Bioinformatics Approaches to Visual Disease Genetics

视觉疾病遗传学的生物信息学方法

• 批准号: 8514000
• 负责人: Jason H. Moore
• 金额: $ 30.78万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8514000
• 关键词: Age related macular degeneration; Algorithms; Biochemical Pathway; Bioinformatics; Clinical; Communities; Complex; Computer Simulation; Computer software; Data; Development; Disease; Entropy; Genes; Genetic; Genetic Markers; Genetic Models; Genetic Structures; Genetic screening method; Genomics; Genotype; Glaucoma; Goals; Hereditary Disease; Knowledge; Machine Learning; Methods; Modeling; Network-based; Ontology; Pathway Analysis; Phenotype; Play; Predisposition; Research Project Grants; Risk; Role; Simulate; Single Nucleotide Polymorphism; Statistical Models; System; Time; Variant; Vision research; Visual; Work; base; database of Genotypes and Phenotypes; genetic analysis; genetic variant; genome wide association study; mathematical model; network models; open source; predictive modeling; protein protein interaction; software development; tool; user-friendly

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.

描述（由申请人提供）：现在认识到许多视觉疾病受到多种不同遗传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严重依赖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，开发网络建模方法，用于识别与视觉疾病终点相关的遗传生物标志物的组合。我们的工作假设是，基于系统的生物信息学方法，使用网络建模将发挥非常重要的作用，面对基因组变异和视觉疾病之间的关系的复杂性。我们将首先开发和评估从全基因组关联研究（AIM 1）中推断大规模遗传相互作用网络的建模方法。然后，我们将应用AIM 1中开发的建模方法，从有和没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的预测遗传模型（AIM 3）的开发。最后，所有的网络建模方法将作为一个流行的用户友好，免费提供和开源软件包（AIM 4）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识别视觉疾病风险所需的基因检测方面发挥重要作用。