Primo

Primo integrates GWAS and omics QTL summary statistics to elucidate molecular mechanisms of trait-associated SNPs and detect pleiotropy across complex traits.

Key Features:

• Integrative Analysis: Integrates GWAS summary statistics with omics QTL data from diverse cellular conditions or studies to interpret how SNPs influence complex traits.
• Conditional Association Analysis: Performs conditional association analysis in gene regions with known susceptibility loci to account for linkage disequilibrium.
• Heterogeneity and Sample Correlation Handling: Manages unknown study heterogeneity and sample correlations across datasets.
• Pleiotropy Detection: Detects pleiotropic effects where single genetic variants influence multiple traits.
• Statistical Framework: Employs advanced statistical methods to analyze association patterns between SNPs and both complex and omics traits.
• Implementation: Implemented as an R package.

Scientific Applications:

• Elucidate Molecular Mechanisms: Dissect how trait-associated SNPs affect molecular phenotypes and contribute to disease susceptibility or other phenotypic variation.
• Detect Pleiotropy: Identify genetic variants that affect multiple complex traits to inform the genetic architecture of diseases.

Methodology:

Uses advanced statistical methods to analyze association patterns between SNPs and complex and omics traits, including conditional association analyses to account for linkage disequilibrium and procedures to accommodate unknown study heterogeneity and sample correlations.