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.

Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data.

Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data.

Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D).

Despite the success of genome-wide association studies (GWAS) in detecting a large number of loci for complex phenotypes such as rheumatoid arthritis (RA) susceptibility, the lack of information on the causal genes leaves important challenges to interpret GWAS results in the context of the disease biology. Here, we genetically fine-map the RA risk locus at 19p13 to define causal variants, and explore the pleiotropic effects of these same variants in other complex traits.

Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals.

Asthma exacerbations are a major cause of morbidity and medical cost.

The most common side effect of angiotensin-converting enzyme inhibitor (ACEi) drugs is cough. We conducted a genome-wide association study (GWAS) of ACEi-induced cough among 7080 subjects of diverse ancestries in the Electronic Medical Records and Genomics (eMERGE) network. Cases were subjects diagnosed with ACEi-induced cough. Controls were subjects with at least 6 months of ACEi use and no cough. A GWAS (1595 cases and 5485 controls) identified associations on chromosome 4 in an intron of KCNIP4.

Using genome-wide data from 253,288 individuals, we identified 697 variants at genome-wide significance that together explained one-fifth of the heritability for adult height. By testing different numbers of variants in independent studies, we show that the most strongly associated ∼2,000, ∼3,700 and ∼9,500 SNPs explained ∼21%, ∼24% and ∼29% of phenotypic variance. Furthermore, all common variants together captured 60% of heritability.

Herpes zoster, commonly referred to as shingles, is caused by the varicella zoster virus (VZV). VZV initially manifests as chicken pox, most commonly in childhood, can remain asymptomatically latent in nerve tissues for many years and often re-emerges as shingles. Although reactivation may be related to immune suppression, aging and female sex, most inter-individual variability in re-emergence risk has not been explained to date. We performed a genome-wide association analyses in 22,981 participants (2280 shingles cases) from the electronic Medical Records and Genomics Network.