Post by Harvard Medical School

Researchers at Harvard Medical School have developed a new statistical framework to link networks of genes identified in yeast cells, mice, or other models systems with human genetic data. This could make it faster and easier for researchers to identify which groups of genes are most likely to contribute to a particular human disease, uncover rare disease-causing mutations, and zero in on promising therapeutic targets.  The framework, called NERINE, can also help uncover disease mechanisms. Studying Parkinson’s disease, the researchers used NERINE to reveal a previously unrecognized link to mutations involved in the production of prolactin — a hormone typically associated with pregnancy and breastfeeding but that is also linked to dopamine, the neurotransmitter depleted in Parkinson’s. Follow-up experiments suggested that prolactin may help protect neurons left vulnerable by other aspects of the disease. The researchers have made the framework available for anyone to use, hoping others will take advantage of it to bridge the gap between complex disease genetics and experimental models. “This work is trying to relate these two universes,” said co-senior author Shamil Sunyaev, professor of biomedical informatics at HMS. “Integrating human genetics with experimental biology will help us reveal disease mechanisms, prioritize potential therapeutic targets, and guide future experiments.” https://bit.ly/4aIpTi1