Analysis of Genomic and Complex Data

基因组和成像技术的出现为我们提供了一个研究和理解 健康状况，包括药物使用和精神疾病，这是复杂的，取决于两者 遗传和环境因素。在过去的几十年里，全基因组关联研究(GWA)已经确定 并有力地复制了大量与复杂疾病相关的基因变异。尽管如此， 尽管成功了，但识别基因和环境因素--即所谓的--仍然是困难的 遗传学家的噩梦。大多数已识别的变异具有较低的相关风险，并且几乎不起作用。 遗传性，人们越来越关注寻找复杂疾病的“缺失遗传性”。 此外，据记载，神经精神病学研究的临床贡献微乎其微。 由于研究的传统样本量小，生物学上不正确的诊断标签，合并症和 疾病的异质性。为了解决这些问题并推进临床科学，我们需要发展 有效利用和理解现有数据的新模型和新方法。这是最主要的动机 为了我们的项目。我们将开发利用生物信息(遗传和/或)的更有效的方法 表型数据)，并直接解决共病问题。此外，我们将分析大型数据集，如 如英国生物库拥有人口统计、临床和基因数据。我们将进一步利用 研究人员在GWA研究的数据收集和分析方面的多年经验，并建立在我们 在复杂研究的统计方法和软件的开发和应用方面取得成功。这个 此应用程序的主要目标是开发、评估和应用新的统计学(包括参数统计和 非参数)模型、方法和软件，用于进行复杂疾病的遗传分析。要处理 为了应对上述挑战，我们提议的方法将涉及以下一个或多个主题：(A) 对遗传、表型和环境数据的分析；(B)通过多变量特征对共病进行建模； 以及(C)确定和纳入新的遗传变异，包括它们与环境的相互作用 通过使用和开发最先进的统计方法和软件，如树和 森林。我们项目的成功将直接影响到我们的理解，并最终影响到治疗 以及预防重大公共卫生问题的疾病。

The advent of genomic and imaging technologies provides us with a great opportunity to study and understand health conditions, including substance use and mental illnesses, which are complex and depend on both genetic and environmental factors. In the past decades genomewide association studies (GWA) have identified and robustly replicated numerous genetic variants that are associated with complex diseases. Despite those successes, it remains persistently difficult to identify genes and environmental factors--the so called geneticist's nightmare. Most of the identified variants have low associated risks and account for little heritability, and there is increasing attention focused on finding the “missing heritability" of complex diseases. Furthermore, it is documented that clinical contributions from neuropsychiatric research have been minimal due to traditionally small sample sizes of studies, biologically incorrect diagnostic labels, comorbidity and heterogeneity of the diseases. To address these problems and advance clinical science, we need to develop novel models and methods to efficiently use and understand the available data. This is the primary motivation for our project. We will develop more efficient approaches that utilize biological information (genetic and/or phenotypic data) and directly address the comorbidity issue. In addition, we will analyze large datasets such as UK BioBank with demographic, clinical, and genetic data. We will further take advantage of the investigators' many years of experience in the data collection and analysis of GWA studies and build on our successes in the development and applications of statistical methods and software for complex studies. The primary aim of this application is to develop, evaluate, and apply new statistical (both parametric and nonparametric) models, methods, and software to conduct genetic analyses of complex diseases. To deal with the challenges stated above, our proposed methods will address one or more of the following topics: (a) analysis of genetic, phenotypic, and environmental data; (b) modeling comorbidity through multivariate traits; and (c) identification and incorporation of novel genetic variants including their interactions with environmental factors by using and developing state-of-the-art statistical methodology and software, such as trees and forests. The success of our project will have a direct impact on our understanding, and ultimately, the treatment and prevention of diseases which are of significant public health concern.