de Marvao Antonio, Dawes Timothy J W, O'Regan Declan P
MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom.
Front Cardiovasc Med. 2020 Jan 21;6:195. doi: 10.3389/fcvm.2019.00195. eCollection 2019.
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
心血管疾病仍是全球死亡和发病的主要原因，基因型对疾病风险有重大影响。心脏影像遗传学旨在识别和表征影响从心血管影像中获得的功能、生理和解剖表型的基因变异。高通量DNA测序和基因分型极大地加速了基因发现，使变异解读成为当代临床遗传学的关键挑战之一。异质性、低可信度的表型分析以及使用传统统计方法整合和分析大规模基因、影像和临床数据集的困难阻碍了这一进程。人工智能（AI）方法，如深度学习，特别适合应对数据可扩展性和高维度的挑战，并在心脏影像遗传学领域显示出前景。在此，我们回顾了应用于影像遗传学研究的人工智能的现状，并讨论了突出的方法学挑战，随着该领域从试点研究转向主流应用，从一维全局描述符转向全器官形状和功能的高分辨率模型，从单变量分析转向多变量分析，从候选基因方法转向全基因组方法。最后，我们考虑人工智能影像遗传学的未来方向和前景，以最终帮助理解心血管健康和疾病的遗传和环境基础。
