论文标题
人群感知的回顾性回归，以检测具有性别差异的全基因组变异频率
A Population-Aware Retrospective Regression to Detect Genome-Wide Variants with Sex Difference in Allele Frequency
论文作者
论文摘要
等位基因频率的性别差异是一个新兴主题，这对于我们对确定偏见的理解以及数据质量至关重要，尤其是在很大程度上被忽视的X染色体。为了检测X染色体和常染色体变体的等位基因频率的性别差异，当现有方法适用于来自多个祖先人群（例如非洲和欧洲人群）的样本时，现有方法是保守的。此外，在人群之间的性别差异是否有所不同，这对于跨性遗传研究很重要，仍然没有探索。因此，我们开发了一种新颖的回顾性测试框架，以提供可解释且易于实施的解决方案来回答这些问题。然后，我们将所提出的方法应用于1000个基因组项目的高覆盖整体基因组序列数据，并稳固地分析了从五个超级选集中获得的所有样本。我们通过识别和建模祖先差异来获得76个新颖的发现。
Sex difference in allele frequency is an emerging topic that is critical to our understanding of ascertainment bias, as well as data quality particularly of the largely overlooked X chromosome. To detect sex difference in allele frequency for both X chromosomal and autosomal variants, existing methods are conservative when applied to samples from multiple ancestral populations, such as African and European populations. Additionally, it remains unexplored whether the sex difference in allele frequency differs between populations, which is important to trans-ancestral genetic studies. We thus developed a novel retrospective regression-based testing framework to provide interpretable and easy-to-implement solutions to answer these questions. We then applied the proposed methods to the high-coverage whole genome sequence data of the 1000 Genomes Project, robustly analyzing all samples available from the five super-populations. We had 76 novel findings by recognizing and modeling ancestral differences.
