Understanding why disease risk varies across different regions of the same organ remains one of the most important unanswered questions in modern biology. In this live scientific webinar, “Decoding Early Disease Risk Using Region-specific Genomics,” researchers will explore how spatially resolved genomic analysis can uncover early mutational processes that shape disease susceptibility long before pathology becomes visible.
The event will take place on February 19, 2026, at 4:00 PM (London time) and will be presented by Laurel Hiatt from the University of Utah Department of Human Genetics.
Why disease risk is not uniform across tissues
Disease does not arise evenly throughout organs. Instead, specific anatomical regions often show increased susceptibility, driven by:
- Local biological microenvironments
- Differential exposure to environmental or microbial factors
- Early somatic mutations that accumulate over time
Traditional genomic studies frequently analyze entire organs or large tissue sections, limiting the ability to connect regional biology with early mutagenic events in otherwise normal tissue.
A new framework: regionally resolved genomics
This webinar introduces a regionally resolved genomics workflow capable of analyzing somatic mosaicism at single-structure resolution. By focusing on discrete anatomical structures within tissues, researchers can:
- Quantify baseline mutation burden
- Compare regional differences in mutation accumulation
- Extract mutational signatures that reveal underlying biological processes
- Link site-specific patterns to clinical outcomes
This approach allows investigators to uncover subtle, biologically meaningful mutational patterns that are often missed by conventional organ-level analyses.
Colorectal cancer as a model system
Colorectal cancer (CRC) provides a powerful case study for this methodology. Tumors arise preferentially in defined segments of the colon, with marked differences in:
- Morphology
- Mutational profiles
- Clinical behavior and outcomes
These location-dependent differences are particularly pronounced in early-onset CRC, yet the mechanisms underlying this spatial variation remain incompletely understood.
By analyzing region-specific somatic mosaicism in normal colon tissue, researchers can test long-standing hypotheses about:
- Site-specific disease risk
- Early mutational processes
- Regional vulnerability to carcinogenesis
What you will learn
Participants will gain insight into:
- How regional differences within a tissue or organ influence disease development and outcomes
- How to interpret common mutational signatures and connect them to endogenous, environmental, or microbial drivers
- How a regionally resolved genomics workflow enables robust detection of early mutational events
- How these techniques can be extended beyond colorectal cancer to broader studies of aging, somatic mosaicism, and cancer biology
Technical workflow overview
The workflow presented in this session integrates:
- Precise microdissection of intact tissue structures
- Library preparation protocols designed to minimize sequencing artifacts
- High-quality next-generation sequencing
- Computational analysis capable of resolving low-frequency mutations and mutational signatures
This combination generates data with sufficient resolution to detect early biological signals that precede overt disease.
Broader implications
While the colon serves as the case study, the methodology has far-reaching applications. Region-specific genomics can be applied to:
- Cancer risk assessment across tissues
- Aging-related somatic mutation studies
- Investigation of microenvironment-driven mutagenesis
- Early disease mechanism research in multiple organ systems
By bridging anatomical precision with genomic analysis, this approach opens new opportunities for understanding how disease begins—and how it might be intercepted earlier.
Event Details
- Event: Decoding Early Disease Risk Using Region-specific Genomics
- Date: February 19, 2026
- Time: 4:00 PM (London)
- Format: Live online webinar
- Presenter: Laurel Hiatt, University of Utah