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Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the ENIGMA-DTI working group.

NeuroImage | 2013

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

Pubmed ID: 23629049 RIS Download

Research resources used in this publication

None found

Antibodies used in this publication

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Associated grants

  • Agency: NIBIB NIH HHS, United States
    Id: P41 EB015922
  • Agency: NIBIB NIH HHS, United States
    Id: R01 EB007813
  • Agency: NIBIB NIH HHS, United States
    Id: R01 EB015611-01
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH078111
  • Agency: Medical Research Council, United Kingdom
    Id: G1001245
  • Agency: NIMH NIH HHS, United States
    Id: U54 MH091657
  • Agency: NIMH NIH HHS, United States
    Id: MH59490
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH059490
  • Agency: Medical Research Council, United Kingdom
    Id: 486682
  • Agency: NIMH NIH HHS, United States
    Id: R37 MH059490
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH083824
  • Agency: NIBIB NIH HHS, United States
    Id: R01 EB008281
  • Agency: NIMH NIH HHS, United States
    Id: MH078111
  • Agency: Biotechnology and Biological Sciences Research Council, United Kingdom
    Id: BB/F019394/1
  • Agency: Wellcome Trust, United Kingdom
    Id: 087727/Z/08/Z
  • Agency: NIBIB NIH HHS, United States
    Id: R01 EB015611
  • Agency: NIMH NIH HHS, United States
    Id: MH083824
  • Agency: Medical Research Council, United Kingdom
    Id: G0700704
  • Agency: NICHD NIH HHS, United States
    Id: R01 HD050735
  • Agency: NIBIB NIH HHS, United States
    Id: R01 EB008432
  • Agency: NIMH NIH HHS, United States
    Id: U54MH091657-03
  • Agency: Wellcome Trust, United Kingdom
  • Agency: NIMH NIH HHS, United States
    Id: MH0708143
  • Agency: Medical Research Council, United Kingdom
    Id: MR/K026992/1

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Human Connectome Coordination Facility (tool)

RRID:SCR_008749

Consortium to comprehensively map long-distance brain connections and their variability. It is acquiring data and developing analysis pipelines for several modalities of neuroimaging data plus behavioral and genetic data from healthy adults.

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Solar Eclipse Imaging Genetics tools (tool)

RRID:SCR_009645

Software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing. The goal of this project is to merge two important research directions in modern science, genetics and neuroimaging. This entails combining modern statistical genetic methods and quantitative phenotyping performed with high dimensional neuroimaging modalities. So far, however, standard imaging tools are unable to deal with large-scale genetics data, and standard genetics tools, in turn, are unable to accommodate large size and binary format of the image data. Their focus is to create imaging genetics tools for classical genetic and epigenetic epidemiological analyses such as heritability, pleiotropy, quantitative trait loci (QTL) and genome-wide association (GWAS), gene expression, and methylation analyses optimized for traits derived from structural and functional brain imaging data

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