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Dietary Intake Mendelian Randomization: Assessment and Development of Methods for Instrument Selection and Robust
Kristen J Sutton1, Julie Gervis2,3, Moomal Jatoi1
1Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA.
Mendelian randomization (MR) studies on diet and health need careful genetic instrument (GI) curation. Refining GIs improves detection of diet-biomarker links, but diet-disease associations require advanced methods like MR-CAUSE to account for confounding.
Area of Science:
- Genetics and Epidemiology
- Nutritional Science
- Biostatistics
Background:
- Mendelian randomization (MR) is used to infer causality between dietary intake and health outcomes.
- Current MR approaches often use all genome-wide significant (GWS) variants, potentially introducing bias from indirect effects.
Purpose of the Study:
- To assess the current literature on diet MR and identify common methodologies.
- To evaluate the efficacy of conventional MR techniques in detecting established diet-health relationships.
- To develop and test methods for refining genetic instruments (GIs) to better estimate direct dietary effects.
Main Methods:
- Literature review of diet MR studies identified via PubMed.
- Empirical testing of conventional two-sample MR with GWS variants on six positive control diet-health relationships.
- Development and application of GI refinement techniques: Phenome-wide association study (PheWAS)-based filtering and multivariable MR (MVMR).
Main Results:
- Conventional MR with GWS GIs showed limited success in detecting expected diet-health associations.
- GI filtering improved detection for diet-biomarker pairs but not diet-disease pairs.
- MR-CAUSE identified a diet-disease association (white vs. whole grain bread on CVD) and highlighted confounding by body composition traits.
Conclusions:
- The widespread use of MR for dietary exposures necessitates caution due to inconsistent findings and potential confounding.
- Thoughtful curation of genetic instruments is crucial for reducing bias and accurately assessing the causal role of diet in health.
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