Scientists have made a groundbreaking advancement in understanding how specific genetic changes influence disease risk and other human health traits. By examining regions of DNA previously linked to disease, researchers have created high-resolution maps of DNA variant activity. This work, published in the journal Nature, helps pinpoint the exact changes that impact blood pressure, cholesterol levels, blood sugar, and other complex human traits.
The study was led by researchers from The Jackson Laboratory (JAX), the Broad Institute, and Yale University. It addresses a long-standing challenge in human genetics: identifying which specific DNA changes within large genomic regions are responsible for disease associations. These regions often contain numerous variants, making it difficult and time-consuming to determine which ones truly matter.
Revolutionizing Genetic Discovery
The key to this breakthrough was scale. The research team utilized a method capable of testing thousands of variants simultaneously, examining over 220,000 previously identified DNA changes across five different cell types. This approach allowed them to resolve about 20 percent of these genomic regions, offering new insights into the role these variants play in health and disease.
“For nearly two decades, genetic studies have identified where in the genome we need to look for disease risk, but not which specific DNA changes are responsible,” said Ryan Tewhey, a geneticist and associate professor at JAX. “Our study helps close this gap by working at the scale needed to confidently pinpoint the specific DNA changes that matter across thousands of regions all at once, rather than one by one.”
Previously, making these connections was likened to searching for a single typo in a massive book. The new experimental approach is akin to speed reading, scanning thousands of pages at once and flagging the exact letters that change meaning, dramatically accelerating genetic discovery.
From Association to Biology
Layla Siraj, the study’s first author, emphasized the significance of bridging the gap from genetic association to biological understanding. “By uncovering the patterns underlying how single-letter changes affect gene regulation, we can start mechanistically connecting genetic risk to the pathways therapies could target,” she explained.
In addition to Tewhey and Siraj, the study was co-led by Jacob Ulirsch from Illumina, with contributions from Steven Reilly of Yale School of Medicine and Hilary Finucane of the Broad Institute and Harvard Medical School.
Building a Foundation for Better Disease Risk Prediction
Most DNA changes linked to common diseases occur not within genes themselves but in non-coding DNA regions that regulate gene expression. Over the past two decades, genetic studies have identified millions of such non-coding variants. The challenge has been determining which of these single-letter changes affect gene activity and, consequently, disease risk.
The researchers employed a technology called a massively parallel reporter assay, allowing them to test the effects of over 220,000 DNA variants simultaneously across different cell types, including brain, liver, and blood cells. Each DNA stretch was paired with a molecular tag to measure its impact on gene activity.
The results revealed over 13,000 single-letter variants that influence gene expression. About 11 percent of these variants behaved differently when combined with nearby variants, suggesting that some genetic risks depend on specific variant combinations.
These insights revealed potential links to human health. For example, some variant pairs were associated with gene activity linked to lower LDL cholesterol levels, while others affected genes associated with blood pressure.
Improving Risk Prediction for Diverse Populations
The study also highlighted the importance of understanding genetic mechanisms across diverse populations. Researchers identified a variant associated with blood sugar control in individuals of European ancestry and predicted similar associations in previously understudied variants found in people of African ancestry. Follow-up analysis confirmed this prediction.
While the study identified which DNA variants regulate specific genes in various cell types, further experiments are needed to understand how these variants influence traits and disease risk across the body’s many tissues and cell types. Millions of genetic variants remain untested, but the findings already strengthen the study of genetic variation and its influence on health traits.
“These findings do more than explain known disease associations. They provide training data for modeling the effects of variants we haven’t yet studied or that remain undiscovered,” Tewhey stated.
The data from this study has proven critical for training predictive models. In related work published in Nature in 2024, Tewhey and colleagues used such a model to design synthetic DNA sequences that could selectively activate genes in specific tissue types. This research builds on previous work by Tewhey and Ulirsch, pointing towards a future where genetic risk can be more accurately predicted and therapies can be tailored to act only in the necessary tissues.
