東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies./
Author:	Yan, Shijia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	74 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	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.
LDR:02136nmm a2200361 4500 001 2349732
005 20221003074941.5
008 241004s2022 eng d
020 $a 9798438757719
035 $a (MiAaPQ)AAI29161732
035 $a AAI29161732
040 $a MiAaPQ $c MiAaPQ
100 1 $a Yan, Shijia. $3 3689149
245 1 0 $a Statistical Methods for Controlling Population Stratification and Gene-Based Association Studies.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 74 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Zhang, Shuanglin.
502 $a Thesis (Ph.D.)--Michigan Technological University, 2022.
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.
590 $a School code: 0129.
650 4 $a Statistics. $3 517247
650 4 $a Biostatistics. $3 1002712
650 4 $a Genetics. $3 530508
653 $a Gene-based association study
653 $a Population stratification
653 $a PRS
653 $a TOW
653 $a TWAS
690 $a 0463
690 $a 0369
690 $a 0308
710 2 0 $a Michigan Technological University. $b Mathematical Sciences. $3 2096669
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 0129
791 $a Ph.D.
792 $a 2022
793 $a English