Statistical, population genetics and genetic epidemiology

统计、群体遗传学和遗传流行病学

• 批准号: 10007476
• 负责人: dmitri v zaykin
• 金额: $ 35.34万
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10007476
• 关键词: Bayesian Method; Chromosome Mapping; Code; Complex; Computer Simulation; Consumption; Data; Data Set; Development; Disease; Disease susceptibility; Drowning; Environmental Exposure; Evaluation; Genetic; Genetic Polymorphism; Genetic Variation; Human; Human Genetics; Manuscripts; Measures; Medicine; Methodology; Methods; Observational Study; Pattern; Phenotype; Population Genetics; Public Health; Publications; Publishing; Reaction; Research; Research Methodology; Signal Transduction; Somatic Mutation; Statistical Data Interpretation; Statistical Methods; Testing; Time; Uncertainty; Work; base; cancer genome; computerized tools; disorder prevention; disorder risk; frontier; genetic association; genetic epidemiology; health related quality of life; interest; mathematical methods; novel; practical application; response; statistics; tool; trait; whole genome

Increased availability of data and accessibility of computational tools in recent years have created an unprecedented upsurge of scientific studies driven by statistical analysis. Limitations inherent to statistics impose constraints on the reliability of conclusions drawn from data, and misuse of statistical methods is a growing concern. We have been developing tools for assessing predictability of common measures of statistical significance of research findings. These methods operate on test statistics or P-values as summaries of data and also incorporate external or prior information for making inference about uncertainty in statistics or parameters of interest, such as P-values or risk of disease. In a currently submitted manuscript we develop approximate Bayesian methods that use information contained in P-values, but overcome their flaws and limitations. A preprint of this research is available at https://doi.org/10.1101/714287 In a manuscript that is tentatively accepted to Frontiers in Genetics -- Statistical Genetics and Methodology we develop new statistical methods to combine top-ranking statistical associations. These methods can be used in observational studies to detect an aggregated effect of multiple weak predictors on complex disorders. They are also being applied in collaborative project with Dr. Gordenin's group to explore patterns of somatic mutations in cancer genomes. Without doubt, practical applications, as well as methodological extensions of methods based on top-ranking statistics, are hindered by their computational complexity. In the course of this work we derived the exact distribution of the rank truncated product (RTP) that substantially simplifies its evaluation. We also suggested an efficient adaptive method that does not require time consuming computer simulations and developed extensions for combining correlated effects with substantial gain in power compared to previously published methods. Further, we proposed a highly promising combination statistic that captures main features of RTP but has higher power and can be implemented using an elementary R code. Preprints of this research are available at https://doi.org/10.1101/665133 and at https://doi.org/10.1101/667238 (both preprints are currently submitted for publication).

近年来，数据的可获得性和计算工具的可获得性的增加创造了由统计分析推动的科学研究的前所未有的热潮。统计数据固有的局限性限制了从数据得出的结论的可靠性，滥用统计方法日益令人担忧。我们一直在开发工具，用于评估研究结果的统计意义的常见衡量标准的可预测性。这些方法以检验统计或P值作为数据的汇总，还纳入外部或先验信息，以推断统计或感兴趣的参数中的不确定性，如P值或疾病风险。在目前提交的手稿中，我们开发了使用P值中包含的信息的近似贝叶斯方法，但克服了它们的缺陷和限制。这项研究的预印本可在https://doi.org/10.1101/714287上查阅。 在一份暂时被《遗传学前沿--统计遗传学和方法论》接受的手稿中，我们开发了新的统计方法来结合顶级的统计关联。这些方法可用于观察性研究，以检测多个弱预测因子对复杂疾病的综合影响。它们还被应用于与戈德宁博士的团队合作的项目中，以探索癌症基因组中的体细胞突变模式。毫无疑问，实际应用以及基于顶级统计的方法的方法扩展都受到其计算复杂性的阻碍。在这项工作的过程中，我们得到了秩截断积(RTP)的精确分布，大大简化了它的估计。我们还提出了一种不需要耗时的计算机模拟的有效的自适应方法，并与以前发表的方法相比，开发了将相关效应与功率大幅提高相结合的扩展。此外，我们提出了一种非常有前景的组合统计量，该统计量捕获了RTP的主要特征，但具有更高的功率，并且可以使用初级R码来实现。这项研究的预印本可以在https://doi.org/10.1101/665133和https://doi.org/10.1101/667238上获得(这两份预印本目前都已提交出版)。