A structural equation model for imaging genetics using spatial transcriptomics.

ABSTRACT: Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly availabl

SUBMITTER: Huisman SMH 

PROVIDER: S-EPMC6429169 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature