Mediation analysis of multiple mediators with incomplete omics data

• Published in: Genetic epidemiology Vol. 47; no. 1; pp. 61 - 77
• Main Authors: Kidd, John, Raulerson, Chelsea K., Mohlke, Karen L., Lin, Dan‐Yu
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2023
• Subjects: adipose tissue; Algorithms; Computer Simulation; DNA methylation; expectation–maximization algorithm; Genetic diversity; Genotype; Genotypes; Humans; Likelihood Functions; maximum likelihood; Mediation Analysis; Metabolic syndrome; Metabolism; Missing data; Models, Genetic; Nucleotide sequence; Phenotypes; Regression analysis; type 2 diabetes; adipose tissue; type 2 diabetes; expectation-maximization algorithm; missing data; maximum likelihood
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.22504

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.