Lucia Corte, Lathan Liou, Paul F O'Reilly, Judit García-González
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引用次数: 0
摘要
全基因组关联和测序研究的最新进展表明,复杂性状和疾病的遗传结构涉及分布在整个基因组中的罕见和常见遗传变异的组合。要更好地理解这种结构,一种方法是将广泛等位基因频率范围内的遗传关联可视化。然而,目前还没有标准化或一致的图形表示法来有效地说明这些结果。在此,我们提出了一种标准化的方法,用于直观显示风险变异在等位基因频率谱中的效应大小。所提出的图具有独特的喇叭形状:大多数变异具有高频率和小效应,而少数变异具有较低频率和较大效应。为了证明喇叭图在说明变体数量、变体频率及其对塑造复杂性状和疾病遗传结构的影响程度之间的关系方面的实用性,我们为英国生物库中的一百多个性状生成了喇叭图。为了便于更广泛地使用,我们开发了一个 R 软件包 "TrumpetPlots"(可在综合 R Archive Network 上获取)和 R Shiny 应用程序 "Shiny Trumpets"(可在 https://juditgg.shinyapps.io/shinytrumpets/ 上获取),允许用户探索这些结果并提交自己的数据。
Trumpet plots: visualizing the relationship between allele frequency and effect size in genetic association studies.
Recent advances in genome-wide association and sequencing studies have shown that the genetic architecture of complex traits and diseases involves a combination of rare and common genetic variants distributed throughout the genome. One way to better understand this architecture is to visualize genetic associations across a wide range of allele frequencies. However, there is currently no standardized or consistent graphical representation for effectively illustrating these results. Here we propose a standardized approach for visualizing the effect size of risk variants across the allele frequency spectrum. The proposed plots have a distinctive trumpet shape: with the majority of variants having high frequency and small effects, and a small number of variants having lower frequency and larger effects. To demonstrate the utility of trumpet plots in illustrating the relationship between the number of variants, their frequency, and the magnitude of their effects in shaping the genetic architecture of complex traits and diseases, we generated trumpet plots for more than one hundred traits in the UK Biobank. To facilitate their broader use, we developed an R package, 'TrumpetPlots' (available at the Comprehensive R Archive Network) and R Shiny application, 'Shiny Trumpets' (available at https://juditgg.shinyapps.io/shinytrumpets/) that allows users to explore these results and submit their own data.