A Mayo Clinic-led study has advanced the detection of DNA mutations across noncancerous human tissues, analyzing over 90 trillion DNA letters of sequencing data. The findings, published in Cell, represent the flagship paper of a series of roughly a dozen publications from the NIH Common Fund’s Somatic Mosaicism across Human Tissues (SMaHT) Network, which includes more than 300 researchers from over two dozen U.S. institutions.
Tracking Mutations Across the Genome
The study focuses on somatic mutations—genetic changes that arise after conception and accumulate over a lifetime as cells divide. Unlike inherited mutations, these alterations create genetic differences within the same person, a phenomenon known as somatic mosaicism. While some somatic mutations are harmless, others can disrupt cell behavior and contribute to cancer, aging, and neurodevelopmental, cardiovascular, and autoimmune diseases.
Detecting these mutations becomes especially challenging when they are rare or located in complex, repetitive regions of the genome. The SMaHT Network evaluated multiple sequencing techniques, including short- and long-read whole-genome sequencing, duplex sequencing, single-cell sequencing, and advanced computational methods.
Long-read sequencing emerged as a particularly effective tool. By reading longer stretches of DNA at once, it provided greater context, making large genetic changes easier to identify. In benchmark tests, long-read sequencing detected roughly twice as many large DNA alterations as short-read methods and identified insertions that were often missed by shorter reads.
Researchers also improved detection in other difficult-to-sequence regions. In one benchmark, a more complete genome reference increased precision from 79% to 89%. Single-cell sequencing of 102 lung and colon cells revealed cell-to-cell variation and helped trace developmental histories back to early embryonic stages.
Mapping Rare Mutations Across Tissues
In a companion study published in The American Journal of Human Genetics, Dr. Alexej Abyzov and colleagues used deep whole-genome sequencing to detect rare, single-letter DNA changes present at levels below 1% in tissue samples. By analyzing DNA from multiple organs, the team identified mutations that originated early in development and spread across tissues.
Combining lung and colon data uncovered 13 additional shared mutations, improving detection by 145% for these very rare variants compared with analyzing a single tissue. These findings suggest that multi-tissue analysis enhances the sensitivity of mutation detection, particularly for changes that occur during early development.
Dr. Abyzov, a Mayo Clinic professor of biomedical informatics and corresponding author of the Cell study, noted that while the human genome is often viewed as a fixed sequence, every individual is actually a mosaic of cells acquiring genetic changes over time. He emphasized that these mutations are relevant not only to cancer but also to a wide range of age-related conditions.
The SMaHT Network plans to catalog somatic mutations across 10 to 15 tissue types from 150 donors while continuing to refine detection tools. The NIH is preparing a second phase focused on understanding how these mutations influence biological processes and disease. Eventually, this research may help identify individuals at higher cancer risk and monitor precancerous changes before symptoms appear.
The studies were supported by the NIH Common Fund through the Office of Strategic Coordination in the NIH Office of the Director. The companion study received additional NIH funding for work conducted at Mayo Clinic.