Mendelian randomization with incomplete measurements on the exposure in the Hispanic Community Health Study/Study of Latinos

Yilun Li, Kin Yau Wong, Annie Green Howard, Penny Gordon-Larsen, Heather M. Highland, Mariaelisa Graff, Kari E. North, Carolina G. Downie, Christy L. Avery, Bing Yu, Kristin L. Young, Victoria L. Buchanan, Robert Kaplan, Lifang Hou, Brian Thomas Joyce, Qibin Qi, Tamar Sofer, Jee Young Moon, Dan Yu Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number100245
JournalHuman Genetics and Genomics Advances
Volume5
Issue number1
DOIs
StatePublished - Jan 11 2024

Keywords

  • causal inference
  • detection limits
  • instrumental variables
  • metabolomics
  • missing data
  • unmeasured confounding

ASJC Scopus subject areas

  • Genetics(clinical)
  • Molecular Medicine

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