Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology

Nat Genet. 2017 Oct 27;49(11):1560-1563. doi: 10.1038/ng.3968.


The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a 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 multi-center 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 3996 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 transforming 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Big Data*
  • Fibrinogen / genetics*
  • Fibrinogen / metabolism
  • Genetics, Population
  • Genome
  • Humans
  • Information Dissemination / methods
  • Mobile Applications
  • Molecular Epidemiology / methods*
  • Regression Analysis
  • Software
  • Workflow


  • Fibrinogen