Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease
Identifieur interne : 002344 ( Istex/Corpus ); précédent : 002343; suivant : 002345Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease
Auteurs : Mun-Gwan Hong ; Chandra A. Reynolds ; Adina L. Feldman ; Mikael Kallin ; Jean-Charles Lambert ; Philippe Amouyel ; Erik Ingelsson ; Nancy L. Pedersen ; Jonathan A. PrinceSource :
- Human Mutation [ 1059-7794 ] ; 2012-03.
English descriptors
- KwdEn :
- Alzheimer, FRMD6, GAB2, GWAS, association, gene‐based.
Abstract
Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around APOE that achieved study‐wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10−14) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10−9). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.
Url:
DOI: 10.1002/humu.22009
Links to Exploration step
ISTEX:E17A5805F575ABC9B435B5B75F3CCA642A15BBDCLe document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title><author><name sortKey="Hong, Mun Wan" sort="Hong, Mun Wan" uniqKey="Hong M" first="Mun-Gwan" last="Hong">Mun-Gwan Hong</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Reynolds, Chandra A" sort="Reynolds, Chandra A" uniqKey="Reynolds C" first="Chandra A." last="Reynolds">Chandra A. Reynolds</name><affiliation><mods:affiliation>Department of Psychology, University of California at Riverside, CA</mods:affiliation></affiliation></author><author><name sortKey="Feldman, Adina L" sort="Feldman, Adina L" uniqKey="Feldman A" first="Adina L." last="Feldman">Adina L. Feldman</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Kallin, Mikael" sort="Kallin, Mikael" uniqKey="Kallin M" first="Mikael" last="Kallin">Mikael Kallin</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Lambert, Jean Harles" sort="Lambert, Jean Harles" uniqKey="Lambert J" first="Jean-Charles" last="Lambert">Jean-Charles Lambert</name><affiliation><mods:affiliation>Inserm U744, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Institut Pasteur de Lille, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Université de Lille Nord de France, Lille, France</mods:affiliation></affiliation></author><author><name sortKey="Amouyel, Philippe" sort="Amouyel, Philippe" uniqKey="Amouyel P" first="Philippe" last="Amouyel">Philippe Amouyel</name><affiliation><mods:affiliation>Inserm U744, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Institut Pasteur de Lille, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Université de Lille Nord de France, Lille, France</mods:affiliation></affiliation></author><author><name sortKey="Ingelsson, Erik" sort="Ingelsson, Erik" uniqKey="Ingelsson E" first="Erik" last="Ingelsson">Erik Ingelsson</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Pedersen, Nancy L" sort="Pedersen, Nancy L" uniqKey="Pedersen N" first="Nancy L." last="Pedersen">Nancy L. Pedersen</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Prince, Jonathan A" sort="Prince, Jonathan A" uniqKey="Prince J" first="Jonathan A." last="Prince">Jonathan A. Prince</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:E17A5805F575ABC9B435B5B75F3CCA642A15BBDC</idno><date when="2012" year="2012">2012</date><idno type="doi">10.1002/humu.22009</idno><idno type="url">https://api-v5.istex.fr/document/E17A5805F575ABC9B435B5B75F3CCA642A15BBDC/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">002344</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">002344</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title><author><name sortKey="Hong, Mun Wan" sort="Hong, Mun Wan" uniqKey="Hong M" first="Mun-Gwan" last="Hong">Mun-Gwan Hong</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Reynolds, Chandra A" sort="Reynolds, Chandra A" uniqKey="Reynolds C" first="Chandra A." last="Reynolds">Chandra A. Reynolds</name><affiliation><mods:affiliation>Department of Psychology, University of California at Riverside, CA</mods:affiliation></affiliation></author><author><name sortKey="Feldman, Adina L" sort="Feldman, Adina L" uniqKey="Feldman A" first="Adina L." last="Feldman">Adina L. Feldman</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Kallin, Mikael" sort="Kallin, Mikael" uniqKey="Kallin M" first="Mikael" last="Kallin">Mikael Kallin</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Lambert, Jean Harles" sort="Lambert, Jean Harles" uniqKey="Lambert J" first="Jean-Charles" last="Lambert">Jean-Charles Lambert</name><affiliation><mods:affiliation>Inserm U744, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Institut Pasteur de Lille, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Université de Lille Nord de France, Lille, France</mods:affiliation></affiliation></author><author><name sortKey="Amouyel, Philippe" sort="Amouyel, Philippe" uniqKey="Amouyel P" first="Philippe" last="Amouyel">Philippe Amouyel</name><affiliation><mods:affiliation>Inserm U744, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Institut Pasteur de Lille, Lille, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Université de Lille Nord de France, Lille, France</mods:affiliation></affiliation></author><author><name sortKey="Ingelsson, Erik" sort="Ingelsson, Erik" uniqKey="Ingelsson E" first="Erik" last="Ingelsson">Erik Ingelsson</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Pedersen, Nancy L" sort="Pedersen, Nancy L" uniqKey="Pedersen N" first="Nancy L." last="Pedersen">Nancy L. Pedersen</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation></author><author><name sortKey="Prince, Jonathan A" sort="Prince, Jonathan A" uniqKey="Prince J" first="Jonathan A." last="Prince">Jonathan A. Prince</name><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</mods:affiliation></affiliation><affiliation><mods:affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Human Mutation</title><title level="j" type="abbrev">Hum. Mutat.</title><idno type="ISSN">1059-7794</idno><idno type="eISSN">1098-1004</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2012-03">2012-03</date><biblScope unit="volume">33</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="521">521</biblScope><biblScope unit="page" to="529">529</biblScope></imprint><idno type="ISSN">1059-7794</idno></series><idno type="istex">E17A5805F575ABC9B435B5B75F3CCA642A15BBDC</idno><idno type="DOI">10.1002/humu.22009</idno><idno type="ArticleID">HUMU22009</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1059-7794</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Alzheimer</term><term>FRMD6</term><term>GAB2</term><term>GWAS</term><term>association</term><term>gene‐based</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around APOE that achieved study‐wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10−14) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10−9). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.</div></front></TEI><istex><corpusName>wiley</corpusName><keywords><teeft><json:string>gwas</json:string><json:string>frmd6</json:string><json:string>genet</json:string><json:string>apoe</json:string><json:string>pathway</json:string><json:string>mutation</json:string><json:string>reiman</json:string><json:string>gab2</json:string><json:string>genotyping</json:string><json:string>alzheimer</json:string><json:string>consortium</json:string><json:string>allele</json:string><json:string>crohn</json:string><json:string>younkin</json:string><json:string>swedish sample</json:string><json:string>allelic</json:string><json:string>nars2</json:string><json:string>present study</json:string><json:string>chromosome</json:string><json:string>annotation</json:string><json:string>dementia</json:string><json:string>datasets</json:string><json:string>heterogeneity</json:string><json:string>heritability</json:string><json:string>alzheimer disease</json:string><json:string>algorithm</json:string><json:string>snp</json:string><json:string>human mutation</json:string><json:string>allelic heterogeneity</json:string><json:string>association studies</json:string><json:string>effect sizes</json:string><json:string>gene</json:string><json:string>marker</json:string><json:string>total number</json:string><json:string>multiple testing</json:string><json:string>locus</json:string><json:string>purcell</json:string><json:string>proxy clusters</json:string><json:string>gwas datasets</json:string><json:string>single marker</json:string><json:string>correction factor</json:string><json:string>common variants</json:string><json:string>disease neuroimaging initiative</json:string><json:string>swedish</json:string><json:string>variant</json:string><json:string>lambert</json:string><json:string>genetic architecture</json:string><json:string>additional gwas datasets</json:string><json:string>additional studies</json:string><json:string>genetic association</json:string><json:string>genebased statistics</json:string><json:string>metabochip array</json:string><json:string>genetic loci</json:string><json:string>gene assignment</json:string><json:string>apoe locus</json:string><json:string>single nucleotide polymorphisms</json:string><json:string>gene windows</json:string><json:string>splice form</json:string><json:string>causal alleles</json:string><json:string>association study</json:string><json:string>total markers</json:string><json:string>genetic marker lists</json:string><json:string>representative gene lists</json:string><json:string>other traits</json:string><json:string>dementia cases</json:string><json:string>second gene</json:string><json:string>wiley periodicals</json:string><json:string>null distribution</json:string><json:string>equivalent number</json:string><json:string>gene region</json:string><json:string>gene ontology</json:string><json:string>swedish study</json:string><json:string>similar annotations</json:string><json:string>same analysis</json:string><json:string>pathway analysis</json:string><json:string>correlated genes</json:string><json:string>total permuted</json:string><json:string>input results list</json:string><json:string>full result</json:string><json:string>bipolar disorder</json:string><json:string>genetic markers</json:string><json:string>eadi1 consortium</json:string><json:string>susceptibility gene</json:string><json:string>hippocampal atrophy</json:string><json:string>other populations</json:string><json:string>full extent</json:string><json:string>strong evidence</json:string><json:string>genetic variation</json:string><json:string>risk alleles</json:string><json:string>different genotyping platforms</json:string><json:string>human populations</json:string><json:string>karolinska institute</json:string><json:string>wellcome trust case control consortium</json:string><json:string>genet bertram</json:string><json:string>medical epidemiology</json:string><json:string>genomewide association study</json:string><json:string>association study variants</json:string><json:string>proc natl acad</json:string><json:string>pathway analyses</json:string><json:string>charge consortium</json:string><json:string>independent tests</json:string><json:string>genetic</json:string><json:string>susceptibility</json:string><json:string>trait</json:string></teeft></keywords><author><json:item><name>Mun‐Gwan Hong</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string></affiliations></json:item><json:item><name>Chandra A. Reynolds</name><affiliations><json:string>Department of Psychology, University of California at Riverside, CA</json:string></affiliations></json:item><json:item><name>Adina L. Feldman</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string></affiliations></json:item><json:item><name>Mikael Kallin</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string></affiliations></json:item><json:item><name>Jean‐Charles Lambert</name><affiliations><json:string>Inserm U744, Lille, France</json:string><json:string>Institut Pasteur de Lille, Lille, France</json:string><json:string>Université de Lille Nord de France, Lille, France</json:string></affiliations></json:item><json:item><name>Philippe Amouyel</name><affiliations><json:string>Inserm U744, Lille, France</json:string><json:string>Institut Pasteur de Lille, Lille, France</json:string><json:string>Université de Lille Nord de France, Lille, France</json:string></affiliations></json:item><json:item><name>Erik Ingelsson</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string></affiliations></json:item><json:item><name>Nancy L. Pedersen</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string></affiliations></json:item><json:item><name>Jonathan A. Prince</name><affiliations><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</json:string><json:string>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</json:string></affiliations></json:item></author><subject><json:item><lang><json:string>eng</json:string></lang><value>Alzheimer</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>GWAS</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>association</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>gene‐based</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>FRMD6</value></json:item><json:item><lang><json:string>eng</json:string></lang><value>GAB2</value></json:item></subject><articleId><json:string>HUMU22009</json:string></articleId><language><json:string>eng</json:string></language><originalGenre><json:string>article</json:string></originalGenre><abstract>Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around APOE that achieved study‐wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10−14) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10−9). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.</abstract><qualityIndicators><score>7.532</score><pdfVersion>1.3</pdfVersion><pdfPageSize>595.245 x 793.66 pts</pdfPageSize><refBibsNative>true</refBibsNative><abstractCharCount>1429</abstractCharCount><pdfWordCount>8725</pdfWordCount><pdfCharCount>51870</pdfCharCount><pdfPageCount>9</pdfPageCount><abstractWordCount>211</abstractWordCount></qualityIndicators><title>Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title><genre><json:string>article</json:string></genre><host><title>Human Mutation</title><language><json:string>unknown</json:string></language><doi><json:string>10.1002/(ISSN)1098-1004</json:string></doi><issn><json:string>1059-7794</json:string></issn><eissn><json:string>1098-1004</json:string></eissn><publisherId><json:string>HUMU</json:string></publisherId><volume>33</volume><issue>3</issue><pages><first>521</first><last>529</last><total>9</total></pages><genre><json:string>journal</json:string></genre><subject><json:item><value>Research Article</value></json:item></subject></host><categories><wos><json:string>science</json:string><json:string>genetics & heredity</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>biomedical research</json:string><json:string>genetics & heredity</json:string></scienceMetrix><inist><json:string>sciences appliquees, technologies et medecines</json:string><json:string>sciences biologiques et medicales</json:string><json:string>sciences medicales</json:string></inist></categories><publicationDate>2012</publicationDate><copyrightDate>2012</copyrightDate><doi><json:string>10.1002/humu.22009</json:string></doi><id>E17A5805F575ABC9B435B5B75F3CCA642A15BBDC</id><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api-v5.istex.fr/document/E17A5805F575ABC9B435B5B75F3CCA642A15BBDC/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api-v5.istex.fr/document/E17A5805F575ABC9B435B5B75F3CCA642A15BBDC/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api-v5.istex.fr/document/E17A5805F575ABC9B435B5B75F3CCA642A15BBDC/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><availability><p>© 2011 Wiley Periodicals, Inc.</p></availability><date>2012</date></publicationStmt><notesStmt><note type="content">*Stephen J. Chanock</note><note>U.S. National Institutes of Health - No. AG028555; No. AG08724; No. AG04563; No. AG10175; No. AG08861;</note><note>the Swedish Research Council (2007‐2722)</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title><author xml:id="author-1"><persName><forename type="first">Mun‐Gwan</forename><surname>Hong</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation></author><author xml:id="author-2"><persName><forename type="first">Chandra A.</forename><surname>Reynolds</surname></persName><affiliation>Department of Psychology, University of California at Riverside, CA</affiliation></author><author xml:id="author-3"><persName><forename type="first">Adina L.</forename><surname>Feldman</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation></author><author xml:id="author-4"><persName><forename type="first">Mikael</forename><surname>Kallin</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation></author><author xml:id="author-5"><persName><forename type="first">Jean‐Charles</forename><surname>Lambert</surname></persName><affiliation>Inserm U744, Lille, France</affiliation><affiliation>Institut Pasteur de Lille, Lille, France</affiliation><affiliation>Université de Lille Nord de France, Lille, France</affiliation></author><author xml:id="author-6"><persName><forename type="first">Philippe</forename><surname>Amouyel</surname></persName><affiliation>Inserm U744, Lille, France</affiliation><affiliation>Institut Pasteur de Lille, Lille, France</affiliation><affiliation>Université de Lille Nord de France, Lille, France</affiliation></author><author xml:id="author-7"><persName><forename type="first">Erik</forename><surname>Ingelsson</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation></author><author xml:id="author-8"><persName><forename type="first">Nancy L.</forename><surname>Pedersen</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation></author><author xml:id="author-9"><persName><forename type="first">Jonathan A.</forename><surname>Prince</surname></persName><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</affiliation></author></analytic><monogr><title level="j">Human Mutation</title><title level="j" type="abbrev">Hum. Mutat.</title><idno type="pISSN">1059-7794</idno><idno type="eISSN">1098-1004</idno><idno type="DOI">10.1002/(ISSN)1098-1004</idno><imprint><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>Hoboken</pubPlace><date type="published" when="2012-03"></date><biblScope unit="volume">33</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="521">521</biblScope><biblScope unit="page" to="529">529</biblScope></imprint></monogr><idno type="istex">E17A5805F575ABC9B435B5B75F3CCA642A15BBDC</idno><idno type="DOI">10.1002/humu.22009</idno><idno type="ArticleID">HUMU22009</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2012</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around APOE that achieved study‐wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10−14) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10−9). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>keywords</head><item><term>Alzheimer</term></item><item><term>GWAS</term></item><item><term>association</term></item><item><term>gene‐based</term></item><item><term>FRMD6</term></item><item><term>GAB2</term></item></list></keywords></textClass><textClass><keywords scheme="Journal Subject"><list><head>article-category</head><item><term>Research Article</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2011-05-13">Received</change><change when="2011-12-02">Registration</change><change when="2012-03">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api-v5.istex.fr/document/E17A5805F575ABC9B435B5B75F3CCA642A15BBDC/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Wiley Subscription Services, Inc., A Wiley Company</publisherName><publisherLoc>Hoboken</publisherLoc></publisherInfo><doi registered="yes">10.1002/(ISSN)1098-1004</doi><issn type="print">1059-7794</issn><issn type="electronic">1098-1004</issn><idGroup><id type="product" value="HUMU"></id></idGroup><titleGroup><title type="main" xml:lang="en" sort="HUMAN MUTATION">Human Mutation</title><title type="short">Hum. Mutat.</title></titleGroup></publicationMeta><publicationMeta level="part" position="30"><doi origin="wiley" registered="yes">10.1002/humu.v33.3</doi><numberingGroup><numbering type="journalVolume" number="33">33</numbering><numbering type="journalIssue">3</numbering></numberingGroup><coverDate startDate="2012-03">March 2012</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="130" status="forIssue"><doi origin="wiley" registered="yes">10.1002/humu.22009</doi><idGroup><id type="unit" value="HUMU22009"></id></idGroup><countGroup><count type="pageTotal" number="9"></count></countGroup><titleGroup><title type="articleCategory">Research Article</title><title type="tocHeading1">Research Articles</title></titleGroup><copyright ownership="publisher">© 2011 Wiley Periodicals, Inc.</copyright><eventGroup><event type="manuscriptReceived" date="2011-05-13"></event><event type="manuscriptAccepted" date="2011-12-02"></event><event type="xmlConverted" agent="Converter:JWSART34_TO_WML3G version:3.1.2.1 standalone mode:FullText" date="2012-02-15"></event><event type="publishedOnlineAccepted" date="2011-12-20"></event><event type="publishedOnlineEarlyUnpaginated" date="2012-01-23"></event><event type="publishedOnlineFinalForm" date="2012-02-14"></event><event type="firstOnline" date="2012-01-23"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:3.8.8" date="2014-01-27"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.3.4 mode:FullText" date="2015-02-24"></event></eventGroup><numberingGroup><numbering type="pageFirst">521</numbering><numbering type="pageLast">529</numbering></numberingGroup><correspondenceTo>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:HUMU.HUMU22009.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="1"></count><count type="tableTotal" number="6"></count><count type="referenceTotal" number="46"></count></countGroup><titleGroup><title type="main" xml:lang="en">Genome‐wide and gene‐based association implicates <i>FRMD6</i> in alzheimer disease<link href="#fn1"></link></title></titleGroup><creators><creator xml:id="au1" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Mun‐Gwan</givenNames><familyName>Hong</familyName></personName></creator><creator xml:id="au2" creatorRole="author" affiliationRef="#af2"><personName><givenNames>Chandra A.</givenNames><familyName>Reynolds</familyName></personName></creator><creator xml:id="au3" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Adina L.</givenNames><familyName>Feldman</familyName></personName></creator><creator xml:id="au4" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Mikael</givenNames><familyName>Kallin</familyName></personName></creator><creator xml:id="au5" creatorRole="author" affiliationRef="#af3 #af4 #af5"><personName><givenNames>Jean‐Charles</givenNames><familyName>Lambert</familyName></personName></creator><creator xml:id="au6" creatorRole="author" affiliationRef="#af3 #af4 #af5"><personName><givenNames>Philippe</givenNames><familyName>Amouyel</familyName></personName></creator><creator xml:id="au7" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Erik</givenNames><familyName>Ingelsson</familyName></personName></creator><creator xml:id="au8" creatorRole="author" affiliationRef="#af1"><personName><givenNames>Nancy L.</givenNames><familyName>Pedersen</familyName></personName></creator><creator xml:id="au9" creatorRole="author" affiliationRef="#af1" corresponding="yes"><personName><givenNames>Jonathan A.</givenNames><familyName>Prince</familyName></personName><contactDetails><email>Jonathan.Prince@ki.se</email></contactDetails></creator></creators><affiliationGroup><affiliation xml:id="af1" countryCode="SE" type="organization"><unparsedAffiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</unparsedAffiliation></affiliation><affiliation xml:id="af2" countryCode="US" type="organization"><unparsedAffiliation>Department of Psychology, University of California at Riverside, CA</unparsedAffiliation></affiliation><affiliation xml:id="af3" countryCode="FR" type="organization"><unparsedAffiliation>Inserm U744, Lille, France</unparsedAffiliation></affiliation><affiliation xml:id="af4" countryCode="FR" type="organization"><unparsedAffiliation>Institut Pasteur de Lille, Lille, France</unparsedAffiliation></affiliation><affiliation xml:id="af5" countryCode="FR" type="organization"><unparsedAffiliation>Université de Lille Nord de France, Lille, France</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">Alzheimer</keyword><keyword xml:id="kwd2">GWAS</keyword><keyword xml:id="kwd3">association</keyword><keyword xml:id="kwd4">gene‐based</keyword><keyword xml:id="kwd5">FRMD6</keyword><keyword xml:id="kwd6">GAB2</keyword></keywordGroup><fundingInfo><fundingAgency>U.S. National Institutes of Health</fundingAgency><fundingNumber>AG028555</fundingNumber><fundingNumber>AG08724</fundingNumber><fundingNumber>AG04563</fundingNumber><fundingNumber>AG10175</fundingNumber><fundingNumber>AG08861</fundingNumber></fundingInfo><fundingInfo><fundingAgency>the Swedish Research Council (2007‐2722)</fundingAgency></fundingInfo><supportingInformation><p> Additional Supporting information may be found in the online version of this article </p><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:10597794:media:humu22009:humu_22009_sm_SuppInfo"></mediaResource><caption>Supporting Information</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around <i>APOE</i> that achieved study‐wide significance in combined samples, the strongest finding being for <i>FRMD6</i> on chromosome 14q (<i>P</i> = 2.6 × 10<sup>−14</sup>) and a weaker signal for <i>NARS2</i> that is immediately adjacent to <i>GAB2</i> on chromosome 11q (<i>P</i> = 7.8 × 10<sup>−9</sup>). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><p>Stephen J. Chanock</p></note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease</title></titleInfo><name type="personal"><namePart type="given">Mun‐Gwan</namePart><namePart type="family">Hong</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Chandra A.</namePart><namePart type="family">Reynolds</namePart><affiliation>Department of Psychology, University of California at Riverside, CA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Adina L.</namePart><namePart type="family">Feldman</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Mikael</namePart><namePart type="family">Kallin</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jean‐Charles</namePart><namePart type="family">Lambert</namePart><affiliation>Inserm U744, Lille, France</affiliation><affiliation>Institut Pasteur de Lille, Lille, France</affiliation><affiliation>Université de Lille Nord de France, Lille, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Philippe</namePart><namePart type="family">Amouyel</namePart><affiliation>Inserm U744, Lille, France</affiliation><affiliation>Institut Pasteur de Lille, Lille, France</affiliation><affiliation>Université de Lille Nord de France, Lille, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Erik</namePart><namePart type="family">Ingelsson</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Nancy L.</namePart><namePart type="family">Pedersen</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jonathan A.</namePart><namePart type="family">Prince</namePart><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden</affiliation><affiliation>Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Nobels väg 12A, 171 77 Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2012-03</dateIssued><dateCaptured encoding="w3cdtf">2011-05-13</dateCaptured><dateValid encoding="w3cdtf">2011-12-02</dateValid><copyrightDate encoding="w3cdtf">2012</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">1</extent><extent unit="tables">6</extent><extent unit="references">46</extent></physicalDescription><abstract lang="en">Genome‐wide association studies (GWAS) that allow for allelic heterogeneity may facilitate the discovery of novel genes not detectable by models that require replication of a single variant site. One strategy to accomplish this is to focus on genes rather than markers as units of association, and so potentially capture a spectrum of causal alleles that differ across populations. Here, we conducted a GWAS of Alzheimer disease (AD) in 2,586 Swedes and performed gene‐based meta‐analysis with three additional studies from France, Canada, and the United States, in total encompassing 4,259 cases and 8,284 controls. Implementing a newly designed gene‐based algorithm, we identified two loci apart from the region around APOE that achieved study‐wide significance in combined samples, the strongest finding being for FRMD6 on chromosome 14q (P = 2.6 × 10−14) and a weaker signal for NARS2 that is immediately adjacent to GAB2 on chromosome 11q (P = 7.8 × 10−9). Ontology‐based pathway analyses revealed significant enrichment of genes involved in glycosylation. Results suggest that gene‐based approaches that accommodate allelic heterogeneity in GWAS can provide a complementary avenue for gene discovery and may help to explain a portion of the missing heritability not detectable with single nucleotide polymorphisms (SNPs) derived from marker‐specific meta‐analysis. Hum Mutat 33:521–529, 2012. © 2011 Wiley Periodicals, Inc.</abstract><note type="content">*Stephen J. Chanock</note><note type="funding">U.S. National Institutes of Health - No. AG028555; No. AG08724; No. AG04563; No. AG10175; No. AG08861; </note><note type="funding">the Swedish Research Council (2007‐2722)</note><subject lang="en"><genre>keywords</genre><topic>Alzheimer</topic><topic>GWAS</topic><topic>association</topic><topic>gene‐based</topic><topic>FRMD6</topic><topic>GAB2</topic></subject><relatedItem type="host"><titleInfo><title>Human Mutation</title></titleInfo><titleInfo type="abbreviated"><title>Hum. Mutat.</title></titleInfo><genre type="journal">journal</genre><note type="content"> Additional Supporting information may be found in the online version of this articleSupporting Info Item: Supporting Information - </note><subject><genre>article-category</genre><topic>Research Article</topic></subject><identifier type="ISSN">1059-7794</identifier><identifier type="eISSN">1098-1004</identifier><identifier type="DOI">10.1002/(ISSN)1098-1004</identifier><identifier type="PublisherID">HUMU</identifier><part><date>2012</date><detail type="volume"><caption>vol.</caption><number>33</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>521</start><end>529</end><total>9</total></extent></part></relatedItem><identifier type="istex">E17A5805F575ABC9B435B5B75F3CCA642A15BBDC</identifier><identifier type="DOI">10.1002/humu.22009</identifier><identifier type="ArticleID">HUMU22009</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2011 Wiley Periodicals, Inc.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Canada/explor/ParkinsonCanadaV1/Data/Istex/Corpus
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002344 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Istex/Corpus/biblio.hfd -nk 002344 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Canada
|area= ParkinsonCanadaV1
|flux= Istex
|étape= Corpus
|type= RBID
|clé= ISTEX:E17A5805F575ABC9B435B5B75F3CCA642A15BBDC
|texte= Genome‐wide and gene‐based association implicates FRMD6 in alzheimer disease
}}
| This area was generated with Dilib version V0.6.29. |  |