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A Flexible Summary Statistics-Based Colocalization Method to Integrate Omics Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Flexible Summary Statistics-Based Colocalization Method to Integrate Omics Data./
作者:	Wang, Fan.
面頁冊數:	1 online resource (217 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-05, Section: B.
Contained By:	Dissertations Abstracts International84-05B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29319156click for full text (PQDT)
ISBN:	9798357550019

A Flexible Summary Statistics-Based Colocalization Method to Integrate Omics Data.
Wang, Fan.

A Flexible Summary Statistics-Based Colocalization Method to Integrate Omics Data. - 1 online resource (217 pages)

Source: Dissertations Abstracts International, Volume: 84-05, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2022.

Includes bibliographical references

Colocalization methods that integrate omics data can help define the mechanism by which Genome-wide association study (GWAS) loci contribute to disease. Contemporary colocalization analytical tools must be valid and powerful under several complex scenarios such as for summary statistics calculated from meta-analyses or using related or overlapping samples; in the presence of a composite null hypothesis; and in regions with high linkage equilibrium (correlation between genetic markers) and/or allelic heterogeneity. In this thesis, we develop colocalization methods and tools to address these complex scenarios through a frequentist and summary statistics-based approach. The proposed methods could assess colocalization, for example, between a primary phenotype and gene expression, or between other omic-data including DNA methylation (meQTLs), protein QTLs (pQTLs) or metabolites (metQTLs). Chapter 1 provides the scientific motivation and genetic background for calculating the summary statistics. Chapter 2 focuses on the development of the Simple Sum (SS) colocalization framework, which is powerful in regions with high linkage disequilibrium and allelic heterogeneity. However, the SS is restricted to implementation in regions with sufficient eQTL evidence to achieve type I error rate control. This inspired the work of Chapter 3 where we develop a more flexible procedure based on the SS (SS2), which avoids a priori eQTL assumptions. We advance the utility of the method to complex scenarios, such as correcting for multiple hypothesis testing in the presence of a composite null hypothesis, analyzing meta-analysis summary statistics with and without related individuals, and adjusting for overlapping samples between studies. Chapter 4 describes the implementation of the SS2 methodology in a web-based tool, LocusFocus, with extended R functions. Currently LocusFocus is used by over 100 new users per month according to Google Analytics. Chapter 5 concludes with a discussion of the impact and limitations of the work in this thesis, and describes some concrete steps that have been taken towards future work.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798357550019Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

ColocalizationIndex Terms--Genre/Form:

542853
Electronic books.

A Flexible Summary Statistics-Based Colocalization Method to Integrate Omics Data.
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520 $a Colocalization methods that integrate omics data can help define the mechanism by which Genome-wide association study (GWAS) loci contribute to disease. Contemporary colocalization analytical tools must be valid and powerful under several complex scenarios such as for summary statistics calculated from meta-analyses or using related or overlapping samples; in the presence of a composite null hypothesis; and in regions with high linkage equilibrium (correlation between genetic markers) and/or allelic heterogeneity. In this thesis, we develop colocalization methods and tools to address these complex scenarios through a frequentist and summary statistics-based approach. The proposed methods could assess colocalization, for example, between a primary phenotype and gene expression, or between other omic-data including DNA methylation (meQTLs), protein QTLs (pQTLs) or metabolites (metQTLs). Chapter 1 provides the scientific motivation and genetic background for calculating the summary statistics. Chapter 2 focuses on the development of the Simple Sum (SS) colocalization framework, which is powerful in regions with high linkage disequilibrium and allelic heterogeneity. However, the SS is restricted to implementation in regions with sufficient eQTL evidence to achieve type I error rate control. This inspired the work of Chapter 3 where we develop a more flexible procedure based on the SS (SS2), which avoids a priori eQTL assumptions. We advance the utility of the method to complex scenarios, such as correcting for multiple hypothesis testing in the presence of a composite null hypothesis, analyzing meta-analysis summary statistics with and without related individuals, and adjusting for overlapping samples between studies. Chapter 4 describes the implementation of the SS2 methodology in a web-based tool, LocusFocus, with extended R functions. Currently LocusFocus is used by over 100 new users per month according to Google Analytics. Chapter 5 concludes with a discussion of the impact and limitations of the work in this thesis, and describes some concrete steps that have been taken towards future work.
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