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Brain Inform. 2018 Nov 2;5(2):13. doi: 10.1186/s40708-018-0091-0.

A structural equation model for imaging genetics using spatial transcriptomics.

Author information

1
Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.
2
Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.
3
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
4
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
5
Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands. m.j.t.reinders@tudelft.nl.
6
Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands. m.j.t.reinders@tudelft.nl.

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 available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer's Disease Neuroimaging Initiative.

KEYWORDS:

ADNI; Allen Brain Atlas; Brain genetics; Imaging genetics; Structural equation modelling

PMID:
30390165
DOI:
10.1186/s40708-018-0091-0
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