Development of Evidential Methodology for Analysis of Genetic Data

遗传数据分析证据方法的发展

• 批准号: RGPIN-2015-03742
• 负责人: Strug, Lisa
• 金额: $ 1.02万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675395
• 关键词: Development; Evidential; Methodology; Analysis; Genetic

The genetics field is in need of novel statistical methodology to deal with data sets of growing size and complexity. Statistical paradigms that penalize investigators for thoroughly interrogating their data (ie. the multiple hypothesis testing problem) will fall out of favour. The evidential paradigm (EP), a less-known alternative to Frequentist and Bayesian paradigms, uses the likelihood ratio (LR) for two simple hypotheses to measure the relative evidence strength in a given body of data. Interpreting evidence strength via LRs is reliable, where false values for the hypotheses are rarely better supported than true values, and the probability of observing evidence favouring false values, M, can be controlled by the sample size. But M does not affect the interpretation of the evidence strength in a given body of data, which is represented by the LR. Due to this decoupling of error probabilities and evidence strength we have shown that, indeed, there is an increase in M due to assessing multiple hypotheses, but this can be controlled by a replication study; a routine practice in the genetics field.***We develop EP statistical methodology for the analysis of genetic data. As a new field there is much work to be done, both in methods development and in training the next generation of EP researchers. Our previous contributions have spurred new research directions for others, including our work in EP methodology for genetic linkage and case-control association studies, and multiple hypothesis testing approaches. Recent theoretical developments by us and others have served to fill important theoretical gaps: Robust adjustments to likelihoods can make the EP robust to model misspecification; while generalizations of the Law of Likelihood provide a framework to weigh evidence for composite hypotheses. Here we propose three specific projects: (1) making composite likelihoods robust for EP genetic association analysis of correlated data; (2) derive the operational characteristics of the generalized Law of Likelihood; and (3) develop robust EP association methodology to integrate large genomic data sets from different technologies.***The EP provides an approach to the analysis of genomic data that is intuitive, resonates with geneticists, and provides unique solutions to issues that hamper progress. However, an absence of EP methodology to address timely problems in statistical genetics limits its application. Here we present a program of research focused on developing EP methodology for state-of-the-art genomic applications, that contribute to theoretical developments towards a sound theory for measuring statistical evidence.**

遗传学领域需要新的统计方法来处理不断增长的规模和复杂性的数据集。统计范式，惩罚调查人员彻底询问他们的数据（即。多重假设检验问题）将失宠。证据范式（EP）是频率主义和贝叶斯范式的一种鲜为人知的替代方法，它使用两个简单假设的似然比（LR）来衡量给定数据体中的相对证据强度。通过LR解释证据强度是可靠的，其中假设的假值很少比真值得到更好的支持，并且观察到有利于假值的证据的概率M可以通过样本量来控制。但是M不影响对给定数据体中证据强度的解释，该数据体由LR表示。由于错误概率和证据强度的这种解耦，我们已经表明，由于评估多个假设，M确实会增加，但这可以通过复制研究来控制;这是遗传学领域的常规做法。我们开发了EP统计方法用于遗传数据的分析。作为一个新的领域，有很多工作要做，无论是在方法的发展和培养下一代的EP研究人员。我们以前的贡献为其他人带来了新的研究方向，包括我们在遗传连锁和病例对照关联研究的EP方法学方面的工作，以及多种假设检验方法。我们和其他人最近的理论发展填补了重要的理论空白：对似然性的鲁棒调整可以使EP对模型误设具有鲁棒性;而对似然定律的推广提供了一个框架来权衡复合假设的证据。在这里，我们提出了三个具体的项目：（1）为相关数据的EP遗传关联分析提供稳健的复合似然;（2）推导出广义李克定律的操作特征;（3）开发稳健的EP关联方法，以整合来自不同技术的大型基因组数据集。EP提供了一种直观的基因组数据分析方法，与遗传学家产生共鸣，并为阻碍进展的问题提供了独特的解决方案。然而，EP方法的缺乏，以解决及时的问题，在统计遗传学限制了它的应用。在这里，我们提出了一个研究计划，重点是为最先进的基因组应用开发EP方法，这有助于理论发展，为测量统计证据提供可靠的理论。