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...
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| Veröffentlicht in: | PLoS genetics 2021-11, Vol.17 (11), p.e1009918-e1009918 |
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| creator | Ng, Bernard Casazza, William Kim, Nam Hee Wang, Chendi Farhadi, Farnush Tasaki, Shinya Bennett, David A De Jager, Philip L Gaiteri, Christopher Mostafavi, Sara |
| 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. |
| doi_str_mv | 10.1371/journal.pgen.1009918 |
| format | Article |
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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.</description><identifier>ISSN: 1553-7404</identifier><identifier>ISSN: 1553-7390</identifier><identifier>EISSN: 1553-7404</identifier><identifier>DOI: 10.1371/journal.pgen.1009918</identifier><identifier>PMID: 34807913</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Alleles ; Biology and Life Sciences ; DNA methylation ; Epigenetic inheritance ; Epigenome - genetics ; Gene expression ; Gene regulation ; Genetic Diseases, Inborn - genetics ; Genetic Diseases, Inborn - pathology ; Genetic diversity ; Genetic Predisposition to Disease ; Genetic regulation ; Genetic variation ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Genotype & phenotype ; Health aspects ; Humans ; Hypotheses ; Medicine and Health Sciences ; Methods ; Phenotype ; Phenotypes ; Polymorphism, Single Nucleotide - genetics ; Quantitative Trait Loci - genetics ; Statistical analysis ; Transcriptome - genetics ; Transcriptomes</subject><ispartof>PLoS genetics, 2021-11, Vol.17 (11), p.e1009918-e1009918</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Ng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Ng et al 2021 Ng et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c726t-7688e1340ecbc5de9a1a1344dc999d0d6f6c858eedb2905bbeefa07af726ccd93</citedby><cites>FETCH-LOGICAL-c726t-7688e1340ecbc5de9a1a1344dc999d0d6f6c858eedb2905bbeefa07af726ccd93</cites><orcidid>0000-0003-3656-7394 ; 0000-0002-8057-2505 ; 0000-0003-3689-554X ; 0000-0002-8688-3873 ; 0000-0002-4636-4710 ; 0000-0002-0686-4024 ; 0000-0003-4698-1177 ; 0000-0003-0497-2459</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648125/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8648125/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34807913$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Kutalik, Zoltán</contributor><creatorcontrib>Ng, Bernard</creatorcontrib><creatorcontrib>Casazza, William</creatorcontrib><creatorcontrib>Kim, Nam Hee</creatorcontrib><creatorcontrib>Wang, Chendi</creatorcontrib><creatorcontrib>Farhadi, Farnush</creatorcontrib><creatorcontrib>Tasaki, Shinya</creatorcontrib><creatorcontrib>Bennett, David A</creatorcontrib><creatorcontrib>De Jager, Philip L</creatorcontrib><creatorcontrib>Gaiteri, Christopher</creatorcontrib><creatorcontrib>Mostafavi, Sara</creatorcontrib><title>Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants</title><title>PLoS genetics</title><addtitle>PLoS Genet</addtitle><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.</description><subject>Alleles</subject><subject>Biology and Life Sciences</subject><subject>DNA methylation</subject><subject>Epigenetic inheritance</subject><subject>Epigenome - genetics</subject><subject>Gene expression</subject><subject>Gene regulation</subject><subject>Genetic Diseases, Inborn - genetics</subject><subject>Genetic Diseases, Inborn - pathology</subject><subject>Genetic diversity</subject><subject>Genetic Predisposition to Disease</subject><subject>Genetic regulation</subject><subject>Genetic variation</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Genotype & phenotype</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Polymorphism, Single Nucleotide - 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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. 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| subjects | Alleles Biology and Life Sciences DNA methylation Epigenetic inheritance Epigenome - genetics Gene expression Gene regulation Genetic Diseases, Inborn - genetics Genetic Diseases, Inborn - pathology Genetic diversity Genetic Predisposition to Disease Genetic regulation Genetic variation Genome-wide association studies Genome-Wide Association Study Genomes Genotype & phenotype Health aspects Humans Hypotheses Medicine and Health Sciences Methods Phenotype Phenotypes Polymorphism, Single Nucleotide - genetics Quantitative Trait Loci - genetics Statistical analysis Transcriptome - genetics Transcriptomes |
| title | Cascading epigenomic analysis for identifying disease genes from the regulatory landscape of GWAS variants |
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