TY - JOUR
T1 - Multivariate Analysis and Modelling of multiple Brain endOphenotypes: Let's MAMBO!
AU - Vilor-Tejedor, Natalia
AU - Garrido-Martin, Diego
AU - Rodriguez-Fernandez, Blanca
AU - Lamballais, Sander
AU - Guigo, Roderic
AU - Gispert, Juan Domingo
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1016/j.csbj.2021.10.019
DO - 10.1016/j.csbj.2021.10.019
M3 - Article
VL - 19
SP - 5800
EP - 5810
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
SN - 2001-0370
ER -