COXMEG

COXMEG performs genome-wide association studies of age-at-onset traits using Cox mixed-effects models with relatedness matrices to account for population structure and family relationships.

Key Features:

• Cox Mixed-Effects Model Implementation: Applies Cox mixed-effects models (CMEMs) to analyze age-at-onset traits in genome-wide association studies.
• Efficient Estimation Algorithms: Implements fast estimation algorithms optimized for general sparse relatedness matrices, including block-diagonal pedigree-based matrices.
• Score Test for Dense Relatedness Matrices: Provides a score test method for analyzing dense relatedness matrices while accounting for population stratification and family structure.
• Support for Positive Semidefinite Matrices: Extends modeling approaches to accommodate positive semidefinite relatedness matrices commonly observed in twin and family studies.
• Computationally Scalable GWAS Analysis: Enables large-scale GWAS using CMEMs with improved computational efficiency compared with traditional methods such as coxme and coxph with frailty.

Scientific Applications:

• Age-at-Onset Genome-Wide Association Studies: Identifies genetic variants associated with disease onset timing in cohort-based genetic studies.
• Familial and Population-Based Genetic Analysis: Analyzes GWAS datasets while accounting for relatedness and population structure.
• Age-Related Disease Genetics: Detects genetic variants influencing risk and progression of age-related diseases such as Alzheimer's disease.

Methodology:

COXMEG fits Cox mixed-effects models using sparse or dense relatedness matrices, applies fast estimation algorithms and score tests to evaluate genetic associations, and supports positive semidefinite relatedness matrices for family-based genetic analyses.