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Zhou, Wei; Zhou, Wei; Wu, Kuan-Han H.; Hirbo, Jibril B.; Lopera-Maya, Esteban A.; Läll, Kristi; Karjalainen, Juha; Kurki, Mitja; Maasha, Mutaamba; Brumpton, Ben M.; Chavan, Sameer; Chen, Tzu-Ting; Ding, Yi; Guare, Lindsay A.; Hornsby, Whitney E.; Ingold, Nathan; Johnson, Ruth; Laisk, Triin; Lv, Jun; Millwood, Iona Y.; Palta, Priit; Uzunovic, Jasmina; Verma, Anurag; Zawistowski, Matthew; Zhong, Xue; Al-Dabhani, Kawthar M.; Campbell, Archie; Damrauer, Scott M.; Douville, Nicholas J.; Finer, Sarah; Fritsche, Lars G.; Fthenou, Eleni; Gonzalez-Arroyo, Gilberto; Hunt, Karen A.; Ioannidis, Alexander; Jansonius, Nomdo M.; Michael Lee, Ming Ta; Lopez-Pineda, Arturo; Marioni, Riccardo E.; Moatamed, Babak; Numakura, Kensuke; Rafaels, Nicholas; Richmond, Anne; Rojas-Muñoz, Agustin; Straub, Peter; Vanderwerff, Brett; Vernekar, Manvi; Barnes, Kathleen C.; Boezen, Marike; Chen, Zhengming; Chen, Chia-Yen; Cho, Judy; Finucane, Hilary K.; Franke, Lude; Ganna, Andrea; Gaunt, Tom R.; Huang, Hailiang; Koskela, Jukka T.; Lajonchere, Clara; Loos, Ruth J.F.; MacGregor, Stuart; Matsuda, Koichi; Olsen, Catherine M.; Porteous, David J.; Shavit, Jordan A.; Takano, Tomohiro; Vonk, Judith M.; Whiteman, David C.; Wright, John; Boehnke, Michael; Bustamante, Carlos D.; Cox, Nancy J.; Fatumo, Segun; Geschwind, Daniel H.; Hayward, Caroline; Mägi, Reedik; Martin, Hilary C.; Medland, Sarah E.; Okada, Yukinori; Palotie, Aarno V.; Pasaniuc, Bogdan; Sanna, Serena; Stefansson, Kari; Zöllner, Sebastian; BioMe; BioVU; CanPath - Ontario Health Study; China Kadoorie Biobank Collaborative Group; Colorado Center for Personalized Medicine; deCODE Genetics; Estonian Biobank, FinnGen; Generation Scotland; National Biobank of Korea; Taiwan Biobank; The Hunt Study; Ucla Atlas Community Health Initiative; Uganda Genome Resource; Uk Biobank; Martin, Alicia R.; Daly, Mark J.
Cell genomics, 12/2022, Volume: 2, Issue: 12Journal Article
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. Display omitted •Extensive simulation of meta-analyses to show substantial fine-mapping miscalibration•SLALOM, a novel method that identifies suspicious loci for meta-analysis fine-mapping•Significant depletion of likely causal variants in SLALOM-predicted suspicious loci•Widespread suspicious loci for fine-mapping in current meta-analysis summary statistics Genome-wide associations studies (GWASs), often performed as meta-analyses, have identified tens of thousands of disease-associated loci. Kanai et al. demonstrate via large-scale simulations and real data analysis that standard tools for pinpointing the causal variants underlying these associations can produce unreliable results when applied to GWAS meta-analyses.
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