CLARITE

CLARITE performs high-throughput quality control, regression analysis, and visualization for environment-wide association studies (EWAS) and related analyses to improve reproducibility and data cleaning of environmental, phenotypic, and clinical laboratory measures.

Key Features:

• Data Cleaning and Quality Control: Provides a high-throughput QC pipeline tailored for EWAS and environmental data to minimize bias from post-hoc or manual processing.
• User-Guided Automation: Enables user-guided automation of analysis workflows while preserving user control to promote reproducibility and consistency across studies.
• Regression Analysis and Visualization: Integrates regression modeling and visualization for comprehensive examination of relationships between environmental variables and complex phenotypes such as BMI.
• Complex Survey Design Support: Incorporates survey weights, cluster, and strata information to account for complex survey designs.
• Discovery and Replication Workflows: Supports analytical phases for discovery and replication across large cohorts and multiple environmental variables.

Scientific Applications:

• Environment-Wide Association Studies (EWAS): Facilitates identification of environmental factors associated with complex diseases by enforcing high-quality input data and standardized analyses.
• Phenome-Wide Association Studies: Assists phenome-wide association studies by enabling systematic association testing across many phenotypes and genetic variants.
• Gene-Environment Interaction Studies: Enhances reliability of analyses testing interactions between genetic factors and environmental exposures through improved data quality and modeling.
• QC for Phenotypes and Clinical Laboratory Measures: Improves quality control for phenotypes and clinical laboratory measures to support downstream association analyses.

Methodology:

Applied to National Health and Nutrition Examination Survey (NHANES) data in discovery and replication phases with over 9,000 participants, analyses examined body mass index (BMI) versus more than 300 environmental variables, adjusted for sex, age, race, socioeconomic status, and survey year, incorporated survey weights, cluster, and strata to account for the complex survey design, used Bonferroni correction for multiple testing, and reported replicated BMI-associated variables including serum g-tocopherol (vitamin E) and iron.