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Detecting, characterizing and interpreting Gene-Gene interactions associated with metabolic traits / Rishika De

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Author	De, Rishika
Title	Detecting, characterizing and interpreting Gene-Gene interactions associated with metabolic traits / Rishika De
Date	2015
Abstract	The ubiquitous complexity of common diseases is one of the biggest challenges facing the field of personalized treatments and precision medicine. This complexity can be attributed to various sources such as the genetic make-up of an individual, their surrounding environment, or the interconnectedness of biological entities and physiological processes. The phenomenon of epistasis, or interactions between genes, is another source of the complexity of diseases. As scientists try to adapt their research paradigms to embrace this genetic complexity, they are faced with the computationally challenging problem of studying interactions between thousands of genetic variants or single nucleotide polymorphisms (SNPs). In this dissertation, we present analytical frameworks that combine innovative bioinformatics methods with insightful biological questions to explore the complex genetic landscape of metabolic traits. We apply these frameworks to genetic data from large genotyping arrays to identify gene-gene interactions that are associated with Body Mass Index (BMI) and lipid traits. Furthermore, we characterize and interpret these interactions to investigate their role in the etiology of these phenotypes. In Chapter 1, we provide an overview of the bioinformatics challenges associated with the analysis of data from large genotyping arrays. In Chapter 2, we use a machine learning algorithm combined with SNP filtering methods and traditional statistics to identify SNP-SNP interactions that are highly associated with BMI. Through this method we identified seven epistatic interactions with a Bonferroni corrected P-value of association < 0.1. Chapter 3 presents the use of a network-based method and Information Theory metrics to analyze interactions between SNPs from twelve candidate obesity genes. We identified a highly connected network of 709 SNPs and 1,241 SNP-SNP interactions, which was examined to characterize the nature of these interactions. In Chapter 4, we use SNP filtering methods and a machine learning algorithm to identify SNP-SNP interactions associated with four quantitative lipid traits. We also combine this with linear regression methods to increase our ability to identify epistatic interactions. These analyses identified 22 SNP-SNP interactions associated with HDL cholesterol, 9 associated with LDL cholesterol, 4 associated with total cholesterol, and 18 associated with triglyceride levels (P-value < 0.05). Chapter 5 presents concluding remarks and future directions for this work.
Description	1 online resource (xiv, 227 leaves) : illustrations (some color)
Format	application/pdf
College	Geisel School of Medicine at Dartmouth -- Department of Genetics.
Issuing Body	Dartmouth Dissertations are made available through the Dartmouth Digital Library.
Degree	Thesis (Ph.D.)--Dartmouth College.
Copyright	All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means without permission in writing from the author.
DOI	http://dx.doi.org/10.1349/ddlp.1604

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