snp

SNP Analysis

Vipin Kumar

Csci 8980 - DMBIO

October 13, 2008

Vipin Kumar SNP Analysis

Messages

Single SNP Methods do not capture multi-locus interactions

Multi SNP Methods can do that,

But they can’t handle high dimensionality

Our work on Myeloma data


Single Nucleotide Polymorphism

SNP is a single nucleotidevariation that occurs at anappreciable frequency (1% to 5%)

12 Million SNPs on humangenome

Spread uniformly across thegenome

Contributes to 90% of the geneticvariation in human genome

AGCGTGCATCAGTCAGCGTGCATCAGTCAGCGTGCATCTGTCAGCGTGCATCAGTCindividual 1

individual 2

individual 3

individual 4

SNP


Linkage Disequilibrium (LD)

probability that no recombinationoccurs in between two alleles

close regions tend to stay togetherduring recombination

Regions that are far apart havelow LD

Regions that are close togetherhave high LD Figure:

Crossoverbetweenchromosomes


Example LD Plot for a sample set of SNPs


Single SNP approaches

Each SNP is tested for its association withthe phenotype

Most prevalent methods for testing SNPassociations are:

Chi-squared statistic testFishers exact testCochran-Armitage test, etc.

These tests give the probability ofassociation by chance

Sub

ject

s

PhenotypeSNPs


Chi-square test

Observed Matrix:

MM Mm mm Row Sum

Affected 8 27 65 100Unaffected 70 20 10 100

Column Sum 78 47 75 200

Expected Matrix:

MM Mm mm Row Sum

Affected 39 23.5 37.5 100Unaffected 39 23.5 37.5 100

Column Sum 78 47 75 200


Chi-square test

Expected Value E = Col .sum×RowsumTotalsamples

χ2 =∑ (O−E)2

E

Degrees of freedom = (m − 1) × (n − 1)

Using χ2 and deg. of freedom, lookup the probability offinding the observed matrix by chance

-log(p) is often used for convenience

Each snp having -log(p) > significance level is associated withthe phenotype


GWAS of Coronary Artery Disease - Samani et. al. 2007

1926 cases (subjects with artery disease before 66 yrs)

2938 controls

377,857 SNPs

Found strong association with SNPs on chromosome 9


GWAS for lung cancer - Amos et. al.

1,154 ever-smoking lung cancer cases

1,137 ever-smoking controls

317,498 SNPs


Multi SNP approaches

Here multiple SNPs are tested forassociation with the phenotype

Most suitable for complex diseases

The following combinatorial methods areused:

Multifactor Dimensionality ReductionCombinatorial Partitioning Method, etc S

ub

ject

s

PhenotypeSNPs


Multifactor Dimensionality Reduction


Application

MDR reveals higher order interaction in sporadic breastcancer, Ritchie et. al. Am. J. Hum. Genet. 2001

200 women with sporadic breast cancer

Age matched controls (patients with other illness)

9 SNPs were considered

Found a 4 locus genotype with highest crossvalidationconsistency.


Combinatorial Partitioning Method (for QTL)


SNP Data Set for finding Associations

Each pixel is either MM (green), Mm (red) or mm (blue).


Statistical Significance


Classification Based Approaches

Controls

Cases

Cases

Cases

Controls

Controls

Test Set

Train set

Model

Classifier

Accuracy

Test

Train


Using Location Information

Non-synonymous X X X X X

Introns X X X X

Synonymous X X X

Admixture X X X X

UTR X X X X

Other X X X

Accuracy 66.43 58.74 51.74 72.72 71.33 54.54 69.99

Nonsyn + Promolign (Syn + Introns): 75.75 %


Statistical Significance


Messages

Single SNP Methods do not capture multi-locus interactions

Multi SNP Methods can do that,

But they can’t handle high dimensionality

Our work on Myeloma data


Questions?


snp

Documents

qtl vipin kumar snp

single snp approacheseach

messagessingle snp methods

snp data set

snp analysisvipin kumarcsci

combinatorial methods

probability of nding

humanagcgtgcatcagtc