Statistical, population genetics and genetic epidemiology

统计、群体遗传学和遗传流行病学

• 批准号: 10260280
• 负责人: dmitri v zaykin
• 金额: $ 80.39万
• 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/10260280
• 关键词: Bayesian Method; Chromosome Mapping; Complex; Data; Data Set; Detection; Development; Disease; Disease susceptibility; Drowning; Environmental Exposure; Genes; Genetic Polymorphism; Genetic Variation; Human; Human Genetics; Measures; Medicine; Methods; Pattern; Phenotype; Population Genetics; Public Health; Publications; Reaction; Research; Research Methodology; Resistance; Signal Transduction; Somatic Mutation; Statistical Data Interpretation; Statistical Methods; Testing; Uncertainty; base; cancer genome; computerized tools; disorder prevention; disorder risk; genetic association; genetic epidemiology; genetic variant; health related quality of life; improved; interest; mathematical methods; novel; 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 improving predictability of research findings using common measures of statistical significance. 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 recent publications we develop meta-analytic (Vsevolozhskaya, Hu, Shi, Zaykin, 2020; Vsevolozhskaya, Hu, Zaykin, 2019) and approximate Bayesian methods that use information contained in P-values, but overcome their flaws and limitations (Vsevolozhskaya, Zaykin, 2020). These methods build on our continuing research concerning analysis of top-ranking genetic associations with disease with applications to detection of aggregated effects of multiple weak predictors on complex disorders. In the course of this research we have been developing approaches for discovery of genetic associations with disease using summaries of data, such as test statistics. Analysis based on summary statistics can be nearly as efficient as analysis of raw data and has some advantages, including ease of transfer, accessibility, and simplified extraction from publications. Combination of summary statistics across genetic variants yields measures of gene-wide associations useful in applications such as gene set analyses. Utilization of summary statistics for construction of Bayesian estimates and intervals improves resistance to the winner's curse and to selection-induced biases, including bias due to multiple testing. Our methods are being applied in collaborative projects with Dr. Gordenin's group to explore patterns of somatic mutations in cancer genomes.

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，使用统计意义的共同措施来提高研究结果的可预测性。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。 在最近的出版物中，我们开发了元分析（Vsevolozhskaya，Hu，Shi，Zaykin，2020; Vsevolozhskaya，Hu，Zaykin，2019）和近似贝叶斯方法，这些方法使用P值中包含的信息，但克服了它们的缺陷和局限性（Vsevolozhskaya，Zaykin，2020）。这些方法建立在我们持续研究的基础上，涉及分析与疾病的顶级遗传关联，并应用于检测多个弱预测因子对复杂疾病的综合影响。在这项研究的过程中，我们一直在开发使用数据摘要（如检验统计）发现遗传与疾病相关性的方法。基于汇总统计的分析几乎与原始数据分析一样有效，并且具有一些优势，包括易于传输、可访问性和简化从出版物中提取。跨遗传变异的汇总统计的组合产生在诸如基因集分析的应用中有用的全基因关联的度量。利用概括统计量的贝叶斯估计和区间的建设，提高抵抗赢家的诅咒和选择引起的偏见，包括偏见，由于多重测试。我们的方法正在与Gordenin博士的小组合作项目中应用，以探索癌症基因组中体细胞突变的模式。