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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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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. |
format | Online Article Text |
id | pubmed-9585674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT schippermarijn demystifyingnoncodinggwasvariantsanoverviewofcomputationaltoolsandmethods AT posthumadanielle demystifyingnoncodinggwasvariantsanoverviewofcomputationaltoolsandmethods |