Abstract
Self-reported race/ethnicity is frequently used in epidemiological studies to assess an individual’s background origin. However, in admixed populations such as Hispanic, self-reported race/ethnicity may not accurately represent them genetically because they are admixed with European, African and Native American ancestry. We estimated the proportions of genetic admixture in an ethnically diverse population of 396 mothers and 188 of their children with 35 ancestry informative markers (AIMs) using the STRUCTURE version 2.2 program. The majority of the markers showed significant deviation from Hardy–Weinberg equilibrium in our study population. In mothers self-identified as Black and White, the imputed ancestry proportions were 77.6% African and 75.1% European respectively, while the racial composition among self-identified Hispanics was 29.2% European, 26.0% African, and 44.8% Native American. We also investigated the utility of AIMs by showing the improved fitness of models in paraoxanase-1 genotype–phenotype associations after incorporating AIMs; however, the improvement was moderate at best. In summary, a minimal set of 35 AIMs is sufficient to detect population stratification and estimate the proportion of individual genetic admixture; however, the utility of these markers remains questionable.
Keywords: population stratification, ancestry informative markers (AIMs), race, ethnicity, genetic epidemiology, forensic genetics, Hispanics
Introduction
Race and ethnicity are widely used interchangeable in population research. The ambiguity and interchangeable nature of these terms raises concerns about the scientific validity of statistical comparisons that use variables based on them (LaVeist 1994; Lee et al. 2001; Braun 2002). Race is linked to individual genetic makeup, which is not necessarily related to cultural or environmental characteristics. To the contrary, ethnicity refers to shared customs, beliefs and traditions among population subgroups that may or may not have a common genetic origin.
According to US Census Bureau, Hispanics, who can be of any race, constitute the largest ethnic minority in United States. They constitute 15.5% of the current US population and are expected to reach 24% by 2050. The Hispanic population is genetically diverse, represening a heterogeneous mix of European, African and Native American ancestry (Gonzalez Burchard et al. 2005; Salari et al. 2005). Therefore, a Hispanic individual may selfidentify as a single race or multiple races. There are wide variations across and within Hispanic ethnic groups in terms of genetic, socioeconomic, cultural and geographic origin.
The complicated genetic structure of contemporary ad-mixed populations has several important implications for conducting genetic epidemiology studies. Population stratification, referring to the difference in allele frequencies between cases and controls, may give rise to false association of genes with disease (Pritchard and Rosenberg 1999). Population stratification exists in ethnically admixed populations; without proper statistical adjustment, such stratification may lead to false-positive or false-negative results and produce incorrect associations between genes and disease. Self-reported ancestry can be inaccurate for determining an individual’s actual genetic ancestry (Choudhry et al. 2007). The use of self-reported race and/or ethnicity for Hispanic study participants in epidemiologic studies is particularly problematic due to the possibility that these individuals may not be fully aware of their own complex ancestry mixture. Genetic markers, such as ancestry informative markers (AIMs), may provide more accurate information on potential population stratification. AIMs and newly developed statistical methods are making the genetic estimation of ancestry increasingly more feasible and accurate (Hoggart et al. 2003; Pritchard et al. 2000a,b). Using a panel of genetic polymorphisms that present large differences in allelic frequencies (> 0.40) between Europeans and Africans, it is possible to estimate the degree of European and African admixture among Hispanics (Ziv et al. 2006).
We used a panel of 35 AIMs to estimate genetic ancestry in an ethnically diverse population in New York City, USA. These markers were selected to maximize the difference between European, African American and Native American populations. The objective is to estimate the extent of genetic admixture in this population with a minimal number of single nucleotide polymorphisms (SNPs). The selection of such a small number of markers also minimizes the cost for genotyping. To demonstrate the utility of these markers, we re-examined the genotype–phenotype relationship of paraoxonase-1 in this population (Chen et al. 2003) by adjusting for population stratification as determined by AIMs.
Materials and methods
Population
We utilized the resources of an ongoing prospective study conducted at the Mount Sinai Center for Children’s Environmental Health and Disease Prevention Research (Berkowitz et al. 2003). The cohort consists of an ethnically diverse group of mother–infant pairs participating in a longitudinal study assessing infant growth and neurodevelopment associated with environmental exposures in urban New York City. Subjects were recruited during early pregnancy from the prenatal clinic and two private practices at Mount Sinai Hospital from March 1998 to March 2002. The details of the study design have been previously described (Berkowitz et al. 2004). Maternal blood samples were obtained in heparin-treated vacutainers, and DNA was extracted and purified using Roche Hi-Pure PCR Template Preparation kits as described by the manufacturer. The study population consists of 404 prenatal patients, 396 with sufficient DNA for genotyping. We also obtained sufficient DNA from 188 cord blood samples from the infants born to the study participants.
Race/ethnicity information
A structured questionnaire was administered by interviewers in English or Spanish to collect parent’s demographic information such as country of birth, socio-demographic characteristics, maternal health, lifestyle habits and residential history. Participants were asked to specify a single race/ethnic group based on six categories; White, Black, Black Hispanic, White Hispanic, Asian or Other.
Selection of AIMs
AIMs were selected to maximize the absolute difference in allele frequency among ancestral populations. A set of 35 AIMs (table 1) was selected based on previously published literatures (Salari et al. 2005; Choudhry et al. 2006; Ziv et al. 2006). These markers were selected because they exhibited largest difference in allele frequency between any two of the three ancestral groups, i.e. European, African and Native American. Europeans were sampled from Utah residents with ancestry from northern and western Europe while Africans from Yoruba in Nigeria. Native American samples were from western United States, Mexico and Central America. These AIMs were distributed across the genome but distant from functional domains of the genome so it is unlikely that these markers would result in any disease phenotype. Detailed information about the flanking sequences and other information for all 35 AIMs are available from the HapMap database (www.hapmap.org).
Table 1.
Thirty-five ancestry informative markers with NCBI reference numbers, chromosomal locations and allele frequencies in European, African and Native Americans.
| Marker name |
dbSNP rs no. |
Location | Allele frequencies | ||
|---|---|---|---|---|---|
| African | European | Native American | |||
| TYR-192 | rs1042602 | 11q21 | 0.00 | 0.47 | 0.05 |
| OCA2 | rs1800404 | 15q13.1 | 0.14 | 0.72 | 0.48 |
| DRD2-Taq | rs1800498 | 11q23.1 | 0.14 | 0.65 | 0.09 |
| TSC0003523 | rs1985080 | 7p14.3 | 0.10 | 0.64 | 0.97 |
| TSC0745571 | rs203096 | 17q21.33 | 0.65 | 0.72 | 0.28 |
| TSC1102055 | rs2065160 | 1q32.1 | 0.50 | 0.92 | 0.17 |
| TSC0042312 | rs2077863 | 18p11 | 0.51 | 0.93 | 0.93 |
| WI-4019 | rs2161 | 7q22.1 | 0.44 | 0.30 | 0.62 |
| MC1R-314 | rs2228478 | 16q24.3 | 0.51 | 0.14 | 0.04 |
| rs223830 | 16q13 | 0.03 | 0.19 | 0.64 | |
| TSC1023401 | rs235936 | 21q21.3 | 0.18 | 0.49 | 0.37 |
| WI-11909 | rs2695 | 9q21.31 | 0.81 | 0.86 | 0.22 |
| WI-11392 | rs2752 | 1q42.2 | 0.88 | 0.43 | 0.63 |
| FY-null | rs2814778 | 1q23.2 | 0.00 | 0.99 | 0.99 |
| WI-7423 | rs2816 | 17p12 | 0.00 | 0.49 | 0.08 |
| LPL | rs285 | 8p21.3 | 0.97 | 0.52 | 0.45 |
| WI-14319 | rs2862 | 15Q14 | 0.38 | 0.17 | 0.69 |
| WI-14867 | rs2891 | 17p13.2 | 0.02 | 0.51 | 0.43 |
| CRH | rs3176921 | 8q13.1 | 0.32 | 0.93 | 0.98 |
| WI-16857 | rs3287 | 2p16.1 | 0.73 | 0.20 | 0.21 |
| SGC30610 | rs3309 | 5q11.2 | 0.40 | 0.28 | 0.69 |
| SGC30055 | rs3317 | 5q23.1 | 0.05 | 0.59 | 0.73 |
| WI-17163 | rs3340 | 5q33.2 | 0.06 | 0.19 | 0.65 |
| CYP19 | rs4646 | 15q21.2 | 0.32 | 0.29 | 0.72 |
| TSC0879894 | rs518116 | 9q33.3 | 0.13 | 0.67 | 0.58 |
| GNB3 | rs5443 | 12p13.31 | 0.80 | 0.33 | 0.36 |
| TSC1157118 | rs584059 | 3q22.3 | 0.49 | 0.14 | 0.47 |
| F13B | rs6003 | 1q31.3 | 0.70 | 0.08 | 0.03 |
| GC1 | rs7041 | 4q13.3 | 0.93 | 0.41 | 0.45 |
| TSC0050288 | rs722098 | 21q21.1 | 0.90 | 0.18 | 0.72 |
| TSC0053441 | rs723632 | 1q32.3 | 0.10 | 0.92 | 0.67 |
| TSC0000409 | rs7349 | 10p11.22 | 0.04 | 0.87 | 0.96 |
| TSC0030203 | rs736394 | 14q32.12 | 0.52 | 0.74 | 0.99 |
| TSC0255473 | rs930072 | 5p13.2 | 0.96 | 0.10 | 0.45 |
| TSC0316844 | rs994174 | 10q23.1 | 0.76 | 0.25 | 0.26 |
AIMs markers and allele frequencies are compiled from Salari et al. 2005; Choudhry et al. 2006; Ziv et al. 2006.
Genotyping for AIMs
Genotyping was performed at the DNA core (Shared Research Facility) of Mount Sinai School of Medicine using SNPlex method under standard conditions (Applied Biosystems, Foster City, USA). It utilizes pre-optimized universal assay reagents kits and a set of SNP-ligation probes to perform genotyping up to 48-plex (48 SNPs genotyped in a single reaction). The analysis system collects and manages raw data and provides automated allele calling and quality metrics (Tobler et al. 2005).
Characterization of population structure and admixture
Using the exact test, we examined deviations from Hardy–Weinberg equilibrium (HWE) of each AIM using a software developed by Paul Lewis (http://www.eeb.uconn.edu/people/plewis/software.php). We estimated individual genetic ancestry and number of ancestral populations (K) among mothers and infants, separately, by Bayesian Markov chain Monte Carlo (MCMC) method implemented in the program STRUCTURE 2.2 available at http://pritch.bsd.uchicago.edu/software.html (Pritchard et al. 2000a; Falush et al. 2003). STRUCTURE arbitrarily groups individuals into clusters based on a Bayesian approach that uses the distribution and likelihood estimation to determine the distribution of population frequency. STRUCTURE was run with a burn-in length of 100,000 and 100,000 iterations after burn in without any prior population assignment under the admixture model using correlated allele frequencies and the number of populations (K = 3), designated as European, African, and Native American.
Statistical analysis
Results on genotype and phenotype of PON1 in the same population have been published previously (Chen et al. 2003). Both race-stratified (based on self-reported category) and race-adjusted genotype–phenotype relationships had been reported in the original paper, in which standard multiple regression techniques were employed using PROC REG and PROC GLM of SAS software, version 9.1. Here, we report the adjusted coefficient of determination, R2, the percentage of the total variation in PON1 phenotype explained by various models. To test for improvements of multivariate models, we contrasted the R2 of these models with and without AIMs.
Results
Our study population consists of multiracial/multiethnic mother–infant pairs in New York City. Of 396 mothers included in these analyses, the composition of the self-reported race/ethnicity was as follows (table 2):White (19.9%), Black (28.3%), Hispanics (47.5%), and non-specified (4.3%). Among 188 self-identified Hispanics, most of whom were of Caribbean origin, the majority (64.0%) identified their race as ‘other Hispanic’, not Black or White.
Table 2.
Estimate of European, African and Native American admixture in mothers of Children Environmental Cohort study, 1998–2002 using STRUCTURE with 35 AIMs in mothers and 34 AIMs in infants.
| Race | Percentage population ancestry | |||||||
|---|---|---|---|---|---|---|---|---|
| (self-reported) | European | African | Native American | |||||
| n | % | Mean | SD | Mean | SD | Mean | SD | |
| Mothers | ||||||||
| White | 79 | 19.9 | 75.1 | 19.0 | 6.6 | 12.0 | 18.3 | 12.8 |
| Black | 112 | 28.3 | 10.2 | 14.0 | 77.6 | 24.2 | 12.2 | 14.6 |
| Hispanic | 188 | 29.2 | 23.0 | 26.0 | 25.9 | 44.8 | 27.0 | |
| Black | 28 | 7.1 | 18.5 | 20.2 | 55.5 | 31.6 | 26.0 | 23.3 |
| White | 39 | 9.8 | 31.3 | 21.8 | 19.6 | 20.3 | 49.1 | 26.6 |
| Other | 121 | 30.6 | 30.9 | 23.4 | 21.3 | 21.3 | 47.8 | 26.3 |
| Non-specified | 17 | 4.3 | ||||||
| Infants | ||||||||
| White | 40 | 23.7 | 68.8 | 21.6 | 7.6 | 13.4 | 23.7 | 16.8 |
| Black | 45 | 26.6 | 11.5 | 11.9 | 73.5 | 22.4 | 15.0 | 14.1 |
| Hispanic | 80 | 28.0 | 19.8 | 30.0 | 25.3 | 42.0 | 23.0 | |
| Black | 8 | 4.7 | 29.1 | 29.9 | 45.6 | 37.1 | 25.4 | 23.1 |
| White | 13 | 7.7 | 28.5 | 23.4 | 17.8 | 15.8 | 53.7 | 24.5 |
| Other | 59 | 34.9 | 27.8 | 17.6 | 31.1 | 24.1 | 41.2 | 21.3 |
| Non-specified | 4 | 2.4 | ||||||
We used a panel of 35 AIMs to examine population stratification of this population. As expected, the majority of the markers (27/35) showed significant deviation (P < 0.05) from HWE indicating significant population stratification. However, the number of markers that depart from HWE drastically decreased when analyses were restricted to each racial group, with 2, 4 and 4 markers in Blacks, White and Hispanics, respectively. This small number of genes that depart from HWE may reflect gene flow between and among different racial groups. Our study population is racially and ethnically mixed; the overall mean proportions of European, African, and Native American in mothers as estimated by the STRUCTURE program were 32.8%, 37.6% and 29.6%, respectively. We then examined the imputed cluster distribution against self-reported race/ethnicity. Agreement between self-identified White and Black and population cluster assignments by STRUCTURE was fairly consistent; imputed ancestry proportions were 75.1% European and 77.5% African for these two groups, respectively. However, there was a less consistent pattern among the self-identified Hispanics, where the imputed cluster distribution was 29.2% European, 26.0% African and 44.8% Native American. Further categorization by race (e.g., Black Hispanics, White Hispanics, Others) among the Hispanics revealed a pattern that is moderately reflective of the self-report of combined race-ethnicity. For example, the women who reported their ancestry as Black Hispanic had a relatively high African cluster (55.5%) while those who self-identified as White Hispanics had higher Native American cluster (49.1%) than the European cluster (31.3%).
The assay for one of the SNPs in our AIMs panel, rs2077863, failed for cord blood, leaving 34 markers. Similar to the mothers, the majority of the markers (22/34) for the newborns showed significant deviation (P < 0.05) from HWE indicating significant population stratification. Not surprisingly, the racial/ethnic composition of the overall infant population was similar to that of their mothers (data not shown). Among the self-identified Hispanics, we also observed a similar pattern when comparing the child’s race/ethnicity inferred from the mother’s self-report to the STRUCTURE estimated population ancestry proportion (table 2). The African ancestry cluster among Black Hispanic infants was 45.6%, whereas the European ancestry cluster among the White Hispanic infants was 28.5%, much lower than the Native American ancestry cluster of 53.7%. This difference may partially reflect the ancestry of the infants’ fathers, which was not included in the self-reported race–ethnicity categorization.
We have previously reported the genotype–phenotype association of paraoxonase-1 in the same population (Chen et al. 2003). The measurements were carried out with phenylacetate as the substrate so that activity could be independent of the PON1 Q192R polymorphism (Chen et al. 2003). In the original report, we observed that the enzymatic activity of PON1 varied by race/ethnicity as well as the distribution of SNPs in PON1. As a result, we reported genotype–phenotype associations either stratified or adjusted by race/ethnicity. Herein, we reanalysed the data by incorporating AIMs into the models. The analysis was restricted to the individuals with complete data for PON1 genotype and phenotype as well as AIMS. To illustrate the utility of AIMs, we included only results on one promoter SNP (−108) and one coding SNP (L55M) as examples. Although R2 values differed, similar trends were observed for −909 and −162 SNPs that were in the original report. In addition, −909 SNP was tightly linked with −108 SNP. L55M is in linkage disequilibrium with the −108 promoter SNP with D′ = 0.5 (Chen et al. 2003). There are 350 mothers and 132 children who have complete data on AIMs, PON1 genotypes and phenotypes. Similar to the original report, the R2 of infants was much larger than that of the mothers (table 3) reflecting the stronger genetic / less environmental influence on the enzymatic activity of PON1. Also shown in table 3, with respect to both PON1 SNPs, models without adjusting for race or AIMs always yielded the smallest R2. A gradual increase in R2 was observed by incorporating self-reported race alone, AIMs alone, and finally race and AIMs together, indicating overall improvement in terms of fitness of these models. The improvement, however, is very modest in mothers and infants. For example, with respect to the L55M SNP, the R2 was 0.082 in the unadjusted model and became 0.123 in the model that was adjusted for race and AIMs in mothers. In comparison, the R2 went from 0.435 to 0.513 in infants. We also performed similar analyses restricted to self-identified Hispanics with additional adjustment by AIMs (table 3, lower panel). Again, AIMs offered very modest improvement in mothers and infants. Lastly, when analyses were restricted to the 132 mother–infant pairs for whom we had complete genotype as well as phenotype data, similar results were observed (data not shown).
Table 3.
Proportion of variance (R2) in PON1 activity explained by PON1 genotype adjusted for self-reported race/ethnicity and AIMs.
| R2 | |||
|---|---|---|---|
| PONI genotype | Mothers (n = 350) |
Infants (n = 132) |
|
| −108 (C/T) | AIMs and race | 0.169 | 0.438 |
| AIMs only | 0.152 | 0.407 | |
| Race only | 0.156 | 0.426 | |
| None | 0.110 | 0.407 | |
| 55 (L/M) | AIMs and race | 0.123 | 0.513 |
| AIMs only | 0.106 | 0.453 | |
| Race only | 0.106 | 0.510 | |
| None | 0.082 | 0.435 | |
| Self-identified Hispanics | Mothers (n = 172) | Infants (n = 62) | |
| −108 (C/T) | AIMs | 0.216 | 0.343 |
| no AIMs | 0.205 | 0.336 | |
| 55 (L/M) | AIMs | 0.159 | 0.390 |
| no AIMs | 0.144 | 0.374 | |
Discussion
Our goal was to estimate the extent of individual ancestry proportion using a small panel of AIMs in our New York City population and to examine the reliability of self-reported race/ethnicity information from questionnaires. Results from this investigation demonstrate that this panel of 35 AIMs provides reliable estimates of ancestry proportion in individuals who self-identified as White or Black. Further, this panel of AIMs highlights the multiracial makeup of the Hispanic women who participated in our study and reflects their heterogeneous origins. However, it also demonstrates that the use of a single ‘Hispanic’ category may be insufficient for characterizing genetic background associated with race in epidemiologic or other analyses.
There are numerous reports on using informative markers for ancestry inference; these reports vary drastically in terms of selection criteria, the nature of the marker (microsatellite versus SNPs) as well as the number of AIMs, especially with respect to Hispanic populations. The minimal cutoff point for AIMs varies from δ = 0.6 (Shaffer et al. 2007) to δ = 0.5 (Reiner et al. 2005; Yaeger et al. 2008) to δ = 0.3 (Choudhry et al. 2006; Ziv et al. 2006). The number of markers varied from 326 microsatellite markers in Tang et al. (2005), 75–100 AIMs in Parra et al. (1998) and Shriver et al. (1997), to 744 AIMs in Smith et al. (2001). Risch et al. (2002) showed that 20 AIMs provide the same information as 120 randomly selected markers when trying to discriminate European from Native American or African. In our study, we demonstrated that the use of a small number of AIMs (n = 35) allowed us to satisfactorily distinguish the ancestral population in our racially diverse population while minimize genotyping costs and DNA usage.
Estimates of Native American ancestry in our cohort are different from other studies among self-identified Hispanics. The mean proportion of Native American ancestry among our self-identified Hispanic participants was 48.8% by using AIMs, which is more than two-fold higher than what was observed in another study of Hispanics of Puerto Rican origin (17.6%) (Bonilla et al. 2004). This may be due to misclassification associated with restricted choices for reporting race/ethnicity in our study questionnaire, in which Puerto Rican, Dominican, Latin American or other Hispanic origins were grouped together as a single response. In addition, the questionnaire did not offer an opportunity for our participants to choose multiple responses on their ancestral heritage, thus prevented detailed analysis of women with differing or multiple Hispanic origins. Salari et al. (2005) showed that Mexicans and Puerto Ricans are different in term of ancestry. Study location may have also contributed to the differences observed between our study and others, since the Hispanic population that resides our study area (East Harlem, New York, USA) is likely to have different origins than those living on the West Coast of USA, where majority of other studies were conducted (Ziv et al. 2006).
While self-identified information on Black and White is generally reliable, self-identified Hispanics are genetically admixed with three ancestral populations (Gonzalez Burchard et al. 2005). Therefore, including ‘Hispanic’ as a race category does not identify a genetically distinct racial group because individuals grouped together under this category do not necessarily possess a similar genetic ancestry. On the other hand, these individuals may share customs, beliefs, traditions, and lifestyles. If there are a large number of individuals who identify as Hispanics a population, AIMs may be necessary to correct for population stratification to prevent bias in estimates of genetic associations which could possibly lead to false-positive or false-negative associations. This small number of AIMs is cost effective as it can be done on small amounts of DNA with a readily available platform. However, the additional information provided by AIMs was limited in our data. Although we demonstrated improved fit in PON1 genotype–phenotype associations after incorporating AIMs into the models, the improvement is far from substantial.
Race is a complex term and when used to define a population for research studies, it may mistakenly imply a biological explanation for social and health disparities. The way we define the population can have significant implications for how we interpret the scientific meaning of genetic findings. Without appropriate control for race and ethnicity, observed associations may be biased. Hence, detailed characterization with regard to self-reported ethnicity, place of birth and country of origin should be given detailed attention in the Hispanic population for future epidemiology and genetic studies.
Acknowledgements
This work was supported by grants from the National Institutes of Health (ES09584) and Environmental Protection Agency (R827039). Susan Teitelbaum is supported by grants from the National Institutes of Health (K01-ES012645) and Department of Defense (W81XWH-04-1-0507). Jia Chen is partially supported by National Institutes of Health (CA109753).
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