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Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies./
作者:	Yan, Shijia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	74 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
標題:	Statistics. -
ISBN:	9798438757719

Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies.
Yan, Shijia.

Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 74 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Michigan Technological University, 2022.

This item must not be sold to any third party vendors.

This dissertation includes three papers with each distributed in one chapter. In chapter 1, we use extensive simulation studies and real data studies to evaluate the performance of using the linkage disequilibrium score regression (LDSC) for controlling population stratification. In chapter 2, we propose a gene-based statistical method that leverage gene expression (GE) measurements and polygenic risk scores (PRS) to identify genes that are associated with a phenotype of interest. In simulation studies, the proposed method has correct type I error rates and can boost power comparing to other methods that use either gene expression or PRS in association tests. The real data analysis based on UK Biobank data for the asthma disease shows that the proposed method is also applicable to GWAS. In chapter 3, we analytically derive the distribution of TOW test statistics and modify TOW to utilize GWAS summary statistics (TOW-S). Simulation studies show that TOW-S has correct type I error rates and can retain power among all scenarios.

ISBN: 9798438757719Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Gene-based association study

Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies.
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506 $a This item must not be sold to any third party vendors.
520 $a This dissertation includes three papers with each distributed in one chapter. In chapter 1, we use extensive simulation studies and real data studies to evaluate the performance of using the linkage disequilibrium score regression (LDSC) for controlling population stratification. In chapter 2, we propose a gene-based statistical method that leverage gene expression (GE) measurements and polygenic risk scores (PRS) to identify genes that are associated with a phenotype of interest. In simulation studies, the proposed method has correct type I error rates and can boost power comparing to other methods that use either gene expression or PRS in association tests. The real data analysis based on UK Biobank data for the asthma disease shows that the proposed method is also applicable to GWAS. In chapter 3, we analytically derive the distribution of TOW test statistics and modify TOW to utilize GWAS summary statistics (TOW-S). Simulation studies show that TOW-S has correct type I error rates and can retain power among all scenarios.
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