Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis
Identifieur interne : 001D62 ( Main/Corpus ); précédent : 001D61; suivant : 001D63Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis
Auteurs : Tricia A. Thornton-Wells ; Jason H. Moore ; Eden R. Martin ; Margaret A. Pericak-Vance ; Jonathan L. HainesSource :
- Genetic Epidemiology [ 0741-0395 ] ; 2008-04.
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Abstract
Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene‐gene interactions in the second stage. We applied this two‐stage analysis strategy to late‐onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family‐based and case‐control datasets, which used the same five markers in leucine‐rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. Genet. Epidemiol. 2007. © 2007 Wiley‐Liss, Inc.
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DOI: 10.1002/gepi.20294
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A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene‐gene interactions in the second stage. We applied this two‐stage analysis strategy to late‐onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family‐based and case‐control datasets, which used the same five markers in leucine‐rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. Genet. Epidemiol. 2007. © 2007 Wiley‐Liss, Inc.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Tricia A. 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A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene‐gene interactions in the second stage. We applied this two‐stage analysis strategy to late‐onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family‐based and case‐control datasets, which used the same five markers in leucine‐rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. Genet. 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In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. Genet. 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interaction</keyword><keyword xml:id="kwd6">epistasis</keyword><keyword xml:id="kwd7">linkage</keyword><keyword xml:id="kwd8">association</keyword><keyword xml:id="kwd9">multifactor dimensionality reduction</keyword><keyword xml:id="kwd10">logistic regression</keyword><keyword xml:id="kwd11">clustering</keyword><keyword xml:id="kwd12">Bayesian classification</keyword></keywordGroup><fundingInfo><fundingAgency>Vanderbilt University Neuroscience Graduate Program</fundingAgency><fundingNumber>T32 MH64913</fundingNumber></fundingInfo><fundingInfo><fundingAgency>National Library of Medicine Training Grant Fellowship</fundingAgency><fundingNumber>LM07450‐01</fundingNumber></fundingInfo><fundingInfo><fundingAgency>National Institutes of Health</fundingAgency><fundingNumber>R01AG019085</fundingNumber><fundingNumber>R01AG019757</fundingNumber><fundingNumber>R01AG021547</fundingNumber><fundingNumber>R01AG0210135</fundingNumber></fundingInfo><supportingInformation><p> This article contains Supplementary Materials which can be found at <url href="http://www.interscience.wiley.com/jpages/0741-0395/suppmat"> www.interscience.wiley.com/jpages/0741‐0395/suppmat </url></p></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene‐gene interactions in the second stage. We applied this two‐stage analysis strategy to late‐onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family‐based and case‐control datasets, which used the same five markers in leucine‐rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. <i>Genet. Epidemiol</i>. 2007. © 2007 Wiley‐Liss, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Confronting Complexity in LOAD</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Confronting complexity in late‐onset Alzheimer disease: application of two‐stage analysis approach addressing heterogeneity and epistasis</title></titleInfo><name type="personal"><namePart type="given">Tricia A.</namePart><namePart type="family">Thornton‐Wells</namePart><affiliation>Biobehavioral Intervention Training Program, Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt University Institute for Imaging Science, Vanderbilt University, Nashville, Tennessee</affiliation><description>Correspondence: Vanderbilt Kennedy Center for Research on Human Development, Vanderbilt University, Peabody Box 40, 230 Appleton Place, Nashville, TN 37203===</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jason H.</namePart><namePart type="family">Moore</namePart><affiliation>Departments of Genetics and Community and Family Medicine, Dartmouth Medical School, Lebanon, New Hampshire</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Eden R.</namePart><namePart type="family">Martin</namePart><affiliation>Miami Institute for Human Genomics, Department of Medicine, Miller School of Medicine, University of Miami, Miami, Florida</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Margaret A.</namePart><namePart type="family">Pericak‐Vance</namePart><affiliation>Miami Institute for Human Genomics, Department of Medicine, Miller School of Medicine, University of Miami, Miami, Florida</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jonathan L.</namePart><namePart type="family">Haines</namePart><affiliation>Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee</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">2008-04</dateIssued><dateCaptured encoding="w3cdtf">2007-07-09</dateCaptured><dateValid encoding="w3cdtf">2007-10-21</dateValid><copyrightDate encoding="w3cdtf">2008</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">2</extent><extent unit="tables">11</extent><extent unit="references">77</extent></physicalDescription><abstract lang="en">Common diseases with a genetic basis are likely to have a very complex etiology, in which the mapping between genotype and phenotype is far from straightforward. A new comprehensive statistical and computational strategy for identifying the missing link between genotype and phenotype has been proposed, which emphasizes the need to address heterogeneity in the first stage of any analysis and gene‐gene interactions in the second stage. We applied this two‐stage analysis strategy to late‐onset Alzheimer disease (LOAD) data, which included functional and positional candidate genes and markers in a region of interest on chromosome 10. Bayesian classification found statistically significant clusterings for independent family‐based and case‐control datasets, which used the same five markers in leucine‐rich repeat transmembrane neuronal 3 (LRRTM3) as the most influential in determining cluster assignment. In subsequent analyses to detect main effects and gene‐gene interactions, markers in three genes—urokinase‐type plasminogen activator (PLAU), angiotensin 1 converting enzyme (ACE) and cell division cycle 2 (CDC2)—were found to be associated with LOAD in particular subsets of the data based on their LRRTM3 multilocus genotype. All of these genes are viable candidates for LOAD based on their known biological function, even though PLAU, CDC2 and LRRTM3 were initially identified as positional candidates. Further studies are needed to replicate these statistical findings and to elucidate possible biological interaction mechanisms between LRRTM3 and these genes. Genet. Epidemiol. 2007. © 2007 Wiley‐Liss, Inc.</abstract><note type="funding">Vanderbilt University Neuroscience Graduate Program - No. T32 MH64913; </note><note type="funding">National Library of Medicine Training Grant Fellowship - No. LM07450‐01; </note><note type="funding">National Institutes of Health - No. R01AG019085; No. R01AG019757; No. R01AG021547; No. R01AG0210135; </note><subject lang="en"><genre>Keywords</genre><topic>Alzheimer disease</topic><topic>complex disease</topic><topic>statistical genetics</topic><topic>heterogeneity</topic><topic>gene‐gene interaction</topic><topic>epistasis</topic><topic>linkage</topic><topic>association</topic><topic>multifactor dimensionality reduction</topic><topic>logistic regression</topic><topic>clustering</topic><topic>Bayesian classification</topic></subject><relatedItem type="host"><titleInfo><title>Genetic Epidemiology</title><subTitle>The Official Publication of the International Genetic Epidemiology Society</subTitle></titleInfo><titleInfo type="abbreviated"><title>Genet. Epidemiol.</title></titleInfo><genre type="Journal">journal</genre><note type="content"> This article contains Supplementary Materials which can be found at www.interscience.wiley.com/jpages/0741‐0395/suppmat</note><subject><genre>article category</genre><topic>Original Article</topic></subject><identifier type="ISSN">0741-0395</identifier><identifier type="eISSN">1098-2272</identifier><identifier type="DOI">10.1002/(ISSN)1098-2272</identifier><identifier type="PublisherID">GEPI</identifier><part><date>2008</date><detail type="volume"><caption>vol.</caption><number>32</number></detail><detail type="issue"><caption>no.</caption><number>3</number></detail><extent unit="pages"><start>187</start><end>203</end><total>17</total></extent></part></relatedItem><identifier type="istex">ED3E3BBC2901FD9F454C23EC21D9BC10AD5AF1EB</identifier><identifier type="DOI">10.1002/gepi.20294</identifier><identifier type="ArticleID">GEPI20294</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2007 Wiley‐Liss, 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)
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