双重麻烦：预测两个异质群体中的新变异数量
Double trouble: Predicting new variant counts across two heterogeneous populations
AI总结：
本文针对跨异质群体的后续测序研究，提出首个可预测群体间共享新变异及总新变异数量的方法，并证明单群体先进预测器的自然扩展存在根本失效。
摘要：
在多个异质群体中（例如跨不同癌症类型）收集基因组学数据，有可能增进我们对疾病的理解。然而，尽管测序技术取得了进展，在收集数据时资源往往仍然是一种约束。因此，如果能够开展预试验的实验者可以预测他们在后续研究中可能发现的新变异数量，这将对实验设计很有用：既包括群体之间共享的新变异数量，也包括各群体中新变异的总数。虽然许多研究者已经为单群体情形开发了预测方法，但我们表明，这些预测在多个异质群体中的表现可能很差。我们证明，令人惊讶的是，将一种最先进的单群体预测器自然扩展到多个群体，会由于根本性原因而失效。我们提供了首个能够处理多个群体中异质性的、用于预测新共享变异和新总变异数量的预测器。我们通过使用真实的癌症和群体遗传学数据表明，所提出的方法在经验上表现良好。
英文摘要：
Collecting genomics data across multiple heterogeneous populations (e.g., across different cancer types) has the potential to improve our understanding of disease. Despite sequencing advances, though, resources often remain a constraint when gathering data. So it would be useful for experimental design if experimenters with access to a pilot study could predict the number of new variants they might expect to find in a follow-up study: both the number of new variants shared between the populations and the total across the populations. While many authors have developed prediction methods for the single-population case, we show that these predictions can fare poorly across multiple populations that are heterogeneous. We prove that, surprisingly, a natural extension of a state-of-the-art single-population predictor to multiple populations fails for fundamental reasons. We provide the first predictor for the number of new shared variants and new total variants that can handle heterogeneity in multiple populations. We show that our proposed method works well empirically using real cancer and population genetics data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
