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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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A structural equation model for imaging genetics using spatial transcriptomics.

Sjoerd M H Huisman1,2, Ahmed Mahfouz1,2, Nematollah K Batmanghelich3

  • 1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.

Brain Informatics
|November 4, 2018
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Summary
This summary is machine-generated.

This study introduces a novel method integrating genetic markers and brain imaging data using causal structural equation models. This approach enhances understanding of genetic influences on brain structure, particularly in diseases like Alzheimer's.

Keywords:
ADNIAllen Brain AtlasBrain geneticsImaging geneticsStructural equation modelling

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Area of Science:

  • Neuroimaging
  • Genetics
  • Computational Biology

Background:

  • Imaging genetics explores links between genetic variation and brain imaging features, often in disease contexts.
  • Existing models for brain volume-gene relationships lack integration of spatial gene activity knowledge.

Purpose of the Study:

  • To develop a method integrating genetic markers (single nucleotide polymorphisms) and imaging features using a causal model and dimension reduction.
  • To leverage spatial transcriptome data for improved model structure and interpretability in imaging genetics.

Main Methods:

  • Utilized structural equation models to identify latent variables explaining brain volume changes influenced by genetic variants.
  • Incorporated spatial transcriptome data from the Allen Human Brain Atlas to inform model structure.
  • Tested the model in simulations and applied it to Alzheimer's Disease Neuroimaging Initiative data.

Main Results:

  • The proposed method successfully integrates genetic and imaging data within a causal framework.
  • Using spatial gene expression data improved model noise reduction and interpretability.
  • The model demonstrated utility in a case study of Alzheimer's disease.

Conclusions:

  • The developed method offers a powerful approach for imaging genetics research by combining causal modeling, dimension reduction, and spatial transcriptome data.
  • This integration enhances the understanding of genetic underpinnings of brain structure and disease.
  • The approach holds promise for future studies in neurodegenerative diseases.