Precision medicine needs trial diversity to reduce bias
Precision medicine marks a shift from the one-size-fits-all approach to healthcare to tailored treatments and interventions that cater to an individual's unique genetic makeup, lifestyle, and environment. While precision medicine has the potential to improve patient outcomes and reduce the risk of trial failures by targeting treatments to individuals with the highest likelihood of response, it relies on the participation of diverse and representative populations. Diversity ensures that medical insights are applicable across a wide range of individuals, preventing biased results that may favor specific ethnic or demographic groups.
This article examines where underrepresentation in precision medicine research persists, why it matters to scientific validity, and what recruitment, design, and policy changes are reducing it.
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: Improving DEI requires community partnerships, culturally-tailored outreach, and inclusive study designs like 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.
The reason for this imbalance is, in part, due to much of the initial push into genetics starting in the United Kingdom. As the field of genetics has grown, much of the research has remained in Europe and America and has still mostly included those of European descent. The absence of diversity in precision medicine studies limits the ability to apply genetic discoveries broadly and hinders the comprehension of the genetic underpinnings of diseases within diverse populations.
Why diversity in precision medicine research matters
Addressing this imbalance is both an ethical requirement and a scientific necessity. 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 generalisability 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, the predictability of certain traits, like height, is easier among people of European descent, but less accurate when applied to other populations, leading to misleading data outside the European model.
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. The risk is not hypothetical. It is a direct consequence of the data these models are built on.
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 in research studies ensures that data represents diverse genetic and medical characteristics. This can lead to more accurate and applicable findings for the diversity of 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:
Fostering ongoing relationships with participants beyond the duration of a study, acknowledging their contributions and keeping them informed about research outcomes.
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. See the FDA’s precision medicine overview.
Various efforts are actively striving to foster diversity within genetics research, for example:
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 — one that affects the scientific validity of genetic findings and the commercial viability of precision therapies targeting underrepresented populations. Progress is being made through both regulatory pressure and targeted research initiatives, but meaningful change will require that diversity is treated as a design requirement from the earliest stages of trial development, not an afterthought addressed at the point of recruitment. As the diversity gap in precision research gradually narrows, researchers will gain access to richer and more representative datasets that uncover insights into the genetic foundations of health and disease for diverse populations. Genetic variation across populations has measurable consequences for diagnostic accuracy, risk prediction, and treatment efficacy. When research cohorts do not reflect that variation, the findings that emerge from them will have limited applicability — both scientifically and commercially — for a globally diverse patient population.