Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issu...

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Autores principales:	Daniel Ho, Denis M. Nyaga, William Schierding, Richard Saffery, Jo K. Perry, John A. Taylor, Mark H. Vickers, Andreas W. Kempa-Liehr, Justin M. O’Sullivan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9509bda596664eab9816f6a97f9db45f
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spelling	oai:doaj.org-article:9509bda596664eab9816f6a97f9db45f2021-12-02T18:34:00ZIdentifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk10.1038/s42003-021-02594-02399-3642https://doaj.org/article/9509bda596664eab9816f6a97f9db45f2021-09-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-02594-0https://doaj.org/toc/2399-3642Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issues and risk of islet autoantibody seroconvernsion.Daniel HoDenis M. NyagaWilliam SchierdingRichard SafferyJo K. PerryJohn A. TaylorMark H. VickersAndreas W. Kempa-LiehrJustin M. O’SullivanNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Daniel Ho Denis M. Nyaga William Schierding Richard Saffery Jo K. Perry John A. Taylor Mark H. Vickers Andreas W. Kempa-Liehr Justin M. O’Sullivan Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
description	Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issues and risk of islet autoantibody seroconvernsion.
format	article
author	Daniel Ho Denis M. Nyaga William Schierding Richard Saffery Jo K. Perry John A. Taylor Mark H. Vickers Andreas W. Kempa-Liehr Justin M. O’Sullivan
author_facet	Daniel Ho Denis M. Nyaga William Schierding Richard Saffery Jo K. Perry John A. Taylor Mark H. Vickers Andreas W. Kempa-Liehr Justin M. O’Sullivan
author_sort	Daniel Ho
title	Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
title_short	Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
title_full	Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
title_fullStr	Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
title_full_unstemmed	Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
title_sort	identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9509bda596664eab9816f6a97f9db45f
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Identifying the lungs as a susceptible site for allele-specific regulatory changes associated with type 1 diabetes risk

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