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Race and ancestry in biomedical research: exploring the challenges

Genome Medicine volume 1, Article number: 8 (2009) Cite this article

Abstract

The use of race in biomedical research has, for decades, been a source of social controversy. However, recent events, such as the adoption of racially targeted pharmaceuticals, have raised the profile of the race issue. In addition, we are entering an era in which genomic research is increasingly focused on the nature and extent of human genetic variation, often examined by population, which leads to heightened potential for misunderstandings or misuse of terms concerning genetic variation and race. Here, we draw together the perspectives of participants in a recent interdisciplinary workshop on ancestry and health in medicine in order to explore the use of race in research issue from the vantage point of a variety of disciplines. We review the nature of the race controversy in the context of biomedical research and highlight several challenges to policy action, including restrictions resulting from commercial or regulatory considerations, the difficulty in presenting precise terminology in the media, and drifting or ambiguous definitions of key terms.

Correspondence

Recent advances in biomedical research promise increasing insights into complex contributions to traits and diseases, and there is hope that these will lead to global health benefits [1, 2]. Analytical and social-justice considerations both recommend thoughtful assessment of the role of social identity, particularly racial or ethnic identity, in the design, conduct and dissemination of clinical and basic science research. Controversies ranging from James Watson's comments on racial differences in intelligence [3] to the adoption of racially targeted pharmaceuticals, such as the African-American heart-failure drug BiDil [4–7], remind us that use of the concept of race in biomedical research can have far-reaching, often unanticipated social consequences.

The problem of race in scientific research is not a new one, and the issue seems to perpetually reappear and remain fundamentally unresolved [8]. We are, however, entering a new era in which the fruits of initiatives, such as the Human Genome Project [9, 10], the International Haplotype Map Project [11], and the recently proposed 1000 Genomes Project [12], promise to elaborate more fully than ever before the nature and extent of human genetic variation and its relation to social identity. A recent interdisciplinary workshop, 'Ancestry in health and medicine; expanding the debate', hosted by the Alberta Health Law Institute and the McLaughlin-Rotman Centre for Global Health, in Toronto, Canada, sought to debate the current status and concerns surrounding these new scientific data, how we relate genetic variation to individual and population-level differences in observable traits, and what this might mean for the effective addressing of significant disparities in health status and disease. A central motivating consideration was how best to secure the anticipated benefits of genetic and related forms of biomedical research in the face of inevitable misunderstandings or misuse concerning genetic variation and race.

Here, we draw together the perspectives of the scholars who participated in the workshop, who have considered the race issue from the vantage point of a variety of disciplines: anthropology, bioethics, clinical medicine, ethical, social, cultural studies, genetic epidemiology, genome sciences, global heath research, law and the social sciences. We review the nature of the race controversy in the context of biomedical research and highlight several challenges to policy action.

The race dilemma in research

At the heart of ongoing debates about the value and use of racial categories in biomedical research are disagreements about the underlying rationale (and motivation) for stratifying study cohorts and what to do with resulting observations. Although there is considerable interest in using social or political categories in the descriptive assessment of health outcome similarities and differences, several scholars have suggested that the subsequent attribution of causality to those categories is unjustified and potentially harmful [13, 14]. So, although there is much agreement that race (and other forms of social identification) matters to health, there is little agreement about why or how race matters, how best to study its effects and how to translate and communicate research results from racially stratified studies (see Box 1).

Box 1.
figure1

Examples of concerns with the use of ‘race’ in genetic research

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Persistent disagreements about how best to understand race as an object of scientific inquiry complicate matters further. Racial definitions can fluctuate according to social context, geographic location, historical period and personal experience. Indeed, it is not uncommon for the same individual to report their racial identity differently in different contexts and at different points in their lives [15–17]. For these and related reasons many scholars view racial identity as primarily a social construct [18–22], and one that can misdirect the categorization of participants in biomedical research. Others see racial identity as correlated with a mix of social and biological risk factors that should be recognized and disentangled, even used to advantage, in an effort to explain and address health disparities [23–26].

Despite research correlating population genetic identity with geographic proximity [27–30], many researchers hold that self-identified racial or ethnic identity is a poor proxy for underlying genetic relatedness [31–34]. As a consequence, scientists with an interest in identifying genetic-association studies with disease are turning to DNA-based estimates of 'ancestry' as a basis for stratifying study samples and controlling for background genetic differences unrelated to disease risk [35–37]. Geneticists have also begun to use ancestry informative markers (AIMs) to identify groups and individuals subject to recent genetic admixture and use this information in methods such as admixture mapping [38–40]. In both gene-association and admixture studies, assessing ancestry is seen as preferable because it circumvents the problems of self-reporting, although major axes of differentiation continue to be drawn along continental lines, recapitulating previously used racial distinctions: African, European, Native American, and so on.

Although ancestry estimation has the potential to control biases due to genetic confounding, in isolation its use defers, rather than addresses, the important problem of how social and biological risk factors interact in the context of health - including the production of racial health disparities. Research that simultaneously assesses both genetic and environmental contributions to disease risk, drug response and other health-related variation, and that deliberately puts such findings in the context of self-identified race, is urgently needed [13, 41–43]. In the absence of such additional evidence, and despite its amorphous nature, the multi-dimensional and contested concept of race will probably continue to have an important place in biomedical research for many years to come.

The continuing salience of race as a research variable places researchers in the unenviable position of having to negotiate complicated, and often controversial, terrain. Given the potential for misinterpretation and misapplication of research findings, great care must be used in the characterization of study samples and the interpretation of observations (Box 2). Available research tells us that such rigor is often absent in the reporting of race and ethnicity in the biomedical literature [44–50]. In addition, researchers must remain aware of the manner in which their work could be translated, both clinically and in the popular press (Box 2).

Box 2.
figure2

Existing recommendations for the use and reporting of race and ethnicity in biomedical research

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Challenges to change

How can we move forward? Many journals, research entities and academic commentators have provided relevant recommendations (Box 2). Yet concerns persist, and race and related concepts continue to be used in an inconsistent and potentially misleading manner within biomedical research [44, 48–50].

The concept of race has a long and complex social history [51], and the research community operates within the constraints imposed by this history and its associated social structures. This overriding reality is one of the primary reasons why the use of race remains a controversial and uneasy concept in research. However, there are other challenges that make progress difficult, despite the numerous policy recommendations. Understanding these tendencies, trends and social forces may help us to more effectively use existing recommendations and address social concerns. What follows, although not comprehensive, is a list of some of the most salient challenges that emerged from the workshop.

Commercial and regulatory imperatives

Decisions on whether or how to use race in biomedical research and clinical practice do not take place in isolation. Often they are shaped by commercial and regulatory imperatives that reward or require the use of racial categories in particular ways that may not serve constructive purposes. Much biomedical data, for example, is produced as a result of regulatory mandates that direct the collection of data using social categories of race derived from such sources as national census tables [13, 46, 52, 53]. Such 'racialized' data necessarily raise questions of how best to manage the relationship between social census categories of race and the biomedical data being produced by researchers and clinicians. Moreover, once introduced into the biomedical arena, race can take on a life of its own, leading to the retrospective framing of data and/or the prospective design of product development in 'racialized' terms that were not originally contemplated by the researchers [54–56]. It also seems likely that market forces will push toward terminology that captures a larger population and has more immediate public recognition [57]. Narrowly defined terms, such as ancestry, are likely to have less public recognition than race.

Media representations

The popular press is an important source of health information, particularly for the general public [58]. Although the relationship between media representations and public perceptions of biomedical research is complex [59], there is some evidence that the media can influence social perceptions and attitudes, even about race [60]. There are certainly examples of news reports that include a thorough examination of the challenges associated with using race in biomedical research [61], but media representations often simplify the science and use concepts such as race without explaining how the social category relates to the research outcome [62]. Given the limited space and time available to write most science stories, this is hardly surprising. The research community, in an effort to translate the research results to the lay public, can also use terminology that does not accurately reflect or represent the research conducted (Box 2). For example, a study might have used ancestry as a variable but, in its media report, racial descriptors are used to describe the significance of the findings in the popular press [63].

Definition drift and slippage

In response to the social concerns associated with the notion of race, new terminology has been suggested - the hope being that this new terminology will be both more scientifically precise and have less historical and social baggage. For example, the term ethnicity emerged as an alternative to race [64]. However, there is often a migration back to the origin term or the new term simply comes to be understood to mean the same thing as the old one [65]. Given that the social category of race has the most cultural resonance, this slippage is likely to be from the more specific terminology toward the broader, and perhaps more inaccurate, notion of race [26, 62].

Definition ambiguity

The final challenge is the need to strategically tolerate the ambiguity of racial identity. Because, as described above, the relevance of race and of race categories far exceeds the arena of scientific discourse and becomes the concern of government regulation, media accounts and language debate, science cannot independently dictate its meaning or invent new terms to replace it. Moreover, the features that make race socially useful - its fluidity, ambiguity and contingency - and that feed its social ubiquity and thus contribute to its scientific utility also work against tidy definitions. These features of race cannot be reasoned away. Nor, however, can they be used as an excuse to ignore standard scientific requirements for explaining research terms and justifying design choices. Instead, they need to be recognized and self-consciously engaged, as part of an iterative process directed at clarifying the import of human genetic variation in the long term and of using genetic insights to help eliminate, rather than reinforce, disparities in health status.

Mitigation strategy

Race is best understood as the result of a process informed by social values and institutional practices that imbue superficial differences between groups, such as skin color, eye shape or language, with unwarranted significance. Historically, this has been informed by hierarchical thinking, in which group differences and social inequalities are naturalized and rearticulated as biological realities [66]. Genomic research that uses racial categories in the investigation of genetic contributions to disease can also inadvertently support such 'racialization' and influence how findings of group differences are interpreted and, in turn, translated into clinical care and health policy.

Clearly, although the recognition that certain susceptibility variants are more prevalent in certain groups can have health benefits, such observations should not validate the politically and historically charged concept of race or support assumptions that the entire range of attributes ascribed to race have a biological basis. There is a need to develop strategies to mitigate the inappropriate and potentially inaccurate use of categorizing terminology. The available recommendations, outlined in Box 2, have merit. But there are numerous social forces and tendencies, such as those outlined above, that challenge progress towards constructive change.

Future policy and social-science work should focus on exploring the influence of these social forces. For example, although some research in this area has already been done [62, 67], a more nuanced understanding of how data on human genomic variation are interpreted by media, and in turn assimilated by the public, is needed. Recommendations for communication strategy could be used to raise the awareness of researchers as to how their work is apprehended by lay audiences (Box 2), especially journalists. Engagement and education of both scientists and media on their social and ethical responsibilities would be a related actionable strategy (Box 2). In the arena of clinical trials and genetic epidemiological research, the social impact and scientific utility of alternative methods of subject identification and reporting of findings could be explored. Most importantly, consideration should be given not only to ensuring that relevant policy recommendations are effectively implemented, but that they are being followed.

Finally, it must be recognized that the discussion and analysis surrounding the use of race, ethnicity or ancestry in medicine is, for the most part, flowing from scholars in North America and Europe. However, a number of countries, including Mexico, India [68, 69], Thailand and South Africa, are already doing, or planning to undertake, projects studying human genetic diversity within their own populations; and in many others, such as Brazil, extensive admixture has created a continuum of ancestral proportions among individuals that challenge racial classification. It will therefore be important that experts from communities in both the emerging economies and developing countries also contribute to this very important debate.

Additional data files

The following file is available: Additional data file 1 , consisting of an extended version of Box 1 that includes supporting quotations from the cited references for each example concern.

References

  1. 1.

    Collins FS, Green ED, Guttmacher AE, Guyer MS, : A vision for the future of genomics research. Nature. 2003, 422: 835-847.

    PubMed  CAS  Article  Google Scholar 

  2. 2.

    Daar AS, Singer PA: Pharmacogenetics and geographical ancestry: implications for drug development and global health. Nat Rev Genet. 2005, 6: 241-246.

    PubMed  CAS  Article  Google Scholar 

  3. 3.

    Black people 'less intelligent' scientist claims. http://www.timesonline.co.uk/tol/news/uk/article2677098.ece

  4. 4.

    Taylor AL, Ziesche S, Yancy C, Carson P, D'Agostino R, Ferdinand K, Taylor M, Adams K, Sabolinski M, Worcel M, Cohn JN, : Combination of isosor-bide dinitrate and hydralazine in blacks with heart failure. New Engl J Med. 2004, 351: 2049-2057.

    PubMed  CAS  Article  Google Scholar 

  5. 5.

    Sankar P, Kahn J: BilDil: race medicine or race marketing. Health Affairs. 2005, Suppl Web Exclusives: W5-455-63.

    Google Scholar 

  6. 6.

    Temple R, Stockbridge NL: BiDil for heart failure in black patients: The U.S. Food and Drug Administration perspective. Ann Intern Med. 2007, 146: 57-62.

    PubMed  Article  Google Scholar 

  7. 7.

    Seguin B, Hardy B, Singer PA, Daar AS: Bidil: recontextualizing the race debate. Pharmacogenomics J. 2008, 8: 169-173.

    PubMed  CAS  Article  Google Scholar 

  8. 8.

    Weiss KM, Fullerton SM: Racing around getting nowhere. Evol Anthropol. 2005, 14: 165-169. 10.1002/evan.20079.

    Article  Google Scholar 

  9. 9.

    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Bladwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stang-Thomann N, : Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921.

    PubMed  CAS  Article  Google Scholar 

  10. 10.

    Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Brider S, Clark AG, Nadeau J, McKusick VA, Zinder N: The sequence of the human genome. Science. 2001, 291: 1304-1351.

    PubMed  CAS  Article  Google Scholar 

  11. 11.

    International HapMap Consortium: A haplotype map of the human genome. Nature. 2005, 437: 1229-1320.

    Article  Google Scholar 

  12. 12.

    1000 Genomes. http://www.1000genomes.org

  13. 13.

    Braun L, Fausto-Sterling A, Fullwiley D, Hammonds EM, Nelcon A, Quivers W, Reverby SM, Shields AE: Racial categories in medical practice: how useful are they?. PLoS Med. 2007, 9: e271-10.1371/journal.pmed.0040271.

    Article  Google Scholar 

  14. 14.

    Montoya MJ: Bioethnic conscription: Genes, race, and mexicana/o ethnicity in diabetes research. Cult Anthropol. 2007, 22: 94-128. 10.1525/can.2007.22.1.94.

    Article  Google Scholar 

  15. 15.

    Surratt HL, Inciardi JA: Unraveling the concept of race in Brazil: issues for the Rio de Janeiro Cooperative Agreement site. J Psychoactive Drugs. 1998, 30: 255-260.

    PubMed  CAS  Article  Google Scholar 

  16. 16.

    Eschbach K, Supple K, Snipp CM: Changes in racial identification and the educational attainment of American Indians. Demography. 1998, 35: 35-43.

    PubMed  CAS  Article  Google Scholar 

  17. 17.

    Hitlin S, Scott JB, Elder GHJ: Racial self-categorization in adolescence: multiracial development and social pathways. Child Dev. 2006, 77: 1298-1308.

    PubMed  Article  Google Scholar 

  18. 18.

    Anonymous: AAA statement on race. Am Anthropol. 1998, 100: 712-713. 10.1525/aa.1998.100.3.712.

    Article  Google Scholar 

  19. 19.

    Anonymous: Genes, drugs and race. Nat Genet. 2001, 29: 239-240.

    Article  Google Scholar 

  20. 20.

    Collins FS, Mansoura MK: The Human Genome Project. Revealing the shared inheritance of all humankind. Cancer. 2001, 91: 221-225.

    PubMed  CAS  Article  Google Scholar 

  21. 21.

    Schwartz RS: Racial profiling in medical research. New Engl J Med. 2001, 344: 1392-1393.

    PubMed  CAS  Article  Google Scholar 

  22. 22.

    Cooper RS: Race, genes, and health - new wine in old bottles?. Int J Epidemiol. 2003, 32: 23-25.

    PubMed  Article  Google Scholar 

  23. 23.

    Risch N, Burchard E, Ziv E, Tang H: Categorization of humans in biomedical research: genes, race and disease. Genome Biol. 2002, 3: comment2007

    Google Scholar 

  24. 24.

    Burchard EG, Ziv E, Coyle N, Gomez SL, Tang H, Karter AJ, Mountain JL, Perez-Stable EJ, Sheppard D, Risch N: The importance of race and ethnic background in biomedical research and clinical practice. New Engl J Med. 2003, 348: 1170-1175.

    PubMed  Article  Google Scholar 

  25. 25.

    Choudhry S, Seibold MA, Borrell LN, Tang H, Serebrisky D, Chapela R, Rodriguez-Santana JR, Avila PC, Ziv E, Rodriguez-Cintron W, Risch NJ, Burchard EG: Dissecting complex diseases in complex populations: asthma in latino americans. Proc Am Thorac Soc. 2007, 4: 226-233.

    PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Koenig B, Lee SSJ, Richardson S: Revisiting Race in a Genomic Age. 2008, New Brunswick: Rutgers University Press

    Google Scholar 

  27. 27.

    Bowcock AM, Ruiz-Linares A, Tomfohrde J, Minch E, Kidd JR, Cavailli-Sforza LL: High resolution of human evolutionary trees with polymorphic satellites. Nature. 1994, 368: 455-457.

    PubMed  CAS  Article  Google Scholar 

  28. 28.

    Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW: Genetic structure of human populations. Science. 2002, 298: 2381-2385.

    PubMed  CAS  Article  Google Scholar 

  29. 29.

    Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, Fung HC, Szpiech ZA, Degnan JH, Wang K, Guerreiro R, Bras JM, Schymick JC, Hernandez DG, Traynor BJ, Simon-Sanchez J, Matarin M, Britton A, Leemput van de J, Rafferty I, Bucan M, Cann HM, Hardy JA, Rosenberg NA, Singleton AB: Genotype, haplotype and copy-number variation in worldwide human populations. Nature. 2008, 451: 998-1003.

    PubMed  CAS  Article  Google Scholar 

  30. 30.

    Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramchandran S, Cann HM, Barsh GS, Feldman M, Cavalli-Sforza LL, Myers RM: Worldwide human relationships inferred from genome-wide patterns of variation. Science. 2008, 319: 1100-1104.

    PubMed  CAS  Article  Google Scholar 

  31. 31.

    Wilson JF, Weale ME, Smith AC, Gatrix F, Fletcher B, Thomas MG, Bradman N, Goldstein DB: Population genetic structure of variable drug response. Nat Genet. 2001, 29: 265-269.

    PubMed  CAS  Article  Google Scholar 

  32. 32.

    Serre D, Paabo S: Evidence for gradients of human genetic diversity within and among continents. Genome Res. 2004, 14: 1679-1685.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  33. 33.

    Barnholtz-Sloan JS, Chakraborty R, Sellers TA, Scwartz AG: Examining population stratification via individual ancestry estimates versus self-reported race. Cancer Epidemiol Biomarkers Prev. 2005, 14: 1545-1551.

    PubMed  Article  Google Scholar 

  34. 34.

    Suarez-Kurtz G, Vargens DD, Struchiner CJ, Bastos-Rodrigues L, Pena SD: Self-reported skin color, genomic ancestry and the distribution of GST polymorphisms. Pharmacogenet Genomics. 2007, 17: 765-771.

    PubMed  CAS  Article  Google Scholar 

  35. 35.

    Hoggart CJ, Parra EJ, Shriver MD, Bonilla C, Kittles RA, Clayton DG, McKeigue PM: Control of confounding of genetic associations in stratified populations. Am J Hum Genet. 2003, 72: 1492-1504.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  36. 36.

    Reiner AP, Ziv E, Lind DL, Nievergelt CM, Schork NJ, Cummings SR, Phong A, Burchard EG, Harris TB, Psaty BM, Kwok PY: Population structure, admixture, and aging-related phenotypes in African American adults: the Cardiovascular Health Study. Am J Hum Genet. 2005, 76: 463-477.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  37. 37.

    Choudhry S, Coyle NE, Tang H, Salari K, Clark SL, Tsai HJ, Naqvi M, Phong A, Ung N, Matallana H, Avila PC, Casal J, Torres A, Nazario S, Castro R, Battle NC, Perez-Stable EJ, Kwok PY, Sheppard D, Shriver MD, Rodriguez-Clinton W, Risch N, Ziv E, Burchard EG, : Population stratification confounds genetic association studies among Latinos. Hum Genet. 2006, 118: 652-664.

    PubMed  Article  Google Scholar 

  38. 38.

    Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C, Pfaff C, Jones C, Massac A, Cameron N, Baron A, Jackson T, Argyropoulos G, Jin L, Hoggart CJ, McKeigue PM, Kittles RA: Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet. 2003, 112: 387-399.

    PubMed  Google Scholar 

  39. 39.

    Reich D, Patterson N, De Jager PL, McDonald GJ, Waliszewska A, Tandon A, Lincoln RR, DeLoa C, Fruhan SA, Cabre P, Bera O, Semana G, Kelly MA, Francis DA, Ardlie K, Khan O, Cree BA, Hauser SL, Oksenberg JR, Hafler DA: A whole-genome admixture scan finds a candidate locus for multiple sclerosis susceptibility. Nat Genet. 2005, 37: 1113-1118.

    PubMed  CAS  Article  Google Scholar 

  40. 40.

    Zhu X, Luke A, Cooper RS, Quertermous T, Hanis C, Mosely T, Gu CC, Tang H, Rao DC, Risch N, Weder A: Admixture mapping for hypertension loci with genome-scan markers. Nat Genet. 2005, 37: 177-181.

    PubMed  CAS  Article  Google Scholar 

  41. 41.

    Krieger N: Stormy weather: race, gene expression, and the science of health disparities. Am J Public Health. 2005, 95: 2155-2160.

    PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Krieger N: Defining and investigating social disparities in cancer: critical issues. Cancer Causes Control. 2005, 16: 5-14.

    PubMed  Article  Google Scholar 

  43. 43.

    Masi CM, Olopade OI: Racial and ethnic disparities in breast cancer: a multilevel perspective. Med Clin North Am. 2005, 89: 753-770.

    PubMed  Article  Google Scholar 

  44. 44.

    Shanawani H, Dame L, Scwartz S, Cook-Deegan R: Non-reporting and inconsistent reporting of race and ethnicity in articles that claim association among genotype, outcome and race or ethnicity. J Med Ethics. 2006, 32: 724-728.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  45. 45.

    L'Observatoire de la genetique: ’Race’ is Not a Scientific Concept: Alternative Directions. http://www.ircm.qc.ca/bioethique/obsgenetique/cadrages/cadr2005/c_no24_05/ca_no24_05_01.html

  46. 46.

    Fullwiley D: The molecularization of race: institutionalizing human difference in pharmacogenetics practice. Sci Cult. 2007, 16: 1-30. 10.1080/09505430601180847.

    Article  Google Scholar 

  47. 47.

    Sankar P, Cho MK, Mountain J: Race and ethnicity in genetic research. Am J Med Genet A. 2007, 143A: 961-970.

    PubMed  Article  Google Scholar 

  48. 48.

    Ma IW, Khan NA, Kang A, Zalunardo N, Palepu A: Systematic review identified suboptimal reporting and use of race/ethnicity in general medical journals. J Clin Epidemiol. 2007, 60: 572-578.

    PubMed  Article  Google Scholar 

  49. 49.

    Hunt LM, Megyesi MS: The ambiguous meanings of the racial/ethnic categories routinely used in human genetics research. Soc Sci Med. 2008, 66: 349-361.

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Hunt LM, Megyesi MS: Genes, race and research ethics: who's minding the store?. J Med Ethics. 2008, 34: 495-500.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  51. 51.

    Frederickson GM: Racism: A Short History. 2002, Princeton, New Jersey: Princeton University Press

    Google Scholar 

  52. 52.

    United States Code 1993: National Institutes of Health Revitalization Act of. 1993, . Public Law 103-143, 131-151. Codified as amended at 42 USC 283-290: Statute 122, 133-140

    Google Scholar 

  53. 53.

    NIH Policy and Guidelines on the Inclusion of Women and Minorities as Subjects in Clinical Research. http://grants.nih.gov/grants/funding/women_min/guidelines_amended_10_2001.htm

  54. 54.

    Lee SS: Racializing drug design: implications of pharmacogenomics for health disparities. Am J Public Health. 2005, 95: 2133-2138.

    PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Kahn J: How a drug becomes 'ethnic': law, commerce, and the production of racial categories in medicine. Yale J Health Policy Law Ethics. 2004, 4: 1-46.

    PubMed  Google Scholar 

  56. 56.

    Kahn J: Genes, race and population: avoiding a collision of categories. Am J Public Health. 2006, 96: 1965-1970.

    PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Caulfield T: Nutrigenomics, popular representations and the reification of 'race'. Health Law Rev. 2008, 16: 50-57.

    Google Scholar 

  58. 58.

    Geller G, Tambor ES, Bernhardt BA, Rodgers J, Holtzman NA: Houseofficers' reactions to media coverage about the sequencing of the human genome. Soc Sci Med. 2003, 56: 2211-2220.

    PubMed  Article  Google Scholar 

  59. 59.

    Bates B: Public culture and public understanding of genetics: a focus study. Public Underst Sci. 2004, 13: 1-19.

    Google Scholar 

  60. 60.

    Condit CM, Parrott RL, Bevan J, Bates BR, Achter PJ: Exploration of the impact of messages about genes and race on lay attitudes. Clin Genet. 2004, 66: 402-408.

    PubMed  CAS  Article  Google Scholar 

  61. 61.

    Caulfield T, Harry S: Popular representations of race: the news coverage of BiDil. J Law Med Ethics.

    Google Scholar 

  62. 62.

    Condit CM: How geneticists can help reporters to get their story right. Nat Rev Genet. 2007, 8: 815-820.

    PubMed  CAS  Article  Google Scholar 

  63. 63.

    Sankar P: Hasty generalizations and exaggerated certainties: Reporting genetic findings in health disparities research. New Genet Soc. 2006, 25: 249-254. 10.1080/14636770601032759.

    Article  Google Scholar 

  64. 64.

    Smart A, Tutton R, Ashcroft R, Martin PA, Ellison GTH: Can science alone improve the measurement and communication of race and ethnicity in genetic research? Exploring the strategies proposed by Nature Genetics. Biosocieties. 2006, 1: 313-324. 10.1017/S1745855206003036.

    Article  Google Scholar 

  65. 65.

    Sankar P, Cho MK: Genetics. Toward a new vocabulary of human genetic variation. Science. 2002, 298: 1337-1338.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  66. 66.

    Omi M, Winant H: Racial Formation in the United States: from the 1960's to the 1990's. 1986, New York: Routledge

    Google Scholar 

  67. 67.

    Bubela TM, Caulfield TA: Do the print media "hype" genetic research? A comparison of newspaper stories and peer-reviewed research papers. CMAJ. 2004, 170: 1399-1407.

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Indian Genome Variation Consortium: The Indian Genome Variation database (IGVdb): a project overview. Hum Genet. 2005, 118: 1-11.

    Article  Google Scholar 

  69. 69.

    Indian Genome Variation Consortium: Genetic landscape of the people of India: a canvas for disease gene exploration. J Genet. 2008, 87: 3-20.

    Article  Google Scholar 

  70. 70.

    Templeton A: Human races: A genetic and evolutionary perspective. Am Anthropol. 1998, 100: 632-650. 10.1525/aa.1998.100.3.632.

    Article  Google Scholar 

  71. 71.

    Long JC, Kittles RA: Human genetic diversity and the nonexistence of biological races. Hum Biol. 2003, 75: 449-471.

    PubMed  Article  Google Scholar 

  72. 72.

    Keita SO, Boyce AJ: "Race": confusion about zoological and social taxonomies, and their places in science. Am J Hum Biol. 2001, 13: 569-575.

    PubMed  CAS  Article  Google Scholar 

  73. 73.

    Oppenheimer GM: Paradigm lost: race, ethnicity, and the search for a new population taxonomy. Am J Public Health. 2001, 91: 1049-1055.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  74. 74.

    Cooper RS, Kaufman JS, Ward R: Race and genomics. New Engl J Med. 2003, 348: 1166-1170.

    PubMed  Article  Google Scholar 

  75. 75.

    Collins FS: What we do and don't know about 'race', 'ethnicity', genetics and health at the dawn of the genome era. Nat Genet. 2004, 36 (11 Suppl): S13-S15.

    PubMed  CAS  Article  Google Scholar 

  76. 76.

    Sankar P, Cho MK, Mountain J: Race and ethnicity in genetic research. Am J Med Genet A. 2007, 143A: 961-970.

    PubMed  Article  Google Scholar 

  77. 77.

    Lee SS, Mountain J, Koenig BA: The meanings of "race" in the new genomics: implications for health disparities research. Yale J Health Policy Law Ethics. 2001, 1: 33-75.

    PubMed  CAS  Google Scholar 

  78. 78.

    Braun L: Race, ethnicity, and health: can genetics explain disparities?. Perspect Biol Med. 2002, 45: 159-174.

    PubMed  Article  Google Scholar 

  79. 79.

    Sankar P, Cho MK, Condit CM, Hunt LM, Koenig B, Marshall P, Lee SS, Spicer P: Genetic research and health disparities. JAMA. 2004, 291: 2985-2989.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  80. 80.

    Center for American Progress. http://www.americanprogress.org

  81. 81.

    Rotimi CN: Are medical and nonmedical uses of large-scale genomic markers conflating genetics and 'race'?. Nat Genet. 2004, 36 (11 Suppl): S43-S47.

    PubMed  CAS  Article  Google Scholar 

  82. 82.

    Duster T: Medicine. Race and reification in science. Science. 2005, 307: 1050-1051.

    PubMed  CAS  Article  Google Scholar 

  83. 83.

    Brewer RM: Thinking critically about race and genetics. J Law Med Ethics. 2006, 34: 513-519. 480.

    PubMed  Article  Google Scholar 

  84. 84.

    Clayton EW: The complex relationship of genetics, groups, and health: what it means for public health. J Law Med Ethics. 2002, 30: 290-297.

    PubMed  Article  Google Scholar 

  85. 85.

    Brandt-Rauf SI, Raveis VH, Drummond NF, Cont JA, Rothman SM: Ashkenazi Jews and breast cancer: the consequences of linking ethnic identity to genetic disease. Am J Public Health. 2006, 96: 1979-1988.

    PubMed  PubMed Central  Article  Google Scholar 

  86. 86.

    Outram SM, Ellison GTH: Anthropological insights into the use of race/ethnicity to explore genetic contributions to disparities in health. J Biosoc Sci. 2006, 38: 83-102.

    PubMed  Article  Google Scholar 

  87. 87.

    Braun L: Reifying human difference: the debate on genetics, race, and health. Int J Health Serv. 2006, 36: 557-573.

    PubMed  Article  Google Scholar 

  88. 88.

    Graves JL, Rose MR: Against racial medicine. Patterns Prejudice. 2006, 40: 481-493. 10.1080/00313220601020189.

    Article  Google Scholar 

  89. 89.

    Suarez-Kurtz G: The implications of population admixture in race-based drug prescription. Clin Pharmacol Ther. 2008, 83: 399-400.

    PubMed  CAS  Article  Google Scholar 

  90. 90.

    Race, Ethnicity and Genetics Working Group: The use of racial, ethnic, and ancestral categories in human genetics research. Am J Hum Genet. 2005, 77: 519-532.

    Article  Google Scholar 

  91. 91.

    Winker M: Measuring race and ethnicity: why and how?. JAMA. 2004, 292: 1612-1614.

    PubMed  CAS  Article  Google Scholar 

  92. 92.

    Rivara FP, Finberg L: Use of the terms race and ethnicity. Arch Pediatr Adolesc Med. 2001, 155: 119-

    PubMed  CAS  Article  Google Scholar 

  93. 93.

    Kaplan JB, Bennett T: Use of race and ethnicity in biomedical publication. JAMA. 2003, 289: 2709-2716.

    PubMed  Article  Google Scholar 

  94. 94.

    Anonymous: Ethnicity, race and culture: guidelines for research, audit and publication. BMJ. 1996, 312: 1094-

    Article  Google Scholar 

  95. 95.

    Greely HT: Informed consent and other ethical issues in human population genetics. Annu Rev Genet. 2001, 35: 785-800.

    PubMed  CAS  Article  Google Scholar 

  96. 96.

    Condit CM, Parrott R, Harris TM: Lay understandings of the relationship between race and genetics: development of a collectivized knowledge through shared discourse. Public Understand Sci. 2002, 11: 373-387. 10.1088/0963-6625/11/4/305.

    Article  Google Scholar 

  97. 97.

    Foster MW, Sharp RR, Freeman WL, Chino M, Berstein D, Carter TH: The role of community review in evaluating the risks of human genetic variation research. Am J Hum Genet. 1999, 64: 1719-1727.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  98. 98.

    The International HapMap Consortium, : Integrating ethics and science in the International HapMap Project. Nat Rev Genet. 2004, 5: 467-475.

    PubMed Central  Article  Google Scholar 

  99. 99.

    CMAJ Editorial Policies. http://www.cmaj.ca/authors/policies.shtml

  100. 100.

    Iverson C, Flanagin A, Fontanarosa PB, Glass RM, Giltman P, Lantz JC, Meyer HS, Smith JM, Winker MA, Young RK: American Medical Association Manual of Style: a Guide for Authors and Editors. 1998, Baltimore: Williams and Wilkins, 9

    Google Scholar 

  101. 101.

    Anonymous: Census, race and science. Nat Genet. 2000, 24: 97-98.

    Article  Google Scholar 

  102. 102.

    CSE's white paper on promoting integrity in scientific journal publications. http://www.councilscienceeditors.org/editorial_policies/whitepaper/2-1_editor.cfm

  103. 103.

    Genetics in Medicine. Online Submission and Review System. http://edmgr.ovid.com/gim/accounts/ifauth.htm

  104. 104.

    Uniform requirements for manuscripts submitted to biomedical journals: writing and editing for biomedical publication. http://www.icmje.org

  105. 105.

    Committee on Pediatric Research: Race/ethnicity, gender, socioeconomic status - research exploring their effects on child health: a subject review. Pediatrics. 2000, 105: 1349-1351.

    Article  Google Scholar 

  106. 106.

    McLaughlin-Rotman Centre for Global Health. http://www.mrcglobal.org

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Acknowledgements

The Toronto workshop was co-hosted by McLaughlin-Rotman Centre for Global Health, University Health Network, University of Toronto (ASD) and the Health Law Institute, University of Alberta (TC). We thank Genome Alberta and the Advanced Food and Material Network for funding support. The McLaughlin-Rotman Centre for Global Health Program on Life Sciences Ethics and Policy is primarily supported by Genome Canada through the Ontario Genomics Institute and the Bill and Melinda Gates Foundation. Other matching partners are listed at [106].

Author information

Affiliations

  1. Faculty of Law and School of Public Health Research, Health Law Institute, University of Alberta, 89 Ave and 111 St., T6G 2H5, Canada

    Timothy Caulfield & Simrat Harry

  2. Department of Medical History and Ethics and Department of Genome Sciences, University of Washington School of Medicine, Seattle, WA, 98195, USA

    Stephanie M Fullerton

  3. Program on Life Sciences Ethics and Policy, McLaughlin-Rotman Centre for Global Health, University Health Network, University of Toronto, MaRS Centre, 101 College St, Toronto, Ontario, M5G 1L7, Canada

    Sarah E Ali-Khan, Billie-Jo Hardy, Béatrice Séguin & Abdallah S Daar

  4. Faculty of Medicine, Island Medical Program, University of British Columbia, 3800 Finnerty Rd, Victoria, British Columbia, V8P 5C2, Canada

    Laura Arbour

  5. Department of Biopharmaceutical Sciences and Department of Medicine, Divisions of Pharmaceutical Sciences and Pharmacogenetics, Pulmonary & Critical Care Medicine, and Clinical Pharmacology, University of California, San Francisco, CA, 94143-2911, USA

    Esteban G Burchard

  6. Department of Epidemiology & Preventive Medicine, Stritch School of Medicine, Loyola University, 2160 South First Avenue, Maywood, IL, 60153, USA

    Richard S Cooper

  7. Genome Alberta, 3553-31 St NW, Calgary, Alberta, T2L 2K7, Canada

    Robyn Hyde-Lay

  8. Hamline University School of Law, 1536 Hewitt Avenue, St. Paul, MN, 55104, USA

    Jonathan Kahn

  9. Department of Medicine, Department of Human Genetics, Section of Genetic Medicine, University of Chicago, 5841 South Maryland Avenue, Chicago, IL, 60637, USA

    Rick Kittles

  10. Program in Professionalism & Bioethics, Mayo College of Medicine, 200 First St SW, Rochester, MN, 55905, USA

    Barbara A Koenig

  11. Stanford Center for Biomedical Ethics, Stanford University Medical School, 701 Welch Rd, Palo Alto, CA, 94304, USA

    Sandra SJ Lee

  12. Paul M Hebert Law Center, Louisiana State University, 1 East Campus Drive, Baton Rouge, LA, 70803, USA

    Michael Malinowski

  13. Department of Medical Ethics and Center for Bioethics, University of Pennsylvania, 3401 Market St, Philadelphia, PA, 19104, USA

    Vardit Ravitsky & Pamela Sankar

  14. Department of Molecular Genetics, The Centre for Applied Genomics, The Hospital for Sick Children, University of Toronto, 555 University Avenue, Toronto, Ontario, M5G 1X8, Canada

    Stephen W Scherer

  15. Leslie Dan School of Pharmacy, University of Toronto, 144 College St, Toronto, Ontario, M5S 3M2, Canada

    Béatrice Séguin

  16. Leeds Institute of Health Sciences, University of Leeds, 101 Clarendon Rd, Leeds, LS2 9LJ, UK

    Darren Shickle

  17. Pharmacology Division, Instituto Nacional de Câncer, Rua André Cavalcanti 37, Rio de Janeiro, 20231-050, Brazil

    Guilherme Suarez-Kurtz

  18. Department of Public Health Sciences and of Surgery, University of Toronto, 155 College St, Toronto, Ontario, M5T 3M7, Canada

    Abdallah S Daar

  19. McLaughlin Centre for Molecular Medicine, University of Toronto, MaRS Centre, 101 College St, Toronto, Ontario, M5G 1L7, Canada

    Abdallah S Daar

  20. Department of Medicine, University of Toronto and University Health Network, 190 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada

    Abdallah S Daar

Authors
  1. Timothy Caulfield
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  2. Stephanie M Fullerton
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  3. Sarah E Ali-Khan
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  4. Laura Arbour
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  5. Esteban G Burchard
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  6. Richard S Cooper
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  7. Billie-Jo Hardy
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  8. Simrat Harry
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  10. Jonathan Kahn
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  11. Rick Kittles
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  12. Barbara A Koenig
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  13. Sandra SJ Lee
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  14. Michael Malinowski
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  15. Vardit Ravitsky
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  16. Pamela Sankar
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  17. Stephen W Scherer
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  18. Béatrice Séguin
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  19. Darren Shickle
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  20. Guilherme Suarez-Kurtz
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  21. Abdallah S Daar
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Corresponding author

Correspondence to Timothy Caulfield.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

All listed authors contributed to the conceptual analysis of the issues discussed in the article. Although Timothy Caulfield, Sarah Ali-Khan and Stephanie M Fullerton wrote the first draft of the manuscript, it was based on a framework developed by all authors at the Toronto workshop. All authors contributed editorial comments, read and approved the final manuscript.

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Caulfield, T., Fullerton, S.M., Ali-Khan, S.E. et al. Race and ancestry in biomedical research: exploring the challenges. Genome Med 1, 8 (2009). https://doi.org/10.1186/gm8

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Keywords

  • Biomedical Research
  • Ethnic Identity
  • Additional Data File
  • Racial Identity
  • Biological Risk Factor

Genome Medicine

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