Analysis of Genomic and Complex Data

基因组和复杂数据分析

• 批准号: 10371032
• 负责人: HEPING ZHANG
• 金额: $ 36.1万
• 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 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10371032
• 关键词: Address; 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; substance use; 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出现为我们提供了一个很好的机会来研究和了解 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。在过去的几十年中，全基因组关联研究（GWA）已经确定了 并稳健地复制了许多与复杂疾病相关的遗传变异。尽管有这些 尽管取得了一些成功，但仍然很难确定基因和环境因素--所谓的 遗传学家的噩梦大多数已识别的变异具有低相关风险， 遗传性，并且越来越多的注意力集中在寻找复杂疾病的“缺失的遗传性”上。 此外，据记载，临床的贡献，从神经精神病学的研究一直是最小的 由于传统的研究样本量小、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，我们需要开发 有效利用和理解现有数据的新模型和方法。这是主要的动机 为了我们的项目。我们将开发更有效的方法，利用生物信息（遗传和/或基因）， 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， 作为英国生物银行，拥有人口统计学，临床和遗传数据。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， 在复杂研究中成功开发和应用统计方法和软件。的 该应用程序的主要目的是开发，评估和应用新的统计（参数和 非参数）模型、方法和软件来进行复杂疾病的遗传分析。处理 为了应对上述挑战，我们提出的方法将解决以下一个或多个主题： 分析遗传、表型和环境数据;（B）通过多变量性状对共显性进行建模; 和（c）鉴定和掺入新的遗传变体，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，如树木和 森林我们项目的成功将对我们的理解产生直接影响，最终，治疗 以及预防严重影响公众健康的疾病。