It is increasingly clear that the development of tools to interrogate and curate the EHR “phenome” opens the way to novel forms of new knowledge development. One approach to generating a curated structured dataset from the EHR is illustrated in Figure 2. For example, a conventional genetic association study starts with a phenotype of interest and searches for associated genetic loci or variants. The phenome-wide association study (PheWAS) reverses this paradigm, using the phenome as the interrogation target. The initial implementations sought associations between genotypes at single nucleotide polymorphism (SNP) sites and the phenome,10,39,40 generated by algorithmically-defined diagnoses for cases (representing about 1500 different diseases and traits) and corresponding controls. For example, in the eMERGE hypothyroidism project, the odds ratios for association between hypothyroidism and FOXE1 variants were near-identical with GWAS and PheWAS; PheWAS also identified specific associated thyroid disorders as well as an association with atrial arrhythmias.10 The top association in a GWAS of normal cardiac conduction (assessed by variability in QRS durations on normal ECGs) was in a sodium channel gene SCN10A, and PheWAS demonstrated that the top
