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MULTIVARIATE VARIANCE-COMPONENTS ANALYSIS IN DTI.

Agatha D Lee1, Natasha Leporé1,2, Jan de Leeuw3

  • 1Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|December 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new restricted maximum likelihood (REML) method for imaging genetics twin studies. REML provides less biased estimates of genetic influences on brain structure, especially with smaller sample sizes.

Keywords:
DTIgeneticsmultivariate statisticstwin studies

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Area of Science:

  • Neuroimaging Genetics
  • Quantitative Genetics
  • Brain Imaging Analysis

Background:

  • Twin imaging studies are crucial for understanding genetic influences on the brain.
  • Intraclass correlation (ICC) is commonly used to measure twin resemblance for phenotypes.
  • Existing methods may be biased with smaller sample sizes.

Purpose of the Study:

  • To extend correlation estimation in twin imaging studies using restricted maximum likelihood (REML).
  • To compute variance components (additive genetic, common, and unique environmental factors) for diffusion tensor-valued signals.
  • To assess the accuracy of REML compared to Pearson correlation in smaller samples.

Main Methods:

  • Applied a novel REML definition for variance components to diffusion tensor imaging (DTI) data.
  • Analyzed data from 25 identical and 25 fraternal twin pairs.
  • Computed genetic effect measures for scalar and multivariate diffusion tensor measures (e.g., tGA, DT).

Main Results:

  • REML estimators showed less bias than Pearson estimators in smaller sample sizes.
  • Voxel-wise genetic contributions to brain fiber microstructure were identified.
  • Quantified proportions of phenotypic variance attributable to additive genetic (A), common environmental (C), and unique environmental (E) factors.

Conclusions:

  • The REML approach offers a more robust method for estimating genetic effects in twin imaging studies, particularly with limited data.
  • This method enhances the understanding of genetic contributions to brain microstructure.
  • The findings support the utility of REML in imaging genetics research for accurate heritability estimation.