SCEBE

SCEBE performs high-dimensional genome-wide association studies (GWAS) on longitudinal phenotypes to detect genetic associations over time.

Key Features:

• High-Dimensional GWAS Capability: Supports GWAS on dynamic longitudinal traits and large-scale datasets including millions of single nucleotide polymorphisms (SNPs).
• Multiple Analytical Approaches: Implements lme (linear mixed-effects model) via the R 'lme4' package, nebe (naive empirical Bayes estimation) following Londono et al. 2013 and Meirelles et al. 2013, gallop (GALLOP) as in Sikorska et al. 2015, and the scebe simultaneous correction method.
• Unbiased and Efficient Analysis: Applies simultaneous correction for empirical Bayes estimation to produce unbiased P-values and effect size estimates for association testing.
• Computational Efficiency: Achieves reported speed improvements up to nearly 10,000-fold, enabling analysis of extensive longitudinal GWAS datasets.

Scientific Applications:

• Longitudinal genetic association studies: Detects temporal genetic effects on phenotypes measured repeatedly over time.
• Neurogenetics: Applied to neurogenetics datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI) data.
• Epidemiology: Enables analysis of longitudinal trajectories and genetic risk factors in epidemiological cohorts.

Methodology:

Implemented as an R package using linear mixed-effects models via lme4, naive empirical Bayes estimation (nebe) per Londono et al. 2013 and Meirelles et al. 2013, GALLOP implementation (gallop) per Sikorska et al. 2015, and the scebe simultaneous correction for empirical Bayes estimates to obtain unbiased P-values and effect size estimates.