Imaging genetics with partial least squares for mixed-data types (MiMoPLS)

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 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.