BIG DATA for DISCOVERY SCIENCE
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The Big Data for Discovery Science Center (BDDS) - comprised of leading experts in biomedical imaging, genetics, proteomics, and computer science - is taking an "-ome to home" approach toward streamlining big data management, aggregation, manipulation, integration, and the modeling of biological systems across spatial and temporal scales.
 
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NeuroImagingPheWAS

Neuroimaging based phenome-wide association study (PheWAS), with a genotype-to-phenotype strategy, explores the associations of a SNP (or SNPs) of interest with diverse brain phenotypes, such as cortical thickness, surface area, cortical volume and etc. at each brain point, extracted from neuroimaging data. This method has the power to detect potential novel genotype-to-phenotype associations and consequently would provide a comprehensive view for the mechanism of how the SNP of interest influences the brain.

NeuroimagingPheWAS is a Matlab toolbox for PheWAS on neuroimaging phenotype data using linear mixed effects models and random field theory (RFT) or false discovery rate (FDR) multiple comparisons correction methods. The analysis results are visualized using 3D rendering of the surface data or statistics on a standard surface model. NeuroimagingPheWAS handles any triangulated surface data, written in FreeSurfer or MNI object format. The only requirement is that the triangulation scheme must be the same for all surfaces, i.e. the data must be registered to a common surface.

NeuroimagingPheWAS package is available for free download at https://github.com/ini-bdds/phewas_neuroimaging/blob/master/NeuroimagingPheWAS_Toolbox.zip. A user manual and sample input file are also included in the package.

The NeuroimagingPheWAS Toolbox has been used for the PheWAS approach