Epistasis Analysis Using Microarrays

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Epistasis Analysis Using Microarrays

• Chris Workman

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Experiments with Microarrays

• Cool technology, but how do we use it? How is it
  useful?
• Identify marker genes in disease tissues
• Classification, diagnostics
• Toxicology, stress response
• Drug candidate screens, basic science
• Genetic factors
• Measuring interactions (chIP-on-chip)

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Overview

• Expression profiling in single-deletions
• Epistasis analysis using single- and
  double-deletions
• Epistasis analysis, genetic and environmental
  factors
• Reconstructing pathways that explain the genetic
  relationships between genes

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Expression Profiling in 276 Yeast Single-Gene
Deletion Strains(The Rosetta Compendium)

• Only 19 of yeast genes are essential in rich
  media
• Giaever, et. al. Nature (2002)

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Clustered Rosetta Compendium Data
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Gene Deletion Profiles Identify Gene Function and
Pathways
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Principle of Epistasis Analysis
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Experimental Design

• Compare single-gene deletions to wild type
• Compare to the double knockout to wild type

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Experimental DesignSingle vs Double-Gene
Deletions
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Classical Epistasis Analysis Using Microarrays to
Determine the Molecular Phenotypes
Time series expression (0-24hrs) every 2hrs
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Mixing Genetic and Environmental Factors
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Expression in Single-Gene Deletions(yeast mec1
and dun1 deletion strains)
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Chen-Hsiang Yeang, PhD Craig Mak MIT
UCSD UC
Santa Cruz Yeang, Jaakkola, Ideker. J Comp Bio
(2004) Yeang, Mak, et. al. Genome Res (2005)
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Measurements
Networks
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Measurements
Networks
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Displaying deletion effects
Published work Epistasis analysis using
expression profiling (2005)
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Relevant Interactions

• Subset of Rosetta compendium used
• 28 deletions were TF (red circles)
• 355 diff. exp. genes (white boxes)
• P lt 0.005
• 755 TF-deletion effects (grey squiggles)

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Network Measurements

• Yeast under normal growth conditions
• Promoter binding
• ChIP-chip / location analysis
• Lee, et. al. Science(2002)
• Protein-protein interaction
• Yeast 2-hybrid
• Database of Interaction Proteins (DIP)
• Deane, et. al. Mol Cell Proteomics (2002)

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ChIP Measurement of Protein-DNA Interactions
(Chromatin Immunoprecipitation)
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Step 1 Network connectivity(chIP-chip analysis)
5k genes (white boxes) 20k interactions
(green lines)
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Step 2 Network annotation(gene expression
analysis)
Measure variables that are a function of the
network (gene expression). Monitor these effects
after perturbing the network (TF knockouts).
What parts are wired together
How and why the parts are wired together the way
they are
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Inferring regulatory paths

Direct

Indirect
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Annotate inducer or repressor
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Annotate inducer or repressor
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Computational methods

• Problem Statement
• Find regulatory paths consisting of physical
  interactions that explain functional
  relationship
• Method
• A probabilistic inference approach
• Yeang, Ideker et. al. J Comp Bio (2004)
• To assign annotations
• Formalize problem using a factor graph
• Solve using max product algorithm
• Kschischang. IEEE Trans. Information Theory
  (2001)
• Mathematically similar to Bayesian inference,
  Markov random fields, belief propagation

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Inferred Network Annotations
A network with ambiguous annotation
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Test Refine
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Which deletion experiments should we do first?

• A mutual information based score
• For each candidate experiment (gene ?)
• Variability of predicted expression profiles
• Predict profile for each possible set of
  annotations
• More variable more information from experiment
• Reuse network inference algorithm to compute
  effect of deletion!

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Ranking candidate experiments
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We target experiments to one region of network
Expression for SOK2?, HAP4 ?, MSN4 ?, YAP6 ?
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Expression of Msn4 targets
Average signed z-score
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Expression of Hap4 targets
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Yap6 targets are unaffected
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Refined Network Model

• Caveats
• Assumes target genes are correct
• Only models linear paths
• Combinatorial effects missed
• Measurements are for rich media growth

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Using this method of choosingthe next experiment

• Is it better than other methods?
• How many experiments?
• Run simulations vs
• Random
• Hubs

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Simulation results
simulated deletions profiles used to learn a
true network
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Current Work
Measurements
Systems level understanding
Treat disease
Networks
Test Refine
Transcriptional response to DNA damage
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Acknowledgments
Trey Ideker
Craig Mak
Chen-Hsiang Yeang Tommi Jaakkola
Scott McCuine Maya Agarwal Mike Daly Ideker lab
members
Tom Begley Leona Samson
Funding grants from NIGMS, NSF, and NIH