CLARITE facilitates the quality control and analysis process for EWAS of metabolic-related traits

While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of non-genetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel R package, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and gene-environment interaction studies. Using CLARITE, we performed and sought replication of an EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall discovery=9063, N overall replication=9874) for body mass index (BMI) and over 300 environment variables, adjusting for sex, age, race, socioeconomic status, and survey year. Seventeen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were g-tocopherol (vitamin E) and serum iron levels. Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes, and as such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies, by establishing a standardized and high-throughput quality control pipeline. Thus, CLARITE is recommended for studying the environmental factors underlying metabolic disease.