2026-09-18T21:57:17Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1129062026-06-17T14:46:31Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Alkes Price.
Finucane, Hilary Kiyo.
Massachusetts Institute of Technology. Department of Mathematics.
Massachusetts Institute of Technology. Department of Mathematics
2017-12-20T18:16:50Z
2017-12-20T18:16:50Z
2017
2017
http://hdl.handle.net/1721.1/112906
1015202578
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
245 pages
eng
Massachusetts Institute of Technology
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
Mathematics.
Functional and cross-trait genetic architecture of common diseases and complex traits
Thesis
application/pdf
2019-06-17T20:30:03Z
Publication
unknown
unknown
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<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.</Abstract>
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