Researchers at Children's Hospital of Philadelphia (CHOP) and the Perelman School of Medicine at the University of Pennsylvania (Penn Medicine) have developed a powerful algorithm to detect potential disease-causing mutations within the noncoding regions of the human genome—areas once considered "junk DNA" that actually play a vital role in regulating gene activity.

Their findings, published in the American Journal of Human Genetics, could pave the way for more precise identification of genetic variants linked to a wide range of common diseases. The study is titled "Characterization of non-coding variants associated with transcription factor binding through ATAC-seq-defined footprint QTLs in liver."

Although just 2% of the human genome codes for proteins, the remaining 98%—the noncoding genome—contains regulatory sequences that control when and how genes are expressed. Many disease-associated variants have been discovered in these regions, but understanding their functional impact remains a major scientific challenge.

Genome-wide association studies (GWAS) have helped locate broad regions of the genome tied to disease risk. However, pinpointing which specific variant in those regions is actually responsible for a disease remains difficult. A significant number of these variants are located near transcription factor binding sites—regions where proteins bind to control gene expression. When transcription factors bind, they briefly obscure the DNA beneath them, leaving what scientists call a "footprint" in genomic data.

“This is like trying to identify a culprit in a police lineup,” said senior author Dr. Struan F.A. Grant, Director of the Center for Spatial and Functional Genomics at CHOP. “It’s hard to tell who’s responsible among similar suspects. Our approach allows us to single out the variant causing disease by tracking these molecular footprints.”

To do this, the research team combined ATAC-seq—a genomic method that identifies open and accessible DNA—with a deep-learning tool called PRINT, which detects transcription factor footprints based on DNA-protein interactions.

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Analyzing data from 170 human liver samples, the researchers identified 809 "footprint quantitative trait loci" (footprint QTLs)—specific locations in the genome where variants influence the binding strength of transcription factors. By measuring how strongly these proteins bind in the presence of different variants, the team was able to isolate which noncoding mutations might be driving gene misregulation linked to disease.

“This technique addresses a core issue we've faced in linking noncoding variants to disease mechanisms,” said Max Dudek, the study's first author and a Ph.D. student in the Grant and Almasy labs at Penn Medicine and CHOP. “With larger datasets, this method could lead to the discovery of causal variants and ultimately help guide the development of targeted treatments.”

The team plans to expand their work beyond liver tissue, applying the technique to other organs and cell types to better understand how regulatory variants influence disease across the body. This breakthrough may bring researchers closer to decoding the vast, underexplored regions of our genome and using that knowledge to combat some of the most prevalent diseases of our time.