A Genomic Map Reveals How Thousands of Genes Drive Disease
Biomedical researchers are increasingly chasing the genetic roots of disease with the aim of designing therapies that directly target these genes to restore health.
When a single gene is to blame, the path to therapy can be relatively straightforward. But for most diseases, where many genes—sometimes in the thousands—contribute, unraveling the connections becomes a much tougher challenge.
Now, a novel genomic-mapping approach could shift that dynamic. In a study published in Nature, researchers from Gladstone Institutes and Stanford University used a broad, cell-wide method to probe every gene in a cell and connect diseases and other traits to their underlying genetic machinery. These maps promise to clarify confusing biology and identify disease-causing genes that are ready for intervention.
“We can now examine every gene in the genome and gauge how each one affects a given cell type,” says Alex Marson, MD, PhD, senior investigator at Gladstone and co-leader of the study. “Our goal is to use this information as a detailed map to gain fresh insights into how specific genes shape particular traits.”
The Quest for Meaning
For decades, researchers have relied on genome-wide association studies (GWAS), which analyze DNA from thousands of people to statistically link genetic variations with diseases and other traits. While GWAS have generated vast data, much of it isn’t directly actionable—especially for complex traits influenced by many genes.
“Even with these studies, there’s still a large gap in understanding disease biology at the genetic level,” notes Mineto Ota, MD, PhD, the study’s first author. Ota is a postdoctoral fellow in Marson’s Gladstone lab and in Jonathan Pritchard’s Stanford lab. “We know many variants are associated with disease; we don’t always know why.”
In some ways, this gap is like having a map that shows the start and end points but lacks the roads in between, Ota explains.
“To understand complex traits, we must look at the network,” emphasizes Pritchard, a Stanford biology and genetics professor who co-led the study with Marson. “What happens when thousands of genes, each with different roles, collectively influence a trait？”
To tackle that question, the researchers drew on two large datasets.
First, they used data from a human leukemia cell line commonly employed to model red blood cell traits. An MIT researcher—unaffiliated with the current study—had previously disabled every gene in this cell line one by one, charting how each loss affected gene activity.
Marson’s team merged those results with UK Biobank data, which includes genome sequences from more than 500,000 people. Ota identified individuals who carry genetic changes that reduce function in ways that shift red blood cell characteristics.
Combining these sources allowed the team to map comprehensively the gene networks that influence red blood cell traits, revealing an extraordinarily intricate genomic landscape. They now had a starting point, an endpoint, and the pathways connecting them.
The researchers discovered that some genes influence multiple mechanisms, dampening some biological activities while boosting others. A notable example is SUPT5H, a gene linked to beta-thalassemia, a blood disorder affecting hemoglobin production that can cause mild to severe anemia. The team connected SUPT5H to three crucial blood-cell programs—hemoglobin synthesis, the cell cycle, and autophagy—and showed how the gene modulates these programs by enhancing or suppressing activity.
“SUPT5H governs all three major pathways that impact hemoglobin,” Pritchard explains. “It stimulates hemoglobin production, slows the cell cycle, and reduces autophagy, together producing a synergistic effect.”
Implications for Immunology
Unveiling the detailed genetic machinery that controls cellular behavior could accelerate biological discovery and drug development.
Although the study highlighted several ways gene networks shape blood cell function, the technique’s true value lies in the method itself. The research team—and potentially many others in life sciences—can now perform similar analyses across a variety of human cells to identify molecular signatures that drive disease.
For Marson’s group, which studies T cells and other immune processes, this method could be transformative, offering the long-sought ability to map the genetic architecture behind immune-related diseases more precisely.
“We know the genetic burden of many autoimmune diseases, immune deficiencies, and allergies is heavily tied to T cells,” Marson notes. “We anticipate creating even more detailed maps that will help us understand the genetic underpinnings of immune-mediated diseases.”
Reference: Ota M, Spence JP, Zeng T, et al. Causal modelling of gene effects from regulators to programs to traits. Nature. 2025. doi:10.1038/s41586-025-09866-3
This article is republished from Gladstone Institutes’ coverage on genomic maps that untangle the complex roots of disease. For more information, see the original source.
