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Race in Precision Medicine: From Social Categories to Genomic Equity

Submitted:

07 November 2026

Posted:

10 November 2026

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Abstract
Race remains embedded in clinical risk algorithms and therapeutic recommendations despite its limitations as a proxy for biological variation. As a sociopolitical construct, race captures aspects of social experience and structural inequality but does not reliably reflect an individual's genetic ancestry or pharmacogenomic profile. This distinction is increasingly important in genetically admixed populations, where broad racial and ethnic classifications can obscure substantial variation in allele frequencies, genetic architecture, and treatment response. Pharmacogenomics offers a more direct approach to individualized prescribing by identifying clinically actionable genetic variants that influence drug metabolism, transport, efficacy, and toxicity. However, realizing its potential requires addressing persistent gaps in the representation of diverse populations in genomic research, including the underrepresentation of non-European ancestry populations and insufficient resolution of variation within broad population categories. Genetic ancestry, subpopulation-level analyses, and individual genotypes can improve the characterization of genetic variation when appropriately matched to the research or clinical question. Yet genomic approaches alone cannot eliminate health disparities and may exacerbate inequities if testing, interpretation, and evidence-based interventions remain inaccessible to underserved populations. Advancing genomic equity therefore requires representative research, equitable access to pharmacogenomic services, responsible data governance, and meaningful community engagement. A precision-medicine framework should move beyond race as a biological proxy while retaining race and ethnicity as relevant variables for examining structural inequities and monitoring disparities in health outcomes.
Keywords: 
race
;  
ancestry
;  
admixed population
;  
diversity
;  
genomic equity
;  
pharmacogenomics
;  
population subgroups
;  
rare variants
;  
community-based genetic research
Subject: 
Medicine and Pharmacology  -   Clinical Medicine

Background

The expansion of precision medicine initiatives and the broader push for health equity in genomics have prompted renewed scrutiny of the use of race and ethnicity in clinical decision-making.[1] Although race has historically served as a convenient surrogate for genetic ancestry, increasing evidence demonstrates that race is a social construct that is an imprecise and often misleading biological proxy.[2] The shortcomings of race as a clinical tool to guide care and the growing population of individuals identified as racially admixed are driving a paradigm shift toward using genetic ancestry biomarkers to describe the individual background rather than self-reported data. As the cost of genomic sequencing declines, access to genetic testing has increased, especially among high-income groups. As genomic technologies have become more routinely integrated into clinical practice, the limitations of race-based therapeutic recommendations have become more apparent, warranting a deliberate discussion on the role of race in medicine. [3,4]
Unlike biological variables such as sex and age, racial and ethnic categories arose primarily through geographic, social, and cultural forces, and as such, are not measurable in a quantitative manner.[5] Great physical distances and geographic barriers have imposed challenges to human interaction and have led to geographically determined endogamous mating patterns, resulting in a genetic substructure that largely follows geographic lines.[6] Although these forces are not biological in nature, they have caused racial and ethnic groups to differ from each other genetically, which has biological ramifications.[6] Sometimes, the race variable flags important inequities or risk factors that are not yet captured by better-known biological variables. For example, African American and Asian American patients have higher rates of hypertension and kidney diseases compared with non-Hispanic whites for reasons linked to structural inequality.[7,8]
Socioeconomic status is strongly correlated with race and ethnic background and is a robust predictor of access to and quality of health care and education. Combined, these correlates might be associated with differences in the incidence of disease and the outcomes of those with the disease. Therefore, the race and ethnicity variables capture important epidemiological information, including environmental exposure and social determinants of health, which can have major ramifications on resource allocation, social services, and adverse health outcomes.[7]
Removing race without substituting the correct social or biological measure can reduce care quality. Therefore, the role of race as a proxy for social determinants of health can provide insights into addressing health challenges in a specific community. Furthermore, racial and ethnic categories in epidemiological research could be useful in generating hypotheses about the environmental and genetic risk factors for important clinical outcomes. [6]
To this end, there are growing calls for a biomarker-based approach to guide therapy and inform interventions, and overcome subjective measures, such as self-reported race, to provide patient-centered care. Concurrently, the need to transition toward ancestry-informed and genotype-based approaches has never been more urgent, particularly as population diversity increases.[7]

Race as a Sociopolitical Construct

Race originated as a sociopolitical classification system shaped by historical, geographic, and cultural contexts rather than biological measures.[8] Its development is closely tied to European colonial expansion beginning in the 15th century, when emerging global power structures and economic interests, particularly the trans-Atlantic slave trade, encouraged the formalization of racial hierarchies to justify enslavement, dispossession, and unequal treatment.[9] Enlightenment-era naturalists later attempted to give these hierarchies scientific legitimacy by categorizing humans into discrete “types,” despite limited empirical evidence and clear ideological motivations.[9] Modern studies of human genetic variation demonstrate that genetic differences occur along continuous gradients rather than discrete categories and that variation within racial groups exceeds variation between them.[10] These findings underscore the discordance between self-reported race and biological ancestry.[8]
Epidemiologically, however, racial categories continue to show associations with patterns of health and disease, not because race captures genetic difference, but because it reflects the cumulative effects of structural determinants such as segregation, discrimination, differential access to care, environmental exposures, and socioeconomic inequality.[11] Historical policies like redlining, exclusionary immigration laws, and unequal labor protections have produced enduring disparities in disease incidence, life expectancy, and chronic stress burden among racialized groups.[12,13] Nevertheless, racial categories remain embedded in clinical practice, often without clear justification, perpetuating the misconception that race reliably reflects underlying genetic traits. This conflation risks obscuring the true drivers of health inequities and highlights the need to distinguish between race as a social exposure and ancestry or genetics as biological constructs.[8,14]

Reevaluating Race in Clinical Risk Algorithms

The debate over including race in clinical formulas to assess patients’ risk for kidney disease or atherosclerotic cardiovascular disease (ASCVD) has sparked intense discussion about whether these adjustments improve accuracy or instead reinforce assumptions that could worsen care for historically marginalized groups. In nephrology, this debate has already resulted in visible changes: professional organizations and laboratories have moved away from using a Black-race coefficient in serum-creatinine eGFR equations and adopted the race-free CKD-EPI 2021 equations along with other race-neutral methods.[15] Many laboratories have implemented these changes, and guideline bodies have endorsed them.[7] This policy shift was followed by operational updates, such as the U.S. transplant network requiring race-neutral eGFR reporting for transplant waitlist calculations, as well as retrospective adjustments to wait-time calculations that have shown measurable benefits for many Black kidney-failure candidates, who were previously disadvantaged by the old formula.[16,17,18]
But removing race from a formula is not a panacea: studies and clinical commentators warn of both intended and unintended consequences. For some patients, the race-free equations lower reported eGFR (raising the prevalence of CKD), which may increase referral rates, testing, or listing for transplant, outcomes many view as corrective, while also raising questions about workload, resource allocation, and how to avoid new forms of misclassification. Robust recalibration, validation across diverse populations, and use of additional biomarkers (e.g., cystatin C) or confirmatory testing are being recommended to preserve diagnostic accuracy while eliminating race as a crude proxy.[19]
The kidney example is part of a broader movement. Health systems and coalitions have begun reconsidering race adjustments in multiple clinical decision tools (e.g., lung-function prediction and donor-quality indices), arguing that race is a sociopolitical label, not a biological one, and that using it can perpetuate inequities.[20,21] Regional efforts to drop race-based algorithmic adjustments have been explicitly framed as efforts to dismantle stereotypes and reduce systematic bias in care. In cardiovascular risk assessment, the picture is evolving too. The widely used 2013 Pooled Cohort Equations include race as an input, and researchers and professional societies have been pursuing new risk models and recalibration strategies to reduce reliance on race while improving predictive performance. This process requires careful revalidation because changes can alter who is recommended preventive therapies (e.g., statins, aspirin, etc.).[22,23]
Developed by the American Heart Association in 2023, the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations estimate 10-year and 30-year risk for total cardiovascular disease (CVD), including atherosclerotic CVD (ASCVD) and heart failure (HF). Unlike the ASCVD calculator, PREVENT is the first risk tool to combine cardiovascular, kidney, and metabolic health measures to guide primary prevention-focused treatment decisions regardless of race. [22,23]
Taken together, these changes highlight three practical and ethical imperatives for clinicians and health systems: (1) accompany algorithmic reform with prospective monitoring and outcome-tracking so that removing race does not quietly shift disparities elsewhere; (2) invest in alternative predictors (better biomarkers, granular social-determinants data, geography-level risk measures) and local validation so risk estimates reflect biology and lived experience rather than crude proxies; and (3) be transparent with patients about what changes in the assessment tools, including why reassessment or additional testing may be warranted before certain clinical decisions may have been influenced by race-adjusted results (e.g., transplant listing, medication thresholds). If implemented thoughtfully, removing race from clinical calculators can reduce one mechanism that contributed to health inequity, but it must be part of a broader effort to address the structural drivers of disparities and to measure whether the reforms could improve equity in access and outcomes.

Race-Based Therapeutics

Race has historically informed several therapeutic recommendations and has been reinforced within major treatment guidelines, such as reduced response to angiotensin-converting enzyme inhibitors among Black patients, the approval of isosorbide dinitrate and hydralazine for heart failure in African Americans, and lower recommended starting doses of rosuvastatin for patients of Asian descent, and genetic testing of selected HLA-B gene variants in specific racial and ethnic groups.[3,24] These practices reflect population-level associations but frequently conflate social identity with biological variation.
Race-based therapeutics, treatments selected or dosed differently based solely on a patient’s racial category, can inadvertently reinforce and widen health inequities. Because race is a social, not biological, construct, using it as an approximation for genetic or physiologic differences risks misclassifying patients and obscuring the true drivers of disease or treatment response. When therapies are targeted to distinct racial groups, they can overlook within-group diversity and deny individuals access to treatments that would benefit them. This was evident in past examples where certain medications were marketed specifically for one racial group, narrowing options for others while implying biological distinctions that lacked scientific grounding. Similarly, labeling certain racial groups with a high or low frequency of genetic variants known to modulate the risk of drug adverse events or the effectiveness of medications, especially when tied to a drug labeling or Boxed Warning, can preclude patients from receiving the treatment simply because of their ethnic background.
Such practices can also delay the pursuit of more precise, evidence-based biomarkers that truly reflect variation in drug metabolism, environmental exposures, and social determinants of health. Embedding race into treatment algorithms may perpetuate the false notion that racial categories carry inherent biological meaning, diverting attention from structural factors, such as inequitable access to care, chronic stress, discrimination, and resource deprivation, that more directly shape health outcomes. Furthermore, when care pathways differ by race, marginalized and admixed communities may experience greater scrutiny or restricted access, while harms go unaddressed due to lower-quality evidence. Ultimately, race-based therapeutics risk codifying bias into medical practice, undermining equity, and obscuring the structural reforms necessary to close existing health gaps.[25,26]

Demographic Changes and the Challenges of Admixture

Demographic trends in the United States further complicate race-based clinical decision-making. Individuals identifying as multiracial represent one of the fastest-growing segments of the population.[7] The proportion of individuals identifying as White non-Hispanic has declined, while genetic admixture has increased across all groups.[7]
The U.S. population is now one of the most genetically admixed in the world. Key demographic trends include the rapid growth of individuals identifying with two or more races, a declining proportion of individuals identifying as White non-Hispanic, rising ethnic diversity among younger generations, and geographic pockets of unique ancestral histories, such as Hawaii’s majority Asian and Native Hawaiian/Pacific Islander populations. [3,7] In this context, self-reported race becomes an increasingly unstable variable, unable to capture the complexity of recent admixture, subpopulation-specific allele frequencies, differences in genetic ancestry among individuals reporting the same race, and phenotype–genotype mismatches (e.g., Hmong vs. Han Chinese). Therefore, precision medicine requires tools that reflect biological ancestry, not sociocultural identity alone.
The collection of race and ethnicity data in clinical trials remains essential for evaluating participant diversity, identifying health disparities, and determining whether research findings can be generalized across populations. However, these efforts are complicated by the increasing demographic diversity and genetic admixture of the US population, in which individuals may have ancestry from multiple ancestral populations while identifying with one or several racial or ethnic groups. Traditional classification systems may oversimplify this complexity, obscure meaningful genetic variation within self-identified groups, and group genetically diverse individuals into broad categories. When race and ethnicity are used as proxies for genetic ancestry, researchers risk misinterpreting genetic associations, overlooking differences in pharmacogenomic variants and treatment responses, and developing precision medicine algorithms that perform less accurately in underrepresented populations.[27] Furthermore, inadequate recruitment, inconsistent demographic data collection, and small sample sizes within specific populations can limit statistical power and perpetuate existing inequities in genomic research.[28]

Pharmacogenomics as a Superior Framework

Self-reported race and ethnicity are inconsistently correlated with genetic ancestry and therefore indirectly related to the presence or absence of genetic variants that may affect disease onset and treatment outcomes.[2,25] Pharmacogenomics directly evaluates genetic variation affecting drug metabolism, transport, and target response. Variants in genes such as CYP2C9, CYP2C19, CYP2D6, SLCO1B1, and VKORC1 have well-established clinical relevance, providing concrete examples of where genotype alone offers superior predictive value compared with race.[29,30,31,32] Having genotype information available can enable healthcare providers to prescribe the right medication for the right patient at the right dose. Precision prescribing is a critical aspect of disease management and improving treatment outcomes, which can usher in a pathway toward greater health equity and ultimately reduce health disparities. A summary of selected pharmacogenomics guidelines to support prescribing medications when pharmacogenetic information is available is shown in Table 1.
While pharmacogenomics often focuses on the presence or absence of specific genetic variants to guide medication selection, its true clinical value depends on the ability to detect clinically actionable variants across all patient populations.[25] It is well established that certain groups, particularly those with specific ancestral backgrounds, may have markedly higher or lower allele frequencies for pharmacogenetically relevant variants.[46] As a result, entire populations have at times been broadly labeled as “optimal” or “poor” responders to particular therapies.
Historical examples illustrate how these generalizations can lead to unintended harm. For instance, concerns about HLA-B*15:02–associated severe cutaneous adverse reactions have led to broad avoidance of carbamazepine in Han Chinese individuals, and reduced-function CYP2C19 alleles have contributed to the perception that patients of Asian ancestry are uniformly poor responders to clopidogrel.[25,47] Such population-wide assumptions risk stigmatizing groups, oversimplifying complex genetic diversity, and depriving individuals, who may not carry the relevant high-risk variants, of therapies that could benefit them.
To counter these limitations, the field must prioritize the development and implementation of inclusive and representative pharmacogenomic testing platforms. By capturing the full spectrum of actionable variation rather than relying on population averages, clinicians can move beyond blanket restrictions that exclude certain patients from specific medications. Ultimately, this approach supports more precise, equitable prescribing, safeguards against preventable treatment-related adverse events, and helps ensure that pharmacogenomics fulfills its promise for all populations.

Ancestry Without Reinforcing Race

Race and ethnicity are dynamic, socially constructed categories shaped by geographic, cultural, and political forces, not fixed biological groupings. Although race itself has no genetic or biological foundation, genetic ancestry remains highly informative for biomedical research because it captures patterns of shared evolutionary history that are relevant to health and disease. Using genetic ancestry rather than racial labels allows researchers to better account for population stratification, reduce background heterogeneity, and more accurately estimate the true effects of genetic variation across different population groups.[2] This distinction is critical, as relying on race can mislead scientists with overly broad or imprecise groupings, whereas ancestry-based analyses can pinpoint meaningful biological variation that may influence drug metabolism, disease susceptibility, or pharmacogenomic responses.[48]
Moreover, genetic ancestry enables the identification of founder variants, population bottlenecks, and subpopulation-specific allele distributions that do not align neatly with racial categories.[2] These biologically grounded patterns can reveal actionable insights. For example, predicting which groups may carry rare pathogenic variants, respond differently to specific therapies, or benefit from tailored drug dosing strategies. By moving beyond racial constructs and instead leveraging genetic ancestry, researchers can conduct more rigorous, equitable, and scientifically accurate studies that ultimately support better clinical decision-making and reduce health disparities.

Ethical Considerations for Genomic Equity

The transition away from race in clinical decision-making requires careful attention to ethical and equity concerns, including safeguarding genetic information to prevent discrimination against high-risk patients. Access to pharmacogenomic testing remains uneven, and insurance coverage varies widely, precipitating the risk of inverse equity.[49,50] Recognizing that eliminating race from biological decision-making does not eliminate racism from health systems, precision medicine must address inequitable access to pharmacogenomic testing, prevent genomic discrimination, ensure diverse representation in biobanks and clinical trials, engage communities historically marginalized in genetics research, and avoid reifying racial categories through misinterpreted genetic data.[28] A precision-medicine framework recognizes race as a sociopolitical factor influencing health, but not as a biological determinant.
Equitable access to pharmacogenomic testing remains an important challenge in translating advances in precision medicine into routine clinical practice.[49] Although the availability of genomic technologies has increased, access to testing and subsequent clinical services may be influenced by insurance coverage, out-of-pocket costs, geographic location, and differences in healthcare infrastructure. [51]These barriers may disproportionately affect patients from historically underserved communities and those receiving care in resource-limited settings. Importantly, access to testing alone does not ensure that patients benefit from precision medicine. Patients must also have access to healthcare professionals capable of interpreting genetic results and incorporating those findings into treatment decisions.[51] Without addressing these barriers throughout the care process, advances in genomic medicine may inadvertently widen existing disparities in healthcare access and outcomes.[27]
Insurance coverage and reimbursement policies also influence how pharmacogenomic testing is adopted across healthcare systems, and those coverage decisions are often informed by evidence demonstrating clinical utility and economic value. [27] However, the evidence supporting pharmacogenomic applications may not be equally representative of all patient populations. The underrepresentation of non-European ancestry populations in genomic research raises questions about the generalizability of findings used to inform clinical guidelines, coverage policies, and resource allocation.[27,52] Consequently, evaluations of pharmacogenomic testing should consider not only overall clinical and cost-effectiveness, but also whether the benefits of testing extend across diverse patient populations.[51] Incorporating equity considerations into evidence generation and reimbursement decisions may help prevent existing disparities from being reinforced as precision medicine becomes more widely implemented. [27,51]
The increasing integration of genomic information into clinical care and research also raises important questions regarding patient autonomy, informed consent, and data privacy.[53] Genetic information differs from many other forms of health data because it may reveal information relevant to biological relatives and retain significance beyond the original purpose of testing. [53,54] Patients should therefore be informed about how their genetic information will be collected, stored, shared, and potentially used for future research.[53] However, variation in informed consent practices for pharmacogenomic testing suggests that patients may not consistently receive the same information regarding potential risks, benefits, and secondary uses of their genetic data.[54] As genomic information becomes increasingly incorporated into electronic health records and large-scale research databases, transparent data governance and appropriate safeguards against unauthorized use or genetic discrimination will be essential to maintaining patient trust. [53]
Community engagement represents another important component of ethical genomic research, particularly among populations historically underrepresented or mistreated in biomedical research. [55] Concerns regarding the collection and use of genetic information should not be viewed simply as barriers to research participation, but as considerations requiring transparency, accountability, and meaningful involvement of the communities being studied.[55,56] Community-based partnerships may help ensure that research priorities, recruitment strategies, informed consent processes, and data governance practices reflect the perspectives and needs of participating populations. [55]Furthermore, increasing representation in genomic datasets should be accompanied by efforts to ensure that participating communities benefit from the knowledge and technologies developed through research. [52,55] Advancing genomic equity requires not only more inclusive evidence generation, but also more equitable translation of research findings into clinical practice. [27,52]

Diversity in Genetic Research for Drug Discovery and Safe Dosing

Diversity in genetic research is essential for advancing precision medicine, improving drug safety, and discovering new therapeutic targets. Human populations carry unique genetic variants shaped by ancestry, geography, admixture, and environmental pressures. When research includes only limited groups, such as individuals of primarily European descent, scientists miss important variants that influence disease risk, drug metabolism, and treatment response. Broad inclusion of diverse populations ensures that medical advances benefit all populations rather than only a subset.[28,57]
Genetic variations in drug response can greatly vary in frequency across different populations. Despite these differences, much of the genetic and pharmacogenetic research has focused predominantly on individuals of European ancestry.[58] The European genetic architecture is more homogeneous than African heritage due to a historical population bottleneck that greatly reduced genomic diversity during out-of-Africa migration.[58] By contrast, the African populations show a high level of genetic diversity due to population sizes and rapid growth, which may have resulted in the evolution of rare and population-specific variants. [58]
The current genetic diversity in humans is the result of extensive migrations across the globe, with many populations today being the result of extensive admixing between different ancestral groups. Due to the lack of large-scale genomic research across these populations, it is likely that much of the genetic heterogeneity linked to complex traits, including drug metabolism in these groups, remains undiscovered.[59] Therefore, the inclusion of multiethnic groups and cultivating a diverse genetic research landscape can increase the power to detect low-frequency or rare variants of significant breakthrough.[59,60] For example, the discovery of naturally occurring PCSK9 loss-of-function variants, more common in individuals of African ancestry, revealed a powerful cholesterol-lowering mechanism that led directly to the development of PCSK9 inhibitors.[61,62,63,64] Similarly, CCR5-Δ32, a variant found primarily in European and western Asian populations, provided insight into HIV resistance and enabled the creation of CCR5 inhibitor drugs.[65,66,67]
Drug dosing is another area where genetic diversity is critically important. The anticoagulant warfarin varies widely in effectiveness and safety depending on variants in genes such as VKORC1 and CYP2C9, which differ in frequency across ancestral groups.[68,69] More recently, variants in NUDT15, most common in East Asian and Latin American populations, were found to strongly influence toxicity to thiopurine drugs used in leukemia and autoimmune disorders.[70,71] Without diverse genetic sampling, these crucial dosing-related variants would have remained undiscovered, placing certain groups at greater risk for adverse drug reactions.
Furthermore, genetic studies in non-European populations are important even if genetic variants are not responsible for the overall differences in disease incidence or outcomes. Specifically, the frequency and effect sizes of genetic variants associated with disease risk may vary across populations. Polygenic risk scores derived from European ancestry have less predictive power when applied to non-European populations, a disparity with clear implications for the future of precision medicine.[73]
Overall, expanding diversity in genetic research accelerates the identification of novel drug targets, ensures drug dosing guidelines are safe and effective for all populations, and reduces health disparities.[28,49,59] It enables the development of therapies that are truly representative, equitable, and grounded in the full spectrum of human genetic variation.[52] A summary of recommendations to improve diversity and the role of race and ancestry in clinical practice is shown in Table 2.

The Importance of Subpopulation Granularity in Genomic Research

The aggregation of broad racial or ethnic categories (e.g., “Asian,” “Black”) obscures substantial within-group genetic diversity.[2,72] Variations in allele frequencies, haplotype structures, and gene–environment interactions can differ markedly between subgroups, with meaningful implications for disease risk, drug metabolism, treatment response, and diagnostic accuracy.[24,46,72,73,74,75,76] Highlighting how subgroup-focused research coupled with community-based genetic research models can illuminate these differences and reinforce the need for more nuanced population descriptors.[77,78] Such approaches can directly inform policy decisions aimed at increasing minority representation in genomic research. While there might be some genetic variation between the subgroups, there might also be ethnographic and cultural differences that modulate penetrance and effect sizes related to their distinct allele enrichment. To this effect, studying subgroup differences and capturing subgroup information can yield a meaningful impact on how lived experiences, subculture, and genetics can collectively contribute to the observed phenotype related to disease risk or the response to treatment.
The admixed population is one of the fastest-growing population subgroups in the United States and worldwide.[7] With allele frequencies varying dramatically across the globe due to features such as demography, genetic drift, and natural selection, tracing from which continental ancestry a variant is derived allows us to compute more accurate expected allele frequencies for each continental background, instead of aggregating frequencies across recently admixed individuals regardless of the ancestral origin of the variant’s haplotype. Therefore, the inclusion of admixed groups in genomic research presents both challenges and opportunities to move beyond broad racial and ethnic categories to better understand how global and local ancestry, genetic variation, and social factors influence health and treatment outcomes.[48] Because admixed individuals carry genomic segments inherited from different ancestral populations, researchers can use this natural genetic variation to identify disease-associated regions, understand ancestry-specific genetic effects, improve pharmacogenomic predictions, and develop more accurate polygenic risk models. This is particularly important because many existing genomic datasets are disproportionately based on European-ancestry populations, which can limit model accuracy and equity of precision medicine for underrepresented groups. Ultimately, studying individuals from admixed populations can shift the focus from “how you identify yourself” to “how your genetic background and contextual factors influence your health”. [48]

Opportunities and Challenges in Precision Medicine

Precision medicine outperforms self-reported race as a tool for directing patient care and overcomes the imprecision of racial self-identification, especially in admixed populations. Beyond improving clinical outcomes, it may challenge cultural biases among clinicians and reduce stigmatization of certain racial groups.[4] However, realizing precision medicine’s full potential faces challenges, including the need for more diverse populations in pharmacogenomic research, privacy protection, and insurance coverage for genetic testing.
One of precision medicine’s most significant opportunities is moving beyond population-level assumptions and incorporating individual genetic variation into clinical decision-making. Pharmacogenomic testing can identify clinically actionable variants associated with differences in drug metabolism, treatment response, and the risk of adverse drug reactions, allowing healthcare professionals to make more informed decisions regarding medication selection and dosing.[51,79]Unlike race-based therapeutic recommendations, which rely on broad population-level associations, genotype-guided approaches can provide information directly relevant to an individual patient’s treatment. [80] This distinction is particularly important for patients whose genetic ancestry may not be adequately represented by conventional racial or ethnic classifications. Furthermore, pharmacogenomic information may remain clinically relevant beyond the initial treatment decision, as variants affecting drug metabolism or transport can influence responses to multiple medications over a patient’s lifetime.[51,79]
The integration of pharmacogenomic information with other patient-specific characteristics offers additional opportunities to improve the precision of clinical assessments. Although genetic variation can contribute to differences in disease susceptibility and treatment response, these outcomes are also influenced by factors such as age, comorbidities, concomitant medications, environmental exposures, and social determinants of health.[81] Combining genomic findings with relevant clinical and contextual information may support more comprehensive approaches to predicting treatment response and identifying patients who could benefit from targeted interventions. Such approaches also recognize that genetic ancestry and individual genotypes provide different types of information and should be interpreted according to the specific clinical question.[80] However, the predictive value of genomic markers varies across diseases and therapies, and genetic information should not be assumed to provide a complete explanation for observed differences in clinical outcomes.
An additional opportunity lies in the potential for genomic information to inform care across multiple clinical encounters and healthcare settings. Unlike many laboratory measures that change over time, inherited genetic variants generally remain stable throughout an individual’s life. [79] When appropriately documented and accessible, pharmacogenomic results may therefore support future prescribing decisions without requiring repeated testing for the same variants. Clinical decision support tools may facilitate this process by making relevant gene-drug information available when medications are prescribed, although the clinical usefulness of these systems depends on accurate interpretation of genetic results and their integration with other patient-specific factors. [51,82]
Despite these opportunities, the clinical application of precision medicine presents challenges related to the interpretation and predictive value of genomic information. Although certain gene-drug associations are well established, the strength of evidence and clinical actionability of pharmacogenomic variants differ across medications and therapeutic areas.[51,82] Genetic variation also represents only one component of treatment response, which may be influenced by drug-drug interactions, comorbidities, organ function, and other patient-specific characteristics.[81] Consequently, the presence of a clinically relevant genetic variant does not necessarily predict an individual’s response to treatment with certainty. These limitations highlight the importance of interpreting pharmacogenomic findings within the broader clinical context rather than treating genetic information as an independent determinant of therapeutic outcomes.[81]
Another challenge involves translating pharmacogenomic evidence into clinical recommendations that can be applied consistently across healthcare settings. Although evidence-based guidelines are available for certain gene-drug associations (Table 1), not all medications have established pharmacogenomic recommendations, and the availability of genetic information does not necessarily indicate that a change in treatment is warranted.[51,82] Healthcare professionals must consider the clinical relevance of available findings alongside established treatment guidelines, patient characteristics, and therapeutic alternatives. This requires distinguishing between genetic associations that may be scientifically informative and those supported by sufficient evidence to guide prescribing decisions.[51] As pharmacogenomic research continues to develop, maintaining consistency between emerging evidence and clinical recommendations will be important for supporting appropriate and individualized patient care.

Conclusions

Precision medicine depends on moving beyond race as a proxy for biological variation without losing sight of race as a marker for social experience and structural inequity. Race and ethnicity remain valuable for identifying disparities and examining the effects of racism, socioeconomic conditions, environmental exposures, and unequal access to healthcare. However, these categories cannot reliably characterize an individual’s genetic ancestry, pharmacogenomic profile, or treatment response. Their continued use as biological proxies risks obscuring meaningful genetic variation, reinforcing population-level assumptions, and contributing to inequitable clinical decisions.
Pharmacogenomics offers a more scientifically grounded framework for understanding variation in drug response and informing individualized treatment. Achieving this potential requires more than replacing race-based recommendations with genetic testing. Genetic research must include populations historically underrepresented in genomic datasets, account for within-group and admixed-population diversity, and generate evidence that is applicable across diverse clinical settings. Similarly, equitable access to testing and clinical interpretation, alongside robust privacy safeguards, transparent data governance, and meaningful community engagement, are warranted to realize the full potential of precision medicine and achieve genomic equity.
Race should be retained where relevant to understanding and addressing health disparities, but should not substitute for validated biological measures in clinical decision-making. By integrating actionable genomic information with clinical characteristics and social determinants of health and ensuring that the benefits of discovery are shared equitably, precision medicine can advance toward safer, more individualized, and more inclusive care.

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Table 1. Selected Actionable Clinical Pharmacogenetic Implementation Consortium (CPIC) Gene–Drug Recommendations.
Table 1. Selected Actionable Clinical Pharmacogenetic Implementation Consortium (CPIC) Gene–Drug Recommendations.
Gene(s) Drug(s) Actionable treatment recommendation Reference
CYP2C19 Clopidogrel For intermediate or poor metabolizers undergoing PCI for acute coronary syndrome, use an alternative antiplatelet agent when clinically appropriate. [32]
HLA-B Abacavir Avoid abacavir in individuals who test positive for HLA-B*57:01. [33]
HLA-B Allopurinol Avoid allopurinol in HLA-B*58:01-positive patients; select an alternative when appropriate. [34]
DPYD Fluorouracil; capecitabine Reduce the initial dose according to DPYD activity score for intermediate metabolizers. Generally, avoid fluoropyrimidines in poor metabolizers unless no suitable alternative exists and a substantially reduced regimen can be justified. [35]
TPMT; NUDT15 Azathioprine; mercaptopurine; thioguanine Use reduced starting doses for intermediate metabolizers. For poor metabolizers, consider alternative therapy or substantial dose reduction and close monitoring, depending on drug and indication. [36]
CYP2C9; VKORC1; CYP4F2 Warfarin Use genotype-informed dose estimation when relevant genotype results are available, alongside clinical factors and INR monitoring. [37]
CYP2C9; HLA-B Phenytoin Adjust the initial dose for CYP2C9 metabolizer status; avoid phenytoin in HLA-B*15:02 positive patients when recommended by the guideline. [38]
HLA-A; HLA-B Carbamazepine; oxcarbazepine Avoid carbamazepine in HLA-B*15:02-positive, treatment-naïve patients. Use HLA-A*31:01 results to guide carbamazepine selection according to the guideline. HLA-B*15:02 also informs oxcarbazepine use. [39]
CYP2C9 NSAIDs Consider a lower starting dose or an alternative agent for CYP2C9 poor metabolizers, depending on the specific non-steroidal anti-inflammatory drug (NSAID) and guideline recommendation. [30]
SLCO1B1; ABCG2; CYP2C9 Statins Select the statin and dose based on genotype and the specific statin recommendation; consider alternatives or lower doses when increased musculoskeletal risk is predicted. [31]
CYP2D6 Codeine; tramadol Avoid codeine and tramadol in poor metabolizers. Avoid codeine and generally avoid tramadol in ultrarapid metabolizers because of toxicity risk. [40]
CYP2D6; CYP2C19 Selected antidepressants Select an alternative or adjust starting dose and titration according to the specific drug and metabolizer phenotype. [41]
CYP2D6 Tamoxifen For poor metabolizers, consider alternative endocrine therapy when clinically appropriate; options depend on menopausal status and cancer-treatment context. [29]
CYP3A5 Tacrolimus Use genotype-informed initial dosing for CYP3A5 expressers, followed by therapeutic drug monitoring. [42]
CYP2C19 Voriconazole Select an alternative antifungal for certain poor or rapid metabolizer phenotypes when recommended; use therapeutic drug monitoring as appropriate. [43]
MT-RNR1 Aminoglycosides Avoid aminoglycosides in individuals with certain high-risk mitochondrial variants unless clinical need outweighs risk and no suitable alternative exists. [44]
RYR1; CACNA1S Volatile anesthetics; succinylcholine Avoid triggering anesthetic agents in patients with a genotype associated with malignant hyperthermia susceptibility. [45]
Table 2. Recommendations for Advancing Genomic Equity in Precision Medicine.
Table 2. Recommendations for Advancing Genomic Equity in Precision Medicine.
Recommendation Description
Replace race with ancestry-informed genetic markers for clinical decision support Use pharmacogenomic genotypes, polygenic risk scores, and ancestry biomarkers rather than racial categories.
Expand representation in genomic datasets. Ensure diverse populations, especially Indigenous, Pacific Islander, African diaspora, and understudied Asian subgroups, are meaningfully represented.
Integrate genomic tools into routine practice. EHR-based decision support, cost-effective testing, and practitioner training are essential.
Preserve race as a sociocultural variable only where relevant. Use race to study structural inequities and racism, not biology.
Foster community trust and ethical stewardship. Transparent consent, equitable data governance, and community partnerships must underpin precision medicine’s expansion.
Improve race and ethnicity data collection in EHR and clinical trials Facilitate the need for resource allocation, targeted intervention, and hypothesis-generating questions
Community-based genetic research Increase recruitment and participation among historically underrepresented populations and allow the community to be a stakeholder in the conduct of research
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