Genome to phenome. Meaningful patterns in human diseases are often only revealed when looking at larger groups of patients. Over the last decade, we have figured out how to make genetics scalable to fit this need. High-throughput genetics can now be performed on an industrial scale with the possibility of assessing almost every base pair in the human genome in thousands of people. Phenotyping, however, has remained a non-scalable task, requiring repeated review, extraction, and interpretation of phenotypic data. In addition, there is no agreed-upon format for phenotypic data that parallels the standards we have in genetics. To overcome this problem, projects such as the Epilepsy Phenome/Genome Project (EPGP) have collected systematic, standardized phenotypic data upfront on every patient. In a recent study in Neurology that analyzed familial clustering of phenotypes within this dataset, we get a first view of what working with the epilepsy phenome may look like. We were asked to provide an editorial for this study where we emphasized that systematic phenotyping in large datasets can reveal phenotypic patterns that are beyond our understanding of disease genetics.  Basically, the phenome suggests patterns that are contradictory to what we think genes would do. Continue reading →