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Strategies for integrated analysis in imaging genetics studies.

Natàlia Vilor-Tejedor1, Silvia Alemany2, Alejandro Cáceres2

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Summary

Imaging Genetics (IG) combines brain scans and genetic data to understand brain development and disorders. This review updates IG methods, offering a framework and R packages for complex data analysis.

Keywords:
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Area of Science:

  • Neuroscience
  • Genetics
  • Biostatistics

Background:

  • Imaging Genetics (IG) integrates neuroimaging and genomic data to study brain development and neurological disorders.
  • Current IG studies often focus on limited associations between brain regions and single genetic variants, which may not capture complex neurobiological mechanisms.
  • Increasing data complexity and sample sizes in IG research necessitate standardized modeling procedures.

Purpose of the Study:

  • To provide a systematic update on current methods and strategies in Imaging Genetics research.
  • To serve as an analytical framework for researchers in the field.
  • To describe R packages that complement the Neuroconductor framework for IG data analysis.

Main Methods:

  • Systematic review of existing methodologies in Imaging Genetics.
  • Description of R packages for implementing IG methods.
  • Overview of the application of these methods in integrating neuroimaging and genetic data.

Main Results:

  • The review systematically updates methods and strategies in Imaging Genetics.
  • Existing R packages that implement IG methodologies are described.
  • An overview of how these approaches are applied to integrate neuroimaging and genetic data is presented.

Conclusions:

  • This review offers a comprehensive update on Imaging Genetics methodologies.
  • It provides a valuable framework and practical tools (R packages) for researchers.
  • The findings facilitate a deeper understanding of the biological mechanisms underlying neurodevelopmental domains and neurological disorders.