Hap Map Project - Bgbunict.it

The International HapMap
Project
Anno 2009/2010
Dott.ssa Laura Rita Duro

Most common diseases, such as diabetes,
cancer, stroke, heart disease, depression, and
asthma, are affected by combinations of multiple
genetic and environmental factors

Genetic and environmental contributions to monogenic and
complex disorders
(A) Monogenic disease. A variant in a single gene is the primary determinant of a
monogenic disease or trait, responsible for most of the disease risk or trait variation
(dark blue sector), with possible minor contributions of modifier genes (yellow
sectors) or environment (light blue sector).
(B) Complex disease. Many variants of small effect (yellow sectors) contribute to
disease risk or trait variation, along with many environmental factors (blue sector).

More than a
thousand genes for
rare, highly heritable
‘mendelian’ disorders
have been identified,
in which variation in a
single gene is both
necessary and
sufficient to cause
disease.
Complex diseases, in
contrast, have proven
much more
challenging to study,
as they are thought to
be due to the
combined effect of
many different
susceptibility DNA
variants interacting
with environmental
factors

Discovering these genetic factors will provide
fundamental new insights into the pathogenesis,
diagnosis and treatment of human disease

Although any two unrelated people are the
same at about 99.9% of their DNA sequences,
the remaining 0.1% is important because it
contains the genetic variants that influence
how people differ in their risk of disease or
their response to drugs.
Discovering the DNA sequence variants that
contribute to common disease risk offers one
of the best opportunities for understanding the
complex causes of disease in humans.

Human Genetic Variations
Primarily two types of genetic mutation events create all
forms of variations:
Single base mutation which substitutes
one nucleotide for another
-Single Nucleotide Polymorphisms (SNP)
Insertion or deletion of one or more
nucleotide(s)
-Tandem Repeat Polymorphisms
-Insertion/Deletion Polymorphisms

Tandem Repeat Polymorphisms
Tandem repeats or variable number of tandem repeats (VNTR) are a very
common class of polymorphism, consisting of variable length of sequence
motifs that are repeated in tandem in a variable copy number.
VNTRs are subdivided into two subgroups based on the size of the
tandem repeat units.
Microsatellites or Short Tandem Repeat (STR)
repeat unit: 1-6 (dinucleotide repeat: CACACACACACA)
Minisatellites
repeat unit: 10-100

SNPs
Sites in the genome where the
DNA sequences of many
individuals differ by a single
base are called single
nucleotide polymorphisms
(SNPs)
For example, some people
may have a chromosome with
an A at a particular site where
others have a chromosome
with a G
Each form is called an allele

Variation Or Mutation ?
Terminology for variation at a single
nucleotide position is defined by allele
frequency

Polymorphism
A sequence variation that occurs at least 1
percent of the time (> 1%)
90% of variations are SNPs
Mutation
If the variation is
present less than
1 percent of the
time (

Transitions and Transversions
SNPs include single base substitutions such as:
Transitions:
change of one purine (A,G) for a purine,
or a pyrimidine (C,T) for a pyrimidine
A G G A C T T C
Transversions:
change of a purine (A,G) for a pyrimidine (C,T),
or viceversa
A C A T G C G T C A C G T A T G

In principle, SNPs could be bi-, tri-, or tetra-allelic
polymorphisms
However, in humans, tri-allelic and tetra-allelic
However, in humans, tri-allelic and tetra-allelic
SNPs are rare almost to the point of
non-existence, and so SNPs are sometimes
simply referred to as bi-allelic markers

Non-coding SNPs:
5’ and 3’ UTRs
Introns
Intergenic Spaces
Non-synonymous Coding
SNPs:
when single base substitutions
cause a change in the resultant
amino acid
Synonymous Coding SNPs:
when single base substitutions do
not cause a change in the
resultant amino acid

Non-coding SNPs
Example: Regulatory SNPs (rSNPs)
Two allelic variants of the same gene are transcribed in different
amounts as a consequence of an adjacent polymorphism. In this
example, allele G, located upstream of the gene, has a higher
transcript level than does allele T.

Coding SNPs
Example: Synonymous, mutation does not change
amino acid.

Coding SNPs
Example: Non-synonymous, mutation change
amino acid.

SNPs
It has been estimated that, in the world’s human population,
about 10 million sites (that is, one variant per 300 bases on
average) vary such that both alleles are observed at a
frequency of > 1%, and that these 10 million common SNPs
constitute 90% of the variation in the population.
The remaining 10% is due to a vast array of variants that are
each rare in the population.
The presence of particular SNP alleles in an individual is
determined by testing (‘genotyping’) a genomic DNA sample.
NATURE |VOL 426 | 18/25 DECEMBER 2003

A particular combination of alleles along a
chromosome is termed a haplotype
Haplotype is a set of SNPs on a single chromatid
that are statistically associated

The coinheritance of SNP alleles on these haplotypes
leads to associations between these alleles in the
population
(known as linkage disequilibrium, LD)

Linkage disequilibrium
Situation in which some combinations of alleles or genetic
markers occur more or less frequently in a population than
would be expected from a random formation of haplotypes
from alleles based on their frequencies.
Non-random associations between polymorphisms at
different loci are measured by the degree of linkage
disequilibrium (LD).

The LD between many neighboring SNPs generally persists because meiotic recombination
does not occur at random, but is concentrated in recombination hot spots.
Adjacent SNPs that lack a hot spot between them are likely to be in strong LD.
r 2 = 1: two SNPs that are perfectly correlated (allele A of SNP1 is always observed with
allele C of SNP2, and viceversa)
r 2 = 0: allele A of SNP1 providing no information at all about which allele of SNP4 is
present.
Complete independence of these 6 SNPs would predict the possibility of 64 different
haplotypes (because n biallelic SNPs could generate 2 n haplotypes), but in reality just 4
haplotypes comprise 90% of observed chromosomes, indicating that LD is present.
Because of the strong associations
among the SNPs in most chromosomal
regions, only a few carefully chosen
SNPs (known as tag SNPs) need to be
typed to predict the likely variants at the
rest of the SNPs in each region
SNP1, SNP2, and SNP3 are strongly correlated, and SNP4, SNP5, and SNP6
are strongly correlated, so that any of SNP1–SNP3 (or SNP4–SNP6) could
serve as tags for the other 2 SNPs in each group.

Many empirical studies have shown highly significant levels of LD, and
often strong associations between nearby SNPs, in the human genome.
Because the likelihood of recombination between two SNPs increases
with the distance between them, on average such associations between
SNPs decline with distance.
Average linkage disequilibrium, |D|, vs.
distance between SNPs for 2597
genes in which accurate distances
were available.
Lower values indicate a stronger effect
of recombination and recurrent
mutation.
LD decreases with distance.
B.A. Salisbury et al. Mutation Research 2003

The strong
associations between
SNPs in a region have
a practical value
Genotyping only a few, carefully chosen SNPs in the region will provide enough
information to predict much of the information about the remainder of the common
SNPs in that region. As a result, only a few of these ‘tag’ SNPs are required to
identify each of the common haplotypes in a region.
On the basis of empirical studies, it has been estimated that most of
the information about genetic variation represented by the 10 million
common SNPs in the population could be provided by genotyping
200.000 to 1.000.000 tag SNPs across the genome
These observations are the conceptual and empirical foundation for
developing a haplotype map of the human genome, the ‘HapMap’.

The International HapMap Project is a partnership of scientists
and funding agencies from Canada, China, Japan, Nigeria, the
United Kingdom and the United States to develop a public resource
that will help researchers find genes associated with human
disease and response to pharmaceuticals.
An initial meeting to discuss the scientific and ethical issues associated
with developing a human haplotype map was held in Washington in 2001.
The International HapMap Project was then formally initiated in 2002.

The goal of the International HapMap Project is to develop a
haplotype map of the human genome, the HapMap,
which will describe the common patterns of human DNA
sequence variation.
The HapMap is expected to be a key resource for researchers
to use to find genes affecting health, disease, and responses
to drugs and environmental factors.
The information produced by the Project is freely available
(www.hapmap.org)
NATURE |VOL 426 | 18/25 DECEMBER 2003

The HapMap was designed to determine the frequencies and
patterns of association among roughly 3 million common
SNPs in four populations, for use in genetic association
studies
The HapMap project focuses only on common SNPs, those
where each allele occurs in at least 1% of the population

The project studied a total of 270 DNA samples:
90 samples from a US Utah population with
Northern and Western European ancestry
(samples collected in 1980 by the Centre
d’Etude du Polymorphisme Humain (CEPH)
and used for other human genetic maps)
new samples collected from 90 Yoruba
people in Ibadan, Nigeria
45 unrelated Japanese in Tokyo, Japan
45 unrelated Han Chinese in Beijing, China

The International HapMap Consortium decided to include several
populations from different ancestral geographic locations to ensure that the
HapMap would include most of the common variation and some of the less
common variation in different populations.
NATURE |VOL 426 | 18/25 DECEMBER 2003

Human Genome Project
vs
International HapMap Project
In its scope and potential consequences, the International HapMap Project
has much in common with the Human Genome Project, which sequenced the
human genome.
Both projects have been scientifically ambitious and technologically
demanding, have involved intense international collaboration, have been
dedicated to the rapid release of data into the public domain, and promise to
have profound implications for our understanding of human biology and
human health.
Whereas the sequencing project covered the entire genome, including the
99.9% of the genome where we are all the same, the HapMap will
characterize the common patterns within the 0.1% where we differ from each
other.

The project had become practical by the confluence of the following:
the availability of the human genome sequence;
databases of common SNPs (subsequently enriched by this
project) from which genotyping assays could be designed;
insights into human LD;
development of inexpensive, accurate technologies for highthroughput
SNP genotyping;
web-based tools for storing and sharing data.
The International HapMap Consortium NATURE October 2005

HapMap Project comprises two phases
The complete data obtained
in Phase I were published
on October 2005.
The analysis of the Phase II
dataset was published in
October 2007.

The Phase I HapMap
Phase I of the HapMap Project set as a
goal genotyping at least one common
SNP every 5 kb across the genome in
each of 269 DNA samples.
For the sake of practicality, and motivated
by the allele frequency distribution of
variants in the human genome, a minor
allele frequency (MAF) of 0.05 or greater
was targeted for study.
Minor Allele Frequency (MAF) : The frequency at which the less abundant
(or minor) allele of a SNP is present in a population. The MAF for a SNP to
be considered common is usually above 1%.

The project required a dense map of SNPs, ideally containing
information about validation and frequency of each candidate SNP.
When the project started, the public SNP
database (dbSNP) contained 2.6 million
candidate SNPs, few of which were
annotated with the required information.
The HapMap Project contributed about 6
million new SNPs to dbSNP. At October
2005 dbSNP contains 9.2 million
candidate human SNPs.

To study patterns of genetic variation were selected ten 500-kb regions
from the ENCODE (Encyclopedia of DNA Elements) Project.
These ten regions were chosen to approximate the genome-wide
average for G+C content, recombination rate, percentage of sequence
conserved relative to mouse sequence, and gene density.
Each 500-kb region was sequenced in 48 individuals, and all SNPs in
these regions (discovered or in dbSNP) were genotyped in the complete
set of 269 DNA samples.

Using the data provided by HapMap, a team of scientists at Harvard Medical
School and the Broad Institute has discovered a new genetic variant
associated with age-related macular degeneration (AMD), the leading cause
of blindness in people over 60 years of age, as well as confirming previously
reported variants
Nature Genetics - 38, 1055 - 1059 (2006)

They estimate that genotypes related to just five variants in three different genes can
explain 50% of the risk of developing AMD
The new genetic common variant identified was found in a non-coding region of the
Complement Factor H (CFH) gene, other variants of which were recently shown to
be associated with the risk of developing AMD.
In addition to CFH on chromosome 1
the complement factor B (BF) gene on chromosome 6
complement component 2 (C2) gene on chromosome 6
a common variant (A69S) is in hypothetical gene LOC387715 on chromosome 10.
Interestingly, these three genes do not appear to interact directly, but instead
contribute to the risk of AMD independently.

Phase II HapMap characterizes over 3.1 million human
SNPs genotyped in 270 individuals from four
geographically diverse populations

Genotyping in phase II was attempted for about 4.4 million
distinct SNPs, of which roughly 1.3 million either could
not be
typed,
were not
polymorphic
in any
of the
populations, or did not pass genotyping quality control
filters.
Certain regions of the genome were recognized as being
challenging to study, such as centromeres, telomeres,
gaps in genome sequence, and segmental duplications,
regions declared to be not HapMapable.

The resulting HapMap has an SNP density of approximately
one per kilobase and is estimated to contain approximately
25–35% of all the 9–10 million common SNPs in the
assembled human genome

Variation in SNP density within the Phase II HapMap
Phase I
Phase II
Example of the fine-scale structure of SNP density for a 100-kb region on chromosome
17 showing polymorphic Phase I SNPs in the consensus data set (red triangles) and
polymorphic Phase II SNPs in the consensus data set (blue triangles)
The Phase II HapMap differs from the Phase I HapMap also in minor allele frequency
(MAF) distribution. SNPs added in Phase II have lower MAF. Phase II HapMap
includes a better representation of rare variation than the Phase I HapMap

Advances in technology for high-throughput SNP genotyping
Advances in genotyping technology have vastly increased the number
of variants that can be typed and decreased the per-sample costs
These advances have made possible the
dense genotyping needed to capture the
majority of SNP variation within an individual
at a sufficiently low cost to allow the large
sample sizes needed for comparison of
individuals with and without disease

Studies in additional populations have shown that the tag
SNPs chosen using the HapMap are generally transferable
across other populations, but there are some limitations.
So additional samples from the populations used to develop
the HapMap as well as from seven more populations have
recently been genotyped across the genome.
Luhya from Webuye, Kenya
Maasai from Kenya
Tuscans from Italy
Indian-Americans (Gujarati) from Houston, TX
Han Chinese from Denver
Mexican-Americans from Los Angeles
Americans of African Descent from the SW USA

It is now clear that the HapMap can be
a useful resource for the design and
analysis of disease association studies
in populations across the world

APPLICATION OF THE HAPMAP TO
COMMON DISEASE

The technological advances directly stimulated or
indirectly facilitated by the HapMap have had a
profound impact on the study of the genetics of
common diseases

The history of high-density GWA
scanning to date has
demonstrated the striking
success of this approach in
finding genetic variants
associated with disease.
Variants or regions associated
with nearly 40 complex diseases
have been identified in diverse
population samples.

Major Autism Gene Found with Help of HapMap
Using data from the HapMap, along with DNA samples collected from many
families who have affected children, researchers have discovered a genetic
variation linked to autism, one of the most heritable mental health
conditions.
They found a variation in the sequence of a gene - the “MET receptor
tyrosine kinase gene” - that is associated with autism. This gene is involved
in brain development, immune function, and digestive system repair.
The MET promoter variant rs1858830 allele "C" is strongly associated with
ASD and results in reduced gene transcription. MET protein levels were
significantly decreased in ASD cases compared with control subjects.
People who have the variation are more than twice as likely as others to
have “autism spectrum disorders”
Campbell DB et al, Ann Neurol. 2007

A genome-wide association study identifies novel risk loci for
type 2 diabetes
Type 2 diabetes mellitus results from the interaction of environmental factors
with a combination of genetic variants.
A systematic search for these variants was recently made possible by the
development of high-density arrays that permit the genotyping of hundreds of
thousands of polymorphisms.
Researchers tested 392,935 SNPs in a French case–control cohort.
Markers with the most significant difference in genotype frequencies between
cases of type 2 diabetes and controls were fast-tracked for testing in a second
cohort.
This identified four loci containing variants that confer type 2 diabetes risk, in
addition to confirming the known association with the TCF7L2 gene.
These loci include a non-synonymous polymorphism in the zinc transporter
SLC30A8, which is expressed exclusively in insulin-producing β-cells, and two
linkage disequilibrium blocks that contain genes potentially involved in β-cell
development or function (IDE–KIF11–HHEX and EXT2–ALX4).
Sladek R et al. Nature 445, 881-885 (2007)

Future of the HapMap Project
Currently, additional samples from the populations used to develop
the initial HapMap, as well as samples from seven additional
populations will be sequenced and genotyped extensively to extend
the HapMap, providing information on rarer variants and helping to
enable genome-wide association studies in additional populations.
There are also ongoing efforts by many groups to characterize
additional forms of genetic variation, such as structural variation, and
molecular phenotypes in the HapMap samples. Finally, in the future,
whole-genome sequencing will provide a natural convergence of
technologies to type both SNP and structural variation.
Nevertheless, until that point the HapMap Project data will provide an
invaluable resource for understanding the structure of human genetic
variation and its link to phenotype.

Beyond SNPs:
Copy Number Variants and Other
Structural Variation

Current generation high-throughput
genotyping platforms are
extraordinarily efficient at genotyping
SNPs, but they are less effective at
genotyping structural variants, such
as insertions, deletions, inversions,
and copy number variants

Although not as common as SNPs, these variants also occur
commonly in the human genome
The distribution of copy number variation in the human genome among 270 HapMap samples

A Copy number variants (CNV) is
a segment of DNA in which copynumber
differences have been
found by comparison of two or more
genomes.
CNV in which stretches of genomic
sequence of roughly 1 kb to 3 Mb in
size are deleted or are duplicated in
varying numbers, have gained
increasing attention because of their
apparent ubiquity and potential
dosage effect on gene expression.

In 2004, the interrogation of genomic variability by array
hybridization methods clearly demonstrated the existence of copy
number variants.
Intense analysis of this type of genomic variability followed, and
the current conservative estimate from studies in a few hundred
individuals is that at least 10% of the genome is subject to copy
number variation

Although a typical SNP affects only one single nucleotide
pair, their genomic abundance (over 10 million) makes
them the most frequent source of polymorphic changes
By contrast, CNVs are far less numerous but can affect
from one kilobase to several megabases of DNA per
event, adding up to a significant fraction of the genome

It is now recognized that the genomes of any two
individuals in the human population differ more at the
structural level than at the nucleotide sequence level
NATURE GENETICS SUPPLEMENT | VOLUME 39 | JULY 2007

Much of what was previously known about the role of CNVs in disease
comes from a rich literature on ‘genomic disorders’.
Genomic disorders are defined as a diverse group of genetic diseases that
are each caused by an alteration in DNA copy number.
These mutations can be relatively large, microscopically visible
imbalances, such as in Prader-Willi syndrome, or they may be much
smaller, requiring higher resolution detection methods, such as in Williams
Syndrome.
Genomic disorders are typically sporadic in nature because the CNV in
most cases is a de novo mutation with nearly complete penetrance, and
because the affected individuals have severe developmental problems and
are unlikely to have offspring.
However, there are notable examples of mendelian disease traits
associated with CNVs. For example, duplications of the gene for peripheral
myelin protein 22 (PMP22) cause the dominant neuropathy Charcot-Marie
Tooth disease type 1A, and deletions of the α-globin gene cluster cause
the recessive anemia α-thalassemia.

Bibliografia
The International HapMap Consortium. The International HapMap Project.
NATURE. 426: 18/25, December 2003.
Deloukas P, Bentley D. The HapMap project and its application to genetic
studies of drug response. The Pharmacogenomics Journal. 4, 88–90 (2004).
The International HapMap Consortium. A haplotype map of the human
genome. NATURE. 437: 27, October 2005.
Manolio TA, Brooks LD, Collins FS. A HapMap harvest of insights into the
genetics of common disease. The Journal of Clinical Investigation. 118: 5,
May 2008.
The International HapMap Consortium. A second generation human
haplotype map of over 3.1 million SNPs. NATURE. 449: 18, October 2007.
Maller J, George S, Purcell S, Fagerness J, Altshuler D, Daly MJ, Seddon JM.
Common variation in three genes, including a noncoding variant in CFH,
strongly influences risk of age-related macular degeneration. Nat Genet. 38:9
(1055-9), Sep 2006.