HiGwas

HiGwas performs longitudinal genome-wide association analyses to integrate longitudinal phenotypic data with high-dimensional single-nucleotide polymorphism (SNP) genotypes and identify additive and dominant genetic effects in "big p, small n" GWAS settings.

Key Features:

• Longitudinal Data Integration: Integrates longitudinal phenotypic data to capture temporal variation in traits that single-time-point measurements may miss.
• Statistical Models: Implements dimension reduction methods and longitudinal data analysis techniques that handle the autocorrelative nature of repeated measures.
• Single-SNP and High-Dimensional SNP-Set Handling: Performs single SNP analyses and high-dimensional SNP-set handling including significance-level adjustment, preconditioning, and model selection.
• Estimation of Genetic Parameters: Provides estimates of genetic parameters with confidence intervals for additive and dominant effects.

Scientific Applications:

• Functional Genomics and Complex Trait Genetics: Facilitates investigation of genetic architectures underlying complex traits using longitudinal GWAS data.
• Longitudinal Disease Studies: Supports analysis of diseases with progressive or fluctuating courses, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, by linking genetic variation to phenotypic change over time.

Methodology:

Implements dimension reduction methods and longitudinal data analysis techniques that account for autocorrelation, performs single-SNP analyses and high-dimensional SNP-set handling with significance-level adjustment, preconditioning, and model selection, and estimates genetic parameters (additive and dominant effects) with confidence intervals.