Genetics and Epigenetics of Obesity

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Maturitas. 2011 May; 69(1): 4149.

doi: 10.1016/j.maturitas.2011.02.018

PMCID: PMC3213306

Genetics and epigenetics of obesity

Blanca M. Herrera, Sarah Keildson, and Cecilia M. Lindgren

Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, United Kingdom

Oxford Centre for Diabetes, Endocrinology and Metabolism University of Oxford, United Kingdom

Cecilia M. Lindgren: [email protected]

Corresponding author. Tel.: +44 1865 287598; fax: +44 1865 287664. Email: [email protected]

Received June 14, 2010; Revised February 14, 2011; Accepted February 14, 2011.

Copyright 2011 Elsevier Ireland Ltd.

This document may be redistributed and reused, subject to certain conditions.

This document was posted here by permission of the publisher. At the time of the deposit, it included all changes made during peer review,

copy editing, and publishing. The U. S. National Library of Medicine is responsible for all links within the document and for incorporating any

publisher-supplied amendments or retractions issued subsequently. The published journal article, guaranteedto be such by Elsevier, is available

for free, on ScienceDirect, at: http://dx.doi.org/10.1016/j.maturitas.2011.02.018

Abstract

Obesity results from interactions between environmental and genetic factors. Despite a relatively high

heritability of common, non-syndromic obesity (4070%), the search for genetic variants contributing

to susceptibility has been a challenging task. Genome wide association (GWA) studies have

dramatically changed the pace of detection of common genetic susceptibility variants. To date, more

than 40 genetic variants have been associated with obesity and fat distribution. However, since these

variants do not fully explain the heritability of obesity, other forms of variation, such as epigeneticsmarks, must be considered.

Epigenetic marks, or imprinting, affect gene expression without actually changing the DNA

sequence. Failures in imprinting are known to cause extreme forms of obesity (e.g. PraderWilli

syndrome), but have also been convincingly associated with susceptibility to obesity. Furthermore,

environmental exposures during critical developmental periods can affect the profile of epigenetic

marks and result in obesity.

We review the most recent evidence for genetic and epigenetic mechanisms involved in the

susceptibility and development of obesity. Only a comprehensive understanding of the underlying

genetic and epigenetic mechanisms, and the metabolic processes they govern, will allow us to

manage, and eventually prevent, obesity.

Abbreviations: BMI, body mass index; WC, waist circumference; WHR, waist:hip ratio; T2D,

type-2-diabetes; LD, linkage disequilibrium; CNV, copy number variants; PWS, PraderWilli

syndrome; QTL, quantitative trait loci; SNP, single nucleotide polymorphism

Keywords: Genetic, Epigenetic, Fat distribution, Obesity, Methylation, Imprinting

1. Introduction

Overweight and obesity are becoming more widespread with global projections of more than 2.16

billion overweight and 1.12 billion obese individuals by 2030 [1]. This clearly presents a worldwide

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clinical and public health burden, associated with social and personal criticism. It is also correlated

with an increased risk of type-2-diabetes (T2D), cardiovascular disease, cancer and mortality [2,3].

Despite intensive research, current efforts to reduce obesity by diet, exercise, education, surgery and

drug therapies are failing to provide effective long-term solutions to this epidemic.

At an individual level, obesity occurs when abnormal amounts of triglycerides are stored in adipose

tissue and released from adipose tissue as free fatty acids (FFA) with detrimental effects [4]. Excess

fat accumulation occurs when energy intake exceeds energy expenditure, although individuals

respond differently to this imbalance owing to genetic predisposition. Twin studies estimateheritability of body mass index (BMI) to be 4070% in children and adults [57], and other

anthropometric measures of obesity and regional fat distribution [skinfold thickness, waist

circumference (WC) and waist:hip ratio (WHR)] show similar heritability [512]. Furthermore, there

are ethnic differences in obesity; admixture mapping studies demonstrate that obesity correlates

closely with the percentage of ancestry derived from ethnic groups with elevated prevalence [13,14].

The goal of obesity research is to elucidate pathways and mechanisms that control obesity and to

improve prevention, management and therapy.

Here we review recent advances in identifying factors contributing to obesity susceptibility. We focus

on:

We discuss the impact of recent findings in these two areas and their joint potential to enhance

understanding of obesity susceptibility mechanisms and aetiology.

2. The identification of susceptibi lity loci for obesity

Until 2006, the main approaches used to track down common variants influencing obesity, involved

either hypothesis-free genome-wide linkage mapping in families with multiple obese subjects or

association studies within candidate genes using casecontrol samples or parentoffspring trios. The

former suffered from being underpowered for any sensible susceptibility models, as linkage is best

placed to detect variants with high penetrance. As far as we can tell, common variants with high

penetrance do not contribute substantially to risk of common forms of obesity and few, if any, robust

signals have emerged from such efforts [15,16]. The latter candidate-gene association approach has

historically been compromised by difficulties in selecting credible candidates. Selection was typically

based on hypotheses about biological mechanisms putatively involved in obesity pathogenesis but, as

the function of much of the genome is poorly characterized, it remains almost impossible to make fully

informed decisions. In addition, all too often these candidate-gene studies were conducted in sample

sets far too small to offer confident detection of variants with the range of effect sizes that are now

known to be realistic. With hindsight, it is easy to appreciate why these approaches yielded fewexamples of genuine obesitysusceptibility variants.

Consequently, over the last two decades, efforts in identifying and replicating genetic variants

predisposing individuals to common forms of obesity were largely characterized by slow progress and

limited success, in sharp contrast to the successful gene identification in monogenic and syndromic

forms of obesity [15]. The most recent edition of the Human Obesity Gene Map gives an excellent

overview of this; it lists 11 single gene mutations, 50 loci related to Mendelian syndromes relevant to

human obesity, 244 knockout or transgenic animal models and 127 candidate genes, of which slightly

less than 20% are replicated by 5 or more studies [15]. A total of 253 quantitative trait loci (QTL), for

recent successes in identification of genetic variation affecting obesity trait susceptibility;(a)

emerging evidence connecting epigenetic (heritable changes which affect gene function but do

not modify DNA sequence) events with obesity.

(b)


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different obesity-related phenotypes, have been reported from 61 genome-wide linkage scans and of

these, only 20% are supported by more than one study [15].

Over the past three years it has become possible, from technical and economic perspectives, to

undertake hypothesis-free GWA testing in samples of sufficient size to generate convincing

association results. The advent of the GWA approach was the result of three components. The first

was the human genome sequence which subsequently enabled cataloguing genome-sequence

variation. Secondly, the International HapMap Consortium (http://www.hapmap.org) [17]taught us

that, in non-African-descent populations, extensive correlations (linkage disequilibrium, LD) betweenneighbouring single nucleotide polymorphisms (SNPs) constrain the number of independent genetic

tests required to survey the genome, such that 80% of all common variation can be sampled using

500 000 carefully selected SNPs [18,19]. Lastly, novel genotyping methods address the challenges of

massively parallel SNP-typing at high accuracy and low cost [20]. The GWA approach has been

hugely successful in identifying loci harbouring common forms of obesity (as defined by

anthropometric measures: BMI, WC and/or WHR) susceptibility genes and hereto results from a total

of 15 high-density GWAs (i.e. 300 000 SNPs, offering genome-wide coverage >65%) have been

published (Table 1). These studies combined, have yielded over 50 loci associated with obesity

(p-values


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association for 7 of the 13 loci previously reported to influence BMI [3234]. This suggests that

variants influencing BMI might also contribute to more severe forms of obesity, which represents the

extreme, of the phenotypic spectrum rather than a distinct condition, although, this needs

confirmation in appropriately powered studies.

Recently, a large-scale meta-analysis of 249 769 individuals confirmed 14 of these previously

identified obesity susceptibility loci and identified 18 novel loci associated with BMI and overall

adiposity (Table 2) [37]. As a consequence of the increased power of this study, new signals with

lower minor allele frequencies and smaller effect sizes, compared to previously identified variants,were discovered. These results suggest that genome-wide association studies are only the first step in

the identification of the causal variants that play a role in common, overall obesity and weight

regulation and further insights into the underlying biological mechanisms and pathways will be

needed for the effective treatment and management of these traits [37].

While no consistent association has been shown between height and BMI-associated variants as a

group, three of the loci (MC4R,RBJADCY3POMCand MTCH2NDUFS3) show individual

associations with height [37]. The BMI-increasing alleles of the variants near POMCand MC4Rwere

associated with decreased and increased height respectively, and this is analogous to the effects of

severe mutations in POMCand MC4Ron height and weight [31,38].

2.1. Genetics of fat distribution

As described above, efforts to identify common and rare variants influencing BMI and risk of obesity

have emphasized a key role for neuronal regulation of overall obesity [2124,2630,3235], but until

recently provided few clues to processes responsible for variation in central obesity and fat

distribution [29,39,40]. Measures of central and general adiposity are highly correlated: BMI has an

r 0.9 with WC and 0.6 WHR. Also, WC and WHR are correlated with measures of intra-

abdominal fat, measured by magnetic resonance imaging in obese women (r 0.6 and 0.5,

respectively). Clinically, central obesity is associated with susceptibility to T2D, cardiovascular disease

and increased mortality [3]. Evidence indicates that individual variability in patterns of fat

distribution involve local, depot-specific and body-shape processes, which are probably independentof the mechanisms that control overall energy balance and general obesity. First, anthropometric

measures of central adiposity are highly heritable [41]and, after BMI correction, heritability

estimates remain high (0.6 for WC and 0.45 for WHR) [10,11,42]. Second, substantial gender-

specific differences in fat distribution, reflect specific genetic influences [43,44]. Third, inherited

lipodystrophies, which are monogenic syndromes, demonstrate that DNA variants can have specific

effects on the development and/or maintenance of specific regional fat-depots and body shape [45].

Hereto, five GWA studies of central obesity (WC and WHR) have been published [29,39,40,44,46](

Table 1) and have identified 17 novel common obesity loci. The associations of three of these loci

(MSRA; TFAP2Band NRXN3, Table 2) to WC appear to be mediated through BMI [36,37,39,44],

implying their involvement in overall adiposity. The remaining 14 loci, however, showed significant

associations with WHR, after correction for BMI, suggesting their role in body fat distribution,

independent of overall obesity. Interestingly, some of these loci showed strong sex-specific effects in

women [39,44]and in a study by Heid et al. [44], the WHR-increasing allele was shown to be

associated with an increase in WC for 14 loci in women, but only six men. This evident heterogeneity

between men and women across many of these WHR loci is a reflection of the sex-specific genetic

effects driving individual patterns of body fat.

3. Epigenetics and obesity

Epigenetics is loosely defined as the study of heritable changes which affect gene function without

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3.1.1. DNA methylation

3.1.2. Histone modif ications

modifying the DNA sequence [47]. The maintenance of epigenetic marks through generations is

poorly understood and the notion of their transmission is contentious [48]. Epigenetic marks are

tissue specific and include DNA methylation and histone modifications which mediate biological

processes such as imprinting. As many imprinted genes are growth factors, or regulators of gene

expression controlling growth, imprinting disorders often feature obesity as one of their clinical

characteristics.

Genomic imprinting determines expression of alleles according to their maternal or paternal origin

[49]and establishes a balance between the expression of the parental alleles influencing growth [50],resulting in counteracting growth effects of paternal and maternal genomes [51]. In addition to

growth, imprinted genes are also involved in differentiation, development, viability and metabolic

functions [52]. Two main clusters of genomic imprinting are known in humans: a region at 11p15

containing several imprinted genes including IGF2,INS,KCNQ10T1(LIT1) (paternally expressed)

and H19,KCNQ1, CDKN1C,PHLDA2,KVLQT1(maternally expressed) [53]. The second cluster at

15q11q12 contains at least 7 imprinted genes, including; MKRN3,MAGEL2,NDN,SNURFSNRPN

(paternally expressed) and UBE3A,ATP10A(maternally expressed) [54].

Failures in imprinting which result in obesity by altering expression of growth and cellular

differentiation factors can arise due to numerous genetic events: translocation, inversion, duplication,

paternal disomy and hyper/hypo-methylation. For instance; paternal deletion or uniparental disomy

at 15q11q13, results in PraderWilli syndrome (PWS). A syndrome characterized by severe

(sometimes life threatening), early onset obesity caused by hyperphagia (due to dysfunction in the

satiety centre [55]). Moderate obesity appears in Albright hereditary osteodystrophy (AHO), due to

disruption of imprinting at the GNASgene (20q13.11 [50]).

3.1. Mediators of genomic imprinting

Genomic imprinting is mediated by DNA methylation as exemplified in the H19

and IGF2loci [56]. Methylation is a widespread feature of the genome, and is obtained through the

addition of a methyl group (CH3) to a cytosine positioned next to a guanine nucleotide (CpGs),

usually in regions with a high presence of CpG dinucleotides (>60%). Methylation in a promoterregion results in the repression (silencing) of gene expression [57], this effect may be achieved by a

number of mechanisms including: obstructing access to transcription factors/activators and

recruitment of co-repressors (like histone deacetylases) which alter chromatin structure [58]resulting

in failure to initiate transcription (Fig. 1).

DNA in cells is packaged as chromatin in a beads on a string

configuration. A length of 147 DNA base pairs wraps around a core histone octamer (a tetramer of

histones H3 and H4, flanked by two H2AH2B dimmers) and these nucleosome beads are

separated by DNA strings between 20 and 60 base pairs. Linker histones (H1/H5) occupy the exit

and entry of the DNA into the histone, these structures are in turn coiled into a compact helical

closed configuration [59]. Packaging DNA in this manner allows its efficient storage and isparamount for regulation of gene expression, as the closed configuration does not allow access to

transcriptional enzymes. Post translational modification of the histones by: H3 and H4

hyperacetylation in the promoter [60], methylation of the lys4 and lys36 of histone H3 open the

structure and allow gene expression. In contrast, methylation of lys9, lys20 and lys27 on H3 [60,61])

and ubiquitination of H2A present at high density CpG promoters [62]result in gene silencing

closed configuration. Further complexity is achieved by the level of methylation, so mono-, bi- or

tri-methylation may also effect the control of gene expression [63].

A feature of both methylation and histone modifications is that they are both tissue specific and can


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vary with age (and developmental stage). Therefore, in order to place findings in an appropriate

context, it is of paramount importance that evaluation of epigenetic factors be carried out on suitable

tissues extracted at specified times [64,65](Fig. 2).

3.2. Epigenetic changes introduced during early development may increase the risk of obesity

Although birth weight (BW) is an imperfect summary index of growth, however, it has been widely

used as a proxy for foetal nutrition and intrauterine growth. Although better measurements of foetal

growth exist, BW measurements are used because they are non-intrusive, easily obtained and oftenfound in birth records, which enabled its use as in retrospective studies linking BW with adult onset

diseases [66]. Many studies focus on the hypothesis that early environmental influences induce

epigenetic variation, thereby permanently affecting metabolism and chronic disease risk. Specifically,

for obesity, it has been shown that obese mothers tend to have obese children [67], it has been shown

that clinical intervention to cause maternal weight loss can have a positive effect on reducing risk of

obesity in the offspring [68]. The mechanisms by which nutritional challenges affect the risk of

disease in later life are poorly understood. However, evidence indicates that the establishment of the

epigenome can be affected by environmental factors during critical developmental periods [69].

Possible disturbances of methylation may arise during foetal development due to lack of availability of

dietary methyl donors [7072]. Potential interactions between the environment and epigenetic

mechanisms mediating the expression of genes associated with increased BMI and adiposity, may

also be possible as suggested for; the FTOlocus is a DNA-demethylase enzyme [23], the MC4Rgene

which has reduced methylation following long-term exposure to a high fat diet [73], the PPAR

protein which interacts with histone acetyltransferases [58]during adipogenesis and on the effect of

diet on methylation ofPOMC[74]and Leptin[75].

With this in mind, it is important to consider the effect of assisted reproduction technology on

epigenetics and subsequently on obesity susceptibility. Despite small numbers, increased incidence of

PWS, when compared to background population prevalence, has been shown [76]and subtler adverse

effects may only become apparent later on in life. We are currently gaining more insight through the

use of high-throughput sequencing methods for the detection of DNA methylation patterns, and

chromatin immunoprecipitation (ChIP) used to detect histone modifications and new integrated

genomics-based technologies which are currently being developed by the human epigenome project

(http://www.epigenome.org) initiative.

4. Remaining challenges

4.1. Identifying novel loci

Despite successes in susceptibility loci identification for obesity through the first wave of GWAs, the

combined effect of the loci explains only 23% of the inherited contribution to obesity risk.

4.2. Collaborativ e stud ies to fo r larger GWA meta-analysis

The GIANT consortium is finalising the next wave of GWA meta-analysis for obesity related traits

incorporating >100 000 samples. This increase in sample size should attain sufficient power to allow

identification of common variants with even smaller effect sizes than hereto. Also, conditional

analysis using the previously identified variants will reveal whether some of the already identified loci

contain more than one independent association with obesity, which would be an additional source of

unexplored variation contributing to the missing heritability.

4.3. Obesity susceptibility associated to rare-low-frequency variants

The obesity risk alleles that have been identified so far by GWAs are quite common (2791%) in


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European populations but the current approaches are limited in the range of minor allele frequencies

(MAF) that are detected due to the range of allele frequencies (>5% MAF) present on current

genotype arrays. The 1000 genomes project (http://www.1000genomes.org/) is designed to catalogue

low and rare frequency variants and this will lead to the design of new genotype arrays including

wider spectra of allele frequencies.

4.4. Copy number variations

Copy number variants (CNVs) are hard to detect due to technical constrains but recently [74], twocomplementary approaches have delivered the first examples of CNVs associated to obesity. The SNPs

associated with obesity at theNEGR1locus are in strong LD with a nearby CNV [32], tagging a 45-kb

deletion polymorphism which is a candidate causal-variant at this locus. Recently by analysing SNP

genotype arrays that are enriched in CNV information and applying novel CNV detecting algorithms

on these arrays deletions on chromosome 16p11.2 have been reported to be associated with extreme,

highly penetrant obesity [77,78]. Further applications of these approaches in larger sample sets

should allow a better understanding of total CNVs contribution to overall variation in obesity

susceptibility.

4.5. Characterization of associated loci and causal variants

It is not clear how much of the remaining variation will be uncovered by the aforementioned steps.

Beyond identifying more susceptibility loci, finding the causal gene(s) and variant(s) in each locus

will be necessary to allow the detailed molecular and physiological investigations that are necessary

to determine the mechanisms and pathways through which these contribute to obesity susceptibility

and ultimately allow this knowledge to be translated into better prediction and treatment options.

4.6. Integration of genetic and epigenetic information

Genetic and epigenetic factors are intimately intertwined, as epigenetic marks and DNA modifications

are the direct consequence of sequence-specific interactions between proteins and DNA [79]. The

completion of the human epigenome project will increase our understanding of the genetic and

epigenetics underlying cellular homeostasis and the integration of this knowledge with the known

environmental risks to obesity, must be applied so that it may eventually be exploited and

manipulated to appease the obesity epidemic.

5. Conclusions

The current obesity epidemic is clearly not of genetic origin per se, but due to unfavourable changes in

lifestyle and environment (the obesogenic environment). The obesogenic environment has different

effects on different individuals in the same environment, highlighting an underlying, inherited

susceptibility to obesity and fat-distribution. For more than a decade, the genetics underlying

common forms of obesity have remained elusive although the advent of the GWA approach has

started to deliver robust associations to obesity. The epigenetic contribution to common forms ofobesity are still largely unknown but, from rare syndromes and animal models we conclude that it is

likely that both genetic and environmental effects on epigenetics will in turn be associated with

obesity. We have started to identify an emerging pattern of effects acting through CNS, suggesting

that a component of an individual's response to the obesogenic environment is partly

neurobehaviourally driven. There is also some evidence of effects acting more peripherally in the

adipose tissue. Despite the success of GWAs in obesity loci identification, we still only explain a low

fraction of the inter-individual variation of obesity. Extensive work including identification of more

obesity susceptibility loci, a better understanding of the gene(s) through which the effect is executed,

as well as further molecular and physiological characterization of the associated genes, is now


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necessary before any of these findings will lead to any useful therapeutic interventions.

Contribu tors and their role

All the authors were involved in the drafting, editing and approval of this manuscript.

Blanca M. Herrera and Sarah L. Keildson were involved in the construction of the tables and figures.

Competing interests

None.

Provenance and peer review

Commissioned and externally peer reviewed.

Acknow ledgement

C.M.L. is a Wellcome Trust Research Career Development Fellow (086596/Z/08/Z).

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Figures and Tables


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Fig. 1

CpG methylation and regulation of gene expression. Unmethylated or hypomethylated DNA (usually in the

promoter region) allows binding of the transcription factors (TF) and other regulatory mechanisms which results in

transcription and mRNA production. Methylated DNA (bottom panel) obstructs binding of the TF, and in some cases

might recruit methyl-CpG binding proteins and other transcription co-repressors, blocking access of the

transcription enzymes and resulting in gene silencing.


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Fig. 2

Histone modification. This simplified diagram of a nucleosome shows a histone octamer bead surrounded by a

DNA strand and try-methylation at lysine-9, this kind of modification exemplifies modifications found at promoter

regions of silenced genes.


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Table 1

Overview of GWA scans or meta-analysis thereof for obesity phenotypes.

Reference Study name

(if any)

Number of samples in

discovery cohort

Ancestry of discovery

cohort

Phenotype

Frayling et al.

[20]

WTCCC 1924 Europeans BMI quantitative analysis

Scuteri et al.

[26]

Sardinia 4741 Europeans BMI waist circumference

(WC) quantitative analysis

Chambers et al.

[28]

LOLIPOP 2684 Indian Asians Insulin resistance and

related quantitative

phenotypes

Loos et al. [27] 16 876 Northern European BMI quantitative analysis

Heard-Costa et

al. [36]

The CHARGE

consortium

31 373 Europeans WC quantitative analysis

Lindgren et al.

[35]

The GIANT

consortium

38 580 Europeans WC and waist:hip-ratio

(WHR) quantitative

analysis

Cotsapas et al.

[33]

775 cases and 3197

unascertained controls

Europeans Extreme obesity/BMI

Meyre et al.

[32]

1380 and 1416 age-matched

normal-weight control

Europeans Early onset and morbid

adult obesity

Thorleifsson et

al. [31]

DeCODE 37 347 Europeans + African

Americans

BMI quantitative analysis

Willer et al.

[30]

The GIANT

consortium

32 387 Europeans BMI quantitative analysis

Hinney et al.

[79]

487 extremely obese young

cases and 442 healthy lean

controls


Scherag et al.

[34]

453 extremely obese young

cases and 435 healthy lean

controls


Cho et al. [41] KARE 8842 Asian BMI, WHR quantitative

analysis

Heid et al. [43] MAGIC 77 167 European WHR quantitative analysis

Speliotes et al.

[37]

123 865 European BMI quantitative analysis


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Table 2

The 54 loci associated to anthropometric obesity phenotypes.

Closest gene(s) Chromosomal

location

Phenotype Associated

lead

SNP(s)

Proposed

molecular or

cellular function

Additional

phenotypes

References

TBX15WARS2 1p12 WHR rs984222 Transcription

factor involved inadipocyte and

specific adipose

depot development

Implicated in

Cousin syndrome

Heid et al.

[43]

PTBP2 1p21.3 BMI rs1555543 Speliotes et al.

[37]

NEGR1 1p31 BMI rs2815752,

rs3101336,

rs2568958

Neuronal

outgrowth

Thorleifsson et

al. [31], Willer

et al. [30],

Speliotes et al.

[37]

TNNI3K 1p31.1 BMI rs1514175 Speliotes et al.

[37]

DNM3PIGC 1q24.3 WHR rs1011731 Dominant, negative

mutations in DNM

enzymes promote

GLUT6 and GLUT8

transporters to

adipocyte cell

surface in rats.

Heid et al.

[43]

SEC16B,RASAL2 1q25 BMI rs10913469 Thorleifsson et

al. [31],

Speliotes et al.

[37]

LYPLAL1; ZC3H11B 1q41 WHR rs2605100 Encodes protein

thought to act as

triglyceride lipase

and is upregulated

in subcutaneous

adipose tissue in

obese patients

Lindgren et al.

[35], Heid et

al. [43]

SDCCAG8 1q43q44 BMI rs12145833 Scherag et al.[34]

FANCL 2p16.1 BMI rs887912 Speliotes et al.

[37]

RBJADCY3POMC 2p23.3 BMI rs713586 Rare POMC

mutations cause

human obesity

Speliotes et al.

[37]

TMEM18 2p25 BMI rs6548238,

rs2867125,

rs4854344,

Neural

development

Associated with

T2D

Willer et al.

[30],

Thorleifsson et


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location


lead

SNP(s)

Proposed

molecular or

cellular function

Additional

phenotypes

References

rs7561317,

rs11127485

al. [31],

Scherag et al.

[34], Speliotes

et al. [37]

ZNRF3KREMEN1 2q12.1 WHR rs4823006 Kremen1 proteinforms a complex

with LDL

receptor-related

protein 6

Heid et al.[43]

LRP1B 2q22.2 BMI rs2890652 LRP1Bdeletions

seen in several

types of human

cancers

Speliotes et al.

[37]

GRB14 2q24.3 WHR rs10195252 Associated with

triglyceride and

insulin levels.

GRB14-deficient

mice exhibit

increased body

weight

Heid et al.

[43]

ADAMTS9 3p14.1 WHR rs6795735 Important for

spatial distribution

of cells in

embryonic

development

Associated with

T2D

Heid et al.

[43]

NISCHSTAB1 3p21.1 WHR rs6784615 Interacts withinsulin receptor

substrate

Heid et al.[43]

CADM2 3p21.1 BMI rs13078807 Speliotes et al.

[37]

ETV5(locus with

three genes,

strongest association

in ETV5)

3q27 BMI rs7647305 Thorleifsson et

al. [31],

Speliotes et al.

[37]

Gene desert;

GNPDA2is one of

three genes nearby

4p13 BMI rs10938397 Associated with

T2D

Willer et al.

[30], Speliotes

et al. [37]

SLC39A8 4q24 BMI rs13107325 Speliotes et al.

[37]

FLJ35779 5q13.3 BMI rs2112347 Speliotes et al.

[37]

ZNF608 5q23.2 BMI rs4836133 Speliotes et al.

[37]


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location


lead

SNP(s)

Proposed

molecular or

cellular function

Additional

phenotypes

References

CPEB4 5q35.2 WHR rs6861681 Regulates

polyadenylation

elongation

Heid et al.

[43]

TFAP2B 6p12 WC, BMI rs987237 Lindgren et al.

[35], Spelioteset al. [37]

Locus containing

NCR3, AIF1and

BAT2

6p21 BMI rs2844479,

rs2260000,

rs1077393

Associated with

weight, not BMI

Thorleifsson et

al. [31]

VEGFA 6p21.1 WHR rs6905288 Involved in vascular

development. Key

mediator of

adipogenesis

VEGFAvariants

nominally

associated with

T2D

Heid et al.

[43]

NUDT3HMGA1 6p21.31 BMI rs206936 Speliotes et al.

[37]

PRL 6p22.2p21.3 BMI rs4712652 Meyre et al.

[32]

LY86 6p25.1 WHR rs1294421 Plays a role in

recognition of

lipopolysaccharide

Associated with

asthma

Heid et al.

[43]

RSPOS 6q22.33 WHR rs9491696 Promotes

angiogenesis and

vascular

development

Oncogene in

mouse mammary

epithelial cells

Heid et al.

[43]

NFE2L3 7p15.2 WHR rs1055144 Heid et al.

[43]

MSRA 8p23.1 WC, BMI rs7826222,

rs17150703

Lindgren et al.

[35], Scherag

et al. [34]

LRRN6C 9p21.3 BMI rs10968576 Speliotes et al.

[37]

PTER 10p12 BMI rs10508503 Meyre et al.

[32]

MTCH2(locus with

14 genes)

11p11.2 BMI rs10838738 Cellular apoptosis Willer et al.

[30], Speliotes

et al. [37]

BDNF(locus with

four genes, strongest

association near

BDNF)

11p14 BMI rs4074134,

rs4923461,

rs925946,

rs10501087,

rs6265

BDNFexpression is

regulated by

nutritional state

and MC4R

signalling

Associated with

T2D. Individuals

with WAGR

syndrome with

BDNFdeletion

have BMI >95th

percentile.BDNF

knockdown in

Thorleifsson et

al. [31],

Speliotes et al.

[37]


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location


lead

SNP(s)

Proposed

molecular or

cellular function

Additional

phenotypes

References

mouse

hypothalamus

causes

hyperphagia and

obesity

RPL27A 11p15.4 BMI rs4929949 Speliotes et al.

[37]

ITPR2SSPN 12p21.1 WHR rs718314 Mice lacking

ITPR2and ITPR3

exhibited

hypoglycaemia

and lean body

type

Heid et al.

[43]

HOXC13 12q13.13 WHR rs1443512 Transcription

factor important in

cell spatial

distribution in

embryonic

development

Heid et al.

[43]

FAIM2(locus also

containsBCDIN3D)

12q13 BMI rs7138803 Adipocyte

apoptosis

Thorleifsson et

al. [31],

Speliotes et al.

[37]

C12orf51 12q24 WHR rs2074356 Cho et al. [41]

MTIF3GTF3A 13q12.2 BMI rs4771122 Speliotes et al.

[37]

PRKD1 14q12 BMI rs11847697 Speliotes et al.

[37]

NRXN3 14q31 WC, BMI rs10146997 Heard-Costa

et al. [36],

Speliotes et al.

[37]

MAP2K5 15q23 BMI rs2241423 Speliotes et al.

[37]

SH2B1(locus with

1925 genes)

16p11.2 BMI rs7498665,

rs8049439,

rs4788102,

rs7498665

Neuronal role in

energy homeostasis

Sh2b1-null mice

are obese and

diabetic

Willer et al.

[30],

Thorleifsson et

al. [31],

Speliotes et al.

[37]

GPRC5B 16p12.3 BMI rs12444979 Speliotes et al.

[37]

MAF 16q22q23 BMI rs1424233 Transcription

factor involved in

adipogenesis and

Meyre et al.

[32]


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location


lead

SNP(s)

Proposed

molecular or

cellular function

Additional

phenotypes

References

insulinglucagon

regulation

FTO 16q22.2 BMI rs9939609,

rs6499640,

rs8050136,rs3751812,

rs7190492,

rs8044769,

rs1558902

Neuronal function

associated with

control of appetite

Associated with

T2D

Frayling et al.

[20], Scuteri

et al. [26],Chambers et

al. [28], Willer

et al. [30],

Thorleifsson et

al. [31], Meyre

et al. [32],

Scherag et al.

[34], Speliotes

et al. [37]

NPC1 18q11.2 BMI rs1805081 Intracellular lipid

transport

NPC1-null mice

show late-onset

weight loss and

poor food intake.

NPC1interferes

with function of

raft-associated

insulin receptor

signalling

Meyre et al.

[32]

MC4R 18q22 BMI rs17782313,

rs12970134,

rs17700144

Hypothalamic

signalling

Haplo-

insufficiency in

humans is

associated with

morbid obesity.

MC4R-deficient

mice show

hyperphagia and

obesity

Willer et al.

[30],

Thorleifsson et

al. [31], Meyre

et al. [32],

Loos et al.

[27],

Chambers et

al. [28],

Scherag et al.

[34], Speliotes

et al. [37]

KCTD15 19q13.11 BMI rs11084753,

rs29941

Willer et al.

[30],

Thorleifsson et

al. [31],

Speliotes et al.

[37]

QPTCL-GIPR 19q13.32 BMI rs2287019 Encodes incretin

receptor

Associated with

fasting and 2-h

glucose

Speliotes et al.

[37]

TMEM160 19q13.32 BMI rs3810291 Speliotes et al.

[37]


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Genetics and Epigenetics of Obesity