Download Marth-Pfizer-2005-PreMeeting

Research for medical discovery
at the Computational Genomics
Laboratory at Boston College
Biology
Gabor T. Marth
Department of Biology, Boston College
[email protected]
http://clavius.bc.edu/~marthlab/MarthLab
We study genetic variations because…
… they underlie
phenotypic
differences
… cause heritable diseases
and determine responses
to drugs
… allow tracking ancestral
human history
We are interested in various aspects of genetic
variations…
• how to discover inherited genetic polymorphisms and
somatic mutations that lead to disease?
• how to model human polymorphism structure to inform
medical research?
• how to select the best genetic markers for clinical
case-control association studies?
• how to use genetic markers to predict individual
responses to drugs, including adverse drug reactions?
1. We build computer tools for variation discovery
1.
inherited (germ line)
polymorphisms
predispose to disease
the most common type of human
polymorphisms are single-nucleotide
polymorphisms (SNPs) and short
insertion-deletions (INDELs)
P( SNP ) 

all var iable
P( S N | RN )
P( S1 | R1 )
 ... 
 PPr ior ( S1 ,..., S N )
PPr ior ( S1 )
PPr ior ( S N )
P( SiN | R1 )
P( Si1 | R1 )
S
 ... 
 PPr ior ( Si1 ,..., SiN )
 ... 
PPr ior ( SiN )
S i1 [ A ,C ,G ,T ] S iN [ A ,C ,G ,T ] PPr ior ( S i1 )
Marth et al.
Nature Genetics 1999
we have developed a computer package,
PolyBayes© , for accurate discovery of
DNA polymorphisms in clonal sequences
Recently received a 5-year research grant from the
NIH to expand our SNP detection capabilities…
Homozygous C
Heterozygous C/T
1. for automated detection of somatic
mutations in diploid individual samples
(medical re-sequencing data)
Homozygous T
2. for new data types produced by the
latest, super-high throughput
sequencing technologies
3. to address the informatics needs of
detecting genetic and epigenetic
changes in somatic cells that lead to
cancer and that occur during cancer
proliferation
copy number changes,
chromosomal rearrangements
changes in DNA
methilation, histone
modifications
2. We quantify the demographic history of human
populations using DNA variation data…
stationary
past
collapse
expansion
bottleneck
history
present
MD
(simulation)
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… and build computational models of human
ancestral demographic history that underlies
present-day genome polymorphism structure
European data
African data
genetic
bottleneck
modest but
uninterrupted
expansion
3. An large NIH project aims to map out human
polymorphism structure to aid gene mapping…
However, the variation structure
observed in the reference DNA
samples genotyped by the HapMap
project…
… often does not match the structure
in another set of samples such as
those used in clinical samples used to
find disease genes and diseasecausing genetic variants
… we build computational tools to help the selection
of optimal genetic markers for clinical studies.
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.
4. We develop methods to connect genotype and
clinical outcome in pharmaco-genetic systems
genetic marker (haplotype)
in genome regions of drug
metabolizing enzyme
(DME) genes
computational prediction
based on haplotype
structure
functional allele (known
metabolic polymorphism)
clinical endpoint
(adverse drug reaction)
molecular phenotype (drug
concentration measured in
blood plasma)