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Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View.

ABSTRACT: BACKGROUND:Phenome-Wide Association Studies (PheWAS) can be used to investigate the association between single nucleotide polymorphisms (SNPs) and a wide spectrum of phenotypes. This is a complementary approach to Genome Wide Association studies (GWAS) that calculate the association between hundreds of thousands of SNPs and one or a limited range of phenotypes. The extensive exploration of the association between phenotypic structure and genotypic variation through PheWAS produces a set of complex and comprehensive results. Integral to fully inspecting, analysing, and interpreting PheWAS results is visualization of the data. RESULTS:We have developed the software PheWAS-View for visually integrating PheWAS results, including information about the SNPs, relevant genes, phenotypes, and the interrelationships between phenotypes, that exist in PheWAS. As a result both the fine grain detail as well as the larger trends that exist within PheWAS results can be elucidated. CONCLUSIONS:PheWAS can be used to discover novel relationships between SNPs, phenotypes, and networks of interrelated phenotypes; identify pleiotropy; provide novel mechanistic insights; and foster hypothesis generation - and these results can be both explored and presented with PheWAS-View. PheWAS-View is freely available for non-commercial research institutions, for full details see http://ritchielab.psu.edu/ritchielab/software.

SUBMITTER: Pendergrass SA

PROVIDER: S-EPMC3476448 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View.

Pendergrass Sarah A SA Dudek Scott M SM Crawford Dana C DC Ritchie Marylyn D MD

BioData mining 20120608 1

<h4>Background</h4>Phenome-Wide Association Studies (PheWAS) can be used to investigate the association between single nucleotide polymorphisms (SNPs) and a wide spectrum of phenotypes. This is a complementary approach to Genome Wide Association studies (GWAS) that calculate the association between hundreds of thousands of SNPs and one or a limited range of phenotypes. The extensive exploration of the association between phenotypic structure and genotypic variation through PheWAS produces a set ...[more]

PMID: 22682510

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Project description:BackgroundWe explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.ResultsIn this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).ConclusionIn conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.

Project description:Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype-phenotype associations, 26 represented phenotypes closely related to previously known genotype-phenotype associations, and 33 represented potentially novel genotype-phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.

Project description:Electronic health records (EHR) provide a comprehensive resource for discovery, allowing unprecedented exploration of the impact of genetic architecture on health and disease. The data of EHRs also allow for exploration of the complex interactions between health measures across health and disease. The discoveries arising from EHR based research provide important information for the identification of genetic variation for clinical decision-making. Due to the breadth of information collected within the EHR, a challenge for discovery using EHR based data is the development of high-throughput tools that expose important areas of further research, from genetic variants to phenotypes. Phenome-Wide Association studies (PheWAS) provide a way to explore the association between genetic variants and comprehensive phenotypic measurements, generating new hypotheses and also exposing the complex relationships between genetic architecture and outcomes, including pleiotropy. EHR based PheWAS have mainly evaluated associations with case/control status from International Classification of Disease, Ninth Edition (ICD-9) codes. While these studies have highlighted discovery through PheWAS, the rich resource of clinical lab measures collected within the EHR can be better utilized for high-throughput PheWAS analyses and discovery. To better use these resources and enrich PheWAS association results we have developed a sound methodology for extracting a wide range of clinical lab measures from EHR data. We have extracted a first set of 21 clinical lab measures from the de-identified EHR of participants of the Geisinger MyCodeTM biorepository, and calculated the median of these lab measures for 12,039 subjects. Next we evaluated the association between these 21 clinical lab median values and 635,525 genetic variants, performing a genome-wide association study (GWAS) for each of 21 clinical lab measures. We then calculated the association between SNPs from these GWAS passing our Bonferroni defined p-value cutoff and 165 ICD-9 codes. Through the GWAS we found a series of results replicating known associations, and also some potentially novel associations with less studied clinical lab measures. We found the majority of the PheWAS ICD-9 diagnoses highly related to the clinical lab measures associated with same SNPs. Moving forward, we will be evaluating further phenotypes and expanding the methodology for successful extraction of clinical lab measurements for research and PheWAS use. These developments are important for expanding the PheWAS approach for improved EHR based discovery.

Project description:OBJECTIVE:We report the first pediatric specific Phenome-Wide Association Study (PheWAS) using electronic medical records (EMRs). Given the early success of PheWAS in adult populations, we investigated the feasibility of this approach in pediatric cohorts in which associations between a previously known genetic variant and a wide range of clinical or physiological traits were evaluated. Although computationally intensive, this approach has potential to reveal disease mechanistic relationships between a variant and a network of phenotypes. METHOD:Data on 5049 samples of European ancestry were obtained from the EMRs of two large academic centers in five different genotyped cohorts. Recently, these samples have undergone whole genome imputation. After standard quality controls, removing missing data and outliers based on principal components analyses (PCA), 4268 samples were used for the PheWAS study. We scanned for associations between 2476 single-nucleotide polymorphisms (SNP) with available genotyping data from previously published GWAS studies and 539 EMR-derived phenotypes. The false discovery rate was calculated and, for any new PheWAS findings, a permutation approach (with up to 1,000,000 trials) was implemented. RESULTS:This PheWAS found a variety of common variants (MAF > 10%) with prior GWAS associations in our pediatric cohorts including Juvenile Rheumatoid Arthritis (JRA), Asthma, Autism and Pervasive Developmental Disorder (PDD) and Type 1 Diabetes with a false discovery rate < 0.05 and power of study above 80%. In addition, several new PheWAS findings were identified including a cluster of association near the NDFIP1 gene for mental retardation (best SNP rs10057309, p = 4.33 × 10(-7), OR = 1.70, 95%CI = 1.38 - 2.09); association near PLCL1 gene for developmental delays and speech disorder [best SNP rs1595825, p = 1.13 × 10(-8), OR = 0.65(0.57 - 0.76)]; a cluster of associations in the IL5-IL13 region with Eosinophilic Esophagitis (EoE) [best at rs12653750, p = 3.03 × 10(-9), OR = 1.73 95%CI = (1.44 - 2.07)], previously implicated in asthma, allergy, and eosinophilia; and association of variants in GCKR and JAZF1 with allergic rhinitis in our pediatric cohorts [best SNP rs780093, p = 2.18 × 10(-5), OR = 1.39, 95%CI = (1.19 - 1.61)], previously demonstrated in metabolic disease and diabetes in adults. CONCLUSION:The PheWAS approach with re-mapping ICD-9 structured codes for our European-origin pediatric cohorts, as with the previous adult studies, finds many previously reported associations as well as presents the discovery of associations with potentially important clinical implications.

Dataset Information

Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View.

Publications

Visually integrating and exploring high throughput Phenome-Wide Association Study (PheWAS) results using PheWAS-View.

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