Liang Yanyu, Nyasimi Festus, Melia Owen, Carroll Timothy J, Brettin Thomas, Brown Andrew, Im Hae Kyung
Section of Genetic Medicine, University of Chicago, Chicago, IL, United States.
Section of Genetic Medicine, University of Chicago, Chicago, IL, United States.
Dev Cogn Neurosci. 2025 Mar 13;73:101542. doi: 10.1016/j.dcn.2025.101542.
Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.
脑部磁共振成像(MRI)技术的进步推动了神经科学领域的诸多发现。对脑部MRI特征进行病例对照比较,凸显了精神和行为障碍的潜在成因。然而,由于收集MRI数据的成本和难度,大多数研究的样本量较小,限制了其可靠性。此外,反向因果关系使解释变得复杂,因为许多观察到的脑部差异是疾病的结果而非病因。在此,我们提出一种方法(BrainXcan),该方法利用大规模全基因组关联研究(GWAS)的力量和参考脑部MRI数据,来发现疾病病因的新机制并验证现有机制。BrainXcan测试与脑部MRI衍生特征和复杂性状的遗传预测因子之间的关联,以确定相关的全脑和区域特异性特征。仅需遗传数据,BrainXcan就能让我们利用公共数据资源,针对多种MRI模式,对一系列关于精神疾病的假设进行检验。例如,我们的方法表明,全脑轴突密度降低与精神分裂症风险相关,这与失连接假说一致。我们还发现海马体体积与精神分裂症风险相关,凸显了我们方法的潜力。综上所述,我们的结果表明BrainXcan有望为GWAS性状的生物学机制提供见解。