PDF(Pubmed)
Abstract：
Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variability is a challenge. We demonstrate a novel technique, 3D transport-based morphometry (TBM), to extract the structural brain changes linked to genetic copy number variation (CNV) at the 16p11.2 region. We identified two distinct endophenotypes. In data from the Simons Variation in Individuals Project, detection of these endophenotypes enabled 89 to 95% test accuracy in predicting 16p11.2 CNV from brain images alone. Then, TBM enabled direct visualization of the endophenotypes driving accurate prediction, revealing dose-dependent brain changes among deletion and duplication carriers. These endophenotypes are sensitive to articulation disorders and explain a portion of the intelligence quotient variability. Genetic stratification combined with TBM could reveal new brain endophenotypes in many neurodevelopmental disorders, accelerating precision medicine, and understanding of human neurodiversity.
摘要：
自闭症是传统的行为诊断，但有很强的遗传基础。遗传学优先的方法可以改变对自闭症的理解和治疗。然而,从混杂的变异性来源中分离基因-大脑-行为关系是一个挑战。我们展示了一种新颖的技术，基于3D传输的形态测量(TBM)，提取16p11.2区域与基因拷贝数变异（CNV）相关的脑结构变化。我们鉴定了两种不同的内表型。在西蒙斯个人变异项目的数据中，通过检测这些内表型,仅从脑图像中预测16p11.2CNV的测试准确率为89-95%.然后，TBM能够直接可视化内表型，驱动准确预测，揭示缺失和重复携带者之间的剂量依赖性大脑变化。这些内表型对关节紊乱敏感，并解释了部分智商变异性。遗传分层结合TBM可以揭示许多神经发育障碍的新脑内表型，加速精准医疗，以及对人类神经多样性的理解。
