A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers

Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.

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Autores principales:	Jonathan D. Mosley, QiPing Feng, Quinn S. Wells, Sara L. Van Driest, Christian M. Shaffer, Todd L. Edwards, Lisa Bastarache, Wei-Qi Wei, Lea K. Davis, Catherine A. McCarty, Will Thompson, Christopher G. Chute, Gail P. Jarvik, Adam S. Gordon, Melody R. Palmer, David R. Crosslin, Eric B. Larson, David S. Carrell, Iftikhar J. Kullo, Jennifer A. Pacheco, Peggy L. Peissig, Murray H. Brilliant, James G. Linneman, Bahram Namjou, Marc S. Williams, Marylyn D. Ritchie, Kenneth M. Borthwick, Shefali S. Verma, Jason H. Karnes, Scott T. Weiss, Thomas J. Wang, C. Michael Stein, Josh C. Denny, Dan M. Roden
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f49b8a4e08894b7bab01494933978a43
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:f49b8a4e08894b7bab01494933978a432021-12-02T16:49:30ZA study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers10.1038/s41467-018-05624-42041-1723https://doaj.org/article/f49b8a4e08894b7bab01494933978a432018-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-05624-4https://doaj.org/toc/2041-1723Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.Jonathan D. MosleyQiPing FengQuinn S. WellsSara L. Van DriestChristian M. ShafferTodd L. EdwardsLisa BastaracheWei-Qi WeiLea K. DavisCatherine A. McCartyWill ThompsonChristopher G. ChuteGail P. JarvikAdam S. GordonMelody R. PalmerDavid R. CrosslinEric B. LarsonDavid S. CarrellIftikhar J. KulloJennifer A. PachecoPeggy L. PeissigMurray H. BrilliantJames G. LinnemanBahram NamjouMarc S. WilliamsMarylyn D. RitchieKenneth M. BorthwickShefali S. VermaJason H. KarnesScott T. WeissThomas J. WangC. Michael SteinJosh C. DennyDan M. RodenNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jonathan D. Mosley QiPing Feng Quinn S. Wells Sara L. Van Driest Christian M. Shaffer Todd L. Edwards Lisa Bastarache Wei-Qi Wei Lea K. Davis Catherine A. McCarty Will Thompson Christopher G. Chute Gail P. Jarvik Adam S. Gordon Melody R. Palmer David R. Crosslin Eric B. Larson David S. Carrell Iftikhar J. Kullo Jennifer A. Pacheco Peggy L. Peissig Murray H. Brilliant James G. Linneman Bahram Namjou Marc S. Williams Marylyn D. Ritchie Kenneth M. Borthwick Shefali S. Verma Jason H. Karnes Scott T. Weiss Thomas J. Wang C. Michael Stein Josh C. Denny Dan M. Roden A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
description	Biomarker identification requires prohibitively large cohorts with gene expression and phenotype data. The approach introduced here learns polygenic predictors of expression from genetic and expression data, used to infer biomarker levels in patients with genetic and disease information.
format	article
author	Jonathan D. Mosley QiPing Feng Quinn S. Wells Sara L. Van Driest Christian M. Shaffer Todd L. Edwards Lisa Bastarache Wei-Qi Wei Lea K. Davis Catherine A. McCarty Will Thompson Christopher G. Chute Gail P. Jarvik Adam S. Gordon Melody R. Palmer David R. Crosslin Eric B. Larson David S. Carrell Iftikhar J. Kullo Jennifer A. Pacheco Peggy L. Peissig Murray H. Brilliant James G. Linneman Bahram Namjou Marc S. Williams Marylyn D. Ritchie Kenneth M. Borthwick Shefali S. Verma Jason H. Karnes Scott T. Weiss Thomas J. Wang C. Michael Stein Josh C. Denny Dan M. Roden
author_facet	Jonathan D. Mosley QiPing Feng Quinn S. Wells Sara L. Van Driest Christian M. Shaffer Todd L. Edwards Lisa Bastarache Wei-Qi Wei Lea K. Davis Catherine A. McCarty Will Thompson Christopher G. Chute Gail P. Jarvik Adam S. Gordon Melody R. Palmer David R. Crosslin Eric B. Larson David S. Carrell Iftikhar J. Kullo Jennifer A. Pacheco Peggy L. Peissig Murray H. Brilliant James G. Linneman Bahram Namjou Marc S. Williams Marylyn D. Ritchie Kenneth M. Borthwick Shefali S. Verma Jason H. Karnes Scott T. Weiss Thomas J. Wang C. Michael Stein Josh C. Denny Dan M. Roden
author_sort	Jonathan D. Mosley
title	A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
title_short	A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
title_full	A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
title_fullStr	A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
title_full_unstemmed	A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
title_sort	study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/f49b8a4e08894b7bab01494933978a43
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A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers

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