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

 描述(申请人提供)：在这项申请中，我们提出了一种系统的方法开发的尝试，以解决在很大程度上未被探索但实际上很重要的问题--基因组变异对复杂疾病表型的遗传效应的个性化推断，以及来自基因组变异的临床结果的个性化预测。虽然基因组测序、临床表型分析以及可能的全基因组患者特征分析方面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，但实现此类承诺所需的合理的数学原理和高效的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，用于对复杂疾病进行稳健、准确和个性化的遗传分析，包括全基因组关联(GWA)和全基因组关联(Phewa)作图，以及全基因组预测(WGP)。针对这一目标，我们提出了以下具体目标：1)开发一种新的关联图谱框架，支持多混杂因子校正和全基因组遗传建模。2)将传统的参数线性模型转化为可任意表达的非参数函数模型，用于增强关联映射和全基因组预测。3)开发一种新的统计范式，用于个性化GWA/PHEWA和表型预测。以及4)与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，开发用于个性化基因组学的交钥匙和基于云的软件平台，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基因研究。我们提出的方法创新与临床基因组学中的传统技术和当前平台有很大的不同，并代表着在存在多个混杂因素、丰富的先前结构知识以及在复杂遗传效应中捕获共享模式和个体签名的需要的情况下，对数学上严格和计算上易于处理的医学遗传推断和预测方法的初步尝试。我们的目标是，由此产生的NE框架将提高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疗提供实践基础。

 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.