Driscoll, Jordan A.
(2023)
Comparison of Genome-Wide Association Study Approaches: Meta-Analysis vs Mega-Analysis.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Mega-analysis and meta-analysis are two different approaches for investigating genotype-phenotype relationships in GWASs. Both methods have been demonstrated to yield comparable results and both approaches have their pros and cons. On occasion, a mega-analysis can yield more accuracy in the reflection of results over a meta-analysis by pooling raw, individual patient data, thereby acting as a large cohort trial facsimile. However, implementing this method can prove to be more arduous and costly compared to a meta-analysis approach; moreover, more accurate findings via meta-analysis are not guaranteed. The decision to employ one method versus the other will likely be contingent upon an investigator’s availability of resources and timeline.
In this thesis, I implemented both a mega-analysis and meta-analysis approach to analyze HDL phenotype derived from multi-cohort Samoan GWAS data and compared the phenotypic output from both methods. I compared the results between a mega-analysis and meta-analysis on Samoan GWASs’ results for chromosome 16 and conclude that these two methods are comparable in evaluating HDL phenotype. The expected log10 p values versus observed log10 p values for variant association with the phenotype for both methods were nearly identical when mapped on quantile-quantile plot. Furthermore, when plotted against one another, these log10 p values for both methods displayed a linear relationship and were highly correlated (Pearson’s r = 0.9813). My research supports prior research claiming the comparability of mega- meta-results.
Since results between mega-analyses and meta-analyses are not always comparable, it is imperative to select the methodological approach to yield results that are most precise and consistent with the integrity of the data. Additionally, due to a paucity in diverse GWASs, elucidating whether one analytical approach affords investigators both advantage in effective resource management and extrapolation of study results will allow for improving public health equity in lesser studied populations such as Samoans.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
3 January 2023 |
| Date Type: |
Publication |
| Defense Date: |
7 December 2022 |
| Approval Date: |
3 January 2023 |
| Submission Date: |
16 December 2022 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
79 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Human Genetics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Mega-analysis, Meta-analysis, GWAS |
| Date Deposited: |
03 Jan 2023 13:42 |
| Last Modified: |
03 Jan 2023 13:42 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/44047 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[img]](http://d-scholarship.pitt.edu/style/images/fileicons/application_pdf.png)
![[feed]](/images/feed-icon-32x32.png){kind=icon}