CRP-Tree:一种二元性状的系统发育关联试验

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Julie Zhang, Gabriel A Preising, Molly Schumer, Julia A Palacios
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引用次数: 1

摘要

摘要在进化基因组学中,一个重要的问题是研究在每个样本上测量到的某一性状是否与样本系统发育树相关联。系统发育树代表了样本的共同进化史,它通常是根据一个位点的分子序列数据或其他类型的遗传数据来估计的。受中国餐馆过程的启发,我们提出了一个性状进化模型,该模型包含一个控制优先依恋程度的参数,即树中节点从同一类型节点的从属趋势。该模型不存在优先依附关系,相当于迁移和聚结事件同时发生的结构化聚结模型,为零模型。我们推导了一种基于线性计算复杂度的系统发育二元性状关联检验方法,并实证证明了它比其他一些方法更有效。我们应用我们的测试来研究剑尾鱼某些性状与乳腺癌、黄热病病毒和甲型H1N1流感病毒的系统发育关系。我们的方法的r包实现可以在https://github.com/jyzhang27/CRPTree上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CRP-Tree: a phylogenetic association test for binary traits
Abstract An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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