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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving com...

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Autores principales: Hall, Molly A., Wallace, John, Lucas, Anastasia, Kim, Dokyoon, Basile, Anna O., Verma, Shefali S., McCarty, Cathy A., Brilliant, Murray H., Peissig, Peggy L., Kitchner, Terrie E., Verma, Anurag, Pendergrass, Sarah A., Dudek, Scott M., Moore, Jason H., Ritchie, Marylyn D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660079/
https://www.ncbi.nlm.nih.gov/pubmed/29079728
http://dx.doi.org/10.1038/s41467-017-00802-2
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author Hall, Molly A.
Wallace, John
Lucas, Anastasia
Kim, Dokyoon
Basile, Anna O.
Verma, Shefali S.
McCarty, Cathy A.
Brilliant, Murray H.
Peissig, Peggy L.
Kitchner, Terrie E.
Verma, Anurag
Pendergrass, Sarah A.
Dudek, Scott M.
Moore, Jason H.
Ritchie, Marylyn D.
author_facet Hall, Molly A.
Wallace, John
Lucas, Anastasia
Kim, Dokyoon
Basile, Anna O.
Verma, Shefali S.
McCarty, Cathy A.
Brilliant, Murray H.
Peissig, Peggy L.
Kitchner, Terrie E.
Verma, Anurag
Pendergrass, Sarah A.
Dudek, Scott M.
Moore, Jason H.
Ritchie, Marylyn D.
author_sort Hall, Molly A.
collection PubMed
description Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.
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spelling pubmed-56600792017-10-31 PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies Hall, Molly A. Wallace, John Lucas, Anastasia Kim, Dokyoon Basile, Anna O. Verma, Shefali S. McCarty, Cathy A. Brilliant, Murray H. Peissig, Peggy L. Kitchner, Terrie E. Verma, Anurag Pendergrass, Sarah A. Dudek, Scott M. Moore, Jason H. Ritchie, Marylyn D. Nat Commun Article Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits. Nature Publishing Group UK 2017-10-27 /pmc/articles/PMC5660079/ /pubmed/29079728 http://dx.doi.org/10.1038/s41467-017-00802-2 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.1
spellingShingle Article
Hall, Molly A.
Wallace, John
Lucas, Anastasia
Kim, Dokyoon
Basile, Anna O.
Verma, Shefali S.
McCarty, Cathy A.
Brilliant, Murray H.
Peissig, Peggy L.
Kitchner, Terrie E.
Verma, Anurag
Pendergrass, Sarah A.
Dudek, Scott M.
Moore, Jason H.
Ritchie, Marylyn D.
PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title_full PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title_fullStr PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title_full_unstemmed PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title_short PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies
title_sort plato software provides analytic framework for investigating complexity beyond genome-wide association studies
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660079/
https://www.ncbi.nlm.nih.gov/pubmed/29079728
http://dx.doi.org/10.1038/s41467-017-00802-2
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