In the vast library of human biology, genes are the books that tell our bodies how to function. But when the pages are misprinted, or entire volumes go missing, the consequences can be illness. For decades, biomedical scientists have been racing to identify which genetic “chapters” contribute to disease, hoping to one day edit the story toward health.
When a single faulty gene is the culprit, the narrative is relatively straightforward. Yet most conditions are sprawling epics, with thousands of genes weaving together in complex, overlapping plots. Untangling those threads has been one of science’s most daunting challenges.
Now, a new genomic mapping approach may offer a breakthrough. In a study published in Nature, researchers at Gladstone Institutes and Stanford University unveiled a sweeping strategy that tests the impact of every gene in a cell. By linking diseases and traits to the underlying genetic systems that shape them, the team has created maps that cut through biological noise and spotlight the genes most likely to become targets for future therapies.
“We can now look across every gene in the genome and get a sense of how each one affects a particular cell type,” says Gladstone Senior Investigator Alex Marson, MD, PhD, the Connie and Bob Lurie Director of the Gladstone-UCSF Institute of Genomic Immunology, who co-led the study. “Our goal is to use this information as a map to gain new insights into how certain genes influence specific traits.”
Innovative tool enhances disease-causing gene discovery
For years, scientists leaned heavily on genome-wide association studies (GWAS), scanning the DNA of thousands to find statistical links between genetic differences and traits. These efforts produced enormous datasets, but translating those signals into clear biological explanations has been like trying to read a book written in code.
“Even with these studies, there remains a huge gap in understanding disease biology on a genetic level,” says first author Mineto Ota, MD, PhD. Ota, a postdoctoral scholar in Marson’s Gladstone lab and Stanford scientist Jonathan Pritchard’s lab, adds: “We understand that many variants are associated with disease; we just don’t understand why.”
Ota compares the challenge to having a map with a clear starting point and endpoint, but no roads connecting them.
“To understand complex traits, we really need to focus on the network,” says Pritchard, professor of Biology and Genetics at Stanford, who co-led the study with Marson. “How do we think about biology when thousands and thousands of genes, with many different functions, are all affecting a trait?”
To tackle this network puzzle, the researchers combined two powerful datasets.
Dataset One: A human leukemia cell line, previously studied at MIT, where each gene had been switched off one at a time to track how its absence altered genetic activity.
Dataset Two: The UK Biobank, containing genomic sequences from more than 500,000 people. Ota searched for individuals with mutations that lowered gene function and altered red blood cell traits.
Together, these sources enabled the team to construct a detailed map of gene networks that influence red blood cell behavior. The resulting picture revealed a labyrinth of connections, genes acting not in isolation but as part of intricate systems.
One striking discovery was the gene SUPT5H, linked to beta thalassemia, a blood disorder that disrupts hemoglobin production. The team found SUPT5H touches three critical programs: hemoglobin production, cell cycle, and autophagy.
“SUPT5H regulates all three main pathways that affect hemoglobin,” Pritchard explains. “It activates hemoglobin synthesis, slows down the cell cycle, and slows down autophagy, which together have a synergistic effect.”
Journal Reference:
- Mineto Ota, Jeffrey P. Spence, Tony Zeng, Emma Dann, Nikhil Milind, Alexander Marson, Jonathan K. Pritchard. Causal modelling of gene effects from regulators to programs to traits. Nature, 2025; DOI: 10.1038/s41586-025-09866-3
