Bioinformatics Approaches to Visual Disease Genetics
视觉疾病遗传学的生物信息学方法
基本信息
- 批准号:8514000
- 负责人:
- 金额:$ 30.78万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Age related macular degenerationAlgorithmsBiochemical PathwayBioinformaticsClinicalCommunitiesComplexComputer SimulationComputer softwareDataDevelopmentDiseaseEntropyGenesGeneticGenetic MarkersGenetic ModelsGenetic StructuresGenetic screening methodGenomicsGenotypeGlaucomaGoalsHereditary DiseaseKnowledgeMachine LearningMethodsModelingNetwork-basedOntologyPathway AnalysisPhenotypePlayPredispositionResearch Project GrantsRiskRoleSimulateSingle Nucleotide PolymorphismStatistical ModelsSystemTimeVariantVision researchVisualWorkbasedatabase of Genotypes and Phenotypesgenetic analysisgenetic variantgenome wide association studymathematical modelnetwork modelsopen sourcepredictive modelingprotein protein interactionsoftware developmenttooluser-friendly
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jason H. Moore其他文献
A disease-specific language model for variant pathogenicity in cardiac and regulatory genomics
用于心脏和调控基因组学中变异致病性的疾病特异性语言模型
- DOI:
10.1038/s42256-025-01016-8 - 发表时间:
2025-03-24 - 期刊:
- 影响因子:23.900
- 作者:
Huixin Zhan;Jason H. Moore;Zijun Zhang - 通讯作者:
Zijun Zhang
ChatGPT and large language models in academia: opportunities and challenges
- DOI:
10.1186/s13040-023-00339-9 - 发表时间:
2023-07-13 - 期刊:
- 影响因子:6.100
- 作者:
Jesse G. Meyer;Ryan J. Urbanowicz;Patrick C. N. Martin;Karen O’Connor;Ruowang Li;Pei-Chen Peng;Tiffani J. Bright;Nicholas Tatonetti;Kyoung Jae Won;Graciela Gonzalez-Hernandez;Jason H. Moore - 通讯作者:
Jason H. Moore
Erratum to: Why epistasis is important for tackling complex human disease genetics
- DOI:
10.1186/s13073-015-0205-8 - 发表时间:
2015-09-07 - 期刊:
- 影响因子:11.200
- 作者:
Trudy F. C. Mackay;Jason H. Moore - 通讯作者:
Jason H. Moore
Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies
组学研究中共享和格式化元数据的感知和技术障碍
- DOI:
10.48550/arxiv.2401.02965 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Yu;Michael I. Love;Cynthia Flaire Ronkowski;Dhrithi Deshpande;L. Schriml;Annie Wong;B. Mons;Russell Corbett;Christopher I Hunter;Jason H. Moore;Lana X. Garmire;T.B.K. Reddy;Winston Hide;A. Butte;Mark D. Robinson;S. Mangul - 通讯作者:
S. Mangul
Cluster Analysis reveals Socioeconomic Disparities among Elective Spine Surgery Patients.
聚类分析揭示了选择性脊柱手术患者的社会经济差异。
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Alena Orlenko;P. Freda;Attri Ghosh;Hyunjun Choi;Nicholas Matsumoto;T. Bright;Corey T. Walker;Tayo Obafemi;Jason H. Moore - 通讯作者:
Jason H. Moore
Jason H. Moore的其他文献
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{{ truncateString('Jason H. Moore', 18)}}的其他基金
Bioinformatics Strategies for Genome Wide Association Studies
全基因组关联研究的生物信息学策略
- 批准号:
10616262 - 财政年份:2022
- 资助金额:
$ 30.78万 - 项目类别:
Bioinformatics Strategies for Genome Wide Association Studies
全基因组关联研究的生物信息学策略
- 批准号:
10654872 - 财政年份:2022
- 资助金额:
$ 30.78万 - 项目类别:
Artificial Intelligence Strategies for Alzheimer's Disease Research
阿尔茨海默病研究的人工智能策略
- 批准号:
10582512 - 财政年份:2021
- 资助金额:
$ 30.78万 - 项目类别:
Artificial Intelligence Strategies for Alzheimer's Disease Research
阿尔茨海默病研究的人工智能策略
- 批准号:
10491672 - 财政年份:2021
- 资助金额:
$ 30.78万 - 项目类别:
Artificial Intelligence Strategies for Alzheimer's Disease Research
阿尔茨海默病研究的人工智能策略
- 批准号:
10907083 - 财政年份:2021
- 资助金额:
$ 30.78万 - 项目类别:
Informatics Algorithms for Genomic Analysis of Brain Imaging Data
用于脑成像数据基因组分析的信息学算法
- 批准号:
10366006 - 财政年份:2020
- 资助金额:
$ 30.78万 - 项目类别:
Informatics Algorithms for Genomic Analysis of Brain Imaging Data
用于脑成像数据基因组分析的信息学算法
- 批准号:
10206271 - 财政年份:2020
- 资助金额:
$ 30.78万 - 项目类别:
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