Functional and cross-trait genetic architecture of common diseases and complex traits
Author(s)Finucane, Hilary Kiyo.
Massachusetts Institute of Technology. Department of Mathematics.
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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.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2017Cataloged from PDF version of thesis.Includes bibliographical references (pages 201-245).
DepartmentMassachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology