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.本数据集包含文献报道的全基因组关联研究（genome-wide association studies）汇总统计量，相关文献为：Bell S、Tozer D J与Markus H S于2022年发表于《科学报告（Scientific Reports）》的《人脑功能连接组的全基因组关联研究揭示全局网络效率背后的强血管组分》一文，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，且可在无外部刺激的条件下被识别。本研究针对英国生物银行（UK Biobank）中多达36150名欧洲血统参与者的静息态固有脑活动网络指标，开展了10项全基因组关联研究。研究发现，全局网络效率的遗传力主要可由血氧水平依赖（blood oxygen level-dependent, BOLD）静息态波动幅度（resting state fluctuation amplitudes, RSFA）解释，而RSFA被认为可反映BOLD信号的血管组分。RSFA本身具有显著的遗传基础，本研究共鉴定出24个与RSFA相关的基因组位点、157个预测表达量与RSFA存在关联的基因，以及背外侧前额叶皮层（dorsolateral prefrontal cortex）中的3种蛋白质与血浆中的4种蛋白质。本研究观察到RSFA与心血管性状存在关联，单细胞RNA特异性分析显示血管相关细胞存在富集。此外，本研究还揭示了脂质转运、存储调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。本研究得出结论：全局网络效率的遗传力主要可通过RSFA所表征的BOLD反应的血管组分得到解释，而RSFA本身具有显著的遗传基础。关于本地上传文件的更多详情可查阅README文档。有批量下载此类汇总统计量需求的用户可借助zenodo_get工具完成操作。