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Polymorphism. Haixu Tang School of Informatics. cause inherited diseases. Genome variations. underlie phenotypic differences. Restriction fragment length polymorphism (RFLP). RFLP. Haplotype. AATG. Microsattelite (short tandem repeats) polymorphysim. 7 repeats. 8 repeats.

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polymorphism

Polymorphism

Haixu Tang

School of Informatics

slide2

cause inherited diseases

Genome variations

underlie phenotypic differences

slide4

RFLP

Haplotype

microsattelite short tandem repeats polymorphysim

AATG

Microsattelite (short tandem repeats) polymorphysim

7 repeats

8 repeats

the repeat region is variable between samples while the flanking regions where PCR primers bind are constant

slide6

Which Suspect,

A or B, cannot

be excluded from

potential perpetrators

of this assault?

single nucleotide polymorphism
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
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
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
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
How do we find sequence variations?
automated polymorphism discovery
Automated polymorphism discovery

Marth et al.

Nature Genetics 1999

large snp mining projects

genome reference

EST

WGS

BAC

~ 8 million

Sachidanandam et al.

Nature 2001

Large SNP mining projects
how to use markers to find disease

question: how to select from all available markers a subset that captures most mapping information (marker selection)

How to use markers to find disease?

genome-wide, dense SNP marker map

  • genotyping: using millions of markers simultaneously for an association study
  • depends on the patterns of allelic association in the human genome
allelic association
Allelic association
  • allelic association is the non-random assortment between alleles i.e. it measures how well knowledge of the allele state at one site permits prediction at another

functional site

marker 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

D=f( ) – f( ) x f( )

Linkage disequilibrium
  • LD measures the deviation from random assortment of the alleles at a pair of polymorphic sites
  • other measures of LD are derived from D, by e.g. normalizing according to allele frequencies (r2)
haplotype diversity

strong association: most chromosomes carry one of a few common haplotypes – reduced haplotype diversity

Haplotype diversity
  • the most useful multi-marker measures of associations are related to haplotype diversity

n markers

2n possible haplotypes

random assortment of alleles at different sites

haplotype blocks
Haplotype blocks

Daly et al.

Nature Genetics 2001

  • experimental evidence for reduced haplotype diversity (mainly in European samples)
the promise for medical genetics

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 promise for medical genetics
  • within blocks a small number of SNPs are sufficient to distinguish the few common haplotypes  significant marker reduction is possible

CACTACCGA

CACGACTAT

TTGGCGTAT

the hapmap initiative
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

A

C

G

C

T

T

C

A

Haplotyping
  • the problem: the substrate for genotyping is diploid, genomic DNA; phasing of alleles at multiple loci is in general not possible with certainty
  • 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
A example of hyplotyping
  • Mother GG AT CA TT
  • Father CC AA AC CT
  • Children GC AA CC CT
  • Children GC AT AA TT
  • Children GC AA AC CT
haplotypes
Haplotypes
  • a b
  • Mother I G A C T G T A T
  • II G T C T G A A T
  • Father I C A A C C A C T
  • II C A A T C A C C
a example of hyplotyping1
A example of hyplotyping
  • Mother GG AT CA TT
  • Father CC AA AC 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
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
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
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
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
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

G/C

3

G/A

2

T/C

4

G/C

5

A/T

1

A/C

6

G

G

A

A

G

G

G

T

T

G

G

A

C

C

C

C

C

C

C

C

C

C

C

C

A

A

A

A

T

T

G

G

G

C

C

C

high r2

high r2

high r2

Pairwise tagging

Tags:

SNP 1

SNP 3

SNP 6

3 in total

Test for association:

SNP 1

SNP 3

SNP 6

After Carlson et al. (2004) AJHG 74:106