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

TitleAnalysis commons, a team approach to discovery in a big-data environment for genetic epidemiology.
Publication TypeJournal Article
Year of Publication2017
AuthorsBrody, JA, Morrison, AC, Bis, JC, O'Connell, JR, Brown, MR, Huffman, JE, Ames, DC, Carroll, A, Conomos, MP, Gabriel, S, Gibbs, RA, Gogarten, SM, Gupta, N, Jaquish, CE, Johnson, AD, Lewis, JP, Liu, X, Manning, AK, Papanicolaou, GJ, Pitsillides, AN, Rice, KM, Salerno, W, Sitlani, CM, Smith, NL, Heckbert, SR, Laurie, CC, Mitchell, BD, Vasan, RS, Rich, SS, Rotter, JI, Wilson, JG, Boerwinkle, E, Psaty, BM, L Cupples, A
Corporate AuthorsNHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, TOPMed Hematology and Hemostasis Working Group, CHARGE Analysis and Bioinformatics Working Group
JournalNat Genet
Volume49
Issue11
Pagination1560-1563
Date Published2017 Oct 27
ISSN1546-1718
Keywordsbig data, Fibrinogen, Genetics, Population, Genome, Humans, Information Dissemination, Mobile Applications, Molecular Epidemiology, Regression Analysis, Software, Workflow
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.

DOI10.1038/ng.3968
Alternate JournalNat Genet
PubMed ID29074945
PubMed Central IDPMC5720686
Grant ListRC2 HL102419 / HL / NHLBI NIH HHS / United States
R01 HL120393 / HL / NHLBI NIH HHS / United States
P30 DK048520 / DK / NIDDK NIH HHS / United States
R01 HL121007 / HL / NHLBI NIH HHS / United States
HHSN268201500001C / HL / NHLBI NIH HHS / United States
U01 HL130114 / HL / NHLBI NIH HHS / United States
N01 HC025195 / HC / NHLBI NIH HHS / United States
R01 HL048157 / HL / NHLBI NIH HHS / United States
UL1 TR000124 / TR / NCATS NIH HHS / United States
U01 HL137181 / HL / NHLBI NIH HHS / United States
R01 HL105756 / HL / NHLBI NIH HHS / United States
U01 HL084756 / HL / NHLBI NIH HHS / United States
P30 DK063491 / DK / NIDDK NIH HHS / United States
P30 DK072488 / DK / NIDDK NIH HHS / United States
HHSN268201500001I / HL / NHLBI NIH HHS / United States
HHSN268201500014C / HL / NHLBI NIH HHS / United States
K23 GM102678 / GM / NIGMS NIH HHS / United States
N01HC25195 / HL / NHLBI NIH HHS / United States
R01 HL117626 / HL / NHLBI NIH HHS / United States
UL1 TR001881 / TR / NCATS NIH HHS / United States
U54 GM115428 / GM / NIGMS NIH HHS / United States
U01 GM074518 / GM / NIGMS NIH HHS / United States

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