The Practitioner's Dilemma: Can We Use a Patient's Race To Predict Genetics, Ancestry, and the Expected Outcomes of Treatment?

The decoding of the human genome has opened the door to an exciting realm that may fundamentally alter the trajectory of many diseases. For many practitioners, however, recent advances in our understanding of genomics and the genetics of ancestry have left us with an unanswered question. In the clinical context, does a patient's race provide an adequate proxy for genetics? The relationship between genomics and what we call race is complex and changes frequently. Understanding it requires familiarity with esoteric methods. A practitioner may find it difficult to know what to do when dealing with the problems of an individual patient. Is it or is it not appropriate to use that patient's ascribed or self-identified race to help decide treatment? Practitioners face this dilemma on a regular basis. The debate over using race as a proxy for genetics and associated treatment outcomes is often heated. A recent op-ed piece in The New York Times opined that [r]ace is merely a shorthand that enables us to speak sensibly, though with no great precision, about genetic rather than cultural or political differences (1). Consistent with this approach, a physician recently described her approach to care (2): Almost every day at the Washington drug clinic where I work as a psychiatrist, race plays a useful diagnostic role. [C]linical experience and pharmacologic research show that blacks metabolize antidepressants more slowly than Caucasians and Asians.So I start all black patients with a lower dose, then take it from there. On the other side of the aisle are scholars who state (3): A racial designation in the context of medical management not only defies everything we have learned from biology, genetics, and history but also opens the door to inequities in medical care.Since race is biologically meaningless, how will a physician know whether a given patient (who may identify with 2 races) has the combination of alleles that will ensure the efficacy of the drug? And what effect will racial profiling in the choice of therapy have on the bond of trust between patients and physicians? Whom should practitioners believe? Recent Research in the Genetics of Race and Ancestry Rosenberg and colleagues (4) published a seminal paper in 2002 that addressed the issue of race as a proxy for genetics. They analyzed the genetic structure of tissue samples taken from 1056 individuals drawn from 52 population groups around the globe. Each group represented a distinct geographic area, and the individuals within each group had remained relatively stable geographically over several generations. The authors asked whether the genetic differences among these population groups were distributed into recognized racial groups. (There is an important caveat to consider here. Studying only geographically stable samples does not consider the effects of intermarriage and other forms of interbreedingoften called genetic admixture. In populations such as the United States, in which extensive genetic admixture has occurred over centuries, sorting population groups reliably on the basis of genetic differences is difficult if not impossible. Rosenberg and colleagues' analysis of the genetic structure of ethnic populations of the Middle East, in which substantial admixture has occurred over time, confirms this.) To appreciate how Rosenberg and colleagues' research was performed, imagine 2 groups of scientists in adjoining rooms separated by a locked door. One group is made up of geneticists, the other of social scientists. The geneticists are provided with a set of 1056 tissue samples with no labeling of their source, and they are asked to sort these samples into groups based on similarities in genetic structure. A computer has been programmed to compare slight differences in the molecular structure of 377 different DNA segments to sort these tissue samples into groups. These segments are called microsatellites, and they represent parts of the DNA that have no known genetic function and are known from previous research to exhibit substantial variation in nucleotide structure across individuals. (The number of segments that optimizes the computer's ability to identify genetic differences among populations is thought to be 377.) The computer doesn't choose the number of groups into which the samples will be sorted; the scientists do. Once the number of groups is arbitrarily chosen by those scientists, the computer uses a statistical algorithm to define the best grouping. Think of a deck of cards. Imagine that you are asked to use a computer to sort them into groups based on similarities and differences. When arbitrarily instructed to sort the cards into 2 groups, you will see the cards divided by color: black and red. If you instruct the computer to sort the cards into 3 groups, you might get a group consisting of face cards, another of numbered red cards, and another of numbered black cards. Changing to 4 groups will sort the cards into the suits you recognize in most card games. Rosenberg and colleagues took a similar approach, asking a computer first to sort the tissue samples into 2 groups, then 3 groups, and so on up to 6 groups. The computer first determined that approximately 95% of the difference identified in DNA structure occurred within populations at the level of the individual tissue sample in ways that did not allow it to differentiate among population groups. Only 5% of the difference in structure occurred among groups. Nevertheless, solely on the basis of that 5% of the difference, the statistical algorithm used by the computer could separate the tissue samples into the various numbers of groups selected by the scientists. We now return to our imaginary scientists in adjoining rooms. While the geneticists have been doing their work, the social scientists in the adjoining room have had a different task. They are told the geographic origin of each tissue sample, and they are asked to sort the samples first by continent of origin and then within continents by region. After both groups of scientists have done their job, the door is unlocked, and the scientists are asked to compare their results. Does grouping the tissue samples by genetic structure recreate the grouping by geographic origin? In Rosenberg and colleagues' study, the answer was yes: Genetic clusters often corresponded closely to predefined regional or population groups or to collections of geographically and linguistically similar populations (4). May information on genetic ancestry be useful in clinical practice? Again, the answer was yes: Information about a patient's population of origin might provide healthcare practitioners with information about risk when direct causes of disease are unknown (4). Does this mean that we can use a patient's race as a proxy for ancestry and to predict genetic structure with accuracy? Although Rosenberg and colleagues did not address this question explicitly, the answer seems to be no. To understand why, let us examine how the genetically defined groups look from a geographic perspective, varying the arbitrarily defined number of groups from 2 to 6. Table 1 shows these results. Table 1. Geographic Origins of Groups Defined by Genetic Clustering on the Basis of the Predefined Number of Groups The Origins of Racial Categories For more than 250 years, there have been 4 races in much of the Western world: white, black, Asian, and Native American. European explorers defined these categories as they set out to identify (and conquer) new continents, and Carl Linnaeus cataloged and described these categories in the 1700s in successive editions of his work Systema Naturae. Linnaeus divided the human species into what he perceived as its 4 subspecies: Afer niger (African black), Americanus rubescus (American red), Asiaticus luridus (Asian yellow), and Europaeus albus (European white). In reporting birth and death rates for 2002, the federal government's National Vital Statistics Reports continued to sort Americans into the same 4 groups described by Linnaeus in the 1700s. The U.S. Census Bureau has also preserved these racial categories largely intact. (There is a movement to separate out the indigenous peoples of the Pacific IslandsOceania in the groupings of Rosenberg and colleagues' studyin both census data and the reporting of vital statistics.) If we look only at the group of 4 origins in Table 1, the geneticists seem to have sorted the tissue samples into what we generally recognize as racesalthough some groups that are considered Asian by race have clustered with the Europeans. However, just as the deck of cards is sorted differently depending on the preselected number of groups, so does the world's population look different on the basis of that preselected number. Select 4, and we have the cards by suits and people by Linnaeus' races. Select 2, and we have cleaved both the deck and the globe approximately in half. Select 6, and the global population starts to get somewhat confusing. Which is the best number for sorting the human population? There cannot be an answer to this question without applying arbitrary measures to the concept of what is best. The genetics of human populations may start with broad continental groupings that bear a rough resemblance to Linnaeus' racial categories, but knowledge of individual ancestry provides much more detailed and relevant information. Consider my ancestors and those of my wife. Mine came on the Mayflower to the Plymouth Colony, and hers fled the 19th-century pogroms aimed at Russian Jews. Both were from Europe. We both check the white box on all the official forms. To learn about our health risks based on our ancestry, you will need to know that I am a mixture of Anglo-Saxon and Celt, while my wife is an Ashkenazi Jew. From our race (white), you can only guess our genetic traits. From knowing the specifics of our ancestry, you can make reasoned projections of our health risks. Let us shift our analysis from my family