Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities
© Mersha and Abebe; licensee BioMed Central. 2015
Received: 26 July 2014
Accepted: 1 December 2014
Published: 7 January 2015
This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals.
KeywordsGenome Race Ethnicity Ancestry Ancestry informative markers Ancestry haplotype Admixture Health disparity
Genetic variation in the human genome
The human genome is composed of over three billion bases of DNA and contains between 25,000 and 30,000 protein-coding genes . On average, any two randomly selected humans have 99.9% identical DNA . Yet, these 0.1% differences spreading over the entire genome contribute to genetic heterogeneity that uniquely distinguishes each person. Because the majority of the human genome contains non-coding DNA, the bulk of this genetic diversity is not visible at the phenotype level. Variable regions on the genome are broadly classified into single nucleotide polymorphisms (SNPs) and structural variations (SVs). SNPs are changes in single DNA bases whereas SVs involve large genomic changes including indels and genomic rearrangements (translocation, transversion). The International HapMap Project was the first multi-institutional effort to catalog variations and develop a haplotype map (HapMap) of the human genome. The HapMap project had identified over 5 million SNPs in the human genome including their distribution among people in different parts of the world . While successful, the HapMap project had two major limitations: 1) it encompassed only SNPs, and 2) it only contained the most common genetic variants (those with frequencies >5%). Many genetic disorders are caused by rare SNPs (with frequencies <5%) and by SVs. The 1000 Genomes Project was formed in 2008 to sequence and generate a catalog of human genetic variation and haplotypes from the genomes of at least 1,000 people around the world (hence the name the 1000 Genome Project). The current phase 3 analysis of the project contains 2,535 individuals from 26 populations and identified a total of over 81 million variants, ranging from SNPs, indels, and other small variants to insertions of mobile elements and large structural variants spanning 100 s of kilobases (http://www.1000genomes.org/). This haplotype resource at finer scales will facilitate the understanding of genetic variation at genomic and geographic levels .
Because of their sheer number, SNPs are the major sources of genetic and phenotypic diversity, accounting for 95% of all known sequence variations . Different versions of the DNA bases present at a SNP locus are referred to as alleles. Alleles with a frequency greater than 5% are called common variants, those with a frequency of 1%–5% are low frequent variants and those less than 1% are rare variants. Because rare variants might have arisen after populations diverged or occurred in recent human history, they are more likely to be population-specific and, therefore, they may not be shared with different populations. Thus, the overrepresentation of rare causal variants in certain population could explain the observed differences in disease prevalence, including asthma .
There are two potential reasons why some variants are relatively common in one population but absent (or nearly so) in another: a) a recent emergence of a variant that has not yet had time to spread to other populations and b) natural selection in a specific local environment. An example of the first scenario is a SNP that causes hereditary hemochromatosis, which is common in Europe but very rare elsewhere. Lactase persistence is an excellent example of the influence of natural selection on allelic frequency. Lactase persistence into adulthood is prevalent in Somali camel herders from Ethiopia where milk consumption continues beyond childhood . Positive selection in a geographic-specific manner has also been seen in genes that affect skin pigmentation  and resistance to malaria .
Anatomically, modern humans first appeared in Africa some 150,000 to 200,000 years ago . About 60,000 years ago, humans left Africa in waves of migrations and, through a sequential chain of colonies, spread to occupy most of today’s land masses. During this journey, they encountered different environments and climates and came in contact with novel pathogens and animals. They formed local communities, separated by geographic, linguistic, cultural, and social barriers. Mutation, genetic drift, and natural selection operated in parallel with demographic and historical events to weave the patterns of human variation in extant populations. The result of this interplay was the imprint of genetic ancestry and population structure carried in the genome of each individual and groups that lead to the development of the remarkable racial and ethnic diversity that we see today.
Comparison between estimates of genetic ancestry and self-reported race in African and European American populations from 1000 genomes project datasets
Ancestral population genetic ancestry
Mean ± SD
Mean ± SD
European ancestry (CEU) (n = 87)
0.976 ± 0.022
0.024 ± 0.022
African ancestry (YRI) (n = 88)
0.013 ± 0.009
0.987 ± 0.009
African American (AA) (n = 61)
0.108 ± 0.152
0.892 ± 0.152
Empirically speaking, ancestry is estimated using ancestry informative markers (AIMs), which are a set of genetic variations for a particular DNA sequence that appear in different frequencies in populations from different regions of the world. The use of AIMs compares an individual’s polymorphisms at these markers with previously analyzed genomic reference sets from people whose ancestral history is fairly well known. AIMs are used to estimate the geographical origins of an individual’s ancestors, typically expressed as proportions of one’s ancestry that comes from different continental regions .
Availability of genetic markers that are ancestry-informative and newly developed statistical methods may overcome concerns regarding race/ethnicity categorization . There is evidence that measures of genetic ancestry can improve clinical care for people of mixed race. For example, physicians assessing lung disease can make more accurate diagnoses when they use a reference standard from the patients’ actual genetic ancestry than self-reported race or ethnicity . A large proportion of Native American ancestry is associated with a greater risk of childhood acute lymphoblastic leukemia. Children with more than 10% Native American ancestry need an additional round of chemotherapy to respond to the treatment . Differences in ancestry proportion in admixed population could introduce variation among individuals of the same race and potentially alter genetic association and the therapeutic efficacy of commonly used asthma therapies, such as β2-adrenergic receptor agonists (β-agonists) [40,41]. So far, pharmacogenetic studies of admixed ethnic groups have been limited to small candidate gene association studies. Large consortium-based whole genome sequencing studies are required to provide a reference “genome map” for population without precise matching reference panel including admixed populations for future genetic/genomic and pharmacogenetic studies.
Genetic markers used to infer ancestry: autosomal SNPs, Y-SNPs, mitochondrial SNPs, and X-SNPs
Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes. Autosomal DNA is inherited from both parents. Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.
Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males. The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother. Y-DNA testing can then be used to trace direct paternal line. Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family. Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree. In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines. When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers. STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.
Mitochondrial DNA (maternal line testing) markers: mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) . All children inherit mtDNA from their mother, with no admixture from the father. Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line. Mitochondrial DNA does not follow any surname. In fact, the surname changes in every generation when women marry. Polymorphisms of mtDNA have been used to understand human population distribution around the world. Before modern human traveled across the world, mitochondrial haplogroups were largely restricted to the geographic regions of their origin . For this reason, they are often superimposed on maps of the globe as representative of the human populations derived from those regions of the planet. The mitochondrial genome is a critical target for inherited disparity due to ethnic-based diversity, which is greatest within Africa. Because of the clear associations of mitochondrial haplogroups and ethnic categories with geography, one might naively expect a simple correlation between the two classifications. While, for instance, there is broad correspondence between the L haplogroups and African ethnicity assignments, African ethnicity assignments are present to varying degrees in virtually every haplogroup analyzed and almost every haplogroup contains members of each of the four ethnicities. This is not particularly surprising due to the fact that mitochondrial DNA represents only a very small segment of the complex mosaic of a human’s genetic ancestry, and it suggests that the ability to infer coarse ethnic identity from mitochondrial sequence would be very limited. In fact, studies found that mitochondrial DNA can be used to infer the probable assignment of coarse ethnicity with almost 90% accuracy . This level of accuracy in predicting investigator-assigned ethnicity could be very useful in forensic investigations .
X chromosome (X-DNA testing) markers: an X chromosome DNA test looks at markers on X chromosome(s). Males have one X chromosome that they inherit exclusively from their mother, and females have two X chromosomes that they inherit from both parents, one from their father and one from their mother. This creates a unique inheritance pattern that may provide many insights into one’s maternal heritage. STR markers on the X chromosome have been used in population genetic studies and forensics.
There are two main benefits in using haploid (Y-DNA and mitDNA) markers over diploid (autosomal) markers: 1) they lack recombination. This allows for more easily recoverable phylogenies than is possible for the autosomal markers, allowing for the easier identification of geographically restricted clades, which could be indicative of past historical migration. The second benefit in using the sex-specific systems is their 2) much small effective population size related to autosomal markers due to their haploid mode of inheritance through one sex only. Genetic diversity of present-day American populations is very complex due to the demographic events that resulted in extremely admixed populations . Through the analysis of lineage markers such as mtDNA andY-DNA, it is possible to isolate the original Native American lineages without the confounding effects of admixture due to the absence of recombination. The Native American share was conserved through the maternal line. Since only the egg, not the sperm, contains cytoplasm, we can use this to distinguish the original mother. Studies have shown that the “Eve” for Cuban population is about 38.8% African, 34.5% Native Americans, and 26.7% Europeans. Conversely, by using the Y chromosome, studies have shown that 82% of Cubans are descendants of European fathers, 17% of African fathers, and 1% of indigenous fathers [33,34].
Multi-locus ancestral haplotype as ancestry-informative regions (AIRs)
Although variation in humans reflect genetic differences at single allele as well as haplotype level, most local ancestry estimators use allele frequency data (locus-by-locus) between parental contributions along the chromosome, ignoring molecular information that is available in haplotype block structure. Individual mutations carry only weak signals about population ancestry. By adding information across the whole genome at haplotype level, we can reconstruct these admixture events more accurately. It has been described that less than 50% of admixture is hard to detect from single locus (or non-recombining genome) data. The power of detecting ancestry switch points between European and African ancestry per person becomes feasible as more and more loci are identified . This approach is referred to as haplotype sharing  and involves sharing several markers to identify regions of interest  rather than relying on differences in allele frequencies at individual markers. However, previous methods do not take into account multiple loci as provided by haplotype structure in ancestral populations. Potential advantages of multipoint ancestral haplotypes include: (1) their use of more information in the data when a susceptibility variant in the region is untyped or partially typed and (2) the fact that likelihoods at nearby variants are based on the same data, so they are formally comparable for the purposes of localization. As a result, multipoint ancestral haplotype methods have the potential to vastly improve and provide high-resolution localization of variants over single-point methods . By considering the genealogy of ancestral haplotype rather than pairs of variants, this approach may allow the joint estimation of other interesting parameters in the admixture model, such as admixture time, divergence time, population size, and mutation rate as described by Wang .
In a founder population, patients with a genetic disease are likely to share predisposing genes from a common ancestor. Depending on the distance of the relationship, patients are expected to share extended segments of DNA around the disease gene, thus the extent of linkage disequilibrium (LD) between the disease and the surrounding marker (about 1 cM) is small enough to be meaningful and large enough to be observed. Because of the size of the shared segment, a genomic search with DNA markers for such regions can efficiently locate the map position of genes using identity by descent (IBD) mapping . IBD mapping is a haplotype sharing statistic (HSS) approach, which uses (hidden) co-ancestry between affected individuals from a founder population. Recently, IBD mapping has been proposed as a useful approach to map genes in a founder population . IBD mapping uses haplotype sharing at several markers rather than differences in allele frequencies at individual markers to identify regions of interest . Devlin et al.  described the possibility of mapping disease genes by analyzing excess haplotype sharing. Using this idea, one could integrate information on LD structure of genotype data and interrogating various SNP densities of the current SNP chips, under various disease models and various levels of informativeness among markers between the ancestral populations to better optimize the power of LD admixture mapping procedures and make them more efficient and powerful to identify and localize liability genes for complex diseases including asthma .
Limitations related to ancestry markers include the reference sets, which are comprised of the genomes of relatively few sampled individuals who are themselves from a relatively few, geographically restricted regions. Thus, to what extent is a panel derived by contrasting a “Yoruban” sample with “Europeans” appropriate for use in African-American samples? How much is the Yoruban population represents Africa and hence African Americans are debatable . However, the same can be said to the CEU population where recent high-density SNP studies showed population gradient including linkage disequilibrium discrepancies across the North–south and even within Finland (East–west) . Therefore, it is prudent to recognize the limitations of ancestry informative markers in genetic/genomic studies of admixed population.
Genetic ancestry and clinical predictive variables
Clinical asthma outcome variables such as pulmonary function tests (PFTs) include forced vital capacity (FVC, a measure of lung size), forced expiratory volume in 1 s (FEV1, a standard measure of lung function), and FEV1/FVC ratios. The variation in ancestry in relation to these clinical predictive variables may help to explain differences in disease phenotypes among ethnic subgroups. Recent study showed that in Mexican Americans, European ancestry was associated with more severe asthma, as measured by FEV1, a quantitative measure of lung function. A decrease of 1.7% baseline FEV1 was observed per 10% increase in European ancestry . FEV1 is a measure of airway caliber and a standard measure of lung function, and FEV1/FVC ratio is a commonly used outcome to assess airway obstruction . Age-, race-, and ethnic-appropriate reference equations will be used for PFT results [57-59]. A recent study by the NHBLI-SARP case-only cohort indicated the predictive role of PFT in asthma severity [60,61].
Studies considering the relationship between degrees of ancestry proportion and asthma and asthma-related outcomes
Study subjects ( n )
Markers ( n )
Increasing Af ancestry associated with lower FEV1 and lower FVC
Kumar et al. 
Increasing Af ancestry associated with increasingly severe asthma exacerbation in males but not females
Rumpel et al. 
Smoking/lung function interaction
Increasing Af ancestry associated with lower FEV1 per pack-year of smoking
Aldrich et al. 
Increasing NA ancestry associated with less severe asthma
Salari et al. 
Increasing Af ancestry associated with decreased FEV1 and FVC pre- and post-bronchodilator
Brehm et al. 
Consortia- and self-reported race/ethnicity information
Limitations of self-reported race/ethnicity and genetic ancestry in disease genetics studies
Recent advance in high-resolution genome-wide genotyping allow the inference of genetic or “biogeographical” ancestry using empirical description of individuals and populations . In determining and quantifying genetic background, this technology can augment or supersede the use of proxy methods, such as self-identified race/ethnicity, physical appearance, language-spoken, or geographical origin, to stratify research participants and maximize their relative genetic homogeneity. As described above, the major problem in performing association studies of admixed populations that are assessed solely by self-reported race/ethnicity as a proxy for genetic ancestry is the possibility of spurious association with false-positive or false-negative results. Self-reported and investigator-assigned ethnicity typically relies on the subjective interpretation of a complex combination of both genetic and non-genetic information including behavior, cultural, and societal norms, skin color, and other influences. It is rarely the case that a study participant will report their ethnicity without errors. Self-reported ethnicity errors may occur for various reasons; some people may not be fully aware of their true ethnicity or only know recent ancestry (or their geographic origin) while others may identify with one ethnic group despite their admixed background. The imposition of racial categories on human populations has been one of the most enduring historical forces that shape our life trajectory . To illustrate, in a recent study, 9 of the 1,247 self-reported African Americans were found to have 100% European ancestry . Similarly, some self-identified European Americans have substantial admixture from African ancestry . Both examples illustrate that researchers should be aware of the limitation of self-identified race and ethnic categories as proxies for genetic ancestry [71,72]. Similarly, although ancestry could play a central role in disease etiology, association studies, and variable drug response, it provides less information in identifying societal construct such as health and income disparities. Furthermore, although disease susceptibility loci can differ in frequency across populations, using genetics as the only basis of explaining for health disparities could reinforce racial stereotypes . Moving forward, the potential of both genetics and race/ethnicity to shed light on health disparities must be considered.
Studies showed that extrapolation of genomics data from genetically homogeneous to genetically structured populations could generate large numbers of false positive and false negative results . Population stratification (or structure) is the existence of groups of individuals within a population that have some degree of reproductive isolation from the rest of the population and for which allele frequencies are likely to be different from the population as a whole. Several approaches have been used to adjust population structure in case–control studies. The most commonly used clustering algorithms is structure . Using ancestry informative markers, a) local ancestry tracked from each individual can be compared with the genome-wide average ancestry, and b) individuals whose ancestry is not typical of the population under study can then be excluded . To investigate the genetic relationships among ancestral groups, one could also compare patterns of population divergence using Wright’s F ST measure . From the F ST analysis, one could reliably identify subpopulations within major geographic regions (i.e., Europe, Africa, Asia, and the Native Americans) that exhibit lower or higher pairwise F ST (and, therefore, lower or higher genetic similarities). For populations of complicated admixture or unknown origins, a large number of loci with high resolution need to be genotyped, followed by principal component analysis (PCA) to individual-level genetic data. PCA can detect the presence of population mixture and admixture in a sample and thus can be used to determine the axis of variation in different dimensions based on biogeographical ancestry. Adjustment made using PCA approach increases investigator confidence that genetic association findings are not spurious due to stratification. Finally, characterization of culture, socioeconomic status, and environment should be made in disease genetic study, otherwise any or all “racial/ethnic” differences in disease risk factors can erroneously be attributed to presumed population genetic differences. Methods such as mixed model regression could help investigate the genetic and non-genetic risk factors. The failure to account ancestral background can thus prevent proper characterization of the genetic structure of a given study population, leading to inaccurate prediction of outcome as well as incorrect inferences about the evolutionary factors driving patterns of diversity .
Race/ethnicity in biomedical research
There are two major questions to answer before applying race/ethnicity category in biomedical research. First, is race/ethnicity a valid and reliable approach to ascertain individual ancestry? If so, should race be considered by those who study diseases and patient responses to treatment? Second, how do we define (or is it at all possible) race/ethnicity in the context of biomedical research? In general, people self-report their population origin correctly in terms of major population descriptors (such as Caucasian, African-American, Hispanics, Asian, etc.). However, these descriptions are not good indicators of the genetic composition of individuals, since genetic makeup of individuals are highly heterogeneous, and can be captured only with large dimensional genomic data. Genetic ancestry estimation at the individual level is bringing us closer to more personalized or individualized genetic-based medicine . Genomic researchers in medicine should focus on how genetic association results can be used to understand disease process in a way that can inform the clinical care of racial disparities rather than focusing merely on explaining health differences .
Large consortium-based next-generation sequencing studies such as the NIH/HLBI GO Exome Sequencing Project, the Consortium on Asthma among African ancestry Populations in the Americas (CAAPA), and the 1000 Genomes Project are currently using next-generation whole exome and whole genome sequencing studies to provide diverse genomic information from different admixed populations . These large-scale sequencing projects have revealed that admixed ethnic groups demonstrate a remarkable degree of genetic diversity related to an ancient African ancestry. Such genetic diversity has resulted in shorter regions of shared chromosomal segments (i.e., linkage disequilibrium) and a greater frequency of rare variants in ethnic groups with an African ancestry compared with European ancestral populations. In addition to increased genome and exome sequencing efforts, it is also critical to assess non-genetic factors such as poverty, education, access to health care, cultural practices, and environmental exposure such as traffic, smoke, and mold, which vary substantially among populations and may interact with genetic risk factors.
Which factors contribute more to health disparity: race/ethnicity or ancestry?
Moving beyond race/ethnicity to guide personalized medicine
As the world becomes multiethnic, and intermarriage between different racial/ethnic groups gets more and more common , it is increasingly difficult to assign a single ethnicity to an individual. There is a need of clear distinction between personalized medicine and guidelines for the application of personalized medicine in the context of homogeneous and an admixed population. Personalized medicine is a dynamic and broad term used to describe the incorporation of patients’ genomic profiles, family history, and social and other health details into clinical decision-making. Personalized medicine is easier to implement in a more uniform population using the genetic variation present in an individual. In admixed populations, it is much more complex to have a “public health” personalized medicine guideline as the context of the variants may be ancestry-sensitive and on an individual basis. For example, one person may have susceptibility variants that are common in one of their ancestral populations, but not the other (and the other way around for another individual from the same admixed population). In order for the personalized medicine to be meaningful and applicable to the global populations, we will need to know how genetic variants found in different parts of the world influence health and drug response. Thus, the application of personalized medicine should not be limited to patients with well-understood genotypes.
Although knowledge gained in genomics has advanced our understanding of biology, the promise of personalized medicine continues to appear far off for minority and admixed populations. For example, recently, pharmacogenomic information has been added to over 70 drug labels , but the studies on which label information are based have mostly focused on European populations. Meanwhile, African populations, who have the greatest genetic variation resulting in more haplotypes, lower levels of linkage disequilibrium, more divergent patterns of linkage disequilibrium, and more complex patterns of population substructure, are grossly underrepresented in the genomic studies that inform pharmaceutical guidance . The result is that clinicians may rely too heavily on data obtained from Europeans to make clinical decisions for Africans and other non-European populations. In addition, this inadequate representation of global populations in the cataloging of genetic variation is hindering the need to move away from the use of group labels such as race, which is often a poor proxy for genetic ancestry. This concern extends to the momentous debate about the development of ‘race-targeted’ drugs, such as BiDil (approved by the US Food and Drug Administration (FDA) to treat heart failure in admixed African Americans), based on subgroup analyses without any adjustment for potential confounders in samples . Intra-ethnic diversity adds complexity to the scientific appraisal, regulatory decisions, and, eventually, prescription of race-targeted drugs. Ignoring admixture or stratification within ethnic groups will complicate the promise of personalized medicine [96-99]. A study by Lee  showed that warfarin dosing algorithms that are based on ‘race’ terms for well-defined ethnic groups are not applicable to the heterogeneous admixed population. In April 2011, the American Congress of Obstetricians and Gynecologists (ACOG) adopted a policy to screen all patients for cystic fibrosis because of the difficulty in assigning ethnicity to individuals . The US FDA recommends screening all groups, regardless of race or ethnicity, for the presence of the HLA-B*5701 allele before starting or restarting therapy with Abacavir or Abacavir-containing medications (http://www.fda.gov/Drugs/DrugSafety/ucm123927.htm). Abacavir is used to treat human immunodeficiency virus (HIV) infection. Patients with the HLA-B*5701 allele have a higher risk of developing a hypersensitivity reaction. Furthermore, several medication dosing algorithms around the world are now being developed using the patient’s own genotype data [79,102,103].
Although conceptual distinction between race/ethnicity and ancestry is widely recognized [104-106], it has not been translated into measurements of how well each accounts for health disparities. Thus, the continued use of race in genetic research obscures the fundamental causes of racial differences in health. Although race and/or ethnicity could serve as good markers to predict socio-economic differentials like housing, income, and/or education, they are poor predictor of genetic ancestry . Increasingly, the world’s populations do not fall into conventional homogeneous ethnic categories, and ancestry informative markers with appropriate statistical methods must be used for quantitative measurement of the genetic ancestry of individuals. Quantifying the contributions of ancestry, environment (such as socio-economic status, life style), and their interactions to disease outcome in the genetically heterogeneous population will be critical to applying genomic-based biomarkers to the practice of medicine. The path to personalized medicine for all ethnic groups requires improvements to our ability to decipher genotype and sequence data using different analysis methods that integrate race/ethnicity information and account for ancestral genetic structure, complex haplotypes, and gene-gene and gene-environment interactions. It is crucial to recognize that disease and health disparities are the products of complex interactions that are not solely limited to genes but also involve environmental factors, socioeconomic status, lifestyle factors, and the biases of health care providers. Thus, it is important to place genetic ancestry factors in context with social, environmental, and economic factors for the purpose of resolving health disparities between populations.
Given higher genetic diversity within races than between races, the use of race/ethnicity as a dissimilarity marker is misleading [107,108]. Genetic ancestry can describe genetic relatedness accurately than race and ethnicity, but it could still exacerbate disparities since it sidesteps the interaction of biological and social factors that contribute to health. The current inference of ancestry based on computer programs with built-in assumptions about how the data should be grouped can sometimes reify racial distinctions by presenting genetic clusters or racial boundaries that do not exist in human population specially in admixed population. In addition, current ancestry inferences are based on reference samples with limited representation of the entire population (e.g., West African ancestry sample for the entire African Americans and Northern European sample for the entire European Americans). Understanding the sources of human genetic variation (using genetic markers) and the causes of health disparities (using race/ethnicity information) could lead to interventions that would improve the public health and bring personalized medicine to all.
ancestry informative markers
genome-wide association study
haplotype map of the human genome
principal component analysis
single nucleotide polymorphism
This work was supported by NIH Grant K01HL103165.
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