Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

影像遗传学（IG）整合了来自同一个人的神经影像和基因组数据，从而加深了我们对神经发育域和神经系统疾病背后的生物学机制的了解。尽管有关IG的文献在过去几年中呈指数增长，但大多数研究主要分析了候选大脑区域与个体遗传变异之间的关联。但是，此策略并非旨在解决行为和神经发育域下的神经生物学机制的复杂性。此外，此类数据的更大样本量和更高的多维性对IG研究中的建模程序标准化提出了挑战。这篇评论提供了IG研究中当前使用的方法和策略的系统更新，并作为该领域研究人员的分析框架。为了补充Neuroconductor框架的功能，我们还描述了实现这些方法的现有R包。此外，我们还概述了如何将这些方法学方法用于整合神经影像和遗传数据。