Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

中文翻译：

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用于挖掘高级成像遗传关联的二维富集分析。

富集分析已广泛应用于全基因组关联研究中，其中检查了与生物途径相对应的基因集与表型的显着关联，以帮助提高统计能力并改善生物学解释。在这项工作中，我们将富集分析的范围扩展到脑成像遗传学，这是一个新兴领域，研究遗传变异如何影响通过神经影像定量特征（QT）衡量的脑结构和功能。考虑到成像和遗传数据的高度维度，我们建议研究成像遗传富集分析（IGEA），这是一种新的富集分析范式，该模型共同考虑了有意义的基因集（GS）和脑回路（BC），并检查是否有给定的GS- BC对丰富了一系列基因QT发现。使用来自艾伦人脑图谱的基因表达数据和来自阿尔茨海默氏病神经影像学计划的遗传基因数据作为测试平台，我们提出了IGEA框架并进行了概念验证研究。这项经验研究确定了25个重要的高级二维成像遗传学模块。这些模块中的许多模块与多种神经生物学途径或神经退行性疾病有关，这表明提议框架有望为复杂疾病的机理提供见解。