Vilor Tejedor, Natàlia, 1988-Garrido Martín, Diego, 1992-Rodríguez-Fernández, BlancaLamballais, SanderGuigó Serra, RodericGispert López, Juan Domingo2021-12-092021-12-092021Vilor-Tejedor N, Garrido-Martín D, Rodriguez-Fernandez B, Lamballais S, Guigó R, Gispert JD. Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO! Comput Struct Biotechnol J. 2021;19:5800-10. DOI: 10.1016/j.csbj.2021.10.0192001-0370http://hdl.handle.net/10230/49155Imaging 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.application/pdfeng© 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)info:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.csbj.2021.10.019GeneticsImage-derived phenotypeImaging geneticsMultiple phenotypesMultivariate modellingNeuroimaginginfo:eu-repo/semantics/openAccess