Ancestry and health

Effects of racial and ethnic identities on health

Racial and ethnic groups can exhibit substantial average differences in disease incidence, disease severity, disease progression, and response to treatment (LaVeist 2002). In the United States, African Americans have higher rates of mortality than does any other racial or ethnic group for 8 of the top 10 causes of death (Hummer et al. 2004). U.S. Latinos have higher rates of death from diabetes, liver disease, and infectious diseases than do non-Latinos (Vega and Amaro 1994). Native Americans suffer from higher rates of diabetes, tuberculosis, pneumonia, influenza, and alcoholism than does the rest of the U.S. population (Mahoney and Michalek 1998). European Americans die more often from heart disease and cancer than do Native Americans, Asian Americans, or Hispanics (Hummer et al. 2004).

Considerable evidence indicates that the racial and ethnic health disparities observed in the United States arise mostly through the effects of discrimination, differences in treatment, poverty, lack of access to health care, health-related behaviors, racism, stress, and other socially mediated forces; however, differences in allele frequencies certainly contribute to group differences in the incidence of some monogenic diseases, and they may contribute to differences in the incidence of some common diseases (Risch et al. 2002; Burchard et al. 2003; Tate and Goldstein 2004). For the monogenic diseases, the frequency of causative alleles usually correlates best with ancestry, whether familial (for example, Ellis – van Creveld syndrome among the Pennsylvania Amish), ethnic (Tay-Sachs disease among Ashkenazi Jewish populations), or geographical (hemoglobinopathies among people with ancestors who lived in malarial regions). To the extent that ancestry corresponds with racial or ethnic groups or subgroups, the incidence of monogenic diseases can differ between groups categorized by race or ethnicity, and health-care professionals typically take these patterns into account in making diagnoses.

Even with common diseases involving numerous genetic variants and environmental factors, investigators point to evidence suggesting the involvement of differentially distributed alleles with small to moderate effects. Frequently cited examples include hypertension (Douglas et al. 1996), diabetes (Gower et al. 2003), obesity (Fernandez et al. 2003), and prostate cancer (Platz et al. 2000). However, in none of these cases has allelic variation in a susceptibility gene been shown to account for a significant fraction of the difference in disease prevalence among groups, and the role of genetic factors in generating these differences remains uncertain (Mountain and Risch 2004).

Allelic architecture of disease

The genetic architecture of common diseases is an important factor in determining the extent to which patterns of genetic variation influence group differences in health outcomes (Reich and Lander 2001; Pritchard and Cox 2002; Smith and Lusis 2002). According to the common disease/common variant hypothesis, common variants present in the ancestral population before the dispersal of modern humans from Africa play an important role in human diseases (Goldstein and Chikhi 2002). Genetic variants associated with Alzheimer disease, deep venous thrombosis, Crohn disease, and type 2 diabetes appear to adhere to this model (Lohmueller et al. 2003). However, the generality of the model has not yet been established and, in some cases, is in doubt (Weiss and Terwilliger 2000; Pritchard and Cox 2002; Cardon and Abecasis 2003). Some diseases, such as many common cancers, appear not to be well described by the common disease/common variant model (Kittles and Weiss 2003; Wiencke 2004).

Another possibility is that common diseases arise in part through the action of combinations of variants that are individually rare (Pritchard 2001; Cohen et al. 2004). Most of the disease-associated alleles discovered to date have been rare, and rare variants are more likely than common variants to be differentially distributed among groups distinguished by ancestry (Risch et al. 2002; Kittles and Weiss 2003). However, groups could harbor different, though perhaps overlapping, sets of rare variants, which would reduce contrasts between groups in the incidence of the disease.

The number of variants contributing to a disease and the interactions among those variants also could influence the distribution of diseases among groups. The difficulty that has been encountered in finding contributory alleles for complex diseases and in replicating positive associations suggests that many complex diseases involve numerous variants rather than a moderate number of alleles, and the influence of any given variant may depend in critical ways on the genetic and environmental background (Risch 2000; Weiss and Terwilliger 2000; Altmüller et al. 2001; Hirschhorn et al. 2002). If many alleles are required to increase susceptibility to a disease, the odds are low that the necessary combination of alleles would become concentrated in a particular group purely through drift (Cooper 2004).

Race and genetic biomedical research

The primary impetus for considering race in biomedical research is the possibility of improving the prevention and treatment of diseases. Many previous studies have observed that disease susceptibility and environmental responses vary by race. Thus, some researchers believe that race may be an informative category for biomedical research. Other researchers believe that racial categories have no valid biomedical applications, and may be socially harmful (Jackson, 2004).

The role of race in biomedicine is actively debated among biomedical researchers. Several questions are considered:

• can the concept of "race" be considered valid?
• When should race be taken into account when studying humans?
• What definition of race is appropriate for biomedical research?
• Do the biological differences between races justify the use of racial categories in research?
• Can genetic assignment to population groups be used in lieu of self-identified race?
• What are the ethical implications of using race in research?

Genetic differences among races

Further information: [[:Race and population genetics]]

Most Americans still believe that there is some biological legitimacy to our socially constructed racial categories. However, our modern scientific understanding of human genetic diversity flies in the face of all of our social stereotypes.

— Joseph L. Graves, Jr., evolutionary biologist

The biomedical relevance of genetic differences among races is a matter of debate. These issues can be illustrated by looking at an example, sickle-cell disease. This disease has a clear relation to geographic origin since the associated gene also provides protection to a common tropical disease, Malaria. Thus, it is much more common in people of African descent than people of European descent. In an emergency room, this may help a doctor doing an initial diagnosis if a patient presents with symptoms compatible with this disease. However, this is still unreliable evidence. Testing the genotype by examining the blood of the patient gives the definitive evidence, not the race. Also, the disease does not follow absolute racial lines, it is most common in African American and Hispanics of Caribbean ancestry, but the trait has also been found in those with Middle Eastern, Indian, Latin American, Native American, and Mediterranean heritage, making it difficult to exclude patients who present with compatible symptoms simply based on race. Most diseases argued to have some correlation to race have much weaker correlation to geographic origin than sickle-cell disease, meaning that the value of knowing the race and not the exact genotype is even weaker.

Disease association studies

Michael Bamshad writes that inference about an individual’s ancestry trough self-identified race can make it easier to predict how likely an individual is to have a some disease-causing variants. HbSallele in sub-Saharan Africans and Southern Europeans or the C282Y-HFEand ∆508-CFTRalleles, which cause haemochromatosis and cystic fibrosis, respectively,in Northern Europeans are well known examples,but many others have been discovered.

The common disease-common variant (often abbreviated CD-CV) hypothesis predicts common disease causing alleles will be found in all populations. An often cited example is an allele of apolipoprotein E, APOE ε4, which is associated in a dose-dependent manner with susceptibility to Alzheimer's disease. This allele is found in Africans, Asians and Europeans. However, many disease causing alleles are found to have different (technically called epistatic) effects in different populations. For example, the increased risk of Alzheimer's disease that is associated with the APOE ε4 allele is 5-fold higher in individuals with Asian rather than African ancestry.

Polymorphisms in the regulatory region of the CCR5 gene affect the rate of progression to AIDS and death in HIV infected patients. While some CCR5 haplotypes are beneficial in multiple populations, other haplotypes have population-specific effects. For example, the HHE haplotype of CCR5 is associated with delayed disease progression in European-Americans, but accelerated disease progression in African-Americans. Similarly, alleles of the CARD15 (also called NOD2) gene are associated with Crohn's disease, an inflammatory bowel disorder, in European-Americans. However, none of these or any other alleles of CARD15 have been associated with Crohn's disease in African-Americans or Asians.

The effects of racial and ethnic identities on health

Although, considerable evidence indicates that the racial and ethnic health disparities observed in the United States arise mostly through the effects of discrimination, differences in treatment, poverty, lack of access to health care, health-related behaviors, racism, stress, and other socially mediated forces, differences in allele frequencies certainly contribute to group differences in the incidence of some monogenic diseases, and they may contribute to differences in the incidence of some common diseases (Risch et al. 2002; Burchard et al. 2003; Tate and Goldstein 2004). For the monogenic diseases, the frequency of causative alleles usually correlates best with ancestry, whether familial (for example, Ellis-van Creveld syndrome among the Pennsylvania Amish), ethnic (Tay-Sachs disease among Ashkenazi Jewish populations), or geographical (hemoglobinopathies among people with ancestors who lived in malarial regions). To the extent that ancestry corresponds with racial or ethnic groups or subgroups, the incidence of monogenic diseases can differ between groups categorized by race or ethnicity, and health-care professionals typically take these patterns into account in making diagnoses.

Even with common diseases involving numerous genetic variants and environmental factors, investigators point to evidence suggesting the involvement of differentially distributed alleles with small to moderate effects. Frequently cited examples include hypertension (Douglas et al. 1996), diabetes (Gower et al. 2003), obesity (Fernandez et al. 2003), and prostate cancer (Platz et al. 2000). However, in none of these cases has allelic variation in a susceptibility gene been shown to account for a significant fraction of the difference in disease prevalence among groups, and the role of genetic factors in generating these differences remains uncertain (Mountain and Risch 2004).

Human Genome Diversity Project

The Human Genome Diversity Project (HGDP) has attempted to map the DNA that varies between humans. In the future, HGDP could possibly reveal new data in disease surveillance, human development and anthropology. HGDP could unlock secrets behind and create new strategies for managing the vulnerability of ethnic groups to certain diseases. It could also show how human populations have adapted to these vulnerabilities. To date, HGDP research has collected samples from 52 distinct ethnic groups, this methodology has been criticised by some on the basis that ethnic groups are considered socio-cultural constructs and not biological populations. Anthropologist Jonathan Marks has stated that: "As any anthropologist knows, ethnic groups are categories of human invention, not given by nature. Their boundaries are porous, their existence historically ephemeral. There are the French, but no more Franks; there are the English, but no Saxons; and Navajos, but no Anasazi...we cannot really know the nature of the actual relationship of the modern group to the ancient one...The worst mistake you can make in human biology is to confuse constructed categories with natural ones. And to overload a big project with cultural categories as the overall sampling strategy would be a serious problem. First it would make those labels appear to be genetic units; indeed, it would make them genetic units, which they had not been previously. Second, it would emphasise the genetic distinctions among these groups; it would force them to be genetically distinct by being labeled at the outset." Many indigenous peoples have refused to take part in the HGDP due to concerns about misuse of the data: "In December [1993], a World Council of Indigenous Peoples in Guatemala repudiated the HGDP." The project has raised ethical questions. Some worry that the results will be misued by racists. However, members of this project have been described as "liberals who argue that the project will help to reduce racism by showing that the concept of race is scientifically unsustainable" by Human Genetics Alert (HGA)

See also

• Ethnicity and health
• Race and health in the United States

References

• "The use of racial, ethnic, and ancestral categories in human genetics research". Am. J. Hum. Genet. 77 (4): 519–32. 2005. doi:10.1086/491747. PMC 1275602. PMID 16175499. {{cite journal}}: Unknown parameter |month= ignored (help)
• Jackson, F. L. C. (2004). Book chapter: Human genetic variation and health: new assessment approaches based on ethnogenetic layering British Medical Bulletin 2004; 69: 215–235 DOI: 10.1093/bmb/ldh012. Retrieved 29 December 2006.