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Polymorphism
Haixu Tang
School of Informatics
Genome variations
underlie phenotypic
differences
cause inherited
diseases
Restriction fragment length
polymorphism (RFLP)
RFLP
Haplotype
Microsattelite (short tandem
repeats) polymorphysim
AATG
7 repeats
8 repeats
the repeat region is variable between samples while the
flanking regions where PCR primers bind are constant
Which Suspect,
A or B, cannot
be excluded from
potential perpetrators
of this assault?
Single nucleotide polymorphism
• The highest possible dense polymorphism
• A SNP is defined as a single base change
in a DNA sequence that occurs in a
significant proportion (more than 1
percent) of a large population.
Some Facts
• In human beings, 99.9 percent bases are same.
• Remaining 0.1 percent makes a person unique.
– Different attributes / characteristics / traits
• how a person looks,
• diseases he or she develops.
• These variations can be:
– Harmless (change in phenotype)
– Harmful (diabetes, cancer, heart disease, Huntington's disease,
and hemophilia )
– Latent (variations found in coding and regulatory regions, are not
harmful on their own, and the change in each gene only
becomes apparent under certain conditions e.g. susceptibility to
lung cancer)
SNP facts
• SNPs are found in
– coding and (mostly) noncoding regions.
• Occur with a very high frequency
– about 1 in 1000 bases to 1 in 100 to 300 bases.
• The abundance of SNPs and the ease with which they can
be measured make these genetic variations significant.
• SNPs close to particular gene can acts as a marker for
that gene.
SNP maps
• Sequence genomes of a large number of
people
• Compare the base sequences to discover
SNPs.
• Generate a single map of the human
genome containing all possible SNPs =>
SNP maps
How do we find sequence variations?
• look at multiple sequences
from the same genome region
• use base quality values to decide if
mismatches are true polymorphisms or
sequencing errors
Automated polymorphism discovery
P( SNP) 

all var iable
P ( S N | RN )
P( S1 | R1 )
 ... 
 PPrior ( S1 ,..., S N )
PPrior ( S1 )
PPrior ( S N )
P( SiN | R1 )
P( Si1 | R1 )
S
... 
 ... 
 PPrior ( Si1 ,..., SiN )

P
(
S
)
P
(
S
)
S i1 [ A,C ,G ,T ] S iN [ A,C ,G ,T ] Prior
i1
Prior
iN
Marth et al.
Nature Genetics 1999
Large SNP mining projects
genome reference
EST
WGS
BAC
~ 8 million
Sachidanandam et al.
Nature 2001
How to use markers to find disease?
genome-wide, dense SNP marker map
• genotyping: using millions of markers simultaneously for
an association study
• question: how to select from all available markers a
subset that captures most mapping information (marker
selection)
• depends on the patterns of allelic association in the
human genome
Allelic association
• allelic association is the nonrandom assortment between alleles
i.e. it measures how well knowledge
of the allele state at one site permits
prediction at another
marker site
functional site
• significant allelic association between a marker and a functional
site permits localization (mapping) even without having the
functional site in our collection
• by necessity, the strength of allelic association is measured between
markers
Linkage disequilibrium
• LD measures the deviation from random assortment of the
alleles at a pair of polymorphic sites
D=f(
) – f( ) x f( )
• other measures of LD are derived from D, by e.g.
normalizing according to allele frequencies (r2)
Haplotype diversity
• the most useful multi-marker measures of associations are related
to haplotype diversity
n markers
n
2 possible haplotypes
random assortment of alleles at
different sites
strong association: most chromosomes carry
one of a few common haplotypes – reduced
haplotype diversity
Haplotype blocks
Daly et al.
Nature Genetics 2001
• experimental evidence for reduced haplotype diversity
(mainly in European samples)
The promise for medical genetics
CACTACCGA
CACGACTAT
TTGGCGTAT
• within blocks a small number of SNPs are
sufficient to distinguish the few common
haplotypes  significant marker reduction is
possible
• if the block structure is a general feature of human variation
structure, whole-genome association studies will be possible at a
reduced genotyping cost
• this motivated the HapMap project
Gibbs et al.
Nature 2003
The HapMap initiative
• goal: to map out human allele and association structure
of at the kilobase scale
• deliverables: a set of physical and informational reagents
Haplotyping
• the problem: the substrate for genotyping is diploid,
genomic DNA; phasing of alleles at multiple loci is in
general not possible with certainty
A
C
G
C
T
T
C
A
• experimental methods of haplotype determination
(single-chromosome isolation followed by whole-genome
PCR amplification, radiation hybrids, somatic cell hybrids)
are expensive and laborious
A example of hyplotyping
• Mother GG AT
• Father CC AA
CA
AC
TT
CT
• Children GC AA CC CT
• Children GC AT AA TT
• Children GC AA AC CT
Haplotypes
•
a
• Mother I G A C T
•
II G T C T
b
G T A T
G A A T
• Father I C A A C
•
II C A A T
C A C T
C A C C
A example of hyplotyping
• Mother GG AT
• Father CC AA
CA
AC
TT
CT
• Children GC AA CC CT (M-Ia & F-IIb)
• Children GC AT AA TT (M-Ib & F-IIa)
• Children GC AA AC CT (M-Ia & F-Ia
or M-IIb & F-IIb) ?
HapMap Project
A freely-available public resource
to increase the power and efficiency
of genetic association studies to medical traits
High-density SNP genotyping across the genome
provides information about
– SNP validation, frequency, assay conditions
– correlation structure of alleles in the genome
All data is freely available on the web for application
in study design and analyses as researchers see fit
HapMap Samples
• 90 Yoruba individuals (30 parent-parent-offspring trios)
from Ibadan, Nigeria (YRI)
• 90 individuals (30 trios) of European descent from Utah
(CEU)
• 45 Han Chinese individuals from Beijing (CHB)
• 45 Japanese individuals from Tokyo (JPT)
HapMap progress
PHASE I – completed, described in Nature paper
* 1,000,000 SNPs successfully typed in all 270
HapMap samples
PHASE II – data generation complete, data released
* >3,500,000 SNPs typed in total !!!
ENCODE-HAPMAP variation project
• Ten “typical” 500kb regions
• 48 samples sequenced
• All discovered SNPs (and any others in dbSNP) typed in
all 270 HapMap samples
• Current data set – 1 SNP every 279 bp
A much more complete variation resource by which
the genome-wide map can evaluated
Tagging from HapMap
• Since HapMap describes the majority of
common variation in the genome,
choosing non-redundant sets of SNPs
from HapMap offers considerable
efficiency without power loss in
association studies
Pairwise tagging
A/T
1
A
A
T
T
G/A
2
G
G
A
A
high r2
G/C
3
G
C
G
C
T/C
4
T
C
C
C
high r2
G/C
5
A/C
6
A
C
C
C
G
C
G
C
high r2
After Carlson et al. (2004) AJHG 74:106
Tags:
SNP 1
SNP 3
SNP 6
3 in total
Test for association:
SNP 1
SNP 3
SNP 6