Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.
Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, United States of America.
PLoS Biol. 2024 Sep 13;22(9):e3002782. doi: 10.1371/journal.pbio.3002782. eCollection 2024 Sep.
An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.
理解人类大脑的个体性需要整合跨人群和脑区、分子和系统尺度、健康和临床状态的大脑组织数据。在这里，我们利用计算基因组学的方法来整合大规模的基因组、转录组、神经影像学和电子健康记录数据集，从而帮助推进这一理解。我们估计了 18647 个基因在 45549 名来自英国生物库的 10 个皮质和皮质下区域的基因表达（gr-expression）。首先，我们表明，估计的 gr-expression 模式反映了已知的遗传祖先关系、区域身份，以及直接检测到的基因表达的区域间相关结构。其次，我们进行了全转录组关联研究（TWAS），以发现 gr-expression 个体变异与人群和脑区的灰质体积之间的 1065 个关联。我们将这些关联与同一样本的全基因组关联研究（GWAS）的结果进行了基准测试，发现与这些 GWAS 相比，有数百个新的关联。第三，我们将我们的结果与范德比尔特生物库中 gr-expression 的临床关联进行了整合。这种整合使我们能够通过 gr-expression 将基因与神经影像学和临床表型联系起来。第四，我们确定了多基因 gr-expression 与人类连接组计划（HCP）的结构和功能 MRI 表型之间的关联，这是一个具有高质量功能成像数据的小型神经影像学-基因组数据集。最后，我们表明，gr-expression 的估计值和 TWAS 的幅度通常具有可重复性，并且 TWAS 的 p 值在大样本中具有可重复性。总的来说，我们的结果为整合 gr-expression 与大脑组织和疾病的群体遗传学提供了一个强大的新资源。
