Meta-analysis fine-mapping is often miscalibrated at single-variant resolution
Masahiro Kanai, Masahiro Kanai, Roy Elzur, Wei Zhou, Wei Zhou, Masahiro Kanai, Masahiro Kanai, Kuan-Han Wu, Humaira Rasheed, Kristin Tsuo, Jibril Hirbo, Ying Wang, Arjun Bhattacharya, Huiling Zhao, Shinichi Namba, Ida Surakka, Brooke N. Wolford, Valeria Lo Faro, Esteban A. Lopera-Maya, Kristi Läll, Marie-Julie Favé, Juulia Partanen, Sinéad B. Chapman, Juha Karjalainen, Mitja Kurki, Mutaamba Maasha, Ben Brumpton, Sameer Chavan, Tzu‐Ting Chen, Michelle Daya, Yi Ding, Yen‐Chen Anne Feng, Lindsay Guare, Christopher R. Gignoux, Sarah E. Graham, Whitney Hornsby, Nathan Ingold, Said I. Ismail, Ruth Johnson, Triin Laisk, Kuang Lin, Jun Lv, Iona Y. Millwood, Sonia Moreno–Grau, Kisung Nam, Priit Palta, Anita Pandit, Michael Preuß, Chadi Saad, Shefali Setia-Verma, Unnur Þorsteinsdóttir, Jasmina Uzunović, Anurag Verma, Matthew Zawistowski, Xue Zhong, Nahla Afifi, Kawthar Al-Dabhani, Asma Al Thani, Yuki Bradford, Archie Campbell, Kristy Crooks, Geertruida H. de Bock, Scott M. Damrauer, Nicholas J. Douville, Sarah Finer, Lars G. Fritsche, Eleni Fthenou, Gilberto Gonzalez-Arroyo, Chris Griffiths, Yu Guo, Karen A. Hunt, Alexander Ioannidis, Nomdo M. Jansonius, Takahiro Konuma, Ming Ta Michael Lee, Arturo Lopez-Pineda, Yuta Matsuda, Riccardo E. Marioni, Babak Moatamed, Marco A. Nava-Aguilar, Kensuke Numakura, Snehal Patil, Nicholas Rafaels, Anne Richmond, Agustin Rojas‐Muñoz, Jonathan Shortt, Péter Straub, Ran Tao, Brett Vanderwerff, Manvi Vernekar, Yogasudha Veturi, Kathleen C. Barnes, Marike Boezen, Zhengming Chen, Chia‐Yen Chen, Judy H. Cho, George Davey Smith, Hilary K. Finucane, Lude Franke, Eric R. Gamazon
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.