Analysis of Genomic and Complex Data

基因组和复杂数据分析

• 批准号: 9927662
• 负责人: HEPING ZHANG
• 金额: $ 36.22万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-08 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9927662
• 关键词: Address; Alcohol or Other Drugs use; Area; Attention; Biological; Biological Markers; Child Health; Childhood; Chromosomes; Clinical; Clinical Sciences; Cohort Studies; Complex; Computer software; Copy Number Polymorphism; Data; Data Analyses; Data Collection; Data Set; Databases; Development; Diagnostic; Disease; Environmental Risk Factor; Eye diseases; Genes; Genetic; Genetic Markers; Genetic study; Genomics; Health; Heritability; Heterogeneity; Human Resources; Image; Imaging technology; Infrastructure; Label; Lead; Learning Disabilities; Medical Genetics; Medicine; Mental Health; Mental disorders; Methodology; Methods; Modeling; Motivation; National Institute of Mental Health; Neurocognition; Nightmare; PF4 Gene; Phenotype; Philadelphia; Population; Positioning Attribute; Prevention strategy; Public Health; Reproductive Health; Research; Research Personnel; Resources; Risk; Sample Size; Single Nucleotide Polymorphism; Source; Statistical Methods; Students; Substance abuse problem; Theoretical model; Translating; Trees; Variant; biobank; comorbidity; complex data; database of Genotypes and Phenotypes; design; disease heterogeneity; disorder prevention; experience; forest; gene environment interaction; genetic analysis; genetic variant; genome wide association study; genomic data; high end computer; innovation; large datasets; large scale data; multidisciplinary; neuropsychiatry; novel; phenotypic data; semiparametric; success; trait; treatment strategy; web site

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.

基因组和成像技术的冒险为我们提供了一个很好的学习和理解的机会 健康状况，包括使用物质和精神疾病，这很复杂，并且依赖 遗传和环境因素。在过去的几十年中，全基因组协会研究（GWA）已经确定 并坚强地复制了许多与复杂疾病相关的遗传变异。尽管如此 成功，确定基因和环境因素仍然很难 - 所谓的 遗传的噩梦。大多数确定的变体的相关风险较低，几乎没有 遗传力和越来越多的关注集中在寻找复杂疾病的“缺失遗传力”。 此外，据报道，神经精神研究的临床贡献很小 由于传统的研究样本量很小，生物学上不正确的诊断标签，合并症和合并症和 疾病的异质性。为了解决这些问题并提高临床科学，我们需要发展 有效使用和理解可用数据的新颖模型和方法。这是主要动机 为我们的项目。我们将开发更有效的方法来利用生物学信息（遗传和/或 表型数据）并直接解决合并症问题。此外，我们将分析这样的大型数据集 作为具有人口，临床和遗传数据的英国生物库。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，并以我们的为基础 在复杂研究的统计方法和软件的开发和应用中取得了成功。 本应用的主要目的是开发，评估和应用新的统计（参数和 非参数模型，方法和软件进行复杂疾病的遗传分析。处理 上面所述的挑战，我们提出的方法将解决以下主题：（a） 分析遗传，表型和环境数据； （b）通过多元特征对合并症进行建模； （c）识别并结合了新型遗传变异，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（例如树木和）的因素 森林。我们项目的成功将直接影响我们的理解，并最终对治疗 以及预防与公共卫生有关的疾病。