{'fraction_non_alphanumeric': 0.02800169707254985, 'fraction_numerical': 0.029698769622401356, 'mean_word_length': 5.605042016806722, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Once doesn't count: Phenotype‐driven gene hunting in cohorts

The demonstration of rare, predicted pathogenic variants in the same gene in multiple unrelated individuals can be used to make a case for the gene being a novel disease-associated gene. Although it is often said that one needs to find a “second” unrelated individual to confirm a candidate disease gene, as genomic databases for diagnostics and gene-discovery research in rare disease grow in size and scope, and as data exchange between databases by means of the MatchMaker Exchange (Philippakis et al., HumMutat 36:915–921, 2015) allows even larger amounts of genotype-phenotype data to be queried, the possibility of spurious matches increases, necessitating more rigorous approaches tomatchmaking. Myriad statistical tests have been developed for assessing the genetic evidence, but to date, the phenotypic evidence has been used in an intuitive and unsystematic manner: clinicians would discuss cases in syndrome clubs or human genetics meetings and occasionally recognize amatch to an undiagnosed case in their care. Recently, computational phenotype analysis using the Human Phenotype Ontology (Köhler et al., Nucleic Acids Res 45:D865–D876, 2017) has enabled computational phenotype matching to be applied to large cohorts. Probabilistic genotype and phenotype matching contributed to the identification of novel disease genes in a study of 4,125 families with diverse developmental disorders (Akawi et al., Nat Genet 47:1363– 1369, 2015), and a Bayesian approach towards estimating the probability of an association together with a cluster of HPO–coded phenotypes enabled the identification of several novel genes associated with inherited bleeding disorders (Greene et al., Am J Hum Genet 98:490–499, 2016). In this issue, Vulto-van Silfhout and colleagues (Hum Mutat 38: 594–599, 2017) present a statistical model to integrate genetic information and shared phenotype features, and demonstrate that their method increases the statistical power to identify novel disease genes (decreasing the number of unrelated cases required for statistical significance) in a cohort of 6,149 patients with intellectual disability and other features encoded using HPO terms. These methods are likely to become trend setters as the movement towards structured phenotyping and data sharing continues.