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Dissertation/Thesis Abstract

Multi-Omics Approaches to Uncover Novel Regulators of Complex Disease
by Cohain, Ariella T., Ph.D., Icahn School of Medicine at Mount Sinai, 2018, 192; 10743996
Abstract (Summary)

The dramatic decrease in sequencing and super-computing costs has enabled the generation of very large-scale datasets and the use of advanced algorithmic solutions applied to those datasets to achieve a better understanding of complex diseases. In this thesis, I integrate multiple modalities of data and apply rigorous statistical and mathematical modeling approaches to analyze these data and create data-driven hypotheses. I start with the exploration of the reproducibility of a commonly used modeling approach, probabilistic causal Bayesian networks, and then application of this modeling method to two complex diseases: Coronary Artery Disease (CAD) and food allergy. Using integrative approaches and a large CAD cohort, I detected downstream effects of GWAS genes via cis- and trans- eQTLs and identified a liver-specific regulatory sub-network that inversely affects plasma cholesterol and blood-glucose levels. Applying a similar framework to longitudinal measurements in peanut allergy patients with and without being challenged with peanut exposure, I found specific transcriptomic changes and highlighted novel regulators of the allergy response. This work emphasizes the importance of using integrative approaches to uncover novel regulators of complex human disease.

Indexing (document details)
Advisor: Schadt, Eric E., Houten, Sander M.
Commitee: Cho, Judy H., Civelek, Mete, Ma'ayan, Avi, Zhu, Jun
School: Icahn School of Medicine at Mount Sinai
Department: Genetics and Genomic Sciences
School Location: United States -- New York
Source: DAI-B 79/05(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Genetics, Systematic biology, Bioinformatics
Keywords: Coronary artery disease, Data integration, Food allergy, Modeling, Multiscale, Omics
Publication Number: 10743996
ISBN: 978-0-355-60473-3
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