Solar-Eclipse Computational Tools for Imaging Genetics

用于成像遗传学的日食计算工具

• 批准号: 10493317
• 负责人: PETER V. KOCHUNOV
• 金额: $ 50.57万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10493317
• 关键词: Accounting; Adult; Advisory Committees; Algorithms; BRAIN initiative; Big Data; Brain; Brain imaging; Cells; Censuses; Cerebral cortex; Cerebrum; Charge; Cognition; Cognitive; Cognitive deficits; Collaborations; Combinatorics; Complex; Computer software; Data; Data Analyses; Dependence; Development; Diagnosis; Dimensions; Disease; Docking; Drug resistance; Epigenetic Process; Equilibrium; Etiology; Evaluation; Family; Funding; Gene Expression; Genes; Genetic; Graph; Heritability; Human; Hybrids; Image; Individual; International; Knowledge; Lead; Link; Linkage Disequilibrium; Magnetic Resonance Imaging; Manuscripts; Mediation; Meta-Analysis; Methods; Methylation; Modernization; Network-based; Nicotine Dependence; Pathway Analysis; Pathway interactions; Pattern; Performance; Phase; Phenotype; Publications; Pythons; Quantitative Trait Loci; Resolution; Resources; Sampling; Schizophrenia; Short-Term Memory; Statistical Methods; Structure; Testing; United States National Institutes of Health; Update; analytical method; application programming interface; base; biobank; cell type; computerized tools; connectome; data format; data sharing; gene regulatory network; genetic analysis; genetic linkage; genetic resource; genetic risk factor; genome analysis; genome wide association study; graph theory; high resolution imaging; imaging genetics; improved; innovation; large datasets; neuroimaging; neuroinformatics; neuropsychiatric disorder; novel; pleiotropism; processing speed; screening; software development; tool; trait; white matter; whole genome

Abstract This application will provide urgently needed analytical methods to develop the field of imaging genetics. SOLAR-Eclipse is an integrated suite of resources for genetic and epigenetic analyses such as heritability, pleiotropy, high-resolution genome-wide association (GWA) and Whole-Genome Analyses (WGA), gene expression, quantitative trait loci-linkage (QTL-L) and methylation analyses optimized for traits derived from structural and functional neuroimaging data. Our focus is on phase 3 development to support full-resolution voxel-and-vertex-wise analyses of imaging genetics networks involved in complex polygenic illnesses. During the first two funding periods, we demonstrated the utility of SOLAR-Eclipse for imaging genetics applications and developed strong “Pull/Push” collaboration with three major NIH brain imaging initiatives: the NIH Big Data 2 Knowledge (BD2K) Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), Human Connectome Project (HCP), MRI-GENIE, CHARGE and UK Bio Bank. During these funding periods, we released over 20 software updates and this support was acknowledged in ~250 manuscripts. We focused the next phase of SE development on the needs identified by our Big Data partners. The first aim is to revise our software to support “Repeat, Rerun, Replicate (R3)” initiative, including Python based application programming interface for straightforward integration of SOLAR-Eclipse into modern processing workflows. It will include a new data format optimized for voxel-and-vertex wise imaging genetic analyses including GWA and WGA, as well as recording the provenance of imaging genetics data analysis workflows. Aim 2 is focused on enabling GWA and WGA at full voxel-vertex-and- genetic resolution. This will derive and perform causality testing and annotation for the imaging-genetic networks while accounting for linkage disequilibrium (LD) and spatial dependency patterns and correcting for multiple comparisons. We will perform causative inference testing for the vertical and horizontal pleiotropies - the two main mechanisms that govern genetic risk factors for complex polygenic illnesses such as schizophrenia. Aim 3, we will execute collaborative studies to tune novel methods in large and diverse samples assembled by our Big Data partners: ENIGMA, HCP, SiGN, UKBB and others. This collaborative piloting and honing of novel methods will serve to popularize and disseminate our developments for individual imaging genetics labs.

抽象的 该应用将为影像遗传学领域的发展提供急需的分析方法。 SOLAR-Eclipse 是一套集成资源，用于遗传和表观遗传分析，例如遗传力、 多效性、高分辨率全基因组关联 (GWA) 和全基因组分析 (WGA)、基因 表达、数量性状基因座连锁 (QTL-L) 和甲基化分析针对衍生性状进行了优化 结构和功能神经影像数据。我们的重点是第 3 阶段的开发，以支持全分辨率 对涉及复杂多基因疾病的成像遗传学网络进行体素和顶点分析。期间 在前两个资助期，我们展示了 SOLAR-Eclipse 在成像遗传学应用中的实用性 并与美国国立卫生研究院 (NIH) 的三个主要脑成像项目开展了强有力的“拉/推”合作：NIH 大数据 2 知识 (BD2K) 通过荟萃分析 (ENIGMA) 增强神经影像遗传学，人类 连接组计划 (HCP)、MRI-GENIE、CHARGE 和英国生物库。在这些资助期间，我们 发布了 20 多个软件更新，并且这种支持在约 250 份手稿中得到了认可。我们重点关注的是 SE 开发的下一阶段将根据我们的大数据合作伙伴确定的需求进行。第一个目标是修改我们的 支持“重复、重新运行、复制 (R3)”计划的软件，包括基于 Python 的应用程序编程 用于将 SOLAR-Eclipse 直接集成到现代处理工作流程中的界面。它将包括一个 针对体素和顶点明智成像遗传分析（包括 GWA 和 WGA）进行了优化的新数据格式，如 以及记录成像遗传学数据分析工作流程的来源。目标 2 的重点是使 全体素顶点和遗传分辨率的 GWA 和 WGA。这将导出并执行因果关系测试和 注释成像遗传网络，同时考虑连锁不平衡（LD）和空间 依赖性模式并纠正多重比较。我们将进行因果推理测试 垂直和水平多效性 - 控制复杂遗传风险因素的两个主要机制 多基因疾病，例如精神分裂症。目标 3，我们将进行合作研究来调整新方法 由我们的大数据合作伙伴收集的大量多样化样本：ENIGMA、HCP、SiGN、UKBB 等。 这种协作试点和磨练新方法将有助于推广和传播我们的方法 个人成像遗传学实验室的发展。