ArticleBrain informatics2018
A structural equation model for imaging genetics using spatial transcriptomics.
Article in Brain informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Fifty years of structural equation modeling: A history of generalization, unification, and diffusion.Social science research · 2022Article
- Article
- A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures.Pacific Symposium on Biocomputing. Pacific Symposium on BiocomputingArticle
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Authors and funding
6 authors.
Funding
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.
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Registered trials
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