Linking genetic variants to disease remains a central challenge in human genetics, especially for traits influenced by many genes. A study published in Nature reports a genomic mapping approach that connects population-scale association signals to cell-level gene networks, offering a clearer path from variant to mechanism.
The work, led by researchers at the Gladstone Institutes and Stanford University, combines genome-wide loss-of-function data with human genetics. “We can now look across every gene in the genome and get a sense of how each one affects a particular cell type,” explained Alex Marson, 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.”
Genome-wide association studies have linked thousands of variants to disease, but many fall in noncoding regions and lack clear functional explanations. “Even with these studies, there remains a huge gap in understanding disease biology on a genetic level,” stated first author Mineto Ota. “We understand that many variants are associated with disease; we just don’t understand why.”
To close that gap, the team integrated two large datasets. One came from a human leukemia cell line used to model red blood cell biology, where an earlier study systematically inactivated each gene and measured downstream effects on gene expression. The second came from the UK Biobank, which includes genomic data from more than 500,000 individuals. The team identified people carrying loss-of-function variants that altered red blood cell traits, then overlaid those signals onto the cell-based gene network.
The resulting maps showed how groups of genes converge on shared programs. One example was SUPT5H, linked to beta thalassemia. “SUPT5H regulates all three main pathways that affect hemoglobin,” said Jonathan Pritchard, a Stanford scientist and corresponding author on the study. “It activates hemoglobin synthesis, slows down the cell cycle, and slows down autophagy, which together have a synergistic effect.”
The study demonstrates how integrating functional genomics with population data can connect disease-associated variants to specific cellular mechanisms in defined cell types. Applied to red blood cell biology, the approach produced genome-wide maps that link genetic variation to coordinated gene programs and measurable human traits.
Publication Details
Ota, M., Spence, J.P., Zeng, T. et al. Causal modelling of gene effects from regulators to programs to traits. Nature (2025). https://doi.org/10.1038/s41586-025-09866-3
The work, led by researchers at the Gladstone Institutes and Stanford University, combines genome-wide loss-of-function data with human genetics. “We can now look across every gene in the genome and get a sense of how each one affects a particular cell type,” explained Alex Marson, 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.”
Genome-wide association studies have linked thousands of variants to disease, but many fall in noncoding regions and lack clear functional explanations. “Even with these studies, there remains a huge gap in understanding disease biology on a genetic level,” stated first author Mineto Ota. “We understand that many variants are associated with disease; we just don’t understand why.”
To close that gap, the team integrated two large datasets. One came from a human leukemia cell line used to model red blood cell biology, where an earlier study systematically inactivated each gene and measured downstream effects on gene expression. The second came from the UK Biobank, which includes genomic data from more than 500,000 individuals. The team identified people carrying loss-of-function variants that altered red blood cell traits, then overlaid those signals onto the cell-based gene network.
The resulting maps showed how groups of genes converge on shared programs. One example was SUPT5H, linked to beta thalassemia. “SUPT5H regulates all three main pathways that affect hemoglobin,” said Jonathan Pritchard, a Stanford scientist and corresponding author on the study. “It activates hemoglobin synthesis, slows down the cell cycle, and slows down autophagy, which together have a synergistic effect.”
The study demonstrates how integrating functional genomics with population data can connect disease-associated variants to specific cellular mechanisms in defined cell types. Applied to red blood cell biology, the approach produced genome-wide maps that link genetic variation to coordinated gene programs and measurable human traits.
Publication Details
Ota, M., Spence, J.P., Zeng, T. et al. Causal modelling of gene effects from regulators to programs to traits. Nature (2025). https://doi.org/10.1038/s41586-025-09866-3