Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs)
to investigate the causal relationship between exposure and outcome. It has become
widely popular due to its versatile applications in epidemiological research. Its rising
popularity is largely driven by the ease of accessing summary-level data from large
consortia, making it a cost-effective choice for researchers.
In this thesis, we focus on three issues in MR that result in potential bias in causal
inference. We first address the “winner’s curse” in MR, which arises from selecting
genetic markers based on their significance or ranking. To mitigate this bias, we adapt
the bootstrap-based BR-squared method to function with summary-level data. Our
findings reveal that the correction methods can effectively reduce bias, albeit with an
increase in variability. We then develop a method that accounts for the correlation
caused by sample overlap while addressing potential bias from weak instruments. This
proposed method yields stable causal estimates, although the standard errors of causal
estimates may not be precisely estimated. Lastly, we introduce a novel approach for
identifying invalid instrumental variables showing signs of horizontal pleiotropy. We
recommend using the bootstrap method to account for the data-driven process of
IV selection. Our results indicate that the bootstrap intervals approach the nominal
level of coverage rate when the proportion of invalid IVs is less than 50%. / Dissertation / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29402 |
| Date | January 2023 |
| Creators | Bian, Mengjie |
| Contributors | Canty, Angelo J., Mathematics and Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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