Genetic Epidemiology of Obesity
Wenjie Yang1, Tanika Kelly1, and Jiang He1,2
1Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA.
2Department of Medicine, School of Medicine, Tulane University, New Orleans, LA.
Accepted for publication January 31, 2007.
Obesity has become a global epidemic and contributes to the increasing burden of type 2 diabetes, cardio-
vascular disease, stroke, some types of cancer, and premature death worldwide. Obesity is highly heritable and
arises from the interactions of multiple genes, environmental factors, and behavior. In this paper, the authors
reviewed recent developments in genetic epidemiologic research, focusing particularly on several promising
genomic regions and obesity-related genes. Gene-gene and gene-environment interactions of obesity were also
discussed. Published studies were accessed through the MEDLINE database. The authors also searched the
Obesity Gene Map Database (http://obesitygene.pbrc.edu/) and conducted a manual search using references
cited in relevant papers. Heritabilities for obesity-related phenotypes varied from 6% to 85% among various
populations. As of October 2005, 253 quantitative trait loci for obesity-related phenotypes have been localized in
61 genome-wide linkage scans, and genetic variants in 127 biologic candidate genes have been reported to be
associated with obesity-related phenotypes from 426 positive findings. Gene-gene interactions were also ob-
served in several genes, and some genes were found to influence the effect of dietary intake and physical activity
on obesity-related phenotypes. Integration of genetic epidemiology with functional genomics and proteomics
studies will be required to fully understand the role of genetic variants in the etiology and prevention of obesity.
body mass index; genes; obesity; overweight
Abbreviations: LOD, logarithm of the odds; SNP, single nucleotide polymorphism; WHO, World Health Organization; WHR,
Obesity is characterized as an excess of adipose tissue.
The most commonly used measurement to assess weight
status is body mass index, defined as weight (kg)/height
(m)2. The World Health Organization (WHO) recommends
the following body mass index cutpoints to classify weight
status in adults 20 yearsof ageorolder: <18.5 kg/m2(under-
weight), 18.5–24.9 kg/m2(normal weight), 25.0–29.9 kg/m2
(overweight), 30.0–39.9 kg/m2(obese), and ?40 kg/m2(ex-
tremely obese) (1). Although it is by far the most commonly
used index for classifying general obesity in an adult, body
mass index cannot distinguish obese from muscular individ-
uals, such as athletes, who have more lean muscle than body
fat. Body fat mass and percentage body fat, which are mea-
sured by dual energy x-ray absorptiometry, can provide
a more accurate estimate of obesity status. Percentage total
body fat is calculated as fat mass/(fat mass 1 lean mass 1
bone mineral content) (2). The WHO-recommended cutoff
point for obesity corresponds to a percentage body fat of 25
percent and 35 percent in men and women, respectively (3).
Waistcircumference andwaist/hipratio (WHR)areotherin-
dicators commonly used to determine abdominal obesity
status. The American Heart Association and the National
Heart, Lung, and Blood Institute recommend waist circum-
ference cutpoints for determining abdominal obesity status
as ?102 cm in men and ?88 cm in women of non-Asian ori-
gin and ?90 cm in Asian men and ?80 cm in Asian women
(4). According to guidelines from the WHO, abdominal
obesity status can be identified as a WHR of >0.90 in
men and >0.85 in women (5).
Obesity is becoming an increasingly important clinical
and public health challenge throughout the world. Recently,
the International Obesity Taskforce estimated a total of 1.1
Correspondence to Dr. Jiang He, Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal
Street, SL 18, New Orleans, LA 70112 (e-mail: email@example.com).
49 Epidemiol Rev 2007;29:49–61
Copyright ª 2007 by the Johns Hopkins Bloomberg School of Public Health
All rights reserved; printed in U.S.A.
Vol. 29, 2007
Advance Access publication June 12, 2007
by guest on June 3, 2013
billion overweight, including 320 million obese, adults
worldwide (6). Economically developed regions have a
higher prevalence of overweight and obesity compared with
developing regions of the world (7). For example, results of
the 1999–2002 National Health and Nutrition Examination
Survey indicated that an estimated 65 percent of US adults
aged 20 years or older (over 131 million people) were either
overweight or obese and that 30 percent of adults (over 60
million people) were obese (8). Despite these estimates, the
developing world actually faces a larger absolute burden of
overweight and obesity because of a larger population size
(9, 10). Obesity is a major risk factor for type 2 diabetes,
cardiovascular disease, stroke, some types of cancer, and
premature death (11–18).
Human obesity arises from the interactions of multiple
genes, environmental factors, and behavior, and this complex
etiology makes management and prevention of obesity es-
pecially challenging. While a genetic basis for obesity ex-
ists, defining the genetic contribution has proven to be a
formidable task. Genetic epidemiologic methods for the
gene discovery of complex traits, such as obesity, can be
divided into two broad classes: hypothesis-free (genome-
wide linkage and genome-wide association) and hypothesis-
driven (candidate gene and biologic pathway) approaches.
The hypothesis-free approach does not involve any spe-
cific biologic hypothesis about the trait of interest. Genome-
wide linkage analysis, typically using a 10-centimorgan (cM)
(or denser 2-cM) marker density usually containing 400 (or
2,000) microsatellite markers evenly covering the entire hu-
man genome, identifies broad intervals of several megabases
that might contain hundreds of susceptibility genes for dis-
eases of interest. This method has been remarkably success-
ful in identifying disease genes for monogenic disorders
(19). When applied to the common complex disease, how-
ever, linkage analysis has less power, and success has been
limited. In addition, relatively high costs and the family-
based data requirement make linkage analysis more re-
stricted in practice. Falling genotype costs and the recent
advancements in the International HapMap Project have
made genome-wide association studies of complex disease
popular (20). For example, the first genome-wide associa-
tion study of obesity conducted by Herbert et al. (21) iden-
tified a common genetic variant near the insulin-induced
gene 2 (INSIG2) associated with obesity in Framingham
Heart Study participants. Genome-wide association studies
have been expected to be more powerful than linkage stud-
ies because of their resolution and ability to narrow down
the genomic target region more precisely and to detect even
small gene effects. In addition, the number of genotyped
variants could be dramatically reduced, taking advantage
of linkage disequilibrium between variants (22). The prac-
tical necessity of having a fixed set of genome-wide asso-
ciation markers has obvious advantages. For example, a
linkage disequilibrium-based set of tag single nucleotide
polymorphisms (SNPs) can maximize the amount of varia-
tion captured per SNP by 300,000 SNPs of Illumina Human-
Hap300 BeadChip or by 500,000 SNPs of Illumina
HumanHap500 BeadChip (Illumina, Inc., San Diego, Cal-
ifornia). A set of SNPs that ignores linkage disequilibrium
patterns can be selected to distribute approximately ran-
domly across the genome available from Affymetrix
111,000 and 500,000 array sets (Affymetrix, Inc., Santa
Clara, California). A combination of these two methods is
also a good choice, consisting of a set of ‘‘random’’ SNPs
augmented by a carefully chosen fill-in set. Therefore, a re-
search group should make decisions about which genome-
wide association genotyping platform to use in order to
balance efficiency, redundancy, and completeness regard-
ing the different marker panels and populations being
The hypothesis-driven approach (candidate gene or bio-
logic pathway analysis) needs an a priori hypothesis that the
genetic polymorphisms in a candidate gene or a biologic
pathway being studied are causal variants or in strong link-
age disequilibrium with a causal variant for a particular phe-
notype of interest. This approach is now considered to be an
efficient strategy for identifying genetic variants with small
or modest effects that underlie susceptibility to common dis-
ease, including obesity. The selection of candidate genes (or
biologic pathways) should consider boththe relevance of the
candidate gene (or biologic pathway) to the pathogenesis of
the disease of interest and the functional effects of a partic-
ular polymorphism (24). Candidate gene analysis is an in-
direct test of association to examine the relation between
a dense map of SNPs and disease, while candidate SNP
analysis is a direct test of association between putatively
functional variants and disease risk (25). The advantage of
indirect association is that it does not require prior determi-
nation of which SNP might be functionally important; how-
ever, the disadvantage is that larger numbers of SNPs need
to be genotyped (25). A combination of functionally impor-
tant SNPs with a collection of tag SNPs covering the entire
candidate gene has been used in many candidate gene asso-
ciation studies. Genetic variants in multiple candidate genes
within the same biologic pathway can be examined, and
their interaction can be tested in pathway analysis. It also
makes sense to do fine mapping in significant linkage peaks
by association analysis with the knowledge of candidate
genes that reside in these regions and are involved in bi-
ologic pathways for developing the disease of interest. One
of the main weaknesses of candidate gene analysis is that it
depends on an a prior hypothesis about disease mechanisms,
so that the discovery of new genetic variants or novel genes
is precluded by previously unknown pathways (26).
This paper reviews recent developments in genetic epi-
demiology research of obesity in human populations and
includes current information from heritability studies,
genome-wide linkage studies, and candidate gene associa-
tion studies, focusing particularly on several important ge-
nomic regions and obesity-related genes. Gene-gene and
gene-environment interactions of obesity are also discussed.
HERITABILITY OF OBESITY
Twin, adoption, and family studies have established that
obesity is highly heritable, and an individual’s risk of obe-
sity is increased when one has relatives who are obese (27–
29). Heritability estimates ranged from 16 percent to 85
50 Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
percent for body mass index (30–34), from 37 percent to 81
percent for waist circumference (35–37), from 6 percent to
30 percent for WHR (38–40), and from 35 percent to 63
percent for percentage body fat (40–43). The Framingham
Heart Study reported a moderate heritability estimate for
body mass index (40–50 percent) (32). In contrast, the Na-
tional Heart, Lung, and Blood Institute family heart study
and twin studies observedhigher estimates of heritability for
body mass index (40–80 percent), and they also reported
a heritability of 70–80 percent for weight gain (27, 44–
46). Davey et al. (46) reported that the heritability estimate
exceeded 90 percent for abdominal fat accumulation in an
Indian population, while a family study in an Old Order
Amish community showed a heritability of 37 percent for
waist circumference and 13 percent for WHR (35). A twin
study and HERITAGE (HEalth, RIsk factors, exercise Train-
ing, And GEnetics) Family Study reported similar heritabil-
ities of 63 percent and 62 percent, respectively, for
percentage body fat (41, 42), while the maximal heritability
estimate in a Taiwanese population was 35 percent (43).
In recent years, molecular approaches have advanced the
understanding of some forms of monogenic obesity in hu-
mans. These forms of obesity are rare and very severe,
generally starting in childhood (47). For example, mutations
in human genes coding for leptin (LEP), leptin receptor
(LEPR), proopiomelanocortin (POMC), and melanocortin-
4 receptor (MC4R) have been associated with juvenile-onset
morbid obesity (48–51). To date, 176 human obesity cases
due to single-gene mutations in 11 different genes have been
reported, 50 loci related to Mendelian syndromes relevant to
human obesity have been mapped to a genomic region, and
causal genes or strong candidates have been identified for
most of these syndromes (52).
GENOME-WIDE LINKAGE STUDIES
Obesity is a complex, heterogeneous group of disorders,
which develops predominantly from a polygenic multifac-
torial trait, with interplay of genetic and environmental fac-
tors. As of October 2005, 253 quantitative trait loci for
obesity-related phenotypes have been localized from 61 ge-
nome-wide linkage studies in human populations. A total of
52 genomic regions harbor quantitative trait loci supported
by two or more studies (52).
The genome-wide linkage studies have linked body mass
index to almost every chromosomal region except Y. Table 1
lists studies that showed evidence for the presence of link-
age with body mass index (logarithm of the odds (LOD)
score: ?3) (36, 53–73). The strongest linkage evidence was
observed in a multipoint analysis with a LOD score of 9.2 in
Utah pedigrees (60).
Few studies have found evidence of linkage with waist
circumference or WHR (74–76). A LOD score of 3.71 was
observed for waist circumference, which was located at
1q21-q25, in the Hong Kong Family Diabetes Study, and
evidence of linkage with waist circumference was shown in
the 6q23-25 region in the Framingham Heart Study (74, 77).
Suggestive linkage was found in European Americans and
African Americans, both with LOD scores of 2.7 at the
Xp21.3 and Xp11.3 regions (75).
Some studies have found evidence of linkage with per-
centage body fat (56, 67, 72, 78–80). LOD scores of 4.27
and 4.21 were observed with the same genetic marker,
D21S1446, in chromosome 21q22.3 by Li et al. (67) and
Dong et al. (80). The HyperGEN Sudy reported a LOD
score of 3.0 for men in chromosome 15q25.3 with marker
D15S655 and 3.8 for women in chromosome 12q24 with
markers D12S395–D12S2078 in non-Hispanic Whites and
African-American populations (79), while in the same year,
marker D12S2070 in European-American families (80).
Most reports of chromosomal regions linked to obesity
and body composition are not robust; only a few regions have
been replicated in some studies. As for body mass index, the
most promising genomic regions (in chromosomes 2, 3, 6,
11, 13, and 20) were replicated in multiple studies. For ex-
ample, two studies reported linkage in the 2q14.3 region
with marker D2S347 (54, 55), and two studies obtained evi-
dence of linkage in the 2p22.3 region with marker D2S1788
(53, 81). Three studies reported evidence of linkage at chro-
mosome 3q26.33 with marker D3S2427 (57–59). In chro-
mosome 6, two studies found linkage in the 6q22.31 region
with marker D6S462 (71, 82). In chromosome 11, three
studies showed evidence of linkage or suggestive linkage
in the 11q24.3 region with marker D11S912 (60, 66, 81),
and three studies observed suggestive linkage (LOD scores
of 2.3, 2.7, and 2.8, respectively) in chromosome 11q24.1
with marker D11S4464 (60, 83, 84). Li et al. (67) and Dong
et al. (80) reported two regions with suggestive linkage lo-
cated at 13q21.32 and 13q32.2 with the same markers,
D13S800 and D13S779, respectively, and North et al. (70)
found evidence of linkage at 13q13.2 with marker
D13S1493, which was also reported by Li et al. (67) with
suggestive linkage. In chromosome 20, at the 20q12 region
with marker D20S438, one study observed linkage, and an-
other study found suggestive linkage (60, 73). As for waist
circumference, two studies observed evidence of linkage in
the 12q24.21 region with marker D12S2070 (67, 80). As for
percentage body fat, aside from chromosomes 12q24 and
21q22.3, suggestive linkage was reported at the chromo-
some 20q13.31-qter region with marker D20S149, which
has already been observed by Lee et al. (72) and Dong
et al. (85).
The general lack of replication of genome scan results
across data sets has been an ongoing concern for genetic
epidemiologic studies. The inconsistency between studies
may be attributed partially to varying sample sizes from
study to study. Relatively small study sample sizes tend to
limit the power of genome scans to detect linkage. In addi-
tion, the multiple statistical tests performed in each scan
increase the risk of type I error. The problem of false pos-
itives could be solved by applying more stringent statistical
significance criteria (86). The study population is also an
important issue when considering inconsistencies of results.
Population heterogeneity decreases the power to detect the
Genetic Epidemiology of Obesity51
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
true linkage signals within studies and makes it difficult to
compare them across studies (87). The genome-scan meta-
analysis method would be useful for combining evidence
from multiple studies and could confirm evidence for re-
gions highlighted in more than one scan or identify new
regions where weak but consistent evidence for linkage
has been seen across studies (87).
CANDIDATE GENE ASSOCIATION STUDIES
Obesity is a complex trait, which does not show a typical
Mendelian transmission pattern and may depend on several
susceptibility genes with low or moderate effects. There is
firm evidence that genes influencing energy homeostasis and
thermogenesis, adipogenesis, leptin-insulin signaling trans-
duction, and hormonal signaling peptides play a role in the
TABLE 1. Evidence for the presence of linkage with body mass index
Study sampleLOD score*
First author, year
D2S1788 2p22.3 66 White families (349 subjects)3.08 Palmer L, 2003 (53)
D2S347 2q14.3 1,249 White European-origin sibling pairs4.44 Deng HW, 2002 (54)
D2S347 2q14.3 53 Caucasian families (758 subjects)3.42 Liu Y, 2004 (55)
2q37 451 Caucasian families (4,247 subjects)3.34 Guo YF, 2006 (56)
D3S17643q22.3 1,055 pairs (White, Black, Mexican American, and Asian)3.45 (Black) Wu X, 2002 (57)
D3S24273q26.33 507 Caucasian families (2,209 subjects)3.3 Kissebah A, 2000 (58)
D3S24273q26.33 128 African-American families (545 subjects)4.3 Luke A, 2003 (59)
D3S24273q26.33 1,055 pairs (White, Black, Mexican American)3.4 Wu X, 2002 (57)
D3S36763q26.33 128 African-American families (545 subjects)4.3 Luke A, 2003 (59)
D4S16274p13 37 Utah families (994 subjects)3.4 Stone S, 2002 (60)
D4S3350 4p15.1 37 Utah families (994 subjects)9.2 Stone S, 2002 (60)
D4S2632 4p15.1 37 Utah families (994 subjects)6.1 Stone S, 2002 (60)
D6S4036q23.3 27 Mexican-American families (261 subjects)4.2 Arya R, 2002 (61)
D6S1003 6q24.1 27 Mexican-American families (261 subjects)4.2 Arya R, 2002 (61)
D7S8177p14.3 182 African families (769 subjects)3.83 Adeyemo A, 2003 (31)
D7S18047q32.3 401 American families (3,027 subjects)4.9 Feitosa MF, 2002 (62)
D8S11218p11.23 10 Mexican-American families (470 subjects)3.2 Mitchell B, 1999 (63)
D10S212 10q26.3 18 Dutch families (198 subjects)3.3 van der Kallen CJ, 2000 (64)
10 region10q26.3 279 White families (1,848 non-Hispanic subjects)3.2 Turner S, 2004 (65)
D11S200011q22.3 182 African families (769 subjects)3.35 Adeyemo A, 2003 (31)
D11S912 11q24.3264 Pima Indian and American families (1,766 pairs) 3.6 Hanson RL, 1998 (66)
D12S105212q21.166 White families (349 subjects)3.41 Palmer L, 2003 (53)
D12S1064 12q21.3366 White families (349 subjects) 3.41Palmer L, 2003 (53)
D12S207012q24.21 260 European-American families (1,297 subjects)3.57 Li W, 2004 (67)
12q24933 Australian families (2,053 subjects) 3.02 Cornes BK, 2005 (68)
D13S257 13q14.2401 American families (3,027 subjects)3.2 Feitosa MF, 2002 (62)
D13S175 13q12.11 580 Finnish families3.3 Watanabe RM, 2000 (69)
D13S221 13q12.13580 Finnish families 3.3 Watanabe RM, 2000 (69)
D13S149313q13.2 1,124 American families (3,383 subjects)3.2 North K, 2004 (70)
D19S57119q109 French Caucasian families (447 subjects) 3.8 Bell CG, 2004 (71)
D20S14920q13.31-qter92 American families (513 subjects, 423 pairs) 3.2 Lee JH, 1999 (72)
D20S47620q13 92 American families (513 subjects, 423 pairs)3.06 Lee JH, 1999 (72)
D20S438 20q12103 Utah families (1,711 subjects) 3.5 Hunt SC, 2001 (73)
D20S10720q12 92 American families (513 subjects, 423 pairs)3.2 Lee JH, 1999 (72)
D20S21120q13.292 American families (513 subjects, 423 pairs) 3.2 Lee JH, 1999 (72)
* LOD score: In genetics, a statistical estimate of whether two loci (the sites of genes) are likely to lie near each other on a chromosome and are
therefore likely to be inherited together as a package. ‘‘LOD’’ stands for logarithm of the odds (to the base 10). (A LOD score of three means that
the odds are a thousand to one in favor of genetic linkage.)
52Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
development of obesity (88). The number ofstudies reporting
associations between DNA sequence variation in specific
genes and obesity phenotypes has increased considerably,
with 426 findings of positive associations in 127 candidate
ported by at least fivepositivestudies (52). A selectivelist of
candidate genes according to biologic pathway is presented
in table 2 (52).
It is necessary to clarify the biologic mechanism under-
lying the putative pathogenic association despite the level of
statistical evidence in favor of an allele-obesity association.
For example, SNP Pro12Ala of the peroxisome proliferative
activated receptor, gamma gene (PPARG), is responsible for
the association with elevated body mass index in some stud-
ies (89–91). The lower trans-activation capacity of the Ala
variant of PPARG suggests a potential molecular mecha-
nism underlying the association of this allele with lower
body mass index and higher insulin sensitivity. The Ala
isoform may lead to less efficient stimulation of PPARG tar-
levels of adipose tissue mass accumulation, which in turn
may be responsible for improved insulin sensitivity (92).
Recently, the best evidence for a causal role in the etiol-
ogy of obesity, other than the rare autosomal recessive forms
of obesity, stems from findings pertaining to diverse muta-
tions in the melanocortin 4 receptor gene (MC4R), of which
more than 40 mutations have been detected so far (93). The
MC4RV103I polymorphism has been found to be negatively
7,500 individuals, which revealed that, among obese cases,
the carrier frequency is about 2.0 percent, whereas in non-
obese controls the rate is 3.5 percent (94).These results indi-
cate that large-scale association studies are most likely
required to pick up such small effects, particularly among
alleles with frequencies below 5 percent.
The b3-adrenergic receptor gene (ADRB3) is predomi-
nantly expressed in adipose tissue and regulates lipid me-
tabolism and thermogenesis (95). Therefore, an impairment
of ADRB3 function may lead to obesity through its effect on
energy expenditure of fat tissue. A meta-analysis including
31 studies with more than 9,000 individuals demonstrated
a significant association of the Trp64Arg polymorphism of
the ADRB3 gene with body mass index (96). For the first
time, an association among diverse population groups ex-
hibited a relatively similar strength, and the ADRB3 locus
has been shown to be a genetic factor associated with body
weight in a universal manner. More recently, another meta-
analysis conducted in Japanese populations supported the
hypothesis that the ADRB3 gene Trp64Arg polymorphism
is associated with body mass index (97).
Uncoupling proteins, designated as ‘‘UCPs,’’ are a family
of proteins whose function is to uncouple oxidative phos-
phorylation of adenosine diphosphate to adenosine triphos-
phate, leading to the generation of heat (98). Three different
is expressed in brown adipose tissue (99), uncoupling pro-
tein 2 (UCP-2) is expressed in most tissues including white
adipose tissue, and uncoupling protein 3 (UCP-3) is ex-
pressed in skeletal muscle (100, 101). The 3# insertion/
deletion (I/D) polymorphism in the UCP-2 gene had a re-
ported association with obesity and body mass index in dif-
ferent populations (102–105). This variant might have an
effect on UCP-2 messenger RNA stability. This, in turn,
could affect protein expression and determine body weight
ger RNA levels in the visceral fat of obese but not lean sub-
jects(106).Although severalstudieshad conflicting findings
about the 2G866A polymorphism of the UCP-2 gene with
obesity, results from one group indicated that the this poly-
and Indian men (107), and results from a Spanish population
indicated that the presence of the A allele increased the
likelihood of developing obesity in the future (108).
Other genes, such as the LEPR gene and the glucocorti-
coid receptor gene (GRL), have been reported to be associ-
atedwith an increased body mass index, an increased weight
gain, or obesity in some populations. However, findings from
two recent meta-analyses indicated that there was no com-
pelling evidence of an association between these two genes
and obesity (109, 110).
Although genetic association studies offer a potentially
powerful approach to detect genetic variants that influence
susceptibilitytocommon disease,the failure toreplicate find-
ings across these studies is a serious concern of this ap-
proach. Several possibilities have been proposed to
explain the inconsistent findings from association studies
(111). First, the association may be due to false positive
results. Recently, a meta-analysis suggested that the false
positives were probably responsible for many failures to
replicate associations between common variants and com-
plex traits; similarly, the estimate of the genetic effect in the
first positive report was always biased upward (111). Sec-
ond, a true association may fail to be replicated in an un-
derpowered replication attempt (false negative), especially
for complex diseases with modest genetic effects (112, 113).
Third,populationstratification resultsininconsistency inrep-
lication, reflecting different ancestral history that includes
events (111, 112). Thus, a true association in one population
is not true in another population because of heterogeneity in
genetic or environmental background. Unmeasured factors,
selection bias, and differential misclassification of exposure
may also be responsible for some nonreplication in associ-
ation studies (114).
In light of the seemingly high proportion of false positive
reports in the literature, more stringent criteria for interpret-
ing association studies are needed (111). A single, nominally
significant association should be viewed as tentative until it
has been independently replicated in other studies. In addi-
laborative efforts probably required to achieve the sample
size of many thousands of case-control pairs that is neces-
sary for definitive studies of common variants with modest
genetic effects. Finally, using large samples to test previ-
ously reported associations, perhaps focusing initially on
those associations that have already been replicated at least
once, would probably identify a significant number of var-
iants that affect the risk of common disease, such as obesity.
The International HapMap Project has identified appropriate
Genetic Epidemiology of Obesity53
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
composition according to biologic pathways*
Candidate genes associated with obesity and body
Gene symbolGene description
Central neuronal signaling pathway
Agouti-related protein homolog
Cocaine- and amphetamine-regulated transcript
Dopamine receptor D2
Dopamine receptor D4
G protein-coupled receptor 24
5-hydroxytryptamine receptor 1B
5-hydroxytryptamine receptor 2A
5-hydroxytryptamine receptor 2C
Melanocortin 3 receptor
Melanocortin 4 receptor
Melanocortin 5 receptor
Natriuretic peptide receptor C
Neuropeptide Y receptor Y2
Neuropeptide Y receptor Y2
Adipose most abundant gene transcript 1
Core-binding factor, runt domain, a subunit 2
Forkhead box C2
Guanine nucleotide binding protein, b polypeptide 3
Insulin-induced gene 2
Low-density lipoprotein receptor
Lipase, hormone sensitive
SAHy family member, acyl-coenzyme A synthetase
for fatty acids
Peroxisome proliferative activated receptor, a
Peroxisome proliferator-activated receptor, d
Peroxisome proliferator-activated receptor, c
SA hypertension-associated homolog
Scavenger receptor class B, member 1
Sorbin and SH3y domain containing 1
Sterol regulatory element binding transcription factor 1
Energy metabolism and thermogenesis
Acid phosphatase 1
Adrenergic, a-2B2, receptor
Adrenergic, b-22, receptor
Adrenergic, b-32, receptor
ATPase,y Na1/K1transporting, a 2 (1) polypeptide
Ectonucleotide pyrophosphatase/phosphodiesterase 1
Fatty acid-binding protein 1
TABLE 2. Continued
Gene symbolGene description
Fatty acid binding protein 2, intestinal
Fatty acid binding protein 4, adipocyte
Fatty acid synthase
Glutamic acid decarboxylase 2
Glycogen synthase 1
Heat shock Mr70,000 protein 1B
Peroxisome proliferator-activated receptor, c,
coactivator 1 a
Protein tyrosine phosphatase, nonreceptor type 1
Tubby, mouse, homolog of
Uncoupling protein 1
Uncoupling protein 2
Uncoupling protein 3
Leptin-insulin signaling pathway
ATPy-binding cassette, subfamily C, member 8
Insulin-like growth factor 2
Insulin receptor substrate 1
Insulin receptor substrate 2
Protein tyrosine phosphatase, receptor type F
TBC1 domain family, member 1
Transcription factor 1, hepatic; LFB1, hepatic nuclear
factor (HNF1), albumin proximal factor
Interleukin 6 receptor
Lymphotoxin alpha (TNFy superfamily, member 1)
Serine proteinase inhibitor, clade E, member 1
Tumor necrosis factor
Hormone signaling pathway
Cholecystokinin A receptor
Corticotropin-releasing hormone receptor 1
Cytochrome P450, family 11, subfamily B,
Cytochrome P450, family 19, subfamily A,
Estrogen receptor 1
Estrogen receptor 2
Growth hormone releasing hormone receptor
Monoamine oxidase A
Monoamine oxidase B
Mediator of RNA polymerase II transcription,
Nuclear receptor subfamily 0, group B, member 2
Nuclear receptor coactivator 3
Solute carrier family 6, member 3
Solute carrier family 6, member 14
Vitamin D receptor
Angiotensin I converting enzyme
Hydroxysteroid (11-beta) dehydrogenase 1
* Please refer to Rankinen et al. (52) for a more comprehensive summa-
rization of obesity-candidate gene associations.
y SAH, SA hypertension-associated homolog (rat) ; SH3, src homology-3;
ATPase, adenosine triphosphatase; ATP, adenosine triphosphate; TNF,
tumor necrosis factor.
54Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
sets of tag SNPs that span the genome, greatly facilitating an
efficient, linkage disequilibrium-based approach (115).
GENE-GENE INTERACTION IN OBESITY
The risk of obesity is determined by not only specific
genotypes but also significant gene-gene interactions. There
is a growing awareness that the failure to replicate single-
locus association studies for obesity may be due to under-
ficulty in detecting gene-gene interactions is a common
problem for current epidemiologic studies. Certain associ-
ation study designs have been shown to be more effective in
identifying gene-gene interactions compared with others.
For example, case-only and unmatched case-control studies
have been shown to be more powerful than matched case-
control studies and family-based designs for detecting
interaction; however, both of these study designs are partic-
ularly sensitive to population stratification (116–118). Ad-
ditionally, study sample sizes are often calculated with the
purpose of capturing the main effect of the candidate gene
and are therefore underpowered to detect any gene-gene in-
teractions (119). Moreover, if an association study fails to
detect the marginal effect of a single locus, subsequent iden-
(120,121).Arecentpaper byMarchinietal.(120) illustrates
the utility of two- and three-locus models for identifying
multiloci interactions in genome-wide association studies.
Recently, some examples of interactions of known genes
on obesity have been reported. Peroxisome proliferator-
receptors implicated in adipocyte differentiation and lipid
and glucose metabolism (122), whereas the ADRB3 gene is
expressed in adipocytes and mediates the rate of lipolysis in
response to catecholamines (123). A gene-gene interaction
was reported between Pro12Ala of the PPARG2 gene and
variants had significantly higher body mass index, insulin,
and leptin levels than those with only the PPARG2 gene
variant in Mexican Americans (124); a synergistic effect be-
tween these two polymorphisms was also found for obesity
risk in a Spanish population (125).
In the Quebec Family Study, gene-gene interactions
were observed among the markers in the a2-, b2-, and
b3-adrenergic receptor genes (ADRs) contributing to the
phenotypic variability in abdominal obesity (126). An in-
teraction was also found in women between the b1- and b3-
adrenergic receptors. Women with Gly/Gly genotypes at the
b1-adrenergic receptor gene (ADRB1) and carrying at least
one b3-Arg allele showed notable increases in body mass
index (127). Additionally, the simultaneous existence of the
ADRB1/ADRB3 gene with the UCP-1 gene and/or the lipo-
protein lipase gene (LPL) might play a role in the develop-
ment of obesity or weight gain and have synergistic effects
when combined with each other (128–131). Genetic inter-
actions between LEP –G2548A and LEPR Q223R may pro-
mote immune dysfunction associated with obesity (132).
Some potential chromosome regions have also been
detected by allowing for interaction between obesity-
susceptibility loci, such as chromosome regions 2p25-p24
and 13q13-21, 20q and chromosome 10 centromere, and the
TBC1 domain family member 1 gene (TBC1D1) and the
4q34-q35 region (80, 85, 133).
GENE-ENVIRONMENT INTERACTION IN OBESITY
The rapidly increasing prevalence of obesity, in spite of
an unchanged gene pool, makes it interesting to search for
responsible environmental factors that increase the suscep-
tibility for obesity at the individual level. Migration studies
help support the impact of environmental factors on obesity
development. For example, Japanese people who have mi-
grated to Hawaii and California are more overweight than
their relatives who remained in Japan (134). Perhaps the
genetic background of most people is not prepared for the
current abundance of food and sedentary lifestyle. However,
even in the obesity-promoting environment, not every in-
dividual becomes obese. Therefore, the importance of a
gene-environment interaction is demonstrated when an in-
dividual with a high-risk genetic profile enters a high-risk
environment, and the effects on risk are so great that obesity
GENES AND DIET INTERACTION
Eating behavioral traits aggregate in families. The familial
environment seems to be the major determinant of correla-
tions in weight status between parents and their offspring,
although a genetic contribution cannot be excluded (136).
At the population level, people with high risk of obesity
could benefit from early diet intervention. However, it is
well documented that there are considerable interindividual
differences in the response of plasma lipid concentrations to
alterations in the amount of fat and cholesterol in the diet
(137). Therefore, in tailoring prevention and treatment pro-
grams for eating behavior, both an individual’s genetic
makeup and family environment should be considered.
Some potential susceptibility genes, which relate to energy
homeostasis, appetite, satiety, lipoprotein metabolism, and
a number of peripheral signaling peptides, may be involved
invariable responses to diets (138). Genes regulating energy
homeostasis and thermogenesis include neuropeptide Y
(NPY), agouti-related protein (AGRP), melanocortin path-
way factors (MC4R), uncoupling proteins (UCPs), and fatty
acid binding protein (FABP) (80, 138). Diet intake control
may be affected by genes encoding taste receptors and a
number of peripheral signaling peptides, such as insulin
(INS), LEP, ghrelin (GHRL), and cholecystokinin (CCK)
(138). Levels of uncoupling proteins (UCP-2 and UCP-3)
were reported to increase during starvation without chang-
ing heat production (85). LEPR genes showed an association
with energy balance in an overfeeding experiment (139).
Clusters of a-amylase genes (AMY1A, AMY2A, and
AMY2B), involved in the digestion of starch, and the insulin-
like growth factor 1 gene (IGF1) may be linked to car-
bohydrate and protein intakes (140, 141). The MC4R gene
and the neuromedin beta gene (NMB) were identified as
having an association with the control of eating behavior
Genetic Epidemiology of Obesity55
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
Some genes have been identified and linked to variable
responses to diet in the lipoprotein metabolism pathway, in-
cluding apolipoprotein E (APOE), apolipoprotein B (APOB),
apolipoprotein AIV (APOA4), apolipoprotein CIII (APOC3),
low-density lipoprotein receptor (LDLR), FABP, LPL, mi-
crosomal transfer protein (MTP), cholesteryl ester transfer
protein (CETP), and hepatic lipase (HPL) (144). For exam-
ple, the subjects with APOA4 T allele showed a better re-
duction in low-density lipoprotein cholesterol under dietary
intervention, and subjects with the FABP 54Thr allele ex-
hibited a much better lowering of triglyceride with dietary
GENES AND PHYSICAL ACTIVITY INTERACTION
Physical activity is a determinant of energy and substrate
metabolism. However, recent cultural changes have engi-
neered physical activity out of the daily lives of humans.
More than 60 percent of American adults are not regularly
active, and 25 percent are sedentary (145). Physical activity
deficiency is predicted to disrupt the optimized expression
of the ‘‘thrifty’’ genes and genotype for the physical activity-
rest cycle. Some of these ‘‘thrifty’’ genes could have been
initially selected to conserve glycogen stores by oxidizing
greater quantities of fatty acids to maximize survival dur-
ing famine and exercise. Therefore, the present sedentary
lifestyle has led to discordance in gene-environmental
A review showed heritability coefficients between 0.29
and 0.62 for daily physical activity, suggesting significant
genetic effects (147). Some genes have been reported to
influence human physical performance and physical activ-
ity,such as the angiotensin I converting enzymegene (ACE),
the guanine nucleotide binding protein, beta polypeptide 3
gene (GNB3), the b2-adrenergic receptor gene (ADRB2),
MC4R, the cocaine- and amphetamine-regulated transcript
gene (CART), UCP-2, and UCP-3 (148–153). For example,
duration of exercise improved significantly for those with
the II and ID genotype of the ACE gene but not for those
with DD genotype (148). Furthermore, the CART gene may
modify the effect of the MC4R genotype (151). The hyp-
oxia-inducible factor 1 gene (HIF1) and the titin gene (TTN)
were associated with maximal oxygen consumption after
aerobic exercise training (154–156). Linkage studies also
discovered some genes related to maximal oxygen uptake
in the sedentary state and in response to training; these in-
cluded the following: the skeletal muscle-specific creatine
kinase gene (CKMM), the b-sarcoglycan gene (SGCB), the
syntrophin b-1 gene (SNTB1), the c-sarcoglycan gene
(SGCG), the dystrophin-associated glycoprotein 1 gene
(DAG1), the lamin A/C gene (LMNA), the liver glycogen
phosphorylase gene (PYGL), the guanosine triphosphate cy-
clohydrolase I gene (GCH1), and the sulfonylurea receptor
gene (SUR) (157).
With the completion of the Human Genome Project and
recent advancements in the International HapMap Project,
our capability in understanding the genetic mechanisms un-
derlying human obesity is rapidly increasing. High-through-
put genotyping and decreased genotyping costs have made
whole-genome association studies a feasible option, and
future studies will likely utilize this method for identifica-
tion of novel genes involved in obesity pathogenesis (158).
However, complete elucidation of this complex trait will
require the integration of many disciplines, combining ad-
vances in genetic epidemiology with the fields of functional
genomics and proteomics. The advent of the DNA micro-
array has made gene-expression profiling a powerful tool for
simultaneous investigation of the expression of a large num-
ber of genes that may provide more clues regarding the
molecular basis of obesity with its continued use (159–
161). Several studies have already examined gene ex-
pression in adipose tissue in obese and nonobese subjects,
identifying some novel genes of interest along with genes
mapped to regions with suggestive linkage to obesity
(162–164). Rodent models may also play an important role
in not only identifying novel genes for further exploration in
human populations but also testing the functional effects of
candidate genes already identified in studies from human
populations (26). Additionally, because a gene can be post-
transcriptionally or posttranslationally modified into many
different protein products, other methods of studying the
molecular mechanisms of obesity must be used. The recent
emergence of proteomics, defined as the analysis of proteins
and their interactions in an organism, holds great promise as
an adjunct technique for unraveling the pathogenesis and
pathophysiology of this complex trait (165, 166).
The global emergence of obesity is one of the greatest
challenges in public health research today. Unhealthy diet
and physical inactivity have been identified as primary de-
terminants of the increase in the incidence of obesity. It is
likely and reasonable to assume that acute changes in be-
havior and the environment have contributed to the rapid
increase in obesity and that genetic factors may be important
in determining an individual’s susceptibility to obesity. Its
sity especially challenging. While exciting advancements in
molecular technology are rapidly expanding the field of
genetic epidemiology and the capabilities of the genetic
epidemiologist, it should be noted that limitations of genetic
epidemiologic studies of obesity still exist. For example,
body mass index is the most widely used phenotype for de-
fining obesity status; however, recent epidemiologic studies
have indicated that it is not the best predictor for the risk of
cardiovascular disease compared with other obesity meas-
ures (167). Moreover, common confounders such as diet and
physical activity are very difficult to measure accurately,
which can result in residual confounding in examining the
association betweencandidategene and obesity-related phe-
notypes (168, 169). Additionally, the effect size of individ-
ual genetic variants on a polygenic disorder such as obesity
is typically moderate to small; therefore, very large sample
sizes may be necessary to detect these effects, particularly
56 Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
when adjusting for other confounders or when examining
gene-gene or gene-environment interactions (170). With the
increasing utilization of large-scale case-control studies of
unrelated individuals, special attention to population strati-
fication is also warranted (114, 171). While methods of
genomic control have been established to correct for pop-
ulation substructure, recent evidence has shown that these
methods may not always be adequate, and, therefore, issues
of population stratification should be considered in the study
design phase (172). Finally, with the advances in genotyping
technology and the emergence of genome-wide association
studies, statistical methods for correcting for multiple com-
parisons have become challenging.
Generally speaking, researchers must always understand
the limitations and potential pitfalls of their study. However,
with careful planning and attention to study design, meth-
ods, conduct, and analytical issues, genetic epidemiologic
studies of obesity can yield important and valid results.
For the novice researcher initiating a linkage study, informa-
tion from Teare and Barrett (173) and Botstein and Risch
(174) would be helpful, and important guidelines for inter-
pretingand reporting linkage results have been illustrated by
Lander and Kruglyak (86). Moreover, recently published
reviews have demonstrated design and statistical issues in-
volved in population-based or family-based association
studies that should also be kept in mind (116, 171, 175).
In addition, gene-gene and gene-environmental interaction
methodological reviews can also be useful references
(118, 119, 176, 177).
Conflict of interest: none declared.
1. Obesity: preventing and managing the global epidemic.
Report of a WHO consultation. World Health Organ Tech
Rep Ser 2000;894:i–xii, 1–253.
2. Lei SF, Liu MY, Chen XD, et al. Relationship of total body
fatness and five anthropometric indices in Chinese aged 20–
40 years: different effects of age and gender. Eur J Clin Nutr
3. Obesity: preventing and managing the global epidemic.
Report of a WHO consultation on obesity. Geneva, Switzer-
land: World Health Organization, 1997.
4. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and
management of the metabolic syndrome. Circulation 2005;
5. Definition, diagnosis and classification of diabetes mellitus
and its complications. Report of a WHO consultation.
Geneva, Switzerland: World Health Organization, 1999.
6. International Obesity Taskforce. About obesity 2005. Lon-
don, United Kingdom: International Obesity Taskforce, 2005.
7. Haslam DW,James WP. Obesity. Lancet 2005;366:1197–209.
8. Centers for Disease Control and Prevention. What is the
prevalence of overweight and obesity among U.S. adults?
Atlanta, GA: Centers for Disease Control and Prevention,
9. Gu D, Reynolds K, Wu X, et al. Prevalence of the metabolic
syndrome and overweight among adults in China. Lancet
10. Reddy KS, Prabhakaran D, Shah P, et al. Differences in body
mass index and waist:hip ratios in North Indian rural and
urban populations. Obes Rev 2002;3:197–202.
11. Chan JM, Rimm EB, Colditz GA, et al. Obesity, fat dis-
tribution and weight gain as risk factors for clinical diabetes
in men. Diabetes Care 1994;17:961–9.
12. Krauss RM, Winston M, Fletcher BJ, et al. Obesity:
impact on cardiovascular disease. Circulation 1998;98:
13. Rexrode KM, Hennekens CH, Willett WC, et al. A pro-
spective study of body mass index, weight change, and risk
of stroke in women. JAMA 1997;277:1539–45.
14. Kurth T, Gaziano JM, Berger K, et al. Body mass index
and the risk of stroke in men. Arch Intern Med 2002;162:
15. Calle EE, Kaaks R. Overweight, obesity and cancer: epide-
miological evidence and proposed mechanisms. Nat Rev
16. Calle EE, Rodriguez C, Walker-Thurmond K, et al. Over-
weight, obesity, and mortality from cancer in a prospectively
studied cohort of US adults. N Engl J Med 2003;348:
17. Hu FB, Willett WC, Li T, et al. Adiposity as compared with
physical activity in predicting mortality among women.
N Engl J Med 2004;351:2694–703.
18. Lee IM, Manson JE, Hennekens CH, et al. Body weight and
mortality: a 27-year follow-up of middle-aged men. JAMA
19. Dean M. Approaches to identify genes for complex human
diseases: lessons from Mendelian disorders. Hum Mutat
20. Barrett JC, Cardon LR. Evaluating coverage of genome-wide
association studies. Nat Genet 2006;38:659–62.
21. Herbert A, Gerry NP, McQueen MB, et al. A common genetic
variant is associated with adult and childhood obesity.
22. Risch N, Merikangas K. The future of genetic studies of
complex human diseases. Science 1996;273:1516–17.
23. Jorgenson E, Witte JS. A gene-centric approach to genome-
wide association studies. Nat Genet 2006;7:885–91.
24. Daly AK, Day CP. Candidate gene case-control association
studies: advantages and potential pitfalls. Br J Clin Pharma-
25. Carlson CS, Eberle MA, Kruglyak L, et al. Mapping complex
disease loci in whole-genome association studies. Nature
26. Cowley AW Jr. The genetic dissection of essential hyper-
tension. Nat Rev Genet 2006;7:829–40.
27. Stunkard AJ, Foch TT, Hrubec Z. A twin study of human
obesity. JAMA 1986;256:51–4.
28. Stunkard AJ, Sorensen TI, Hanis C, et al. An adoption study
of human obesity. N Engl J Med 1986;314:193–8.
29. Rice T, Perusse L, Bouchard C, et al. Familial aggregation of
body mass index and subcutaneous fat measures in the
longitudinal Quebec family study. Genet Epidemiol 1999;
30. Platte P, Papanicolaou GJ, Johnston J, et al. A study of
linkage and association of body mass index in the Old Order
Amish. Am J Med Genet C Semin Med Genet 2003;121:
Genetic Epidemiology of Obesity57
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
31. Adeyemo A, Luke A, Cooper R, et al. A genome-wide scan
for body mass index among Nigerian families. Obes Res
32. McQueen MB, Bertram L, Rimm EB, et al. A QTL genome
scan of the metabolic syndrome and its component traits.
BMC Genet 2003;4(suppl 1):S96.
33. Allison DB, Kaprio J, Korkeila M, et al. The heritability of
body mass index among an international sample of mono-
zygotic twins reared apart. Int J Obes Relat Metab Disord
34. Pietilainen KH, Kaprio J, Rissanen A, et al. Distribution and
heritability of BMI in Finnish adolescents aged 16y and 17y:
a study of 4884 twins and 2509 singletons. Int J Obes Relat
Metab Disord 1999;23:107–15.
35. Hsueh WC, Mitchell BD, Aburomia R, et al. Diabetes in the
Old Order Amish: characterization and heritability analysis
of the Amish Family Diabetes Study. Diabetes Care 2000;23:
36. Hunt KJ, Duggirala R, Goring HH, et al. Genetic basis of
variation in carotid artery plaque in the San Antonio Family
Heart Study. Stroke 2002;33:2775–80.
37. Sakul H, Pratley R, Cardon L, et al. Familiality of physical
and metabolic characteristics that predict the development of
non-insulin-dependent diabetes mellitus in Pima Indians. Am
J Hum Genet 1997;60:651–6.
38. Poulsen P, Vaag A, Kyvik K, et al. Genetic versus environ-
mental aetiology of the metabolic syndrome among male and
female twins. Diabetologia 2001;44:537–43.
39. Freeman MS, Mansfield MW, Barrett JH, et al. Heritability of
features of the insulin resistance syndrome in a community-
based study of healthy families. Diabet Med 2002;19:994–9.
40. Wu DM, Hong Y, Sun CA, et al. Familial resemblance of
adiposity-related parameters: results from a health check-up
population in Taiwan. Eur J Epidemiol 2003;18:221–6.
41. Schousboe K, Visscher PM, Erbas B, et al. Twin study of
genetic and environmental influences on adult body size,
shape, and composition. Int J Obes 2004;28:39–48.
42. Rice T, Saw EW, Gagnon J, et al. Familial resemblance for
body composition measures: the HERITAGE Family Study.
Obes Res 1997;5:557–62.
43. Luke A, Guo X, Adeyemo AA, et al. Heritability of obesity-
related traits among Nigerians, Jamaicans and US black
people. Int J Obes 2001;25:1034–41.
44. Borecki IB, Higgins M, Schreiner PJ, et al. Evidence for
multiple determinants of the body mass index: the National
Heart, Lung, and Blood Institute Family Heart Study. Obes
45. Fabsitz RR, Sholinsky P, Carmelli D. Genetic influences on
adult weight gain and maximum body mass index in male
twins. Am J Epidemiol 1994;140:711–20.
46. Davey G, Ramachandran A, Snehalatha C, et al. Familial
aggregation of central obesity in southern Indians. Int J Obes
Relat Metab Disord 2000;24:1523–7.
47. Farooqi IS, O’Rahilly S. Monogenic human obesity syn-
dromes. Recent Prog Horm Res 2004;59:409–24.
48. Montague CT, Farooqi IS, Whitehead JP, et al. Congenital
leptin deficiency is associated with severe early-onset obesity
in humans. Nature 1997;387:903–8.
49. Clement K, Vaiesse C, Lahlou N, et al. A mutation in the
human leptin receptor gene causes obesity and pituitary
dysfunction. Nature 1998;392:398–401.
50. Krude H, Biebergmann H, Luck W, et al. Severe early-onset
obesity, adrenal insufficiency and red hair pigmentation
caused by POMC mutations in humans. Nat Genet 1998;2:
51. Vaisse C, Clement K, Guy-Grand B, et al. A frameshift
mutation in human MC4R is associated with a dominant form
of obesity. Nat Genet 1998;20:113–14.
52. Rankinen T, Zuberi A, Chagnon YC, et al. The human obesity
gene map: the 2005 update. Obesity (Silver Spring) 2006;
53. Palmer L, Buxbaum S, Larkin E, et al. Awhole-genome scan
for obstructive sleep apnea and obesity. Am J Hum Genet
54. Deng HW, Deng H, Liu YJ, et al. A genomewide linkage scan
for quantitative-trait loci for obesity phenotypes. Am J Hum
55. Liu Y, Xu F, Shen H, et al. A follow-up linkage study for
quantitative trait loci contributing to obesity-related pheno-
types. J Clin Endocrinol Metab 2004;89:875–82.
56. Guo YF, Shen H, Liu YJ, et al. Assessment of genetic linkage
and parent-of-origin effects on obesity. J Clin Endocrinol
57. Wu X, Cooper RS, Borecki I, et al. A combined analysis
of genomewide linkage scans for body mass index from
the National Heart, Lung, and Blood Institute Family
Blood Pressure Program. Am J Hum Genet 2002;70:
58. Kissebah A, Sonnenberg G, Myklebust J, et al. Quantitative
trait loci on chromosomes 3 and 17 influence phenotypes of
the metabolic syndrome. Proc Natl Acad Sci U S A 2000;
59. Luke A, Wu X, Zhu X, et al. Linkage for BMI at 3q27 region
confirmed in an African-American population. Diabetes
60. Stone S, Abkevich V, Hunt SC, et al. A major predisposition
locus for severe obesity, at 4p15-p14. Am J Hum Genet
61. Arya R, Blangero J, Williams K, et al. Factors of insulin
resistance syndrome-related phenotypes are linked to genetic
locations on chromosomes 6 and 7 in nondiabetic Mexican-
Americans. Diabetes 2002;51:841–7.
62. Feitosa MF, Borecki IB, Rich SS, et al. Quantitative-trait loci
influencing body-mass index reside on chromosomes 7 and
13: the National Heart, Lung, and Blood Institute Family
Heart Study. Am J Hum Genet 2002;70:72–82.
63. Mitchell B, Cole S, Comuzzie A, et al. A quantitative trait
locus influencing BMI maps to the region of the beta-3
adrenergic receptor. Diabetes 1999;48:1863–7.
64. van der Kallen CJ, Cantor RM, van Greevenbroek MM, et al.
Genome scan for adiposity in Dutch dyslipidemic families
reveals novel quantitative trait loci for leptin, body mass
index and soluble tumor necrosis factor receptor superfamily
1A. Int J Obes Relat Metab Disord 2000;24:1381–91.
65. Turner S, Kardia S, Boerwinkle E, et al. Multivariate linkage
analysis of blood pressure and body mass index. Genet
66. Hanson RL, Ehm MG, Pettitt DJ, et al. An autosomal ge-
nomic scan for loci linked to type II diabetes mellitus and
body-mass index in Pima Indians. Am J Hum Genet 1998;
67. Li W, Dong C, Li D, et al. An obesity-related locus in
chromosome region 12q23-24. Diabetes 2004;53:812–20.
68. Cornes BK, Medland SE, Ferreira MA, et al. Sex-limited
genome-wide linkage scan for body mass index in an
unselected sample of 933 Australian twin families. Twin Res
Hum Genet 2005;8:616–32.
69. Watanabe RM, Ghosh S, Langefeld CD, et al. The Finland-
United States investigation of non-insulin-dependent diabetes
mellitus genetics (FUSION) study. II. An autosomal genome
58Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
scan for diabetes-related quantitative-trait loci. Am J Hum
70. North K, Rose K, Borecki I, et al. Evidence for a gene on
chromosome 13 influencing postural systolic blood pressure
change and body mass index. Hypertension 2004;43:780–4.
71. Bell CG, Benzinou M, Siddiq A, et al. Genome-wide linkage
analysis for severe obesity in French Caucasians finds sig-
nificant susceptibility locus on chromosome 19q. Diabetes
72. Lee JH, Reed DR, Li WD, et al. Genome scan for human
obesity and linkage to markers in 20q13. Am J Hum Genet
73. Hunt SC, Abkevich V, Hensel CH, et al. Linkage of body
mass index to chromosome 20 in Utah pedigrees. Hum Genet
74. Ng MCY, So WY, Lam VKL, et al. Genome-wide scan for
metabolic syndrome and related quantitative traits in Hong
Kong Chinese and confirmation of a susceptibility locus on
chromosome 1q21-q25. Diabetes 2004;53:2676–83.
75. Price RA, Kilker R, Li WD. An X-chromosome scan reveals
a locus for fat distribution in chromosome region Xp21-22.
76. Hsueh WC, Mitchell BD, Schneider JL, et al. Genome-wide
scan of obesity in the Old Order Amish. J Clin Endocrinol
77. Fox CS, Heard-Costa NL, Wilson PW, et al. Genome-wide
linkage to chromosome 6 for waist circumference in the
Framingham Heart Study. Diabetes 2004;53:1399–402.
78. Chen G, Adeyemo AA, Johnson T, et al. A genome-wide scan
for quantitative trait loci linked to obesity phenotypes among
West Africans. Int J Obes (Lond) 2005;29:255–9.
79. Lewis CE, North KE, Arnett D, et al. Sex-specific findings
from a genome-wide linkage analysis of human fatness in
non-Hispanic whites and African Americans: the HyperGEN
study. Int J Obes (Lond) 2005;29:639–49.
80. Dong CH, Li WD, Li D, et al. Interaction between obesity-
susceptibility loci in chromosome regions 2p25-p24 and
13q13-q21. Eur J Hum Genet 2005;13:102–8.
81. Moslehi R, Goldstein AM, Beerman M, et al. A genome-wide
linkage scan for body mass index on Framingham Heart
Study families. BMC Genet 2003;4(suppl 1):S97.
82. Meyre D, Lecoeur C, Delplanque J, et al. A genome-wide
scan for childhood obesity-associated traits in French fam-
ilies shows significant linkage on chromosome 6q22.31-
q23.2. Diabetes 2004;53:803–11.
83. Arya R, Duggirala R, Jenkinson C, et al. Evidence of a
novel quantitative-trait locus for obesity on chromosome
4p in Mexican Americans. Am J Med Genet 2004;74:
84. Lindsay RS, Kobes S, Knowler WC, et al. Genome-wide
linkage analysis assessing parent-of-origin effects in the
inheritance of type 2 diabetes and BMI in Pima Indians.
85. Dong C, Wang S, Li W, et al. Interacting genetic loci on
chromosomes 20 and 10 influence extreme human obesity.
Am J Hum Genet 2003;72:115–24.
86. Lander E, Kruglyak L. Genetic dissection of complex traits:
guidelines for interpreting and reporting linkage results.
Nat Genet 1995;11:241–7.
87. Altmuller J, Palmer LJ, Fischer G, et al. Genome-wide scans
of complex human diseases: true linkage is hard to find.
Am J Hum Genet 2001;69:936–50.
88. Loktionov A. Common gene polymorphisms and nutrition:
emerging links with pathogenesis of multifactorial chronic
diseases (review). J Nutr Biochem 2003;14:426–51.
89. Deeb SS, Fajas L, Nemoto M, et al. A Pro12Ala substitution
in PPARgamma2 associated with decreased receptor activity,
lower body mass index and improved insulin sensitivity.
Nat Genet 1998;20:284–7.
90. Cole SA, Mitchell BD, Hsueh WC, et al. The Pro12Ala
variant of peroxisome proliferator-activated receptor-
gamma2 (PPAR-gamma2) is associated with measures of
obesity in Mexican Americans. Int J Obes Relat Metab
91. Ek J, Andersen G, Urhammer SA, et al. Homozygosity of the
Pro12Ala variant of the peroxisome proliferation-activated
receptor-gamma2 (PPAR-gamma2): divergent modulating
effects on body mass index in obese and lean Caucasian men.
92. Vidal-Puig AJ, Considine RV, Jimenez-Linan M, et al.
Peroxisome proliferator-activated receptor gene expression
in human tissues. Effects of obesity, weight loss, and
regulation by insulin and glucocorticoids. J Clin Invest
93. Marti A, Moreno-Aliaga MJ, Hebebrand J, et al. Genes,
lifestyles and obesity. Int J Obes Relat Matab Disord 2004;
94. Dempfle A, Hinney A, Heinzel-Gutenbrunner M, et al. Large
quantitative effect of melanocortin-4 receptor gene mutations
on body mass index. J Med Genet 2004;41:795–800.
95. Emorine L, Blin N, Strosberg AD. The human b3-adrenergic
receptor: the search for a physiological function. Trends
Pharmacol Sci 1994;15:3–7.
96. Fujisawa T, Ikegami H, Kawaguchi Y, et al. Meta-analysis of
the association of Trp64Arg polymorphism of b3-adrenergic
receptor genewith body mass index. J Clin Endocrinol Metab
97. Kurokawa N, Nakai K, Kameo S, et al. Association of BMI
with the b3-adrenergic receptor gene polymorphism in
Japanese: meta-analysis. Obes Res 2001;9:741–5.
98. Flier JS, Lowell BB. Obesity research springs a proton leak.
Nat Genet 1997;15:223–4.
99. Cannon B, Houstek J, Nedergaard J. Brown adipose tissue.
More than an effector of thermogenesis? Ann N YAcad Sci
100. Fleury C, Neverova M, Collins S, et al. Uncoupling protein-2:
a novel gene linked to obesity and hyperinsulinemia. Nat
101. Vidal-Puig A, Solanes G, Grujic D, et al. UCP3: an
uncoupling protein homologue expressed preferentially and
abundantly in skeletal muscle and brown adipose tissue.
Biochem Biophys Res Commun 1997;235:79–82.
102. Cassell PG, Neverova M, Janmohamed S, et al. An uncou-
pling protein 2 gene variant is associated with a raised body
mass index but not type II diabetes. Diabetologia 1999;
103. Evans D, Minouchehr S, Hagemann G, et al. Frequency of
and interaction between polymorphisms in the b3-adrenergic
receptor and in uncoupling proteins 1 and 2 and obesity
in Germans. Int J Obes Relat Metab Disord 2000;24:
104. Wang H, Chu WS, Lu T, et al. Uncoupling protein-2 poly-
morphisms in type 2 diabetes, obesity, and insulin secretion.
Am J Physiol Endocrinol Metab 2004;286:E1–7.
105. Walder K, Norman RA, Hanson RL, et al. Association
between uncoupling protein polymorphisms (CUP2-UCP3)
and energy metabolism/obesity in Pima Indians. Hum Mol
106. Oberkofler H, Liu YM, Esterbauer H, et al. Uncoupling
protein 2 gene: reduced mRNA expression in intraperitoneal
Genetic Epidemiology of Obesity59
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
adipose tissue of obese humans. Diabetologia 1998;41:
107. Shen H, Qi L, Tai ES, et al. Uncoupling protein 2 promoter
polymorphism -866G/A, central adiposity, and metabolic
syndrome in Asians. Obesity 2006;14:656–61.
108. Zurbano R, Ochoa MC, Moreno-Aliaga MJ, et al. Influence
of the -866G/A polymorphism of the UCP2 gene on an obese
pediatric population. Nutr Hosp 2006;21:52–6.
109. Marti A, Ochoa MC, Sanchez-Villegas A, et al. Meta-
analysis on the effect of the N363S polymorphism of the
glucocorticoid receptor gene (GRL) on human obesity.
BMC Med Genet 2006;7:50260.
110. Paracchini V, Pedotti P, Taioli E. Genetics of leptin and
obesity: a HuGE review. Am J Epidemiol 2005;162:101–14.
111. Lohmueller KE, Pearce CL, Pike M, et al. Meta-analysis of
genetic association studies supports a contribution of com-
mon variants to susceptibility to common disease. Nat Genet
112. Tan NC, Mulley JC, Berkovic SF. Genetic association studies
in epilepsy: ‘‘the truth is out there.’’ Epilepsy 2004;45:
113. Colhoun HM, McKeigue PM, Smith DG. Problems of re-
porting genetic associations with complex outcomes. Lancet
114. Clayton DG, Walker NM, Smyth DJ, et al. Population
structure, differential bias and genomic control in a large-
scale, case-control association study. Nat Genet 2005;37:
115. De Bakker PI,YelenskyR, Pe’er I, et al.Efficiencyand power
in genetic association studies. Nat Genet 2005;37:1217–23.
116. Balding D. A tutorial on statistical methods for population
associations. Nat Rev Genet 2006;7:781–91.
117. Cordell H, Clayton D. Genetic association studies. Lancet
118. Wang S, Zhao H. Sample size needed to detect gene-gene
interactions using association designs. Am J Epidemiol
119. Gaudermann WJ. Sample size requirements for association
studies of gene-gene interaction. Am J Epidemiol 2002;
120. Marchini J, Donnelly P, Cardon L. Genome-wide strategies
for detecting multiple loci that influence complex diseases.
Nat Genet 2005;37:413–17.
121. Culverhouse R, Suarez B, Lin J, et al. A perspective on
epistasis: limits of models displaying no main effects. Am J
Hum Genet 2002;70:461–71.
122. Desvergne B, Wahli W. Peroxisome proliferator-activated
receptors: nuclear control of metabolism. Endocr Rev 1999;
123. Collins S, Daniel KW, Rohlfs EM, et al. Impaired expression
and functional activity of the b3- and b1-adrenergic receptors
in adipose tissue of congenitally obese (C57BL/6J ob/ob)
mice. Mol Endocrinol 1994;8:518–27.
124. Hsueh WC, Cole SA, Shuldiner AR, et al. Interactions
between variants in the b3-adrenergic receptor and peroxi-
some proliferator-activated receptor-c2 genes and obesity.
Diabetes Care 2001;24:672–7.
125. Ochoa MC, Marti A, Azcona C, et al. Gene-gene interaction
between PPAR gamma 2 and ADR beta 3 increases obesity
risk in children and adolescents. Int J Obes Relat Metab
Disord 2004;28(suppl 3):S37–41.
126. Ukkola O, Rankinen T, Weisnagel SJ, et al. Interactions
among the a2-, b2-, and b3-adrenergic receptor genes and
obesity-related phenotypes in the Quebec Family Study.
127. Ellsworth DL, Coady SA, Chen W, et al. Interactive effects
between polymorphisms in the b-adrenergic receptors and
longitudinal changes in obesity. Obes Res 2005;13:519–26.
128. San Millan JL, Corton M, Villuendas G, et al. Association of
the polycystic ovary syndrome with genomic variants related
to insulin resistance, type 2 diabetes mellitus, and obesity.
J Clin Endocrinol Metab 2004;89:2640–6.
129. Proenza AM, Poissonnet CM, Ozata M, et al. Association of
sets of alleles of genes encoding beta3-adrenoreceptor,
uncoupling protein 1 and lipoprotein lipase with increased
risk of metabolic complications in obesity. Int J Obes Relat
Metab Disord 2000;24:93–100.
130. Sivenius K, Valve R, Lindi V, et al. Synergistic effect of
polymorphisms in uncoupling protein 1 and beta3-adrenergic
receptor genes on long-term body weight change in Finnish
type 2 diabetic and non-diabetic control subjects. Int J Obes
Relat Metab Disord 2000;24:514–19.
131. Li S, Chen W, Srinivasan SR, et al. Influence of lipoprotein
lipase gene Ser447Stop and b1-adrenergic receptor gene
Arg389Gly polymorphisms and their interaction on obesity
from childhood to adulthood: the Bogalusa Heart Study. Int J
Obes (Lond) 2006;30:1183–8.
132. Skibola CF, Holly EA, Forrest MS, et al. Body mass index,
leptin and leptin receptor polymorphisms, and non-Hodgkin
lymphoma. Cancer Epidemiol Biomarkers Prev 2004;13:
133. Stone S, Abkevich V, Russell DL, et al. TBC1D1 is a can-
didate for a severe obesity gene and evidence for a gene/gene
interaction in obesity predisposition. Hum Mol Genet 2006;
134. Lissner L, Heitmann BL. Dietary fat and obesity: evidence
from epidemiology. Eur J Clin Nutr 1995;49:79–90.
135. Talmud PJ, Stephens JW. Lipoprotein lipase gene variants
and the effect of environmental factors on cardiovascular
disease risk. Diabetes Obes Metab 2004;6:1–7.
136. Provencher V, Perusse L, Bouchard L, et al. Familial resem-
blance in eating behaviors in men and women from the
Quebec Family Study. Obes Res 2005;13:1624–9.
137. Perusse L, Bouchard C. Gene-diet interactions in obesity. Am
J Clin Nutr 2000;72(suppl):1285S–90S.
138. Spitz MR, Detry MA, Pillow P, et al. Variant alleles of the D2
139. Ukkola O, Bouchard C. Role of candidate genes in the
responses to long-term overfeeding: review of findings. Obes
140. Groot PC, Bleeker MJ, Pronk JC, et al. The human alpha-
amylase multigene family consists of haplotypes with vari-
able numbers of genes. Genomics 1989;5:29–42.
141. Collaku A, Rankinen T, Rice T, et al. A genome-wide linkage
scan for dietary energy and nutrient intakes: the Health, Risk
Factors, Exercise Training, and Genetics (HERITAGE)
Family Study. Am J Clin Nutr 2004;79:881–6.
142. Branson R, Potoczna N, Kral JG, et al. Binge eating as
a major phenotype of melanocortin 4 receptor gene muta-
tions. N Engl J Med 2003;348:1096–103.
143. Bouchard L, Drapeau V, Provencher V, et al. Neuromedin
beta: a strong candidate gene linking eating behaviors
and susceptibility to obesity. Am J Clin Nutr 2004;80:
144. Vincent S, Planells R, Defoort C, et al. Genetic polymor-
phisms and lipoprotein responses to diets. Proc Nutr Soc
145. Surgeon General’s report on physical activity and health.
From the Centers for Disease Control and Prevention. JAMA
60 Yang et al.
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013
146. Chakravarthy MV, Booth FW. Eating, exercise, and ‘‘thrifty’’
genotypes: connecting the dots toward an evolutionary
understanding of modern chronic diseases. J Appl Physiol
147. Beunen G, Thomis M. Genetic determinants of sports
participation and daily physical activity. Int J Obes Relat
Metab Disord 1999;23(suppl 3):S55–63.
148. Montgomery HE, Marshall R, Hemingway H, et al. Human
gene for physical performance. Nature 1998;393:221–2.
149. Siffert W. G protein b3 subunit 825T allele, hypertension,
obesity, and diabetic nephropathy. Nephrol Dial Transplant
150. Corbalan MS, Marti A, Forga L, et al. The 27Glu poly-
morphism of the b2-adrenergic receptor gene interacts with
physical activity influencing obesity risk among female
subjects. (Letter). Clin Genet 2002;61:305–7.
151. Loos RJ, Rankinen T, Tremblay A, et al. Melanocortin-4
receptor gene and physical activity in the Quebec Family
Study. Int J Obes (Lond) 2005;29:420–8.
152. Buemann B, Schierning B, Toubro S, et al. The association
between the val/ala-55 polymorphism of the uncoupling
protein 2 gene and exercise efficiency. Int J Obes Relat Metab
153. Otabe S, Clement K, Dina C, et al. A genetic variation in the
5# flanking region of the UCP3 gene is associated with body
mass index in humans in interaction with physical activity.
154. Prior SJ, Hagberg JM, Phares DA, et al. Sequencevariation in
hypoxia-inducible factor 1 alpha (HIF1A): association with
maximal oxygen consumption. Physiol Genomics 2003;15:
155. Rankinen T, Rice T, Boudreau A, et al. Titin is a candidate
gene for stroke volume response to endurance training:
the HERITAGE Family Study. Physiol Genomics 2003;15:
156. Franks PW, Barroso I, Luan J, et al. PGC-1 alpha genotype
modifies the association of volitional energy expenditure
with [OV0312] O2max. Med Sci Sports Exerc 2003;35:
157. Bouchard C, Rankinen T, Chagnon YC, et al. Genomic scan
for maximal oxygen uptake and its response to training in
the HERITAGE Family Study. J Appl Physiol 2000;88:
158. Lawrence R, Evans D, Cardon L. Prospects and pitfalls in
whole genome association studies. Phil Trans R Soc 2005;
159. Clement K. Genetics of human obesity. Proc Nutr Soc
160. Baranova A, Schlauch K, Gowder S, et al. Microarray
technology in the study of obesity and non-alcoholic fatty
liver disease. Liver Int 2005;25:1091–6.
161. Challis B, Yeo G. Past, present and future strategies to study
the genetics of body weight regulation. Brief Funct Genomic
162. Permana P, Parigi A, Tataranni A. Microarray gene expres-
sion and profiling in obesity and insulin resistance. Nutrition
163. Baranova A, Collantes R, Gowder S, et al. Obesity-related
differential gene expression in the visceral adipose tissue.
Obes Surg 2005;15:758–65.
164. Gomez-Ambrosi J, Catalan V, Diez-Caballero A, et al. Gene
expression profile of omental adipose tissue in human obe-
sity. FASEB J 2004;18:215–17.
165. Thongboonkerd V. Genomics, proteomics and integrative
‘omics’ in hypertension research. Curr Opin Nephrol
166. Wang J, Li D, Dangott S, et al. Proteomics and its role in
nutritional research. J Nutr 2006;136:1759–62.
167. Wildman RP, Gu D, Reynolds K, et al. Are waist circum-
ference and body mass index independently associated with
cardiovascular disease risk in Chinese adults? Am J Clin Nutr
168. Sempos C, Liu K, Ernst N. Foodand nutrient exposures: what
to consider when evaluating epidemiologic evidence. Am J
Clin Nutr 1999;69(suppl):1330S–8S.
169. Mahabir S, Baer D, Giffen C, et al. Comparison of energy
expenditure estimates from 4 physical activity questionnaires
with doubly labeled water estimates in postmenopausal
women. Am J Clin Nutr 2006;84:230–6.
170. Ionnidis J, Trikalinos T, Khoury M. Implications of small
effect sizes of individual genetic variants on design and
interpretation of genetic association studies of complex
diseases. Am J Epidemiol 2006;164:609–14.
171. Wang W, Barratt B, Clayton D, et al. Genome-wide associa-
tion studies: theoretical and practical concerns. Nat Rev
172. Marchini J, Cardon L, Phillips M, et al. The effects of human
population structure on large genetic association studies. Nat
173. Teare MD, Barrett JH. Genetic linkage studies. Lancet
174. Botstein D, Risch N. Discovering genotypes underlying
human phenoytpes: past successes for mendelian disease,
future approaches for complex disease. Nat Genet 2003;
175. LairdNM,LangeC.Family-baseddesigns intheageoflarge-
176. Hunter DJ. Gene-environment interactions in human dis-
eases. Nat Rev Genet 2005;6:287–98.
177. Manolio TA, Bailey-Wilson JE, Collins FS. Genes, environ-
ment and the value of prospective cohort studies. Nat Rev
Genetic Epidemiology of Obesity61
Epidemiol Rev 2007;29:49–61
by guest on June 3, 2013