A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility
Utilizing data from published tuberculosis (TB) genome-wide association studies (GWAS), we use a bioinformatics pipeline to detect all polymorphisms in linkage disequilibrium (LD) with variants previously implicated in TB disease susceptibility. The probability that these variants had a predicted re...
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| Veröffentlicht in: | PloS one 2017-04, Vol.12 (4), p.e0174738-e0174738 |
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| creator | Uren, Caitlin Henn, Brenna M Franke, Andre Wittig, Michael van Helden, Paul D Hoal, Eileen G Möller, Marlo |
| description | Utilizing data from published tuberculosis (TB) genome-wide association studies (GWAS), we use a bioinformatics pipeline to detect all polymorphisms in linkage disequilibrium (LD) with variants previously implicated in TB disease susceptibility. The probability that these variants had a predicted regulatory function was estimated using RegulomeDB and Ensembl's Variant Effect Predictor. Subsequent genotyping of these 133 predicted regulatory polymorphisms was performed in 400 admixed South African TB cases and 366 healthy controls in a population-based case-control association study to fine-map the causal variant. We detected associations between tuberculosis susceptibility and six intronic polymorphisms located in MARCO, IFNGR2, ASHAS2, ACACA, NISCH and TLR10. Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease. |
| doi_str_mv | 10.1371/journal.pone.0174738 |
| format | Article |
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Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0174738</identifier><identifier>PMID: 28384278</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aging ; Analysis ; Bacteria ; Bioinformatics ; Biology and Life Sciences ; Cadherins ; Cardiovascular diseases ; Case-Control Studies ; Cell surface ; Children ; Cholesterol ; Chromosome 18 ; Computer applications ; Data processing ; Deoxyribonucleic acid ; Disease susceptibility ; DNA ; Ecology ; Ethnic Groups - genetics ; Female ; Filters ; Gene frequency ; Gene polymorphism ; Genetic aspects ; Genetic factors ; Genetic linkage ; Genetic Predisposition to Disease ; Genetics ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Genotype ; Genotyping ; Haploinsufficiency ; Heterogeneity ; Histocompatibility antigen HLA ; Humans ; Hypersensitivity ; Immune response ; Infectious diseases ; Linkage disequilibrium ; Lung diseases ; Male ; Medicine and Health Sciences ; Middle Aged ; Molecular biology ; People and Places ; Polymorphism ; Polymorphism, Single Nucleotide ; Population genetics ; Population studies ; Research and Analysis Methods ; Skin tests ; South Africa ; Suburbs ; Tuberculosis ; Tuberculosis - genetics ; Zinc ; Zinc finger proteins</subject><ispartof>PloS one, 2017-04, Vol.12 (4), p.e0174738-e0174738</ispartof><rights>COPYRIGHT 2017 Public Library of Science</rights><rights>2017 Uren 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>2017 Uren et al 2017 Uren et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-bc257c9adb98f35955069bda90cb873bab0bae65b67b111dcbafd31bf3ea892e3</citedby><cites>FETCH-LOGICAL-c692t-bc257c9adb98f35955069bda90cb873bab0bae65b67b111dcbafd31bf3ea892e3</cites><orcidid>0000-0003-2358-0135</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/PMC5383035/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5383035/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28384278$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Li, Yun</contributor><creatorcontrib>Uren, Caitlin</creatorcontrib><creatorcontrib>Henn, Brenna M</creatorcontrib><creatorcontrib>Franke, Andre</creatorcontrib><creatorcontrib>Wittig, Michael</creatorcontrib><creatorcontrib>van Helden, Paul D</creatorcontrib><creatorcontrib>Hoal, Eileen G</creatorcontrib><creatorcontrib>Möller, Marlo</creatorcontrib><title>A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Utilizing data from published tuberculosis (TB) genome-wide association studies (GWAS), we use a bioinformatics pipeline to detect all polymorphisms in linkage disequilibrium (LD) with variants previously implicated in TB disease susceptibility. 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Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease.</description><subject>Adult</subject><subject>Aging</subject><subject>Analysis</subject><subject>Bacteria</subject><subject>Bioinformatics</subject><subject>Biology and Life Sciences</subject><subject>Cadherins</subject><subject>Cardiovascular diseases</subject><subject>Case-Control Studies</subject><subject>Cell surface</subject><subject>Children</subject><subject>Cholesterol</subject><subject>Chromosome 18</subject><subject>Computer applications</subject><subject>Data processing</subject><subject>Deoxyribonucleic acid</subject><subject>Disease susceptibility</subject><subject>DNA</subject><subject>Ecology</subject><subject>Ethnic Groups - genetics</subject><subject>Female</subject><subject>Filters</subject><subject>Gene frequency</subject><subject>Gene polymorphism</subject><subject>Genetic aspects</subject><subject>Genetic factors</subject><subject>Genetic linkage</subject><subject>Genetic Predisposition to Disease</subject><subject>Genetics</subject><subject>Genome-wide association studies</subject><subject>Genome-Wide Association Study</subject><subject>Genomes</subject><subject>Genotype</subject><subject>Genotyping</subject><subject>Haploinsufficiency</subject><subject>Heterogeneity</subject><subject>Histocompatibility antigen HLA</subject><subject>Humans</subject><subject>Hypersensitivity</subject><subject>Immune response</subject><subject>Infectious diseases</subject><subject>Linkage disequilibrium</subject><subject>Lung diseases</subject><subject>Male</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Molecular biology</subject><subject>People and Places</subject><subject>Polymorphism</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Population genetics</subject><subject>Population studies</subject><subject>Research and Analysis Methods</subject><subject>Skin tests</subject><subject>South Africa</subject><subject>Suburbs</subject><subject>Tuberculosis</subject><subject>Tuberculosis - 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The probability that these variants had a predicted regulatory function was estimated using RegulomeDB and Ensembl's Variant Effect Predictor. Subsequent genotyping of these 133 predicted regulatory polymorphisms was performed in 400 admixed South African TB cases and 366 healthy controls in a population-based case-control association study to fine-map the causal variant. We detected associations between tuberculosis susceptibility and six intronic polymorphisms located in MARCO, IFNGR2, ASHAS2, ACACA, NISCH and TLR10. Our post-GWAS approach demonstrates the feasibility of combining multiple TB GWAS datasets with linkage information to identify regulatory variants associated with this infectious disease.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>28384278</pmid><doi>10.1371/journal.pone.0174738</doi><tpages>e0174738</tpages><orcidid>https://orcid.org/0000-0003-2358-0135</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aging Analysis Bacteria Bioinformatics Biology and Life Sciences Cadherins Cardiovascular diseases Case-Control Studies Cell surface Children Cholesterol Chromosome 18 Computer applications Data processing Deoxyribonucleic acid Disease susceptibility DNA Ecology Ethnic Groups - genetics Female Filters Gene frequency Gene polymorphism Genetic aspects Genetic factors Genetic linkage Genetic Predisposition to Disease Genetics Genome-wide association studies Genome-Wide Association Study Genomes Genotype Genotyping Haploinsufficiency Heterogeneity Histocompatibility antigen HLA Humans Hypersensitivity Immune response Infectious diseases Linkage disequilibrium Lung diseases Male Medicine and Health Sciences Middle Aged Molecular biology People and Places Polymorphism Polymorphism, Single Nucleotide Population genetics Population studies Research and Analysis Methods Skin tests South Africa Suburbs Tuberculosis Tuberculosis - genetics Zinc Zinc finger proteins |
| title | A post-GWAS analysis of predicted regulatory variants and tuberculosis susceptibility |
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