Division of Research, Kaiser Permanente, 2000 Broadway, Oatland, CA 94612, USA. E-mail: andy.j.karter{at}kp.org
In the article, Race, genes, and healthnew wine in old bottles? in this issue of the International Journal of Epidemiology, Dr Cooper1 presents an interesting perspective about the role of race in the study of genetics and disease. That paper is largely a critique of a recent paper in Genome Biology in which Risch et al.2 reported evidence supporting categorization based on five major categories of self-identified race (Africans, Caucasians, Pacific Islanders, Asians, and Native Americans) and suggested that identifying genetic differences between these groups was scientifically appropriate. Cooper counters that there is insufficient evidence that race or continental ancestry has a biological (genetic) significance. He suggests that race is too crude a measure to have value in public health, and that finer-grained (sub-) categorizations (e.g. Scandinavians or Bantus) would be more informative than continental ancestry. He predicts that although populations show racial variation in genetic risk of rare, Mendelian diseases (e.g. Tay Sachs, sickle cell, thalassaemia), susceptibility allele distributions for the common, complex conditions that have world-wide distribution (e.g. diabetes or heart disease) are more likely pan-ethnic. Therefore, Cooper believes that racial differences for common, complex diseases do not have a genetic origin. He argues that evaluating the role of race in genomic research is premature given the current lack of understanding of the genetic susceptibility for common complex disorders. Cooper concludes the concept of race is unlikely to have value in public health.
In recent years, there has been a flurry of papers debating issues surrounding race, genetics, and disease, with Cooper being a major contributor. Unfortunately, much of the controversy raised in this paper stems from misunderstanding rather than true disagreement. Cooper cites the hype surrounding the genomics era and the tendency of geneticists to view the world from a purely genetic dimension, ignoring the role of environmental factors. However, almost all genetic epidemiologists, Risch et al. included, would fully agree that environmental factors are important determinants of disease. While social epidemiologists have justifiably criticized molecular scientists espousing genetic-determinism, many social epidemiologists have promoted the equally unsubstantiated perspective that dismisses the influence of genetics on racial disparities in disease. Indeed, proposals from both extremes lack scientific merit. As the genomics era unfolds, the opportunity to test empirically whether genetics is a relevant factor in determining racial differences in complex diseases has arrived. The challenge will be to find the reasonable middle road that leads to appropriate research methodologies, a better understanding of disease aetiology, and practical public health interventions.3
Coopers paper (as well as numerous previous papers)410 focuses on the processes of naming races and the appropriateness (scientific as well as political) of such naming (whether to lump or split) given the complex, multi-dimensional construct that is race. The variable race has received special attention, largely because of societys deplorable history of racism and eugenics. However, from a purely scientific and methodological standpoint, this special attention is out of proportion. Risch et al. were simply posing a practical methodological solution to a vexing design problem in genetic studies of disease: appropriate stratification under the suspicion of racial differences in allelic distributions across populations. Note that I use the term stratification in the classic epidemiological sense to mean analyses conducted separately in sub-groups (not to be confused with the term population stratification used by population geneticists to mean statistical adjustment).
Stratified analyses are indicated when significant interactions (effect modification, e.g. racial differences in the effect of alleles) are found in preliminary studies. While easily handled in the analysis, confounding (e.g. racial differences in the distribution of alleles, each of which have similar effect size across races) can also be minimized by studying restricted populations. It may be impossible to distinguish between effect modification and confounding by race with regards to the genetic effect before susceptibility alleles have been identified in non-stratified samples. However, since stratified analyses handle both situations, it seems the optimal solution. Additionally, since races are often not uniformly distributed in population-based samples, post-hoc strata-specific analyses may lack sufficient statistical power for the minority groups. Stratified sampling is therefore frequently designed into a study based on prior expectations. There exists adequate evidence of racial disparities to warrant stratification for genomic studies of many complex diseases. There are numerous examples of such racial disparities in disease (e.g. prostate cancer,11 glaucoma,12 chronic kidney disease,13 and diabetes-related lower extremity amputation)14 and response to therapy (e.g. high-dose interferon treatment for chronic hepatitis C)15 that persist after accounting for a wide range of potential confounders (e.g. access to care, socioeconomic status, health behaviours). Such residual race effects should at the very least raise some suspicion regarding the distribution and effect size of susceptibility alleles associated with these and other complex disorders. If we fail to detect genetic differences across races, then the stratified sampling will have no detrimental impact and pooled analyses will be fully appropriate.
A group of genetic scientists have suggested ignoring race in genetic studies.16,17 Justification for the race-neutral approach is typically based on Landers 99.9% identical rule.18 However, these scientists often fail to mention that Lander also contends that although individuals are genotypically almost identical, the tenth of a per cent of the genome that is different translates into roughly 3 million sequence differences, with some conferring dramatically differing risk of disease. For example, a single base pair change can cause haemochomatosis. By failing to design studies to accommodate the contingencies for interactions between race and genes, important racial differences in genetic susceptibility, if they exist, would likely remain undetected.
Cooper and others8 have justifiably questioned the analytical framework that proposes the utility of residual effects due to race as a proxy for genetics (after adjusting for expected alternative explanatory variables, e.g. socioeconomic status). Fixed attributes such as race fail in the context of causal modelling because they are not substitutable (see discussions of the counterfactual model of causality)19 and do not yield the answers that we actually seek (e.g. what would the health of individuals of race X be given they experienced life as race Y?). Because of these caveats, testing population-specific hypotheses (e.g. race specific alleledisease associations) makes more sense than making inferences based on residual effects. Clearly, for scientists who have evidence suggesting potential racial differences in disease susceptibility, the only way to accomplish this is to use stratified (by race) designs.
The looming question is how to operationalize population-stratification. Cooper objects to the use of self-identified race because of its lack of precise definition. Risch et al. suggest that self-identified race is an adequate approach to human categorization in biomedical and genetic research, but do not deny that it is a very crude categorization. These authors simply suggest that genetic differentiation, including disease susceptibility alleles, although certainly also occurring on a finer scale, is greatest when defined by these five major racial categories. Moreover, finer-scaled categorization is quite impractical from a research operations standpoint.
Instead of relying on self-reported race, many scientists, including the leadership of the Human Genome Project, support using race-specific markers (e.g. microsatellite markers) to facilitate population stratification. However, marker-based studies would require genotyping before population stratification could take place, demanding expensive data collection and laboratory assays on inflated sample frames to accommodate the identification of sufficient individuals in minority populations. Moreover, Risch et al. suggest that the confluence of social, cultural, behavioural, and environmental variables that are associated with self-identified race introduce confounding between genetic and environmental risk in an ethnically heterogeneous, race-neutral study. By ignoring self-identified race, or even when stratifying on race specific genetic markers, environmental culprits may be missed due to our inability to disentangle the residual effects of confounding. If disease variation was due to racially varying cultural practices, then self-identified race would be a better adjuster than genetic markers given that cultural practices would not be maintained over time if its members could not identify each other.20,21 The use of self-identified race provides the most practical resolutions for problems of confounding (e.g. matching, adjusting, or restriction) and effect modification (stratification).
Epidemiologists have a long history of benefiting from designs that stratify samples in rather crude categories that are surrogates for ill-defined constructs (e.g. social position indicators such as income, education, and occupation). Despite the imperfection of such measures, they have frequently moved us closer to understanding the determinants of disease. Similarly, self-identified race, albeit a crude proxy, may accomplish the same in the context of genomic research. Many, if not most, scientific hypotheses that are tested and later shown to be important findings were stimulated not by a priori hypotheses and well-formed theory, but rather by exploration of data. I suggest that our abilities to move epidemiological and genetic research forward would be greatly impeded if we were to avoid the use of surrogates such as self-identified race.
Cooper correctly points out the enormous gaps in our understanding of the mechanism by which genetic variation influences common complex conditions. However, the fact that genomic research has made relatively little progress in untangling complex genetic disorders is not grounds for ignoring race. In fact, our progress may have been hampered due to the lack of adequate stratification to date in the face of differing effect size or distribution of susceptibility alleles across populations. Cooper should embrace the use of self-identified race as a design variable, as it may be the only practical way that we may fill those large gaps in our understanding. There is no denying that epidemiological studies have demonstrated relevant racial variation in the incidence, prevalence, and natural history of many diseases. I concur with Coopers suggestion that the dominant cause is likely environment (social, cultural, economic, political) in the US where, unlike countries with socialized medicine, access and quality of health care varies greatly by race22,23 and socioeconomic position. In general, the poorer US minority populations experience reduced access to and quality of health care. Moreover, there is evidence that internalized racism impacts health,24,25 and physicians perceptions of and interactions with patients may differ by race.26
However, these assertions in no way preclude the possibility that complex diseases may also differ by race due to genetics. I agree with Coopers assertion that these effects may be attributable to residual confounding by unspecified environmental factors. However, if Coopers allegation is true that geneticists are using scientific ideas for their social purpose, it is certainly more the exception than the rule. In fact, by avoiding a full exploration of the potential genetic contribution because of the social baggage that accompanies the concept of race (however it may be measured), we may do an even greater disservice to those at-risk populations we are trying to protect. Indeed Cooper himself seems also to support a genetic approach to racial differences in a recent paper27 on admixture linkage disequilibrium mapping, the primary assumption of which is that racial differences in prevalence are due to allele frequency differences at genetic loci.
Cooper must be congratulated for his meaningful contributions, present and past, to this important public health debate. It is clear that both geneticists and epidemiologists have a similar goal in mind, which is to understand causes of disease in general, and in so doing, reduce racial disparities. He predicts that identifying race-specific genetic aetiologies will not have a significant impact on public health. Although environmental risk factors (e.g. behavioural, social, cultural, political, economic) may be a bigger culprit than genetics with regard to racial disparities in disease, identifying genetic aetiologies with even small attributable risks will further our understanding of the tangled web of aetiological pathways involved in complex disease. If this understanding leads to identification of novel pharmacological interventions, then the impact of our genetics (i.e. gene products) may be in a sense modifiable. Paradoxically, while environmental factors are typically thought of as modifiable, they may ultimately prove less yielding to change than our biology. Regardless, epidemiologists cannot afford to ignore either genes or environment.
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2 Risch N, Burchard E, Ziv E, Tang H. Categorization of humans in biomedical research: genes, race and disease. Genome Biol 2002;3: COMMENT2007.
3 Lin SS, Kelsey JL. Use of race and ethnicity in epidemiologic research: concepts, methodological issues, and suggestions for research. Epidemiol Rev 2000;22:187202.[ISI][Medline]
4 Cooper R. A note on the biologic concept of race and its application in epidemiologic research. Am Heart J 1984;108:71522.[ISI][Medline]
5 Cooper R, David R. The biological concept of race and its application to public health and epidemiology. J Health Polit Policy Law 1986; 11:97116.[ISI][Medline]
6 Cooper R, Steinhauer M, Miller W, David R, Schatzkin A. Racism, society, and disease: an exploration of the social and biological mechanisms of differential mortality. Int J Health Serv 1981;11: 389414.[ISI][Medline]
7 Cooper RS. Health and the social status of blacks in the United States. Ann Epidemiol 1993;3:13744.[Medline]
8 Kaufman JS, Cooper RS. Commentary: considerations for use of racial/ethnic classification in etiologic research. Am J Epidemiol 2001; 154:29198.
9 Kaufman JS, Cooper RS. Seeking causal explanations in social epidemiology. Am J Epidemiol 1999;150:11320.[Abstract]
10 Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and health in blacks and whites: the problem of residual confounding and the resiliency of race. Epidemiology 1997;8:62128.[ISI][Medline]
11 Shibata A, Whittemore AS. Genetic predisposition to prostate cancer: possible explanations for ethnic differences in risk. Prostate 1997;32: 6572.[CrossRef][ISI][Medline]
12 Tielsch JM, Sommer A, Katz J, Royall RM, Quigley HA, Javitt J. Racial variations in the prevalence of primary open-angle glaucoma. The Baltimore Eye Survey. JAMA 1991;266:36974.[Abstract]
13 Tarver-Carr ME, Powe NR, Eberhardt MS et al. Excess risk of chronic kidney disease among African-American versus white subjects in the United States: a population-based study of potential explanatory factors. J Am Soc Nephrol 2002;13:236370.
14 Karter AJ, Ferrara A, Liu JY, Moffet HH, Ackerson LM, Selby JV. Ethnic disparities in diabetic complications in an insured population. JAMA 2002;287:251927.
15 De Maria N, Colantoni A, Idilman R, Friedlander L, Harig J, Van Thiel DH. Impaired response to high-dose interferon treatment in African-Americans with chronic hepatitis C. Hepatogastroenterology 2002;49: 78892.[ISI][Medline]
16 Schwartz RS. Racial profiling in medical research. N Engl J Med 2001; 344:139293.
17 Wilson JF, Weale ME, Smith AC et al. Population genetic structure of variable drug response. Nat Genet 2001;29:26569.[CrossRef][ISI][Medline]
18 Lander ES. Mapping heredity: using probabilistic models and algorithms to map genes and genomes. In: Lander ES, Waterman MS (eds). Calculating the Secrets of Life, Applications of the Mathematical Sciences in Molecular Biology. Washington, DC: National Academy Press, 1995, pp. 2555.
19 Maldonado G, Greenland S. Estimating causal effects. Int J Epidemiol 2002;31:42229.
20 Wacholder S, Rothman N, Caporaso N. Counterpoint: bias from population stratification is not a major threat to the validity of conclusions from epidemiological studies of common polymorphisms and cancer. Cancer Epidemiol Biomarkers Prev 2002;11:51320.
21 Thomas DC, Witte JS. Point: population stratification: a problem for case-control studies of candidate-gene associations? Cancer Epidemiol Biomarkers Prev 2002;11:50512.
22 Schneider EC, Zaslavsky AM, Epstein AM. Racial disparities in the quality of care for enrollees in medicare managed care. JAMA 2002; 287:128894.
23 Smedley BD, Stith AY, Nelson AR. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. Institute of Medicine, 2002. Washington, DC: National Academy Press.
24 Krieger N, Sidney S, Coakley E. Racial discrimination and skin color in the CARDIA study: implications for public health research. Coronary Artery Risk Development in Young Adults. Am J Public Health 1998;88:130813.[Abstract]
25 Tull ES, Chambers EC. Internalized racism is associated with glucose intolerance among Black Americans in the U.S. Virgin Islands. Diabetes Care 2001;24:1498.
26 van Ryn M, Burke J. The effect of patient race and socio-economic status on physicians perceptions of patients. Soc Sci Med 2000;50: 81328.[CrossRef][ISI][Medline]
27 Collins-Schramm HE, Phillips CM, Operario DJ et al. Ethnic-difference markers for use in mapping by admixture linkage disequilibrium. Am J Hum Genet 2002;70:73750.[CrossRef][ISI][Medline]