Large-Scale Study Advances Detection of DNA Mutations Across the Human Genome

Researchers in a Mayo Clinic-led study have shown how to detect a broad range of DNA mutations in noncancerous human tissues and across the genome. The analysis, drawing on more than 90 trillion DNA letters of sequencing data, could help scientists study when these changes arise — from early development through aging — and how they contribute to cancer and other diseases.

Published in Cell, the study is the flagship paper in a package of roughly a dozen publications from the National Institutes of Health (NIH) Common Fund's Somatic Mosaicism across Human Tissues, or SMaHT, Network. The network includes more than 300 members from more than two dozen U.S. universities and hospitals. Researchers evaluated approaches for detecting somatic mutations ranging from single-letter DNA changes to large deletions, insertions and rearrangements.

Alexej Abyzov, Ph.D., a Mayo Clinic professor of biomedical informatics who leads the Somatic Mutations in Development and Aging Laboratory, is the corresponding author of the Cell study.

"We often think of the human genome as a single, fixed sequence, but every person is a mosaic of cells that acquire genetic changes over time," Dr. Abyzov says. "As we have increasingly recognized in recent years, these mutations are relevant not only to cancer but also to a broad range of age-related diseases."

Reading the Cellular Mosaic

Unlike inherited genetic changes, somatic mutations arise after conception and accumulate over a lifetime. As cells divide, they can pass those changes to new cells, creating genetic differences within the same person, known as somatic mosaicism. Some somatic mutations can alter how cells behave and contribute to cancer, aging, and neurodevelopmental, cardiovascular and autoimmune diseases.

These mutations can be difficult to detect when they are rare or occur in complex, repetitive regions of the genome. The SMaHT Network compared short- and long-read whole-genome sequencing, duplex sequencing, single-cell sequencing, and computational approaches.

This illustration shows examples of DNA mutations.

One of the clearest findings came from long-read sequencing, which reads longer stretches of DNA at a time. Much like reading a full sentence instead of a few scattered words, the added context made large genetic changes easier to find. Long-read sequencing detected about twice as many large DNA changes as short-read sequencing. It also found insertions that shorter reads often missed.

Researchers improved detection in other hard-to-read parts of the genome too. In one benchmark, a more complete genome reference raised precision from 79% to 89%. Single-cell sequencing of 102 lung and colon cells revealed differences from cell to cell and helped trace their history back to early development.

Companion Study Maps Rare Mutations Across Tissues

Dr. Abyzov also co-led a companion SMaHT paper published in The American Journal of Human Genetics. Deep whole-genome sequencing accurately detected rare, single-letter DNA changes present at levels below 1% in a tissue sample.

Alexej Abyzov, Ph.D.

Analyzing DNA from different organs also helped detect mutations that arose early in development and spread to multiple tissues. Combining lung and colon data uncovered 13 additional shared mutations, improving detection by 145% for these very rare mutations compared with analyzing either tissue alone.

SMaHT aims to catalog somatic mutations across 10 to 15 tissue types from 150 donors while improving the tools used to detect them. NIH is planning a second stage focused on understanding how these mutations influence biological processes and disease.

Dr. Abyzov says the research could eventually help identify people at higher cancer risk and track precancerous changes in tissues before developing symptoms.

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 support for work conducted at Mayo Clinic. For complete author lists, disclosures and funding information, review the Cell study and the companion The American Journal of Human Genetics study.