Partial clonality is widespread across the tree of life, but most population genetic models are designed for exclusively clonal or sexual organisms. This gap hampers our understanding of the influence of clonality on evolutionary trajectories and the interpretation of population genetic data. We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto β) and genetic (F_IS and linkage disequilibrium) indices, and tested predictions of c from population genetic data through supervised machine learning. Two complementary behaviours emerged from the probability distributions of genotypic and genetic indices with increasing c. While the impact of c on R and Pareto β was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c > 0.95). Consequently, genotypic indices allowed reliable estimates of c, while genetic descriptors led to poorer performances when c < 0.95. These results provide clear baseline expectations for genotypic and genetic diversity and dynamics under partial clonality. Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness, should be reappraised. We propose future avenues to derive realistic confidence intervals for c and show that, although still approximate, a supervised learning method would greatly improve the estimation of c from population genetic data.

中文翻译：

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克隆性对种群基因型和遗传状态的可辨别和隐藏的影响：改进我们对克隆率的估计

部分克隆性在整个生命之树中广泛存在，但大多数种群遗传模型专为克隆或有性生物而设计。这种差距阻碍了我们对克隆性对进化轨迹和种群遗传数据解释的影响的理解。我们以增加的克隆率 ( c )对二倍体种群进行了正向模拟，分析了它们与基因型（克隆丰富度，R和克隆大小分布，帕累托β）和遗传（F _IS和连锁不平衡）指数的关系，并测试了预测的ç从种群遗传数据到监督机器学习。随着c 的增加，基因型和遗传指数的概率分布出现了两种互补行为。虽然c对R和帕累托β 的影响很容易用简单的数学方程来描述，但它对遗传指数的影响只有在最高水平 ( c  > 0.95)时才明显。因此，基因型指数允许可靠地估计c，而当c时遗传描述符导致较差的性能 < 0.95。这些结果为部分克隆性下的基因型和遗传多样性和动态提供了明确的基线预期。然而，令人担忧的是，使用实际样本量来系统地获取经验数据导致c 的严重低估（通常为一到两个数量级），这表明迄今为止文献中提出的许多解释，主要基于基因型丰富度，应该是重新评估。我们提出了未来的途径来推导出c 的真实置信区间，并表明，尽管仍然是近似的，但监督学习方法将大大改善从种群遗传数据中对c的估计。