longGWAS

longGWAS performs mixed-model genome-wide association mapping using phenotype measurements collected at multiple time points to increase statistical power and partition genetic, environmental, and residual contributions to traits.

Key Features:

• Utilization of Multiple Phenotype Measurements: Incorporates phenotype measurements collected at multiple time points per individual to model temporal trait dynamics.
• Mixed-Model Framework: Employs a mixed-model framework for association mapping to separate genetic, environmental, and residual error contributions.
• Increased Statistical Power: Implements an analytical method to calculate statistical power and demonstrates increased power relative to single-time-point approaches.
• Prediction of Phenotypic Contributions: Estimates the proportion of phenotypic variation attributable to genetic and environmental factors and ranks individuals based on these predictions.

Scientific Applications:

• Longitudinal cohort studies: Analyzes traits measured at multiple time points in longitudinal cohort studies.
• Dissection of genetic and environmental influences: Dissects genetic versus environmental contributions to complex trait variation over time.
• Genetic association discovery: Enhances identification of genetic associations, supporting research in personalized medicine and evolutionary biology.

Methodology:

Mixed-model association mapping using longitudinal phenotype measurements; analytical power calculation; estimation of genetic, environmental, and residual variance components; prediction and ranking of individual contributions.