Despite great strides in the development and wide acceptance of standards for exchanging structured information about genomic variants, the development of standards for computational phenotype analysis for translational genomics has lagged behind. Phenotypic features (signs, symptoms, laboratory and imaging findings, results of physiological tests, etc.) are of essential clinical importance, yet exchanging them in conjunction with genomic variation is often overlooked or even neglected. In the clinical domain, significant work has been dedicated to the development of computational phenotypes.1 Traditionally, these approaches have largely relied on rule-based methods and large sources of clinical data to identify cohorts of patients with or without a specific disease.2–5 However, they were not developed to enable deep phenotyping of phenotypic abnormalities, to facilitate computational analysis of interpatient phenotypic similarity, or to support computational decision support. To address this, the Global Alliance for Genomics and Health6 (GA4GH) has developed the Phenopacket schema, which supports exchange of computable longitudinal case-level phenotypic information for diagnosis of and research on all types of disease including Mendelian and complex genetic diseases, cancer, and infectious diseases