals can have parentage in single populations or involving hybridization between differentiated populations. Accounting for this mixture and structure is important when mapping the genetics of traits and learning about the formative evolutionary processes that shape genetic variation among individuals and populations. Stratified genetic relatedness among individuals is commonly quantified using estimates of ancestry that are derived from a statistical model. Development of these models for polyploid and mixed-ploidy individuals and populations has lagged behind those for diploids. Here, we extend and test a hierarchical Bayesian model, called entropy, which can use low-depth sequence data to estimate genotype and ancestry parameters in autopolyploid and mixed-ploidy individuals (including sex chromosomes and autosomes within individuals). Our analysis of simulated data illustrated the trade-off between sequencing depth and genome coverage and found lower error associated with low-depth sequencing across a larger fraction of the genome than with high-depth sequencing across a smaller fraction of the genome. The model has high accuracy and sensitivity as verified with simulated data and through analysis of admixture among populations of diploid and tetraploid Arabidopsis arenosa.

中文翻译：

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具有混合倍性的潜在杂种的基于模型的基因型和祖先估计

个体之间的非随机交配会导致遗传相似个体的空间聚类和种群分层。这种与panmixia的偏差在自然种群中很常见。因此，个体可以在单一种群中具有亲子关系或涉及不同种群之间的杂交。在绘制性状遗传学和了解形成个体和种群遗传变异的形成进化过程时，考虑这种混合和结构很重要。个体之间的分层遗传相关性通常使用源自统计模型的祖先估计来量化。这些用于多倍体和混合倍体个体和种群的模型的开发落后于二倍体模型。在这里，entropy，它可以使用低深度序列数据来估计同源多倍体和混合倍体个体（包括个体内的性染色体和常染色体）的基因型和祖先参数。我们对模拟数据的分析说明了测序深度和基因组覆盖率之间的权衡，并发现与在基因组的一小部分进行高深度测序相比，在较大部分的基因组中进行低深度测序的相关错误更低。通过模拟数据和二倍体和四倍体拟南芥种群间混合物的分析，该模型具有较高的准确性和灵敏度。