dc.contributor.author | Kanai, M | en_US |
dc.contributor.author | Elzur, R | en_US |
dc.contributor.author | Zhou, W | en_US |
dc.contributor.author | Global Biobank Meta-analysis Initiative | en_US |
dc.contributor.author | Daly, MJ | en_US |
dc.contributor.author | Finucane, HK | en_US |
dc.date.accessioned | 2022-11-17T14:01:52Z | |
dc.date.issued | 2022-12-14 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/82566 | |
dc.description.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. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Cell Genom | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.title | Meta-analysis fine-mapping is often miscalibrated at single-variant resolution. | en_US |
dc.type | Article | |
dc.identifier.doi | 10.1016/j.xgen.2022.100210 | en_US |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/36643910 | en_US |
pubs.issue | 12 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
pubs.volume | 2 | en_US |