Polymorphism
description
Transcript of Polymorphism
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
the repeat region is variable between samples while the flanking regions where PCR primers bind are constant
7 repeats
8 repeats
AATG
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
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Marth et al. Nature Genetics 1999
Large SNP mining projects
Sachidanandam et al. Nature 2001
~ 8 million
EST
WGS
BAC
genome reference
How to use markers to find disease?
• genotyping: using millions of markers simultaneously for an association study
genome-wide, dense SNP marker map
• depends on the patterns of allelic association in the human genome
• question: how to select from all available markers a subset that captures most mapping information (marker selection)
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
marker site functional site
• by necessity, the strength of allelic association is measured between markers
• significant allelic association between a marker and a functional site permits localization (mapping) even without having the functional site in our collection
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)
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
2n possible haplotypesn
markers
random assortment of alleles at different sites
Haplotype blocks
Daly et al. Nature Genetics 2001
• experimental evidence for reduced haplotype diversity (mainly in European samples)
The promise for medical genetics
CACTACCGACACGACTATTTGGCGTAT
• 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
• experimental methods of haplotype determination (single-chromosome isolation followed by whole-genome PCR amplification, radiation hybrids, somatic cell hybrids) are expensive and laborious
A
T
C
T
G
C
C
A
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
• 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 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
High-density SNP genotyping across the genome provides information about– SNP validation, frequency, assay conditions– correlation structure of alleles in the genome
A freely-available public resource to increase the power and efficiency
of genetic association studies to medical traits
All data is freely available on the web for applicationin 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 whichthe 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
Tags:
SNP 1SNP 3SNP 6
3 in total
Test for association:
SNP 1SNP 3SNP 6
A/T1
G/A2
G/C3
T/C4
G/C5
A/C6
high r2 high r2 high r2
AATT
GC
CG
GC
CG
TCCC
ACCC
GC
CG
TCCC
GGAA
GGAA
After Carlson et al. (2004) AJHG 74:106