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Identifying imaging genetic associations via regional morphometricity estimation.

Jingxuan Bao1, Zixuan Wen, Mansu Kim

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

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|December 10, 2021
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Summary

This study introduces regional morphometricity to link single nucleotide polymorphisms (SNPs) with specific brain regions. Alzheimer's disease-related SNPs show stronger associations with brain morphology, highlighting imaging genetics for AD research.

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Area of Science:

  • Neuroscience
  • Genetics
  • Medical Imaging

Background:

  • Brain imaging genetics investigates the genetic underpinnings of brain structure and function.
  • Morphometricity assesses the association between genetic variations and overall brain morphology.
  • Extending morphometricity to a regional level (Region of Interest - ROI) allows for more focused genetic association studies.

Purpose of the Study:

  • To develop a novel framework for identifying single nucleotide polymorphism (SNP)-ROI associations using regional morphometricity.
  • To evaluate the utility of regional morphometricity in understanding the genetic basis of Alzheimer's disease (AD).
  • To compare the effectiveness of voxel-level versus average ROI measures in detecting imaging genetic associations.

Main Methods:

  • Proposed a novel framework for regional morphometricity estimation to identify SNP-ROI associations.
  • Empirically studied structural MRI and genotyping data from an Alzheimer's disease (AD) biobank.
  • Compared regional morphometricity estimates for AD-related SNPs versus non-AD SNPs.

Main Results:

  • AD-related SNPs exhibited higher regional morphometricity estimates compared to non-AD SNPs.
  • Identified 11 ROIs demonstrating strong dependency between AD/non-AD SNPs and morphometricity estimations.
  • Supplementary motor area (SMA) and dorsolateral prefrontal cortex (DPC) were among the enriched ROIs.
  • Voxel-level morphometric information within ROIs improved the power to detect imaging genetic associations over average ROI measures.

Conclusions:

  • Regional morphometricity is a valuable approach for dissecting the genetic architecture of brain morphology in relation to diseases like AD.
  • Imaging traits serve as effective targets for studying AD genetics.
  • Detailed, voxel-level morphometric data within ROIs enhance the detection of genetic associations in brain imaging studies.