Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 201-245). / In this thesis, I introduce new methods for learning about diseases and traits from genetic data. First, I introduce a method for partitioning heritability by functional annotation from genome-wide association summary statistics, and I apply it to 17 diseases and traits and many different functional annotations. Next, I show how to apply this method to use gene expression data to identify diseaserelevant tissues and cell types. I next introduce a method for estimating genetic correlation from genome-wide association summary statistics and apply it to estimate genetic correlations between all pairs of 24 diseases and traits. Finally, I consider a model of disease subtypes and I show how to determine a lower bound on the sample size required to distinguish between two disease subtypes as a function of several parameters. / by Hilary Kiyo Finucane. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Department of Mathematics
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/112906 |
| Date | January 2017 |
| Creators | Finucane, Hilary Kiyo. |
| Contributors | Alkes Price., Massachusetts Institute of Technology. Department of Mathematics., Massachusetts Institute of Technology. Department of Mathematics |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 245 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
Page generated in 0.0064 seconds