CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

Abstract

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 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. We offer 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 geneenvironment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel 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 post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10-12 , Replication Bonferroni p: 2.70x10-9) and iron (Discovery Bonferroni p: 1.09x10-8 , Replication Bonferroni p: 1.73x10-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. 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 high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study

“… The phenotype quality control was performed in CLARITE software [ 15 ]. The genotype quality control and the principle component analysis (PCA) were performed in PLINK 1.90 [ 13 ].…”

“…Thirty-three cardiac traits and 27 fatty acids for 3,316 samples were selected. Quality control for cardiac traits and fatty acids were mainly performed with the CLARITE R package (https:// github.com/HallLab/clarite) (Fig 1) [15]. Participants were filtered to remove children/adolescents and those with missing covariates.…”

“…All G×G models were run assuming an additive genetic model and adjusting for age, sex, BMI, waist-hip ratio based on CDC report recommendations [22] and additional ANOVA tests (S2 Table), as well as the first three principal components which explained 0.23% of genetic variation (S2A and S2B Fig). SNP-SNP interaction models were assessed using a likelihood ratio test (LRT) between the [15]. The genotype quality control and the principle component analysis (PCA) were performed in PLINK 1.90 [13].…”

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults

“…We assembled these laboratory traits using a MySQL database and applied QC using R. We retained only individuals with available genotype data, and traits that were normally distributed and met the criterion of phenotype missing rate < 80%. Frequency distributions of traits were inspected using hist_plot.R that facilitates manual inspection of continuous traits by providing fast, highthroughput visualization along with necessary summary statistics of each visualized traits [53].…”

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease

“…The PheWAS method has replicated many known gene-disease associations [10] and has identified novel pleiotropic genetic effects [11], opportunities for drug repurposing, and unintended drug consequences [12]. QualityLab builds off the success of previous measurement quality control methods, such as CLARITE [13]. While, CLARITE focuses on minimal cleaning of survey data, QualityLab conducts extensive cleaning of quantitative lab measurements derived from EHRs.…”