Novel analytic paradigms allowing for a fully integrated interrogation of regulatory elements, protein-coding genes, demographic characteristics and environmental factors on evoked and dynamic traits are essential for providing new insight into the mechanistic underpinnings of genetic associations. In this proposal we aim to develop, evaluate and apply sound statistical methods for leveraging and integrating the vast amount of publicly available multi-omic data resources to improve understanding of the mechanistic relationships among genes and regulatory elements associated with complex traits. As activation of innate immunity is a fundamen- tal pathophysiological process in cardiometabolic disease, e.g., atherosclerosis and type 2 diabetes, as well as complex in?ammatory disorders, e.g., response to sepsis and trauma, our understanding of the genetic under- pinnings of these evoked in?ammatory biomarkers, provides clinically relevant impact toward development of novel prognostic markers and therapeutic targets in complex diseases. Advancing knowledge of the molecular and physiological underpinnings of complex diseases will deepen insight into disease etiology, while providing opportunity to develop targeted interventions and lessen disease morbidity and mortality. The Speci?c Aims are to: (1) Develop a novel statistical framework for inferential transcriptome association analysis using reference data for rigorous interrogation of regulatory and gene-level underpinnings of dynamic response(s) to stimulus. We will develop novel and precise estimation and hypothesis testing strategies to inves- tigate and characterize the mechanistic foundations of genomic class-level associations with biological response to in?ammatory stress. (2) Extend the methodology of Aim 1 to incorporate repeatedly measured transcriptome data, multiple expression patterns, and high linkage disequilibrium within genomic classes.. We will advance the solid conceptual framework of Aim 1 to develop strategies for evaluating time varying transcriptome pro- ?les as well as data on multiple cell and tissue types and several genes and regulatory elements. (3) Apply and evaluate the methods of Aims 1 and 2 through leveraging multiple sources of layered -omics data, including cell and tissue speci?c expression. In addition to fully vetting the proposed methods and comparing to existing alternative strategies using extensive simulation studies, we will further unravel and elucidate the mechanisms of gene and regulatory element control of induced response using multiple publicly-available reference tran- scriptome data resources and repeatedly measured biomarker data arising from the GENE study. In addition to supporting rigorous and novel statistical research at the forefront of precision medicine, this NIH Academic Research Enhancement Award (AREA) Program (R15) application aims to meet the speci?c NIH AREA objectives by offering a unique opportunity to expose and engage underrepresented undergradu- ate students in STEM to biomedical big data science, while strengthening the research environment at Mount Holyoke College (MHC), the world's longest standing institution of higher education for women. This applica- tion launches from an extensive, decade-long and highly productive trans-disciplinary collaboration. Building on a strong research and mentoring record, the proposed research offers novel statistical research addressing pressing challenges in precision medicine, while offering an important and unique opportunity to engage young women in cutting-edge biomedical big data research.
The collection of big data in biomedicine promises unprecedented opportunities to elucidate complex disease etiology and inform clinical management strategies. Using biomarker response to in?ammatory stress as our model system, we propose to develop, evaluate and apply new analytic paradigms for integrated analysis of publicly-available multi-omic data and repeated measures biomarker data, with the goal of improving under- standing of the mechanisms of complex diseases. Ultimately, these methods will allow us to derive information from the vast quantities of genomics data for personalized, clinical decisions and thus serve as a central com- ponent of precision medicine.
