Solar-Eclipse Computational Tools for Imaging Genetics

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Abstract This application will provide urgently needed analytical methods to develop the field of imaging genetics. SOLAR-Eclipse is an integrated suite of resources for genetic and epigenetic analyses such as heritability, pleiotropy, high-resolution genome-wide association (GWA) and Whole-Genome Analyses (WGA), gene expression, quantitative trait loci-linkage (QTL-L) and methylation analyses optimized for traits derived from structural and functional neuroimaging data. Our focus is on phase 3 development to support full-resolution voxel-and-vertex-wise analyses of imaging genetics networks involved in complex polygenic illnesses. During the first two funding periods, we demonstrated the utility of SOLAR-Eclipse for imaging genetics applications and developed strong “Pull/Push” collaboration with three major NIH brain imaging initiatives: the NIH Big Data 2 Knowledge (BD2K) Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), Human Connectome Project (HCP), MRI-GENIE, CHARGE and UK Bio Bank. During these funding periods, we released over 20 software updates and this support was acknowledged in ~250 manuscripts. We focused the next phase of SE development on the needs identified by our Big Data partners. The first aim is to revise our software to support “Repeat, Rerun, Replicate (R3)” initiative, including Python based application programming interface for straightforward integration of SOLAR-Eclipse into modern processing workflows. It will include a new data format optimized for voxel-and-vertex wise imaging genetic analyses including GWA and WGA, as well as recording the provenance of imaging genetics data analysis workflows. Aim 2 is focused on enabling GWA and WGA at full voxel-vertex-and- genetic resolution. This will derive and perform causality testing and annotation for the imaging-genetic networks while accounting for linkage disequilibrium (LD) and spatial dependency patterns and correcting for multiple comparisons. We will perform causative inference testing for the vertical and horizontal pleiotropies - the two main mechanisms that govern genetic risk factors for complex polygenic illnesses such as schizophrenia. Aim 3, we will execute collaborative studies to tune novel methods in large and diverse samples assembled by our Big Data partners: ENIGMA, HCP, SiGN, UKBB and others. This collaborative piloting and honing of novel methods will serve to popularize and disseminate our developments for individual imaging genetics labs.