@article{b446daf5b8244b04a60dc0f0b05d6a5c,
title = "Meta-analysis fine-mapping is often miscalibrated at single-variant resolution",
abstract = "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.",
keywords = "biobank, fine-mapping, genome-wide association study, GWAS, heterogeneity, linkage disequilibrium, meta-analysis, miscalibration, summary statistics",
author = "{Global Biobank Meta-analysis Initiative} and Masahiro Kanai and Roy Elzur and Wei Zhou and Wu, {Kuan Han H.} and Humaira Rasheed and Kristin Tsuo and Hirbo, {Jibril B.} and Ying Wang and Arjun Bhattacharya and Huiling Zhao and Shinichi Namba and Ida Surakka and Wolford, {Brooke N.} and {Lo Faro}, Valeria and Lopera-Maya, {Esteban A.} and Kristi L{\"a}ll and Fav{\'e}, {Marie Julie} and Partanen, {Juulia J.} and Chapman, {Sin{\'e}ad B.} and Juha Karjalainen and Mitja Kurki and Mutaamba Maasha and Brumpton, {Ben M.} and Sameer Chavan and Chen, {Tzu Ting} and Michelle Daya and Yi Ding and Feng, {Yen Chen A.} and Guare, {Lindsay A.} and Gignoux, {Christopher R.} and Graham, {Sarah E.} and Hornsby, {Whitney E.} and Nathan Ingold and Ismail, {Said I.} and Ruth Johnson and Triin Laisk and Kuang Lin and Jun Lv and Millwood, {Iona Y.} and Sonia Moreno-Grau and Kisung Nam and Priit Palta and {de Bock}, {Geertruida H.} and Jansonius, {Nomdo M.} and Marike Boezen and Lude Franke and Harold Snieder and Vonk, {Judith M.} and Cisca Wijmenga and Serena Sanna and Daly, {Mark J.} and Finucane, {Hilary K.}",
note = "Funding Information: We acknowledge all the participants and researchers of the 23 biobanks that have contributed to the GBMI. Biobank-specific acknowledgments are included in the Data S3 . We thank H. Huang, A.R. Martin, B.M. Neale, Y. Okada, K. Tsuo, J.C. Ulirsch, Y. Wang, and all the members of Finucane and Daly labs for their helpful feedback. M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation . H.K.F. was funded by NIH grant DP5 OD024582 . Publisher Copyright: {\textcopyright} 2022 The Author(s)",
year = "2022",
month = dec,
day = "14",
doi = "10.1016/j.xgen.2022.100210",
language = "English",
volume = "2",
journal = "Cell Genomics",
issn = "2666-979X",
publisher = "Cell Press",
number = "12",
}