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      Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data.

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      Journal: Nature biotechnology
      Keywords: Chromosome Mapping, methods, Data Mining, Electronic Health Records, statistics & numerical data, Genetic Predisposition to Disease, epidemiology, genetics, Genome-Wide Association Study, Humans, Medical Record Linkage, Phenotype, Polymorphism, Single Nucleotide

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          Abstract

          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.

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          The "meaningful use" regulation for electronic health records.

          David Blumenthal, Marilyn Tavenner (2010)
          Article 0 comments Cited 437 times – based on 0 reviews     Review now
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            Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease.

            Kirk Lohmueller, Celeste Pearce, Malcolm C. Pike … (2003)
            Association studies offer a potentially powerful approach to identify genetic variants that influence susceptibility to common disease, but are plagued by the impression that they are not consistently reproducible. In principle, the inconsistency may be due to false positive studies, false negative studies or true variability in association among different populations. The critical question is whether false positives overwhelmingly explain the inconsistency. We analyzed 301 published studies covering 25 different reported associations. There was a large excess of studies replicating the first positive reports, inconsistent with the hypothesis of no true positive associations (P < 10(-14)). This excess of replications could not be reasonably explained by publication bias and was concentrated among 11 of the 25 associations. For 8 of these 11 associations, pooled analysis of follow-up studies yielded statistically significant replication of the first report, with modest estimated genetic effects. Thus, a sizable fraction (but under half) of reported associations have strong evidence of replication; for these, false negative, underpowered studies probably contribute to inconsistent replication. We conclude that there are probably many common variants in the human genome with modest but real effects on common disease risk, and that studies using large samples will convincingly identify such variants.
            Article 0 comments Cited 314 times – based on 0 reviews     Review now
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              TRAF1-C5 as a risk locus for rheumatoid arthritis--a genomewide study.

              Robert Plenge, Mark Seielstad, Leonid Padyukov … (2007)
              Rheumatoid arthritis has a complex mode of inheritance. Although HLA-DRB1 and PTPN22 are well-established susceptibility loci, other genes that confer a modest level of risk have been identified recently. We carried out a genomewide association analysis to identify additional genetic loci associated with an increased risk of rheumatoid arthritis. We genotyped 317,503 single-nucleotide polymorphisms (SNPs) in a combined case-control study of 1522 case subjects with rheumatoid arthritis and 1850 matched control subjects. The patients were seropositive for autoantibodies against cyclic citrullinated peptide (CCP). We obtained samples from two data sets, the North American Rheumatoid Arthritis Consortium (NARAC) and the Swedish Epidemiological Investigation of Rheumatoid Arthritis (EIRA). Results from NARAC and EIRA for 297,086 SNPs that passed quality-control filters were combined with the use of Cochran-Mantel-Haenszel stratified analysis. SNPs showing a significant association with disease (P<1x10(-8)) were genotyped in an independent set of case subjects with anti-CCP-positive rheumatoid arthritis (485 from NARAC and 512 from EIRA) and in control subjects (1282 from NARAC and 495 from EIRA). We observed associations between disease and variants in the major-histocompatibility-complex locus, in PTPN22, and in a SNP (rs3761847) on chromosome 9 for all samples tested, the latter with an odds ratio of 1.32 (95% confidence interval, 1.23 to 1.42; P=4x10(-14)). The SNP is in linkage disequilibrium with two genes relevant to chronic inflammation: TRAF1 (encoding tumor necrosis factor receptor-associated factor 1) and C5 (encoding complement component 5). A common genetic variant at the TRAF1-C5 locus on chromosome 9 is associated with an increased risk of anti-CCP-positive rheumatoid arthritis. Copyright 2007 Massachusetts Medical Society.
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                Author and article information

                Journal
                PubMed ID:: 24270849
                PMC ID:: 3969265
                DOI:: 10.1038/nbt.2749

                ScienceOpen disciplines: Chemistry
                Keywords: Chromosome Mapping,methods,Data Mining,Electronic Health Records,statistics & numerical data,Genetic Predisposition to Disease,epidemiology,genetics,Genome-Wide Association Study,Humans,Medical Record Linkage,Phenotype,Polymorphism, Single Nucleotide
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                ScienceOpen disciplines: Chemistry
                Keywords: Chromosome Mapping, methods, Data Mining, Electronic Health Records, statistics & numerical data, Genetic Predisposition to Disease, epidemiology, genetics, Genome-Wide Association Study, Humans, Medical Record Linkage, Phenotype, Polymorphism, Single Nucleotide

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