title: Comparison of variants of canonical correlation analysis and partial least squares for combined analysis of MRI and genetic data.
abstract: The standard analysis approach in neuroimaging genetics studies is the mass-univariate linear modeling (MULM) approach. From a statistical view, however, this approach is disadvantageous, as it is computationally intensive, cannot account for complex multivariate relationships, and has to be corrected for multiple testing. In contrast, multivariate methods offer the opportunity to include combined information from multiple variants to discover meaningful associations between genetic and brain imaging data. We assessed three multivariate techniques, partial least squares correlation (PLSC), sparse canonical correlation analysis (sparse CCA) and Bayesian inter-battery factor analysis (Bayesian IBFA), with respect to their ability to detect multivariate genotype-phenotype associations. Our goal was to systematically compare these three approaches with respect to their performance and to assess their suitability for high-dimensional and multi-collinearly dependent data as is the case in neuroimaging genetics studies. In a series of simulations using both linearly independent and multi-collinear data, we show that sparse CCA and PLSC are suitable even for very high-dimensional collinear imaging data sets. Among those two, the predictive power was higher for sparse CCA when voxel numbers were below 400 times sample size and candidate SNPs were considered. Accordingly, we recommend Sparse CCA for candidate phenotype, candidate SNP studies. When voxel numbers exceeded 500 times sample size, the predictive power was the highest for PLSC. Therefore, PLSC can be considered a promising technique for multivariate modeling of high-dimensional brain-SNP-associations. In contrast, Bayesian IBFA cannot be recommended, since additional post-processing steps were necessary to detect causal relations. To verify the applicability of sparse CCA and PLSC, we applied them to an experimental imaging genetics data set provided for us. Most importantly, application of both methods replicated the findings of this data set.
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany; Leipzig University Hospital, IFB Adiposity Diseases, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany. Electronic address: grellmann@cbs.mpg.de.
authors.forename: Claudia
authors.initials: C
authors.lastname: Grellmann
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany. Electronic address: bitzer@cbs.mpg.de.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bitzer
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany; Leipzig University Hospital, IFB Adiposity Diseases, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany. Electronic address: neumann@cbs.mpg.de.
authors.forename: Jane
authors.initials: J
authors.lastname: Neumann
authors.affiliation: Oslo University Hospital, NORMENT KG Jebsen Centre for Psychosis Research, Kirkeveien 166, PO Box 4956, Nydalen, 0424 Oslo, Norway; University of Oslo, Department of Psychology, PO Box 1094, Blindern, 0317 Oslo, Norway. Electronic address: l.t.westlye@psykologi.uio.no.
authors.forename: Lars T
authors.initials: LT
authors.lastname: Westlye
authors.affiliation: Oslo University Hospital, NORMENT KG Jebsen Centre for Psychosis Research, Kirkeveien 166, PO Box 4956, Nydalen, 0424 Oslo, Norway. Electronic address: o.a.andreassen@medisin.uio.no.
authors.forename: Ole A
authors.initials: OA
authors.lastname: Andreassen
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany; Leipzig University Hospital, IFB Adiposity Diseases, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany; Leipzig University Hospital, Clinic of Cognitive Neurology, Liebigstraße 16, 04103 Leipzig, Germany; Mind and Brain Institute, Berlin School of Mind and Brain, Humboldt-University, Unter den Linden 6, 10099 Berlin, Germany. Electronic address: villringer@cbs.mpg.de.
authors.forename: Arno
authors.initials: A
authors.lastname: Villringer
authors.affiliation: Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A, 04103 Leipzig, Germany; Leipzig University Hospital, IFB Adiposity Diseases, Philipp-Rosenthal-Straße 27, 04103 Leipzig, Germany. Electronic address: horstmann@cbs.mpg.de.
authors.forename: Annette
authors.initials: A
authors.lastname: Horstmann
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D005823:Genetics / Q000706:statistics & numerical data*; D005838:Genotype; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D015810:Linkage Disequilibrium / Q000235:genetics; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D059906:Neuroimaging / Q000706:statistics & numerical data; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011597:Psychomotor Performance / Q000502:physiology
keywords: Canonical correlation analysis; Functional magnetic resonance imaging; Partial least squares correlation; Single nucleotide polymorphisms