SOLAR-Eclipse Computational Tools for Imaging Genetics

描述（申请人提供）：该应用程序将为成像遗传学这一新兴领域提供急需的分析方法。我们的重点是创建SOLAR-Eclipse成像遗传学工具，用于经典遗传和表观遗传流行病学分析，如遗传性，多效性，数量性状位点（QTL）和全基因组关联（GWAS），基因表达和甲基化分析，使用结构和功能脑成像（AIM 1）衍生的性状。我们还将开发满足遗传和成像要求的多种测试的智能校正（AIM 2）。SOLAR-Eclipse工具将进行优化，以使用体素脑功能和结构成像特征以及高通量现代分子遗传数据。拟议的工具将具有灵活性，以适应以家庭为基础的结构化和非结构化流行病学样本。这些工具将作为一个独立的应用程序发布，并集成到现有的神经科学网络中。在单个分析包中实现这一目标将极大地增强和加快对影响大脑神经解剖和功能特征的基因的搜索，并提供全面的工具来说明从基因到大脑结构/功能的途径。在开发过程中，我们将测试和优化这些工具（AIM 3），使用由美国国立卫生研究院资助的三个成像遗传学项目，大脑结构遗传学，人类连接体项目和尼古丁大脑产生的数据。这些工具、源代码和文档化的分析策略将通过神经成像信息学工具和资源交换所（NITRC.org）分发。该应用程序的PI是nibib资助的k奖获得者，他致力于优化成像遗传学研究的标准遗传分析方法。这项工作产生了25同行评议的出版物在过去的四年。这些经验将用于创建一个新的工具库，名为SOLAR- eclipse，它将以美国国立卫生研究院资助的统计遗传方法SOLAR库为基础。我们的初步数据表明，当对成像性状进行优化时，这些工具可以实现100-200倍的性能提高（比标准方法），从而在多节点Linux集群上实现全尺寸（103个受试者的约105个性状）单变量和双变量遗传分析。这项资助的合作者结合了成像和遗传学方面的专家，他们在这两个领域都有开发工具的经验，并开发了流行的分析工具，如SOLAR、FSL、SPM、BrainMap、Talairach Daemon、JIST、Mango等。

DESCRIPTION (provided by applicant): This application will provide urgently needed analysis methods to the emerging field of imaging genetics. Our focus is to create SOLAR-Eclipse imaging genetics tools for classical genetic and epigenetic epidemiological analyses such as heritability, pleiotropy, quantitative trait loci (QTL) and genome-wide association (GWAS), gene expression, and methylation analyses using traits derived from structural and functional brain imaging (AIM 1). We will also develop intelligent correction for multiple testing that meets both genetic and imaging requirements (AIM 2). The SOLAR-Eclipse tools will be optimized to use voxel-wise brain functional and structural imaging traits and high throughput modern molecular genetic data. The proposed tools will be flexible to accommodate both structured family-based and unstructured epidemiological samples. These tools will be released as a standalone application and integrated into the existing neuroscience eScience networks. Achieving this goal in a single analysis package will greatly enhance and speed up the search for genes that influence brain's neuroanatomic and functional traits and provide comprehensive tools to illustrate pathways from genes to brain structure/function. During the development process we will test and optimize these tools (AIM 3) using the data generated by three NIH-funded imaging genetics projects, the Genetic of Brain Structure, the Human Connectom project, and the NicotineBrain. The tools, source codes and documented analyses strategies will be distributed via the Neuroimaging Informatics Tool and Resources Clearing house (NITRC.org). The PI for this application is a NIBIB-funded K-awardee who has worked on optimizing the standard genetic analysis methods for imaging genetics research. This work generated twenty-five peer- reviewed publications in the last four years. These experiences will be used to create a new library of tools termed, SOLAR-Eclipse, which will be based on the NIH-funded SOLAR library of statistical genetic methods. Our preliminary data shows that when optimized for imaging traits these tools can achieve 100-200 fold performance improvement (over standard methods) enabling full scale (~105 traits in 103 subjects) voxel-wise univariate and bivariate genetic analyses on a multi-node Linux cluster. The collaborators on this grant combine experts in imaging and genetics who have demonstrated their experience in developing tools in both domains and developed popular analyses tools such as SOLAR, FSL, SPM, BrainMap, Talairach Daemon, JIST, Mango and others. PUBLIC HEALTH RELEVANCE: Merging two important research directions in modern science, genetics and neuroimaging, led to the emergence of a new field, termed imaging genetics. This field combines modern statistical genetic methods with quantitative phenotyping performed with high dimensional neuroimaging modalities. This discipline seeks to understand the biological basis of neurological and psychiatric illnesses by characterizing genes that contribute to the risk for these disorders. However, standard imaging tools are unable to deal with large-scale genetics data and standard genetics tools, in turn, are unable to accommodate large size and format of the imaging traits. Thus, there is in immediate need for software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing.