With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi‐modality imaging, genetic, neurocognitive and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function and brain‐related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention and treatment of numerous complex brain‐related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high‐dimensional imaging phenotypes and high‐dimensional genetic data, a big data squared (BD²) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high‐dimensional nature of BD², various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel‐wise approaches, reduced rank regression, mixture models and group sparse multi‐task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research. The Canadian Journal of Statistics 47: 108–131; 2019 © 2019 Statistical Society of Canada

中文翻译：

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影像遗传学统计方法综述。

随着现代技术的迅速发展，正在进行许多生物医学研究，以收集海量数据集，其中包括来自越来越多的同类人群的大量多模态成像，遗传，神经认知和临床信息。从这些大型数据集中同时提取和整合丰富多样的神经影像学和/或基因组学中的异构信息，可能会改变我们对遗传变异如何影响整个生命周期的大脑结构和功能，认知功能以及与脑相关的疾病风险的理解。这种理解对于诊断，预防和治疗多种复杂的与大脑有关的疾病（例如精神分裂症和阿尔茨海默氏病）至关重要。但是，用于联合分析高维成像表型和高维遗传数据的分析方法的发展，²）问题，对现有的分析方法提出了主要的计算和理论挑战。除了BD ²的高维性质外，各种神经影像学测量通常还表现出较强的空间平滑性和依赖性，并且遗传标记可能具有由连锁不平衡引起的自然依赖性结构。我们回顾了用于成像遗传学的各种统计技术的最新进展，包括大规模单变量和体素化方法，降阶回归，混合模型和小组稀疏多任务回归。通过这样做，我们希望这次审查可以鼓励统计界的其他人进入这一新的令人兴奋的研究领域。加拿大统计杂志47：108-131；2019©2019加拿大统计学会