MRLocus

MRLocus estimates gene-to-trait effect sizes and quantifies allelic heterogeneity using a Bayesian framework to assess mediation between eQTLs and GWAS traits.

Key Features:

• Bayesian framework: Uses Bayesian statistics to estimate gene-to-trait effect sizes and quantify uncertainty.
• Allelic heterogeneity handling: Tailored for loci with allelic heterogeneity characterized by multiple LD-independent eQTLs.
• Per-eQTL colocalization: Performs a colocalization step for each eQTL to identify shared signals with GWAS loci.
• Mendelian Randomization across eQTLs: Conducts MR analysis across independent eQTLs to assess mediation of expression on traits.
• Dispersion parameter: Estimates a dispersion parameter to quantify variability in mediation effects from individual eQTLs.
• Simulation-evaluated uncertainty: Demonstrates improved interval coverage and accuracy in simulation studies compared to state-of-the-art methods.

Scientific Applications:

• Interpretation of non-coding GWAS loci: Elucidates regulatory functions of non-coding GWAS risk loci by linking eQTLs to trait-associated variants.
• Causal inference of gene expression: Assesses causal relationships between gene expression and downstream traits, providing evidence of mediation and context-specific effects in tissues or developmental stages.

Methodology:

Per-eQTL colocalization followed by Mendelian Randomization across LD-independent eQTLs, with Bayesian estimation of effect sizes and a dispersion parameter to model allelic heterogeneity.