Imaging Genetics with Partial Least Squares for Mixed-Data Types (MiMoPLS)

  • Derek BeatonEmail author
  • Michael Kriegsman
  • ADNI
  • Joseph Dunlop
  • Francesca M. Filbey
  • Hervé Abdi
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 173)


“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 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.


Imaging genetics Partial least squares Alzheimer disease (Multiple) Correspondence analysis Burt’s stripe SNPs Heterogenuous data 



DB is currently supported via training grant by the NIH and National Institute on Drug Abuse (F31DA035039).

FMF is currently supported by the NIH and National Institute on Drug Abuse (R01DA030344). HA would like to acknowledge the support of an EURIAS fellowship at the Paris Institute for Advanced Studies (France), with the support of the European Union’s 7th Framework Program for research, and from a funding from the French State managed by the “Agence Nationale de la Recherche (program: Investissements d’avenir, ANR-11-LABX-0027-01 Labex RFIEA+).” ADNI: Data collection and sharing for this project was funded by the ADNI (NIH Grant U01 AG024904) and DOD ADNI (W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare;; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Derek Beaton
    • 1
    Email author
  • Michael Kriegsman
    • 1
  • ADNI
    • 1
  • Joseph Dunlop
    • 2
  • Francesca M. Filbey
    • 3
  • Hervé Abdi
    • 1
  1. 1.School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonUSA
  2. 2.SAS Institute Inc.CaryUSA
  3. 3.Center for BrainHealth and School of Behavioral and Brain SciencesThe University of Texas at DallasRichardsonUSA

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