Scientists have made a significant breakthrough in genetic research by enhancing genetic maps to pinpoint specific DNA changes that influence disease risk and other human health traits. This advancement, achieved by probing regions of DNA previously linked to disease, has resulted in high-resolution maps of DNA variant activity. These maps are instrumental in identifying the exact changes that affect blood pressure, cholesterol levels, blood sugar, and other complex human traits.
The study, published in the prestigious journal 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: determining which specific DNA changes within genome regions, often spanning tens of thousands to millions of DNA letters, are truly significant. The complexity of these regions has historically made it difficult and time-consuming to pinpoint the variants that drive disease associations.
Revolutionizing Genetic Research with Scale
The solution to this challenge was scale. The research team employed a method capable of testing thousands of DNA variants simultaneously, examining over 220,000 previously identified changes across five different cell types. This approach resolved about 20 percent of these regions across the genome, providing new insights into the function of these variants. This knowledge can 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 leading 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 likened the previous efforts to searching for a single typo on one page of a massive book, whereas the new method is akin to speed reading, scanning thousands of pages at once to flag the exact letters that change meaning. This dramatically accelerates genetic discovery.
Connecting Genetic Variants to Biological Mechanisms
Layla Siraj, the study’s first author, emphasized the importance of this research as a bridge from association to biology. “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. Siraj conducted this research while at the Lander Lab at the Broad Institute and is now in her residency in obstetrics and gynecology at Columbia University/New York Presbyterian.
In addition to Tewhey and Siraj, the study was co-led by Jacob Ulirsch, currently a group leader at Illumina. Key contributors included 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 occur not within the genes themselves but in the vast stretches of non-coding DNA. These regions contain regulatory elements that control gene expression. Over the last two decades, genetic studies have identified millions of non-coding disease-related variants throughout the genome. The challenge has been identifying which single-letter changes in these regulatory DNA regions affect gene activity, fine-tuning protein production, and shaping disease risk.
The researchers utilized a technology called a massively parallel reporter assay, which allowed them to test the effects of more than 220,000 single-letter DNA variants simultaneously across different cell types, including brain, liver, and blood cells. Each DNA stretch was paired with a molecular tag to measure whether a variant increased, decreased, or had no effect on gene activity—an essential step in understanding how regulatory DNA changes may impact health.
The results revealed over 13,000 single-letter variants that influence gene expression. While most act independently, about 11 percent behaved differently when combined with a nearby variant. This suggests that some genetic risk of disease depends on specific combinations of variants whose whole is greater than the sum of its parts.
Implications for Human Health and Future Research
The study’s 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 appeared 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 expected from either variant alone.
Improving Equity in Genetics-Driven Advances
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 similar but previously understudied variants, predominantly found in people of African ancestry, would show a similar association. Follow-up analysis confirmed this 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 are necessary 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. Moreover, millions of genetic variants remain untested. However, the researchers assert that the findings can already begin strengthening how scientists study genetic variation and its influence on 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.
This breakthrough in genetic mapping not only enhances our understanding of disease risk but also paves the way for more targeted and effective therapies, offering hope for improved health outcomes across diverse populations.
