Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population
Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to ev...
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| description | Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.
Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P |
| doi_str_mv | 10.1371/journal.pone.0009903 |
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Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.
These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.]]></description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0009903</identifier><identifier>PMID: 20361036</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Alleles ; Apoptosis ; Case-Control Studies ; Committees ; Data mining ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - genetics ; Endocrinology ; Endoplasmic reticulum ; Environmental factors ; Female ; Gene Expression Profiling ; Gene loci ; Genes ; Genetic analysis ; Genetic aspects ; Genetic Predisposition to Disease ; Genetics ; Genetics and Genomics/Complex Traits ; Genetics and Genomics/Disease Models ; Genetics and Genomics/Genetics of Disease ; Genomes ; Health risks ; Hepatocyte Nuclear Factor 1-alpha - genetics ; Hepatocyte nuclear factor 4 ; Humans ; Hypoglycemia ; Identification methods ; Internal medicine ; Jews ; Male ; Medicine ; Membrane Proteins - genetics ; Metabolism ; Middle Aged ; Mutation ; Polymorphism, Single Nucleotide ; Population ; Regression analysis ; Risk ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; Studies ; Type 2 diabetes</subject><ispartof>PloS one, 2010-03, Vol.5 (3), p.e9903-e9903</ispartof><rights>COPYRIGHT 2010 Public Library of Science</rights><rights>2010 Neuman et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://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>Neuman et al. 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c757t-419e614b7adbac937592d4caa7be4f13ae0e8071060aa83727d4f3d4ce7c371a3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845632/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845632/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/20361036$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Westermark, Per</contributor><creatorcontrib>Neuman, Rosalind J</creatorcontrib><creatorcontrib>Wasson, Jon</creatorcontrib><creatorcontrib>Atzmon, Gil</creatorcontrib><creatorcontrib>Wainstein, Julio</creatorcontrib><creatorcontrib>Yerushalmi, Yair</creatorcontrib><creatorcontrib>Cohen, Joseph</creatorcontrib><creatorcontrib>Barzilai, Nir</creatorcontrib><creatorcontrib>Blech, Ilana</creatorcontrib><creatorcontrib>Glaser, Benjamin</creatorcontrib><creatorcontrib>Permutt, M Alan</creatorcontrib><title>Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population</title><title>PloS one</title><addtitle>PLoS One</addtitle><description><![CDATA[Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.
Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.
These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.]]></description><subject>Adult</subject><subject>Aged</subject><subject>Alleles</subject><subject>Apoptosis</subject><subject>Case-Control Studies</subject><subject>Committees</subject><subject>Data mining</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Endocrinology</subject><subject>Endoplasmic reticulum</subject><subject>Environmental factors</subject><subject>Female</subject><subject>Gene Expression Profiling</subject><subject>Gene loci</subject><subject>Genes</subject><subject>Genetic analysis</subject><subject>Genetic aspects</subject><subject>Genetic Predisposition to Disease</subject><subject>Genetics</subject><subject>Genetics and Genomics/Complex Traits</subject><subject>Genetics and Genomics/Disease Models</subject><subject>Genetics and Genomics/Genetics of Disease</subject><subject>Genomes</subject><subject>Health risks</subject><subject>Hepatocyte Nuclear Factor 1-alpha - genetics</subject><subject>Hepatocyte nuclear factor 4</subject><subject>Humans</subject><subject>Hypoglycemia</subject><subject>Identification methods</subject><subject>Internal medicine</subject><subject>Jews</subject><subject>Male</subject><subject>Medicine</subject><subject>Membrane Proteins - genetics</subject><subject>Metabolism</subject><subject>Middle Aged</subject><subject>Mutation</subject><subject>Polymorphism, Single Nucleotide</subject><subject>Population</subject><subject>Regression analysis</subject><subject>Risk</subject><subject>Single nucleotide polymorphisms</subject><subject>Single-nucleotide polymorphism</subject><subject>Studies</subject><subject>Type 2 diabetes</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk91u1DAQhSMEoqXwBggsIYG42MWOs3F8g7SqoCyqVIm_W2uSTBJvvXGwk0J5epxuWm1QL5AVOxp_cyY59kTRc0aXjAv2bmsH14JZdrbFJaVUSsofRMdM8niRxpQ_PHg_ip54v6V0xbM0fRwdhVDKwnMc2TNscVGHiei2RwdFr23riUEoSW9Jo-sGHXHaX5LKOlLiFRrb7bDtia1If90hiUmpIccefdAg0JK1by6xhT-afMZf2jeks91gYFR-Gj2qwHh8Nq0n0fePH76dflqcX5xtTtfni0KsRL9ImMSUJbmAModCcrGScZkUACLHpGIckGJGBaMpBci4iEWZVDwQKIpgDvCT6OVetzPWq8krr1icSS7ThLFAbPZEaWGrOqd34K6VBa1uAtbVClyvC4MqS1ZsNK8EhIQzIVcJz9Miz2NZ5ijHau-nakO-w7II7jgwM9H5TqsbVdsrFQfplMdB4M0k4OzPAX2vdtoXaAy0aAevBOdZktE4DeSrf8j7f26iagjfr9vKhrLFqKnWieCSBnPHqst7qDBK3Oki3KtKh_gs4e0sITA9_u5rGLxXm69f_p-9-DFnXx-wDYLpG2_NcHMX52CyBwtnvXdY3XnMqBrb4tYNNbaFmtoipL04PJ-7pNs-4H8B5zcH-Q</recordid><startdate>20100326</startdate><enddate>20100326</enddate><creator>Neuman, Rosalind J</creator><creator>Wasson, Jon</creator><creator>Atzmon, Gil</creator><creator>Wainstein, Julio</creator><creator>Yerushalmi, Yair</creator><creator>Cohen, Joseph</creator><creator>Barzilai, Nir</creator><creator>Blech, Ilana</creator><creator>Glaser, Benjamin</creator><creator>Permutt, M Alan</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20100326</creationdate><title>Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population</title><author>Neuman, Rosalind J ; Wasson, Jon ; Atzmon, Gil ; Wainstein, Julio ; Yerushalmi, Yair ; Cohen, Joseph ; Barzilai, Nir ; Blech, Ilana ; Glaser, Benjamin ; Permutt, M Alan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c757t-419e614b7adbac937592d4caa7be4f13ae0e8071060aa83727d4f3d4ce7c371a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Alleles</topic><topic>Apoptosis</topic><topic>Case-Control Studies</topic><topic>Committees</topic><topic>Data mining</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus, Type 2 - genetics</topic><topic>Endocrinology</topic><topic>Endoplasmic reticulum</topic><topic>Environmental factors</topic><topic>Female</topic><topic>Gene Expression Profiling</topic><topic>Gene loci</topic><topic>Genes</topic><topic>Genetic analysis</topic><topic>Genetic aspects</topic><topic>Genetic Predisposition to Disease</topic><topic>Genetics</topic><topic>Genetics and Genomics/Complex Traits</topic><topic>Genetics and Genomics/Disease Models</topic><topic>Genetics and Genomics/Genetics of Disease</topic><topic>Genomes</topic><topic>Health risks</topic><topic>Hepatocyte Nuclear Factor 1-alpha - genetics</topic><topic>Hepatocyte nuclear factor 4</topic><topic>Humans</topic><topic>Hypoglycemia</topic><topic>Identification methods</topic><topic>Internal medicine</topic><topic>Jews</topic><topic>Male</topic><topic>Medicine</topic><topic>Membrane Proteins - genetics</topic><topic>Metabolism</topic><topic>Middle Aged</topic><topic>Mutation</topic><topic>Polymorphism, Single Nucleotide</topic><topic>Population</topic><topic>Regression analysis</topic><topic>Risk</topic><topic>Single nucleotide polymorphisms</topic><topic>Single-nucleotide polymorphism</topic><topic>Studies</topic><topic>Type 2 diabetes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Neuman, Rosalind J</creatorcontrib><creatorcontrib>Wasson, Jon</creatorcontrib><creatorcontrib>Atzmon, Gil</creatorcontrib><creatorcontrib>Wainstein, Julio</creatorcontrib><creatorcontrib>Yerushalmi, Yair</creatorcontrib><creatorcontrib>Cohen, Joseph</creatorcontrib><creatorcontrib>Barzilai, Nir</creatorcontrib><creatorcontrib>Blech, Ilana</creatorcontrib><creatorcontrib>Glaser, Benjamin</creatorcontrib><creatorcontrib>Permutt, M Alan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Neuman, Rosalind J</au><au>Wasson, Jon</au><au>Atzmon, Gil</au><au>Wainstein, Julio</au><au>Yerushalmi, Yair</au><au>Cohen, Joseph</au><au>Barzilai, Nir</au><au>Blech, Ilana</au><au>Glaser, Benjamin</au><au>Permutt, M Alan</au><au>Westermark, Per</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2010-03-26</date><risdate>2010</risdate><volume>5</volume><issue>3</issue><spage>e9903</spage><epage>e9903</epage><pages>e9903-e9903</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract><![CDATA[Evidence has accumulated that multiple genetic and environmental factors play important roles in determining susceptibility to type 2 diabetes (T2D). Although variants from candidate genes have become prime targets for genetic analysis, few studies have considered their interplay. Our goal was to evaluate interactions among SNPs within genes frequently identified as associated with T2D.
Logistic regression was used to study interactions among 4 SNPs, one each from HNF4A[rs1884613], TCF7L2[rs12255372], WFS1[rs10010131], and KCNJ11[rs5219] in a case-control Ashkenazi sample of 974 diabetic subjects and 896 controls. Nonparametric multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) were used to confirm findings from the logistic regression analysis. HNF4A and WFS1 SNPs were associated with T2D in logistic regression analyses [P<0.0001, P<0.0002, respectively]. Interaction between these SNPs were also strong using parametric or nonparametric methods: the unadjusted odds of being affected with T2D was 3 times greater in subjects with the HNF4A and WFS1 risk alleles than those without either (95% CI = [1.7-5.3]; P<or=0.0001). Although the univariate association between the TCF7L2 SNP and T2D was relatively modest [P = 0.02], when paired with the HNF4A SNP, the OR for subjects with risk alleles in both SNPs was 2.4 [95% CI = 1.7-3.4; P<or=0.0001]. The KCNJ11 variant reached significance only when paired with either the HNF4A or WFSI SNPs: unadjusted ORs were 2.0 [95% CI = 1.4-2.8; P<or=0.0001] and 2.3 [95% CI = 1.2-4.4; P<or=0.0001], respectively. MDR and GMDR results were consistent with the parametric findings.
These results provide evidence of strong independent associations between T2D and SNPs in HNF4A and WFS1 and their interaction in our Ashkenazi sample. We also observed an interaction in the nonparametric analysis between the HNF4A and KCNJ11 SNPs (P<or=0.001), demonstrating that an independently non-significant variant may interact with another variant resulting in an increased disease risk.]]></abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>20361036</pmid><doi>10.1371/journal.pone.0009903</doi><tpages>e9903</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_plos_journals_1289396411 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Aged Alleles Apoptosis Case-Control Studies Committees Data mining Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - genetics Endocrinology Endoplasmic reticulum Environmental factors Female Gene Expression Profiling Gene loci Genes Genetic analysis Genetic aspects Genetic Predisposition to Disease Genetics Genetics and Genomics/Complex Traits Genetics and Genomics/Disease Models Genetics and Genomics/Genetics of Disease Genomes Health risks Hepatocyte Nuclear Factor 1-alpha - genetics Hepatocyte nuclear factor 4 Humans Hypoglycemia Identification methods Internal medicine Jews Male Medicine Membrane Proteins - genetics Metabolism Middle Aged Mutation Polymorphism, Single Nucleotide Population Regression analysis Risk Single nucleotide polymorphisms Single-nucleotide polymorphism Studies Type 2 diabetes |
| title | Gene-gene interactions lead to higher risk for development of type 2 diabetes in an Ashkenazi Jewish population |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T18%3A00%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Gene-gene%20interactions%20lead%20to%20higher%20risk%20for%20development%20of%20type%202%20diabetes%20in%20an%20Ashkenazi%20Jewish%20population&rft.jtitle=PloS%20one&rft.au=Neuman,%20Rosalind%20J&rft.date=2010-03-26&rft.volume=5&rft.issue=3&rft.spage=e9903&rft.epage=e9903&rft.pages=e9903-e9903&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0009903&rft_dat=%3Cgale_plos_%3EA473906142%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1289396411&rft_id=info:pmid/20361036&rft_galeid=A473906142&rft_doaj_id=oai_doaj_org_article_84510053daea43179543b6cbb29dbe9a&rfr_iscdi=true |