lab technology
as published in CLI May 2006
S NPs
Using SNPs to unravel the genetic
basis of obesity
by Dr M. Smith and Dr M. Olivier
Obesity is a multifactorial disorder influenced by both genetics and the environment, and its incidence is increasing rapidly worldwide. The analysis of
single nucleotide polymorphisms (SNPs) across the human genome, and
their relation to obesity and associated traits is helping to determine some
of the underlying genetic mechanisms controlling the onset and progression
of the disorder. This review discusses how SNPs can be used to identify
genes and/or variants associated with obesity.
What is obesity?
Obesity is a disorder influenced by multiple genes
and sequence variants within these genes. These
variants predispose individuals to an obese phenotype without necessarily having a direct influence on
food intake or waist circumference. In addition, an
individual's lifestyle affects the development of the
condition. Both physical activity level and food consumption will have an influence on body mass. The
generally reduced level of activity and increased
caloric intake experienced with a westernised diet
and lifestyle is fuelling the current increase in levels
of obesity worldwide. Levels of both adult and childhood obesity are increasing and reaching epidemic
proportions; and whilst absolute levels vary, the
upward trend is ubiquitous [Figure 1].
Obesity in adults is defined by the World Health
Organisation (WHO) as a body mass index (BMI)
greater than 30 kg/m2. For children, age and sex specific centile curves have been defined, which reach
the adult values at age 18 [1]. A BMI of greater than
25 kg/m2 is used to classify individuals as overweight. Studies investigating the genetic influences
on increased weight use these classifications in their
recruitment criteria.
The health and economic impact of obesity is significant, due in no small part to its associated comorbidities such as insulin resistance (leading to type 2
diabetes), hypertension, heart disease and dyslipidaemia. The presence of any three of these criteria in
a patient constitutes syndrome X, or the metabolic
syndrome [2, 3].
What is a single nucleotide polymorphism?
The completion of the Human Genome Project [4]
has determined that humans are 99.9% identical,
and it is the 0.1% of an individual's genome that
harbours the genetic predispositions to disease. A
single nucleotide polymorphism (SNP) is the variation of a single base pair in the DNA sequence, such
as an insertion, deletion or substitution of a base,
and they are the most common form of genetic variation. There are an estimated seven million SNPs
present in the human genome at a minor allele frequency (MAF) greater than 5%, i.e. more than 5% of
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living humans have the rarer nucleotide (allele) for a
particular SNP. Due to their prevalence and the simplicity of genotyping SNPs, they are the marker of
choice for disease association and fine-mapping
studies, to elucidate disease-causing genes and their
mutations. A wide variety of methods are available
for SNP genotyping, ranging from simple single
SNP assays to those designed to type up to 500,000
SNPs at once [5].
Haplotypes, linkage disequilibrium and tagSNPs
The physical assembly of individual SNP alleles
along a chromosome is known as a haplotype. Over
generations, recombination and mutation events
cause a re-arrangement of SNP alleles along chromosomes. This results in SNP alleles remaining on
the same chromosomal segment as others within
close proximity of them, but those further away will
differ from the ancestral chromosome. Linkage disequilibrium (LD) is the term used to describe this
nonrandom association of loci which, in effect,
maintains small sections of the ancestral chromosome. Many haplotypes can be detected where SNPs
are not in LD (2n for n SNPs), whereas in regions of
high LD the number of haplotypes is significantly
reduced. The low number of haplotypes in high LD
regions is representative of ancestral chromosomal
patterns unbroken by recombination. Information
about these common patterns allows the determination of tagSNPs, individual non-redundant markers
that allow the differentiation of haplotypes without
the need to genotype all SNPs in the region [Figure
2].
The recent completion of the International
Hap(lotype)Map Project (http://www.hapmap.org,
[6, 7]) has determined the patterns of LD (and thus
haplotypes) across all chromosomes in four reference populations; these are North American individuals of European descent, African, Chinese, and
Japanese. Whilst these patterns need to be confirmed in cohorts selected for disease association
studies, initial reports suggest a broad applicability
of results, and provide an excellent resource for
tagSNP selection.
Application to obesity research
To date SNPs have been used mainly to analyse obesity candidate gene regions, selected on either a functional or positional (or combination of both) basis.
Functional candidates are genes thought to play a
role in a pathway(s) which will lead to an effect on
body weight, such as the peroxisome proliferativeactivated receptor-gamma (PPARG) gene. This gene
is involved in the differentiation of adipocytes and
susceptibility to type 2 diabetes. Positional candidates
are genes within genomic regions, usually identified
by a genome-wide linkage analysis to determine areas
linked to traits such as BMI [Table 1]. An example
would be the ghrelin receptor which plays a role in
the regulation of food intake, and is located within a
quantitative trait locus (QTL) for BMI on chromosome 3. In addition, SNPs can also be used to finemap an identified quantitative trait locus (QTL) to
further narrow down the region of interest. The
intention is to identify one (or few) potential candidate genes for further investigation.
As the technology for high-throughput genotyping
of SNPs has improved, and continues to improve, so
their use in genetic investigations has increased.
Initially rare SNPs, or those located in candidate gene
regions thought to be rare or extreme causes of obesity, were identified and genotyped. It was then possible to demonstrate the effect of the mutation on the
resulting phenotype [8, 9]. Whilst these studies were
able to determine rare causes of obesity, they did not
produce results more applicable to the majority of
obese individuals in the general population.
The use of genome scans, which genotype
microsatellite markers (two-, three- or fournucleotide repeats of varying length) in large
cohorts of individuals, created optimism for the
identification of gene loci involved in obesity. These
investigations are able to link obesity (and associated phenotypes) to particular areas of the genome
[for example 10], and the regions can then be examined more closely for putative candidate genes causing the linkage. Once selected, a candidate gene can
be interrogated in detail by genotyping all known
SNPs within its region. These can either be selected
from
public
databases
(e.g.
dbSNP;
http://www.ncbi.nlm.nih.gov/projects/SNP/) or
identified through DNA sequencing of the region in
a subset of the total study cohort. Once chosen, all
SNPs can then be genotyped in the full cohort and
attempts can be made to correlate individual SNP
genotypes to particular phenotypes. Whilst initial
optimism was high, successful investigations of individual candidate genes have been limited.
The ability to identify haplotypes and tagSNPs has
greatly improved the mapping and dissection of
genomic regions of interest. This was demonstrated
S NPs
Figure 1. Obesity trends worldwide. Increasing percentage of population (male and female
data combined) diagnosed as obese (BMI>30 kg/m2) in the USA (blue), UK (black) and
other European countries (red) over time.
*European data are combined for the following countries: Austria, Belgium, Denmark,
Finland, Greece, Ireland, Italy, Portugal, and Spain.
in a recent investigation of the LD pattern of the serotonin receptor 2C (HTR2C;
[11]) which has been reported to influence obesity. Inconsistent associations
between the gene and phenotypes may be related to the pattern of LD across the
gene. Twenty-one common SNPs flanking a SNP commonly used in studies of
this region, and in the promoter region were identified and genotyped. Whilst
the commonly used SNP was in LD with the rest of the genic region investigated,
haplotypes of the promoter region may be associated with a BMI equal to, or
greater than 30 kg/m2 [11].
The 2004 update of the human obesity gene map [12], which reviews all data up
to October 2004, reports ten genes with 69 mutations as causes of obesity.
However, these are mainly rare mutations detected in one or few individuals
causing extremes of the phenotype. Further studies detected a total of 358 associations with obesity in 113 genes, of these, 18 were identified in five or more
investigations.
Chromosome
Region
1
1
2
3
7
7
10
10
11
11
12
20
p36
p31
p22
q26
q31
q32
p12
q26
q22
q24
q24
q13
Phenotypes
BMI, skinfolds
BMI, leptin
BMI, leptin
BMI
BMI
BMI, skinfolds
BMI
BMI
BMI
BMI
BMI, fat levels
BMI, fat levels,
protein/fat/carbohydrate intake
Table 1. Regions of the genome identified in four or more studies with significant linkage
to obesity and related traits [data derived from 12].
Future prospects
Obesity is a multi-factorial disease associated with a number of interacting phenotypes, and so the causes are also many and varied. Elucidation of the underlying genetic mechanisms leading to an increased BMI will no doubt necessitate the
greater understanding of all possible interactions. This means that investigation
of traits such as lipid levels, glucose and insulin concentrations, hypertension and
heart disease will need to be performed alongside analyses of obesity.
The use of SNPs will be two-fold. In one respect, high density arrays will be used
to genotype thousands of SNPs in large cohorts to localise regions of the genome
linked to obesity or related traits. Secondly, the prospect of custom arrays individually designed to target particular regions of the genome will allow fine-mapping of the identified regions. However this option is not without the need for
considerable investment and is likely to only be available to the best funded laboratories. The completion of the HapMap project has generated vast amounts of
publicly available data regarding SNP genotypes and their frequencies in four
major populations. Incorporation of this data into the design of high-density
arrays may permit effective genotyping of larger areas through the use of
tagSNPs.
As the ability to type more SNPs in more people increases, it is likely that the
genes or genetic variants underlying regions of linkage will be identified at a
greater rate than has occurred in the past. It is the hope that the knowledge
gained from these studies will help in the control of adverse health effects for
future generations.
References
Figure 2. The use of haplotypes and tagSNPs. [Adapted from [6]]. Location of SNPs (a)
indicated by arrows within a hypothetical gene (b), the closed boxes indicate exons. Four
putative haplotypes (c) generated by genotyping all SNPs are displayed, red squares indicate
the major allele for each SNP and blue squares the minor allele. TagSNPs (d) have been
identified; genotyping these SNPs alone would be enough to differentiate the four haplotypes, e.g. if the major allele was present at all loci (red, red, red) this matches the pattern of
the first haplotype.
1. Cole TJ et al. BMJ 2000; 320: 1240-3.
2. Reaven GM. Diabetes 1988; 37: 1595-607.
3. Grundy SM. Arterioscler Thromb Vasc Biol 2005; 25: 2243-4.
4. McPherson JD et al. Nature 2001; 409: 934-41.
5. Kwok PY and Chen X. Curr Issues Mol Biol 2003; 5: 43-60.
6. The International HapMap Consortium. Nature 2003; 426: 789-96.
7. Altshuler D et al. Nature 2005; 437: 1299-320.
8. Hinney A et al. J Clin Endocrinol Metab 2003; 88: 4258-67.
9. Hegele RA et al. Physiol Genomics 2000; 3: 39-44.
10. Kissebah AH et al. PNAS 2000; 97: 14478-83.
11. McCarthy S et al. Hum Genet 2005; 117: 545-57.
12. Perusse L et al. Obes Res 2005; 13: 381-490.
The authors
Edward M. Smith, PhD,
Michael Olivier, PhD,
Department of Physiology,
Human and Molecular Genetics Center,
Medical College of Wisconsin,
Milwaukee, WI 53222, USA Tel.: +1 414 456 4213