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From Craig H. Warden and Janis S. Fisler, Genetics of Nonsyndromic Human Obesity, With Suggestions
for New Studies From Work in Mouse Models. In: Ann M. Coulston, Carol J. Boushey, Mario G.
Ferruzzi and Linda M. Delahanty, editors, Nutrition in the Prevention and Treatment of Disease. Oxford:
Academic Press, 2017, pp. 453-476.
ISBN: 978-0-12-802928-2
Copyright © 2017 Elsevier Inc.
Academic Press.
Chapter 21
Genetics of Nonsyndromic Human
Obesity, With Suggestions for New
Studies From Work in Mouse Models
Craig H. Warden and Janis S. Fisler
University of California, Davis, CA, United States
I INTRODUCTION
Complex and incompletely defined interactions between
environment and genetics determine each individual’s
height and weight, as well as other human quantitative
traits. The result is a population in which individuals vary
widely for height and weight, but no one factor can be iden-
tified as controlling either trait in most people. In humans,
long-term adult weight is relatively stable, as evidenced by
the difficulty of sustaining intentional weight loss and the
almost automatic return to previous weight following brief
periods of overeating. This drive to constancy of body
weight is due to both behavioral and physiological altera-
tions that accompany weight change. Convincing evidence
of the biological basis of the regulation of body fat stores
comes from the identification of dozens of rare single-
gene Mendelian mutations and syndromes that result in
spontaneous massive obesity or in adipose tissue atrophy.
Most human obesity, however, is not due to mutations
in single genes that have overwhelming effects, but is
inherited as a complex, multigenic, quantitative trait influ-
enced by many genetic and environmental variables.
There are likely to be interactions among genes and
between genes and environmental factors such that some
alleles of one gene will not cause obesity unless specific
alleles of another gene or environmental pressures are also
present. Dietary effects on parents and parental genetics,
independent of progeny genotype, also exert powerful but
indirect effects on obesity. Genetic heterogeneity, where
similar phenotypes are caused by more than one gene, and
incomplete penetrance of the trait, where not all people
with the gene develop the phenotype, also make dissection
of complex phenotypes difficult. Expression of an obesity
gene may also be age- or gender dependent. Thus, identifi-
cation of all the genes promoting human obesity has not
been, and will never be, a trivial task.
This chapter is not an exhaustive compendium of all
things of genetics and obesity. This chapter does not
include discussion of congenital lipodystrophies [1] nor rare
genetic syndromes that include obesity in the phenotype,
such as BardetBiedl and PraderWilli, as these do not
seem to contribute significantly to common causes of obe-
sity and are reviewed elsewhere [2,3]. The present chapter
is focused on human genetics, thus we do not plan to dis-
cuss the many effects of gut microbiota on metabolism and
obesity [4]. The present chapter will discuss both mono-
genic and multigenic obesity, some of the techniques used
to discover them, and general principles derived from these
studies. We will list and discuss the most important known
obesity genes, but we will not attempt to provide an exhaus-
tive catalog of obesity genes (see [5,6]). Genetics is a rap-
idly progressing field, and knowledge of the genetic basis
for obesity is expanding exponentially. Therefore, the
reader should use this chapter to understand the most com-
mon genes and mechanisms, general ideas for finding more
human obesity genes based on what has already been dem-
onstrated in mice, and an appreciation of the wide variety
of mechanisms by which genetics influences obesity.
II THE BIG PICTURE—HOW MUCH
OBESITY IS DUE TO GENETICS
Genetic epidemiology of human obesity is the study of
the relationships of the various factors determining the
455
Nutrition in the Prevention and Treatment of Disease. DOI: http://dx.doi.org/10.1016/B978-0-12-802928-2.00021-7
©2017 Elsevier Inc. All rights reserved.
Author’s personal copy
frequency and distribution of obesity in the population.
Such studies of obesity are limited in that they do not
examine genetic variations and rarely directly measure
the amount or location of body fat. However, genetic epi-
demiology studies do provide information as to whether
there is a genetic basis for the trait, whether a major gene
is involved in the population, whether inheritance is
maternal or paternal, the relative importance of genes and
shared or nonshared environment, and whether expression
of the trait is gender or age dependent. Genetic epidemiol-
ogy studies of human obesity employ a variety of designs
and statistical methods, each giving somewhat different
estimates for heritability of obesity. For a discussion of
genetic epidemiology methods employed in the study of
obesity, see [7].
The heritability estimates for human obesity are
derived from a large number of studies of adoptees, twins,
families, or communities. Population or family studies
tend to have lower, and twin studies to have higher, herita-
bility for body mass index (BMI). Heritability of BMI
has been estimated from adoption studies to be as low as
10% and from twin studies to be as high as 85% [7,8]
(Fig. 21.1). In a pediatric twin study, genetic influences
contributed 7580% in the percent of body fat [11]. The
heritability for BMI in a study of childhood obesity in a
Hispanic population was 40% and, in that study, heritabil-
ity of diet and physical activity phenotypes ranged from
32% to 69% [12]. By using data from all types of studies,
it is estimated that 4070% of the within population vari-
ation in obesity is due to genetic variation [10] (Fig. 21.1).
Most studies indicate that familial environment has only a
minor impact on obesity.
III WHY FINDING OBESITY GENES
MATTERS
During 201114, in the United States the prevalence of
obesity in adults aged 20 years and over was 36% and in
youths was 17% [13]. According to the World Health
Organization in 2014 of the world population more than 1.9
billion adults were overweight (39%) and of these over 600
million were obese (13%). A total of 42 million children
under the age of 5 years were overweight or obese in 2013
(http://www.who.int/mediacentre/factsheets). Obesity rates
worldwide are predicted to rise to between 42% and 51% of
the adult population by 2030 [14]. If obesity rates were to
remain at 2010 levels the savings in medical expenditures
over the next two decades could approach $550 billion.
Obesity is not just a financial burden. Sometimes the
problems caused by obesity are social, such as discrimina-
tion, sometimes obesity influences quality of life by, for
example, limiting physical activity, and sometimes
obesity is associated with diseases that shorten lifespan,
such as heart disease, type 2 diabetes, hypertension, and
cancer. Additional obesity comorbidities include arthritis,
limitations on mobility, sleep apnea, gallstones, and
kidney disease. Until recently there was no way to deter-
mine if obesity caused these comorbidities or if they were
simply correlated with obesity. Mendelian randomization,
a study design that incorporates genetic information into
traditional epidemiological methods, now provides a
method to determine if genes simultaneously cause both
obesity and comorbidity [15]. Causal relations then mean
that treatment for obesity becomes even more urgent and
that treatment can target specific genetic pathways that
cause both obesity and comorbidity.
Studies of people who have lost weight by diet or bariat-
ric surgery prove reduced mortality and improved quality of
life. A person’s genes influence weight gain, weight loss,
and health consequences of obesity. Finding obesity genes
may provide tools to improve health of people worldwide.
IV THE SEARCH FOR OBESITY GENES
A Lessons for Human Obesity From Genetic
Studies in Mice
Although many decades ago genetic epidemiology studies
provided evidence that obesity is highly genetic, there
FIGURE 21.1 Heritability of obesity as determined by different study
types. Data for studies of twins, nuclear families, and adoption studies
are taken from C. Bouchard, L. Perusse, T. Rice, D.C. Rao, The genetics
of human obesity, in: G.A. Bray, C. Bouchard, W.P.T. James (Eds.),
Handbook of Obesity, Marcel Dekker, New York, NY, 1998. Data for
community-based studies are taken from A. Herbert, N.P. Gerry, M.B.
Mcqueen, I.M. Heid, A. Pfeufer, T. Illig, et al., A common genetic variant
is associated with adult and childhood obesity. Science 312 (2006)
279283. Range of heritability estimated from all study types is taken
from A.G. Comuzzie, D.B. Allison, The search for human obesity genes.
Science 280 (1998) 13741377.
456 PART | C Overweight and Obesity
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was no understanding of the molecular basis for obesity
until the identification of genes that cause Mendelian
forms of obesity in mice. Five genes were known for
many decades to cause monogenic obesity syndromes in
mice. Positional cloning of the mouse obesity genes,
Lep
ob
,Lepr
db
,Tub,Cpe
fat
, and A
y
, from naturally occur-
ring mutant models between 1992 and 1996 led to an
explosion of knowledge of the genetic causes of obesity
[16]. When the third edition of this chapter [17] was pub-
lished, human orthologs of three mouse obesity genes
were known to cause obesity in humans and a fourth
mouse obesity gene identified a pathway that caused
human obesity. Subsequent studies have now demon-
strated that human versions of all five mouse Mendelian
obesity genes act in the brain to either directly cause
obesity or identify a pathway that causes obesity. These
mouse monogenic obesity genes in most instances are
recessive and their human orthologs are expected to rarely
cause obesity in the human population.
Mouse models of obesity provide information that
often replicates causes of human obesity. Several hundred
different knockout and transgenic mice have been devel-
oped where absence or replacement of a single-gene
affects obesity or its phenotypes (for a listing of knockout
and transgenic mouse models of obesity and related pheno-
types see [6]). These are all possible human obesity genes.
These and other genetic studies in mice also show that
separate genes control (a) body weight, (b) BMI, (c) sizes
of individual fat depots, and (d) responses of individual fat
depots to dieting and exercise. Feeding different diets to
mice revealed that some mice resist weight gain on diets
that make other strains obese, indicating genediet inter-
actions. These diet responsive genes are mostly separate
from genes for spontaneous obesity on healthy chow diets.
Human genetic studies have extensively investigated BMI,
have produced smaller studies of overall fatness, but have
produced virtually no data on genetics of individual fat
depots, and despite many underpowered efforts, almost no
significant results on genediet interactions.
Mice are valuable in the study of parental effects.
Parents may exert indirect effects where female genotype
influences progeny phenotype independent of progeny
genotype [18,19] possibly by influencing milk composi-
tion or quantity, or quality of maternal care. Diets fed
to male or female parents may influence weight and
health of progeny through epigenetic effects that are
heritable changes not due to changes in the underlying
DNA sequence. Although similar maternal diet effects are
well known in people, the paternal effects in mice have
been a surprise as they occur through sperm. Recent evi-
dence from mice suggests that RNA found in sperm can
cause obesity and metabolic disorders in progeny of males
fed high-fat diets [20]. No comparable studies exist for
humans.
Although studies directly in humans have now
successfully identified almost 200 obesity genes, taken
altogether these account for a limited fraction of herit-
ability and for only a few traits, such as BMI and overall
fat distribution. The studies in mice and rats strongly
suggest that work to find genes with similar effects on fat
depots, diet, and parental effects in humans will identify
entire new classes of human obesity genes. Doing so will
likely increase the total heritability of obesity that can be
explained in humans. Human geneticists have either not
explored at all, or only begun to explore, these funda-
mental aspects of obesity that have been reproducibly
demonstrated in mice and rats.
B Identification of Human Obesity Genes by
Sequencing
Several approaches are currently used to find new human
obesity genes, the most direct of which is to sequence
DNA. One can sequence all DNA (whole genome
sequencing), or only parts, such as the exome, the protein
coding portion of the gene [21]. One recent paper used
whole genome sequencing of Sardinians to identify genes
influencing height, inflammatory markers, and lipids [22].
Although whole exome sequencing in obesity has been
reported [23], no whole genome sequencing studies for
obesity have yet been published.
Whether sequencing whole genome or whole exome,
most investigators look for genes with mutations that
obviously alter function such as stop codons, insertions, or
deletions. One of the primary limitations of this approach
is that missense mutations that substitute one amino acid
for another in genes not previously known to cause obesity
tend to be ignored, despite the fact that missense muta-
tions can alter protein function. The practical problem is
that each person has many thousands of missense variants
and investigators cannot directly test functional effects of
all to determine which of these are causal for obesity.
Much effort is being devoted to methods to predict which
missense mutations in protein coding regions will have
functional effects on proteins, but at present there is no
substitute for direct studies showing that a missense muta-
tion alters protein function. And since many alleles that
cause obesity are not in protein coding regions, ability to
predict functional effects of these alleles ranges from non-
existent for alleles far from any gene to sometimes useful
predictions for alleles in obvious gene promoter regions.
Once again, there remains no convincing substitute for
determining direct functional effects.
One exception is that some missense mutations in
known obesity genes can be labeled as putative obesity
causing. The most common results from sequencing are
identification of novel mutations in known obesity genes,
Genetics of Nonsyndromic Human Obesity Chapter | 21 457
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for example, LEP [24,25],LEPR [24], and MC4R [25].In
ideal cases, investigators can show that missense muta-
tions present in obese people will alter the function of a
protein. For instance, missense mutations in MC4R may
alter binding of α-melanocyte-stimulating hormone
(α-MSH), localization on the cell surface, or production
of cGMP on binding of α-MSH. Currently, mutations
have been identified by sequencing in human orthologs of
the known obesity genes LEP [24,25],LEPR [24],MC4R
[25],CPE [26],TUB [27], and PCSK1 [28]. New papers
reporting discovery of mutations in these genes occur
regularly, so we will not attempt to provide a comprehen-
sive list.
Selected sequencing papers are presented in Table 21.1.
Saeed and colleagues found that 30% of severe obesity in
children in consanguineous or inbred families was due to
variants in LEP,LEPR,orMC4R [24]. Philippe et al. [28]
sequenced coding regions of 34 obesity genes in 201 indi-
viduals, including 126 who were obese. They report
discovery of a nonsense loss of function mutation in
PCSK1 that causes a dominantly inherited familial obesity
in a single three-generation pedigree. These investigators
[28] also report finding another missense mutation in
PCSK1 and a missense mutation in POMC that were previ-
ously identified as putative obesity mutations but which
are not associated with obesity in their study. This empha-
sizes the necessity for functional studies of missense muta-
tions for putative obesity genes. Tan et al. [29] provide
experimental evidence that exome sequencing did not iden-
tify all the obesity-causing mutations in MC4R, confirming
prior hypotheses that whole genome sequencing will be
needed to more completely catalog obesity-causing alleles.
TABLE 21.1 Sequencing Studies of Human Obesity
Gene Protein Coded Population Method (Ref.) Functional
effects of
variants
confirmed
Comments
Mutations Identified by Sequencing Targeted at Known Obesity Genes
LEP Leptin 22 probands from
consanguineous
families
Targeted sequencing
[24]
No 30% of severe obesity in
children of consanguineous
families due to LEP,LEPR,
or MC4R
LEPR Leptin receptor
MC4R Melanocortin receptor 4
CPE Carboxypeptidase E 1 individual from
consanguineous
family
Whole exome
sequencing [26]
Yes Obesity, intellectual
disability, abnormal
glucose, hypogonadism
TUB Tubby bipartite
transcription factor
1 individual from
consanguineous
family
Exome sequencing
[27]
Yes Frameshift mutation of TUB
likely cause of obesity and
retinal degeneration in
humans
Incomplete penetrance
PCSK1 Proprotein convertase
subtilisin/kexin type 1
General population Sequenced coding
regions [28]
No One 3-generation family
with dominantly inherited
obesity
206 of whom 126
obese
MC4R Melanocortin receptor 4 267 obese children Sequenced
promoter region of
MC4R [29]
Yes Found novel promoter
polymorphism that greatly
reduced transcriptional
activity in 1 child
Novel Obesity Genes Identified by Sequencing
COA3 Cytochrome C oxidase
assembly factor 3
1 obese adult Whole exome
sequencing [30]
Yes Subject with exercise
intolerance, obesity,
neuropathy
DYRK1B Duel specificity
tyrosine-
phosphorylation-
regulated kinase 1B
3 multigenerational
families
Linkage analysis and
whole exome
sequencing [31]
Yes Missense mutation
associated with increase of
BMI from 23 to 33.
Separate variants associated
with central obesity and
metabolic syndrome
300 obese
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Several recent papers identify novel obesity genes and
provide functional data demonstrating that the identified
mutations have causal roles. Ostergaard et al. [30] studied
a single subject with exercise intolerance, obesity, and
neuropathy using whole exome sequencing and found
compound heterozygous mutations in cytochrome c oxi-
dase assembly factor 3 (COA3). COA3 is an autosomal
gene that is localized to mitochondria with expression
highest in metabolically active tissues such as brain, liver,
heart, kidney, and small intestine. Thus, effects of this
mutation may be tissue specific which may explain the
mild phenotype. Keramati et al. [31] used linkage analysis
and whole exome sequencing to identify the gene
DYRK1B as the cause of autosomal dominant coronary
heart disease and metabolic syndrome in three multigener-
ational families. Initial studies identified significant
logarithm of the odds (LOD) scores for linkage between
markers in DYRK1B to BMI, blood pressure, and type 2
diabetes. Whole exome sequencing then identified a mis-
sense mutation in DYRK1B located in the LOD peak. In
the families this mutation is associated with an increase of
BMI from 23 to 33. The investigators screened 300 addi-
tional obese people and identified a separate variant that
was also associated with central obesity. Subsequent letters
to the editor by other groups confirmed that yet other mis-
sense mutations in DYRK1B influence metabolic syndrome.
Sometimes whole exome sequencing does not identify
a mutation in a specific gene that causes Mendelian obe-
sity but does identify susceptibility genes that increase
risk. Pima Indians have one of the highest incidences of
obesity and type 2 diabetes in the United States. They
have been participants in, and subjects of, a long running
study aimed at discovering if there is a genetic basis for
this high incidence of obesity. Whole exome sequencing
of 177 Pima Indians identified 31,441 coding variants,
none of which had genome-wide significant association
with adiposity or measures of type 2 diabetes [32]. A total
of 345 of these variants that were predicted to have func-
tional effects were genotyped in additional Pima Indians.
CYB5A and RNF10 showed significant association with
adiposity and type 2 diabetes but the effects on type 2 dia-
betes were eliminated when the data were adjusted for
BMI. Individuals with the risk allele of CYB5A were
about 1 BMI unit heavier, while those with the risk allele
for RNF10 were about 3 BMI units heavier. Although
these genes are risk factors for obesity, they cannot pres-
ently be considered as genes causing Mendelian forms of
nonsyndromic obesity.
A small number of papers report the identification of
novel genes through whole exome sequencing with pos-
sible causal mutations for obesity but do not demon-
strate that the mutations identified influence function of
the candidate obesity gene. Nevertheless, the utility of
the sequence-based approach is likely to grow rapidly
because costs are dropping and software for analyzing
the results is improving [21]. Several low cost for-profit
and nonprofit vendors already offer sequence-based
diagnosis to primary care physicians with difficult to
diagnose cases. However, most exome sequencing
studies do not identify causal genes, and those that do,
identify causal genes in only a fraction of the obese
people studied. Thus, methods to determine causality of
specific mutations remain essential. As discussed in
Section IV-A, Lessons for Human Obesity from Genetic
Studies in Mice, mouse genetic models often have
phenotypes similar to those observed in humans with
mutations in orthologous genes. Thus, one method to
determine causality is to make the corresponding muta-
tion in mice and then evaluate phenotype. Availability
of clustered regularly interspaced short palindromic
repeat (CRISPR)/Cas9 technology means that geneti-
cally engineered mice can be made and phenotyped
much more quickly than was possible with traditional
knockout or transgenic methods [33]. Characterizing the
genetic basis of obesity will likely require much more
than just exome sequencing.
C Genome-Wide Association Studies—
Finding Most of the Common Disease
Variants
Starting in 2005, human genetics entered a new era with
the introduction of genome-wide association studies
(GWAS) that examine many genetic variants in different
individuals to see if any variant is associated with a trait.
In one of the GWAS several hundred thousand single
nucleotide polymorphism (SNP) markers, spread through-
out the genome, are used to identify chromosomal regions
influencing traits anywhere. GWAS owe their existence
to several converging discoveries; sequencing of the
human genome, identification of millions of naturally
occurring SNPs, and discovery of technologies for
determining which allele a person has for hundreds of
thousands of SNPs in a single experiment.
One of the key analytical features of GWAS is that
investigators do not need to use SNPs that cause disease;
they only need to use SNPs that are near the disease-
causing allele. More specifically, they need to be in link-
age disequilibrium, or close, to the causal alleles. GWAS
examine SNPs throughout the genomes of individuals to
identify associations between those markers and diseases
or specific traits, often comparing genomes of cases
(disease) with controls (no disease). GWAS SNP panels
are efficient at finding common variants that cause com-
mon diseases, but they cannot find rare disease-causing
variants. The SNPs used are themselves ones where the
minor alleles are relatively common, for instance many
Genetics of Nonsyndromic Human Obesity Chapter | 21 459
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have frequencies of 15%, meaning that there cannot be
a unique association of any one common SNP with a rare
allele. Rare allele discovery requires other methods, such
as sequencing. Whole genome association studies have
several other disadvantages. In some cases SNPs associ-
ated with obesity are located in introns of one gene but
appear to act by influencing expression of an adjacent
gene. In other cases SNPs associated with obesity are not
located within a gene but are between genes.
Many GWAS have been performed for obesity
[34,35]. The first obesity locus identified by GWAS was
the fat mass and obesity-associated (FTO) gene [36],
which has significant effects on feeding and on adipose
tissue [37]. Although explaining only 13% of the vari-
ance in BMI, FTO polymorphisms have been found in
multiple studies of populations worldwide.
The studies with the largest number of patients and
providing higher statistical power are meta-analyses from
the Genetic Investigation of ANthropomorphic Traits
(GIANT) consortium that published several papers in
2015. One paper examined GWAS for BMI in adult men
and women [38], while another examined GWAS for
waist-to-hip ratio (WHR) after adjustment for BMI [39],
and yet another examined the data stratified by gender
and age [40].
The BMI study [38] identified 97 genome-wide SNPs,
56 of which were novel and 41 SNPs that had previously
been significantly associated with BMI. Table 21.2 lists
13 of the 41 loci with significant association with BMI in
multiple GWAS and highlights five of these genes that
are components of the leptin-melanocortin pathway.
When the data were stratified by gender and age, there
was a larger effect in younger rather than older subjects at
most of the loci [40]. There were no gender differences.
Estimation of overall heritability explained was con-
ducted in two ways. First, just using genome-wide signifi-
cant SNPs it was found that about 4% of heritability in
BMI was explained. However, using all SNPs in the
entire GWAS it was estimated that about 20% of herita-
bility was explained. Of course, this would include some
false positives, but may be a better estimate because it
includes all the genes with very small effects on BMI.
A total of 35 of the BMI significant SNPs are also
identified as associated with other diseases in the
National Human Genome Research Institute (NHGRI)
GWAS catalog. These include genes that are associated
with cardiovascular disease, schizophrenia, smoking,
irritable bowel syndrome, and Alzheimer’s disease. As we
will discuss later, these SNPs and traits are candidates for
having causal relationships with BMI in Mendelian ran-
domization studies.
Genes consistently and strongly associated with com-
mon obesity (Table 21.2), as measured by BMI in GWAS,
include FTO (found only in GWAS), MC4R (identified as
causing single-gene obesity, from sequencing studies, and
from GWAS), TMEM18,SEC16B, and TFAP2B (all iden-
tified in GWAS), BDNF (identified as causing single-gene
obesity and from GWAS), NEGR1 and FAIM2 (identified
from GWAS), SH2B1 (identified as causing single-gene
obesity and from GWAS), and GIPR (identified from
GWAS) [38,41,43,44].
The FTO was first identified through GWAS [45] and
consistently shown to be associated with common human
obesity across populations and ethnic groups [46]. The
function of the FTO protein is unknown although mouse
studies suggest it is a 2-oxoglutarate-dependent oxygenase
that catalyzes nucleic acid demethylation [47].FTO was
recently found to interact with promoters of IRX3 and
IRX5 that are involved in early neural development and
may play a role in adipocyte, especially brown adipocyte,
development [48].FTO is highly expressed in the hypo-
thalamus, a primary site for regulation of energy balance
and satiety [45]. Risk alleles of FTO are associated with
increased food intake, increased hunger, and reduced sati-
ety [42], as well as with increased protein intake [49,50].
FTO variants interact with fat and carbohydrate intakes to
affect BMI [51]. Individuals carrying homozygous FTO
obesity predisposing alleles may lose more weight through
diet or lifestyle intervention than noncarriers [52].FTO
variants have also been examined for interaction with phys-
ical activity, but the studies to date are not consistent [53].
MC4R,BDNF,SH2B1,POMC, and TUB, as compo-
nents of the melanocortin pathway, contribute to single-
gene obesity. However, variants of these genes also
contribute to common obesity as measured by BMI in
GWAS. Transmembrane protein 18 gene (TMEM18)
expression levels are related to phenotypes of obesity and
glucose metabolism [43,54].TMEM18 is widely
expressed in the body, both centrally and in adipose
tissue. However, its function in energy metabolism is not
yet known. NEGR1 codes for a neuronal growth-
promoting factor which may be involved in synaptogen-
esis, neurite outgrowth, and cell-cell recognition/adhesion
[43], and the gene is expressed in the hypothalamus
and in peripheral adipose tissue and muscle. The gastric
inhibitory polypeptide receptor (GIPR) gene codes for a
receptor for an appetite-linked hormone, GIP, which is
produced in the alimentary tract and mediates enhanced
release of insulin from the pancreas. GIPR is also
expressed in the hypothalamus and adipocytes.
Statistically significant SNPs are rarely causal for
common diseases. Locke et al. [38] sought to determine
if the significant SNPs were in linkage disequilibrium
(close to) coding variants. They found coding variants
predicted to have damaging effects on protein function
in five genes in linkage disequilibrium with BMI SNPs;
ZNF142,STK36,TRIM66,BDNF,andGIPR.Further
study of these variants is needed to determine if they are
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causal for the BMI effects. The authors also identified
many genes where the BMI SNPs were associated with
mRNA levels for adjacent genes, consistent with hypoth-
esis that some of the SNPs influence BMI by altering
mRNA levels.
The WHR GWAS, which focused on finding genes for
upper versus lower body fat distribution rather than fat
mass or mass of individual fat depots, showed several
novel features [55]. Shungin et al. [55] found a total of 49
significant SNPs for WHR and another 19 associated with
waist or hip circumference measures. Twenty of the SNPs
showed strong gender dependence with 19 having stron-
ger effects in women and only a one having stronger
effects in men. When stratified for both gender and age,
only gender differences were apparent in the data [40].
An additional GWAS paper focusing on adiposity or fat
depots is consistent with the GIANT consortium findings.
Sung et al. [56] identified multiple SNPs in several genes
with gender-specific effects on visceral and subcutaneous
adipose tissue.
TABLE 21.2 Selected BMI Loci Identified in Multiple GWAS (Loci Listed in the Sequence of Strength of Association
with BMI) [38]
Notable Gene Gene Name Chr Function Reference
FTO
a,c
Fat mass and obesity
associated
16 Catalyzes demethylation of RNA. Increased hypothalamic
FTO expression associated with regulation of energy intake.
[38,41,42]
MC4R
a,b,c
Melanocortin 4
receptor
18 MC4 protein binds α-MSH and is involved in regulation of
feeding behavior and metabolism.
[38,41]
TMEM18
b,c
Transmembrane protein
18
2 Transcription repressor. Cell migration modulator that
enhances the glioma-specific ability of neuronal stem cells.
[38,41,43,44]
SEC16B
b,c
SEC16 homolog B,
endoplasmic reticulum
export factor
1 Required for organization of transitional endoplasmic
reticulum sites and protein export.
[38,41,44]
TFAP2B
c
Transcription factor AP-
2 beta
6 Transcription factor thought to stimulate cell proliferation
and suppress terminal differentiation of specific cell types
during embryogenesis.
[38,41]
BDNF
a,c
Brain-derived
neurotrophic factor
11 Helps support growth and differentiation of new neurons
and synapses and support the survival of existing neurons.
[38,44]
NEGR1
b,c
Neuronal growth
regulator 1
1 May function as a trans-neural growth-promoting factor. [38,43,44]
FAIM2
c
Fas apoptotic inhibitory
molecule 2
12 Protects cells from Fas-induced apoptosis. [38,41]
POMC
a
Proopiomelanocortin 2 Polypeptide hormone precursor that undergoes extensive,
tissue specific, posttranslational processing to produce
biologically active peptides, including α-MSH, important in
regulation of appetite.
[38]
SH2B1
a
SH2B adapter protein 1 16 The protein mediates activation of various kinases including
LEP signaling and other genes of the leptin-melanocortin
pathway.
[38,43]
GIPR Gastric inhibitory
polypeptide receptor
19 Stimulates insulin release in the presence of elevated
glucose.
[38,43]
POC5 POC5 centriolar protein 5 Essential for the assembly of the distal half of centrioles,
required for centriole elongation.
[38,43]
LINGO2 Leucine-rich repeat and
Ig domain containing 2
9 unknown [38,43]
TUB
a
Tubby bipartite
transcription factor
11 Plays a role in obesity and sensorineural degradation [38]
a
Loci associated with genes involved in the leptin-melanocortin pathway. Each of these genes has variants that can cause single-gene obesity in humans.
b
SNPs located near these genes have stronger association with BMI in younger versus older adults.
c
SNPs located near these genes are significantly associated with BMI in children and adolescents.
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Alternative measures of obesity may produce differ-
ent GWAS results. Lu et al. [57] measured percent body
fat in 100,716 people by either bioelectric impedance or
dual-energy x-ray absorptiometry (DEXA) and found 12
genome-wide statistically significant SNP loci. Seven
loci had larger effects on percent body fat than BMI.
Five had larger effects on BMI than percent fat. None of
the genes was significant for WHR adjusted for BMI.
Thus, GWAS contrasting results for BMI and WHR
adjusted for BMI, or examining percent body fat or dif-
ferent adipose depots, show different genes for weight
and for individual fat depots and substantial genetic
differences between males and females. The results are
strongly consistent with mouse studies showing that
body weight or BMI only partially overlap with percent
fatandfatpadgenetics.
Other GWAS examined children and various ethnic
groups. Studies of children found that most genes for
BMI are common with adults and only a few are child
specific [41,44]. The results could mean that there are
some different genetic controls between adult and child-
hood obesity, or they could mean that both adult and
childhood obesity studies were underpowered and would
find the same genes if enough people were studied.
Also, most genes in other ethnic groups were the same
as those observed in Caucasians; genes that were differ-
ent may or may not indicate true ethnically different
obesity pathways.
GWAS led to the development of genetic risk scores
(GRS), multilocus profiles calculated by summing up the
number of risk alleles for elevated BMI and obesity. GRS
are very useful for studying effects of genetics on
response to diet and exercise. They have also been used
for the technique of Mendelian randomization, which
determines if obesity has a causal effect on correlated
comorbidities.
D Mendelian Randomization or Genetic
Correlations and Causal Relationships
Obesity is correlated with many diseases, for example,
hypertension, type 2 diabetes, cardiovascular disease,
serum triglycerides, and more. Until recently there was no
method to determine if obesity caused these other
diseases, or if one or more other diseases caused obesity.
Several methods have just recently become available to
identify causal relationships between correlated complex
traits. One method looks for genetic correlations. The
method does not require individual genotypes, genome-
wide significant SNPs, nor even measuring multiple traits
for the same individuals. Thus, genetic correlations can
be measured for large numbers of traits. Bulik-Sullivan
et al. [58] estimated genetic correlations between 24
traits, including BMI. They report positive genetic corre-
lations between BMI and type 2 diabetes, coronary artery
disease, and serum triglycerides. They report statistically
significant negative correlations between BMI and HDL
cholesterol, age at menarche, height, and years of educa-
tion. These results, and limitations, are quite similar to
those observed using the technique of Mendelian
randomization. Note that they did not have data on fat
mass or fat distribution so comparisons of genetic correla-
tions of BMI with fat mass or fat distribution were not
possible.
Mendelian randomization combines genetics with
traditional epidemiologic methods to provide causal infor-
mation about correlated phenotypes, without conducting a
randomized controlled trial. The Mendelian randomiza-
tion approach limits both confounding and reverse causal-
ity errors, but assumes there is no linkage disequilibrium
or pleiotropy where one gene has a primary effect on
more than one phenotype.
A number of recent studies used Mendelian randomi-
zation to determine if obesity, as measured by BMI, is
causal for disease. Using GRS generated from BMI risk
alleles, Todd et al. [59] found evidence that obesity is
causal for diabetic kidney disease in type 1 diabetes and
Cole et al. [60] found evidence that obesity is a causal
risk factor for coronary artery disease. On the other hand,
Davies et al. [61] found little evidence that genetically
determined BMI and height had influence on prostate
cancer risk, but were associated with increased mortality
in low-grade disease. Furthermore, Nordestgaard et al.
[62] concluded that, although high coffee intake was cor-
related observationally with low risk of obesity, metabolic
syndrome, and type 2 diabetes, there was no evidence to
support a causal relationship.
Observational studies suggest that a leptin surge in the
perinatal period may program the long-term risk of obe-
sity. A study by Allard et al. [63] using DNA methylation
levels near the LEP locus in a Mendelian randomization
study supports causality between maternal hyperglycemia
and epigenetic regulation of leptin in the newborn.
We expect to see more studies using Mendelian
randomization to determine causal relations between
obesity and disease and between environmental factors
and obesity.
E The Problem of Missing Heritability
As much as 70% of any one person’s risk for being obese
may be heritable, that is, genetic. Yet, to date, less than
10% of that heritability has been identified. The gene
most strongly associated with common human obesity,
the fat mass and obesity-associated gene (FTO), only
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contributes about 1% of the variance in BMI [36]. The
problem of missing heritability is true not only for obesity
but also for most common genetically complex traits and
diseases. For example, height is estimated to be 8090%
heritable, yet large-scale population studies identify less
than 10% of height’s heritability [64].
This missing heritability may be due to the presence
of rare variants, variants with low penetrance, copy num-
ber variants, epigenetic tags, numerous variants with
small effects, or may be due to overestimation of herita-
bility due to epistasis (genegene interaction), none of
which are readily identified by GWAS.
Rare variants are likely to be missed by the GWAS
approach despite the fact that they may have larger effects
than common variants [6567]. Rare variants with large
effects seem to be more common in people with early onset
or more severe obesity [68,69]. Indeed, sequence-based
studies have already found that cohorts with extreme phe-
notypes of obesity are enriched with highly penetrant but
rare alleles. It is likely that sequencing will continue to dis-
cover rare variants.
Copy number variation (CNV) is when the number of
copies of a particular gene varies among individuals. CNVs
comprise more total nucleotide content than SNPs and may
encompass one or more partial or entire genes. Yet, in cur-
rent SNP analytic methods homozygous (A/A), hemizy-
gous (A/O), and duplicative (A/A/A) tend to be lumped.
One study of CNVs showed that deletion at the 16p11.2
locus resulted in altered satiety response and subsequent
obesity in children whereas duplication at that locus was
associated with leanness [70]. Taking account of the struc-
tural dimension of the genome might recover some of the
missing heritability for many traits [71,72].
Epigenetics refers to changes in gene expression that
are not the result of DNA sequence, but can be the result
of chemical modifications of DNA or of proteins that
bind DNA. Common epigenetic tags are those resulting
from DNA methylation or deacetylation of DNA-binding
histones. These often occur as the result of early life envi-
ronment. Evidence now suggests that epigenetic changes
are important contributors to inheritance of obesity pheno-
types. Since standard sequencing or GWAS techniques do
not measure epigenetic tags, they may be a major compo-
nent of missing heritability. Epigenetics will be discussed
in more detail in Section V-A, Epigenetics.
Estimates of heritability assume that there are no
genegene interactions (epistasis), a phenomenon found
in all animal models of obesity. Epistasis is where
a gene (or genes) masks or amplifies the effects of
another gene (or genes). A classic example of the effect
of genegene interactions on a complex trait comes
from mouse studies. The severity of diabetes in both
Lep
ob
(leptin) and Lepr
db
(leptin receptor) mutant mice is
determined by the genetic background upon which the
mutation is expressed. There are many different inbred
mouse strains. These strains differ from each other at
millions of places through their genomes. Thus, if the
same mutation is moved from one strain to another by
breeding, then one can determine if all these other
variants influence the phenotypes produced by the muta-
tion. Both Lep
ob
and Lepr
db
mutations in C57BL/6 strain
mice result in hyperinsulinemia and obesity, whereas
these mutations in C57BLKs strain mice result in severe
diabetes and early death [73]. This means that genes
other than Lep
ob
and Lepr
db
have dramatic effects on the
phenotype that is observed. Characterization of epistasis
influencing polygenic obesity in the BSB mouse model,
produced by breeding C57BL/6J with Mus spretus (a
different mouse species) F1 3C57BL/6J, found inter-
action between genes on different chromosomes that
contributed significant variation to obesity and related
phenotypes [74]. Each BSB mouse is genetically unique
and weights, fat distribution, and fat mass vary widely
from mouse to mouse. Quantitative trait locus mapping
demonstrated both direct genetic effects and epistatic
effects [74].
Genegene interactions are likely a universal phe-
nomenon in common human diseases and may be more
important in determining the phenotype than the indepen-
dent main effects of any one susceptibility gene [75,76].
Zuk et al. [77] argue that a high proportion of heritability
for certain traits could be due to genetic interactions.
Genegene interactions are difficult to identify using tra-
ditional genetic studies in humans and studies searching
for interactions of any gene with all genes are lacking for
human obesity. Instead, investigators have performed
more limited studies searching for epistasis of pairs of
specific genes chosen by investigators. Genegene inter-
action effects have been shown on BMI and waist circum-
ference [78], extreme obesity [79], abdominal fat [80],
and on immune dysfunction in obesity [81]. Genegene
interactions among variants of the β-adrenergic receptor
genes (ADRB1,ADRB2, and ADRB3) contribute to longi-
tudinal weight changes in African and Caucasian
American subjects [82]. Epistasis affecting obesity was
also found in African derived populations in Brazil where
interactions between LEPR and ADRB2 polymorphisms
as well as a third-order effect between LEPR,ADRB2,
and INSIG2 were found [83]. In the study of Feitosoa
et al., blood lipid profile and dietary habits were found
to have confounding effects in the analysis [78].Itis
possible to have both genegene and geneenvironment
interactions affecting the same pathway.
Zuk et al. [77] describe a method for estimating herita-
bility not inflated by genetic interactions, but the method
requires isolated populations.
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V GENEENVIRONMENT INTERACTIONS
A Epigenetics
Epigenetics is the study of mitotic and/or meiotic changes
due to environmental factors that switch genes on and off
without changes in the DNA sequence [84]. Common epi-
genetic changes result from DNA methylation or histone
deacetylation and often occur as the result of environmen-
tal exposure in utero, in the early neonatal period or early
in life [85]. Epigenetic processes include genome imprint-
ing, gene silencing, and noncoding microRNA, among
other effects. Technically, the term epigenetics applies to
only those changes that are stably inherited. However, the
term epigenetics is also commonly used to describe
processes that have not been shown to be heritable but
that effect the development of the organism. There is
increasing evidence that epigenetics is a mediator of
geneenvironment interactions underlying the develop-
ment of obesity and comorbidities [8690].
Nutrition and activity levels can both affect metabo-
lism through epigenetic gene regulation. Studies that
show epigenetic changes associated with weight regula-
tion include studies of the Dutch winter famine that
occurred in 1944 showing that children conceived during
famine were small and underweight with increased risk
for obesity and type 2 diabetes as adults [91,92]. In the
Chinese famine of 195861, only females developed obe-
sity in later life [93]. DNA isolated from individuals dec-
ades after the famine showed abnormal DNA methylation
[94]. Studies of identical twins with discordant BMIs
identified DNA methylation and expression differences in
subcutaneous adipose tissue that distinguish one twin
from the other, differences that tended to increase with
diverging life experience suggesting that the difference in
obesity is epigenetically regulated [95].
Epigenetic studies of human obesity are just begin-
ning. A large-scale epigenome-wide study found signifi-
cant associations between DNA methylation and BMI and
waist circumference in European Americans, which was
then replicated in two independent populations including
both European and African Americans [96].
Studies showing inheritance of epigenetic changes
have been done in rodents. Increased maternal energy
intakes affect the epigenetic changes of rats [97]. Feeding
a high-fat diet to female mice resulted in increased
growth and insulin insensitivity in the progeny [98]. Wei
et al. [99] found that prediabetes in male mice increased
susceptibility to diabetes in progeny through gamete
methylation changes. Mice with the Agouti viable yellow
obesity mutation given dietary methyl group supplements
have epigenetic changes that prevent passage of obesity
to subsequent generations through the agouti viable yel-
low allele [100]. Integrating mouse to human approaches
will be essential to the understanding of the epigenetic
contribution to the current obesity epidemic.
B Genetic Effects on Weight Gain or Loss
Due to Diet or Exercise
Why some people in modern societies become obese,
despite considerable effort and expense to avoid this
condition, whereas others stay lean without such effort,
appears to have a genetic basis [101,102]. Chronic over-
feeding studies by Sims and colleagues beginning in the
1960s showed interindividual differences in weight gain
[103,104]. More recently, Bouchard and colleagues deter-
mined the response to changes in energy balance by
submitting pairs of monozygotic twins either to positive
energy balance induced by overeating [105] or to negative
energy balance induced by exercise training [106].
Significant intrapair resemblance was observed for
changes in body composition and was particularly striking
for changes in regional fat distribution and amount of
visceral fat. One explanation for these differences is that
some twin pairs were better oxidizers of lipid, as evidenced
by reduced respiratory quotient, during the submaximal
work than were the other twin pairs [106].
Recent epigenome-wide association studies may help
explain some of these interindividual differences in body
weight response to diet or exercise [107,108]. Young men
with low birth weight, suggesting in utero undernutrition,
were compared to men of normal birth weight both on a
control diet and after 5 days of a high-fat diet. There
was no difference in skeletal muscle DNA methylation
between low and normal birth weight cohorts on the con-
trol diet. However, after the high-fat diet the normal
weight group had widespread skeletal muscle DNA meth-
ylation, whereas the low birth weight group had few
methylation changes [108]. In obese adolescents DNA
regions were differentially methylated consistent with
weight loss response to diet [109].
Several papers now report DNA methylation changes
as a result of acute [110] or chronic [107] exercise train-
ing including altered DNA methylation patterns in adi-
pose tissue of healthy young men in candidate genes for
obesity including FTO,GRB14, and TUB [107]. A meta-
analysis of 10 studies found that individuals carrying
homozygous FTO obesity predisposing allele lose more
weight through diet or lifestyle intervention than noncar-
riers [52]. These studies are consistent with the hypothesis
that exercise may modify DNA methylation, and thus
gene expression, for many genes that influence BMI, fat
mass, or fat distribution.
Other studies examined genes in the lipolysis pathway
for influence on weight loss success but results are not
consistent (see [111] for review).
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C Genetic Effects on Weight Loss Due to
Bariatric Surgery
Several studies with small numbers of subjects report on
weight loss in individuals with MC4R mutations follow-
ing bariatric surgery. Patients with MC4R mutations are
able to lose as much weight as those without such muta-
tions with bariatric surgery in children [112], adolescents
[113], and adults [114]. GWAS-type studies with small
numbers of subjects are just beginning to look at SNPs
associated with weight loss success following bariatric
surgery and need confirmation. At the present time, obe-
sity GRS does not predict weight loss results following
bariatric surgery [115].
VI GENETIC PATHWAYS OF OBESITY
The hypothalamus is of great importance in obesity as it
integrates peripheral hormonal and neuronal signals of
satiety and nutritional status, senses nutrients, controls
glucose homeostasis and peripheral lipid metabolism,
and functions to control whole body energy balance.
Much of this control is through the leptin-melanocortin
pathway in the hypothalamus. Recent studies also impli-
cate adipose tissue as important in obesity, both as an
active endocrine organ [116,117] andinregardtobody
fat distribution [55].
A The Leptin-Melanocortin Pathway in the
Hypothalamus
The first five causal human obesity genes were identified
using the mouse models Lep
ob
,Lepr
db
,Tub,Cpe
fat
, and
A
y
. Once these genes were identified in mice, geneticists
began searching for obese humans with mutations in these
same genes. Study of the yellow obese A
y
mouse led to
the discovery of the leptin-melanocortin pathway, the pri-
mary pathway in the brain which functions in the regula-
tion of body weight (Table 21.3). Searching in highly
TABLE 21.3 Single Gene Mutations Causing Uncomplicated Obesity in Humans and Confirmed in Mouse Models
Gene Name Chromosomal
Transmission
Function of Gene Product Relative to Obesity Mouse Ortholog
Components of the Leptin-Melanocortin Pathway of the Hypothalamus
Leptin (LEP)[118] 7q31.3 Hormone secreted from adipocytes that plays a
critical role in regulation of body weight by inhibiting
food intake and stimulating energy expenditure.
Deficiency causes hyperphagia, early onset obesity,
hypogonadotropic hypogonadism, and altered
carbohydrate metabolism.
Lep
ob
cloned from the ob/
ob mouse [154]Recessive
Leptin receptor (LEPR)
[119]
1p31 Receptor for the hormone leptin. LEPR deficiency
causes same phenotype as LEP deficiency.
Lepr
db
cloned from the
db/db mouse [120]Recessive
SH2B adaptor protein 1
(SH2B1)(
155,156)
16p11.2 Adaptor protein enhances intracellular leptin
signaling in the brain. Loss of function mutation
results in hyperphagia, childhood onset obesity,
disproportionate leptin resistance, and reduced height
as adult.
Sh2b1
Recessive No obesity identified in
homozygous null mice.
Proopiomelanocortin
(POMC)[121]
2p23.3 Located in centrally projecting neurons that contain
peptide products of proopiomelanocortin (POMC)
and cocaine and amphetamine-regulated transcript
(CART). POMC is a precursor protein that is
ultimately cleaved into ACTH, α-MSH, β-MSH,
γ-MSH, and β-endorphin. Mutation causes
hyperphagia, early onset obesity, hypocortisolism,
and skin and hair hypopigmentation.
Pomc [157]
Recessive
Tubby bipartite
transcription factor (TUB)
[27]
11p15.5 Functions as a membrane-bound transcription
regulator in the hypothalamus that translocates to the
nucleus in response to phosphoinositide hydrolysis.
Deficiency results in hyperphagia, obesity, altered
glucose metabolism, and sensorineural degradation.
Tub cloned from the
tubby mouse [158]Recessive
(Continued )
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consanguineous families including obese individuals, a
mutation in the leptin (LEP) gene was discovered [118]
and confirmed in additional homozygous LEP-deficient
patients [25]. As with the LEP gene, homozygous leptin
receptor (LEPR) deficiencies were found in severely
obese siblings [119] and in families where severe obesity
segregated with mutations in LEPR [127,128]. Individuals
homozygous for mutations in either LEP or LEPR are
hyperphagic and gain weight rapidly in the first year of
life [118,129] and have delayed puberty due to hypogona-
dotropic hypogonadism [128]. Heterozygotes for LEP and
LEPR mutations have increased fat mass but are not mor-
bidly obese.
Borman et al. [130] identified a homozygous mutation
in TUB in a child of consanguineous marriage. The 11-
year old was identified with mild obesity and a 2-year
history of deteriorating vision. Functional studies demon-
strated that the mutated protein is expressed at low levels
in the retina. Alsters et al. [131] identified a homozygous
mutation in carboxypeptidase E (CPE) in a severely obese
woman from a consanguineous marriage. The proband
had severe obesity, intellectual disability, abnormal
TABLE 21.3 (Continued)
Gene Name Chromosomal
Transmission
Function of Gene Product Relative to Obesity Mouse Ortholog
Carboxypeptidase E (CPE)
[26]
4q32.3 Involved in the synthesis of most neuropeptides and
peptide hormones. Deficiency results in severe
obesity, type 2 diabetes, intellectual disability, and
hypogonadotrophic hypogonadism.
Cpe
fat
, cloned from the fat
mouse [159]Recessive
Melanocortin 4 receptor
(MC4R)[122,123]
18q22 The encoded protein is a membrane-bound
receptor and member of the melanocortin receptor
family, interacts with adrenocorticotropic and
MSH hormones, and is mediated by G proteins.
Defects in this gene are a cause of hyperphagia,
early onset obesity, increased height, and
fasting hyperinsulinemia. MC4R mutations cause
the most common form of human monogenic
obesity B5%.
Mc4r [160]
Dominant
Proprotein convertase
subtilisin/kexin type 1
(PCSK1)[161]
5q15-q21 PC1/3 is a neuroendocrine convertase encoded by
PCSK1 that cleaves POMC and also proinsulin to
insulin. Mutation causes hyperphagia, early onset
obesity, hypogonadism, and altered carbohydrate
metabolism.
Pcsk1 mutation in the
HRS/J inbred mouse
results in late onset
obesity
Recessive
Melanocortin 2 receptor
assembly protein 2
(MRAP2)[162]
6q14.2 Regulates energy homeostasis through signaling of
MC4R and PKR1.
Mrap2 knockout produces
severe early obesity [163]Recessive
Components of the Paraventricular Pathway of the Hypothalamus
Brain-derived
neurotrophic factor
(BDNF)[124]
11p13 Plays a role in the growth, maturation, and
maintenance of cells in the brain and is active where
cell-to-cell communication occurs. BDNF protein is
found in regions of the brain that control eating,
drinking, and body weight. Mutation causes
hyperphagia, early onset obesity and cognitive
impairment.
Bdnf [164]
Recessive
Neurotrophic tyrosine
kinase, receptor, type 2
(NTRK2)(
[165,125])
9q22.1 Receptor for BDNF. Mutation results in early onset
obesity, hyperphagia, and developmental delay.
Ntrk2 knock-in results in
adiposity [166]Recessive
Single-minded homolog 1
(SIM1)(
[167,126])
6q16.3 Transcription factor that is essential for the
development of the PVN of the hypothalamus.
Haploinsufficiency causes hyperphagia, early onset
obesity, altered carbohydrate metabolism,
dysmorphic features, and mental retardation.
Sim1 heterozygous
mutants exhibit
hyperphagic obesity [168]
Recessive
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glucose, and hypogonadotropic hypogonadism. The pro-
band’s symptoms closely match those of Cpe
fat
mice.
To date, mutations in 12 genes, all components of the
leptin-melanocortin or paraventricular pathways, have been
reliably shown to cause spontaneous Mendelian increased
BMI in humans; leptin (LEP), leptin receptor (LEPR), tubby
bipartite transcription factor (TUB), SH2B adapter protein 1
(SHR2B1), proopiomelanocortin (POMC), proprotein con-
vertase subtilisin/kexin type 1 (PCSK1), carboxypeptidase
E(CPE), melanocortin 2 receptor assembly protein 2
(MRAP2), melanocortin 4 receptor (MC4R), brain-derived
neurotrophic factor (BDNF), neurotrophic tyrosine kinase
receptor type 2 (NTRK2), and single-minded homolog
1(SIM1)(
Table 21.3;Fig. 21.2). Mutations in these
genes in humans are recessive, with the exception of
MC4R, and therefore are rare, are associated with hyperpha-
gia and severe obesity beginning in childhood, and may
include developmental, endocrine, and behavioral disorders.
(For a description of the most common single-gene obesity
disorders and syndromes, see [132].)
Leptin (product of LEP) is secreted by adipocytes and
its concentration in blood is proportional to fat mass.
Leptin crosses the bloodbrain barrier and activates lep-
tin receptors (product of LEPR) on the surface of neurons
in the arcuate nucleus of the hypothalamus. This activa-
tion, with the assist of the tubby bipartite transcription
FIGURE 21.2 Simplified schematic of the genes involved in the leptin-melanocortin and paraventricular pathways in the hypothalamus. Mutations
of genes in bold are known to cause monogenic obesity in humans. These pathways are essential components of the central control of energy homeo-
stasis, propagating the signals that result in satiety and increased energy intake. Leptin is secreted from adipocytes, crosses the bloodbrain barrier
and activates leptin receptors. This activation, mediated by tubby bipartite transcription factor, stimulates POMC/CART neurons producing melanocor-
tins that activate melanocortin 4 receptors in the paraventricular and ventromedial nuclei, resulting in satiety. SIM1 is essential for development of the
paraventricular nucleus. Brain-derived neurotrophic factor and its receptor, neurotrophic tyrosine kinase receptor, type 2, are part of the MC4R cascade
leading to satiety. When stimulated by ghrelin receptors in the arcuate nucleus, agouti-related protein inhibits MC4R activity. AgRP, agouti-related
protein; BDNF, brain-derived neurotrophic factor; CART, cocaine- and amphetamine-related transcript; CPE, carboxypeptidase E; GHSR, ghrelin
receptor; INSR, insulin receptor; LEP, leptin; LEPR, leptin receptor; MC3R, melanocortin 3 receptor; MC4R, melanocortin 4 receptor; MRAP2, mela-
nocortin 2 receptor assembly protein 2; α-MSH, α-melanocyte-stimulating hormone; β-MSH, β-melanocyte stimulating hormone; NPY, neuropeptide
Y; NTRK2, neurotrophic tyrosine kinase receptor, type 2; PCSK1, proprotein convertase subtilisin/kexin-type 1; POMC, proopiomelanocortin; SIM1,
single-minded homolog 1 of Drosophila.Figure illustration by Venus Nguyen.
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factor (product of TUB) and SH2B adapter protein 1
(product of SH3B1), stimulates proprotein convertase
(product of PCSK1) and carboxypeptidase E (product of
CPE) to cleave proopiomelanocortin (product of POMC)
into the melanocortins including α-MSH, the primary
ligand for melanocortin receptors and activation of
downstream signaling to regulate energy balance. (For
reviews of the leptin-melanocortin pathway and down-
stream signaling in obesity see [6,133,134].) Activation of
the agouti-related protein (AgRP)/neuropeptide Y (NPY)
neurons in the hypothalamus stimulates feeding. Leptin
binding inhibits the AgRP protein, thereby inhibiting feed-
ing. Thus, leptin functions as an afferent signal in a negative
feedback loop to maintain constancy of body fat stores.
Leptin acts through the leptin receptor, a single-
transmembrane-domain receptor of the cytokine-receptor
family [120]. The leptin receptor is found in many tissues
in several alternatively spliced forms, raising the possibil-
ity that leptin affects many tissues in addition to the
hypothalamus.
Leptin clearly has a broader physiological role than
just the regulation of body fat stores. Leptin deficiency
results in many of the abnormalities seen in starvation,
including reduced body temperature, reduced activity,
decreased immune function, and infertility. (For reviews
of the physiological role of leptin see [135137].) Leptin
deficiency results in severe hyperphagia and early onset
obesity. Replacement with human recombinant leptin in
children with severe leptin deficiency normalizes food
intake and body composition [136]. Studies of long-term
replacement therapy in patients with congenital leptin
deficiency show that leptin regulates many body functions
including the endocrine system, energy balance, the adi-
poinsular axis, inflammation, and immunity [138].
Sequential cleavage of the precursor protein proopio-
melanocortin (product of POMC) generates the melano-
cortin peptides adrenocorticotrophin (ACTH), the MSHs
(α-, β- and δ-MSH), and the opioid-receptor ligand
β-endorphin (for review see [139]). α-MSH plays a cen-
tral role in the regulation of food intake by the activation
of the brain melanocortin 4 receptor (product of MC4R).
The dual role of α-MSH in regulating food intake and
influencing hair pigmentation predicts that the phenotype
associated with a defect in POMC function would include
obesity, alteration in pigmentation (e.g., red hair and pale
skin in Caucasians), and ACTH deficiency. The observa-
tions of these symptoms in two probands led to the identi-
fication of three separate mutations within their POMC
genes [121]. Another POMC variant in a region encoding
β-MSH results in severe early onset obesity, hyperphagia,
and increased linear growth, a phenotype much like that
seen with mutations in MC4R [140]. Heterozygosity for a
POMC mutation having subtle effects on proopiomelano-
cortin expression and function was shown to influence
susceptibility to obesity in a large family of Turkish ori-
gin [141].
A wide variety of hormones, enzymes, and receptors
are initially synthesized as large inactive precursors. To
release the active hormone, enzyme, or receptor, these
precursors must undergo limited proteolysis by specific
convertases. An example is the clipping of proopiomela-
nocortin by proprotein convertase subtilisin/kexin type 1,
also known as prohormone convertase-1 (product of
PCSK1). Mutations in PCSK1 were found in individuals
with extreme childhood obesity and elevated proinsulin
and proopiomelanocortin concentrations but very low
insulin levels (for review see [28]). Carboxypeptidase E
(CPE) then removes a single basic amino acid from the
C-terminus of many different hormones. For example,
ACTH is produced from POMC by action of proteases,
including PCSK1, then an intermediate product is pro-
duced by another protease, and finally α-MSH is pro-
duced when CPE removes a final C-terminal amino acid
from this last intermediate. A recessive mutation of the
gene producing carboxypeptidase E causes obesity in the
Cpe
fat
mouse. Since the human cases and the Cpe
fat
mouse share similar phenotypes, it can be inferred that
molecular defects in prohormone conversion represent a
generic mechanism for obesity.
Several melanocortin receptors are highly expressed
in the hypothalamus. Mutations in MC4R are found in
various ethnic groups and cause the most common form
of monogenic obesity in humans. The global presence of
obesity-specific MC4R mutations is estimated to vary
from 2% to 7% among population groups [139,142].
MC4R-linked obesity in humans is dominantly inherited
with incomplete penetrance. Homozygotes have been
observed in consanguineous families and have more
severe phenotypes than heterozygotes. Subjects with
MC4R deficiency are obese from an early age. Adrenal
function is not impaired but severe hyperinsulinemia is
present in the MC4R-deficient subjects. Sexual develop-
ment and fertility are normal. Affected subjects are hyper-
phagic and have increased linear growth, similar to what
occurs in heterozygous Mc4r-deficient mice. MC4R-defi-
cient humans also have increased lean mass and bone
mineral density and mild central hypothyroidism. Female
haploinsufficiency carriers who have only a single func-
tioning copy of MC4R are heavier then male carriers in
their families, a pattern also seen in Mc4r-deficient mice.
These data are strong evidence for dominantly inherited
obesity, not associated with infertility, due to haploinsuf-
ficiency mutations in MC4R.
MC3R, while not known to cause single-gene obesity,
acts as an autoreceptor indicating the tight regulation of
the melanocortin system in energy balance. MC3R modi-
fies energy balance by decreasing feed efficiency.
Mutations in MC3R are not as common as in MC4R and
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do not result in an autosomal dominant form of obesity,
but may be important contributors to susceptibility to obe-
sity. Two variants of the MC3R gene interacted with diet
to affect weight loss success in an Italian clinic treating
severe childhood obesity [143].
B The Paraventricular Pathway
Three genes important in downstream signaling of the
melanocortin system have also been shown to cause
single-gene obesities. Single-minded homolog 1, product
of SIM1, is a regulator of neurogenesis and is essential to
the development of the paraventricular nucleus of the
hypothalamus [126]. Brain-derived neurotrophic factor,
product of BDNF, and its receptor, neurotrophic tyrosine
kinase receptor type 2, product of NTRK2, are involved in
signaling in the ventromedial nucleus of the hypothalamus
and contribute to memory and learning [125].BDNF-defi-
cient rodents are hyperphagic and obese. Case reports
associate mutation in BDNF or NTRK2 with massive obe-
sity and impaired cognitive function [124].
C Genetic Pathways Involved in Common
Obesity
Locke et al. [38], using data from all loci significantly
associated with BMI in the GIANT Consortium GWAS
meta-analysis, examined the data using pathway analysis.
Biochemical analysis identified several gene sets with sig-
nificant enrichment; neurotrophin signaling, general
growth and patterning, basal cell carcinoma, acute mye-
loid leukemia, and hedgehog signaling. Pathway analysis
showed that genes expressed in the nervous system were
particularly enriched in the BMI GWAS, with genes
expressed in the immune and hemic systems second most
abundant. Genes for monogenic obesity, hypothalamic
function, and energy homeostasis were frequently
observed. Pathway analysis provided “strong support for a
role of the central nervous system in obesity susceptibility
and implicated new genes and pathways, including those
related to synaptic function, glutamine signaling, insulin
secretion/action, energy metabolism, lipid biology and
adipogenesis” [38].
D Genetic Pathways Involved in Body Fat
Distribution
Fewer studies have looked at body fat distribution by
GWAS and, except for the GIANT study, sample sizes
have been limiting. However, the GIANT consortium data
showed that there was little or no overlap between genes
associated with BMI and genes associated with WHR.
Pathway analysis also demonstrated that most WHR
genes are expressed primarily in adipocytes and adipose
tissue. Lack of evidence for association with brown
adipose tissue and other adipose depots is likely due to
absence of data for these traits. Using predefined
gene sets Shungin et al. [55] observed enrichment for vas-
cular endothelial growth factor (VEGF), phosphatase and
tensin homolog (PTEN), insulin receptor (INSR), and per-
oxisome proliferator activated receptors (PPARs). PPARs
regulate expression of genes involved in, among other
things, adipocyte differentiation, lipid metabolism, and
energy balance. Pathway analysis implicated adipogen-
esis, angiogenesis, transcriptional regulation, and insulin
resistance as processes affecting fat distribution. Of note,
there was no overlap of these pathways with those identi-
fied for BMI.
VII CLINICAL IMPLICATIONS OF THE
DISCOVERY OF OBESITY GENES
A Identification of Monogenic Causes of
Obesity
Until recently, only the rare Mendelian syndromes, such
as PraderWilli and BardetBiedl, were known to cause
heritable obesity. These disorders are easily recognized,
both by a wide spectrum of phenotypes [132,144] and by
the use of cytogenetics assays that are widely available.
However, the Mendelian, nonsyndromic obesity disorders
are not so easily diagnosed, because obesity is often the
only apparent phenotype and clinical assays for known
obesity gene mutations are rarely practical. It is estimated
that 27% of morbidly obese patients have mutations in
MC4R [122,123,145,146] and an unknown, but smaller,
percent have mutations in other obesity genes, including
POMC [147] and NTRK2 [125]. Thus, only about 1 in
10 morbidly obese patient has a known mutation that
explains the obesity, and molecular assays for the cur-
rently known Mendelian obesities would be negative in
the majority of morbidly obese patients. Also, there are
many known distinct mutations in each of these genes.
Thus, no clinical laboratories yet provide diagnosis of
these mutations, rather they have only been diagnosed by
research laboratories. However, inability to make specific
molecular diagnosis does not mean that one cannot iden-
tify people with increased risk for genetic obesity, and
this may influence choices or approaches to treatment.
Several criteria can be used to estimate the probability
that an individual’s obesity has a genetic cause (Table 21.4).
At the present time, due to the lack of data, these estimates
do not produce any quantitative values revealing individual
risk that obesity is monogenic, but rather just generic classi-
fication, such as likely genetic, uncertain, and likely not
genetic.
Genetics of Nonsyndromic Human Obesity Chapter | 21 469
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Factors indicating a genetic basis for obesity are: (1) a
family history of obesity is consistent with the presence
of an obesity gene shared among family members; (2)
early age of onset and extreme obesity indicate a genetic
basis for obesity; and (3) children with single-gene obe-
sity are normal weight at birth but severe early hyperpha-
gia, often associated with aggressive food-seeking
behavior, results in rapid weight gain, usually beginning
in the first year of life. Severe obesity in children has
been variously defined as a standard deviation score for
BMI of more than 2.5 [148] or 3 [128] relative to the
appropriate reference population. Extreme trait values are
more likely to be genetic for many complex diseases, sim-
ply because extremes tend to result from the actions of
severe mutations or from mutations in genes that have
larger effects [149].
At present, a few diagnostic tools are available for the
medical evaluation of patients suspected of having mono-
genic obesity. The only screening tests available are for
those mutations that cause endocrine abnormalities.
Serum leptin should be measured. Very low or very high
serum leptin levels will indicate mutation in LEP or
LEPR, respectively. However, lack of very high leptin
levels cannot rule out homozygous mutations in LEPR
[128]. A subset of obese individuals has inappropriately
low leptin levels for their fat mass, suggesting a less
severe defect in leptin regulation [150]. ACTH and proin-
sulin should be measured to indicate defects in POMC or
in prohormone processing. Insulin should be measured to
evaluate the appropriateness of the degree of hyperinsuli-
nemia as this may indicate an MC4R mutation.
Physical appearance provides evidence of POMC
mutations or the syndromic obesities. POMC defects can
cause red hair and obesity [121], although most red hair
results from mutations in melanocortin 1 receptor (MC1R)
[151], which does not influence obesity. Thus, red hair is
only informative when red hair, ACTH deficiency, and
obesity cosegregate within a family.
PraderWilli, BardetBiedl, and other syndromic
obesities can be diagnosed by a variety of characteristic
TABLE 21.4 Suggestions to Evaluate Suspected Monogenic Etiology of Severe Obesity
a,b
Phenotype Phenotype Indicative of Genetic Etiology
Characteristic of All Genetic Obesities
Family history Having first-degree relatives with severe obesity
Age of onset Normal birth weight but age of onset of obesity before age 10
Hyperphagia Hyperphagia developing within first year of life
Aggressive food-seeking behavior
Phenotype Associated with Specific Gene Mutation
Very low leptin levels Mutation in LEP
Hypogonadism, delayed puberty, lack of growth spurt Mutation in LEP or LEPR or PCSK1
Disproportionate insulin resistance Mutation in SH2B1
Developmental delay Mutation in SH2B1 or SIM1
Low ACTH or high proinsulin levels Mutation in POMC or PCSK1
Frequent infections Mutation in POMC (ACTH), LEP or LEPR
Defective prohormone processing Mutation in CPE or PCSK1
Red hair segregating with obesity Mutation in POMC
Severe hyperinsulinemia, acanthosis nigricans Mutation in MC4R
Accelerated linear growth, increased bone mass Mutation in MC4R
Delayed language skills, impaired short-term memory Mutation in NTRK2 or BDNF
a
Data adapted from ([169,170,155,161],[172],[171,128,132,148]).
b
For a complete algorithm for the assessment of a severely obese individual, see [132].
BDNF, brain-derived neurotrophic factor; LEP, leptin; LEPR, leptin receptor; SH2B1, SH2B adapter protein 1; SIM1, single-minded homolog 1; MC4R,
melanocortin 4 receptor; NTRK2, neurotrophic tyrosine kinase receptor, type 2; PCSK1, proprotein convertase subtilisin/kexin-type 1; CPE,
carboxypeptidase E; POMC, proopiomelanocortin.
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phenotypes, such as small hands and feet, polydactyly,
and mental retardation as well as by cytogenetic assays.
Thus, one should rule out these diagnoses by phenotype
determination and by absence of characteristic chromo-
somal abnormalities.
B Personalized Treatment Based on
Genotype
At present the impact of genetics on diet effectiveness has
been the subject of many papers, but all current studies
have severe limitations. First, there are some large longi-
tudinal or cohort studies that have reported statistically
significant dietgenotype interactions. However, diet-
based correlations have yet to provide evidence that
stands the test of time. For example, correlations formed
the basis for advice to avoid cholesterol and saturated fat,
which have rarely been supported by randomized con-
trolled trials. Second, all current randomized controlled
trials are underpowered for genetics and thus find no or
few significant results. Third, the underlying diet studies
test too few diets for too short a time. Not even one large,
well-powered study has examined dietgenotype interac-
tions for diets that range from ketogenic to low carbohy-
drate or the typical U.S. diet to extreme low fat and
vegan. Many basic questions are thus unanswered. For
instance, does each person have one ideal diet for weight
management or many possible equally healthy diets?
Matching diets to genotype is a goal for personalized
medicine. Goals of personalized medicine are sometimes
called P4; predictive, preventative, personalized, and partic-
ipatory. The ability to calculate GRS is now well estab-
lished but surprisingly, GRS may not predict weight gain or
loss. Other components of P4 are not as advanced. Diet pre-
dictions based on questionnaire are flawed because diet
questionnaires are unreliable. If people are resistant to try-
ing new diets on their own, will they also resist when some
professional or expert says “your obesity GRS means that
you should be eating...?” One recent study reported that
subjects told that they have higher genetic risk alleles of
FTO had enhanced readiness to control weight but the
knowledge of FTO status had no impact on behavior [152].
A 2016 NIH Working Group Report [153] on using
genomic information to guide weight management
pointed out that technologies are available for the fast
characterization of the transcriptome, proteome, epigen-
ome, and metabolome of an individual. But effective
algorithms are yet to be developed to combine these data
with classical medical and behavioral measures of the
individual to personalize weight loss recommendations in
the clinical setting.
Despite the ability to generate overwhelming amounts
of genetic and other data for people, P4 recommendations
for diet cannot be implemented. It is not known which of
the many natural variants detected matter, nor do the diet
studies needed to evaluate variants for causal effects on
dietgenotype interactions exist. Thus, for a long foresee-
able future, individuals will need to determine optimal
diets by personally testing several different diets. The first
step toward generalized discovery of personalized diets
will require large highly powered randomized diet studies
testing a full range of diets.
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