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

被引：0

作者：

Jennifer A Brody

Alanna C Morrison

Joshua C Bis

Jeffrey R O'Connell

Michael R Brown

Jennifer E Huffman

Darren C Ames

Andrew Carroll

Matthew P Conomos

Stacey Gabriel

Richard A Gibbs

Stephanie M Gogarten

Namrata Gupta

Cashell E Jaquish

Andrew D Johnson

Joshua P Lewis

Xiaoming Liu

Alisa K Manning

George J Papanicolaou

Achilleas N Pitsillides

Kenneth M Rice

William Salerno

Colleen M Sitlani

Nicholas L Smith

Susan R Heckbert

Cathy C Laurie

Braxton D Mitchell

Ramachandran S Vasan

Stephen S Rich

Jerome I Rotter

James G Wilson

Eric Boerwinkle

Bruce M Psaty

L Adrienne Cupples

机构：

[1] Cardiovascular Health Research Unit,Department of Medicine

[2] University of Washington,Department of Epidemiology

[3] Human Genetics Center,Department of Medicine, Division of Endocrinology

[4] Human Genetics,Department of Biostatistics

[5] and Environmental Sciences,Division of Cardiovascular Sciences

[6] School of Public Health,Department of Medicine

[7] University of Texas Health Science Center at Houston,Department of Veteran Affairs Office of Research and Development

[8] Diabetes,Department of Epidemiology

[9] and Nutrition,Department of Medicine

[10] University of Maryland,Department of Epidemiology

[11] Framingham Heart Study,Departments of Pediatrics and Medicine

[12] National Heart,Department of Physiology and Biophysics

[13] Lung,Departments of Medicine

[14] and Blood Institute and Boston University,Department of Biostatistics

[15] DNAnexus,undefined

[16] Inc.,undefined

[17] University of Washington,undefined

[18] Program in Medical and Population Genetics,undefined

[19] Broad Institute,undefined

[20] Human Genome Sequencing Center,undefined

[21] Baylor College of Medicine,undefined

[22] National Heart,undefined

[23] Lung,undefined

[24] and Blood Institute,undefined

[25] Center for Human Genetics Research,undefined

[26] Massachusetts General Hospital,undefined

[27] Program in Medical and Population Genetics,undefined

[28] Broad Institute of Harvard and MIT,undefined

[29] Harvard Medical School,undefined

[30] Kaiser Permanente Washington Health Research Institute,undefined

[31] Seattle Epidemiologic Research and Information Center,undefined

[32] University of Washington,undefined

[33] Geriatrics Research and Education Clinical Center,undefined

[34] Baltimore Veterans Administration Medical Center,undefined

[35] Sections of Preventive Medicine and Epidemiology,undefined

[36] and of Cardiology,undefined

[37] Boston University School of Medicine,undefined

[38] Boston University School of Public Health,undefined

[39] Center for Public Health Genomics,undefined

[40] University of Virginia,undefined

[41] Institute for Translational Genomics and Population Sciences,undefined

[42] LABioMed at Harbor -UCLA Medical Center,undefined

[43] University of Mississippi Medical Center,undefined

[44] Epidemiology,undefined

[45] and Health Services,undefined

[46] University of Washington,undefined

[47] Boston University School of Public Health,undefined

来源：

Nature Genetics | 2017年 / 49卷

关键词：

D O I：

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中图分类号：

学科分类号：

摘要：

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.

引用

页码：1560 / 1563

页数：3

共 50 条

[1] Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology
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.
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Gibbs, Richard A.
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Gupta, Namrata
Jaquish, Cashell E.
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