Toward PanOmic and Personalized Association Study of Complex Diseases - A New Statistical and Computational Paradigm for Personalized Medicine

复杂疾病的全景和个性化关联研究——个性化医疗的新统计和计算范式

• 批准号: 8963539
• 负责人: Sally E Wenzel
• 金额: $ 57.16万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8963539
• 关键词: Adult asthma; Algorithmic Software; Algorithms; Aptitude; Asthma; Big Data; Childhood Asthma; Clinical; Clinical Informatics; Collaborations; Complex; Computer software; Data; Data Set; Development; Diagnosis; Disease; Disease model; Face; Family; Gene Expression; Genetic; Genetic Models; Genetic Polymorphism; Genetic Variation; Genome; Genomics; Genotype; Goals; Individual; Investigation; Knowledge; Light; Linear Models; Linear Regressions; Maps; Medical; Medical Genetics; Medical center; Medicine; Methodology; Methods; Modeling; Morphologic artifacts; Non-linear Models; Outcome; Patients; Pattern; Pharmaceutical Preparations; Phenotype; Population; Research; Research Personnel; Risk Assessment; SNP genotyping; Statistical Models; Structure; Techniques; Technology; Time; Variant; Visit; base; clinical phenotype; cloud based; cohort; combinatorial; cost; disease diagnosis; disease phenotype; flexibility; genetic analysis; genetic element; genome sequencing; genome wide association study; human disease; improved; innovation; medical schools; multitask; next generation; personalized medicine; phenome; precision medicine; programs; public health relevance; response; sound; success; theories; tool; trait; user-friendly

 DESCRIPTION (provided by applicant): In this application, we propose a systematic attempt on methodological development for the largely unexplored but practically important problem-personalized inference of genetic effects of genome variations to complex disease phenotypes, and personalized prediction of clinical outcomes from genome variations. While technological advancements and cost reduction in genome sequencing, clinical phenotyping, and possibly panomic patient profiling promise to bring people closer to an era of personalized and precision medicine, sound mathematical principles and efficient analytical programs needed to deliver such promises remain to be developed. We intend to develop next-generation statistical frameworks, algorithms, and software for robust, yet accurate and personalizable genetic analysis of complex diseases, including genome-wide association (GWA) and phenome-wide association (PheWA) mapping, and whole genome prediction (WGP). Toward this goal, we propose the following specific aims: 1) Develop a new framework for association mapping enabling multi-confounder correction and panomic genetic modeling. 2) Transform traditional parametric linear models to arbitrarily expressive nonparametric functional models for enhanced association mapping and whole-genome prediction. 3) Develop a new statistical paradigm for personalized GWA/PheWA and phenotype prediction. And 4) develop a turnkey and cloud-based software platform for personalized genomics, and application of our methods and programs to an in-depth genetic investigation of childhood and adult asthma using the CAMP and SARP datasets, in collaboration with clinicians from U Pitt School of Medicine/U Pitt Medical Center (UPMC), and Penn State Hershey Medical Center (PSMC). Our proposed methodological innovations depart significantly from conventional technologies and current platforms in clinical genomics, and represent an initial foray into a mathematically rigorous and computationally tractable way for medical genetic inference and prediction in presence of multiple confounders, rich prior structural knowledge, and needs for capturing both shared patterns and individual signatures in complex genetic effects. It is our goal that the resultant ne framework will improve the understanding, diagnosis, and treatment of complex human diseases such as asthma, and offer a practical basis for personalized medicine in the Big Data era of genomic medicine.

 描述（由应用程序提供）：在本应用中，我们提出了一种系统性的尝试，对方法论开发的系统性尝试在很大程度上出乎意料但实际上重要的问题是个性化的，对基因组变异对复杂疾病表型的遗传作用的推断以及基因组变异的临床结果的个性化预测。尽管技术进步和基因组测序，临床表型和可能的全态患者分析的成本降低有望使人们更接近个性化和精确的医学时代，合理的数学原理和有效的分析计划需要提供此类诺言。我们打算开发下一代统计框架，算法和软件，用于对复杂疾病（包括全基因组关联（GWA）和现象范围范围的关联（PHEWA）映射，以及整个基因组预测（WGP）的鲁棒性但准确且可靠的遗传分析。目标，我们提出以下特定目的：1）开发一个新的框架，用于关联映射，以实现多方面的校正和全态遗传建模。 2）将传统的参数线性模型转换为任意表达的非参数功能模型，以增强关联映射和全基因组预测。 3）为个性化的GWA/Phewa和表型预测开发了一个新的统计范式。 4）开发了一个基于交钥匙和云的软件平台，用于个性化基因组学，并使用CAMP和SARP数据集将我们的方法和程序应用于儿童和成人哮喘的深入遗传投资，并与UPITT医学院/U PITT医学学院/U PITT医学中心（UPMC）以及Penn State State Hershey Medical Center（PSMC）合作。我们提出的方法论创新部门从传统的技术和临床基因组学的当前平台中显着，代表了在数学上进行了严格且在计算方面的涉及方式，用于在多个混杂的结构知识的存在下，以及在复杂的遗传遗传中捕获共享模式和个人信号的需求，以在医疗遗传推理和预测中进行医学遗传推理和预测。我们的目标是，由此产生的NE框架将改善对哮喘等复杂人类疾病的理解，诊断和治疗，并在基因组医学的大数据时代为个性化医学提供了实际的基础。