Abstract
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.
Original language | English |
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Pages (from-to) | 5800-5810 |
Number of pages | 11 |
Journal | Computational and Structural Biotechnology Journal |
Volume | 19 |
DOIs | |
Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:At the time of writing this review, NV-T is funded by a postdoctoral grant, Juan de la Cierva Programme (FJC2018-038085-I), Ministerio de Ciencia, Innovación y Universidades – Spanish State Research Agency. Her research is also supported by the “la Caixa'' Foundation (LCF/PR/GN17/10300004), the Health Department of the Catalan Government (Health Research and Innovation Strategic Plan (PERIS) 2016–2020 grant #SLT002/16/00201), and the Alzheimer Nederland Project (WE.15–2019-09). DG-M is funded by grant number CZF2019-002436 from the Chan Zuckerberg Initiative. JDG holds a ‘Ramón y Cajal’ fellowship (RYC-2013–13054). All CRG authors acknowledge the support of the Spanish Ministry of Science, Innovation, and Universities to the EMBL partnership, the Centro de Excelencia Severo Ochoa, and the CERCA Programme / Generalitat de Catalunya.
Publisher Copyright:
© 2021 The Authors