Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect meta-analysis methods. However, the power to detect genetic associations under fixed-effect models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. Han and Eskin developed a modified random-effects meta-analysis approach (RE2) that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, updating the method (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a fixed-effect meta-analysis.