PC-select

PC-select computes association statistics for genome-wide association studies (GWAS) using a data-adaptive genetic relationship matrix (GRM) combined with principal components to increase power and control population stratification.

Key Features:

• Data-adaptive GRM: Employs a genetic relationship matrix tailored to the input data to improve detection of genetic associations beyond standard mixed models.
• Principal component hybridization: Integrates principal components into the analysis to control population structure while retaining association signal.
• Reduced SNP subset within linear mixed model: Selects a reduced subset of single nucleotide polymorphisms (SNPs) for inclusion in a linear mixed model to boost statistical power.
• Control of confounding from population stratification: Uses the combined GRM and principal components approach to avoid inflation of association statistics due to population structure.

Scientific Applications:

• Genome-wide association studies (GWAS): Enhances power and precision in identifying genetic variants associated with complex traits and diseases.
• Population genetics analyses: Facilitates analyses of genetic variation across populations while mitigating confounding from population structure.

Methodology:

Selects a subset of SNPs, constructs a data-adaptive GRM, incorporates principal components, and fits a linear mixed model to compute association statistics.