SLALOM suggests caution with meta-analysis fine-mapping interpretation
After researchers combine multiple genome-wide association studies into a meta-analysis, they often seek causal variants using methods built for single-cohort studies. CGM PI’s Hilary Finucane, Mark Daly, and colleagues showed that this fine-mapping approach is often miscalibrated due to heterogeneous characteristics of the individual cohorts, such as different genotyping arrays or imputation panels. They built a quality control method, SLALOM, and applied it to 14 disease endpoints from the Global Biobank Meta-analysis Initiative (GBMI), finding that 68 percent of fine-mapped loci showed signs of potential inaccuracy. The findings suggest caution when interpreting meta-analysis fine-mapping results until improved methods are available.