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Mendelian randomization analysis using multiple biomarkers of an underlying common exposure.
Jin, Jin; Qi, Guanghao; Yu, Zhi; Chatterjee, Nilanjan.
Afiliação
  • Jin J; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, United States.
  • Qi G; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104-6021, United States.
  • Yu Z; Department of Biomedical Engineering, Johns Hopkins University, 720 Rutland Avenue, Baltimore, MD 21205, United States.
  • Chatterjee N; Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195-1617, United States.
Biostatistics ; 25(4): 1015-1033, 2024 Oct 01.
Article em En | MEDLINE | ID: mdl-38459704
ABSTRACT
Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores / Estudo de Associação Genômica Ampla / Análise da Randomização Mendeliana Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article