Bioinformatics Approaches to Visual Disease Genetics

描述(由申请人提供)：现在人们认识到，许多视觉疾病受到多种不同基因变异之间复杂相互作用的影响。因此，我们预测视觉疾病易感性的能力将关键取决于可用于理解高维遗传数据的计算、数学和统计建模方法和软件。我们在这里提出了一个生物信息学研究项目，以开发网络建模方法来识别与视觉疾病终点相关的遗传生物标记物的组合。我们的工作假设是，使用网络建模的基于系统的生物信息学方法将在面对基因组变异和视觉疾病之间复杂关系的过程中发挥非常重要的作用。我们将首先开发和评估从全基因组关联研究中推断大规模遗传相互作用网络的建模方法(目标1)。然后，我们将应用AIM 1中开发的建模方法，从患有和不患有视觉疾病的受试者的全基因组关联数据中推断遗传相互作用网络(AIM 2)。接下来，我们将利用推断的遗传相互作用网络来指导视觉疾病预测遗传模型的发展(目标3)。最后，所有网络建模方法将作为流行的用户友好、免费提供和开放源码的软件包(AIM4)的一部分发布给视觉研究社区。我们预计，作为该项目的一部分开发和分发的网络建模方法和软件将在开发基因测试方面发挥重要作用，这将是识别视力疾病风险所必需的。 公共卫生相关性：作为生物信息学研究项目的一部分开发和分发的网络建模方法和软件将在基因测试的开发中发挥重要作用，这些测试将是识别青光眼和老年性黄斑变性等常见疾病风险所必需的。

DESCRIPTION (provided by applicant): It is now recognized that many visual diseases are influenced by complex interactions between multiple different genetic variants. As a result, our ability to predict susceptibility to visual diseases will depend critically on the computational, mathematical and statistical modeling methods and software that are available for making sense of high-dimensional genetic data. We propose here a bioinformatics research project to develop network modeling approaches for identifying combinations of genetic biomarkers associated with visual disease endpoints. Our working hypothesis is that a systems-based bioinformatics approach using network modeling will play a very important role in confronting the complexity of the relationship between genomic variation and visual diseases. We will first develop and evaluate modeling methods to infer large-scale genetic interaction networks from genome-wide association studies (AIM 1). We will then apply the modeling methods developed in AIM 1 to the inference of genetic interaction networks from genome-wide association data in subjects with and without visual diseases (AIM 2). Next, we will utilize the inferred genetic interaction networks to guide the development of predictive genetic models of visual diseases (AIM 3). Finally, all network modeling methods will be released to the vision research community as part of a popular user-friendly, freely available and open-source software package (AIM 4). We anticipate that the network modeling methods and software developed and distributed as part of this project will play an important role in the development of the genetic tests that will be necessary to identify those at risk for visual diseases. PUBLIC HEALTH RELEVANCE: The network modeling methods and software developed and distributed as part of this bioinformatics research project will play an important role in the development of the genetic tests that will be necessary to identify those at risk for common diseases such as glaucoma and age-related macular degeneration.