Drug target discovery often begins with a disease diagnosis. Your team has identified genetic associations, shortlisted candidate genes, and started assessing which targets deserve further investigation.
But a diagnosis is not a complete representation of disease biology. Patients with the same condition can have different underlying molecular mechanisms, while diseases classified separately may share genetic factors and biological pathways. Yet conventional diagnostic categories do not always reflect these relationships.
For drug discovery teams, could looking beyond the single disease label reveal additional promising hypotheses for target discovery and prioritisation?
Why Disease Definitions Matter for Drug Target Discovery
Diseases are commonly classified by their clinical presentation, affected organs, or pathology. These categories provide a shared framework for clinical practice and biomedical research, but they do not always map neatly onto the molecular and physiological processes underlying disease.
Patients with the same diagnosis may differ in their symptoms, severity, comorbidities, disease progression, and underlying molecular mechanisms. Conversely, conditions classified as distinct diseases may share genetic factors, molecular pathways, or physiological traits.
The disease terminologies and ontologies used in biomedical research add another layer of complexity. They provide standardised ways to name, classify, and organise diseases, enabling data integration and analysis across studies and resources. However, they differ in scope, granularity, and how they represent relationships between diseases and phenotypes. As a result, the terminology used to annotate and retrieve evidence can influence which biological associations are captured and how they are connected.
For target discovery, this creates two challenges.
Within a disease, an overall picture can conceal more specific biological relationships. Analyses focused on a broad diagnostic category may overlook genetic associations relevant to particular manifestations, molecular subtypes, or patient subgroups.
Across diseases, shared biology can be harder to recognise when conditions are studied separately. Genetic and molecular relationships spanning diagnostic categories may reveal common mechanisms and suggest additional therapeutic hypotheses worth exploring.
Connecting Genetic Evidence Across Diseases and Phenotypes
To better understand disease biology, it is useful to look beyond diagnostic categories and consider the broader genotype–phenotype spectrum across biological scales: the relationships connecting genetic variation with molecular and cellular processes, physiological traits, clinical manifestations, and disease outcomes.
Clinical phenotypes, quantitative traits, subclinical abnormalities, and intermediate phenotypes provide complementary perspectives on these relationships. Representing and connecting these different levels of biological information can help researchers investigate disease heterogeneity, identify biology shared across diagnostic boundaries, and explore how genetic variation relates to specific disease manifestations.
For target discovery, this broader perspective can reveal additional biological hypotheses that may be missed when analyses are restricted to a single disease label. The challenge is not simply to collect more associations, but to organise and interpret them in ways that preserve their biological context and support the evaluation of potentially relevant therapeutic mechanisms.
What Genetic Evidence Reveals About Diseases
Expanding genetic investigations beyond a single disease diagnosis can reveal biological relationships that may be difficult to identify through disease-centred analyses alone. Recent studies illustrate how broader representations of diseases and phenotypes can uncover additional genetic associations, reveal shared biology, and inform drug target prioritisation.
How Phenotype Definitions Influence Genetic Discovery
Genetic studies often rely on binary disease definitions used to classify individuals as cases or controls. While valuable, these classifications may not fully capture the complexity of disease biology.
A 2024 study published in Nature Communications by Chen and colleagues demonstrated that incorporating predicted disease phenotypes across 112 chronic diseases identified additional genetic associations and improved the prioritisation of potential therapeutic targets.
The way disease phenotypes are defined and represented can influence which genetic associations are discovered and which targets emerge as candidates.
How Shared Genetics Reveals Relationships Between Diseases
Diseases classified as distinct conditions may share genetic factors and biological mechanisms. Investigating them together can uncover relationships that might otherwise remain overlooked.
In a 2024 Nature Genetics study, Lincoln and colleagues analysed six autoimmune diseases, identifying shared genetic risk factors and demonstrating that combining evidence across diseases improved the resolution of genetic fine-mapping.
Looking across diagnostic boundaries can reveal shared disease biology and strengthen the genetic evidence supporting potential therapeutic mechanisms.
How Quantitative Traits Provide Additional Genetic Evidence
Clinical diagnoses often encompass multiple interacting physiological processes. Quantitative traits, such as blood pressure, lipid levels, or glucose metabolism, can provide more specific insights into these processes.
A 2024 Nature Genetics study by Park and colleagues jointly analysed genetic associations across components of metabolic syndrome, revealing genetic factors underlying their shared variation.
Investigating quantitative traits alongside disease diagnoses can uncover genetic relationships that may be less apparent when analysing individual conditions or traits in isolation.
From Genetic Associations to Target Discovery Hypotheses
Together, these studies highlight the value of exploring genetic evidence across the broader genotype–phenotype spectrum, from physiological traits and intermediate phenotypes to clinical manifestations and disease diagnoses.
For drug discovery, this perspective can help identify additional candidate genes, investigate mechanisms shared across diseases, and explore associations linked to specific biological characteristics.
Importantly, broader phenotype coverage does not automatically establish causality or identify better therapeutic targets. It expands the range of biologically relevant hypotheses that can be investigated and evaluated.
The opportunity is not simply to find more associations, but to uncover biological relationships that might otherwise remain unexplored.
Connecting Disease and Phenotype Evidence for Target Discovery
Disease diagnoses remain essential for clinical practice and drug development, but they do not always capture the full complexity of underlying biology. As the studies discussed illustrate, looking beyond individual diagnostic categories can reveal additional genetic associations, shared biological mechanisms, and new opportunities for target discovery.
Making this broader perspective part of drug discovery requires access to genetic evidence spanning diseases, clinical phenotypes, and quantitative traits, together with the ability to explore and evaluate these associations systematically.
DISGENET supports this work by integrating gene–disease and variant–disease associations across a broad spectrum of diseases, phenotypes, and traits. Through standardised disease and phenotype annotations, traceable supporting evidence, and association-level metrics, researchers can investigate genetic relationships beyond a single diagnostic label and assess their relevance to target discovery and prioritisation.
The aim is not simply to identify more genes associated with a disease, but to explore a wider range of evidence-informed biological hypotheses for target discovery and prioritisation.