Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations. Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many...
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| Veröffentlicht in: | Nature biotechnology 2013-12, Vol.31 (12), p.1102-1111 |
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| creator | Denny, Joshua C Bastarache, Lisa Ritchie, Marylyn D Carroll, Robert J Zink, Raquel Mosley, Jonathan D Field, Julie R Pulley, Jill M Ramirez, Andrea H Bowton, Erica Basford, Melissa A Carrell, David S Peissig, Peggy L Kho, Abel N Pacheco, Jennifer A Rasmussen, Luke V Crosslin, David R Crane, Paul K Pathak, Jyotishman Bielinski, Suzette J Pendergrass, Sarah A Xu, Hua Hindorff, Lucia A Li, Rongling Manolio, Teri A Chute, Christopher G Chisholm, Rex L Larson, Eric B Jarvik, Gail P Brilliant, Murray H McCarty, Catherine A Kullo, Iftikhar J Haines, Jonathan L Crawford, Dana C Masys, Daniel R Roden, Dan M |
| description | When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations.
Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with
P
< 4.6 × 10
−6
(false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (
n
= 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease. |
| doi_str_mv | 10.1038/nbt.2749 |
| format | Article |
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Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with
P
< 4.6 × 10
−6
(false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (
n
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Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with
P
< 4.6 × 10
−6
(false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (
n
= 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.</description><subject>631/208/205</subject><subject>692/308/575</subject><subject>692/699</subject><subject>Agriculture</subject><subject>Analysis</subject><subject>Bioinformatics</subject><subject>Biomedical Engineering/Biotechnology</subject><subject>Biomedicine</subject><subject>Biotechnology</subject><subject>Chromosome Mapping - methods</subject><subject>Data analysis</subject><subject>Data Mining - methods</subject><subject>Disease susceptibility</subject><subject>Electronic health records</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Genetic aspects</subject><subject>Genetic Predisposition to Disease - epidemiology</subject><subject>Genetic Predisposition to Disease - genetics</subject><subject>Genetic variance</subject><subject>Genome-Wide Association 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(Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Nature biotechnology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Denny, Joshua C</au><au>Bastarache, Lisa</au><au>Ritchie, Marylyn D</au><au>Carroll, Robert J</au><au>Zink, Raquel</au><au>Mosley, Jonathan D</au><au>Field, Julie R</au><au>Pulley, Jill M</au><au>Ramirez, Andrea H</au><au>Bowton, Erica</au><au>Basford, Melissa A</au><au>Carrell, David S</au><au>Peissig, Peggy L</au><au>Kho, Abel N</au><au>Pacheco, Jennifer A</au><au>Rasmussen, Luke V</au><au>Crosslin, David R</au><au>Crane, Paul K</au><au>Pathak, Jyotishman</au><au>Bielinski, Suzette J</au><au>Pendergrass, Sarah A</au><au>Xu, Hua</au><au>Hindorff, Lucia A</au><au>Li, Rongling</au><au>Manolio, Teri A</au><au>Chute, Christopher G</au><au>Chisholm, Rex L</au><au>Larson, Eric B</au><au>Jarvik, Gail P</au><au>Brilliant, Murray H</au><au>McCarty, Catherine A</au><au>Kullo, Iftikhar J</au><au>Haines, Jonathan L</au><au>Crawford, Dana C</au><au>Masys, Daniel R</au><au>Roden, Dan M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data</atitle><jtitle>Nature biotechnology</jtitle><stitle>Nat Biotechnol</stitle><addtitle>Nat Biotechnol</addtitle><date>2013-12-01</date><risdate>2013</risdate><volume>31</volume><issue>12</issue><spage>1102</spage><epage>1111</epage><pages>1102-1111</pages><issn>1087-0156</issn><eissn>1546-1696</eissn><abstract>When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations.
Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with
P
< 4.6 × 10
−6
(false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort (
n
= 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>24270849</pmid><doi>10.1038/nbt.2749</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1087-0156 |
| ispartof | Nature biotechnology, 2013-12, Vol.31 (12), p.1102-1111 |
| issn | 1087-0156 1546-1696 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_3969265 |
| source | MEDLINE; SpringerLink Journals; Nature Journals Online |
| subjects | 631/208/205 692/308/575 692/699 Agriculture Analysis Bioinformatics Biomedical Engineering/Biotechnology Biomedicine Biotechnology Chromosome Mapping - methods Data analysis Data Mining - methods Disease susceptibility Electronic health records Electronic Health Records - statistics & numerical data Genetic aspects Genetic Predisposition to Disease - epidemiology Genetic Predisposition to Disease - genetics Genetic variance Genome-Wide Association Study - methods Genome-Wide Association Study - statistics & numerical data Genomics Genotype & phenotype Genotypes Humans Life Sciences Medical Record Linkage - methods Medical records Phenotype Polymorphism, Single Nucleotide - genetics |
| title | Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-02T03%3A46%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Systematic%20comparison%20of%20phenome-wide%20association%20study%20of%20electronic%20medical%20record%20data%20and%20genome-wide%20association%20study%20data&rft.jtitle=Nature%20biotechnology&rft.au=Denny,%20Joshua%20C&rft.date=2013-12-01&rft.volume=31&rft.issue=12&rft.spage=1102&rft.epage=1111&rft.pages=1102-1111&rft.issn=1087-0156&rft.eissn=1546-1696&rft_id=info:doi/10.1038/nbt.2749&rft_dat=%3Cgale_pubme%3EA353323604%3C/gale_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1558804611&rft_id=info:pmid/24270849&rft_galeid=A353323604&rfr_iscdi=true |