A Genomic Map Reveals How Thousands of Genes Shape Disease
Today’s biomedical researchers are relentlessly pursuing the genes that drive disease, aiming to develop therapies that target these genes to restore health. When a single gene is the culprit, the path to treatment can be straightforward. But for most diseases, where many genes — sometimes thousands — contribute, deciphering the connections becomes far more complex.
A new genomic mapping technique could change that. In a study published in Nature, researchers from Gladstone Institutes and Stanford University used a comprehensive approach to probe every gene in a cell, linking diseases and other traits to their underlying genetic machinery. These maps have the potential to clarify confusing biology and identify disease-causing genes that are good targets for intervention.
“We can now examine every gene in the genome and see how each one influences a particular 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 map to gain new insights into how certain genes influence specific traits.”
The Quest for the Why
For decades, researchers have relied on genome-wide association studies (GWAS), which analyze genomes from thousands of people to statistically link DNA variants with diseases and other traits. While productive, this data is not always actionable, especially for complex traits shaped by many genes.
“Even with these studies, there remains a big gap in understanding disease biology at the genetic level,” says Mineto Ota, MD, PhD, the study’s first author. Ota is a postdoctoral scholar at both the Marson lab and Stanford’s lab of Jonathan Pritchard. “We know many variants are associated with disease; we just don’t understand why.”
In some ways, it’s like having a map that shows a starting point and a destination but none of the roads in between.
“To understand complex traits, we need to focus on the network,” adds Pritchard, a Stanford professor of Biology and Genetics who co-led the study. “How do we think about biology when thousands of genes, with diverse functions, all influence a trait?”
To answer this, the team drew on two data sources.
First, they used data from a human leukemia cell line, a classic model for red blood cell traits. An MIT researcher — not involved in this study — previously disabled every gene in that cell line to observe how each gene's loss affected genetic activity.
Marson’s team merged those findings with data from the UK Biobank, which contains genomic sequences from more than 500,000 people. Ota then searched for individuals whose genetic mutations reduced function in a way that altered red blood cells.
By integrating these datasets, the researchers could map the networks of genes that influence red blood cell traits, uncovering a strikingly intricate genomic landscape. They now had a start point, a target, and the network of routes connecting them.
A key discovery was that some genes act through multiple mechanisms, dampening some processes while boosting others. SUPT5H, a gene linked to beta-thalassemia—a blood disorder affecting hemoglobin production and potentially causing anemia—serves as a prime example. The researchers linked SUPT5H to three essential blood-cell programs: hemoglobin production, the cell cycle, and autophagy. More importantly, they showed how the gene modulates these programs—either upregulating or downregulating activity.
“SUPT5H controls all three major pathways impacting hemoglobin,” Pritchard explains. “It promotes hemoglobin synthesis, slows the cell cycle, and slows autophagy, producing a synergistic effect.”
Implications for Immunology
This method’s ability to reveal the detailed genetic mechanisms that govern cellular behavior could dramatically accelerate biological discovery and drug development.
While the study highlighted several ways gene networks influence blood cell function, the method’s value lies in its versatility. The team’s approach can be applied to other human cells to uncover the molecular signatures driving disease.
For the Marson lab, which studies T cells and other immune processes, this technique could fulfill a long-standing wish: to map the genetic architecture underpinning immune-related diseases with greater clarity.
“The genetic burden linked to many autoimmune diseases, immune deficiencies, and allergies mostly centers on T cells,” Marson notes. “We’re excited to build more detailed maps that reveal how genetics shapes these immune-mediated conditions.”
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
Note: This article is republished from Gladstone Institute materials. Content may have been edited for length and clarity. For more information, please consult the original sources linked in the reference.
