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

Global Biobank Meta-analysis Initiative

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.

Original languageEnglish
Article number100210
JournalCell Genomics
Issue number12
StatePublished - 14 Dec 2022


  • GWAS
  • biobank
  • fine-mapping
  • genome-wide association study
  • heterogeneity
  • linkage disequilibrium
  • meta-analysis
  • miscalibration
  • summary statistics


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