Genomic analyses of glycine decarboxylase neurogenic mutations yield a large-scale prediction model for prenatal disease

Hundreds of mutations in a single gene result in rare diseases, but why mutations induce severe or attenuated states remains poorly understood. Defect in glycine decarboxylase (GLDC) causes Non-ketotic Hyperglycinemia (NKH), a neurological disease associated with elevation of plasma glycine. We unified a human multiparametric NKH mutation scale that separates severe from attenuated neurological disease with new in silico tools for murine and human genome level-analyses, gathered in vivo evidence from mice engineered with top-ranking attenuated and a highly pathogenic mutation, and integrated the data in a model of pre- and post-natal disease outcomes, relevant for over a hundred major and minor neurogenic mutations. Our findings suggest that highly severe neurogenic mutations predict fatal, prenatal disease that can be remedied by metabolic supplementation of dams, without amelioration of persistent plasma glycine. The work also provides a systems approach to identify functional consequences of mutations across hundreds of genetic diseases. Our studies provide a new framework for a large scale understanding of mutation functions and the prediction that severity of a neurogenic mutation is a direct measure of pre-natal disease in neurometabolic NKH mouse models. This framework can be extended to analyses of hundreds of monogenetic rare disorders where the underlying genes are known but understanding of the vast majority of mutations and why and how they cause disease, has yet to be realized. Author summary Building models of human genetic disease, both computational and animal, is an essential part of understanding the disease, designing treatments, and testing therapies. Here, we have developed new in silico tools to build models for the rare neurological disorder non-ketotic hyperglycinemia (NKH), which is caused by mutations in glycine decarboxylase (GLDC), a protein that degrades glycine. We first applied a mutation scoring tool to GLDC in both the human and mouse genomes, and then used this data to develop a computational model for predicting which mutations would be well-modeled in mice, and how severe their disease would be. We then validated this computational model by genetically-engineering a mutation predicted to cause mild disease and another predicted to cause severe disease. Our predictions were correct and we used them to develop a model relevant for over a hundred major and minor neurogenic mutations that suggests that the more severe the mutation, the greater chance it will cause disease that starts before birth and is likely to be fatal unless rescued by modifying diet. This study also demonstrates the power of in silico analyses for guiding the development of genetic disease models and incorporating them into scalable models that can be applied to understand hundreds of mutations that cause disease.