Imaging Genetics (IG) integrates neuroimaging and genomic data from the same individual, deepening our knowledge of the biological mechanisms behind neurodevelopmental domains and neurological disorders. Although the literature on IG has exponentially grown over the past years, the majority of studies have mainly analyzed associations between candidate brain regions and individual genetic variants. However, this strategy is not designed to deal with the complexity of neurobiological mechanisms underlying behavioral and neurodevelopmental domains. Moreover, larger sample sizes and increased multidimensionality of this type of data represents a challenge for standardizing modeling procedures in IG research. This review provides a systematic update of the methods and strategies currently used in IG studies, and serves as an analytical framework for researchers working in this field. To complement the functionalities of the Neuroconductor framework, we also describe existing R packages that implement these methodologies. In addition, we present an overview of how these methodological approaches are applied in integrating neuroimaging and genetic data.

Natalia Vilor-Tejedor was funded by a pre-doctoral grant from theAgència de Gestió d’Ajuts Universitaris i de Recerca (2017 FI_B 00636),Generalitatde Catalunya–Fons Social Europeu. This work has beenpartially supported by a STSM Grant from EU COST Action 15120 OpenMultiscale Systems Medicine (OpenMultiMed) and Centro deInvestigación Biomédica en Red de Epidemiología y Salud Pública(CIBERESP). Silvia Alemany thanks the Institute of Health Carlos III forher Sara Borrell postdoctoral grant (CD14/00214). The researchleading to these results has also received funding from the EuropeanResearch Council under the ERC Grant Agreement number 268479–the BREATHE project and by grant MTM2015-68140-R from theMinisterio de Economía e Innovación (Spain). ISGlobal is a member ofthe CERCA Programme, Generalitat de Catalunya.