Solar-Eclipse Computational Tools for Imaging Genetics

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 DESCRIPTION (provided by applicant): This application will provide urgently needed analytical methods to the emerging field of imaging genetics. Our focus is on phase 2 development of SOLAR-Eclipse integrated suite of resources for genetic and epigenetic analyses such as heritability, pleiotropy, quantitative trait loci-linkage (QTL-L), genome-wide association (GWA) and Whole-Genome Sequencing (WGS), gene expression, and methylation analyses optimized for traits derived from structural and functional neuroimaging data. During the first short and intensive funding period (2.5 years), 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) and Stroke Genetics Network (SiGN). During the first funding period, we released 12 major software updates and authored and co-authored 47 manuscripts. We established an annual workshop on the use of SOLAR-Eclipse at the Imaging Genetics Conference (2012, 2013, 2014, 2015) and at the genetic imaging workshop at the Organization for Human Brain Mapping conference (2012, 2013, 2014). We structure this renewal in the "Pull/Push" sprit of collaborative Big Data research, where "Pull" refers to development of novel tools by our team and "Push" refers to collaboration with Big Data partners to apply and test cutting edge analyses. We propose to focus the next phase of SE development at the needs identified by our Big Data partners. We propose three "Pull" AIMS (1-3) to develop leading and enabling imaging genetics analysis techniques, high performance computing tailored to unique imaging genetics challenges, and novel data formats. In "Push" AIM 4, SOLAR-Eclipse team partnered with imaging genetics collaborations, ENIGMA, HCP, SiGN, IMAGEN and others to "Push" the state of science through collaborative studies. AIM 1 centers on high performance computing with the aim of achieving real-time GWA/WGS analyses of voxel-wise imaging traits in family based samples such as HCP. By combining novel data transformations for fast approximation of likelihood analyses and Graphics Processor Unit (GPU) computing, we will achieve ~105-6-fold computation acceleration as compared with traditional, maximum likelihood calculation methods. High performance computing will require a new data format for storage of imaging genetics data. In AIM 2, we propose to draft Gen.Gii data format and application programming interface (APIs) optimized for imaging genetic analyses, as well as recording the provenance of imaging genetics data analysis workflows. Building off a first draft created by a working group of imaging genetics experts, we will seek broad community input to ensure that Gen.Gii standard will be embraced. In AIM 3, we propose to integrate newly developed empirical kinship techniques variance component kernel use in imaging genetics applications. Empirical kinship methods calculate "genetic distances" among subjects directly from genome-wide data and partition the trait variance based on the "empirical kinship"; for example, computing additive genetic variance in white matter integrity in schizophrenia patients that is contributed by the regulatory regions of genome. Empirical kinship methods will be generalized to perform classical genetic variance analyses of the imaging phenotypes, including their heritability, pleiotropy, rare variant and quantitative trait linkage analyses in "unrelated" (but actually distantly related) subjects (2). In AIM 4, we will execute collaborative studies to fine tune novel methods in large and diverse samples assembled by our Big Data partners: ENIGMA, HCP and SiGN. This collaborative piloting and honing of novel methods will serve to popularize and disseminate our developments for individual imaging genetics labs.