Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWAS) into a more powerful whole. To resolve causal variants, meta-analysis studies typically apply summary statistics-based fine-mapping methods as they are applied to single-cohort studies. However, it is unclear whether heterogeneous characteristics of each cohort (e.g., ancestry, sample size, phenotyping, genotyping, or imputation) affect fine-mapping calibration and recall. Here, we first demonstrate that meta-analysis fine-mapping is substantially miscalibrated in simulations when different genotyping arrays or imputation panels are included. To mitigate these issues, we propose a summary statistics-based QC method, SLALOM, that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics based on ancestry-matched local LD structure. Having validated SLALOM performance in simulations and the GWAS Catalog, we applied it to 14 disease endpoints from the Global Biobank Meta-analysis Initiative and found that 67% of loci showed suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci were significantly depleted for having likely causal variants, such as nonsynonymous variants, as a lead variant (2.7x; Fishers exact P = 7.3 x 10-4). Compared to fine-mapping results in individual biobanks, we found limited evidence of fine-mapping improvement in the GBMI meta-analyses. Although a full solution requires complete synchronization across cohorts, our approach identifies likely spurious results in meta-analysis fine-mapping. We urge extreme caution when interpreting fine-mapping results from meta-analysis.