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Demystifying non-coding GWAS variants: an overview of computational tools and methods

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and...

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Autores principales: Schipper, Marijn, Posthuma, Danielle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585674/
https://www.ncbi.nlm.nih.gov/pubmed/35972862
http://dx.doi.org/10.1093/hmg/ddac198
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author Schipper, Marijn
Posthuma, Danielle
author_facet Schipper, Marijn
Posthuma, Danielle
author_sort Schipper, Marijn
collection PubMed
description Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.
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spelling pubmed-95856742022-10-24 Demystifying non-coding GWAS variants: an overview of computational tools and methods Schipper, Marijn Posthuma, Danielle Hum Mol Genet Review Article Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants. Oxford University Press 2022-08-16 /pmc/articles/PMC9585674/ /pubmed/35972862 http://dx.doi.org/10.1093/hmg/ddac198 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Schipper, Marijn
Posthuma, Danielle
Demystifying non-coding GWAS variants: an overview of computational tools and methods
title Demystifying non-coding GWAS variants: an overview of computational tools and methods
title_full Demystifying non-coding GWAS variants: an overview of computational tools and methods
title_fullStr Demystifying non-coding GWAS variants: an overview of computational tools and methods
title_full_unstemmed Demystifying non-coding GWAS variants: an overview of computational tools and methods
title_short Demystifying non-coding GWAS variants: an overview of computational tools and methods
title_sort demystifying non-coding gwas variants: an overview of computational tools and methods
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585674/
https://www.ncbi.nlm.nih.gov/pubmed/35972862
http://dx.doi.org/10.1093/hmg/ddac198
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