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The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases
BACKGROUND: The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. METHODS: To determine how the field is addressing this urgent need, we performed a comprehensi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973751/ https://www.ncbi.nlm.nih.gov/pubmed/35365203 http://dx.doi.org/10.1186/s12920-022-01216-w |
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author | Alsheikh, Ammar J. Wollenhaupt, Sabrina King, Emily A. Reeb, Jonas Ghosh, Sujana Stolzenburg, Lindsay R. Tamim, Saleh Lazar, Jozef Davis, J. Wade Jacob, Howard J. |
author_facet | Alsheikh, Ammar J. Wollenhaupt, Sabrina King, Emily A. Reeb, Jonas Ghosh, Sujana Stolzenburg, Lindsay R. Tamim, Saleh Lazar, Jozef Davis, J. Wade Jacob, Howard J. |
author_sort | Alsheikh, Ammar J. |
collection | PubMed |
description | BACKGROUND: The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. METHODS: To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. RESULTS: We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). CONCLUSIONS: This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01216-w. |
format | Online Article Text |
id | pubmed-8973751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-89737512022-04-02 The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases Alsheikh, Ammar J. Wollenhaupt, Sabrina King, Emily A. Reeb, Jonas Ghosh, Sujana Stolzenburg, Lindsay R. Tamim, Saleh Lazar, Jozef Davis, J. Wade Jacob, Howard J. BMC Med Genomics Research Article BACKGROUND: The remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants. METHODS: To determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles. RESULTS: We identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33). CONCLUSIONS: This review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-022-01216-w. BioMed Central 2022-04-01 /pmc/articles/PMC8973751/ /pubmed/35365203 http://dx.doi.org/10.1186/s12920-022-01216-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Alsheikh, Ammar J. Wollenhaupt, Sabrina King, Emily A. Reeb, Jonas Ghosh, Sujana Stolzenburg, Lindsay R. Tamim, Saleh Lazar, Jozef Davis, J. Wade Jacob, Howard J. The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title | The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title_full | The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title_fullStr | The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title_full_unstemmed | The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title_short | The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
title_sort | landscape of gwas validation; systematic review identifying 309 validated non-coding variants across 130 human diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8973751/ https://www.ncbi.nlm.nih.gov/pubmed/35365203 http://dx.doi.org/10.1186/s12920-022-01216-w |
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