Functional and cross-trait genetic architecture of common diseases and complex traits
Name
1015202578-MIT.pdf
Size
23.64 MB
Format
Adobe PDF
Checksum (MD5)
30a0d0cf30453f1513d21538c2bb2ad9
Author(s)
Finucane, Hilary Kiyo.
Advisor(s)
Alkes Price.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 201-245).
Subjects
Mathematics.
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
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