A Permutation based Mixed E ect Model in Rare Variants Association Study

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Luke Vilaseca (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/
Jianping Sun

Abstract: In the past decade, rare variant association study has become popular in the scientific field to identify disease associated rare genetic variants. A lot of statistical methods have been developed since then, including the method of mixed effect score test (MiST) proposed by Sun et. al., 2013. Though MiST is a more robust method comparing with other existing methods, it has the limitation of potential type I error inflation under some circumstances. Hence, the aim of my research is to improve MiST so that it can control type I error well. Particularly, we will use permutation based method to improve MiST and use simulations under various scenarios to examine the performance of this improvement in terms of type I error rate.

Additional Information

Honors Project
Language: English
Date: 2020
MiST, Mixed Effect Model, Rare Variants, Statistics, Mathematics, Permutation, Genetics

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