Obesity and genomics: role of technology in
unraveling the complex genetic architecture
of obesity
Yamunah Devi Apalasamy & Zahurin
Mohamed
Human Genetics
ISSN 0340-6717
Hum Genet
DOI 10.1007/s00439-015-1533-x
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DOI 10.1007/s00439-015-1533-x
REVIEW PAPER
Obesity and genomics: role of technology in unraveling
the complex genetic architecture of obesity
Yamunah Devi Apalasamy · Zahurin Mohamed Received: 9 October 2014 / Accepted: 2 February 2015
© Springer-Verlag Berlin Heidelberg 2015
Abstract Obesity is a complex and multifactorial disease
that occurs as a result of the interaction between “obesogenic” environmental factors and genetic components.
Although the genetic component of obesity is clear from
the heritability studies, the genetic basis remains largely
elusive. Successes have been achieved in identifying the
causal genes for monogenic obesity using animal models
and linkage studies, but these approaches are not fruitful
for polygenic obesity. The developments of genome-wide
association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of
new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability.
The arrival of NGS technologies and completion of 1000
Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use
of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In
this review, we summarize and discuss the developments of
these genetic approaches in human obesity.
Introduction
Obesity has reached epidemic proportions globally, with
an estimation of more than 2.16 billion overweight people
and 1.12 billion obese people by 2030 (Kelly et al. 2008).
The World Health Organization reported that 65 % of the
world’s population resides in countries in which obesity
Y. D. Apalasamy (*) · Z. Mohamed Department of Pharmacology, Pharmacogenomics Laboratory,
Faculty of Medicine, University of Malaya,
50603 Kuala Lumpur, Malaysia
e-mail: [email protected]
and overweight have led to death. As obesity is a major
health and economic burden, the American Medical Association has considered it a disease in 2013 (American Medical Association 2013). It has a significant health burden,
as obesity is also associated with other comorbidities such
as diabetes, cardiovascular disease, liver and gallbladder
disease, cancer, gynecological problems, sleep apnea, and
osteoarthritis (Pi-Sunyer 2009).
Obesity is a chronic condition in which excessive fat
accumulates as a result of imbalance between energy consumption and expenditure (Galgani and Ravussin 2008).
Interactions among environmental, genetic, and behavioral
factors lead to a complex pathogenesis (Chaput et al. 2014).
The modern lifestyle has partially led to a rapid increase
in consumption of energy-dense food and physical inactivity. But even in the presence of an obesogenic environment,
genetic components play an important role in contributing
to individual risk of obesity. Twin, family, and adoption
studies have shown that heritability of obesity is high, ranging from 40 to 70 % (Allison et al. 1996; Maes et al. 1997;
Stunkard et al. 1990). Various genetic approaches such as
linkage analysis and candidate gene association studies
have been performed to identify susceptibility genetic loci
in an attempt to dissect the biological mechanisms underlying body-weight regulation; however, these attempts have
met with limited success.
Study designs to dissect the genetic architecture of obesity have progressively shifted from linkage and candidate
gene association studies to genome-wide association studies (GWAS) after 2005. In the early studies of obesity, in
vivo, in vitro, linkage, and candidate gene association studies were employed to study obesity-susceptibility genes.
The Human Genome Project and the International HapMap Project are the major developments which have significantly enhanced our knowledge on genetic variations in
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the human genome. The human genome sequencing was
completed in 2003, and the subsequent developments such
as characterization of the LD patterns among the SNPs
through the HapMap Project endeavor have led to the
breakthrough discovery of the genetic variants of human
diseases through GWAS (Schmutz et al. 2004). Rapid
developments in technologies at a relatively low cost, such
as high-throughput genotyping arrays and next generation
sequencing (NGS) technologies, together with advanced
statistical methods, have enabled GWAS and copy number variation (CNV) studies to identify numerous genetic
loci associated with obesity (Naidoo et al. 2011). To date,
GWAS have identified at least 58 novel loci associated
with obesity/body mass index (BMI) in European and
non-European populations (Lu and Loos 2013). The availability of well-curated Human Gene Mutation Databases
has also facilitated large-scale meta-analyses of diseaseassociated variants These databases include the Human
Gene Mutation Database, A Catalog of Published GWAS,
Online Mendelian Inheritance in Man, ClinVar, and many
other locus-specific mutation databases (Amberger et al.
2009; Sherry et al. 2001; Stenson et al. 2013). Nevertheless, the SNPs that have been identified thus far collectively explain only a relatively small proportion of the
heritability. Unraveling the complex genetic architecture
of obesity remains a challenging task for researchers. In
this review, we summarize recent advances in genetic
studies of obesity. We also discuss various technological
approaches used to dissect the genetics of obesity, especially those that leverage high-throughput genotyping and
NGS technologies.
In vivo and in vitro models of obesity
Molecular mechanisms of body-weight regulation and
obesity development were first discovered through animal
models (Zhang et al. 1994; Zucker 1975). Mouse models are critical in obesity research in pre- and post-GWAS
era, as reviewed in recent articles (Cox and Church 2011;
Speakman et al. 2008). The quantitative genetic approach
with mice models offers an excellent tool to study the
genetic architecture of obesity. Studies in rodent genetic
models to understand the pathways of body-weight regulation and feeding behavior have been useful in demonstrating the monogenic type of obesity (Leibel et al. 1997). For
example, the localization of mutant genes in animal model
has led to the discovery of monogenic genes such as leptin,
a mutation in the LEP gene causing obesity in the ob/ob
mouse (Zhang et al. 1994). Apart from single-gene mutations, fine-mapping of quantitative trait loci in mouse models also unraveled more than 200 novel targets related to
obesity for candidate gene studies (Snyder et al. 2004).
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Polygenic animal models have been crucial in various studies of environmental effects such as epigenetics, responses
to high-fat or sugar, and low-calorie and other weightloss diets, as well as in the development of pharmaceutical agents such as leptin and cannabinoid receptor type 1
antagonist agents for treating obesity (Speakman et al.
2008). In addition, adipogenic cellular models have been
useful in understanding adipogenesis, energy homeostasis,
and insulin resistance in obesity.
In vitro approaches such as primary cell cultures, coculture models, and three-dimensional cell cultures have
also shed light on the important components of obesity
such as growth factors, hormones, and potential pharmacological compounds (Armani et al. 2010; Green
and Meuth 1974). For instance, in vitro studies have
shown that PPARG is extensively regulated by CCAAT/
enhancer-binding proteins, which subsequently provide a
path towards the development of PPARG in the treatment
of obesity and type 2 diabetes (Mikkelsen et al. 2010).
Notably, in vivo and in vitro models are important in the
study of genetics for both monogenic and polygenic obesity (Nilsson et al. 2012). Monogenic obesity is caused
by a single-gene mutation, whereas polygenic obesity is
associated with multiple genetic factors that interact with
an “at-risk” environment (Mutch and Clement 2006).
Despite the success in terms of development of some
therapeutic targets, there are limited successes in other
instances in translating the molecular compounds discovered by in vivo and in vitro studies into usable drugs for
humans. For example, treatments of rodents with leptin
and MC4R (MTII) antagonists resulted in weight loss, but
these compounds were shown to be not clinically useful
because they were short acting and also the occurrence
of adverse effects (Gibson et al. 2004; Hadley 2005). The
major shortcoming of the currently available in vitro and
in vivo models is that these models only resemble certain
aspects of human obesity; therefore, it would be useful if
animal- and cell-based models of therapeutic approaches
could be improved in future to reflect the pathophysiology
of obesity in humans.
Monogenic obesity
Monogenic obesity refers to a rare form of severe obesity
that resulted from mutations with a large effect size. These
mutations are known to cause severe early onset obesity
with hyperphagia as a key feature and multiple endocrine
anomalies (Table 1) (Clement 2006). To date, eight candidate genes (LEP, LEPR, PSCK1, POMC, MC4R, SIM1,
BDNF, and NTRK2) listed in Table 1, have been identified
for monogenic obesity, which have shed light into the biological pathways of the disease. Considerable success in
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Table 1 Genes in monogenic obesity
Gene
Main clinical characteristics
Severe early onset of obesity; hypogonadism; very
low circulating leptin levels
LEPR Severe early onset of obesity; hypogonadism; very
low circulating leptin levels
POMC Severe obesity; hyperphagia; hypoadrenalism;
hypopigmentation
MC4R Severe early onset of hyperphagia; rapid rise in fat
mass
BDNF Severe obesity; hyperphagia; hyperactivity; impaired
cognitive function; developmental delay
NTRK2 Severe obesity; hyperphagia; hyperactivity; impaired
cognitive function; developmental delay
PSCK1 Hyperphagia; hypogonadism; hyperglycemia;
elevation in proinsulin to insulin ratio
LEP
SIM1
Discovery method
References
Animal model
Montague et al. (1997), Strobel et al. (1998)
Animal model
Clement et al. (1998), Farooqi et al. (2007)
Animal model
Farooqi et al. (2006), Krude et al. (2003)
Animal model
Animal model
Dubern et al. (2007), Farooqi et al. (2003), Huszar
et al. (1997), Vaisse et al. (2000)
Gray et al. (2006), Han et al. (2008), Kernie et al.
(2000), Rios et al. (2001)
Yeo et al. (2004)
Animal model
Benzinou et al. (2008), Jackson et al. (1997)
Animal model
Severe early onset obesity; Prader–Willi-like (PWL) Cell-based in vitro study Holder et al. (2000), Hung et al. (2007)
syndrome; developmental delay
understanding monogenic obesity is derived from animal
models; the discovery of all candidate genes were made
on rodent models, except for SIM1, of which the genetic
aberrations were identified using in vitro cell lines (Holder
et al. 2000). Contribution of genes involved in monogenic
obesity to polygenic obesity in the general population is
largely unknown.
The arrival of NGS and the development of exome
sequencing have offered a new approach to identify causal
genes in monogenic diseases. This approach has been
shown to be highly successful in identifying causal genes
for monogenic diseases with hitherto unknown genetic etiologies (Ku et al. 2012). Furthermore, exome sequencing
works well for small sample sizes without the need of a
large pedigree. Thus, exome sequencing can be applied
to individual cases from different families, and the variants identified are then filtered accordingly to identify the
mutations causing monogenic diseases which are likely
residing in protein. Various bioinformatic tools are also
developed and used to predict the functional effect on the
protein and evolutionary conservation. Although exome
sequencing has yet to be applied in monogenic obesity, it
is anticipated that it will achieve similar success as with
other monogenic diseases. The presentation of clinical
features such as early onset obesity, hyperphagia, hypoadrenalism, and low levels of leptin concentration despite
severe obesity and hypothyroidism is useful to identify
monogenic obesity cases for exome sequencing approach.
The genes identified in monogenic obesity such as BDNF,
MC4R, and PSCK1 have also been found to be strongly
associated with obesity in GWAS, suggesting the importance of using exome sequencing to explore additional
monogenic loci.
The focus on the single-gene defect in obesity as a molecular target for pharmacological interventions had shown to
be effective. This is well exemplified by the development of
leptin treatment. The LEP mutation is the first and the only
genetic form of human obesity to be successfully treated
pharmacologically (Hainerova and Lebl 2013). Leptin treatment results in weight loss that is mainly due to loss of fat
mass in patients with leptin deficiency, but it has no therapeutic effect on those with LEPR deficiency (Farooqi et al.
2002). Eventually it became clear that most of these obese
subjects are not leptin deficient; rather, they have high levels of leptin but are leptin resistant (Considine et al. 1996).
Despite leptin is not an effective treatment for polygenic
obesity, the reversal effect of leptin on hyperglycemia has
provided a path for the development of other potential therapeutic entities for diabetes in the future (Perry et al. 2014).
Polygenic obesity
Although studies on monogenic obesity have provided significant insights into the biology of obesity, particularly
the severe phenotype, the genetic loci of polygenic obesity remain elusive until the GWAS era. Different genetic
approaches such as candidate gene association studies,
genome-wide linkage studies (GWLSs), and GWAS have
been used to investigate the polygenic basis of obesity, of
which the results are summarized and discussed as follows.
Candidate gene studies
Candidate gene association studies have been widely used
to study obesity-susceptibility loci, but achieved limited
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successes. This is a hypothesis-driven approach of which
the loci/genes to be studied are selected based on a priori
knowledge. Small sample sizes with limited statistical
power have been used to identify associations with common variants with small effect sizes, which led to irreproducible results. To date, candidate gene studies have
investigated various genes involved in energy balance regulation such as those encoding factors that regulate food
intake and energy homeostasis. For example, LEP, LEPR,
GHRL, CCK, NPY, CRH, POMC, AGRP, MC1R, MC3R,
MC4R, MC5R, and CART have been commonly studied for
their associations with obesity (Loktionov 2003). Genes
involved in peripheral regulation of energy expenditure
such as ADRB1, ADRB2, ADRB3, UCP2, and UCP3 have
also been shown to be associated with obesity (Loktionov
2003).
It is well known that the link between obesity and diabetes is based on insulin resistance. Despite this tight relationship, these conditions do not seem to share a common
genetic background, as only a few common susceptibility loci have been discovered such as PPARG2, PGC-1,
CEBPE, and GIPR (Grarup et al. 2014; Ling et al. 2004;
Loktionov 2003; Oberkofler et al. 2004). These genes in
the PPARs pathway have a regulatory role in lipid metabolism, glucose homeostasis, and adipogenesis (Barbier et al.
2003; Kim et al. 2011; Lehrke and Lazar 2005; Tontonoz
and Spiegelman 2008). Genome-wide analysis has demonstrated that most of the genes induced in adipogenesis are
bound by both PPARs and CEBPE (Lefterova et al. 2008).
Although candidate gene studies have limited success in
detecting risk variants, certain genes with known biological
functions in obesity have provided a path to the development of therapeutic interventions in metabolic abnormalities. This was evidenced in the use of a PPAR ligand, thiazolidinedione (TZD), in type 2 diabetes to reduce insulin
resistance (Gastaldelli et al. 2007). Currently, the use of
TZD had been withdrawn due to its adverse effects; a safer
new class of PPAR-targeted antidiabetic drugs is still being
explored, however, this is the only antidiabetic agent available currently for increasing insulin sensitivity (Choi et al.
2011; Fuentes et al. 2013; Monsalve et al. 2013; Soccio
et al. 2014).
GWLS
GWLS is a hypothesis-generating method, where the
whole genome is interrogated in an agnostic approach
(Altmuller et al. 2001). This requires a large pedigree to
study the segregation patterns between the genotype and
phenotype, and hence to identify the loci and mutations.
However, this study design might not be applicable to individual cases without a multigenerational pedigree, and
it is not robust to phenotypic and genetic heterogeneity.
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GWLSs have thus far identified many genetic loci associated with obesity but most were not reproducible. For
example, this was shown in a meta-analysis of 37 genomewide linkage scans that reported no evidence of significant
linkage to any of the chromosomal regions being studied
(Saunders et al. 2007). PSCKI1 is the only region reported
by linkage studies to be strongly linked to obesity to date
(Benzinou et al. 2008). Attempts to identify novel loci
using linkage mapping have been largely unsuccessful,
although novel loci such as TBC1D22A and THUMPD2
have been recently reported, but further validation would
be needed (Liu et al. 2014).
GWAS
In contrast to the candidate gene association study design,
GWAS are a non-hypothesis-driven approach in which
a large number of genetic markers (SNPs) spanning the
entire genome are interrogated for their associations with
the phenotypes of interest. This approach has been very
successful in identifying common SNPs that contribute to
a relatively low risk (as measured by odds ratio [OR] < 1.5)
of developing complex diseases and phenotypes, including
obesity-related phenotypes such as diabetes and hypertension (Hindorff et al. 2009).
INSIG2 is the first gene reported to be associated with
obesity from the Framingham Heart Study that involved
case–control and family studies of different ethnic groups
(Herbert et al. 2006). However, the association later
became controversial because of non-replication in three
subsequent studies (Dina et al. 2007; Loos et al. 2008;
Rosskopf et al. 2007). Replication studies in Caucasians,
and Asians including Indians, Japanese, and Chinese had
also failed to identify this locus (Feng et al. 2007; Kumar
et al. 2007; Loos et al. 2007; Tabara et al. 2008). These
inconsistent results could be due to differences in the levels of physical activity in different ethnicities, as physical
activity appears to exert an effect on the INSIG2 SNP association (Andreasen et al. 2008a). This initial discovery was
then followed by the identification of an additional two new
loci for BMI/obesity, i.e., FTO and MC4R. These genes
have strong biological plausibility, as they play a major role
in the central regulation of energy homeostasis (Frayling
et al. 2007; Loos et al. 2008). In addition to genetic studies, mouse model studies also revealed the role of FTO in
controlling food intake, energy homeostasis, and energy
expenditure (Church et al. 2009, 2010). Following these
first reports, the association between obesity and the FTO
gene has been very well replicated, and found to be associated with both adulthood and childhood obesity in European and Asian populations (Li et al. 2012; Okada et al.
2012; Rees et al. 2011; Speliotes et al. 2010; Thorleifsson
et al. 2009; Wen et al. 2012; Willer et al. 2009; Wu et al.
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2010; Xi and Mi 2009; Xi et al. 2010). Interestingly, FTO
is associated with an increased risk of obesity and type 2
diabetes, with similar effect sizes in East and South Asians
(OR = 1.13–1.18) and in Europeans (OR = 1.20–1.32) (Li
et al. 2012).
Like FTO, the MC4R gene has also been well studied
in obesity research. To date, more than 90 obesity-associated MC4R mutations mostly missense were reported (Tao
2005). The first study (Loos et al. 2008) reported that MC4R
SNP (rs1778231) is associated with obesity in Europeans,
but a subsequent GWAS found association of another SNP
(rs129070134) with BMI/obesity in Indian Asians, which
had also been confirmed later in European and East Asian
populations (Chambers et al. 2008; Xi et al. 2012). Association between MC4R SNPs and obesity in the Chinese population was confirmed in meta-analyses; however, the results
are inconsistent (Tao et al. 2012; Xi et al. 2012).
To date, most of the GWAS on BMI and obesity have
been performed in Caucasian populations, and to a much
lesser extent in other populations. Nonetheless, because of
population and genetic heterogeneity (including SNP frequency and LD differences) between populations in different continental regions, this suggests that additional new
loci might be discovered from GWAS in diverse populations. Table 2 summarizes the obesity-susceptibility loci discovered in Caucasian, Asian and Africans through GWAS.
In essence, the GWAS of obesity can be divided into
two waves, i.e., individual studies followed by large-scale
meta-analyses and replication studies performed by consortia. The first wave resulted in the identification of common SNPs in four genes namely FTO, PFKP, CTNNBL1,
and FDFT1 (Frayling et al. 2007; Liu et al. 2008; Scuteri
et al. 2007). However, only the SNPs in the FTO gene
were consistently replicated (Bradfield et al. 2012; Frayling et al. 2007; Hinney et al. 2007; Jiao et al. 2011; Meyre
et al. 2009; Scherag et al. 2010; Scuteri et al. 2007; Speliotes et al. 2010; Thorleifsson et al. 2009; Willer et al.
2009). GWAS are an approach based on the “commondisease common-variant” hypothesis, and thus a large sample size is required to detect SNPs with small effect sizes
(OR < 1.5). Furthermore, the reliance on LD between surrogate markers and disease variants and the statistical correction to test a larger number of SNPs also contribute to
the need for a large sample size to ensure an adequate statistical power. The SNP associations identified in GWAS
require replication in additional cohorts which is mandatory to prove its genuine associations. For this reason, a
large sample size of up to tens of thousands is required for
GWAS and subsequent replications, which is unachievable
through individual studies. As a result, several large consortia have been formed, for example “Genetic Investigation
of Anthropometric Traits” (GIANT), to ensure that adequate sample size is available for discovery and replication
phases in GWAS (Willer et al. 2009). The collaborative
works from GIANT resulted in the second wave of discovery for obesity loci and SNPs. A meta-analysis of multiple GWAS performed by GIANT with a sample size of
approximately 17,000 Caucasians identified a new gene for
obesity, namely, MC4R (Loos et al. 2008). The MC4R SNP
associations have been consistently replicated in different
populations, including Europeans and Indians; however,
inconsistent results were reported for Africans and East
Asians (Chambers et al. 2008; Grant et al. 2009; Tao et al.
2012; Wu et al. 2010). The GIANT consortium initially
used waist circumference as a proxy trait for overall body
fat distribution. However, in 2010, GIANT was expanded
by combining data from 32 GWAS, using the waist–hip
ratio (WHR) adjusted for BMI as the parameter to measure
overall adiposity. The GIANT consortium reported a total
of 18 BMI-associated and 14 WHR-associated novel loci
from this effort (Andreasen et al. 2008a; Loos et al. 2007).
The loci identified from the first few GWAS have been
quite limited in terms of accounting for the heritability of obesity. To further enrich the genetic components
of obesity, selecting extreme phenotype samples (e.g.,
BMI ≥ 40 kg/m2) was taken to identify additional new
loci for obesity. (Meyre et al. 2009), and this approach had
been proven fruitful with the identification of three new
loci (SDCCAG8, TNKS, and KCNMA1) (Jiao et al. 2011;
Scherag et al. 2010).
Most of the earlier GWAS and almost all of the international consortia studies discussed above have focused
mainly on Caucasians despite the high prevalence of obesity in other populations. Collaborative work was lacking
to generalize the earlier findings to different populations
that have been underreported such as African-Americans,
Asians and American Indians. Recently, this gap has been
closed by the formation of a consortium known as “Population Architecture using Genomics and Epidemiology”
(PAGE) by recruiting populations of diverse genetic backgrounds into the studies, which successfully showed that
FTO is a BMI-susceptible locus in all ethnic groups including American Indians, African-Americans, East Asians,
Hispanics, Pacific Islanders and European Americans (Fesinmeyer et al. 2013). Another interesting finding of PAGE
is that the LD pattern of the FTO gene is similar in European Americans, Hispanics, East Asians, and American
Indians, compared to African-Americans (Fesinmeyer et al.
2013). Because PAGE investigated the previously GWASidentified SNPs, novel associations were not reported in
non-Caucasians.
GWAS in various populations
As PAGE focused on generalizing the associations that had
been previously reported in Europeans, further studies are
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Table 2 Overview of obesity-susceptibility loci identified through GWAS
Gene
Phenotype
Ancestry of discovery
ADAMTS9; GRB14
BDNF
WHR
BMI; extreme obesity
European
European
BMI
European
FTO
CDKAL1/GIPR
CAMD2
FAIM2
MC4R
TMEM18
NEGRI
MTCH2
GNPDA2
PCSKI; GP2
LHX; RREB1
KCTD15; SH2BI
ETV5; SEC16B
NRXN3
TFAP2B
SDCCAG8; TNKS;
KCNMA1
POMC
References
Heid et al. (2010)
Jiao et al. (2011), Speliotes et al. (2010),
Thorleifsson et al. (2009)
BMI; WC; fat percentage; extreme obesity European
Frayling et al. (2007), Kilpelainen et al.
(2011), Meyre et al. (2009), Scuteri et al.
(2007)
BMI
East Asian
Okada et al. (2012), Wen et al. (2012)
BMI
European
Heard-Costa et al. (2009)
BMI; extreme obesity
European
Paternoster et al. (2011), Speliotes et al.
(2010), Thorleifsson et al. (2009)
BMI; WC; extreme obesity
European: Indian Asian Chambers et al. (2008), Loos et al. (2008),
Meyre et al. (2009)
BMI; extreme obesity
European
Scherag et al. (2010), Thorleifsson et al.
(2009), Willer et al. (2009)
BMI
European
Speliotes et al. (2010), Thorleifsson et al.
(2009), Willer et al. (2009)
BMI; WC
European
Lindgren et al. (2009), Willer et al. (2009)
BMI
European
Speliotes et al. (2010), Willer et al. (2009)
BMI
East Asian
Wen et al. (2012)
WHR
Africans
Liu et al. (2013)
BMI
European
Speliotes et al. (2010), Thorleifsson et al.
(2009), Willer et al. (2009)
BMI
European
Speliotes et al. (2010), Thorleifsson et al.
(2009)
BMI; WHR
European
Heard-Costa et al. (2009), Speliotes et al.
(2010)
BMI; WC
European
Heard-Costa et al. (2009), Lindgren et al.
(2009), Speliotes et al. (2010)
Extreme obesity
European
Jiao et al. (2011), Scherag et al. (2010)
MAP2K5
BMI
European
Heard-Costa et al. (2009), Speliotes et al.
(2010), Wen et al. (2012)
Speliotes et al. (2010), Wen et al. (2012)
GALNT10; MIR148
BMI
African
Monda et al. (2013)
WHR waist–hip ratio
needed to identify population-specific susceptibility loci
in non-European descents. The first GWAS on East Asian
ancestry, involving approximately 8,800 subjects, confirmed that FTO, MC4R, and CTNNBL1 are BMI-associated loci, and HECTD4 as a WHR-susceptibility locus, but
failed to identify any novel associations (Cho et al. 2009).
Subsequently, a meta-analysis was performed with a larger
sample size of 27,715 individuals in the discovery stage,
preceded by in silico and de novo replication studies of
37,691 and 17,642 individuals that identified three novel
loci (CDKAL1, PCSK1, and GP2) and confirmed eight
previously identified loci (FTO, MC4R, GIPR, QPCTL,
ADCY3-DNAJC27, BDNF, SEC16B, and MAP2K5) (Wen
et al. 2012). Another study of East Asians also confirmed
CDKAL1 as a novel BMI-susceptibility locus, with KLF9
as an additional locus (Okada et al. 2012).
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To date, only a limited number of GWAS have been conducted in African populations. Findings from large cohort
studies on African ancestry have shown an association
between FTO and obesity (Grant et al. 2008; Hassanein
et al. 2010), and two smaller GWAS with a sample size of
1,800 replicated the association with MC4R (Kang et al.
2010; Ng et al. 2012). A recent GWAS discovered variants in LHX, RREB1, GALNT10, and MIR148 as the novel
loci for obesity in Africans (Liu et al. 2013; Monda et al.
2013). The replication of the well-studied loci has also
been observed in other populations. For example, the first
GWAS in the Filipino population involving approximately
1,700 participants replicated the GWAS-identified loci such
as MC4R, FTO, and BDNF (Croteau-Chonka et al. 2011)
and a GWAS for waist circumference in Indian Asians
with approximately 2,600 subjects identified MC4R as
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being associated with adiposity (Chambers et al. 2008). An
association of MC4R with obesity was also confirmed in a
replication study comprising more than 1,500 native Asian
Indians (Been et al. 2010). Population-based studies and
meta-analyses in Asian Indians also confirmed that FTO
is associated with obesity (Moore et al. 2012; Vasan et al.
2012, 2014). However, a large-scale GWAS are still to be
done in this population with a high prevalence of obesity to
identify new population-specific loci (if any).
Challenges of GWAS of obesity and the “missing
heritability”
Despite substantial effort over the past 10 years to identify
the genetic loci for obesity, a large portion of the heritability remains unexplained. This could be attributed to multiple reasons such as inadequate coverage of rare variants,
genetic heterogeneity across the populations, gene–gene
and gene–environment interactions and possibly epigenetic
factors (Marian 2012). Most of the risk alleles identified in
obesity GWAS are common (allele frequency > 10 %) with
the exception of SNPs in several genes such as CTNNBL1,
PTER, SLC39A8, and PRKD1 (Sandholt et al. 2012). However, the effect sizes of identified variants are generally
small (OR < 1.5). Most of the identified variants are located
in the non-coding region, including intronic and intergenic
regions, making it difficult to elucidate their functional
roles and their involvement in the pathophysiology of obesity. This will also hinder the further understanding of the
novel biological pathway in the development of obesity.
The functions of a few genes are known, such as those
expressed in hypothalamic regions (FTO, MC4R, MTCH2,
FAIM2, GNPDA2, KCTD15, NPC1, and ETV5), which
may play a pivotal role in controlling appetite (Farooqi
2008); BDNF in eating disorders (Gratacos et al. 2007);
and SH2B1 in leptin and insulin signaling pathways (Maures et al. 2007); however, the impact of the SNPs in these
genes on the pathophysiology of obesity is still to be elucidated, which should then be the focus of future studies.
The presence of gene–environment (G × E) interactions
in obesity has been reported for several loci. These include
interactions of FTO, PCSKI, and INSIG2 with physical
activity (Andreasen et al. 2008a, b; Kilpelainen et al. 2009;
Kilpelainen et al. 2011). FTO is the most well-studied and
replicated region for gene–physical activity interaction in
obesity (Andreasen et al. 2008b; Kilpelainen et al. 2011).
In addition, studies have shown an interaction of FTO with
dietary factors such as fat, carbohydrate, protein, and fiber
intake in Caucasians (Sonestedt et al. 2009). Although the
importance of systemic interrogation of G × E interactions is well appreciated, it is challenging because of poor
availability and lack of high-throughput environmental
data (Patel and Ioannidis 2014). Recently, genome-wide
approaches to identify loci involved in G × E interactions
have been reported for several diseases such as asthma and
Parkinson disease (Ege et al. 2011; Hamza et al. 2011).
However, G × E interaction datasets from diverse populations have not yet been combined for obesity. In addition, epistasis (gene–gene interactions) is also a potential
factor of missing heritability, but it still remains largely
unexplored in GWAS (Eichler et al. 2010). This is because
it is extremely challenging to detect epistatic interactions
in complex traits in existing GWAS which required a huge
sample size to account for the statistical correction for
the countless number of possible interactions. Nevertheless, gene–gene interactions have been detected for some
genes such as PPARs and ADRs, mostly in the pre-GWAS
stage (Luo et al. 2013; Ukkola et al. 2000; Warden et al.
2004). Robust and systematic approaches using genomewide analysis to study epistatic interactions are still being
developed. The detection of significant epistatic and G × E
interactions for complex traits, including obesity, requires a
combination of large sample size from multiple cohorts and
the use of high-density SNP genotyping platforms (microarray and DNA sequencing) and robust statistical tools
(Wei et al. 2014).
Although GWAS have identified many genetic variants associated with obesity, the results do not really provide additional biological insights, and the SNPs also have
poor risk predication value. (Sandholt et al. 2010). Genetic
risk prediction for rare variants seems to be more powerful
than that for GWAS-identified SNPs. Rare variants appear
to account for a much stronger effect in complex diseases
such as obesity and thus may potentially fill the gap of
missing heritability (Zuk et al. 2014). Therefore, there is
a need to study rare variants using new technologies such
exome genotyping arrays and NGS platforms.
Exome array genotyping of obesity genes
Before the completion of the 1000 Genomes Project, genotyping arrays were focused on common SNPs, and GWAS
were based on the hypothesis of “common-disease, common-variants”. However, it is clear that common SNPs with
small effect sizes explain only a small portion of heritability.
The results from the 1000 Genomes Project have enabled
the development of exome arrays allowing the selection of
rare (1–5 % allele frequency) coding SNPs for genotyping.
Despite having high-throughput NGS technologies to detect
rare variants, this approach remains costly when applied to
large-scale association studies. Thus exome array genotyping platform has become a more practical method to study
rare coding variants that are difficult to capture via standard
genotyping methods used in GWAS. The exome genotyping array has succeeded in identifying new variants and in
13
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fine-mapping GWAS-identified loci in Caucasians for complex traits such as hematological traits (Yang et al. 2013),
insulin processing and secretion (Matsunami et al. 2013),
and blood lipids and coronary heart disease (Merikangas
et al. 2014; Wheeler et al. 2013). Furthermore, exome arrays
were also used to study psychiatric diseases such as schizophrenia to identify large CNVs (Sha et al. 2009). To date,
exome arrays have not been used to identify rare coding
variants associated with obesity; however, it is an important research area that may yield valuable insights into the
pathophysiology of obesity. In contrast to common variants,
rare variants are more population specific; hence inadequate
representation of rare coding variants of diverse populations in the current exome genotyping arrays may hinder the
identification of population-specific variants for obesity.
CNV studies of obesity
Studies that used high-throughput genotyping array platforms have so far focused on SNP associations; other
genetic variants such as short indels and CNVs have not
been interrogated. The importance of CNVs has been
increasingly recognized since the first report in 2004 and
subsequent studies also revealed the commonness of CNVs
in the human genome. It has also found that CNVs are
in strong LD with SNPs, which can be interrogated indirectly using SNPs in GWAS. In addition to the findings
of CNVs in the genomes of normal healthy individuals,
CNVs have also been found to be associated with various
human diseases such as autism, breast cancer, colorectal
cancer, and lung cancer (Bergamaschi et al. 2006; Glessner
et al. 2009; Moroni et al. 2005). These findings, together
with the fact that SNPs identified by GWAS only partially
explain the heritability, have motivated the research community to start interrogating the association of CNVs in
human diseases and phenotypes, including obesity. Studies of CNVs in obesity have generated encouraging results
(Ahituv et al. 2007; Bochukova et al. 2010; Glessner et al.
2010; Jacquemont et al. 2011; Sofos et al. 2012; Walters
et al. 2010; Wang et al. 2010). Notably, a meta-analysis of
15 GWAS involving ~ 32,000 individuals reported a strong
association between BMI and a 45-kb deletion located near
to NEGR1 (Willer et al. 2009). This deletion was in strong
LD (r2 = 1.0) with the BMI-associated SNP rs2815752,
and this finding was confirmed by other studies (Jarick
et al. 2011; Speliotes et al. 2010). In addition, rs12444979
tagging the 21-kb deletion (CNV 16p12.3) upstream of
GPRC5B was found to be associated with obesity in Europeans but not in Chinese populations (Willer et al. 2009;
Yang et al. 2013). This study showed that CNV 16p12.3
could be population specific, highlighting the importance to
study the CNV associations in different populations.
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Hum Genet
In addition to the common CNVs, which are tagged
by SNPs, rare CNVs have also been identified for human
diseases. This is well demonstrated in the identification
of rare CNVs for schizophrenia and autism (Matsunami
et al. 2013; Merikangas et al. 2014). Similar success has
also been seen in obesity. For example, several studies performed on extreme early onset obesity in Caucasians have
consistently shown the association of a highly penetrant
deletion of ~ 593 kb at 16p11.2, which contains SH2B1
gene (Bochukova et al. 2010; Jacquemont et al. 2011; Walters et al. 2010). Interestingly, this highly penetrant rare
deletion was found only in morbidly obese patients and
was not detected in lean Caucasians (Walters et al. 2010).
In addition, another study in Europeans also identified 11
rare CNVs associated with obesity (Wang et al. 2010),
and multiple deletions were also identified in GWAS of
extremely obese Europeans and Africans (Glessner et al.
2010; Wheeler et al. 2013). Other examples of CNV associations with obesity/BMI include the finding of a CNV at
10q11.22 (Sha et al. 2009) and AHI1 copy number gains
in the Chinese population (Huang et al. 2012). Although
numerous CNVs have been found, but in comparison to the
validation performed for SNPs, replication studies in different populations are lacking to show their genuine association, except for the deletion of 593 kb at 16p11.2 (Jacquemont et al. 2011; Walters et al. 2010, 2013). Most of the
CNVs detected in obesity so far are rare and large; further
studies of smaller CNVs are needed to further assess the
importance of CNVs for obesity and other diseases.
NGS analysis of obesity genes
The arrival of NGS has led to a paradigm shift in the genetic
approaches to study human diseases. Whole-genome and
whole-exome sequencing have made a significant contribution to identify new mutations and genes especially for both
Mendelian disorders and complex diseases such as cancer,
mental disorders, and neurodegenerative disorders (Guerreiro et al. 2013; Ning et al. 2014; Schreiber et al. 2013). It
is clear that the common variants identified so far explain
only a proportion of the total attributable risk for complex
disease (Lander 2011). Arguments have increasingly been
put forward that the missing heritability in complex disease can be explained by rare and de novo mutations (Yang
et al. 2010). Rare variants are estimated and shown to have a
larger effect and are implicated in the early onset of certain
diseases such as autism, inflammatory bowel disease, and
schizophrenia (Carroll et al. 2010; Pinto et al. 2010; Rivas
et al. 2011). However, these variants are not readily detectable by SNP-based methods (Gorlov et al. 2011). The application of NGS technologies for studies in individuals at the
extreme of the BMI distribution is also useful in detecting
Author's personal copy
Hum Genet
rare variants, as these individuals seem to carry an increased
burden of obesity-risk alleles (Cotsapas et al. 2009).
Whole-genome and whole-exome sequencing have yet to
be applied in obesity research. However, a few studies have
employed targeted sequencing of obesity genes, for example, sequencing of FTO exons in ~ 3,000 Europeans revealed
known and novel rare mutations (Meyre et al. 2010). This
study identified functional mutations of FTO in extremely
obese and lean subjects and provided insight into the functional effects of the FTO gene in energy balance (Meyre
et al. 2010). Another study also sequenced FTO gene in the
Swedish population and identified indels as causative variants for obesity and reported that intron 1 is the only region
within the FTO gene that is associated with obesity (Sallman
Almen et al. 2013). Sequencing was also performed on 29
loci previously identified through GWAS, and identified candidate genes for obesity and non-alcoholic fatty liver disease
such as FTO, GNPDA2, BDNF and MTCH (Gerhard et al.
2013). Since this study was conducted only in extremely
obese Caucasians, the results may not be applicable to the
general population (Gerhard et al. 2013). A recent study
with microdroplet PCR-based enrichment and NGS analysis of targeted coding exons of 26 monogenic obesity genes
revealed two novel LEPR mutations associated with the early
onset of severe obesity in Pakistani children (Saeed et al.
2013). Similar to other approaches, replication in additional
samples is needed to substantiate the genetic associations.
Summary
Although quite a significant development has been made
over the past 10 years in dissecting the genetic basis of
human obesity, it remains much to be done in order to
account for the entire heritability of this disorder. Welldesigned GWAS with adequate statistical power should
continue to be applied in different populations which have
yet to be studied to identify additional novel loci. On the
other hand, exome genotyping array should also be applied
to identify the associations with rare variants on those
sample cohorts which had been previously studied by
GWAS. This will nicely complement the earlier GWAS
that focused on common SNPs. In addition to SNP associations, focus should also be given to CNVs which can also
be studied using array technologies; however, high-density
arrays would be required to study smaller CNVs or indels
which remain largely unexplored for their associations with
human diseases including obesity. Although exome genotyping array is a cost-effective approach applying to large
sample sizes, deep sequencing remains the most powerful tool to study rare variants which are more population
specific. It is probably a financial constraint to individual
efforts, to apply whole-exome or targeted gene sequencing
approaches in large sample sizes; this can be achieved
through consortium effort which had shown to be highly
successful. These continued efforts are only to identify
additional risk variants and loci which is still far from complete, other efforts should also be taken simultaneously to
fine-mapping GWAS loci unraveling the ‘true disease variants’ and to study their functional effects to provide more
biological insights of the disorder. When whole-genome
sequencing comes to the point where it is affordable to be
applied for a large sample size, it would be the most comprehensive approach to study the different types of genetic
variants ranging from SNPs, to small indels, CNVs and
other structural variants in a single dataset, and it would
encompass both common and rare frequency variants.
Acknowledgments This work was supported by High Impact
Research Ministry of Higher Education (HIR-MOHE) of Malaysia Grants E000049-20001 and FL009-2011 from the University of
Malaya.
Conflict of interest The authors declare that they have no conflict
of interest.
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