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Analytical strategies for large imaging genetic datasets: experiences from the IMAGEN study
Author(s) -
Nymberg Charlotte,
Jia Tianye,
Ruggeri Barbara,
Schumann Gunter
Publication year - 2013
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.12088
Subject(s) - endophenotype , univariate , neuroimaging , heritability , imaging genetics , genome wide association study , missing heritability problem , genetic association , multivariate statistics , computer science , psychology , artificial intelligence , computational biology , data science , machine learning , single nucleotide polymorphism , biology , neuroscience , genotype , evolutionary biology , cognition , genetics , gene
Large imaging genetic studies are becoming increasingly common in psychiatric research. In order to fully explore the collected information, analytical strategies that allow comprehensive investigations of the genetic and neural underpinnings of psychiatric disorders are needed. On the basis of our experience with the IMAGEN study, this review evaluates univariate and multivariate analytical strategies for exploring large imaging genetic datasets, with particular focus on reinforcement mechanisms in adolescents. Heritability estimates of functional and structural MRI endophenotypes are presented along with analytical strategies, ranging from those used in univariate candidate gene studies to genome‐wide association studies. Finally, data reduction strategies are discussed at both the genotype level, in the form of expression SNPs and pathway analyses, and the phenotype level, as network analyses of neuroimaging data. Overall, imaging genetic studies have the potential to significantly contribute to our understanding of neurophysiological processes underlying human behavior. The analytical strategies presented here may aid in the comprehensive investigation of reinforcement and other neurobehavioral phenotypes.