Computational Tools for Finding

and Interpreting Genetic

Variations

Gabor T. Marth Department of Biology, Boston Collegemarth@bc.eduhttp://clavius.bc.edu/~marthlab/MarthLab

Sequence variations (polymorphisms)

A reference sequence of the human genome is available…

… but every individual is unique, and is different from others at millions of nucleotide locations

genetic polymorphisms

Our research interests

1. How to find genetic polymorphisms??

?

?

?

2. How to use variation data to track our pre-historic past?

3. How to utilize polymorphism data for medical research?

Tools for polymorphism discovery

SNP discovery in clonal sequences

Siablevarall

]T,G,C,A[S ]T,G,C,A[SiiiorPr

iiorPr

i

iiorPr

i

NiorPrNiorPr

NN

iorPr

i Ni

N

N

N )S,...,S(P)S(P

)R|S(P...

)S(P

)R|S(P...

)S,...,S(P)S(P)R|S(P

...)S(P)R|S(P

)SNP(P

1

1

1

1 11

11

11

Redevelopment and expansion

Homozygous T

Homozygous C

Heterozygous C/TAutomated detection of heterozygous positions in diploid individual samples

(visit Aaron Quinlan’s poster)

Redevelopment and expansion

Discovery of short deletions/insertions (both bi-allelic and micro-satellite repeats)

Redevelopment and expansion

• Improve the detection of very rare alleles by taking into

account recent results in Population Genetics (i.e. a priori, rare

alleles are more frequent than common alleles)

• Developing a rigorous statistical framework both for

heterozygote polymorphisms and INDELs

• Calculating a probability value that a SNP found in one set of

samples will also be present in another

• Complete software rewrite

• Graphical User Interface (GUI)

• Ease of use for small laboratories without UNIX expertise

Genetic and epigenetic changes in cancer

changes in DNA methilation, histone

modificationcopy number changes,

chromosomal rearrangements

nucleotide changes, short insertions / deletions

We want to develop tools for detecting inherited polymorphisms and somatic mutations in a variety of new data types, representing both genetic and epigenetic changes

Human pre-history

Demographic history

0

0.05

0.1

0.15

1 2 3 4 5 6 7 8 9 10

minor allele count

0

0.05

0.1

0.15

1 2 3 4 5 6 7 8 9 10

minor allele count

0

0.05

0.1

0.15

1 2 3 4 5 6 7 8 9 10

minor allele count

European data

African data

bottleneck

modest but uninterrupted

expansion

Tools for Medical Genetics

http://pga.gs.washington.edu/

The polymorphism structure of individuals follow strong patterns

The international HapMap project

However, the variation structure observed in the reference DNA samples…

… often does not match the structure in another set of samples such as those used in a clinical case-control association study aimed to find disease genes and disease-causing genetic variants

Tools to test sample-to-sample variability

Instead of genotyping additional sets of (clinical) samples with costly experimentation, and comparing the variation structure of these consecutive sets directly…

… we generate additional samples with computational means, based on our Population Genetic models of demographic history. We then use these samples to test the efficacy of gene-mapping approaches for clinical research.

Tools to test sample-to-sample variability

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

r2 (data)

r2 (

4-si

te c

om

po

site

#2)

computational sample

experimental sample

(visit Dr. Eric Tsung’s poster)

Tools to connect genotype and clinical outcome

clinical endpoint (adverse drug

reaction)computational prediction

based on haplotype structure

genetic marker (haplotype) in genome

regions of drug metabolizing enzyme

(DME) genes

functional allele (known metabolic

polymorphism)

molecular phenotype (drug concentration measured in blood

plasma)

The Computational Genetics Lab

http://clavius.bc.edu/~marthlab/MarthLab