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Analysis of Big Data Squared in Biomedical Studies

生物医学研究中的大数据平方分析

基本信息

批准号：
10361461
负责人：
HEPING ZHANG
金额：
$ 43.52万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-05 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10361461
关键词：
Accounting Address Affect Alzheimer&apos s Disease Big Data Big Data Methods Brain Brain imaging Childhood Clinical Clinical Data Collaborations Companions Complex Computer software Data Data Analyses Data Collection Data Set Development Diagnosis Dimensions Disease Environmental Risk Factor Etiology Functional Imaging Genes Genetic Genetic study Genomics Grouping Heritability Human Image Joints Journals Longevity Maps Measurement Methodology Methods Modeling Modernization Molecular Multimodal Imaging Neurocognition Neurocognitive Neurodegenerative Disorders Neurosciences Onset of illness Outcome Pathway interactions Phenotype Philadelphia Prevention Prevention approach Psychiatry Psychology Public Health Publications Radiogenomics Recording of previous events Research Schizophrenia Son Statistical Methods Structure Substance Use Disorder Technology Testing The Cancer Imaging Archive Thick Time advanced analytics analytical method analytical tool base biobank cancer imaging cognitive function cohort computer science connectome disorder risk experience experimental study genetic analysis genetic variant genome analysis genome wide association study genomic data high dimensionality imaging genetics imaging study interest large scale data member method development multidisciplinary neuroimaging neuropsychiatric disorder new technology novel precision medicine predict clinical outcome predictive modeling rapid growth screening simulation statistics success theories tool user friendly software white matter whole genome

项目摘要

Project Summary/Abstract With the rapid growth of modern technology, many large-scale biomedical studies generate massive datasets with multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. We consider 6 publicly available datasets: the Human Connectome project (HCP) study, the UK biobank study, the Pediatric Imaging, Neurocognition, and Genetics study, the Philadelphia Neurodevelopmental Cohort, the Alzheimer's Disease Neuroimaging Initiative study, and the UNC early brain development study. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets may transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. This is critical for diagnosis, prevention, and treatment of brain-related disorders (e.g., schizophrenia and Alzheimer's). However, the development of methods for the joint analysis of high-dimensional imaging-genetic data, called big data squared, presents major theoretical and computational challenges due to complexities of imaging phenotypes such as regional volumetric measurements, cortical thickness maps, subcortical structures, structural and functional connectivity matrices, white matter tracts, and activation images. We will address three imminent challenges in the analysis of big data squared: (CH1) carrying out genome-wide association analysis for functional imaging phenotypes (e.g., white matter tracts, cortical thickness, and subcortical structures); (CH2) carrying out genome-wide association anal- ysis for high-dimensional imaging phenotypes with strong spatial structure (e.g., regional volumetric measure- ments, and structural and functional connectivity matrices); and (CH3) integrating multi-modality imaging, ge- netic, and clinical data to predict clinical outcomes (e.g., disease status or time-to-disease onset). To this end, we will develop (Aim 1) a functional genome-wide association analysis (FGWAS) framework for (CH1); (Aim 2) a net- work genome-wide association analysis (NGWAS) framework for (CH2); (Aim 3) a multi-scale prediction modeling (MSPM) framework for (CH3); and (Aim 4) verify the efﬁcacy of the newly developed analytical tools using simula- tions and the 6 extremely valuable imaging genetic datasets. Finally, we will develop companion software for the methods to be developed in this project. The software, which will provide much needed analytic tools for the big data squared, will be disseminated to the public through http://c2s2.yale.edu/software/, https://github.com/BIG- S2, http://odin.mdacc.tmc.edu/bigs2/software.html, and http://www.nitrc.org/. Our novel methods are applicable to a variety of imaging genetic studies for neuropsychiatric disorders, major neurodegenerative diseases, sub- stance use disorders, and normal brain development. A deeper understanding of genetic mechanism, brain development, and neurocognitive maturation has the potential to inspire new and urgently needed approaches to prevention, diagnosis, and treatment of many illnesses (e.g., schizophrenia and Alzheimer's).

项目总结/摘要随着现代科技的快速发展，许多大规模的生物医学研究产生了大量的数据集多模态成像，遗传，神经认知和临床信息，从越来越大的队列。我们考虑了6个公开可用的数据集：人类连接组项目（HCP）研究，英国生物银行研究，儿科成像、神经认知和遗传学研究，费城神经发育队列研究，阿尔茨海默氏病神经影像学研究和早期大脑发育研究。同时在神经成像和/或基因组学中提取和整合丰富多样的异质信息，这些大数据集可能会改变我们对遗传变异如何影响大脑结构和功能的理解，认知功能和脑相关疾病的风险。这对于诊断、预防、以及脑相关疾病的治疗（例如，精神分裂症和阿尔茨海默氏症）。但发展高维成像-遗传数据联合分析的方法（称为大数据平方）提出了主要的由于成像表型的复杂性，如局部体积，测量、皮质厚度图、皮质下结构、结构和功能连接矩阵，白色物质束和激活图像。我们将解决大数据分析中的三个迫在眉睫的挑战平方：（CH 1）进行功能成像表型的全基因组关联分析（例如，白色物质束，皮质厚度和皮质下结构）;（CH 2）进行全基因组关联分析，用于具有强空间结构的高维成像表型的分析（例如，区域体积测量- 单元，以及结构和功能连接矩阵）;以及（CH 3）整合多模态成像，遗传学和临床数据来预测临床结果（例如，疾病状态或疾病发作时间）。为此我们将开发（目标1）一个功能性全基因组关联分析（FGWAS）框架（CH 1）;（目标2）一个净- 工作全基因组关联分析（NGWAS）框架（CH 2）;（目的3）多尺度预测建模（MSPM）框架（CH 3）;和（目标4）使用模拟验证新开发的分析工具的有效性- 和6个非常有价值的成像遗传数据集。最后，我们将为在这个项目中开发的方法。该软件将为大型企业提供急需的分析工具，数据平方，将通过http：//c2s2.yale.edu/software/，https://github.com/BIG-向公众传播 http：//odin.mdacc.tmc.edu/bigs2/software.html和http://www.nitrc.org/。我们的新方法适用于神经精神疾病、主要神经退行性疾病、亚神经系统疾病、站姿使用障碍，大脑发育正常。更深入地了解遗传机制，大脑发展，神经认知成熟有可能激发新的和迫切需要的方法，预防、诊断和治疗许多疾病（例如，精神分裂症和阿尔茨海默氏症）。

项目成果

期刊论文数量（55）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The emergence of a functionally flexible brain during early infancy.

DOI：
10.1073/pnas.2002645117
发表时间：
2020-09-22
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Yin W;Li T;Hung SC;Zhang H;Wang L;Shen D;Zhu H;Mucha PJ;Cohen JR;Lin W
通讯作者：
Lin W

Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression.