Environment-wide association studies (EWAS) highlights the contribution of nongenetic 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. The Hall Lab offers a novel software, 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. It exists as both an R package and a Python package. 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. An example workflow is shown in figure 1.

Figure 1: CLARITE Flowchart
Figure 1 - This is a sample flowchart depicting a typical workflow when using the CLARITE package with the NHANES data. The user starts with raw data and alternates between summary steps (dashed lines) and filtering/quality control (QC) steps (solid lines) based on variable type (indicated by color) and either user-defined or default thresholds informed by the summary output. Once data are sufficiently cleaned, environment-wide association studies (EWAS) can be run.

With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES).. The results are shown in figure 2.

Figure 2: Manhattan plot generated by CLARITE
Figure 2 - A Manhattan plot generated using CLARITE’s visualization tool, displaying the results of exposure categories predictive of body mass index (BMI). Along the x- axis with -log10(p-value) along the y-axis, are results included for Discovery (circle) and Replication (triangle) datasets. The red line denotes the Bonferroni threshold (alpha: 0.05) for the number of tests run in the Discovery dataset (305), and the blue line denotes the Bonferroni threshold (alpha: 0.05) for the number of tests run in the Replication dataset (99). The 16 replicating results with Bonferroni-corrected p-value < 0.05 are labeled.

CLARITE Documentation

CLARITE R Package CLARITE Python Package CLARITE GUI


Questions?

If you have any questions not answered by the documentation, feel free to open an Issue or contact John McGuigan (John.McGuigan at psu.edu).

This work is/was supported by the USDA National Institute of Food and Agriculture and Hatch Appropriations under Project #PEN04275 and Accession #1018544

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