학술논문
Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology
Document Type
article
Author
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; Laurie, Cathy C; Mitchell, Braxton D; Vasan, Ramachandran S; Rich, Stephen S; Rotter, Jerome I; Wilson, James G; Boerwinkle, Eric; Psaty, Bruce M; Cupples, L Adrienne
Source
Nature Genetics. 49(11)
Subject
Language
Abstract
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.