Developing and applying integrative computational methods to study autoimmune disease
Abstract/Contents
- Abstract
- Autoimmune diseases are painful and debilitating conditions which affect millions of people in the United States and all over the world. At present such conditions are often difficult to diagnose and many have no satisfactory treatment. Given the wealth and availability of genomic data such as genetic variation and gene expression, computational integrative methods provide a powerful opportunity to improve human health by refining the current knowledge about diagnostics, therapeutics and disease mechanism. For instance numerous genome-wide association studies (GWAS) performed across autoimmune diseases, provide a great opportunity to study disease relationships based on genetic variation. Comparing such profiles allows us to quantify allele-specific pair-wise relationships between these diseases to find two broad clusters of autoimmune disease. We furthermore find that certain polymorphisms, toggle SNPs, predispose individuals to one class of autoimmune disease but are protective against the other class. While studying allelic differences between diseases may point to key novel disease-specific genes and pathways, studying similarities across diseases might lead to discovery of common therapeutic options as well as common disease mechanisms. In particular we integrate genetic variation data across several studies to discover the role of a complement factor in Rheumatoid Arthritis and Multiple Sclerosis. Gene expression microarrays are also often used to study human diseases as well as the perturbation of biological systems by drug compounds providing an opportunity to discover novel relationships between diseases and drugs. We present a systematic computational method to predict novel therapeutic indications based on gene expression. We tested our top prediction for Crohn's disease (CD) using the rat model of inflammatory bowel disease (IBD), and successfully demonstrated the predicted efficacy of an anti-seizure drug in treating disease. In this work, we have showed that integrative computational tools can be used to improve diagnostics, learn more about disease mechanism and discover novel therapeutics for autoimmune disease.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Copyright date | 2011 |
| Publication date | 2010, c2011; 2010 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sirota, Marina |
|---|---|
| Associated with | Stanford University, Department of Biomedical Informatics. |
| Primary advisor | Butte, Atul J |
| Thesis advisor | Butte, Atul J |
| Thesis advisor | Batzoglou, Serafim |
| Thesis advisor | Robinson, William (William Hewitt) |
| Advisor | Batzoglou, Serafim |
| Advisor | Robinson, William (William Hewitt) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Marina Sirota. |
|---|---|
| Note | Submitted to the Department of Biomedical Informatics. |
| Thesis | Ph.D. Stanford University 2011 |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Marina Sirota
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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