Hautakangas Heidi, Kartau Joonas, Palotie Aarno, Pirinen Matti
Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry Massachusetts General Hospital, Boston, MA, USA.
Nat Commun. 2026 Jan 12;17(1):355. doi: 10.1038/s41467-025-64880-3.
Migraine is a highly prevalent neurovascular disorder for which genome-wide association studies (GWAS) have identified over one hundred risk loci, yet the causal variants and genes remain mostly unknown. Here, we meta-analyze three migraine GWAS including 98,374 cases and 869,160 controls and identify 122 independent risk loci of which 35 were new. Fine-mapping of a meta-analysis is challenging because some variants may be missing from some participating studies and accurate linkage disequilibrium (LD) information of the variants is often not available. Here, using the exact in-sample LD, we first investigate which statistics could reliably capture the quality of fine-mapping when only reference LD is available. We observe that the posterior expected number of causal variants best distinguishes between the high- and low-quality results. Next, we perform fine-mapping for 102 autosomal risk regions using FINEMAP. We produce high-quality fine-mapping for 93 regions and define 181 distinct credible sets. Among the high-quality credible sets are 7 variants with very high posterior inclusion probability (PIP > 0.9) and 2 missense variants with PIP > 0.5 (rs6330 in NGF and rs1133400 in INPP5A). For 35 association signals, we manage to narrow down the set of potential risk variants to at most 5 variants.
偏头痛是一种高度流行的神经血管疾病，全基因组关联研究（GWAS）已确定了一百多个风险位点，但其因果变异和基因大多仍不清楚。在此，我们对三项偏头痛GWAS进行荟萃分析，包括98374例病例和869160例对照，确定了122个独立风险位点，其中35个是新发现的。荟萃分析的精细定位具有挑战性，因为一些参与研究中可能缺少某些变异，而且变异的准确连锁不平衡（LD）信息往往无法获得。在此，我们使用样本内精确LD，首先研究在仅可获得参考LD的情况下，哪些统计量能够可靠地捕捉精细定位的质量。我们观察到，因果变异的后验期望数量最能区分高质量和低质量结果。接下来，我们使用FINEMAP对102个常染色体风险区域进行精细定位。我们对93个区域进行了高质量精细定位，并定义了181个不同的可信集。在高质量可信集中，有7个变异的后验包含概率非常高（PIP>0.9），还有2个错义变异的PIP>0.5（NGF中的rs6330和INPP5A中的rs1133400）。对于35个关联信号，我们设法将潜在风险变异集缩小到最多5个变异。
