Genetic Background of Patients from a University
Medical Center in Manhattan: Implications for
Bamidele O. Tayo1*, Marie Teil2, Liping Tong1, Huaizhen Qin3, Gregory Khitrov2, Weijia Zhang2, Quinbin
Song2, Omri Gottesman2, Xiaofeng Zhu3, Alexandre C. Pereira4, Richard S. Cooper1, Erwin P. Bottinger2*
1Department of Preventive Medicine and Epidemiology, Loyola University Chicago Stritch School of Medicine, Maywood, Illinois, United States of America, 2Charles R.
Bronfman Institute for Personalized Medicine, Mount Sinai School of Medicine, New York, New York, United States of America, 3Department of Biostatistics and
Epidemiology, Case Western University, Cleveland, Ohio, United States of America, 4University of Sao Paulo Medical School, Sao Paulo, Brazil
Background: The rapid progress currently being made in genomic science has created interest in potential clinical
applications; however, formal translational research has been limited thus far. Studies of population genetics have
demonstrated substantial variation in allele frequencies and haplotype structure at loci of medical relevance and the genetic
background of patient cohorts may often be complex.
Methods and Findings: To describe the heterogeneity in an unselected clinical sample we used the Affymetrix 6.0 gene
array chip to genotype self-identified European Americans (N=326), African Americans (N=324) and Hispanics (N=327)
from the medical practice of Mount Sinai Medical Center in Manhattan, NY. Additional data from US minority groups and
Brazil were used for external comparison. Substantial variation in ancestral origin was observed for both African Americans
and Hispanics; data from the latter group overlapped with both Mexican Americans and Brazilians in the external data sets.
A pooled analysis of the African Americans and Hispanics from NY demonstrated a broad continuum of ancestral origin
making classification by race/ethnicity uninformative. Selected loci harboring variants associated with medical traits and
drug response confirmed substantial within- and between-group heterogeneity.
Conclusion: As a consequence of these complementary levels of heterogeneity group labels offered no guidance at the
individual level. These findings demonstrate the complexity involved in clinical translation of the results from genome-wide
association studies and suggest that in the genomic era conventional racial/ethnic labels are of little value.
Citation: Tayo BO, Teil M, Tong L, Qin H, Khitrov G, et al. (2011) Genetic Background of Patients from a University Medical Center in Manhattan: Implications for
Personalized Medicine. PLoS ONE 6(5): e19166. doi:10.1371/journal.pone.0019166
Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America
Received December 1, 2010; Accepted March 28, 2011; Published May 4, 2011
Copyright: ? 2011 Tayo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The Andrea and Charles Bronfman Philantropies (to EPB); NIH grant National Heart Lung and Blood Institute RO1 HL53353 (to RC). The funders had no
role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org (BOT); email@example.com (EPB)
With the dramatic decline in the cost of sequencing and the
availability of well annotated databases genomic research is moving
rapidly toward the clinical arena [1,2,3]. Risk loci have been
identified for many common diseases and while they collectively
explain a small proportion of the heritable risk modest effects have
been noted for markers associated with conditions such as focal
segemental glomerular sclerosis, hyperlipidemia, Crohn’s disease,
adult macular degeneration, type 2 diabetes, rheumatoid arthritis,
schizophrenia and bipolar disorder, and coronary artery disease
among others [4,5,6,7,8,9,10,11,12]. Of more immediate clinical
relevance has been the discovery of genetic variants which influence
the action of pharmacologic agents . Because these loci are
unlikely tohavebeen underselectivepressure,variantsaltering drug
metabolism have in some cases increased to reasonable frequency
and can be associated with large effects . As more geographic
populations are studied with high density genotype arrays it is also
becoming apparent that allele frequencies for the relevant markers
can vary widely [5,15,16,17].
These emerging data must be incorporated into a strategy that
positions genomic medicine for a clinical role. Because virtually all
risk loci have been identified via proxy markers, it cannot be
assumed that the haplotypes that are being identified in the
populations where the original findings are made will carry the
causal mutations in other geographically separated groups. For
example, a recent analysis of the three HapMap populations
showed considerable heterogeneity of allele frequencies for loci
associated with 26 common diseases . Likewise, the fat mass
and obesity associated (FTO) locus, which has the strongest known
association with obesity in Europeans, shows a complex and
inconsistent pattern in African-origin populations [15,17]. The
generalizability of the current generation of published risk markers
in all racial/ethnic groups cannot therefore be taken for granted.
In the initial phase much of the interest in molecular studies of
population differentiation was focused on large continental
PLoS ONE | www.plosone.org1 May 2011 | Volume 6 | Issue 5 | e19166
groupings [18,19,20]. Fine-scale population structure has now
been examined using dense genotype data in Europe and North
America, and somewhat more limited data has become available
from Latin America, Africa and Asia [18,19,21,22,23]. Most of
these studies have attempted to identify ‘‘source’’ populations and
describe migration and other demographic patterns over a
historical framework [21,23,24]. In a sense, therefore, this research
has been ‘‘backward looking’’, as formalized in the Human
Diversity Project . However migration and gene flow between
populations that had historically been geographically distant has
accelerated in the modern era and many large metropolitan areas
are now exceedingly diverse. Considered as a ‘‘city region’’, New
York City had an estimated population of 20 million; in 2005 36%
of the residents of New York City proper were foreign born,
speaking 170–200 languages [26,27]. In this cosmopolitan setting
the standard US racial/ethnic categories – ie, black, white,
Hispanic, Asian – become particularly problematic.
Mount Sinai Medical Center serves a diverse community in
northern Manhattan with outpatient visits totaling 800,000/year.
The Charles R. Bronfman Institute for Personalized Medicine at
Mount Sinai has initiated a program of research aimed at
translating the growing body of evidence on genetic susceptibility
for chronic disease and drug responsiveness into clinical practice.
As an initial step we collected dense genotype data on a sample of
977 outpatients served by our institution who self-identified into 3
major racial/ethnic groups. Genotype array analysis was used to
assess patterns of gene flow between the groups and consistency
and distribution of haplotypes at a series of loci known to
predispose to common disease or influence the metabolism of
This research study was reviewed and approved by the ethics
review board of the Program for the Protection of Human Subjects
(PPHS) of Mount Sinai School of Medicine under project #
HSD09-00030. The Mount Sinai Biobank Project (IRB # 07-
0529 0001 02 ME) is an IRB-approved research protocol with
IRB-approved informed consent forms. All study participants
provided written informed consent.
Study participants were recruited from the Biobank Program of
the Institute of Personalized Medicine at Mount Sinai Medical
Center. The primary sample consisted of 1030 self-identified
African Americans, European Americans or Hispanics. The
majority of the Hispanic participants were from the Caribbean,
primarily the Dominican Republic and Puerto Rico. One subject
from each group and one CEPH trio family were replicated for the
purpose of quality control of genotype data. The project was
reviewed and approved by the Institutional Review Boards of both
Mount Sinai Medical Center and Loyola University Chicago
Stritch School of Medicine.
Genotyping and quality assessment
Genotyping was carried out on genomic DNA from 1030
subjects using the Affymetrix 6.0 gene chip. Genotyping was
performed in batches and within each batch samples were
randomized with respect to race/ethnicity, gender and diagnostic
status. Selected samples from each race plus a CEPH trio sample
were also replicated in each batch for the purpose of assessing
batch effect on genotypes. Quality control procedures were
performed with the Whole Genome Analysis software (Golden
Helix) and PLINK  (http://pngu.mgh.harvard.edu/purcell/
plink/). The chip analysis provided data on 909,600 SNPs of
which 905,384 mapped to the dbSNP rsID. Of the 1030 samples,
those that broadly failed genotyping (n=36), had gender
inconsistency (n=5) or had missing genotype proportion .0.05
(n=7) were excluded. Similarly, 60,869 SNPs with missing
genotype rate .5% and 10,889 SNPs with MAF,0.01 were
excluded. Using the data on batch-replicated samples, mean
genotype concordance rate between batches was estimated to be
99.6560.08%. Investigation of batch effect on genotypes revealed
1,236 SNPs with substantial deviations associated with batch effect
and these were dropped. In addition, SNPs found to have
significantly differential missing rates (n=217) between patients
with specific diagnoses and those failing Hardy-Weinberg
equilibrium (HWE) test (p-value=0.001) (n=2,587) were also
excluded. We estimated inbreeding coefficients and genome-wide
identity-by-descent (IBD) sharing among pairs of samples using the
software PLINK . Finally, one additional sample with an
inbreeding coefficient greater than four standard deviations of the
mean coefficient was dropped. There was no significant evidence
of excess sharing of IBD proportion either due to sample
contamination, duplication, or cryptic relatedness. The final
quality-controlled cleaned dataset thus consisted of 977 unrelated
adult subjects – 324 African Americans, 326 European Americans
and 327 Hispanics with genome-wide information on 829,586
Creation of marker sets for population structure analysis
For population structure analysis the cleaned dataset was
merged with datasets from the International HapMap Project 
and prior studies conducted by the Department of Preventive
Medicine at Loyola. The HapMap data consisted of samples of
African ancestry in the southwest USA (ASW) (n=71) and of
Mexican ancestry in Los Angeles, California (MEX) (n=71).
Descriptions of the sample, genotyping and quality control of the
genotype data have been provided elsewhere [29,30]. The Loyola
dataset consisted of a population-based Yoruba sample from
Nigeria (YOR) (n=334), African Americans from Maywood, IL
(AMW) (n=204), and Brazilians (BRZ) (n=109). The YOR and
AMW samples were recruited as controls in a study of genetics of
hypertension [31,32]. Data from appropriate Native American
groups were not available. The genotype data were generated on
Affymetrix 6.0 chip and details of the samples, genotyping and
quality control procedures have been described elsewhere .
There were 1770 samples in the combined dataset with genotypes
on 599,857 SNPs. The sample genotyping rate in the combined
dataset was .0.99 and the MAF of every SNP in each of the
subpopulations was at least 0.01.
Two different marker sets were created from the combined
dataset for population structure analysis. These markers were
chosen to ensure that the SNPs were not in strong linkage
disequilibrium (LD) and to make the analysis computationally
efficient. For the first marker set, we excluded SNPs with missing
genotype rate .0.1%, then used the software PLINK  to
prune the remaining 238,533 SNPs using pairwise linkage
shifting and recalculating every 5 SNPs. The resulting subset of
SNPs consisted of 100,133 SNPs distributed across the genome.
The second marker set was created based on average genetic
distance difference d ð Þ between African and European ancestral
populations. Using the HapMap allele frequencies, data for
samples of Yoruba in Ibadan, Nigeria (YRI) and CEPH (Utah
residents with ancestry from northern and western Europe) (CEU),
d was computed as the sum of the absolute differences between the
? ?maximum threshold of 0.2 in 50 SNP widows,
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allele frequencies  in the two samples. SNPs with dv50%
were then excluded from the combined dataset and the remaining
67,124 SNPs were pruned using r2maximum threshold of 0.2 in
50 SNP widows, shifting and recalculating every 5 SNPs. The
resulting subset consisted of 28,783 SNPs spread across the
genome. The two marker sets were subsequently used separately
for population structure analysis.
Principal component and multidimensional scaling
Principal component analysis was performed with the Whole
Genome Analysis software (Golden Helix). The first two
components had large eigenvalues compared to the remaining
components. These two components were therefore extracted and
used as covariates to adjust for stratification in the candidate gene
association analysis in the samples of African Americans and
Hispanic Americans as described below. Also, using the genome-
wide identity-by-state (IBS) estimated with PLINK, we performed
multidimensional scaling analysis on the matrix of IBS. Distribu-
tions of samples on the first to fourth dimensions were used to
assess clustering and diversity within and between the groups.
Global and local ancestries
To evaluate potential discrepancies between global and local
population structures or ancestries in the samples we use the
method of squared coefficients of canonical correlation
described by Qinet al . Briefly, let N denote the sample size,
global principal components (PCs), and B~ b1,:::,bK
N|K matrix consisting of the first K local PCs in a local window.
The coefficient of multiple-determination R2
in the linear regression of bj on A. The jthlargest squared
coefficient of canonical correlation l2
largest coefficient of determination between any linear combina-
tion of B0s columns and any linear combination of A0s columns.
The local PCs were computed from the local 20 Mb-window
defined on each autosome. Squared coefficient was computed as
the square of the largest canonical correlation between the first 10
local PCs of each local 20 Mb-window and the first 10 global PCs
in each population sample. For this evaluation, we restricted
analysis to the three Biobank samples (ANY, ENY and HNY) and
three external comparison samples (AMW, BRZ and HapMap
? denote the N|K matrix consisting of the first K
½? denote the
jfor bjand A is the R2
jbetween A and B is the jth
Structure analysis was performed separately for the two marker
sets using the software STRUCTURE [35,36]. STRUCTURE
applies a Bayesian model-based clustering algorithm to assign
subjects into pre-assumed K ancestral populations each of which is
characterized by a set of allele frequencies at each SNP. Based on
their allele frequency profiles for the loci and under the
assumption that loci are at HWE and linkage equilibrium within
each population (racial group), the subjects are then probabilis-
tically assigned to populations, or jointly to two or more
populations if their genotypes indicated recent gene flow. For
each of the two marker sets, analysis was run under an assumed
number of ancestral populations ranging from K=2 to K=7.
Analysis parameters included admixture model, correlated allele
frequencies among populations, estimation of separate alpha for
each population, and burn-in period of 20,000 iterations followed
by 10,000 Markov chain Monte Carlo replications. Graphical
displays of results of population structure were produced using the
program DISTRUCT .
Linkage disequilibrium and haplotype analysis
To compare LD structure and organization of haplotypes
harboring published disease loci or loci that alter drug metabolism
we carried out analyses of obesity-related (viz, fat mass and obesity
associated (FTO) and melanocortin 4 receptor (MC4R)), and
pharmacogenomic variants (viz, solute carrier organic anion
transporter family, member 1B1 (SLCO1B1) and cytochrome
P450, family 4, subfamily F, polypeptide 2 (CYP4F2)). This analysis
was restricted to the Biobank sample. The software Haploview
 was used to compute estimates of pair-wise LD by the
standard D-prime method  and haplotype blocks defined by
the confidence interval  using the standard parameter setting.
All available SNPs in each gene were included in the analysis but
comparisons between groups were restricted to haplotypes and LD
blocks bearing or flanking the selected published disease or
Candidate gene association analysis for body mass index
We carried out single SNP test for association between BMI and
SNPs in FTO and MC4R genes. For each racial group, BMI was
log-transformed to approximate trait normality. The residuals
controlling for age and sex were standardized and used in
association analysis with the SNPs. Only an additive genetic model
was tested. The association analysis for the African-American and
Hispanic samples was adjusted for population structure by
inclusion of principal components as covariates.
The primary study sample was drawn from the 7,266 consented
adult patients enrolled in Mount Sinai Medical Center Biobank
from September 2007 to December 2009. Although the institution
serves a diverse community in northern Manhattan, sampling was
limited to self-identified African American (ANY), European
American (ENY) and Hispanic (HNY) participants. Characteristics
of the 977 individuals included in these analyses are presented in
Table 1, including the frequencies of a set of common chronic
conditions (asthma, CKD, diabetes and morbid obesity). All
population structure analyses additionally included the ASW,
MEX, YOR, AMW and BRZ samples (see Methods). The
combined sample size including all population groups was 1770
and 2 separate pruned genotype marker sets with 100,133 and
28,732 SNPs were chosen, as described. Because the results from
both marker sets were broadly similar, only results from the larger
marker set are presented here.
Multidimensional scaling of identity by state pair-wise
To assess the within- and between-group clustering and
diversity we performed multidimensional scaling analysis on the
matrix of estimated IBS pair-wise distances and extracted the first
four dimensions. Plots of the samples in the 1stvs. 2nd, 2ndvs. 3rd,
and 3rdvs. 4thdimensions are presented in Figure 1; the upper
panel describes all available population samples, including those
from Nigeria and Brazil, while the lower panel is restricted to the 3
groups of patients from New York. Based on the 1stand 2nd
dimension, the YOR sample formed a distinct non-overlapping
cluster separated from all other samples, while the ENY sample
also formed a small cluster. However, as best seen with extraction
of the 2nddimension (middle panel, upper row), the groups
designated as MEX, BRZ and HNY showed much greater
dispersion, while partially overlapping with the ANY and ENY
samples. This pattern is clearly consistent with recent gene flow
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from geographically distant populations among the Latin Amer-
ican groups. As anticipated, the three Biobank samples (ANY,
ENY and HNY) tended to cluster with samples from similar
reference population groups (i.e., ANY with AMW and ASW; and
HYN with both the BRZ and MEX samples). When restricted to
the Biobank samples, the HNY sample can be observed to cluster
between the ANY and ENY samples and exhibited high within-
group diversity which resulted in the observed dispersion (Figure 1).
Global and local ancestry
The squared canonical correlation coefficients for the evalua-
tion of discrepancies between global and local ancestry for each of
the Biobank samples and corresponding external comparison
samples are presented in Figure 2. In each sample, the squared
coefficient is the largest canonical correlation between the first 10
local PCs of a 20 Mb-window and the first 10 global PCs. Criteria
for choice of top 10 PCs and window size for local ancestry
evaluation are as described in our previous study . In each
sample, we observed variation in the distributions of the squared
canonical correlation coefficients from one autosome to the other,
showing that ancestry or population structure is not uniform across
the genome. It is expected that local genomic regions could be
subject to varying forms of population structure as a result of
natural selection, demographic history differences and local
random fluctuations of admixture, among others . These
differential distributions of local population structures are also, to
some extent, evident between samples of similar ancestry and this
could have been amplified by sampling variation or genotype
batch effects associated with the genotype calling algorithms of
different platforms [41,42,43,44,45,46,47].
Table 1. Characteristics of subjects.
African Americans European AmericansHispanic AmericansAll
N (% females) 324 (50.31)326 (41.10)327 (49.54) 977 (46.98)
Age (years)52.69613.7749.55614.3956.11613.72 52.79614.20
Diagnosis (#without) 217 (107)88 (238)217 (110) 522 (455)
Diabetes 13241 147320
CKD 56 1038 104
Obesity90 3874 202
Asthma 833592 210
Mean 6 SD.
Figure 1. Multidimensional scaling plots for all samples (TOP) and only biobank samples (BOTTOM). Plots of subjects in the 1stand 2nd
dimensions (left column), 2ndand 3rddimensions (middle column), and 3rdand 4thdimensions right column). Abbreviation for samples: African
American biobank sample (ANY); European American biobank sample (ENY); Hispanic American biobank sample (HNY); African ancestry in Southwest
USA (ASW); Mexican ancestry in Los Angeles, California (MEX); Yoruba from Nigeria (YOR); African American from Maywood, Illinois (AMW); Brazilians
from Brazil (BRZ).
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We carried out further analysis of population diversity using the
software STRUCTURE (Figure 3). Both the YOR and ENY
samples were assigned to separate populations. Evidence of recent
gene flow was can be seen as shared colors that correspond to the
proportions from each ancestral group. The ANY (and also AMW
and ASW) sample exhibited a higher proportion of ancestry from
Africa than from Europe. On the other hand, the HNY sample on
average exhibited higher proportion of ancestry from Europe than
from Africa. We likewise observed more within-population
variation of individual ancestral proportions in the HNY sample.
As K increased from 2, each sample with the exception of the YOR
showed varied levels of admixture of the assumed ancestral
populations. Closer inspection indicated that the admixture seen in
ANY and HNY best supports the presence of 2 and 3 ancestral
populations, respectively. In addition, evaluation of model
estimates for the different values of K indicated a better fit for
K=2 and K=3 for the ANY and HNY samples, respectively.
While clear differences exist in terms of the proportion of
continental ancestry between the Hispanics and African Ameri-
cans in this sample, this result is in part an artifact of the use of
group labels. In fact, ancestral heritage at the individual level
among persons from these two groups is best represented as a
continuum (Figure 4).
LD structure and haplotypes harboring published disease
To assess within–population group LD structure and the
distributions of haplotypes harboring known disease variants, we
selected published obesity and pharmacogenomic variants that
were genotyped in our Biobank sample of African Americans,
European Americans and Hispanic Americans. For obesity
variants, we selected 7 FTO SNPs (rs1421085, rs1121980,
rs8057044, rs8050136, rs9939609, rs9941349 and rs9930506)
and 2 MC4R SNPs (rs17782313 and rs12970134). For pharma-
cogenomic variants, we selected rs4149056 and rs11045819 in the
SLCO1B1 gene and rs2108622 in the CYP4F2 gene. The SLCO1B1
variants affect response to 3-hydroxy-3-methylglutaryl-coenzyme
A (statins) [48,49,50] and the CYP4F2 variant affects response to
warfarin [1,51]. We included all available SNP genotypes in these
genes in the determination of the LD blocks and hyplotypes within
each group. Here, we present results from same region across
groups for the purpose of comparison.
Results of the LD structure and distribution of haplotypes
bearing FTO and MC4R variants are shown in Figures 5a and 5b.
Substantial differences were observed between groups in the
number and length of LD blocks in the targeted regions. For
instance, in the FTO region there were 5 LD blocks in the African
American sample, 2 LD blocks in the European Americans, and 4
Figure 2. Canonical correlation based on window-wide local PCs for biobank samples and selected external samples. Each circle
represents the squared coefficient of the largest canonical correlation between the first 10 local PCs of a local 20 Mb-window and the first 10 global
PCs. Abbreviation for samples: African American biobank sample (ANY); European American biobank sample (ENY); Hispanic American biobank
sample (HNY); African American from Maywood, Illinois (AMW); CEPH (Utah residents with ancestry from northern and western Europe (CEU);
Brazilians from Brazil (BRZ).
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Figure 3. Population structure results for ancestral populations K=2 to K=6. Each subject is represented by a thin vertical line colored in
proportion to their estimated ancestry within each cluster. The colors represent the proportion of inferred ancestry from each of the ancestral
populations within each specific K value. Abbreviation for samples: African American biobank sample (ANY); European American biobank sample
(ENY); Hispanic American biobank sample (HNY); African ancestry in Southwest USA (ASW); Mexican ancestry in Los Angeles, California (MEX) ; Yoruba
from Nigeria (YOR); African American from Maywood, Illinois (AMW); Brazilians from Brazil (BRZ).
Figure 4. Population structure results for K=2 ancestral populations sorted by ancestry proportions for African American biobank
sample (ANY) and Hispanic American biobank sample (HNY) (Top) and pooled sample of both ANY and HNY (Bottom).
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in the Hispanics (Figure 5a). Similarly in the MC4R region, there
were 6, 2 and 4 LD blocks in the African American, European
(Figure 5b). The observed strength of LD within blocks varied
widely between groups just as the number of haplotypes in every
block also varied between the three groups. As a result of the
variation in the number and length of the LD blocks, haplotypes
bearing or flanking the susceptibility variants were clearly not
Similar discordance between LD structures and haplotype
distributions was observed for the pharmacogenomic variants. In
the SLCO1B1 gene, there were 4, 1 and 5 LD blocks with widely
different number of haplotypes in the African American,
European American and Hispanic American samples, respectively
(Figure 6a). It is important to note that the length of the single
block in the European-American sample spanned the same region
in which multiple blocks were found in the other two groups. As a
consequence few SNPs tagging this haplotype would be observed
in the African-American and Hispanic samples making it difficult
to validate associations originally discovered in European
Americans. Results for the CYP4F2 variants are shown in
Figure 6b. Again, the racial groups differ from each other in
terms of length of LD block and number of haplotypes.
Candidate gene association
As an illustrative example, results of comparative association
analysis of the association between BMI and haplotypes tagged by
published SNPs in the FTO and MC4R loci are presented in
Figure 7. Although these loci have provided the strongest signals
for adioposity, the effect sizes are admittedly modest (#,1.3 for
BMI.30) and therefore large sample sizes are required to obtain
stable results. Nonetheless, it is clear that among European
Americans the association is consistent across this region, while
highly variable for African Americans and Hispanics. The MC4R
region includes fewer haplotypes and the underlying effect size is
smaller than for FTO, and is therefore less informative. The
pattern in Figure 7, however, suggests that the association with
BMI is less well captured by the common haplotypes than for the
other ethnic groups.
Genomic technology has rapidly transformed the character of
biomedical research, however the translational impact of this new
science for clinical medicine remains undefined. Some investiga-
tors have suggested that the current state of knowledge justifies
sequencing individual patients’ genomes and placing this infor-
mation in the electronic medical record, although others hold that
the current utility of genome sequence data is still far too limited,
while the storage burden and interpretation are unsupportable,
and this state of affairs is unlikely to change in the foreseeable
future [2,13,52,53]. Clearly more incremental steps will be
required in order to adapt genomic science for clinical purposes
and evaluate its contribution to patient outcomes. One of the most
immediate challenges is the need to quantify and properly account
for the genetic diversity present in clinical populations. Formal
research studies have virtually always stratified on a conventional
descriptor of population structure and diversity is subsequently
assessed within and between groups [18,20,23,54]. While this
approach has been useful in describing global patterns, others have
argued that it narrowly ‘‘packages’’ our view of human variation
. Even more problematic in the clinical arena, racial/ethnic
labels can vary widely by geographic location and time (eg,
Hispanic and Asian) and cosmopolitan cities now include many
individuals whose genetic heritage is drawn from multiple
The analyses presented here attempt to capture the pattern of
genetic diversity in patients seen at a major medical center in New
York City. Consistent with previous studies of population genetics,
broad ancestral clustering is apparent in the 3 patient groups
[18,23,54]. At the same time, wide divergence is seen in the
Hispanic samples. Using the traditional perspective that ‘‘packag-
es’’ geographic populations, these sub-clusters roughly represent
Mexican Americans, persons of Caribbean origin (who would
overlap with Brazilians), and a third group that clusters with
African Americans . Conversely, if one dispenses with the
conventional labels and relies solely on genotype the Hispanics in
the NY sample can be appropriately merged with the African
Americans (Figure 3). These data forcefully underscore the
diminishing relevance of the descriptors currently used for the
two principle minority groups in the US.
A complementary layer of complexity is demonstrated by the
illustrative examples based on genetic variants associated with
common traits and drug response. As is well recognized, both the
spectrum of allele frequencies for ‘‘causal’’ mutations and proxy
haplotypes can vary widely across population groups . As this
phenomenon has become better appreciated, the relevance of
‘‘diagnosis by proxy’’ using race/ethnicity to predict genotype, has
dissipated . Since the vast majority of current findings from
GWAs are based on SNPs that tag the relevant haplotype, until
the ‘‘causal mutation’’ is known for these findings it may not be
possible to transfer this information from the original study
population, virtually always of European ancestry . The
relevance of cross-population haplotype diversity was perhaps
most clearly demonstrated in the analyses that defined the causal
mutation at the SORT1 locus . A variant at this locus confers the
largest effect on LDL-cholesterol of any known common allele .
While a broad haplotype carrying numerous SNPs was captured
on GWAs in a European-origin sample, the apparent causal
mutation was isolated to a smaller haplotype present in persons of
African descent . It must be assumed, therefore, that proxy
haplotypes cannot be used for individual-level patient analyses.
While the sample included in this study was restricted to a single
hospital the inferences are generalizable to most urban centers in
the US. The combination of rapid changes in migration away
from Europe and the realization that very few genetic variants are
sufficiently differentiated even between historically unrelated
populations mean that clinical decisions about genotypic effects
will require very detailed knowledge of the locus in question, and
analyses of individual patients. In a sense, therefore, much of the
research emphasizing continental origin and ancestry stands in
contradiction to the clinical imperative and a shift away from a
paradigm that is founded on racial/ethnic categories will be
required. We would suggest that this shift in perspective will be
one of many required before genomic science can be fully adapted
to use in the clinical arena.
This report has limitations which must be recognized. The
racial/ethnic background of patient populations will, of course,
vary widely across the US the specific composition observed in
New York may not be observed. We did not have access to
appropriate data on Native Americans that might have helped
define genetic ancestry of migrants from Mexico or other parts of
Central and South America. Recent analyses by Bryc et al
demonstrate the heterogeneity with Hispanics sub-groups and our
data are consistent with their results, albeit weighted toward
persons from the Caribbean [18,23,54]. We also recognize that
clinical testing must take place in approved laboratories and will be
restricted to genotypes that have been widely validated as relevant
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for patient outcomes therefore data from GWAs must be filtered
extensively before application in any group. This process of
defining the functional variant will by itself de-emphasize the
broad framework of race/ethnicity.
In conclusion, based on a consecutive series of patients from an
urban medical center in New York City we demonstrate that a
spectrum of mixed ancestry is emerging in the largest US minority
groups. While consistent with previous descriptive studies, when
viewed from the clinical perspective this evidence invites a re-
evaluation of the relevance of racial/ethnic labels. In combination
with evidence of locus heterogeneity within and between
populations, this picture of extensive gene flow lends credence to
Figure 5. a: Linkage disequilibrium structure (Top) and organization of haplotypes harboring published obesity variants (rsIDs indicated with blue
boxes) in FTO gene in biobank sample of African Americans (left column), European Americans (middle column), and Hispanic Americans (right
column). b: Linkage disequilibrium structure (Top) and organization of haplotypes harboring published obesity variants (rsIDs indicated with blue
boxes) in MC4R gene in biobank sample of African Americans (left column), European Americans (middle column), and Hispanic Americans (right
Genetic Diversity Background in Patients
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Figure 6. a: Linkage disequilibrium structure (Top) and organization of haplotypes harboring published pharmacogenomic variants (rsIDs indicated
with blue boxes) in SLCO1B1 gene in biobank sample of African Americans (left column), European Americans (middle column), and Hispanic
Americans (right column). b: Linkage disequilibrium structure (Top) and organization of haplotypes harboring published pharmacogenomic variants
(rsIDs indicated with blue boxes) in CYP4F2 gene in biobank sample of African Americans (left column), European Americans (middle column), and
Hispanic Americans (right column).
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the argument that the transfer of historical population labels which
reflect language and other social categories onto patient samples
will in many cases be unwarranted.
We would like to thank the participants from New York City, United
States, for participating in the ‘‘Mount Sinai IPM Biobank Program’’ and
the participants from Ibadan, Nigeria for their willing participation in the
‘‘Genetics of Hypertension in Blacks’’ project.
Conceived and designed the experiments: EPB RSC. Performed the
experiments: GK WZ QS MT. Analyzed the data: BOT LT HQ WZ XZ
OG RSC EPB. Contributed reagents/materials/analysis tools: BOT XZ.
Wrote the paper: BOT RSC EPB. Provided Brazilian population genetic
data set: ACP.
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