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Using Gene Expression to Annotate Cardiovascular GWAS Loci
Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996083/ https://www.ncbi.nlm.nih.gov/pubmed/29922679 http://dx.doi.org/10.3389/fcvm.2018.00059 |
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author | Heinig, Matthias |
author_facet | Heinig, Matthias |
author_sort | Heinig, Matthias |
collection | PubMed |
description | Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription—so called expression quantitative trait loci—has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification. |
format | Online Article Text |
id | pubmed-5996083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-59960832018-06-19 Using Gene Expression to Annotate Cardiovascular GWAS Loci Heinig, Matthias Front Cardiovasc Med Cardiovascular Medicine Genetic variants at hundreds of loci associated with cardiovascular phenotypes have been identified by genome wide association studies. Most of these variants are located in intronic or intergenic regions rendering the functional and mechanistic follow up difficult. These non-protein-coding regions harbor regulatory sequences. Thus the study of genetic variants associated with transcription—so called expression quantitative trait loci—has emerged as a promising approach to identify regulatory sequence variants. The genes and pathways they control constitute candidate causal drivers at cardiovascular risk loci. This review provides an overview of the expression quantitative trait loci resources available for cardiovascular genetics research and the most commonly used approaches for candidate gene identification. Frontiers Media S.A. 2018-06-05 /pmc/articles/PMC5996083/ /pubmed/29922679 http://dx.doi.org/10.3389/fcvm.2018.00059 Text en Copyright © 2018 Heinig. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Heinig, Matthias Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title | Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title_full | Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title_fullStr | Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title_full_unstemmed | Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title_short | Using Gene Expression to Annotate Cardiovascular GWAS Loci |
title_sort | using gene expression to annotate cardiovascular gwas loci |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5996083/ https://www.ncbi.nlm.nih.gov/pubmed/29922679 http://dx.doi.org/10.3389/fcvm.2018.00059 |
work_keys_str_mv | AT heinigmatthias usinggeneexpressiontoannotatecardiovasculargwasloci |