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
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Acceso en línea:https://doaj.org/article/9509bda596664eab9816f6a97f9db45f
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