Mendelian gene identification through mouse embryo viability screening

The diagnostic rate of Mendelian disorders in sequencing studies continues to increase, along with the pace of novel disease gene discovery. However, variant interpretation in novel genes not currently associated with disease is particularly challenging and strategies combining gene functional evidence with approaches that evaluate the phenotypic similarities between patients and model organisms have proven successful. A full spectrum of intolerance to loss-of-function variation has been previously described, providing evidence that gene essentiality should not be considered as a simple and fixed binary property. Here we further dissected this spectrum by assessing the embryonic stage at which homozygous loss-of-function results in lethality in mice from the International Mouse Phenotyping Consortium, classifying the set of lethal genes into one of three windows of lethality: early, mid or late gestation lethal. We studied the correlation between these windows of lethality and various gene features including expression across development, paralogy and constraint metrics together with human disease phenotypes, and found that the members of the early gestation lethal category show distinctive characteristics and a strong enrichment for genes linked with recessive forms of inherited metabolic disease. Based on these findings, we explored a gene similarity approach for novel gene discovery focused on this subset of lethal genes. Finally, we investigated unsolved cases from the 100,000 Genomes Project recruited under this disease category to look for signs of enrichment of biallelic predicted pathogenic variants among early gestation lethal genes and highlight two novel candidates with phenotypic overlap between the patients and the mouse knockout.

A computational pipeline for functional gene discovery

Many computational pipelines exist for the detection of differentially expressed genes. However, computational pipelines for functional gene detection rarely exist. We developed a new computational pipeline for functional gene identification from transcriptome profiling data. Key features of the pipeline include batch effect correction, clustering optimization by gap statistics, gene ontology analysis of clustered genes, and literature analysis for functional gene discovery. By leveraging this pipeline on RNA-seq datasets from two mouse retinal development studies, we identified 7 candidate genes involved in the formation of the photoreceptor outer segment. The expression of top three candidate genes (Pde8b, Laptm4b, and Nr1h4) in the outer segment of the developing mouse retina were experimentally validated by immunohistochemical analysis. This computational pipeline can accurately predict novel functional gene for a specific biological process, e.g., development of the outer segment and synapses of the photoreceptor cells in the mouse retina. This pipeline can also be useful to discover functional genes for other biological processes and in other organs and tissues.

Diagnostic testing laboratories are valuable partners for disease gene discovery: 5-year experience with GeneMatcher

Clinical and research laboratories extensively use exome sequencing due to its high diagnostic rates, cost savings, impact on clinical management, and efficacy for disease gene discovery. While the rates of disease gene discovery have steadily increased, only ~16% of genes in the genome have confirmed disease associations. Here we describe our diagnostic laboratory’s disease gene discovery and ongoing data-sharing efforts with GeneMatcher. In total, we submitted 246 candidates from 243 unique genes to GeneMatcher, of which 45.93% are now clinically characterized. Submissions with at least one case meeting our candidate genes reporting criteria were significantly more likely to be characterized as of October 2021 compared to genes with no candidates meeting our reporting criteria (p=0.025). We reported relevant findings related to these gene-disease associations for 480 probands. In 219 (45.63%) instances, these results were reclassifications after an initial candidate gene (uncertain) or negative report. Since 2013, we have co-authored 105 publications focused on delineating gene-disease associations. Diagnostic laboratories are pivotal for disease gene discovery efforts and can screen phenotypes based on genotype matches, contact clinicians of relevant cases, and issue proactive reclassification reports. GeneMatcher is a critical resource in these efforts.