자료유형  

 학위논문

Control Number  

0016931555

International Standard Book Number  

9798379756536

Dewey Decimal Classification Number  

575

Main Entry-Personal Name  

Bone, William.

Publication, Distribution, etc. (Imprint  

[S.l.] : University of Pennsylvania., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(143 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Includes supplementary digital materials.

General Note  

Advisor: Voight, Benjamin F.;Ritchie, Marylyn D.

Dissertation Note  

Thesis (Ph.D.)--University of Pennsylvania, 2023.

Restrictions on Access Note  

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

Summary, Etc.  

요약The success of biobanks in collecting phenotype and genotype data from millions of people has dramatically changed the scale of genetic associations studies. The collection of these data has made it possible to study the genetics of many more human traits in larger cohorts, and has shown that pleiotropy, the phenomenon where a single genetic locus has an effect on multiple traits, is ubiquitous in the human genome. Pleiotropy is particularly common between cardiometabolic traits and complex diseases, such as circulating lipid levels and coronary artery disease. We can study pleiotropy to better understand the relationships between these traits and detect novel therapeutic opportunities for these diseases. Using a number of different methods, I worked to first detect pleiotropic loci that involved cardiometabolic traits and then understand the genetic mechanisms behind these pleiotropic loci. I did this by using multi-trait genetic association methods to detect loci associated with multiple traits, both in common variants via multi-trait genome-wide association studies (GWAS) and in rare variants using multi-trait gene burden analyses. A vital tool for identifying candidate causal genes at pleiotropic loci identified from multi-trait GWAS was genetic colocalization analysis between GWAS signals and expression quantitative trait loci (eQTL) and splicing quantitative trait loci (sQTL). These analyses allowed us to identify which eQTL and sQTL signals for genes have evidence of sharing the same causal variants as the GWAS signals. Part of the work presented here is the development of a framework for performing these QTL-GWAS colocalization analyses at scale. Throughout these analyses we detected several loci with evidence of pleiotropy and identify many candidate causal genes supported by statistical genetics work as well as functional work. Some of these genes, such as DOCK4 and PCSK6, may be good candidates for therapeutic targets to treat multiple diseases. These experiments show how we can use large-scale genetic and phenotypic data from biobanks to better understand the relationships between human diseases and leverage this to identify potential therapeutic targets. Supplemental files for this document include: Supplementary Methods, Supplementary Tables 1-15, and Supplementary Figures S1-S6 for Chapter 3, Supplementary Methods, Supplementary Tables 1-8, and Supplementary Figures S1-S36 for Chapter 4, and Supplementary Tables 1-3 for Chapter 5.

Subject Added Entry-Topical Term  

Genetics.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Medicine.

Index Term-Uncontrolled  

Cardiometabolic traits

Index Term-Uncontrolled  

Colocalization

Index Term-Uncontrolled  

Multi-trait GWAS

Index Term-Uncontrolled  

Biobanks

Index Term-Uncontrolled  

Pleiotropy

Added Entry-Corporate Name  

University of Pennsylvania Genomics and Computational Biology

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640035