Mondragon-Estrada Enrique, Newburger Jane W, DePalma Steven R, Brueckner Martina, Cleveland John, Chung Wendy K, Gelb Bruce D, Goldmuntz Elizabeth, Hagler Donald J, Huang Hao, McQuillen Patrick, Miller Thomas A, Panigrahy Ashok, Porter George A, Roberts Amy E, Rollins Caitlin K, Russell Mark W, Tristani-Firouzi Martin, Grant P Ellen, Im Kiho, Morton Sarah U
Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA.
iScience. 2024 Dec 28;28(2):111707. doi: 10.1016/j.isci.2024.111707. eCollection 2025 Feb 21.
Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals. Predicted impact was compared between participants with CHD and a jointly called cohort without CHD. We then assessed the relationship of the predicted impact of ncDNVs with their sulcal folding patterns. ncDNVs predicted to increase H3K9me2 modification were associated with larger disruptions in right parietal sulcal patterns in the CHD cohort. Genes predicted to be regulated by these ncDNVs were enriched for functions related to neuronal development. This highlights the potential of deep learning models to generate hypotheses about the role of noncoding variants in brain development.
与先天性心脏病（CHD）相关的神经发育障碍可能源于大脑发育途径的扰动，包括脑沟模式的形成。虽然遗传因素会影响脑沟特征，但非编码变异（ncDNV）与CHD患者脑沟模式之间的关联仍知之甚少。利用深度学习模型，我们研究了ncDNV对基因调控信号的预测影响。比较了CHD患者与无CHD的联合分析队列之间的预测影响。然后，我们评估了ncDNV的预测影响与其脑沟折叠模式之间的关系。预测会增加H3K9me2修饰的ncDNV与CHD队列中右侧顶叶脑沟模式的更大破坏有关。预测受这些ncDNV调控的基因在与神经元发育相关的功能方面富集。这凸显了深度学习模型在生成关于非编码变异在大脑发育中作用的假设方面的潜力。
