New tool helps uncover rare genetic mutations in common diseases, including Parkinson's

by Laura Castañón, Harvard Medical School

edited by Sadie Harley, reviewed by Andrew Zinin

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Studies of genetics conducted in yeast cells, human neurons, mice or other model systems often reveal networks of genes that could contribute to complex diseases, such as breast cancer, type 2 diabetes and Parkinson's disease. But those findings don't always translate to human biology. Human genetics offers a path to determining which genes among those networks are most relevant to human disease.

Researchers at Harvard Medical School have developed a new statistical framework to link networks identified in models 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 work was published in Cell Genomics.

"We can take a network of genes and their connections and test whether there is a signal in real humans for the phenomenon we're interested in," said co-senior author Shamil Sunyaev, a professor of biomedical informatics in the Blavatnik Institute at HMS. "We can test if humans with mutations in a particular network are, for example, more likely to develop breast cancer."

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 also linked to dopamine, the neurotransmitter depleted in Parkinson's.

"The genetic data pointed us toward a role for prolactin within neurons that was completely unexpected," said co-senior author Vikram Khurana, an HMS associate professor of neurology at Brigham and Women's Hospital and chief of the Division of Movement Disorders at Mass General Brigham. "It opened up an entirely new line of investigation for Parkinson's disease."

A network-level approach

It is widely understood that genes frequently work together to carry out biological functions and that a disruption within that network—whether caused by a genetic mutation or environmental factors—could interfere with, say, the creation of an important hormone. But scientists don't have a good way to study these networks and pathways as a whole—most studies investigating disease in human genetics focus on single genes.

With NERINE, researchers can examine genes at a network level, enabling them to search for rare mutations within a connected group of genes that might be associated with a disease.

"This approach helps us move beyond looking at genes one at a time and instead understand how groups of genes may work together to influence a disease," Khurana said.

Using genetic data from the UK Biobank and Mass General Brigham Biobank, the researchers identified variants associated with breast cancer, cardiovascular disease and type 2 diabetes that could not be detected in single-gene tests.

For cardiovascular disease in particular, the framework uncovered genetic connections outside known pathways, providing new insights into biological mechanisms that may contribute to disease risk.

The researchers went a step further with Parkinson's disease. Starting with a gene network derived from yeast and neuronal models, they used NERINE to discover a potential connection with certain variants in a subnetwork containing the prolactin gene.

Follow-up experiments showed that prolactin loss had a negative effect on human neurons left vulnerable by stress from the abnormal protein alpha-synuclein, suggesting that prolactin may serve a protective role in Parkinson's.

"Human genetic signals can guide experimental studies toward the specific genes and biological interactions most likely to be relevant to disease," said first author Sumaiya Nazeen, an HMS research fellow in the Sunyaev and Khurana Labs.

"NERINE helps prioritize which genes, pathways and interactions are most promising to investigate in a particular disease, making experimental follow-up more focused and efficient."

Connecting two universes

Sunyaev specializes in developing computational and statistical methods to study genetic complexity. Over a decade ago, he began creating a framework similar to NERINE, but the work was hampered by the small size of available data sets. The founding and expansion of large-scale biobanks with genetic information from hundreds of thousands of individuals, along with technical innovations from Nazeen that could effectively harness the data, changed that.

"Suddenly we have this massive statistical dataset of observations on individual human genetics," said Sunyaev, who is also an HMS professor of medicine at Brigham and Women's. "We can bring humans to the table."

The researchers are already working on additional collaborations, using NERINE to analyze different disease systems and help prioritize new hypotheses to test. They 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," Sunyaev said. "Integrating human genetics with experimental biology will help us reveal disease mechanisms, prioritize potential therapeutic targets and guide future experiments."

Publication details

Sumaiya Nazeen et al, NERINE reveals rare variant associations in gene networks across phenotypes and implicates an SNCA-PRL-LRRK2 subnetwork in Parkinson's disease, Cell Genomics (2026). DOI: 10.1016/j.xgen.2026.101284

Journal information: Cell Genomics

Key medical concepts

Parkinson's DiseaseProlactinalpha-Synuclein

Clinical categories

Clinical geneticsNeurology Provided by Harvard Medical School Who's behind this story?

Sadie Harley

BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile →

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