Meta-analysis fine-mapping is often miscalibrated at single-variant resolution
Meta-analysis fine-mapping is often miscalibrated at single-variant resolution
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DOI：
10.1016/j.xgen.2022.100210
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发表时间：
2022-12-14
期刊：
影响因子：
--
通讯作者：
Finucane, Hilary K.
中科院分区：
文献类型：
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作者：
Kanai, Masahiro;Elzur, Roy;Finucane, Hilary K.
Meta分析被广泛用于组合多个全基因组关联研究(GWASs)。Meta分析研究的精细映射通常与单队列研究一样进行。在这里，我们首先论证了异质性(例如，样本量、表型、归因性)损害了Meta分析精细图谱的校准。我们提出了一种基于汇总统计的质量控制(QC)方法--元分析汇总统计的可疑位点分析(SLALOM)，该方法通过检测关联统计中的离群值来识别用于Meta分析精细映射的可疑位点。我们在模拟和GWASTALOG中验证了SLALOM。对来自全球生物库荟萃分析倡议(GBMI)的14项荟萃分析应用SLALOM，我们发现67%的基因座显示出可疑的模式，这对精细定位的准确性提出了质疑。这些预测的可疑基因座由于有非同义变异作为先导变量而显著减少(2.73；Fisher‘s精确p=7.33 10-4)。与单个生物库相比，我们在GBMI荟萃分析中发现了有限的精细映射改进的证据。我们敦促在解释来自不同类别队列的荟萃分析的精细图谱结果时极其谨慎。
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 demon-strate 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 (2.73; Fisher's exact p = 7.3 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.