Imaging Genetics with Partial Least Squares for Mixed-Data Types (MiMoPLS)
“Imaging genetics” studies the genetic contributions to brain structure and function by finding correspondence between genetic data—such as single nucleotide polymorphisms (SNPs)—and neuroimaging data—such as diffusion tensor imaging (DTI). However, genet
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Imaging Genetics with Partial Least Squares for Mixed-Data Types (MiMoPLS) Derek Beaton, Michael Kriegsman, ADNI*, Joseph Dunlop, Francesca M. Filbey, and Hervé Abdi
Abstract “Imaging genetics” studies the genetic contributions to brain structure and function by finding correspondence between genetic data—such as single nucleotide polymorphisms (SNPs)—and neuroimaging data—such as diffusion tensor imaging (DTI). However, genetic and neuroimaging data are heterogenous data types, where neuroimaging data are quantitative and genetic data are (usually) categorical. So far, methods used in imaging genetics treat all data as quantitative, and this sometimes requires unrealistic assumptions about the nature of genetic data. In this article we present a new formulation of Partial Least Squares Correlation (PLSC)—called Mixed-modality Partial Least Squares (MiMoPLS)—specifically tailored for heterogeneous (mixed-) data types. MiMoPLS integrates features of PLSC and Correspondence Analysis (CA) by using special properties of quantitative
D. Beaton () • M. Kriegsman • H. Abdi School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA e-mail: [email protected]; [email protected]; [email protected] for the Alzheimer’s Disease Neuroimaging Initiative *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf
J. Dunlop SAS Institute Inc., Cary, NC, USA e-mail: [email protected] F.M. Filbey Center for BrainHealth and School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, TX, USA e-mail: [email protected] © Springer International Publishing Switzerland 2016 H. Abdi et al. (eds.), The Multiple Facets of Partial Least Squares and Related Methods, Springer Proceedings in Mathematics & Statistics 173, DOI 10.1007/978-3-319-40643-5_6
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data and Multiple Correspondence Analysis (MCA). We illustrate MiMoPLS with an example data set from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with DTI and SNPs. Keywords Imaging genetics • Partial least squares • Alzheimer disease • (Multiple) Correspondence analysis • Burt’s stripe • SNPs • Heterogenuous data
6.1 Introduction Imaging genetics (and “imaging genomics”) combines two scientific disciplines: neuroimaging—often from the cognitive neuroscience perspective—and genetics— often from the genomics perspective (Meyer-Lindenberg 2012; Thompson et al. 2010). Imaging genetics integrates neuroimaging and genetic data to understand how genetics contributes to brain structure and function—often with respect to diagnostic criteria or complex behavior
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