A gene-to-patient approach uplifts novel disease gene discovery and identifies 18 putative novel disease genes

Exome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck.We apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries.Results from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction-constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries.Using large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.