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

Carnegie-Mellon University, Pittsburgh PA

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Abstract

? 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.