Analysis of Genomic Data for Complex Traits

描述(由申请者提供)：基因是多种疾病的基础。基因-基因和基因-环境的相互作用可能存在于大多数常见的复杂疾病中，包括物质使用障碍、癌症、早产及其后遗症。要证明这种相互作用不是偶然的，是非常困难的。人类基因组计划和HapMap计划极大地提高了我们研究复杂疾病背后的遗传和环境因素的能力。特别是，高通量基因分型技术一直在迅速发展。然而，许多复杂疾病的病因仍然知之甚少，利用丰富的信息来理解复杂疾病仍然是一个巨大的挑战。在这一努力中，先进的数据分析和数据挖掘技术变得不可或缺。开发强大的分析方法来了解生物系统是利用基因组信息的最大挑战。在过去的几年里，几组研究人员利用全基因组关联研究成功地识别了各种常见、复杂疾病的潜在基因。这些成功导致了全美卫生研究院的基因环境倡议，以确定复杂的遗传变异。最近，PI在使用全基因组联合研究方法的两个主要国家遗传研究网络的规划、设计、数据库开发、统计分析和研究协调方面发挥了主导作用。该项目将利用他在这两项研究中的参与，并在他之前成功开发遗传学研究的统计方法的基础上再接再厉。这个应用程序的主要目的是继续我们在开发、评估和应用新的统计(包括参数和非参数)模型、方法和软件方面的努力和成功，以进行复杂疾病的GWA分析。具体地说，我们将开发(A1)多性状遗传分析的统计方法；以及(A2)复杂性状的关联分析的基于树和森林的模型。一旦完成，将为所有这些型号开发配套软件，并在张博士的网站上向公众提供。我们的方法和软件将应用于PI可用的数据，我们将实现以下次要目标：识别烟草使用、药物使用及其与包括焦虑在内的精神障碍的共病的基因和环境因素。这是我们先前努力的继续；并确定早产及其后遗症(包括脑室出血)的遗传变异和环境因素。尽管技术和方法的巨大进步导致了最近在识别复杂疾病的遗传变异方面的成功，但新的统计方法的发展对于解决复杂表型遗传研究中固有的困难至关重要。公共卫生相关性：该项目将对基因数据的分析产生重大影响，从而对公众产生重大影响，因为我们的方法和软件可以帮助研究人员了解常见和复杂疾病的遗传和环境因素，包括药物使用、癌症和早产。这反过来又会带来更好的预防和治疗策略。

DESCRIPTION (provided by applicant): Genes underlie numerous diseases. Gene-gene and gene-environment interactions are likely to be present in most of the common, complex diseases including substance use disorders, cancer, preterm birth and its sequelae. Demonstrating such interactions beyond chance is very difficult. The Human Genome Project and HapMap Project have greatly advanced our ability to study genetic and environmental factors underlying complex diseases. In particular, high throughput genotyping technologies have been evolving rapidly. However, the etiologies of many complex diseases remain poorly understood, and use of the rich information to understand the complex diseases remains a tremendous challenge. Advanced data analysis and data mining techniques become indispensable in this endeavor. Developing powerful analytic methods to understand biological systems is the greatest challenge in using genomic information. In the last few years, several groups of investigators have successfully identified genes underlying various common, complex diseases using genome-wide association studies. Those successes have led to the NIH-wide Gene Environment Initiatives to identify genetic variants for complex. Recently, the PI has played a leading role in the planning, design, database development, statistical analysis, and study coordination for two major national networks of genetic studies using the genome-wide association study approach. This project will take advantage of the PI involvement in those two studies and build on his success in the development of statistical methods for genetic studies in the previous period. The primary aim of this application is to continue our effort and successes in developing, evaluating, and applying new statistical (both parametric and nonparametric) models, methods, and software to conduct GWA analyses of complex diseases. Specifically, we will develop (A1) statistical methods for genetic analysis of multiple traits; and (A2) tree- and forest-based models for association analyses of complex traits. Once accomplished, companion software will be developed for all of these models and made available to the public on Dr. Zhang's website. Our methods and software will be applied to the data available to the PI, and we will achieve the following secondary aims: to identify genes and environmental factors for tobacco use, substance use and its comorbidity with psychiatric disorders including anxiety. This is a continuation of our previous effort; and to identify genetic variants and environmental factors for preterm deliveries and its sequelae including Intraventricular Hemorrhage. Despite great advance in technology and methodology that led to recent successes in identifying genetic variants for complex diseases, developments of novel statistical methods are critically important to deal with difficulties inherent in genetic studies of complex phenotypes. PUBLIC HEALTH RELEVANCE: This project will have significant impact in analysis of genetic data and hence public, because our methods and software can help investigators understand genetic and environmental factors of common and complex diseases including substance use, cancer, and preterm birth. That in turn leads to better prevention and treatment strategies.