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Related Experiment Video

Updated: Jan 1, 2026

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

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Regional imaging genetic enrichment analysis.

Xiaohui Yao1, Shan Cong1, Jingwen Yan2

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Bioinformatics (Oxford, England)
|December 21, 2019
PubMed
Summary

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This study introduces a new framework for brain imaging genetics, enhancing the discovery of genetic associations with brain structure and function. The method significantly increases statistical power and biological interpretability by analyzing voxelwise data and integrating anatomical information.

Area of Science:

  • Neuroimaging
  • Genetics
  • Computational Biology

Background:

  • Brain imaging genetics studies typically analyze regions of interest (ROIs), often using summary statistics that do not fully utilize voxelwise data.
  • Existing methods may lack the statistical power to detect subtle genetic effects due to dimensionality and multiple comparison correction burdens.

Purpose of the Study:

  • To develop a novel framework for mining regional brain imaging genetic associations using voxelwise enrichment analysis.
  • To enhance statistical power, integrate brain anatomical information for biological interpretation, and leverage fine-grained voxelwise signals.

Main Methods:

  • Proposed a voxelwise enrichment analysis framework integrating brain anatomical annotation.
  • Applied the method to analyze genetic effects on FDG-PET measures across 116 ROIs and 565,373 SNPs in the Alzheimer's Disease Neuroimaging Initiative cohort.

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  • Assessed collective regional genetic effects by embracing weak voxel-level signals.

    • Main Results:

    • Identified 2946 novel imaging genetic associations, significantly outperforming traditional ROI-wise and voxelwise approaches.
    • 33 associations overlapped with benchmark methods, and two novel variants were supported by transcriptome evidence.
    • Demonstrated increased statistical power and improved biological interpretability through integrated annotation.

    Conclusions:

    • The proposed framework offers a powerful and flexible approach for exploring brain imaging genetic effects.
    • This method effectively mines regional associations by utilizing voxelwise data and anatomical information.
    • The findings highlight the potential for discovering new genetic variants influencing brain phenotypes.