Details

Autor(en) / Beteiligte

• 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.

Titel

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Ist Teil von

• Cell genomics, 2022-12, Vol.2 (12), p.100210, Article 100210

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.