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

作者：Brody Jennifer A^*; Morrison Alanna C; Bis Joshua C; O'Connell Jeffrey R; Brown Michael R; Huffman Jennifer E; Ames Darren C; Carroll Andrew; Conomos Matthew P; Gabriel Stacey; Gibbs Richard A; Gogarten Stephanie M; Gupta Namrata; Jaquish Cashell E; Johnson Andrew D; Lewis Joshua P; Liu Xiaoming; Manning Alisa K; Papanicolaou George J; Pitsillides Achilleas N; Rice Kenneth M; Salerno William; Sitlani Colleen M; Smith Nicholas L; Heckbert Susan R

来源：Nature Genetics, 2017, 49(11): 1560-1563.

DOI：10.1038/ng.3968

The 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.

• 出版日期2017-11