Statistical, population genetics and genetic epidemiology

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驱动的科学研究的前所未有的热潮。统计固有的局限性限制了从数据中得出结论的可靠性，滥用统计方法日益令人关切。我们一直在开发工具，用于评估研究结果的统计意义的共同措施的可预测性。这些方法对检验统计量或P值进行操作，作为数据的总结，并且还包含外部或先验信息，用于对统计量或感兴趣的参数（例如P值或疾病风险）中的不确定性进行推断。在目前提交的手稿中，我们开发了近似贝叶斯方法，使用P值中包含的信息，但克服了它们的缺陷和局限性。这项研究的预印本可在https://doi.org/10.1101/714287上获得。 在遗传学前沿-统计遗传学和方法学暂时接受的手稿中，我们开发了新的统计方法来结合联合收割机顶级统计协会。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。它们也被应用于与Gordenin博士小组的合作项目，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，受到其计算复杂性的阻碍。在这项工作的过程中，我们推导出确切的分布的秩截断产品（RTP），大大简化了其评估。我们还提出了一种有效的自适应方法，不需要耗时的计算机模拟和开发扩展相结合的相关效应与大量增益的功率相比，以前公布的方法。此外，我们提出了一个非常有前途的组合统计，捕捉RTP的主要功能，但具有更高的功率，可以使用一个基本的R代码来实现。该研究的预印本可在https://doi.org/10.1101/665133和https://doi.org/10.1101/667238上获得（两种预印本目前都已提交出版）。

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).