Abstract Molecular QTLs (xQTLs) are widely studied to identify functional variation and possible mechanisms underlying genetic associations with diseases. Larger xQTL sample sizes are critical to help identify causal variants, improve predictive models, and increase power to detect rare associations. This will require scalable and accurate methods for analysis of tens of thousands of molecular traits in large cohorts, and/or from summary statistics in meta-analysis, both of which are currently lacking. We developed APEX (All-in-one Package for Efficient Xqtl analysis), an efficient toolkit for xQTL mapping and meta-analysis that provides (a) highly optimized linear mixed models to account for relatedness and shared variation across molecular traits; (b) rapid factor analysis to infer latent technical and biological variables from molecular trait data; (c) fast and accurate trait-level omnibus tests that incorporate prior functional weights to increase statistical power; and (d) compact summary data files for flexible and accurate joint analysis of multiple variants (e.g., joint/conditional regression or Bayesian finemapping) without individual-level data in meta-analysis. We applied the methods to data from three LCL eQTL studies and the UK Biobank. APEX is open source: https://corbinq.github.io/apex .

A versatile toolkit for molecular QTL mapping and meta-analysis at scale