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: 9116901
• 负责人: Sally E Wenzel
• 金额: $ 53.08万
• 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/9116901
• 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; Genomic medicine; Genomics; Genotype; Goals; Health; 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; individual patient; innovation; medical schools; multitask; next generation; personalized medicine; phenome; precision medicine; predict clinical outcome; profiles in patients; programs; response; sound; success; theories; tool; trait; user-friendly; whole genome

 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（由申请人提供）：在本申请中，我们提出了一种系统的尝试，用于方法学开发，用于大部分未探索但实际上重要的问题-基因组变异对复杂疾病表型的遗传效应的个性化推断，以及基因组变异对临床结果的个性化预测。虽然基因组测序、临床表型分析和可能的全基因组患者分析的技术进步和成本降低有望使人们更接近个性化和精确医学的时代，但实现这些承诺所需的合理数学原理和高效分析程序仍有待开发。我们打算开发下一代统计框架，算法和软件，用于复杂疾病的稳健，准确和个性化的遗传分析，包括全基因组关联（GWA）和全表型关联（PheWA）映射，以及全基因组预测（WGP）。为了实现这一目标，我们提出了以下具体目标：1）开发一个新的关联映射框架，使多混淆校正和panomic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函数模型，以增强关联映射和全基因组预测。3)开发个性化GWA/PheWA和表型预测的新统计范式。和4）开发一个交钥匙和基于云的软件平台，用于个性化基因组学，并将我们的方法和程序应用于使用CAMP和SARP数据集对儿童和成人哮喘进行深入的遗传调查，与来自U Pitt医学院/U Pitt医学中心（UPMC）和宾夕法尼亚州立大学好时医学中心（PSMC）的临床医生合作。我们提出的方法创新显著偏离了临床基因组学的传统技术和当前平台，并代表了在存在多个混杂因素，丰富的先验结构知识以及捕获复杂遗传效应中的共享模式和个体签名的需求的情况下，医学遗传推断和预测的数学上严格且计算上易于处理的方式的初步尝试。我们的目标是，由此产生的新框架将改善对哮喘等复杂人类疾病的理解、诊断和治疗，并为基因组医学大数据时代的个性化医学提供实践基础。