Solar-Eclipse Computational Tools for Imaging Genetics

摘要 这一应用将为发展成像遗传学领域提供急需的分析方法。 太阳-日食是一套用于遗传和表观遗传学分析的综合资源，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 农艺性状的表达、数量性状连锁(QTL-L)和甲基化分析 结构和功能神经影像数据。我们的重点是支持全分辨率的阶段3开发 涉及复杂多基因疾病的成像遗传学网络的体素和顶点分析。在.期间 在前两个资助期，我们演示了Solar-Eclipse在成像遗传学应用中的应用 并与三个主要的NIH脑成像计划发展了强大的“拉/推”协作：NIH大数据 2知识(BD2K)通过荟萃分析增强神经成像遗传学(谜题)，人类 Connectome Project(HCP)、MRI-Genie、Charge和UK Bio Bank。在这些资助期间，我们 发布了20多个软件更新，这一支持在约250份手稿中得到了认可。我们把重点放在 根据我们的大数据合作伙伴确定的需求进行SE开发的下一阶段。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的界面。它将包括一个 针对体素和顶点成像遗传分析优化的新数据格式，包括GWA和WGA，AS 以及记录成像遗传学数据分析工作流程的出处。目标2的重点是实现 在全体素顶点和遗传分辨率下的GWA和WGA。这将派生并执行因果关系测试和 解释连锁不平衡(LD)和空间的成像-遗传网络 依赖关系模式和针对多个比较的更正。我们将执行因果推理测试，以 垂直和水平多向性--控制复杂遗传风险因素的两种主要机制 多基因疾病，如精神分裂症。目标3，我们将进行协作研究以调整新方法 在我们的大数据合作伙伴收集的大型和多样化的样本中：Enigma、HCP、SIGN、UKBB等。 这种新方法的合作试点和磨练将有助于普及和传播我们的 个体成像遗传学实验室的发展。

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.