Underrepresentation in precision medicine research is not a peripheral concern. It is a structural issue that affects scientific validity, regulatory submissions, and the commercial viability of targeted therapies.
Key Takeaways
The Diversity Gap: While Europeans make up only 16% of the global population, they represent approximately 80% of genetic research participants.
Scientific Imperative: Diverse datasets are essential to identify genetic variants unique to specific ethnic groups and ensure treatment efficacy across all populations.
Health Equity: Addressing the "eurocentricity" of genetics is critical to preventing misleading medical advice and reducing global health disparities.
Actionable Strategies: Reducing the diversity gap requires deliberate changes to trial design, recruitment infrastructure, and community engagement, including culturally tailored outreach, expanded eligibility criteria, and decentralized models such as remote monitoring and mobile clinics.
The diversity gap in precision medicine trials
Historically, clinical trials have lacked diversity due to several key factors:
- Systemic barriers: Limited access to healthcare and research opportunities for marginalized communities.
- Inadequate outreach: Recruitment strategies that fail to effectively engage diverse populations.
- Community biases: Mistrust within minority communities and implicit biases within the medical establishment.
The Eurocentricity of genetic research is well documented, with a 2009 study showing that 96% of participants in genetic research were of European descent. Ten years later, improvements have been made, but only to bring that down to 80%. This is a major issue because European ancestry only makes up about 16% of the global population.
This imbalance reflects where the field developed. Much of the foundational work in human genetics was conducted in the United Kingdom and the United States, using locally accessible populations. As the field expanded, the infrastructural and institutional concentration of research remained in these geographies, and the participant composition of studies did not shift proportionally. The absence of diversity in precision medicine studies limits the ability to apply genetic discoveries broadly. This matters not only in genetics research but across the therapeutic domains where precision medicine is already being applied, including oncology, pharmacogenomics, and rare diseases. When the foundational data is unrepresentative, the treatments, risk models, and diagnostic tools built on it inherit that limitation.
The importance of diversity in precision medicine
Diversity matters because precision medicine's core function is predicting which treatments will work for which patients. If the research base excludes entire populations, those predictions are unreliable for the groups not represented. When trial cohorts are not genetically representative, variant associations may be population-specific, polygenic risk scores lose predictive validity across groups, and treatment response data reflects a narrow slice of the intended patient population, reducing the external validity of findings.
Genetic variation is not uniformly distributed across populations. Variants in frequency that are common in one ancestral group may be rare or absent in another. Studies designed around a single-ancestry reference population will systematically miss these differences, producing risk models that perform poorly when applied to underrepresented groups.
Diverse cohorts also increase the likelihood of identifying shared genetic markers — variants with cross-population relevance that would be invisible in single-ancestry data. These findings have direct implications for drug target selection and for the generalizability of efficacy and safety data across regulatory submissions.
Dr. Alicia Martin spoke to Sano Genetics in one of our podcast episodes about how a lack of diversity in research can affect patient outcomes. Dr. Martin raises the crucial question of how knowledge gained from genetic studies can effectively apply to globally diverse populations. While fundamental biology is shared among human populations, genetic variants differ in frequency due to unique human population histories. For instance, polygenic scores for traits such as height show substantially higher predictive accuracy in European-ancestry cohorts than in others. Applied beyond those populations, the same scores lose validity because the variant frequencies and linkage patterns underlying the model are population-specific.
This issue becomes even more critical in medical contexts, such as predicting heart conditions, cancer relapse, or schizophrenia, where incorrect predictions can result in misguided medical advice and potentially adverse outcomes.
Another key reason to drive diversity in precision medicine research is to address health disparities across different communities, both globally and nationally. Including more diversity in clinical trials can help address this inequality by finding treatments that work for everyone and understanding the differences in genotypes and patient outcomes across different populations.
Strategies for advancing diversity and inclusion in precision medicine research
To address the lack of diversity in research, stakeholders across the healthcare sector are taking concerted actions to promote inclusion in clinical trials. Some examples of types of initiatives and efforts that can improve DEI and drive appropriate representation in precision medicine clinical research are as follows:
Community partnerships
Collaborating with community organizations and leaders to build trust and engage underrepresented populations in research. These partnerships can help tailor recruitment strategies, address cultural concerns, and ensure that research aligns with community needs.
Culturally-tailored outreach
Developing culturally-relevant materials and messages for recruitment efforts, ensuring that potential participants understand the importance of their involvement and feel respected and included.
Data transparency
Making research data more accessible and transparent to participants, which can enhance trust and participation from underrepresented communities.
Diversity in genetic databases
Prioritizing diversity in the collection and analysis of genetic data to prevent bias and improve the accuracy of findings. Projects like the Human Heredity and Health in Africa Initiative (H3Africa)focus on building diverse genetic databases.
Health literacy programs
Offering educational programs that explain precision medicine concepts, genetic research, and clinical trials in accessible language. This empowers individuals to make informed decisions about participating.
Inclusive study design
Expanding eligibility criteria, using centralized translation services, and implementing remote consent and monitoring options to reduce the burden on participants. Actively recruiting individuals from underrepresented backgrounds ensures that data represents diverse genetic and medical characteristics. This produces more accurate and broadly applicable findings for the populations these therapies are designed to serve.
Leadership diversity
Ensuring diversity at leadership levels within research institutions and companies, which can drive a more inclusive organizational culture and decision-making processes.
Long-term engagement
Maintaining structured engagement with participants beyond individual study milestones, including planned recontact pathways and outcome communication, supports both retention in long-term programs and the development of reusable participant cohorts across successive trials.
Mobile clinics and telemedicine
Using mobile clinics and telemedicine to reach individuals in remote or underserved areas, reducing barriers related to transportation and access to healthcare facilities.
Patient advisory boards
Establishing advisory boards composed of patients and community representatives to provide input on research priorities, study designs, and dissemination strategies.
Policy advocacy
Advocating for policy changes that incentivize diversity in research and address systemic barriers to participation, such as increased access to healthcare and reduced healthcare disparities.
Public awareness campaigns
Raising awareness about the importance of diversity in research through public campaigns, media, and community events.
Representation in research teams
Increasing diversity within research teams, including scientists, clinicians, and staff. Diverse teams are better equipped to design studies that consider various perspectives and to communicate effectively with diverse populations.
Researcher training
Providing researchers with training on cultural competency, implicit bias, and equitable communication to ensure respectful and inclusive interactions with participants.
Regulatory requirements
Implementing regulatory changes that require diversity in trial enrollment, incentivizing researchers to prioritize inclusion. For example, the FDA expects sponsors to address diversity and inclusion in their development programs and provide a rationale if the demographics do not represent the intended patient population.
Several established research programs are working to close the diversity gap in genetics research, including:
- The All of Us Research Program (NIH): Enlisting over a million participants with a focus on underrepresented racial, ethnic, and socioeconomic groups.
- The African Genome Variation Project (AGVP): Dedicated to charting genetic diversity across African populations to rectify the scarcity of data in global research.
Conclusion
The diversity gap in precision medicine research is a structural problem that affects the scientific validity of genetic findings and the commercial viability of precision therapies targeting underrepresented populations. Regulatory pressure and targeted research initiatives are driving progress, but the gap will not close until diversity is treated as a design requirement from the earliest stages of trial development, not an afterthought addressed at the point of recruitment.
As cohort composition improves, the genetic evidence base will more accurately reflect the variation present in the patient populations these therapies are intended to serve. As precision medicine extends further into oncology, pharmacogenomics, and rare diseases, the cost of unrepresentative research compounds. Every model, risk score, and treatment decision built on narrow data carries that limitation forward. Addressing the diversity gap will allow the field to deliver on its foundational promise: treatments that work because they account for who the patient actually is.