Large-scale meta-analyses of brain images, such as the ENIGMA project ([1
) reveal common variants on the human genome that are associated with measurable brain differences. Each of these variants individually explains less than 5% of the variance in the brain measures, so the quest to identify them has been empowered by multivariate models of the image and genome [2
], as well as meta-analysis.
Large imaging genetics consortia, such as ENIGMA, have recently combined structural imaging measures and genome-wide scans (GWAS) from 20,800 individuals assessed at over 21 sites worldwide, and have identified robust gene effects not detectable in any single cohort but replicated by meta-analysis. These meta-analyses have identified common genetic variants associated with hippocampal volume and total brain volume [1
]. In a multi-site study, any differences across sites in scanning protocols (different scanner field strengths, image resolutions) and in image analysis protocols (such as segmentation methods) may reduce power to find and replicate genetic associations. Segmentation programs, for example, do not always work well across all datasets [3
]. Realizing that scanner effects may matter, some studies have recently compared 1.5 and 3 Tesla scans of the same subjects for detecting neurodegenerative changes with morphometric analysis [4
Recently, DTI measures have shown promising associations with common genetic variants, such as those in the Alzheimer’s disease risk gene, CLU
] and the growth factor gene, BDNF
]. However, the reproducibility and signal-to-noise ratio in DTI depends on the spatial and angular resolution (the number of directional diffusion-weighted gradients applied) [7
]. Structural MRI images are typically acquired with ~1-mm3
spatial resolution, but 2–5 mm voxels are common for diffusion-weighted scans. Additionally, the number of diffusion-weighted gradients applied may vary drastically, from the minimum needed to reconstruct a tensor (6), to high angular resolution (HARDI) scans with hundreds of diffusion-weighted scans.
To determine a stable DTI phenotype for genetic analysis, here we analyzed DTI scans from 417 genotyped twins and siblings scanned with two diffusion imaging protocols, differing in spatial (2mm vs 5mm slices) and angular resolution (27 vs 94 directions). Common voxelwise and region-of-interest analyses were performed on FA maps and on the FA ‘skeletons’ from the widely-used method, TBSS (‘tract-based spatial statistics’ from FSL [8
]). Various levels of Gaussian smoothing were also applied to the FA images prior to analysis. As genetic associations are likely to be discovered only for traits that are heritable, a formal twin-based heritability analysis was performed using an A/C/E-type structural equation model on the mean FA of various regions defined by the JHU-DTI atlas. A region of the corpus callosum, with high heritability regardless of to the protocol, was then carried forward for genome-wide association analyses (GWAS) where the most significantly associated single nucleotide polymorphisms (SNPs) from different protocols and analyses were compared.