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

Neda Jahanshad, Peter V. Kochunov, Emma Sprooten, René C. Mandl, Thomas E. Nichols, Laura Almasy, John Blangero, Rachel M. Brouwer, Joanne E. Curran, Greig I. de Zubicaray, Ravi Duggirala, Peter T. Fox, L. Elliot Hong, Bennett A. Landman, Nicholas G. Martin, Katie L. McMahon, Sarah E. Medland, Braxton D. Mitchell, Rene L. Olvera, Charles P. Peterson, John M. Starr, Jessika E. Sussmann, Arthur W. Toga, Joanna M. Wardlaw, Margaret J. Wright, Hilleke E. Hulshoff Pol, Mark E. Bastin, Andrew M. McIntosh, Ian J. Deary, Paul M. Thompson, David C. Glahn

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/).