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Description

More and more large-scale imaging genetic studies are being widely conducted to collect a rich set of imaging, genetic, and clinical data to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. Several major big-data challenges arise from testing millions of genome-wide associations with functional signals sampled at millions of locations in the brain from thousands of subjects. In this talk, we are presenting a Fast Functional Genome Wide Association Study (FFGWAS) framework to carry out whole-genome analyses of multimodal imaging data. FFGWAS consists of three components including (1) a multivariate varying coefficient model for modeling the relation between multiple functional imaging responses and a set of covariates (both genetic and non-genetic predictors), (2) a global sure independence screening (GSIS) procedure for reducing the dimension from a very large scale to a moderate scale, and (3) a detection procedure for detect significant cluster-locus pairs. We also successfully applied FFGWAS to a large-scale imaging genetic data analysis of ADNI data with 708 subjects, 30,000 vertices on hippocampal surface, and 501,584 SNPs.