Ontology type: schema:ScholarlyArticle Open Access: True
2017-10-27
AUTHORSJennifer A Brody, Alanna C Morrison, Joshua C Bis, Jeffrey R O'Connell, Michael R Brown, Jennifer E Huffman, Darren C Ames, Andrew Carroll, Matthew P Conomos, Stacey Gabriel, Richard A Gibbs, Stephanie M Gogarten, Namrata Gupta, Cashell E Jaquish, Andrew D Johnson, Joshua P Lewis, Xiaoming Liu, Alisa K Manning, George J Papanicolaou, Achilleas N Pitsillides, Kenneth M Rice, William Salerno, Colleen M Sitlani, Nicholas L Smith, Susan R Heckbert, Cathy C Laurie, Braxton D Mitchell, Ramachandran S Vasan, Stephen S Rich, Jerome I Rotter, James G Wilson, Eric Boerwinkle, Bruce M Psaty, L Adrienne Cupples
ABSTRACTThe increasing volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created the cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multicenter WGS analyses, including data-sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3,996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for translating WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations. More... »
PAGES1560-1563
http://scigraph.springernature.com/pub.10.1038/ng.3968
DOIhttp://dx.doi.org/10.1038/ng.3968
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1092405015
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/29074945
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