Principal Investigator: Dr Lu Zhao
University of Southern California, INI, 2025 Zonal Avenue, Los Angeles CA 90033, United States.Tags: 25641, PheWAS
Funding body: University of Southern California
1a: We aim to develop a new, big data discovery framework for neuroimaging based phenome-wide association study (PheWAS). This big data discovery approach will be applied to systematically explore relationships between SNPs of interest and a wide variety of neuroimaging phenotypes using a unified genotype-to-phenotype strategy, so as to achieve a broad survey for true system-level genetic influences on the brain.
1b: The extensive resource of neuroimaging and genomic data provided by Biobank will allow us to evaluate the performance of the developed tools and software in big, complex data management and its processing and analysis. And the biobank resource will allow us to conduct true PheWAS to validate existing GWAS or single-phenotype studies’ findings, and more importantly to discover novel gene-brain associations. The expected finding will improve our understanding of the genetic pathways shaping the function, anatomy and connectivity of the brain in healthy populations, and further the pathology of diseased brains.
1c: Some advanced software and tools will be developed to manage and structure the complex, heterogenous, big-volume neuroimaging genomic data archives, and to implement sophisticated image processing and statistical analyses. The biobank data will be applied to evaluate the performance of these tools. Scientific researches will then be conduced using the developed tools and approaches for mining the biobank database for true system-level associations of SNPs of interest with diverse brain phenotypes.
1d: Full cohort.
The current scope: We aim to develop a new, big data discovery framework for neuroimaging based phenome-wide association study (PheWAS). This big data discovery approach will be applied to systematically explore relationships between SNPs of interest and a wide variety of neuroimaging phenotypes using a unified genotype-to-phenotype strategy, so as to achieve a broad survey for true system-level genetic influences on the brain.
New scope: Our analyses showed significant associations of various imaging markers with a set of environmental factors, such as smoking, alcohol consumption, etc. It is recently suggested that there may exist emerging models where genetics and environmental factors interact to determine brain longevity and diseases. Thus, we need to extend our current scope from the direct imaging genetics associations to model the joint influences of various environmental and genetic factors and medical conditions on the brain for a complete understanding of the normal and pathological brain changes in middle-aged to older adults. We aim to apply cutting-edge data mining techniques to conduct system-level surveys of impacts/interactions of diverse non-imaging variables on/with imaging genetics associations so as to 1) differentiate the genetic pathways in populations with different health and medical conditions, social demographics and lifestyle behaviors; 2) obtain a comprehensive understanding of relationships of brain structural and functional changes with individual and joint influences of various environmental, lifestyle and genetic factors; 3) identify new biomarkers modeling complex interactions of imaging, genetics and other factors to predict major neurological disorders in middle and old ages.