When one faulty gene is responsible, the path to understanding the problem can be fairly direct. Many conditions, however, are far more complicated. In these cases, multiple genes, sometimes even thousands, play a role, and it becomes much harder to sort out how they connect to the disease.

A new genomic mapping approach could make that challenge easier to tackle. In a Nature study, researchers at Gladstone Institutes and Stanford University used a broad strategy that tests the impact of every gene in a cell, linking diseases and other traits to the underlying genetic systems that shape them. The resulting maps could cut through confusing biology and spotlight the genes most likely to be useful targets for new 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."

Finding the 'Why' Behind Genetic Risk

For years, scientists have relied heavily on "genome-wide association studies," which scan the DNA of thousands of people to find statistical links between genetic differences and traits, including disease risk. These efforts have generated enormous datasets, but turning those signals into clear biological explanations can be difficult, especially for traits 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."

Mineto compares it to having a map with a clear starting point and endpoint, but no routes connecting the two.

"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 Big Population Data

To dig into that network problem, the researchers pulled information from two databases.

One dataset came from a human leukemia cell line that is commonly used to study red blood cell traits. In earlier work, an MIT researcher who was not involved in this study had switched off each gene in that cell line, one at a time, and tracked how losing that gene changed genetic activity.

Marson's team then paired those results with UK Biobank data, which includes genomic sequences from more than 500,000 people. Ota searched for individuals with genetic mutations that lowered gene function in ways that changed their red blood cells.

Putting the two sources together allowed the researchers to build a detailed map of the gene networks that influence red blood cell traits. The picture that emerged showed a remarkably complex genetic landscape. With this approach, they could see the starting point, the destination, and the intricate set of connections in between.

They also discovered that some genes affect several biological processes at the same time, weakening certain activities while increasing others. One example is SUPT5H, a gene associated with beta thalassemia, a blood disorder that disrupts hemoglobin production and can lead to 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 influences each program, either increasing or reducing 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 This Mapping Method Could Matter for Immunology

Being able to reveal the detailed genetic pathways that control how cells function could reshape both basic biology and drug development.

Although the team identified multiple ways gene networks shape blood cell behavior, the bigger story is the tool itself. The research group, and potentially many other scientists, can now apply the same strategy to other human cell types to uncover the molecular patterns that drive disease.

For the Marson lab, which focuses on T cells and other parts of the immune system, the method could open the door to many more 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."

The study, "Causal modeling of gene effects from regulators to programs to traits," appears in the December 10, 2025 issue of Nature. 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.

The approach, known as V2P (Variant to Phenotype), is intended to speed up genetic testing and support the development of new therapies for rare and complex illnesses. The research was published in the December 15 online issue of Nature Communications.

Predicting disease from genetic variation

Most existing genetic analysis tools are able to estimate whether a mutation is potentially damaging, but they typically stop there. They do not explain what kind of disease may result. V2P is designed to overcome this limitation by using advanced machine learning to connect genetic variants with their expected phenotypic outcomes -- meaning the diseases or traits a mutation may produce. In this way, the system helps predict how a person's DNA could affect their health.

"Our approach allows us to pinpoint the genetic changes that are most relevant to a patient's condition, rather than sifting through thousands of possible variants," says first author David Stein, PhD, who recently completed his doctoral training in the labs of Yuval Itan, PhD, and Avner Schlessinger, PhD. "By determining not only whether a variant is pathogenic but also the type of disease it is likely to cause, we can improve both the speed and accuracy of genetic interpretation and diagnostics."

Training the AI to find the right mutation

To build the model, the researchers trained V2P on a large dataset containing both harmful and harmless genetic variants, along with detailed disease information. This training allowed the system to learn patterns linking specific variants to health outcomes. When tested using real, de-identified patient data, V2P frequently ranked the true disease-causing mutation within the top 10 candidates, demonstrating its potential to simplify and accelerate genetic diagnosis.

"Beyond diagnostics, V2P could help researchers and drug developers identify the genes and pathways most closely linked to specific diseases," says Dr. Schlessinger, co-senior and co-corresponding author, Professor of Pharmacological Sciences, and Director of the AI Small Molecule Drug Discovery Center at the Icahn School of Medicine at Mount Sinai. "This can guide the development of therapies that are genetically tailored to the mechanisms of disease, particularly in rare and complex conditions."

Expanding precision medicine and drug discovery

At present, V2P sorts mutations into broad disease categories, such as nervous system disorders or cancers. The research team plans to enhance the system so it can make more detailed predictions and combine its results with additional data sources to further assist drug discovery.

The researchers say this advance marks meaningful progress toward precision medicine, where treatments are selected based on an individual's genetic profile. By linking genetic variants to their likely disease effects, V2P could help clinicians reach diagnoses faster and help scientists uncover new targets for therapy.

"V2P gives us a clearer window into how genetic changes translate into disease, which has important implications for both research and patient care," says Dr. Itan, co-senior and co-corresponding author, Associate Professor of Artificial Intelligence and Human Health, and Genetics and Genomic Sciences, a core member of The Charles Bronfman Institute for Personalized Medicine, and a member of The Mindich Child Health and Development Institute at the Icahn School of Medicine at Mount Sinai. "By connecting specific variants to the types of diseases they are most likely to cause, we can better prioritize which genes and pathways warrant deeper investigation. This helps us move more efficiently from understanding the biology to identifying potential therapeutic approaches and, ultimately, tailoring interventions to an individual's specific genomic profile."

The paper is titled "Expanding the utility of variant effect predictions with phenotype-specific models."

The study's authors, as listed in the journal, are David Stein, Meltem Ece Kars, Baptiste Milisavljevic, Matthew Mort, Peter D. Stenson, Jean-Laurent Casanova, David N. Cooper, Bertrand Boisson, Peng Zhang, Avner Schlessinger, and Yuval Itan.

This research was supported by National Institutes of Health (NIH) grants R24AI167802 and P01AI186771, funding from the Fondation Leducq, and the Leona M. and Harry B. Helmsley Charitable Trust grant 2209-05535. Additional support came from NIH grants R01CA277794, R01HD107528, and R01NS145483. The work also received partial support through Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences, as well as support from the Office of Research Infrastructure of the NIH under award numbers S10OD026880 and S10OD030463.