Probabilistic colocalization of genetic variants from complex and molecular traits: promise and limitations

Abstract

Colocalization analysis has emerged as a powerful tool to uncover the overlapping of causal variants responsible for both molecular and complex disease phenotypes. The findings from colocalization analysis yield insights into the molecular pathways of complex diseases. In this paper, we conduct an in-depth investigation of the promise and limitations of the available colocalization analysis approaches. Focusing on variant-level colocalization approaches, we first establish the connections between various existing methods. We proceed to discuss the impacts of various controllable analytical factors and uncontrollable practical factors on outcomes of colocalization analysis through realistic simulations and real data examples. We identify a single analytical factor, the specification of prior enrichment levels, which can lead to severe inflation of false-positive colocalization findings. Meanwhile, the combination of many other analytical and practical factors all lead to diminished power. Consequently, we recommend the following strategies for the best practice of colocalization analysis: (1) estimating prior enrichment level from the observed data and (2) separating fine-mapping and colocalization analysis. Our analysis of 4,091 complex traits and the multi-tissue expression quantitative trait loci (eQTL) data from the GTEx (v.8) suggests that colocalizations of molecular QTLs and causal complex trait associations are widespread. However, only a small proportion can be confidently identified from currently available data due to a lack of power. Our findings set a benchmark for current and future integrative genetic association analysis applications.

Keywords: Bayesian; GWAS; colocalization; eQTL; integrative genetic analysis; probabilistic.