Scientists have made a significant breakthrough in understanding how specific genetic changes function within cells, influencing disease risk and various human health traits. By exploring DNA regions previously linked to diseases, researchers have developed high-resolution maps of DNA variant activity. These maps help pinpoint the exact changes that affect blood pressure, cholesterol levels, blood sugar, and other complex human traits.
The study, published today in Nature, was led by researchers from The Jackson Laboratory (JAX), the Broad Institute, and Yale University. It addresses a long-standing challenge in human genetics. For years, scientists have known that certain genome regions—often spanning tens of thousands to millions of DNA letters—are associated with diseases. However, these regions usually contain many variants, making it difficult and time-consuming to determine which specific DNA changes are significant.
Revolutionizing Genetic Research Through Scale
The solution to this challenge was scale. Using a method capable of testing thousands of variants simultaneously, the team examined over 220,000 previously identified DNA changes in five different cell types. This approach allowed them to resolve about 20 percent of these regions across the genome, offering new insights into the role of these variants. These findings could improve risk prediction and guide the development of new therapies.
“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 who led the team 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.”
Tewhey explained that previously, making these connections was akin to searching for a single typo on one page of a massive book. The new experimental approach is like speed reading, scanning thousands of pages at once and flagging the exact letters that change meaning, dramatically accelerating genetic discovery.
Connecting Genetic Changes to Disease Mechanisms
“What excites me is that this is a bridge from association to biology,” said Layla Siraj, first author of the study, which she spearheaded while in the Lander Lab at the Broad Institute, and now in her residency in obstetrics and gynecology at Columbia University/New York Presbyterian. “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.”
In addition to Tewhey and Siraj, the study was co-led by Jacob Ulirsch, currently a group leader at Illumina. Key authors also include Steven Reilly, assistant professor at Yale School of Medicine; and Hilary Finucane, associate member at the Broad Institute and assistant professor at Harvard Medical School and Massachusetts General Hospital.
Building a Foundation for Better Disease Risk Prediction
Most DNA changes linked to common diseases like heart disease and type 2 diabetes occur not within genes themselves—which only constitute about 2 percent of the genome—but in the vast stretches of non-coding DNA. These regions contain regulatory elements that control when, where, and how strongly our genes are expressed. Over the last two decades, genetic studies have identified millions of such non-coding disease-related variants throughout the genome. The challenge has been identifying which of the many single-letter changes in these regulatory DNA regions affect gene activity, fine-tuning protein production, and in turn shaping disease risk.
To address this challenge, the researchers used a technology called a massively parallel reporter assay. This method allowed them to test the effects of more than 220,000 single-letter DNA variants at the same time across different cell types, including brain, liver, and blood cells. Each stretch of DNA was paired with a molecular tag, or reporter, that they could directly measure to see whether a variant increased, decreased, or had no effect on gene activity—an important step in understanding how regulatory DNA changes may affect health.
The results revealed over 13,000 single-letter variants that influence how strongly a gene is expressed. While most act independently, about 11 percent behaved differently than expected when combined with a nearby variant. This surprising result suggests some genetic risk of disease depends on specific combinations of variants whose whole is greater than the sum of its parts.
Implications for Diverse Populations
These insights revealed potential links to human health. In some cases, pairs of variants were associated with gene activity linked to lower levels of LDL, or “bad” cholesterol. Other combinations appear to affect a gene associated with blood pressure. The team also identified two variants near the ESS2 gene—associated with developmental disorders—whose combined effect on gene expression was greater than would be expected from either variant alone.
In another example, researchers pinpointed a single variant associated with long-term blood sugar control discovered in people of European ancestry. Based on its molecular behavior, they predicted that similar but previously understudied variants, found predominantly in people of African ancestry, would show a similar association. Follow-up analysis confirmed that prediction, underscoring the importance of understanding genetic mechanisms across diverse populations.
While the study identified which DNA variants regulate specific protein-coding genes in the brain, liver, and blood cells, additional experiments will be needed to determine how those variants ultimately influence traits and disease risk. Given the body’s many tissues and thousands of distinct cell types, switching genes on or off in a single cell type is only one piece of a much larger puzzle in determining health outcomes. In addition, millions of genetic variants remain untested. Even so, the researchers say the findings can already begin strengthening how scientists study genetic variation and how they influence health traits.
“These findings do more than explain known disease associations. They provide training data to build predictive models of the effects of variants we haven’t yet studied or that remain undiscovered,” Tewhey said.
The study marks a significant step forward in the field of genetics, offering a more precise understanding of how DNA changes impact health. As research continues, these insights could pave the way for more personalized and effective treatments for a range of diseases, ultimately improving health outcomes for diverse populations worldwide.
