PhenoMan

PhenoMan facilitates management and quality control of phenotype datasets for genetic association studies, particularly in the context of next-generation sequencing.

Key Features:

• Individual Selection: Selects individuals based on multiple phenotype criteria or population membership.
• Data Quality Control: Controls for missing covariate data and removes related individuals, duplicate samples, and individuals with incorrect sex specifications.
• Data Transformation and Recoding: Recodes primary traits and covariates, applies data transformations, and removes or winsorizes outliers.
• Covariate Management: Selects appropriate covariates and creates residuals to control confounders in association studies.
• Consistency and Harmonization: Generates detailed reports and summary statistics in graphical and text formats to support dataset consistency and harmonization.
• Quantitative and Case-Control Data Handling: Manipulates quantitative traits and disease and control datasets for downstream analysis.
• Error Mitigation and Effect Estimation: Helps mitigate type I and type II errors, enhances the reliability of effect estimates, and supports consistent results across studies by enforcing phenotype QC before association analysis.
• Context with Association Tools: Complements association-focused software such as PLINK and GenABEL by providing phenotype selection, QC, and confounder management.

Scientific Applications:

• Genetic Association Studies: Prepares and quality-controls phenotype data for genetic association studies in the context of next-generation sequencing.
• Public Repository Data Preparation: Harmonizes and QCs datasets obtained from public repositories for association analysis.
• Phenotype Harmonization: Standardizes and documents phenotypic measures across studies to enable consistent comparative analyses.
• Quantitative and Case-Control Analyses: Prepares quantitative traits and disease/control datasets for downstream statistical and association analyses.

Methodology:

Computational steps explicitly include individual selection by phenotype criteria or population membership; handling missing covariate data; removal of related individuals, duplicate samples, and sex-mismatches; recoding and transforming traits and covariates; outlier removal or winsorization; covariate selection and residual creation; and generation of graphical and text summary statistics and reports.