dc.contributor.advisor | Alkes Price. | en_US |
dc.contributor.author | Finucane, Hilary Kiyo. | en_US |
dc.contributor.other | Massachusetts Institute of Technology. Department of Mathematics. | en_US |
dc.date.accessioned | 2017-12-20T18:16:50Z | |
dc.date.available | 2017-12-20T18:16:50Z | |
dc.date.copyright | 2017 | en_US |
dc.date.issued | 2017 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/112906 | en_US |
dc.description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017 | en_US |
dc.description | Cataloged from PDF version of thesis. | en_US |
dc.description | Includes bibliographical references (pages 201-245). | en_US |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | by Hilary Kiyo Finucane. | en_US |
dc.format.extent | 245 pages | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Massachusetts Institute of Technology | en_US |
dc.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. | en_US |
dc.rights.uri | http://dspace.mit.edu/handle/1721.1/7582 | en_US |
dc.subject | Mathematics. | en_US |
dc.title | Functional and cross-trait genetic architecture of common diseases and complex traits | en_US |
dc.type | Thesis | en_US |
dc.description.degree | Ph. D. | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mathematics | en_US |
dc.identifier.oclc | 1015202578 | en_US |
dc.description.collection | Ph.D. Massachusetts Institute of Technology, Department of Mathematics | en_US |
dspace.imported | 2019-06-17T20:30:03Z | en_US |