New 3D Genome Mapping Tool Reveals Hidden Complexity in DNA

Standard laboratory tests can fail to detect many disease-causing DNA changes. Now, a novel 3D chromosome mapping method can reliably reveal these hidden structural variants and lead to new discoveries. The findings on this groundbreaking tool can be found in a new study in The Journal of Molecular Diagnostics, published by Elsevier, and are poised to transform diagnostic testing and treatment for genetic disorders.

Traditional methods sequence DNA in a linear, one-dimensional way, reading the genetic code as if it were a flat line of text. In contrast, 3D chromosome mapping captures the spatial relationships between different parts of the genome. It reveals how the long strands of DNA fold and interact with each other in the three-dimensional space of the cell nucleus, which is vital for detecting certain structural changes that are invisible to conventional linear tests.

Researchers applied genomic proximity mapping (GPM), a genome-wide Hi-C (high-throughput chromosome conformation capture sequencing)-based NGS assay, to DNA from 123 individuals with suspected genetic disorders. This approach captured the 3D contacts in the genome, which allowed the detection of both copy-number changes and rearrangements in DNA. GPM correctly identified all known large chromosomal variants (110 deletions/duplications and 27 rearrangements) with 100% concordance. It also uncovered 12 novel structural variants that were missed by standard clinical tests.

“We were excited by how much hidden complexity GPM revealed,” says co-lead investigator He Fang, PhD, Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle. “Using modern tools like GPM allows us to uncover hidden DNA rearrangements that standard tests miss. For example, one case with a known three-way translocation actually had 13 breakpoints across four chromosomes when mapped by GPM. In every patient with multiple rearrangements, GPM uncovered additional cryptic changes. It was also impressive that GPM detected low-level mosaic variants, or cells with different genetic makeup, with high sensitivity. These discoveries went beyond our expectations and highlight the power of this new method.”

Key results of the study are as follow:

• 100% detection of known variants: GPM found all 110 previously identified copy-number variants and 27 rearrangements in the cohort.

• High precision on complex events: Breakpoints of both balanced and unbalanced rearrangements were pinpointed to a high degree of accuracy (within ~10 kb), and GPM even worked on challenging samples like preserved tissue while detecting mosaic changes.

• New discoveries: GPM revealed 12 additional structural variants that standard methods had missed.

• Hidden complexity: In every case that had multiple rearrangements by traditional tests, GPM found extra cryptic changes.

GPM requires substantially less DNA than is typically needed for conventional cytogenetic methods or emerging technologies such as optical genome mapping (OGM) and long-read sequencing (LRS), thereby enhancing its practicality for real-world clinical implementation.

Identifying the exact genetic rearrangement may open the door to targeted therapies or clinical trials specific to those variants.

Co-lead investigator Yajuan J. Liu, PhD, Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, concludes, “GPM offers broad clinical benefits. It enables high-resolution, comprehensive genomic characterization, even from compromised samples such as low-quality or archived preserved tissue. As genomic medicine moves toward precision diagnostics, this new tool addresses current limitations in genetic testing, improving diagnostics and empowering doctors to provide personalized treatment, tailored monitoring, better prognosis, and improved family counseling.”

Notes for editors

The article is “Evaluation of Genomic Proximity Mapping for Detecting Genomic and Chromosomal Structural Variants in Constitutional Disorders,” by He Fang, Stephen M. Eacker, Yu Wu, Cate Paschal, Mary Wood, Brad Nelson, Alexander Muratov, and Yajuan J. Liu (https://doi.org/10.1016/j.jmoldx.2025.07.005). The article appears in The Journal of Molecular Diagnostics, volume 27, issue 11 (November 2025), published by Elsevier.

The article is openly available at https://www.jmdjournal.org/article/S1525-1578(25)00194-1/fulltext.

Full text of this article and additional information are also available to credentialed journalists upon request; contact Eileen Leahy or Emily Essex at [email protected]. Journalists wishing to interview the study authors should contact He Fang, PhD, at [email protected].

This study was funded in part by the Brotman Baty Institute grant and the National Institute of Child Health and Human Development/NIH grant R44HD104323.

Stephen M. Eacker is an employee of Phase Genomics, Inc., the company that developed the GPM technology. Yajuan J. Liu is currently affiliated with the TPMG Regional Genomics Laboratory, Kaiser Permanente, San Jose, CA.

About The Journal of Molecular Diagnostics

The Journal of Molecular Diagnostics, the official publication of the Association for Molecular Pathology, co-owned by the American Society for Investigative Pathology, and published by Elsevier, seeks to publish high quality original papers on scientific advances in the translation and validation of molecular discoveries in medicine into the clinical diagnostic setting, and the description and application of technological advances in the field of molecular diagnostic medicine. The editors welcome review articles that contain: novel discoveries or clinicopathologic correlations, including studies in oncology, infectious diseases, inherited diseases, predisposition to disease, or the description of polymorphisms linked to disease states or normal variations; the application of diagnostic methodologies in clinical trials; or the development of new or improved molecular methods for diagnosis or monitoring of disease or disease predisposition. www.jmdjournal.org

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