New Genetic Map Reveals How Thousands of Genes Drive Disease

A new genome-wide mapping method finally shows how thousands of genes connect to drive disease.

Biomedical researchers are working intensively to identify the genes that contribute to disease, with the long-term aim of developing treatments that precisely target those genes and help restore normal health.

When illness can be traced back to a single faulty gene, the path forward is often relatively clear. Most diseases, however, are far more complex. In many cases, dozens, hundreds, or even thousands of genes are involved, making it extremely difficult to understand how they interact and lead to disease.

A newly developed genomic mapping approach could help overcome this challenge. In a study published in Nature, scientists from Gladstone Institutes and Stanford University used a large-scale method that examines the effects of every gene within a cell. This strategy allowed them to connect diseases and traits to the genetic systems that control them. The resulting maps may help untangle complicated biology and identify genes that could be promising targets for treatment.

“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.”

Finding the ‘Why’ Behind Genetic Risk

For many years, scientists have relied on “genome-wide association studies,” which examine the DNA of large populations to identify genetic differences linked to diseases and other traits. These studies have generated vast amounts of data, but translating those findings into clear biological explanations has often proven difficult, especially for conditions influenced by many genes.

“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 is a postdoctoral scholar in Marson’s Gladstone lab, as well as in the lab of Stanford scientist Jonathan Pritchard, PhD. “We understand that many variants are associated with disease; we just don’t understand why.”

Ota likens the situation to having a map that shows where a journey begins and ends, but offers no information about the routes connecting the two points.

“To understand complex traits, we really need to focus on the network,” says Pritchard, a 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?”

Combining Cell Experiments With Human Genetic Data

To tackle this problem, the research team drew on two different data sources.

One came from a human leukemia cell line commonly used to study red blood cell characteristics. In earlier work, an MIT researcher not involved in the current study systematically turned off each gene in this cell line and recorded how the loss of each gene changed genetic activity.

Marson’s group then combined this information with data from the UK Biobank, which includes genetic sequences from more than 500,000 people. Ota analyzed the dataset to identify individuals with genetic changes that reduced gene function and altered red blood cell traits.

Bringing these datasets together allowed the researchers to build a detailed map of the gene networks that influence red blood cells. The result was a highly complex picture of genetic interactions, providing not just the starting point and endpoint, but also the many connections that link them.

The team also found that certain genes influence several biological processes at once, increasing some activities while reducing others. One example is SUPT5H, a gene associated with beta thalassemia, a blood disorder that interferes with hemoglobin production and can cause moderate to severe anemia. The researchers connected SUPT5H to three key blood cell programs: hemoglobin production, cell cycle, and autophagy. They also showed how the gene alters each process by either increasing or decreasing gene activity.

“SUPT5H regulates all three main pathways that affect hemoglobin,” Pritchard says. “It activates hemoglobin synthesis, slows down the cell cycle, and slows down autophagy, which together have a synergistic effect.”

Why These Genetic Maps Matter for Immunology

The ability to uncover detailed genetic pathways that govern how cells function could significantly influence future biological research and drug development.

Although the study revealed specific insights into how gene networks shape blood cell behavior, the larger advance lies in the method itself. This approach can now be applied to other types of human cells, enabling researchers to uncover the molecular patterns that underlie many diseases.

For the Marson lab, which focuses on understanding T cells and the immune system, the technique could open the door to many new discoveries.

“The genetic burden associated with many autoimmune diseases, immune deficiencies, and allergies are overwhelmingly linked to T cells,” Marson says. “We look forward to developing additional detailed maps that will help us really understand the genetic architecture behind these immune-mediated diseases.”

Reference: “Causal modelling of gene effects from regulators to programs to traits” by Mineto Ota, Jeffrey P. Spence, Tony Zeng, Emma Dann, Nikhil Milind, Alexander Marson and Jonathan K. Pritchard, 10 December 2025, Nature.
DOI: 10.1038/s41586-025-09866-3

Authors include: Mineto Ota, Jeffrey Spence, Tony Zeng, Emma Dann, Nikhil Milind, Alexander Marson, and Jonathan Pritchard. This research was funded by the National Institutes of Health, the Simons Foundation, the Lloyd J. Old STAR Award, the Parker Institute for Cancer Immunotherapy, the Innovative Genomics Institute, the Larry L. Hillblom Foundation, the Northern California JDRF Center of Excellence, the Byers family, K. Jordan, the CRISPR Cures for Cancer Initiative, the Astellas Foundation for Research on Metabolic Disorders, the Chugai Foundation for Innovative Drug Discovery Science, and the EMBO Postdoctoral Fellowship.

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