Solar-Eclipse Computational Tools for Imaging Genetics

用于成像遗传学的日食计算工具

• 批准号: 9761288
• 负责人: PETER V. KOCHUNOV
• 金额: $ 40万
• 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 2021-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9761288
• 关键词: Acceleration; Advisory Committees; Algorithms; Analysis of Variance; Big Data; Biological; Brain Mapping; Brain imaging; Collaborations; Communities; Computer software; DNA; Data; Data Analyses; Data Set; Development; Discipline; Disease; Distant; Educational workshop; Ensure; Epigenetic Process; Family; Feedback; Funding; Gene Expression; Gene Library; Genes; Genetic; Genetic Markers; Genetic Research; Genome; Genotype; Heritability; High Performance Computing; Human; Image; Image Analysis; Imaging Device; Individual; International; Internet; Knowledge; Linear Models; Manuscripts; Measures; Mental disorders; Meta-Analysis; Methods; Methylation; Modality; Modernization; Multimodal Imaging; Multivariate Analysis; Neurologic; Nucleic Acid Regulatory Sequences; Patients; Performance; Phase; Phenotype; Quantitative Trait Loci; Reproducibility; Research; Resolution; Rest; Sampling; Schizophrenia; Science; Science of genetics; Site; Software Tools; Speed; Stroke; Structure; Techniques; Testing; Time; United States National Institutes of Health; Update; Work; analytical method; application programming interface; base; cohort; computerized tools; connectome; dashboard; data format; data sharing; disorder risk; endophenotype; genetic analysis; genetic linkage analysis; genetic resource; genome sequencing; genome wide association study; genome-wide; high dimensionality; high resolution imaging; imaging genetics; innovation; interest; neuroimaging; neuroinformatics; novel; pleiotropism; public health relevance; rare variant; symposium; tool; trait; white matter; whole genome; working group

 DESCRIPTION (provided by applicant): This application will provide urgently needed analytical methods to the emerging field of imaging genetics. Our focus is on phase 2 development of SOLAR-Eclipse integrated suite of resources for genetic and epigenetic analyses such as heritability, pleiotropy, quantitative trait loci-linkage (QTL-L), genome-wide association (GWA) and Whole-Genome Sequencing (WGS), gene expression, and methylation analyses optimized for traits derived from structural and functional neuroimaging data. During the first short and intensive funding period (2.5 years), 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) and Stroke Genetics Network (SiGN). During the first funding period, we released 12 major software updates and authored and co-authored 47 manuscripts. We established an annual workshop on the use of SOLAR-Eclipse at the Imaging Genetics Conference (2012, 2013, 2014, 2015) and at the genetic imaging workshop at the Organization for Human Brain Mapping conference (2012, 2013, 2014). We structure this renewal in the "Pull/Push" sprit of collaborative Big Data research, where "Pull" refers to development of novel tools by our team and "Push" refers to collaboration with Big Data partners to apply and test cutting edge analyses. We propose to focus the next phase of SE development at the needs identified by our Big Data partners. We propose three "Pull" AIMS (1-3) to develop leading and enabling imaging genetics analysis techniques, high performance computing tailored to unique imaging genetics challenges, and novel data formats. In "Push" AIM 4, SOLAR-Eclipse team partnered with imaging genetics collaborations, ENIGMA, HCP, SiGN, IMAGEN and others to "Push" the state of science through collaborative studies. AIM 1 centers on high performance computing with the aim of achieving real-time GWA/WGS analyses of voxel-wise imaging traits in family based samples such as HCP. By combining novel data transformations for fast approximation of likelihood analyses and Graphics Processor Unit (GPU) computing, we will achieve ~105-6-fold computation acceleration as compared with traditional, maximum likelihood calculation methods. High performance computing will require a new data format for storage of imaging genetics data. In AIM 2, we propose to draft Gen.Gii data format and application programming interface (APIs) optimized for imaging genetic analyses, as well as recording the provenance of imaging genetics data analysis workflows. Building off a first draft created by a working group of imaging genetics experts, we will seek broad community input to ensure that Gen.Gii standard will be embraced. In AIM 3, we propose to integrate newly developed empirical kinship techniques variance component kernel use in imaging genetics applications. Empirical kinship methods calculate "genetic distances" among subjects directly from genome-wide data and partition the trait variance based on the "empirical kinship"; for example, computing additive genetic variance in white matter integrity in schizophrenia patients that is contributed by the regulatory regions of genome. Empirical kinship methods will be generalized to perform classical genetic variance analyses of the imaging phenotypes, including their heritability, pleiotropy, rare variant and quantitative trait linkage analyses in "unrelated" (but actually distantly related) subjects (2). In AIM 4, we will execute collaborative studies to fine tune novel methods in large and diverse samples assembled by our Big Data partners: ENIGMA, HCP and SiGN. This collaborative piloting and honing of novel methods will serve to popularize and disseminate our developments for individual imaging genetics labs.

 描述（由申请人提供）：该应用程序将为新兴的成像遗传学领域提供急需的分析方法，我们的重点是用于遗传性、多效性、定量等遗传和表观遗传分析的 SOLAR-Eclipse 综合资源套件的第二阶段开发。性状位点连锁 (QTL-L)、全基因组关联 (GWA) 和全基因组测序 (WGS)、基因表达和甲基化分析，针对源自结构的性状进行优化在第一个短期密集资助期间（2.5 年），我们展示了 SOLAR-Eclipse 在成像遗传学应用中的实用性，并与 NIH Big 的三个主要脑成像项目开展了强有力的“拉/推”合作。数据 2 知识 (BD2K) 通过荟萃分析 (ENIGMA)、人类连接组项目 (HCP) 和中风遗传学网络 (SiGN) 增强神经影像遗传学。在此期间，我们发布了 12 个主要软件更新，并撰写和合着了 47 篇手稿。我们在成像遗传学会议（2012 年、2013 年、2014 年、2015 年）和基因成像研讨会上建立了关于 SOLAR-Eclipse 使用的年度研讨会。人脑图谱组织会议（2012、2013、2014）协作大数据研究的“拉/推”精神，其中“拉”是指我们团队开发新颖的工具，“推”是指与大数据合作伙伴合作应用和测试前沿分析，我们建议重点关注下一步。根据我们的大数据合作伙伴确定的需求，我们提出了三个“拉动”目标 (1-3)，以开发领先且可行的成像遗传学分析技术、针对独特成像遗传学挑战的高性能计算以及新颖的数据格式。 。在“推动”AIM 4、SOLAR-Eclipse 团队与影像遗传学合作机构、ENIGMA、HCP、SiGN、IMAGEN 等合作，通过合作研究“推动”AIM 1 以高性能计算为中心，旨在实现真正的科学发展。通过结合新颖的数据转换来快速近似似然分析和图形处理器单元，对基于家族的样本（例如 HCP）中的体素成像特征进行全时 GWA/WGS 分析。 (GPU) 计算，与传统的最大似然计算方法相比，我们将实现约 105-6 倍的计算加速。在 AIM 2 中，我们建议起草一种新的数据格式来存储遗传成像数据。 Gen.Gii 数据格式和应用程序编程接口 (API) 针对成像遗传学分析进行了优化，并记录了成像遗传学数据分析工作流程的出处，我们将在成像遗传学专家工作组创建的初稿的基础上寻求广泛的支持。社区投入以确保在 AIM 3 中，我们将采用新开发的经验亲缘关系技术，将方差分量核集成到成像遗传学应用中，经验亲缘关系方法直接从全基因组数据计算受试者之间的“遗传距离”并划分性状。基于“经验亲缘关系”的方差；例如，计算由基因组调控区域贡献的精神分裂症患者白质完整性的附加遗传方差将被推广到执行经典遗传。成像表型的方差分析，包括“不相关”（但实际上关系较远）受试者的遗传性、多效性、罕见变异和数量性状连锁分析 (2) 在 AIM 4 中，我们将进行合作研究以微调新颖的研究。 我们的大数据合作伙伴 ENIGMA、HCP 和 SiGN 收集的大量不同样本中的方法，这种对新方法的协作试验和磨练将有助于推广和传播我们在个体成像遗传学实验室的发展。