- 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
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.