Data quality assessment in Genome Wide Association Studies (GWAS)
Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 5 |
|---|---|
| container_issue | |
| container_start_page | 1 |
| container_title | |
| container_volume | |
| creator | Etcheverry, Lorena Marotta, Adriana Ruggia, R |
| description | Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis since it generates lack of confidence and inhibits its exploitation. This paper addresses GWAS data quality issues and presents a domain specific model for data quality assessment, which has been developed taking into account meta-analysis requirements. |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5556605</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5556605</ieee_id><sourcerecordid>5556605</sourcerecordid><originalsourceid>FETCH-ieee_primary_55566053</originalsourceid><addsrcrecordid>eNp9ybsOgjAUgOEaNRGUJ3DpqANJKb3ISLzgjglspJFjUsNFOWXg7XVw9l--4Z8RPzkkieJCx-Wc-JHgQmjOdbkgHo-UCpnmekUCxCf7JiSXMfNIejLO0PdoGusmahABsYXOUdvRDLq-BVrYGmiK2N-tcbbvaO7G2gLSXVak-X5Dlg_TIAQ_12R7Od-O19ACQPUabGuGqZJSKsVk_P9-AMvANxA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Data quality assessment in Genome Wide Association Studies (GWAS)</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Etcheverry, Lorena ; Marotta, Adriana ; Ruggia, R</creator><creatorcontrib>Etcheverry, Lorena ; Marotta, Adriana ; Ruggia, R</creatorcontrib><description>Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis since it generates lack of confidence and inhibits its exploitation. This paper addresses GWAS data quality issues and presents a domain specific model for data quality assessment, which has been developed taking into account meta-analysis requirements.</description><identifier>ISSN: 2166-0727</identifier><identifier>ISBN: 142447227X</identifier><identifier>ISBN: 9781424472277</identifier><identifier>EISBN: 989962473X</identifier><identifier>EISBN: 9789899624733</identifier><language>eng</language><publisher>IEEE</publisher><subject>Accuracy ; Bioinformatics ; Biological system modeling ; Data models ; data quality ; Genomics ; GWAS ; meta-analysis</subject><ispartof>5th Iberian Conference on Information Systems and Technologies, 2010, p.1-5</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5556605$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5556605$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Etcheverry, Lorena</creatorcontrib><creatorcontrib>Marotta, Adriana</creatorcontrib><creatorcontrib>Ruggia, R</creatorcontrib><title>Data quality assessment in Genome Wide Association Studies (GWAS)</title><title>5th Iberian Conference on Information Systems and Technologies</title><addtitle>CISTI</addtitle><description>Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis since it generates lack of confidence and inhibits its exploitation. This paper addresses GWAS data quality issues and presents a domain specific model for data quality assessment, which has been developed taking into account meta-analysis requirements.</description><subject>Accuracy</subject><subject>Bioinformatics</subject><subject>Biological system modeling</subject><subject>Data models</subject><subject>data quality</subject><subject>Genomics</subject><subject>GWAS</subject><subject>meta-analysis</subject><issn>2166-0727</issn><isbn>142447227X</isbn><isbn>9781424472277</isbn><isbn>989962473X</isbn><isbn>9789899624733</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9ybsOgjAUgOEaNRGUJ3DpqANJKb3ISLzgjglspJFjUsNFOWXg7XVw9l--4Z8RPzkkieJCx-Wc-JHgQmjOdbkgHo-UCpnmekUCxCf7JiSXMfNIejLO0PdoGusmahABsYXOUdvRDLq-BVrYGmiK2N-tcbbvaO7G2gLSXVak-X5Dlg_TIAQ_12R7Od-O19ACQPUabGuGqZJSKsVk_P9-AMvANxA</recordid><startdate>201006</startdate><enddate>201006</enddate><creator>Etcheverry, Lorena</creator><creator>Marotta, Adriana</creator><creator>Ruggia, R</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201006</creationdate><title>Data quality assessment in Genome Wide Association Studies (GWAS)</title><author>Etcheverry, Lorena ; Marotta, Adriana ; Ruggia, R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_55566053</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Accuracy</topic><topic>Bioinformatics</topic><topic>Biological system modeling</topic><topic>Data models</topic><topic>data quality</topic><topic>Genomics</topic><topic>GWAS</topic><topic>meta-analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Etcheverry, Lorena</creatorcontrib><creatorcontrib>Marotta, Adriana</creatorcontrib><creatorcontrib>Ruggia, R</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Etcheverry, Lorena</au><au>Marotta, Adriana</au><au>Ruggia, R</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Data quality assessment in Genome Wide Association Studies (GWAS)</atitle><btitle>5th Iberian Conference on Information Systems and Technologies</btitle><stitle>CISTI</stitle><date>2010-06</date><risdate>2010</risdate><spage>1</spage><epage>5</epage><pages>1-5</pages><issn>2166-0727</issn><isbn>142447227X</isbn><isbn>9781424472277</isbn><eisbn>989962473X</eisbn><eisbn>9789899624733</eisbn><abstract>Genome Wide Association Studies (GWAS) are developed to find direct or indirect relations from given genomic configurations to physical characteristics or specific diseases. In order to build new GWAS, avoiding the complexities of field based studies, a statistical technique called meta-analysis can be used. Bad or unknown data quality has been largely identified as a major problem in meta-analysis since it generates lack of confidence and inhibits its exploitation. This paper addresses GWAS data quality issues and presents a domain specific model for data quality assessment, which has been developed taking into account meta-analysis requirements.</abstract><pub>IEEE</pub></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2166-0727 |
| ispartof | 5th Iberian Conference on Information Systems and Technologies, 2010, p.1-5 |
| issn | 2166-0727 |
| language | eng |
| recordid | cdi_ieee_primary_5556605 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Bioinformatics Biological system modeling Data models data quality Genomics GWAS meta-analysis |
| title | Data quality assessment in Genome Wide Association Studies (GWAS) |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T19%3A40%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Data%20quality%20assessment%20in%20Genome%20Wide%20Association%20Studies%20(GWAS)&rft.btitle=5th%20Iberian%20Conference%20on%20Information%20Systems%20and%20Technologies&rft.au=Etcheverry,%20Lorena&rft.date=2010-06&rft.spage=1&rft.epage=5&rft.pages=1-5&rft.issn=2166-0727&rft.isbn=142447227X&rft.isbn_list=9781424472277&rft_id=info:doi/&rft_dat=%3Cieee_6IE%3E5556605%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=989962473X&rft.eisbn_list=9789899624733&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5556605&rfr_iscdi=true |