This application will provide urgently needed analysis methods to the emerging field of imaging genetics. Our focus is to create SOLAR-Eclipse imaging genetics tools for classical genetic and epigenetic epidemiological analyses such as heritability, pleiotropy, quantitative trait loci (QTL) and genome-wide association (GWAS), gene expression, and methylation analyses using traits derived from structural and functional brain imaging (AIM 1). We will also develop intelligent correction for multiple testing that meets both genetic and imaging requirements (AIM 2). The SOLAR-Eclipse tools will be optimized to use voxel-wise brain functional and structural imaging traits and high throughput modern molecular genetic data. The proposed tools will be flexible to accommodate both structured family-based and unstructured epidemiological samples. These tools will be released as a standalone application and integrated into the existing neuroscience eScience networks. Achieving this goal in a single analysis package will greatly enhance and speed up the search for genes that influence brain's neuroanatomic and functional traits and provide comprehensive tools to illustrate pathways from genes to brain structure/function. During the development process we will test and optimize these tools (AIM 3) using the data generated by three NIH-funded imaging genetics projects, the Genetic of Brain Structure, the Human Connectom project, and the NicotineBrain. The tools, source codes and documented analyses strategies will be distributed via the Neuroimaging Informatics Tool and Resources Clearing house (NITRC.org). The PI for this application is a NIBIB-funded K-awardee who has worked on optimizing the standard genetic analysis methods for imaging genetics research. This work generated twenty-five peer- reviewed publications in the last four years. These experiences will be used to create a new library of tools termed, SOLAR-Eclipse, which will be based on the NIH-funded SOLAR library of statistical genetic methods. Our preliminary data shows that when optimized for imaging traits these tools can achieve 100-200 fold performance improvement (over standard methods) enabling full scale (~105 traits in 103 subjects) voxel-wise univariate and bivariate genetic analyses on a multi-node Linux cluster. The collaborators on this grant combine experts in imaging and genetics who have demonstrated their experience in developing tools in both domains and developed popular analyses tools such as SOLAR, FSL, SPM, BrainMap, Talairach Daemon, JIST, Mango and others.
Merging two important research directions in modern science, genetics and neuroimaging, led to the emergence of a new field, termed imaging genetics. This field combines modern statistical genetic methods with quantitative phenotyping performed with high dimensional neuroimaging modalities. This discipline seeks to understand the biological basis of neurological and psychiatric illnesses by characterizing genes that contribute to the risk for these disorders. However, standard imaging tools are unable to deal with large-scale genetics data and standard genetics tools, in turn, are unable to accommodate large size and format of the imaging traits. Thus, there is in immediate need for software tools optimized for performing univariate and multivariate imaging genetics analyses while providing practical correction strategies for multiple testing.
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