Meta-analysis fine-mapping is often miscalibrated at single-variant resolution
Kanai M., Elzur R., Zhou W., Wu KHH., Rasheed H., Tsuo K., Hirbo JB., Wang Y., Bhattacharya A., Zhao H., Namba S., Surakka I., Wolford BN., Lo Faro V., Lopera-Maya EA., Läll K., Favé MJ., Partanen JJ., Chapman SB., Karjalainen J., Kurki M., Maasha M., Brumpton BM., Chavan S., Chen TT., Daya M., Ding Y., Feng YCA., Guare LA., Gignoux CR., Graham SE., Hornsby WE., Ingold N., Ismail SI., Johnson R., Laisk T., Lin K., Lv J., Millwood IY., Moreno-Grau S., Nam K., Palta P., Pandit A., Preuss MH., Saad C., Setia-Verma S., Thorsteinsdottir U., Uzunovic J., Verma A., Zawistowski M., Zhong X., Afifi N., Al-Dabhani KM., Al Thani A., Bradford Y., Campbell A., Crooks K., de Bock GH., Damrauer SM., Douville NJ., Finer S., Fritsche LG., Fthenou E., Gonzalez-Arroyo G., Griffiths CJ., Guo Y., Hunt KA., Ioannidis A., Jansonius NM., Konuma T., Michael Lee MT., Lopez-Pineda A., Matsuda Y., Marioni RE., Moatamed B., Nava-Aguilar MA., Numakura K., Patil S., Rafaels N., Richmond A., Rojas-Muñoz A., Shortt JA., Straub P., Tao R., Vanderwerff B., Vernekar M., Veturi Y., Barnes KC., Boezen M., Chen Z., Chen CY., Cho J., Smith GD., Finucane HK., Franke L., Gamazon ER., Ganna A., Gaunt TR., Ge T., Huang H.
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.