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NIH Commits $12M to Fund Omics Data Analysis Projects

NEW YORK (GenomeWeb) – The National Heart, Lung, and Blood Institute announced this week that it will provide up to $12 million in funding over the next three years to support the analysis of data from its Trans Omics for Precision Medicine (TOPMed) program.

TOPMed was created to use high-throughput omics technologies to characterize molecular abnormalities or signatures associated with heart, lung, blood, and sleep (HLBS) disorders. Part of the National Institutes of Health's Precision Medicine Initiative, TOPMed is building a repository of whole-genome sequencing, omics, and clinical data across diverse patient populations.

"Having produced an unprecedented volume of high-throughput data, TOPMed now seeks to turn its attention to effectively leveraging this resource through novel systems biology analyses to uncover disease pathobiology," the NHLBI said.

"Although lower costs and technological improvements in sequencing technology have vastly expanded our ability to generate large volumes of omics data, the ability to analyze such large datasets to extract biologically meaningful insights from them remains challenging," it added. "Thus, advanced analyses that incorporate genotype and phenotype datasets from thousands to tens of thousands of individuals are required to move TOPMed to the next phase of discovery."

To that end, the NHLBI has earmarked $12 million — $3 million in fiscal 2018, $6 million in fiscal 2019, and $3 million in fiscal 2020 — to fund up to 12 research projects that will use existing TOPMed omics data to uncover the molecular mechanisms driving HLBS disease. The agency is particularly interested in systems level approaches incorporating computational modeling to bring together high-throughput genotype and phenotype datasets.

Research appropriate to this funding opportunity includes studies investigating pleiotropic gene effects and gene expression patterns across several cardiovascular risk factors; the identification of biomarkers related to severity of outcomes in sickle cell patients; and the development of machine learning approaches to search for likely areas of the genome related to hypertension and chronic kidney disease in African Americans.

Additional details about the funding opportunity can be found here.