Mapping the genome in 3D to reveal new drug targets

Advances in genomics have transformed the study of disease biology, but many complex and common disorders remain without effective treatments. Inflammatory bowel disease, multiple sclerosis and other immune-mediated conditions continue to challenge researchers who seek to identify which genes are truly responsible for disease risk.

Dr Dan Turner, Chief Scientific Officer at Enhanced Genomics, has spent much of his career addressing that challenge. With more than 20 years in genetics, molecular biology and genomics research, he now leads scientific strategy and research at Enhanced Genomics.

Moving beyond association

Turner describes his role as ensuring scientific rigour and alignment across the company’s research programmes. “I oversee the company’s scientific direction and look after all of our research activities,” he explains. “Essentially, I make sure that the work we do is rigorous, focused and aligned with our goals. It’s a role that keeps me involved in everything from high-level strategy to the fine details of our scientific projects.”

At the heart of the company’s research lies 3D multi-omics – an approach that layers the physical folding of the genome with other molecular readouts to map how genes are switched on or off. By capturing that three-dimensional context, researchers can move beyond statistical association and start uncovering the causal biology that drives disease.

Despite decades of genetic research, most common diseases still lack effective treatments, largely because it’s so difficult to identify the relevant genes.

“At Enhanced, we’re focused on rapidly identifying high-confidence, genetically validated drug targets for common diseases,” Turner says. “Despite decades of genetic research, most common diseases still lack effective treatments, largely because it’s so difficult to identify the relevant genes.”

Over the past two decades, thousands of genome-wide association studies (GWAS) have pinpointed variants associated with disease, but the majority of those variants sit in non-coding regions of the genome. Such regions influence how genes are expressed rather than altering protein sequences, which makes them harder to interpret.

“For variants in coding regions, we can predict whether they change a protein’s amino acid sequence,” Turner explains. “But most GWAS variants lie in non-coding regions, where they act differently – usually in regulatory regions that control how much protein is produced.”

In the cell nucleus, DNA folds into an intricate 3D structure. This folding brings regulatory elements into physical proximity with their target genes, often over long genomic distances. Understanding that folding, Turner says, is key to linking non-coding variants to their effects.

Folding brings regulatory regions close to the genes they control. At Enhanced, we’ve developed an assay that profiles this 3D genome folding across the entire genome in a single experiment.

“Folding brings regulatory regions close to the genes they control. At Enhanced, we’ve developed an assay that profiles this 3D genome folding across the entire genome in a single experiment.”

By integrating genome folding data with other layers of information – such as chromatin accessibility and gene expression – researchers can identify the true regulatory networks underlying disease. “This is what we mean by 3D multi-omics,” Turner says. “It allows us to pinpoint which genes matter, in which cell types, and in which contexts.”

Why context matters

Traditional genomics approaches often assume that a disease-associated variant affects the nearest gene in the linear DNA sequence, but that assumption frequently fails.

“This is wrong about half the time,” Turner says. “Without 3D context, conventional approaches often miss valuable targets or prioritise incorrect ones, adding cost and time to drug discovery. By providing an integrated view of the genome, 3D multi-omics allows us to focus on the highest-confidence targets, accelerating development and increasing the likelihood of success.”

Without 3D context, conventional approaches often miss valuable targets or prioritise incorrect ones, adding cost and time to drug discovery.

The emphasis on confidence is not about speed for its own sake. In drug discovery, the cost of pursuing the wrong target can be immense. By building genetic validation into the discovery process, 3D multi-omics helps researchers focus only on targets with causal evidence rather than correlation.

“With our 3D multi-omics approach, genetic validation is built into the technology itself,” Turner says. “Rather than stopping at genetic associations, we can map long-range physical interactions between regulatory regions of the genome and the genes they control, turning an association into validation.”

Constructing an atlas of healthy cells

To make sense of disease-associated variants, it is first necessary to understand what a healthy cell looks like. Enhanced Genomics and other groups are building multi-omic atlases that characterise normal gene regulation across different cell types. These atlases provide a baseline for comparison when studying disease.

We’ve systematically developed full multi-omic profiles, including 3D genomics, across a wide range of healthy cells. This gives us a reference atlas of what the genome should look like in its normal state.

“Our platform is disease-agnostic, but we are currently applying it to immune-mediated and autoimmune conditions,” Turner says. “We’ve systematically developed full multi-omic profiles, including 3D genomics, across a wide range of healthy cells. This gives us a reference atlas of what the genome should look like in its normal state.”

“Once a cell type is profiled, it’s effectively ‘done’. We can repeatedly use that reference to interpret new GWAS data.”

By overlaying disease-associated variants on top of this healthy 3D structure, researchers can identify where normal gene–regulatory relationships are disrupted. These disruptions can point directly to causal genes and pathways involved in disease.

From data to decision

A major goal of this work is to make target selection more data-driven. Turner describes how combining large-scale genomics data with structured prioritisation can narrow down candidates for therapeutic development.

“We start by taking a GWAS for the chosen disease indication and systematically interrogating all the relevant omics data across cell types. Because we’ve already generated these datasets, the process is rapid and comprehensive,” he says.

This process produces a longlist of genes with genetic support. A second layer of assessment then considers practical and commercial factors such as safety, feasibility and intellectual property. “From there, we refine that longlist into a very high-confidence shortlist of targets to advance,” Turner explains.

Although such workflows are often proprietary, the underlying principle – integrating genetic and functional data to raise confidence – is increasingly common across the field. As more groups adopt similar approaches, the emphasis in early discovery is shifting from quantity of hypotheses to quality of evidence.

Choosing where to focus

Selecting disease areas where genetically validated targets can have the most impact is a strategic part of this approach. Turner outlines the reasoning that guides these decisions.

“We’ve designed a two-phase selection process that weighs suitability for our platform from both quantitative and qualitative perspectives,” he says. “Quantitatively, we draw on historical industry data to identify therapeutic areas where genetically backed targets have a strong record of success, and within those, we highlight diseases with unexplained genetic risk factors in intergenic regions.”

Qualitative considerations include whether new cell types need to be characterised, how tractable biological validation might be and the potential clinical and commercial value.

Using this framework, Enhanced Genomics has prioritised inflammatory bowel disease as its initial focus, given the condition’s unmet need and its strong genetic component.

Broader implications for drug discovery

Turner sees 3D multi-omics as part of a broader evolution in how researchers approach complex disease. Instead of viewing genomics, transcriptomics and epigenomics as separate disciplines, the aim is to integrate them within a structural framework that reflects how biology actually operates inside the cell.

3D multi-omics makes the process of defining causality direct, scalable and accessible at a genome-wide level in the most relevant cell types. This clarity is hugely significant.

“I think that 3D multi-omics will reshape drug discovery in the next decade as profoundly as next-generation sequencing has reshaped genetics,” he says. “3D multi-omics makes the process of defining causality direct, scalable and accessible at a genome-wide level in the most relevant cell types. This clarity is hugely significant.”

“Instead of building discovery programs on partial signals or investing heavily just to validate a handful of hypotheses, we can start with genetically grounded insights that are ready to translate into drug development.”

The approach moves research beyond single-gene studies towards understanding how genome structure shapes regulation, with the aim of informing more precise therapeutic strategies.

“Complex and common diseases, whether autoimmune, neurodegenerative or metabolic, have been notoriously resistant to traditional discovery approaches,” Turner says. “With 3D multi-omics we finally have a way to illuminate the biology behind them, and that gives us a credible path to developing the next wave of truly effective therapies.”

He adds: “In my view, this isn’t just an incremental step forward. Ten years from now, I believe we’ll look back on this moment as the beginning of a new era in pharma, one defined by sharper insight, greater conviction and a new generation of therapeutics that genuinely change lives.”