Vilor-Tejedor Natalia, Garrido-Martín Diego, Rodriguez-Fernandez Blanca, Lamballais Sander, Guigó Roderic, Gispert Juan Domingo
Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.
Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain.
Comput Struct Biotechnol J. 2021 Oct 13;19:5800-5810. doi: 10.1016/j.csbj.2021.10.019. eCollection 2021.
Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual. Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial. In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.
影像遗传学研究旨在通过结合基于神经成像的脑特征和来自同一个体的遗传数据,来测试遗传信息如何影响脑结构和功能。大多数研究集中于基因变异与脑的单一测量值之间的个体相关性和关联性测试。尽管单变量方法取得了巨大成功,但鉴于神经成像方法能够提供多种脑表型,多变量方法的开发和应用变得至关重要。在本文中,我们综述了专注于多表型和遗传数据分析的新方法和策略。我们还在神经成像数据的背景下讨论了多性状建模的相关方面。