The Big Data for Discovery Science Center (BDDS) - comprised of leading experts in biomedical imaging, genetics, proteomics, and computer science - is taking an "-ome to home" approach toward streamlining big data management, aggregation, manipulation, integration, and the modeling of biological systems across spatial and temporal scales.
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Semi-Automated Quantitative Trait Genome-Wide Association Studies (SAQT-GWAS)

Conducting genome-wide association studies (GWAS) on 1000s of quantitative traits, such as can be derived from neuroimaging data or compiled from biospecimen data, is a technical, computational, and organizational challenge. We have developed the semi-automated quantitative trait genome-wide association study approach (SAQT-GWAS) to address this challenge. The SAQT-GWAS approach consists of a LONI Pipeline workflow and a collection of R functions that together allow a user to quickly and easily conduct GWAS on thousands of different quantitative traits simultaneously and subsequently visualize the results. It enables anyone, regardless of technical ability or access to a computer cluster, to test for associations between quantitative trait data and associated genetics data for a collection of subjects. The SAQT-GWAS code, LONI Pipeline workflow, and documentation are available for download at