Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Genetic Commonalities in Gynecologic Cancers Using Publicly Available Genome-Wide Association Study Summary Results: An Exploratory Meta-Analysis

Vater, Mark (2021) Genetic Commonalities in Gynecologic Cancers Using Publicly Available Genome-Wide Association Study Summary Results: An Exploratory Meta-Analysis. Master's Thesis, University of Pittsburgh. (Unpublished)

[img]
Preview
PDF
Download (4MB) | Preview

Abstract

Public Health Significance: Gynecologic cancers are responsible for millions of deaths worldwide every year. The goal of this research is to further scientific understanding of such cancers, potentially leading to improved diagnosis and treatment. The public health significance of this project is to uncover potential genetic underpinnings of gynecologic cancers, aiding in efforts to reduce the mortality rate of gynecologic cancers, which is imperative in protecting the health of susceptible persons across the globe.

Gynecologic cancers are those which arise in the female reproductive system, chiefly, those of the ovaries, cervix, vulva, endometrium, and vagina. These conditions present a serious threat to the health of susceptible persons, leading to nearly three million deaths worldwide each year. Questions remain about the genetic origins and risk factors for each of them. Specifically, there is uncertainty regarding potential overlap in genetic architecture for these conditions. This analysis sought to answer the questions: are there shared genetic variants between ovarian cancers and endometrial and cervical cancers? If so, what are the genetic implications of these overlapping variants? This was accomplished by gathering summary-level data from published genome-wide association studies (GWAS) and analyzing them using fixed effects inverse variance meta-analysis. Four datasets were included, three ovarian cancer datasets, one cervical and one endometrial cancer dataset. Three meta-analyses were performed: ovarian + endometrial cancer, ovarian + cervical cancer, and ovarian cancer only. Several significant variants were found in the ovarian + endometrial cancer meta-analysis, including those located on genes TERT (p=3.1e-17), ABO (p=3.4e-11), and ATAD5 (p=3.7e-12). The ovarian + cervical cancer meta-analysis was inconclusive, but the ovarian-only meta-analysis provided evidence for significant variants across datasets, including those located on genes TIPARP (p=2.3e-14) and SKAP1 (p=3.0e-10). Some variants found may yield new insight if studied in conjunction with both types of cancer in the meta-analysis, such as ABO for endometrial cancer, a known genetic factor in ovarian cancer risk. Further study is needed to determine the relevance and level of involvement for shared genetic variants in multiple types of gynecologic cancers, potentially leading to new or improved treatments.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Vater, Markmav130@pitt.edumav130
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee MemberBuchanich, Jeaninejeanine@pitt.edujeanine
Committee MemberShaffer, Johnjohn.r.shaffer@pitt.edujohn.r.shaffer
Committee MemberYouk, Adaayouk@pitt.eduayouk
Thesis AdvisorCarlson, Jennajnc35@pitt.edujnc35
Date: 27 August 2021
Date Type: Publication
Defense Date: 4 August 2021
Approval Date: 27 August 2021
Submission Date: 9 August 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 151
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Gynecologic cancer Meta-analysis GWAS
Date Deposited: 27 Aug 2021 19:38
Last Modified: 27 Aug 2021 19:38
URI: http://d-scholarship.pitt.edu/id/eprint/41622

Metrics

Monthly Views for the past 3 years

Plum Analytics


Actions (login required)

View Item View Item