The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how ...essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery. Discovery of causal variants for monogenic disorders has been facilitated by whole exome and genome sequencing, but does not provide a diagnosis for all patients. Here, the authors propose a Full Spectrum of Intolerance to Loss-of-Function (FUSIL) categorization that integrates gene essentiality information to aid disease gene discovery.
A wealth of genotypic and phenotypic information about inbred strains of laboratory mice is being collected and assembled in large databases. Sophisticated mining of this information can be useful in ...generation of hypotheses regarding the sources and nature of phenotypic variability, both environmental and genetic. As genotypic databases become complete, computational methods for identification of the genetic loci associated with complex polygenic traits may be possible. The common genetic origin of the inbred strains, and the genetic similarity of members of these strains make possible these approaches to the genetic study of pain and other complex phenotypes. In the first study, the relative role of laboratory environmental factors and genetic factors in pain related phenotypes are explored in a large data archive containing over 8000 observations of a single pain related phenotype. Classification and Regression Tree Analysis revealed that the experimenter was a more important factor than genotype and that other laboratory factors also influence studies of pain. Linear modeling allowed parametric estimation of some of the effects, and results of the CART analysis were confirmed in a balanced prospective experiment. In the second study, the possibility of detecting genetic loci contributing to trait variability through the use of databased genetic information and inbred strain phenotype studies is evaluated. Two algorithms are considered, and compared to results from more commonly employed experimental crosses. Statistical power issues and methods of controlling error-rates are evaluated for each method. The use of permutation analysis for the empirical derivation of significance thresholds may enhance the performance of inbred strain based mapping, potentially making this theoretically interesting method viable for use in practice.
The International Mouse Phenotyping Consortium (IMPC) continues to expand the catalogue of mammalian gene function by conducting genome and phenome-wide phenotyping on knockout mouse lines. The ...extensive and standardized phenotype screens allow the identification of new potential models for human disease through cross-species comparison by computing the similarity between the phenotypes observed in the mutant mice and the human phenotypes associated to their orthologous loci in Mendelian disease. Here, we present an update on the novel disease models available from the most recent data release (DR10.0), with 5861 mouse genes fully or partially phenotyped and a total number of 69,982 phenotype calls reported. With approximately one-third of human Mendelian genes with orthologous null mouse phenotypes described, the range of available models relevant for human diseases keeps increasing. Among the breadth of new data, we identify previously uncharacterized disease genes in the mouse and additional phenotypes for genes with existing mutant lines mimicking the associated disorder. The automated and unbiased discovery of relevant models for all types of rare diseases implemented by the IMPC constitutes a powerful tool for human genetics and precision medicine.
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