Faculty, Staff and Student Publications
Publication Date
1-11-2024
Journal
Human Genetics and Genomics Advances
Abstract
Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.
Keywords
Humans, Mendelian Randomization Analysis, Public Health, Prospective Studies, Causality, Hispanic or Latino, causal inference, detection limits, instrumental variables, metabolomics, missing data, unmeasured confounding
DOI
10.1016/j.xhgg.2023.100245
PMID
37817410
PMCID
PMC10628889
PubMedCentral® Posted Date
10-28-2023
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Genetic Phenomena Commons, Medical Genetics Commons, Public Health Commons, Race and Ethnicity Commons