Admixture mapping of 15,280 African Americans identifies obesity susceptibility loci on chromosomes 5 and X.

Abstract

The prevalence of obesity (body mass index (BMI) > or =30 kg/m(2)) is higher in African Americans than in European Americans, even after adjustment for socioeconomic factors, suggesting that genetic factors may explain some of the difference. To identify genetic loci influencing BMI, we carried out a pooled analysis of genome-wide admixture mapping scans in 15,280 African Americans from 14 epidemiologic studies. Samples were genotyped at a median of 1,411 ancestry-informative markers. After adjusting for age, sex, and study, BMI was analyzed both as a dichotomized (top 20% versus bottom 20%) and a continuous trait. We found that a higher percentage of European ancestry was significantly correlated with lower BMI (rho = -0.042, P = 1.6x10(-7)). In the dichotomized analysis, we detected two loci on chromosome X as associated with increased African ancestry: the first at Xq25 (locus-specific LOD = 5.94; genome-wide score = 3.22; case-control Z = -3.94); and the second at Xq13.1 (locus-specific LOD = 2.22; case-control Z = -4.62). Quantitative analysis identified a third locus at 5q13.3 where higher BMI was highly significantly associated with greater European ancestry (locus-specific LOD = 6.27; genome-wide score = 3.46). Further mapping studies with dense sets of markers will be necessary to identify the alleles in these regions of chromosomes X and 5 that may be associated with variation in BMI.

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Admixture Mapping of 15,280 African Americans
Identifies Obesity Susceptibility Loci on Chromosomes 5
and X
Ching-Yu Cheng1,2,3*, W. H. Linda Kao1,4*, Nick Patterson5, Arti Tandon5,6, Christopher A. Haiman7,
Tamara B. Harris8, Chao Xing9,10,11, Esther M. John12,13,14, Christine B. Ambrosone15, Frederick L.
Brancati1,4, Josef Coresh1,4, Michael F. Press16, Rulan S. Parekh17,18, Michael J. Klag1,18,19, Lucy A.
Meoni18,20, Wen-Chi Hsueh21, Laura Fejerman21,22,23, Ludmila Pawlikowska22,24, Matthew L.
Freedman5,25, Lina H. Jandorf26, Elisa V. Bandera27, Gregory L. Ciupak15, Michael A. Nalls8,28, Ermeg L.
Akylbekova29, Eric S. Orwoll30, Tennille S. Leak31, Iva Miljkovic31, Rongling Li32, Giske Ursin7,33, Leslie
Bernstein7,34, Kristin Ardlie5,35, Herman A. Taylor36,37,38, Eric Boerwinckle39, Joseph M. Zmuda31, Brian E.
Henderson7, James G. Wilson38,40, David Reich5,6*
1Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland, United States of America, 2Department of Ophthalmology, National Yang Ming University
School of Medicine, Taipei, Taiwan, 3Taipei Veterans General Hospital, Taipei, Taiwan, 4Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins
University, Baltimore, Maryland, United States of America, 5Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts,
United States of America, 6Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America, 7Department of Preventive Medicine, Keck
School of Medicine, University of Southern California, Los Angeles, California, United States of America, 8Laboratory of Epidemiology, Demography and Biometry,
National Institute on Aging, Bethesda, Maryland, United States of America, 9Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas,
Texas, United States of America, 10McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, Texas, United
States of America, 11Donald W. Reynolds Cardiovascular Clinical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of
America, 12Northern California Cancer Center, Fremont, California, United States of America, 13Department of Health Research and Policy, Stanford University School of
Medicine, Stanford, California, United States of America, 14Stanford Cancer Center, Stanford, California, United States of America, 15Department of Cancer Prevention
and Control, Roswell Park Cancer Institute, Buffalo, New York, United States of America, 16Department of Pathology, Keck School of Medicine, University of Southern
California, Los Angeles, California, United States of America, 17Department of Pediatrics, Johns Hopkins University, Baltimore, Maryland, United States of America,
18Department of Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America, 19Department of Health Policy and Management, Johns Hopkins
University, Baltimore, Maryland, United States of America, 20Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, United States of America,
21Department of Medicine, University of California San Francisco, San Francisco, California, United States of America, 22Institute for Human Genetics, University of
California San Francisco, San Francisco, California, United States of America, 23Helen Diller Family Comprehensive Cancer Center, University of California San Francisco,
San Francisco, California, United States of America, 24Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, California,
United States of America, 25Department of Medical Oncology, Dana–Farber Cancer Institute, Boston, Massachusetts, United States of America, 26Department of
Oncological Sciences, Mount Sinai School of Medicine, New York, New York, United States of America, 27The Cancer Institute of New Jersey, Robert Wood Johnson
Medical School, New Brunswick, New Jersey, United States of America, 28Molecular Genetics Section, Laboratory of Neurogenetics, Intramural Research Program,
National Institute on Aging, Bethesda, Maryland, United States of America, 29Jackson Heart Study Analysis Group, Jackson State University, Jackson, Mississippi, United
States of America, 30Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States of America,
31Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 32Department of
Preventive Medicine, Division of Biostatistics and Epidemiology, University of Tennessee, Memphis, Tennessee, United States of America, 33Department of Nutrition,
University of Oslo, Oslo, Norway, 34Department of Cancer Etiology, Division of Population Science, City of Hope National Medical Center, Duarte, California, United States
of America, 35Genomics Collaborative, Cambridge, Massachusetts, United States of America, 36Jackson State University, Jackson, Mississippi, United States of America,
37Tougaloo College, Tougaloo, Mississippi, United States of America, 38University of Mississippi Medical Center, Jackson, Mississippi, United States of America,
39Human Genetics Center, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, 40G. V. (Sonny) Montgomery Veterans Affairs
Medical Center, Jackson, Mississippi, United States of America
Abstract
The prevalence of obesity (body mass index (BMI) $30 kg/m2) is higher in African Americans than in European Americans,
even after adjustment for socioeconomic factors, suggesting that genetic factors may explain some of the difference. To
identify genetic loci influencing BMI, we carried out a pooled analysis of genome-wide admixture mapping scans in 15,280
African Americans from 14 epidemiologic studies. Samples were genotyped at a median of 1,411 ancestry-informative
markers. After adjusting for age, sex, and study, BMI was analyzed both as a dichotomized (top 20% versus bottom 20%)
and a continuous trait. We found that a higher percentage of European ancestry was significantly correlated with lower BMI
(r=20.042, P=1.661027). In the dichotomized analysis, we detected two loci on chromosome X as associated with
increased African ancestry: the first at Xq25 (locus-specific LOD=5.94; genome-wide score=3.22; case-control Z=23.94);
and the second at Xq13.1 (locus-specific LOD=2.22; case-control Z=24.62). Quantitative analysis identified a third locus at
5q13.3 where higher BMI was highly significantly associated with greater European ancestry (locus-specific LOD=6.27;
genome-wide score=3.46). Further mapping studies with dense sets of markers will be necessary to identify the alleles in
these regions of chromosomes X and 5 that may be associated with variation in BMI.
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Page 2

Citation: Cheng C-Y, Kao WHL, Patterson N, Tandon A, Haiman CA, et al. (2009) Admixture Mapping of 15,280 African Americans Identifies Obesity Susceptibility
Loci on Chromosomes 5 and X. PLoS Genet 5(5): e1000490. doi:10.1371/journal.pgen.1000490
Editor: Mark I. McCarthy, University of Oxford, United Kingdom
Received November 4, 2008; Accepted April 22, 2009; Published May 22, 2009
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: The ARIC Study was carried out as a collaborative study supported by the National Heart, Lung, and Blood Institute (NHLBI) contracts N01-HC-55015,
N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021 and N01-HC-55022 with support for this analysis by R21DK073482 and
K01DK067207 (WHLK). The BCFR was supported by the National Cancer Institute (NCI), National Institutes of Health (NIH) under RFA-CA-06-503 and through
cooperative agreements with members of the BCFR and P.I.s. The Northern California BCFR site was funded by U01 CA69417. The Los Angeles component of the
Women’s Contraceptive and Reproductive Experiences study received support from contracts with the National Institute of Child Health and Human
Development (N01-HD-3-3175), the NCI (N01-PC-67010) and the California Department of Health Services (050G 8709), and grants from the National Institute of
Environmental Health Sciences (R01-ES-07084) and the California Breast Cancer Research Program (PB-0051). The DHS was supported by the Donald W. Reynolds
Foundation and grant 1RL-1HL092550 from the NIH. The FIND Study included in this analysis was supported by the following research grants: U01DK070657,
U01DK57304, K01DK067207 (WHLK), and U01DK57292 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and, in part, by the
Intramural Research Program of the NIDDK; this research was supported in part by the Intramural Research Program of the NIH, NCI, Center for Cancer Research,
and has been funded in part with federal funds from the NCI, NIH, under contract N01-CO-12400. Research support for Health ABC was provided by the Intramural
Research Program of the National Institute on Aging, and contracts N01-AG-6-2101, N01-AG-6-2103, and N01-AG-6-2106. Genotyping in Health ABC was
supported by National Center for Research Resources grant U54-RR020278. Research support for JHS studies was provided by R01-HL-084107 (JGW) from the
NHLBI and contracts N01-HC-95170, N01-HC-95171, and N01-HC-95172 from the NHLBI and the National Center on Minority Health and Health Disparities. The
LIFE Study was supported by grants CA 17054, CA 74847 from the NCI, and by grant 4PB-0092 from the California Breast Cancer Research Program of the
University of California. The MEC Study was supported by NCI grants CA63464 and CA54281. MrOS is supported by grants: U01 AR45580, U01 AR45614, U01
AR45632, U01 AR45647, U01 AR45654, U01 AR45583, U01 AG18197, U01-AG027810, UL1 RR024140 and R01-AR051124. SFBABCS was supported by grants R01
CA63446 and R01 CA77305 from the NCI, grant DAMD17-96-607 from the United States Army Medical Research Program, and grant 7PB-0068 from the California
Breast Cancer Research Program. SOF was supported by grants AG05407, AR35582, AG05394, AR35584, and AR35583. The WCHS was initially funded as part of a
Center of Excellence for Biobehavioral Breast Cancer Research (United States Army Medical Research Program, DAMD17-01-1-0334) and subsequently by R01
CA100598 from the NCI. Genotyping for SFBABCS and WCHS was supported by grant KG080165 from the Susan Komen Foundation for Breast Cancer Research.
DR was supported by a Burroughs Wellcome Career Development Award in the Biomedical Sciences, and methodological work was supported by grant U01-
HG004168 to DR, NP and AT. TL is postdoctoral fellow funded on 5T32AG000181-19, Training in the Epidemiology of Aging. The content of this manuscript does
not necessarily reflect the views or policies of the NCI, any of the collaborating centers in the BCFR, or the Department of Health and Human Services, nor does
mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. 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: cycheng@jhsph.edu (CYC); wkao@jhsph.edu (WHLK); reich@genetics.med.harvard.edu (DR)
Introduction
Obesity is a highly prevalent condition that increases the risk of
many illnesses such as cardiovascular disease, diabetes, and some
cancers. Familial aggregation studies have shown that both genetic
and environmental factors are involved in the development of
common forms of obesity, and heritability estimates suggest that
approximately 40% of variation in body mass index (BMI) can be
attributed to genetic factors [1,2].
The current increase in prevalence of obesity in the U.S. has
been hypothesized to be the result of genetic susceptibility in an
environment that promotes obesity [3]. James V. Neel in 1962
proposed the ‘‘thrifty gene hypothesis’’ to put these epidemiological
observations in an evolutionary context [4]. He suggested that the
genetic factors that predispose to weight gain might have been
selectively advantageous in ancient environments where food was
scarce, but might have become deleterious in modern environments
where food is plentiful and lifestyles are generally sedentary. Based
on epidemiologic evidence, specific racial/ethnic groups seem to be
particularly susceptible to obesity in the U.S., especially African
Americans, Pima Indians, and Pacific Islanders [3,5]. Data from the
2003–2004 National Health and Nutritional Examination Survey
(NHANES) indicate that African Americans are about 1.5 times
more likely to be obese (defined as BMI $30 kg/m2) than European
Americans even in homogeneous socioeconomic groups [6,7].
Recent genome-wide association studies have shown that
variants in the fat mass and obesity-related gene (FTO) are
significantly associated with obesity in populations of European
origin [8–10]. It was estimated that a ,0.4 kg/m2rise in BMI is
associated with each copy of the A allele at rs9939609 in
populations of European descent [8]. While the association was
replicated in East Asian populations [11–13], no association was
observed in African Americans [9], although there is evidence that
another SNP (rs3751812) affects the risk of obesity in African
Americans as well [14]. These results suggest that the genetic
factors predisposing to obesity in African Americans at FTO may
be different from that in other populations, although an alternative
explanation for these observations is that the causal variant has not
been identified, and that the linkage disequilibrium patterns to the
causal variant are different in African and non-African popula-
tions.
To screen for genetic variants modulating BMI in African
Americans, we used admixture mapping, a technique that scans
the genomes of recently admixed populations and searches for
genomic regions in people with disease where there is substantial
deviation in one of the parental ancestries compared with the
Author Summary
Obesity is about 1.5-fold more prevalent in African
Americans than European Americans. To determine
whether genetic background may contribute to this
observed disparity, we scanned the genomes of African
Americans, searching for genomic regions where obese
individuals have a difference from the average proportion
of African ancestry. By examining genetic data from more
than 15,000 African Americans, we show that the
proportion of European ancestry is inversely correlated
with BMI. In obese individuals, we detect two loci with
increased African ancestry on chromosome X (Xq13.1 and
Xq25) and one locus with increased European ancestry on
chromosome 5 (5q13.3). The 5q13.3 and Xq25 regions
both contain genes that are known to be involved in
appetite regulation. Our results suggest that genetic
factors may contribute to the difference in obesity
prevalence between African Americans and European
Americans. Further studies of the regions may identify
the causative variants affecting susceptibility to obesity.
Admixture Mapping of Obesity
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Page 3

genome average [15–20]. To maximize power to detect variants
affecting BMI, we carried out a pooled admixture mapping
analysis of 15,280 African-American samples from 14 studies,
including the Atherosclerosis Risk in Communities (ARIC) Study,
the Breast Cancer Family Registry (BCFR), the Los Angeles
component of the Women’s Contraceptive and Reproductive
Experiences (CARE) Study, the Dallas Heart Study (DHS), the
Family Investigation of Nephropathy and Diabetes (FIND) Study,
the Genomics Collaborative (GCI) Study, the Health, Aging and
Body Composition (Health ABC) Study, the Jackson Heart Study
(JHS), the Learning the Influence of Family and the Environment
(LIFE) Study, the Multiethnic Cohort of Los Angeles and Hawaii
(MEC), the Osteoporotic Fractures in Men Study (MrOS), the San
Francisco Bay Area Breast Cancer Study (SFBABCS), the Study of
Osteoporotic Fractures (SOF), and the Women’s Circle of Health
Study (WCHS).
Methods
Study Populations and SNP Genotyping
Our analysis was carried out in 15,280 African Americans.
Samples were scanned with at least one of three iteratively
improved and partially overlapping panels of ancestry-informative
markers. The Phase 1 panel was published in Smith et al. 2004
[20] and Reich et al. 2005 [21]. The Phase 2 panel was first
published in Reich et al. 2007 [22]. The Phase 3 panel was first
published in Nalls et al. 2008 [23] (http://www.illumina.com/
downloads/AfricanAmericanAdmixture_DataSheet.pdf).
gether 4,372 markers were genotyped in the present study, with
a median of 1,411 markers genotyped per sample. We found in
practice that all marker panels provided at least 60% of the
maximum possible information about ancestry.
The samples were assembled from 14 studies (Table 1). Of
these, six (ARIC, DHS, Health ABC, JHS, MrOS and SOF) were
prospective cohort studies that did not oversample any particular
phenotype, and eight (BCFR, CARE, FIND, GCI, LIFE, MEC,
SFBABCS and WCHS) were studies that oversampled individuals
with particular phenotypes, such as breast cancer, end-stage renal
disease, type 2 diabetes, hypertension, and prostate cancer. Brief
description of each study as well as the number of samples we
analyzed after applying various data quality filters are provided in
Text S1.
In the six prospective cohort studies, anthropometric mea-
surements were performed using study-specific standardized
protocols, and BMI was calculated as weight (in kg) divided by
height (in meters) squared. In the BCFR, SFBABCS and WCHS,
BMI was also calculated from height and weight measures taken
at the time of study interview by trained research staff. In the
remaining studies, BMI was calculated using self-reported weight
and height.
Alto-
Table 1. Characteristics of 15,280 African American adults by study population.
Diabetes status
Study
No. of
samples
DNA
source
Phase of
marker
panel
Female,
%Age (yrs)
European
ancestry from
autosomes, %
European
ancestry from
the X
chromosome, %
BMI
(Kg/m2)
No. with
information
on diabetes
status
Diabetes,
%
ARIC3,522 genomic362.153.565.817.6610.2 14.267.4 29.666.2a
3,450 19.5
BCFRb
268genomic2100.0 50.369.422.7612.4 17.969.2 30.366.7a
0-
CAREb
365WGA3100.0 48.968.0 22.0611.5 17.368.4 27.766.10-
DHS1,718genomic1 57.5 44.8610.2 16.268.2 13.166.1 31.568.2a
1,71813.6
FINDb
1,445genomic3 50.448.4612.417.168.413.966.128.867.21,44522.0
GCIb
503genomic254.557.9613.615.3611.212.668.231.667.0503 8.9
Health ABC1,172 WGA257.173.462.920.9612.816.769.428.565.2a
1,16421.4
JHS 2,141genomic 2/360.052.4611.117.969.214.566.731.967.1a
2,10617.9
LIFEb
108WGA3100.042.265.321.3611.017.167.929.066.90-
MECb
3,199 genomic1/2/331.3 62.768.123.4614.0 18.4610.1 28.365.3 1,55158.9
MrOS199 WGA3 0.071.765.221.2613.616.969.928.564.4a
18227.5
SFBABCSb
152genomic2100.055.1611.822.6614.717.9611.030.566.0a
0-
SOF368WGA3 100.0 75.064.7 24.4613.8 19.1610.129.965.8a
36816.3
WCHSb
120genomic2 100.050.169.316.7614.813.6611.230.366.5a
0-
Total 15,280--55.756.0612.219.3611.515.568.429.866.612,48723.4
Ranges are given in terms of 61 standard deviation. ARIC, Atherosclerosis Risk in Communities Study; BCFR, Breast Cancer Family Registry; CARE, Los Angeles
component of the Women’s Contraceptive and Reproductive Experiences Study, DHS, Dallas Heart Study; FIND, Family Investigation of Nephropathy and Diabetes
Study; GCI, Genomics Collaborative Study; Health ABC, Health, Aging and Body Composition Study; JHS, Jackson Heart Study; LIFE, Learning the Influence of Family and
the Environment Study; MEC, Multiethnic Cohort of Los Angeles and Hawaii; MrOS, Osteoporotic Fractures in Men Study; SFBABCS, the San Francisco Bay Area Breast
Cancer Study; SOF, Study of Osteoporotic Fractures; WCHS, Women’s Circle of Health Study; WGA, whole genome amplification.
aBMI were measured in an actual clinical visit in the six prospective cohort studies and in the BCFR, SFBABCS and WCHS; for others, BMI was calculated from self-
reported weight and height.
bThese studies include case-control studies and so are not a representative cross-section of the populations. BCFR, CARE, LIFE, SFBABCS and WCHS oversampled women
with breast cancer. FIND oversampled individuals with nephropathy. GCI focused on individuals with hypertension. MEC oversampled individuals with type 2 diabetes,
prostate cancer, breast cancer, and hypertension.
doi:10.1371/journal.pgen.1000490.t001
Admixture Mapping of Obesity
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Estimates of Allele Frequencies in West African and
European American Ancestral Populations
We used previously published genotyping data to estimate the
frequency of each SNP in West Africans and European Americans
[20,24,25]. We only used SNPs for which we were able to obtain
data from both West African (Yoruba) and European American
(CEU) populations from the International Haplotype Map. For
SNPs in the Phase 1 panel, we also added additional genotyping
data from African and European samples, which was the same as
the data collected in Smith et al. 2004 [20].
SNP Filters
To decrease the likelihood of false-positives in our admixture
scans, we applied a series of filters that had the goal of detecting
and removing any SNPs with problematic genotyping, as
described previously [20–22]. Briefly, we applied three previously
published filters. (1) We applied a ‘‘mapcheck’’ filter that tests
whether the estimate of ancestry obtained based on the
information from that SNP alone is consistent with the estimate
of ancestry obtained from neighboring markers; SNPs with
discrepancies are removed from analysis. (2) We applied a
‘‘freqcheck’’ filter that tests whether the observed frequency of a
SNP in African Americans is statistically consistent with being a
mixture of the frequencies observed in the West Africans and
European American samples that we used to represent the
ancestral populations. (3) We finally applied an ‘‘ldcheck’’ filter
that for each sample, iteratively removes SNPs that are less
informative (in terms of the information content about ancestry)
until none are within 200 kilobases of each other or are in
detectable linkage disequilibrium with each other in the ancestral
West African or European American populations [21,25].
Elimination of Samples with Incomplete Genotype and
Phenotype Data
We required all individuals included in the study to have
complete phenotypic information, including BMI, age at the time
of measurement, and gender. We also required all individuals to
have a full admixture scan, and we removed samples that were
outliers with respect to others in the same cohort in the sense of
having many fewer genotypes, as we found that this predicts less
reliable data. The data for the great majority of the samples we
analyze in this study was reported previously [22–26], and hence
we do not report further details of the sample genotyping here.
Estimating Local and Genome-Wide Ancestry in the
African American Samples
We estimated the European and African ancestry at each locus
and genome-wide using the ANCESTRYMAP software [19].
ANCESTRYMAP uses a Hidden Markov Model (HMM) to
combine the weak information about local ancestry that is
provided by each marker, into a more confident estimate that
takes into account the information from many neighboring
markers. The HMM is nested within a Markov Chain Monte
Carlo method, which accounts for uncertainty in the unknown
parameters: SNP allele frequencies in the West African and
European American ancestral populations, the number of
generations since mixture and the average proportion of ancestry
inherited from ancestral populations. All Markov Chain Monte
Carlo runs used 100 burn-in and 200 follow-on iterations, as
previously recommended [19], except for one longer run of 1,000
burn-in and 2,000 follow-on iterations, which we carried out to
check the stability of our results. Samples with an estimated
percentage of European ancestry of more than 0.85 (n=27) were
excluded from this analysis.
Calculation of Covariate-Adjusted BMI
Body mass index was defined as described above. For most of
our admixture analysis runs, BMI was adjusted for age, age-
squared, sex and study, using multivariate linear regression
analyses, and the residuals that emerged from this regression
analysis were used for subsequent analysis.
Admixture Mapping Scans Treating BMI as a
Dichotomous Trait
ANCESTRYMAP [19] was used to test whether individuals
with high or low BMI had a proportion of ancestry that was
significantly different from the genome average in the same
samples.
For the dichotomous admixture scans, we defined the top 20%
of samples with the highest residuals of BMI as cases and the
bottom 20% as controls. Because a prior distribution on risk
models is required for the Bayesian statistical analysis in
ANCESTRYMAP [19], we tested a total of 24 pre-specified risk
models and assessed overall evidence of association by averaging
all models. The first eight models specified 0.5-, 0.6-, 0.7-, 0.8-,
1.3-, 1.5-, 1.7- and 2.0-fold increased risk due to inheritance of one
copy of European ancestral allele for cases, with a control risk of 1.
The next eight models used the same set of risk models for cases,
and the control risks were set to be the reciprocal of the case risks.
The last eight models used a case risk of 1, but specified that
controls had risks of 0.5, 0.6, 0.7, 0.8, 1.3, 1.5, 1.7 and 2.0. These
risk models equally tested for both positive and negative
associations of BMI with African ancestry [19].
To assess statistical significance, the ANCESTRYMAP software
provided two scores: a locus-specific score and a case-control score. A
locus-specific score is obtained in cases (i.e., case-only analysis) by
calculating the likelihood of the genotyping data at the SNPs at the
locus under the risk model and comparing it to the likelihood of
the genotyping data at the SNPs at the locus assuming that the
locus is uncorrelated to the phenotype [19]. The ratio of these two
likelihoods is the ‘‘likelihood ratio’’, and the log-base-10 of this
quantity is the ‘‘LOD’’ score. A locus-specific LOD score of .5
has been recommended as criterion for genome-wide significance
and .4 has been recommended as a criterion for genome-wide
suggestiveness [27].
To obtain an assessment of the evidence for a risk locus
anywhere in the genome—which we call the ‘‘genome-wide
score’’—we averaged the likelihood ratio for association across all
loci in the genome, and took the log10 to obtain a genome-wide
score. We interpret a genome-wide score.2 as significant and .1
as suggestive as previously recommended [27].
A case-control score was calculated by comparing locus-specific
deviations in European ancestry in cases versus controls at each
locus across the genome. This score tests whether any deviation in
ancestry from the genome-wide average is significantly different
comparing cases with controls [19]. If there is no locus associated
with disease, the case-control score is expected to be distributed
approximately according to a standard normal distribution. For
loci identified by this score, the level of genome-wide significance
was defined as a case-control Z score,24.06 or .4.06 (i.e., an
uncorrected nominal P,561025, or a corrected nominal P,0.05
after conservatively correcting for 1,000 hypotheses tested,
corresponding to independent chromosomal chunks assigned to
either African or European ancestry). The case-control score is
particularly important for X chromosome analyses. Case-only
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admixture analyses of the X chromosome are complicated by the
fact that African Americans tend to have lower proportions of
European ancestry on the X chromosome than on the autosomes,
and thus an X-chromosome-wide-specific estimate of ancestry is
required [19]. However, such an X-chromosome-wide estimate of
ancestry is difficult to obtain because of the relatively short size of
the X chromosome. By contrast, a case-control score is robust to
uncertainty in the X-chromosome-wide European ancestry
proportion. A systematic bias in the estimate of ancestry at a
locus is expected to affect controls as much as cases, and hence is
not expected to generate a significant difference between cases and
controls.
Quantitative Admixture Mapping Scans
We have now extended ANCESTRYMAP to also allow for
association analyses of quantitative traits (Text S2). Briefly, we
applied a normal-quantile transformation to the covariate-adjusted
BMI to obtain normally distributed values for subsequent
quantitative admixture scans and regression-based association
analysis. To test for association to a quantitative trait, we applied a
feature, ‘‘qtmode’’, in ANCESTRYMAP (see Text S2 for
mathematical details). In qtmode, each risk model represented a
correlation coefficient (r) of European ancestry with the normally
distributed value of the trait. For this analysis, we tested equally
spaced risk models of r=0.1, 0.08, 0.06, 0.04, 0.02, 20.02,
20.04, 20.06, 20.08 and 20.1. To determine statistical
significance, we used the same thresholds of locus-specific LOD
and genome-wide scores as described above for the dichotomous
analyses.
Credible Interval for the Position of a Genetic Locus
To calculate a 95% credible interval (CI) for the position of a
locus, we obtained the likelihood ratio for association at each
marker across the chromosome where we found an association.
This provided a Bayesian posterior probability for the position of
the underlying causal variant assuming a flat prior distribution
across the region for the position of the disease locus. The central
region of this peak containing 95% of the area was used as the CI.
Assessing Associations of BMI to Local Ancestry at the
Admixture Peak
Local estimates of ancestry at the admixture peak were obtained
using the ANCESTRYMAP software [19]. Heterogeneity of the
correlations between the local ancestry and BMI across studies was
quantified using the I2inconsistency metric [28]. To determine the
association of BMI with local ancestry at the admixture peak, we
performed a linear regression analysis, with the transformed BMI
as the dependent variable and the local estimates of ancestry as
independent variables. To determine whether there was evidence
of residual association with local ancestry after adjustment for
global ancestry, we included each individual’s percentage of
genome-wide European ancestry as a covariate in the regression
models. This enabled us to increase power by including all samples
in a quantitative analysis, rather than using only a subset of
samples with the highest 20% and lowest 20% values in the
dichotomous admixture scans described above.
Ethics Statement
This study was conducted according to the principles expressed
in the Declaration of Helsinki. All sample collections were carried
out according to institutionally approved protocols for study of
human subjects and written informed consent was obtained from
all subjects.
Results
The demographic and phenotypic characteristics of the 15,280
African Americans included in the admixture scan are summa-
rized in Table 1. Because the individual studies differed in aims,
design and methods of data collection, there was considerable
variation across studies in the distribution of age, sex, BMI, and
frequency of diabetes. For example, there was an extremely high
proportion of type 2 diabetes among the MEC samples (58.9%),
reflecting the fact that these samples included a group of cases with
type 2 diabetes who had been specifically genotyped as part of an
admixture scan (http://www.broad.mit.edu/node/549). Com-
bined across all studies, the mean BMI was 29.866.6 kg/m2,
and 40.2% of the population had BMI $30 kg/m2. Mean BMI
differed significantly across studies (P,0.001).
Estimates of European Ancestry
The average percentage of genome-wide European ancestry in
these samples was 19.3611.5% based on estimates from the
autosomes, and the average percentage of European ancestry on
the X chromosome was 15.568.4%. Because the study samples
came from different resources and locations across the U.S., there
was significant variation in average European ancestry, either
estimated from autosomes or the X chromosome, across studies
(P,0.001).
Percent European Ancestry Was Inversely Associated
with BMI among African Americans
The relationship between BMI and percentage of European
ancestry is shown in Figure 1. BMI was inversely correlated with
European ancestry as estimated from autosomes, an effect that was
weak (r=20.042) but statistically significant (P=1.661027) given
the large sample size. It was also significantly correlated with
European ancestry as estimated from the X chromosome
(r=20.046, P=1.261028).
Dichotomous Admixture Scans Identified Two Signals on
Chromosome X
The dichotomous admixture scans detected evidence of
genome-wide significant associations between markers on the X
chromosome and higher BMI (Table 2 and Figure 2). By
comparing the 20% of samples with the highest and lowest
covariate-adjusted BMI, we identified the strongest association for
high BMI at Xq25 (locus-specific LOD=5.94). The genome-wide
score was 3.22, substantially exceeding our genome-wide threshold
for significance. At the same locus, we also observed a case-control
Z score of 23.94 standard deviations (nominal P=8.161025),
which also supported an association at this locus, with obese cases
having lower European ancestry than non-obese controls.
Interestingly, we found another admixture peak at Xq13.1.
Although the LOD score at Xq13.1 was far from significant
(locus-specific LOD=2.22), this locus had the strongest case-
control Z score, 24.62 (nominal P=3.861026), anywhere in the
genome. The associations at Xq13.1 was nominally genome-wide
significant (P=3.861023) after conservatively correcting for 1,000
hypotheses tested.
To examine the potential impact of heterogeneity across the
studies on our admixture-generated signals, we carried out a series
of subgroup analyses (Table 2). When BMI was adjusted for
diabetes in samples with information on diabetes status, the
association signal at Xq25 grew stronger, with the locus-specific
LOD score rising to 6.92, the genome-wide score rising to 3.98,
and the case-control Z-score becoming less significant at 23.25.
To take into account potential measurement errors from self-
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Page 6

reported BMI in 40% of the samples, we also performed
admixture scans restricting the samples to those from the six
prospective cohort studies where BMI was clinically measured.
Similarly strong evidence of association at Xq25 (locus-specific
LOD=6.00; genome-wide score=3.03) was found. We also
carried out an analysis in which we excluded individuals with
diabetes to avoid problems related to co-morbidity and treatment.
After removing these samples (a drop of 23.4% of the sample size),
the signal of association became weaker but remained suggestive
(locus-specific LOD=4.21).
Because the admixture peaks we identified were located on
chromosome X, which has a different copy number in men and
women, we also performed analyses for each gender separately to
explore whether the strength of association differed significantly
Figure 1. Scatter-plots of BMI vs. the estimated percentage of European ancestry. (A) Percentage of European ancestry was estimated
based on the autosomes. (B) Percentage of European ancestry was estimated based on chromosome X. Data are plotted using 20% of the samples
(selected at random) for better visualization.
doi:10.1371/journal.pgen.1000490.g001
Table 2. Summary of results from dichotomous admixture scans for BMI.
Xq25 Xq13.15q13.3
RunDescription
No. of cases/
controlsa
No. of
SNPsb
Genome-
wide score
Peak LOD
score
Case-
control Z
score
Peak
LOD
score
Case-
control Z
score
Peak
LOD
score
Case-
control Z
score
1All African Americans 3,055/3,0563,9023.22c
5.94d
23.94 2.22
24.622.484.03
2106more iterations for run 1 3,055/3,056 3,902 3.10c
5.80d
23.94 2.07
24.62 2.12 4.02
3 African Americans with information on
diabetes status, BMI additionally adjusted for
diabetes
2,496/2,498 3,7573.98c
6.92d
23.25 2.55
24.572.584.23
4African Americans in the six prospective
cohort studiese
1,824/1,825 3,5433.03c
6.00d
23.781.68
24.19 2.24 3.02
5 Non-diabetic African Americans 1,914/1,9143,7371.464.21
23.132.25
24.281.503.05
6 Male African Americans 1,351/1,3533,716 1.380.96
22.694.40
24.12 0.401.60
7 Female African Americans1,703/1,7053,6461.36 4.15
23.00
21.06
22.10
20.23 3.30
8All African Americans, drop every even SNP 3,055/3,0562,0572.02c
4.78
24.301.29
24.74 1.583.80
9All African Americans, drop every odd SNP 3,055/3,0562,049 1.29 4.10
23.83 0.90
24.781.343.24
10 Best-fit multiplicative model for run 1 (0.73
multiplicative risk for chromosome X)
3,055/3,0563,902 4.57c
7.24d
23.942.71
24.62
2
4.03
11Best-fit multiplicative model for run 3 (0.70
multiplicative risk for chromosome X)
2,496/2,4983,7575.25c
8.16d
23.253.25
24.57
2
4.23
BMI was adjusted for age, age-squared, sex and study for all runs, except for runs 6 and 7, where analysis was performed in each gender group and thus not adjusted for
sex.
aCases: 20% with the highest covariate-adjusted BMI; controls: 20% with the lowest values.
bThe number of SNPs analyzed after applying a series of quality filters.
cGenome-wide scores.2 are formally significant; scores.1 are suggestive.
dLOD scores.5 are formally significant; scores.4 are suggestive.
eThe six cohorts composed 94% of all samples with clinically measured BMI.
doi:10.1371/journal.pgen.1000490.t002
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between males and females. We found that the evidence of
association at Xq25 was stronger in females (locus-specific
LOD=4.15;N=1,703)than
LOD=0.96; N=1,351), and that the association signal at
Xq13.1 in males grew stronger with the local LOD score rising
to 4.40 (Run 6 and 7 in Table 2). In the more comprehensive
linear regression analysis of local ancestry, there was a significant
gender difference at Xq13.1 (P,0.026; see below for details).
In addition to the two peaks on chromosome X, using
dichotomous admixture scans we observed a few interesting
regions (Figure 2 and Table S1), particularly locus 5q13.3 (locus-
specific LOD=2.48, Table 2). This locus is unique in that even
though its LOD score was far from statistical significance, it had
the strongest increase in European ancestry in individuals with
high BMI compared to individuals with low BMI (case-control Z
score=4.03, nominal P=5.661025). The case-control score was
marginally significant at genome-wide level, suggesting that higher
BMI was, though counter-intuitively, associated with greater
European ancestry at 5q13.3 locus.
inmales(locus-specific
Quantitative Admixture Scans Detected a Third Locus on
Chromosome 5
By including all African-American samples and using BMI as a
continuous trait, our quantitative admixture scan supported and
strengthened the evidence of association at 5q13.3 locus (Figure 3).
The peak locus-specific LOD score was 6.27 and the genome-wide
score was 3.46, both reaching the thresholds for genome-wide
significance.
Evidence of Association between Admixture-Generated
Signals and Continuous BMI
The local estimate of European ancestry was also extracted for
each individual at each of the three admixture peaks and analyzed
for association with continuous BMI (Model 1 in Table 3). Higher
local European ancestry both at Xq13.1 and Xq25 was
significantly and inversely associated with lower values of
transformed BMI (P=2.2610211and P=4.5610210, respective-
ly). To examine whether these associations could be fully explained
by the significant association between BMI and genome-wide
ancestry (discussed above), we further adjusted for genome-wide
European ancestry in the multivariate analysis. The residual
association of local ancestry with BMI after adjusting for genome-
wide ancestry remained significant at both Xq13.1 (P=1.961027)
and Xq25 (P=4.161026) (Model 2 in Table 3), indicating that
local ancestry had an effect on BMI above and beyond genome-
wide ancestry. Both associations were nominally genome-wide
significant (P=1.961024and P=4.161023) after conservatively
correcting for 1,000 hypotheses tested. A naive analysis suggests
that each additional copy of a European ancestral allele at either
the Xq13.1 or the Xq25 peak is independently associated with a
BMI decrease of ,0.1 Z-score units on average (equivalent to
,0.64 kg/m2and accounting for 0.3% of the variance in BMI,
after adjusting for age, age-squared, sex and study). The true
genetic effects are expected to be somewhat weaker because of
discovery bias.
The association at the 5q13.3 peak was particularly interesting
in that it did not achieve statistical significance until the genome-
Figure 3. The quantitative admixture scans for genetic loci affecting BMI. The quantitative admixture scans identified an association peak at
5q13.3 with a locus-specific LOD score of 6.27 and a genome-wide score of 3.46, both reaching the thresholds for genome-wide significance.
doi:10.1371/journal.pgen.1000490.g003
Figure 2. The dichotomous admixture scans for genetic loci affecting BMI. The locus-specific LOD score (red line) and the case-control Z
score (blue gray line) are shown for Run 1 in Table 2: BMI was adjusted for age, age-squared, sex and studies. A signal at genome-wide significant
level (locus-specific LOD=5.94) was detected at Xq25. The Xq25 peak was also supported by the case-control statistic (Z score=23.94, P=8.161025).
Another peak on chromosome X was observed at Xq13.1 (locus-specific LOD=2.22). Although its LOD score did not reach statistical significance, it
had the largest magnitude case-control Z score of 24.62 (P=3.861026) anywhere in the genome. Moreover, we observed an admixture signal at
5q13.3 (locus-specific LOD=2.48), which did not reach significance, but had the strongest positive case-control Z score across the genome (Z
score=4.03, P=5.661025).
doi:10.1371/journal.pgen.1000490.g002
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wide estimate of European ancestry was added into the analysis.
This was presumably because the locus effect was in the opposite
direction to the genome-wide ancestry effect (thus, the effects
cancel in the unadjusted analysis). Each additional copy of a
Europeanancestral alleleat
(P=5.861027) associated with an increase in BMI of 0.09 Z-
score units (naively equal to ,0.59 kg/m2, accounting for 0.3% of
thevariance inBMI),which
(P=5.861024) after correcting for the approximately 1,000
independent hypotheses tested.
For the two peaks on chromosome X, we further examined
whether the effects of the local ancestry on BMI were modified by
gender. The local ancestry at Xq13.1 tended to be more strongly
associated with BMI in males than in females. After adjusting for
genome-wide European ancestry, the gender difference at Xq13.1
was significant (P for heterogeneity=0.026, Model 2 in Table 3),
which was in line with our results of dichotomous admixture scans.
At Xq25, the effects of local ancestry did not show significant
heterogeneity (P.0.05) between the two gender groups, either
before or after adjusting for genome-wide European ancestry. A
potential mechanism for the difference in the strength of
association in men and women at the Xq13.1 locus is that women
carry two copies of chromosome X whereas men carry only one,
and hence this may simply reflect a difference in the genetics of the
two genders on chromosome X.
Since our analysis pooled data from 14 studies, we also
examined whether the strength of the admixture associations to
BMI on chromosomes X and 5 differed across studies. Local
ancestry estimates at each of the three admixture peaks were used
to check for homogeneity of their correlation with BMI across
studies. There was no evidence of heterogeneity across studies (all
P.0.05, I2=0%) at any of the three peaks (Table S2).
5q13.3was significantly
was nominallysignificant
95% Credible Interval for the Three Loci
We constructed 95% CI for each of the three loci identified.
The 95% CI for the chromosome 5 locus spanned from 69.2 to
77.2 Mb (an ,8 Mb region) on build 35 of the human genome
reference sequence. The 95% CI for the higher admixture peak on
chromosome X spanned from 114.4 to 124.4 Mb (an ,10 Mb
region), and then 95% CI for the other chromosome X admixture
peak spanned from 47.8 to 89.2 Mb, a much broader region
(,40 Mb).
Discussion
We have carried out admixture mapping analyses to search for
genomic regions associated with BMI. This pooled analysis of
samples from 14 studies is the largest admixture scan reported to
date. In more than 15,000 individuals, we identified a locus on
chromosome 5 where greater local European ancestry was
associated with higher levels of BMI (P=5.861027), and two
regions on chromosome X where greater local European ancestry
was associated with lower levels of BMI (both P,5.061026). Each
of these three associations was above and beyond the contribution
of genome-wide European ancestry, and each reached genome-
wide significance.
One of the major strengths of this study is its large sample size,
with over 15,000 African Americans. However, the large sample
also introduced complications in that it required the pooling of
several studies which potentially introduced various types of
heterogeneity to the study samples. For example, we included
individuals with either self-reported BMI or clinically measured
BMI in the present study. It is well known that individuals tend to
under report their body weight and that this measurement error is
potentially more common among heavier individuals. Moreover,
this type of measurement error can reduce the statistical power of
a study. To assess the potential effects of such measurement error,
we performed subgroup analysis by restricting the samples to those
from the six population-based cohort studies, where body weight
and height were clinically measured according to study protocols
(samples in the six cohorts represented 94% of all samples with
measured BMI) and found the two sets of results to be largely
comparable. Additional subgroup analyses, as shown in Table 2,
also confirmed the robustness of our findings [21,25,26].
The inverse correlation between BMI and percentage of
European ancestry estimated on the genome-wide scale confirmed
Table 3. Linear regression analysis of BMI on local European ancestry at the three admixture peaks.
Model 1: Local ancestry only
Model 2: Local ancestry, ancestry from
autosomes as a covariate
Model 3: Local ancestry, ancestry from
the X chromosome as a covariate
Admixture peaks Reg. Coef. (95% CI)
P valueReg. Coef. (95% CI)P valueReg. Coef.(95% CI)P value
5q13.3 0.03(20.01, 0.06) 0.071 0.09(0.06, 0.13) 5.861027
---
Xq13.1
Both sexes
20.13 (20.16, 20.09)2.2610211
20.11(20.14, 0.07)1.961027
20.10(20.14, 0.06)2.261026
Males
20.15(20.23, 20.08)3.561025
20.16(20.23, 20.08) 4.061025
20.16(20.24, 20.08) 6.461025
Females
20.10(20.14, 20.06) 7.261026
20.06(20.10, 20.01)0.022
20.04(20.09, 0.01)0.089
P for heterogeneity between sexesa=0.676P for heterogeneity between sexesa=0.026P for heterogeneity between sexesa=0.016
Xq25
Both Sexes
20.13(20.17, 20.09)4.5610210
20.10(20.15, 20.06)4.161026
20.10 (20.14, 20.05)4.361025
Males
20.11(20.19, 20.03)0.008
20.11(20.20, 20.03)0.011
20.11(20.19, 20.02)0.017
Females
20.12(20.16, 20.07)5.261026
20.06(20.12, 20.01)0.022
20.05(20.11, 0.01)0.091
P for heterogeneity between sexesa=0.882P for heterogeneity between sexesa=0.358P for heterogeneity between sexesa=0.281
Reg. Coef., regression coefficient: the change in Z score for each additional copy of the European ancestry allele; CI, confidence interval. In both-sex-combined analysis,
BMI was adjusted for age, age-squared, sex and study, and then normal-quantile transformed. Sex-stratified analysis was performed in each gender group and thus not
adjusted for sex.
aBy Z test for difference between the two the regression coefficients.
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the results from previous studies of smaller sample size and fewer
markers [29,30]. However, while genome-wide ancestry is likely
correlated with local ancestry, it cannot fully capture ancestry
information at each locus as there exists variation across the
genome in the effects of locus-specific ancestry on obesity. In
particular, local European ancestry at 5q13.3 was positively
associated with BMI, providing the first evidence of a genome-
wide significant ancestry association being in the opposite direction
to the overall epidemiological association.
The 95% CI for the chromosome 5 peak harbors a number of
genes, including the cocaine and amphetamine regulated tran-
script (CART) gene, which is a candidate for modulating obesity.
CART is a hypothalamic neuropeptide that transmits a physio-
logical anorexigenic signal and is involved in appetite regulation
[31,32]. Experiments have also shown that CART knock-out mice
have increased body weight compared with wild type mice [33].
Genomic regions containing the CART gene have also been linked
to both BMI and serum leptin levels in a study of French
Caucasian families [34]. SNPs in the 59 upstream region have
been reported to be associated with obesity in Japanese [35] and
French [36]. However, association studies in European-related
populations [37,38] and Pima Indians [39] have not found
associations between BMI and the CART gene in these
populations, and to our knowledge no published studies have
studied CART variants in African Americans. Further mapping
work is needed to determine whether the CART gene or other
genetic variants in the interval may influence the risk of obesity.
There have been very few studies reporting linkage of obesity
with markers on the X chromosome [40], yet three prior studies
also reported either suggestive or significant linkage of obesity to
the q arm of chromosome X [41–43]. Although these three studies
were performed in European-American families, they all mapped
the obesity locus to the Xq23–q24 region, which overlaps with the
95% CI of the highest admixture peak on chromosome X in our
study. The 95% CI in our study contains one particular gene that
may be a candidate for obesity susceptibility. The gene solute
carrier family 6 member 14 (SLC6A14) is involved in serotonin
synthesis and serotonergic receptor mechanisms that have been
implicated in appetite control and body weight regulation [44–46].
Nominally significant evidence of association between BMI and a
SNP (22510C/G) in SLC6A14 was observed in ,1,800 samples
from Finland and Sweden (P=0.003), and females were found to
contribute most to this particular observed association [43]. The
gender difference observed in the previous study [43] is in line with
the results from our dichotomous admixture scans at this locus,
although the difference observed between men and women in our
study did not reach statistical significance. Another potential
candidate gene near the highest admixture peak is the cullin 4B
(CUL4B) gene. CUL4B was recently identified as a causative gene
for an X-linked mental retardation syndrome, which was
associated with several clinical features, including central obesity
[47].
Although we did not detect a significant association in the
region of the FTO gene, we noticed that the second highest
admixture peak (locus-specific LOD=3.68) identified in our
quantitative scans was on chromosome 16, about 5.6 Mb away
from the FTO gene, and its 99% CI spanned 51.5 to 66.8 Mb (on
build 35 of the human reference sequence), which is a region that
includes the FTO gene. (However, FTO is outside the 95% CI.)
Further fine-mapping analysis may determine whether additional
variations in FTO may explain the intriguing admixture signal in
this region. The melanocortin-4 receptor (MC4R) gene, located on
chromosome 18q21.32, is the second obesity-susceptibility gene
discovered by genome-wide association studies in individuals of
European origin [48,49]. However, our dichotomous and
quantitative admixture scans did not identify any admixture
signals on chromosome 18q.
In summary, we have carried out a genome-wide admixture
mapping scan in 15,280 African Americans and have identified
three loci, 5q13.3, Xq13.1 and Xq25, that may harbor genetic
variants associated with variations in BMI. The local ancestry
associations to BMI at each of the three admixture-generated
peaks were statistically significant, suggesting the presence of a
genetic effect at these loci above and beyond the effects of genome-
wide ancestry. Follow-up fine mapping and focused analysis of
each locus using data that emerge from genome-wide association
studies in African Americans with measured BMI will be crucial to
determine whether these regions harbor genetic variants predis-
posing to obesity.
The present study is also methodologically significant in
illustrating how searches for genes in African Americans and
diverse populations can result in the detection of genetic loci that
have eluded discovery in European-derived populations, perhaps
because the underlying variants are too rare in the latter
populations. However, there is no reason to think that the three
loci we have identified are biologically important only in African
Americans. Replication and fine-mapping studies in other ethnic
groups, including Hispanic Americans and Pacific Islanders, with a
similar risk of obesity to African Americans, and even European
Americans and East Asians with a lower but still important rate of
this condition, may further elucidate these regions of the genome.
Studying multiple populations to fine-map a locus highlighted in
an admixture scan can be more informative than studying any one
population, as was previously demonstrated by our use of a multi-
ethnic cohort to fine-map prostate cancer risk factors at 8q24 [50].
Supporting Information
Table S1
Xq13.1 and 5q13.3, observed in the dichotomous admixture scans
for BMI.
Found at: doi:10.1371/journal.pgen.1000490.s001 (0.01 MB PDF)
Interesting chromosome regions, other than Xq25,
Table S2
BMI by study.
Found at: doi:10.1371/journal.pgen.1000490.s002 (0.14 MB PDF)
Correlations between local European ancestry and
Text S1
Found at: doi:10.1371/journal.pgen.1000490.s003 (0.04 MB PDF)
Description of each study.
Text S2
Found at: doi:10.1371/journal.pgen.1000490.s004 (0.07 MB PDF)
Details of quantitative trait analysis.
Acknowledgments
The authors thank the staff and participants of the ARIC, BCFR, CARE,
DHS, FIND, GCI, Health ABC, JHS, LIFE, MEC, MrOS, SFBABCS,
SOF, and WCHS studies for major contributions.
Author Contributions
Conceived and designed the experiments: CYC WHLK CAH TBH CX
EMJ CBA GU LB KA JMZ BEH JGW DR. Performed the experiments:
WHLK AT CAH TBH CX LF EB JMZ BEH JGW DR. Analyzed the
data: CYC WHLK NP AT DR. Contributed reagents/materials/analysis
tools: WHLK NP AT CAH TBH CX EMJ CBA FLB JC MFP RSP MJK
LAM WCH LF LP MLF LHJ EVB GLC MAN ELA ESO TSL IM RL
GU LB KA HAT EB JMZ BEH JGW DR. Wrote the paper: CYC WHLK
NP DR.
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Admixture Mapping of Obesity
PLoS Genetics | www.plosgenetics.org10May 2009 | Volume 5 | Issue 5 | e1000490