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Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants
The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcripto...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648125/ https://www.ncbi.nlm.nih.gov/pubmed/34807913 http://dx.doi.org/10.1371/journal.pgen.1009918 |
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author | Ng, Bernard Casazza, William Kim, Nam Hee Wang, Chendi Farhadi, Farnush Tasaki, Shinya Bennett, David A. De Jager, Philip L. Gaiteri, Christopher Mostafavi, Sara |
author_facet | Ng, Bernard Casazza, William Kim, Nam Hee Wang, Chendi Farhadi, Farnush Tasaki, Shinya Bennett, David A. De Jager, Philip L. Gaiteri, Christopher Mostafavi, Sara |
author_sort | Ng, Bernard |
collection | PubMed |
description | The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms. |
format | Online Article Text |
id | pubmed-8648125 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86481252021-12-07 Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants Ng, Bernard Casazza, William Kim, Nam Hee Wang, Chendi Farhadi, Farnush Tasaki, Shinya Bennett, David A. De Jager, Philip L. Gaiteri, Christopher Mostafavi, Sara PLoS Genet Research Article The majority of genetic variants detected in genome wide association studies (GWAS) exert their effects on phenotypes through gene regulation. Motivated by this observation, we propose a multi-omic integration method that models the cascading effects of genetic variants from epigenome to transcriptome and eventually to the phenome in identifying target genes influenced by risk alleles. This cascading epigenomic analysis for GWAS, which we refer to as CEWAS, comprises two types of models: one for linking cis genetic effects to epigenomic variation and another for linking cis epigenomic variation to gene expression. Applying these models in cascade to GWAS summary statistics generates gene level statistics that reflect genetically-driven epigenomic effects. We show on sixteen brain-related GWAS that CEWAS provides higher gene detection rate than related methods, and finds disease relevant genes and gene sets that point toward less explored biological processes. CEWAS thus presents a novel means for exploring the regulatory landscape of GWAS variants in uncovering disease mechanisms. Public Library of Science 2021-11-22 /pmc/articles/PMC8648125/ /pubmed/34807913 http://dx.doi.org/10.1371/journal.pgen.1009918 Text en © 2021 Ng et al 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 use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ng, Bernard Casazza, William Kim, Nam Hee Wang, Chendi Farhadi, Farnush Tasaki, Shinya Bennett, David A. De Jager, Philip L. Gaiteri, Christopher Mostafavi, Sara Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title_full | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title_fullStr | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title_full_unstemmed | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title_short | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
title_sort | cascading epigenomic analysis for identifying disease genes from the regulatory landscape of gwas variants |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648125/ https://www.ncbi.nlm.nih.gov/pubmed/34807913 http://dx.doi.org/10.1371/journal.pgen.1009918 |
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