Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)

Summary statistics for genome-wide association studies reported in: Bell, S., Tozer, D.J., &amp; Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: 10.1038/s41598-022-19106-7. <strong>Abstract</strong> Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component. Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find zenodo_get helpful.本数据集对应发表于以下文献的全基因组关联研究汇总统计量：Bell, S., Tozer, D.J. & Markus H.S. (2022). 《人脑功能连接组全基因组关联研究揭示支撑全局网络效能的强血管成分》，*Scientific Reports*，DOI: 10.1038/s41598-022-19106-7。**摘要** 复杂脑网络在整合人脑全脑活动中发挥核心作用，且无需外部刺激即可被识别。我们基于英国生物银行（UK Biobank）中至多36150名欧洲血统参与者，开展了10项针对静息态脑活动网络指标的全基因组关联研究。我们发现，全局网络效能的遗传力很大程度上可由血氧水平依赖（blood oxygen level-dependent, BOLD）静息态波动振幅（resting state fluctuation amplitude, RSFA）解释，而RSFA被认为反映了BOLD信号的血管成分。RSFA本身具有显著的遗传组分，我们共鉴定出24个与RSFA相关的基因组位点、157个其预测表达量与之相关的基因，以及背外侧前额叶皮层中的3种蛋白质与血浆中的4种蛋白质。我们观察到其与心血管性状存在关联，单细胞RNA特异性分析揭示了血管相关细胞的富集。我们的分析还揭示了脂质转运、钙池操控性钙通道活性以及肌醇1,4,5-三磷酸结合在静息态BOLD波动中的潜在作用。我们得出结论：全局网络效能的遗传力很大程度上可由RSFA所表征的BOLD反应的血管成分解释，而RSFA本身具有显著的遗传组分。本数据集上传文件的更多详细信息可参阅README。有批量下载这些汇总统计量需求的用户可借助zenodo_get工具完成下载。