Epigenetic Analysis

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Epigenetic Analysis

• BIOS 691- 803
• Statistics for Systems Biology
• Spring 2008

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Kinds of Questions

• Where are the epigenetic modifications?
• How do they co-vary?
• How do epigenetic changes affect expression of
  genes?

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Covariation of Epigenetic Measures

• Motivating questions
• How are epigenetic modifications related?
• What are the major determinants of epigenetic
  state?
• Statistical techniques
• Covariance calculation
• Principal component analysis
• Linear models

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Location and Covariance

• Question do epigenetic modifiers act on specific
  targets or do they act on whole regions of DNA?
• Direct experimental evidence contradictory
• Statistics may help
• Covariation patterns may be evidence

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CalcA in NCI60

• Calcitonin A gene
• Two CpG clusters plus 3 odd CpGs
• High correlation within clusters

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CDH1 in NCI 60
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Covariation in Methylation of 7 Genes

• Individual genes have multiple CpG sites
• Most variation overall methylation

Correlation Map of 108 CpG sites in 6 genes
across 5 ECOG pilot samples Red 1 White
0 Blue lt 0
Epigenomic Analysis
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Methylation and Expression

• Single gene (E-cadherin) results suggest overall
  methylation correlated with expression

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Methylation and Expression

• HELP assay gives genome-wide sampling of
  methylation sites at 15K genes
• If select genes with S/N gt 2 in both measures,
  then correlations with associated genes are
  bi-modal

Epigenomic Analysis
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What Causes Methylation?

• NCI-60 derived from various tissues
• Tissue characteristic profile specific history
  of cells
• Fit linear model to each methylation site
• 9 tissues for 60 observations
• 51 error df
• Overall 41 of variance attributable to tissue
• What causes the remainder of methylation
  differences?

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PCA for Cell-specific Factors

• Residual variance has one strong PC
• Remainder are noise
• 1st PC is almost constant
• Reflects overall level of methylation
• Is this an artifact or is it real?
• Significantly correlated with expression of DNMT1
  DNMT3A

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Relations Between Epigenetic Measures - III

• Stem Cells Cancer

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Issue Cancer Stem Cells?

• Hypothesis cancers arise from stem cells rather
  than differentiated epithelial cells
• How would you tell the difference between
  partially differentiated stem cells and
  de-differentiated epithelial cells?
• Proposal compare characteristic epigenetic
  modifications of stem cells with cancers
• Epigenetic modifications are distinct
• PRC2 (stem cells) vs methylation (cancer)

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Statistical Methodology

• Test of association 2 x 2 table
• Fisher Exact p 10-5

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Statistical Methodology

• Test of association 2 x 2 table
• Fisher Exact p 10-5
• Alternatives
• T-test (predictor PRC2)
• Linear model (predictor methylation T N )

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PRC2 Methylation Association
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Are CIMPs Stem Cell Clones?

• Distinctive PRC2 sites appear preferentially
  methylated in CIMP tumors

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Correlations between epigenetic and expression
measures I

• Copy Number and Expression

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Copy Number and Expression

• Large sections of DNA containing many genes are
  often copied or deleted
• We think most control elements are copied or
  deleted also
• If more (or fewer) copies of a gene then ceteris
  paribus there should be more (fewer) copies of
  RNA

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Integrative Studies of CGH Gene Expression

• Expect to see strong correlation between copy
  number and expression in data
• Previous studies report report weak effects
• Average correlations from (0.04 to 0.27)
• NCI 60 study average correlation 0.16

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Why Not?

• H1 there really isnt much effect biology
• Somehow the cells are compensating
• In any case there shouldnt be any effect on
  non-expressed genes
• H2 we may not be able to measure the effect that
  is there technical error
• Probes may be insensitive/cross-hybridizing
• Signal/noise too low even when probes are
  sensitive

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Eliminating Uninformative Genes

• Genes which are silenced will not show effect of
  copy number variation
• Mean signal a rough proxy
• Remove genes with mean signal above 6.3
• Only genes with significant copy number variation
  (above measurement noise) will show effect
• Select genes with SD of copy number gt 0.5

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Correlations of Selected Measures
Black All correlations Red Reliably measured
correlations
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Estimating True Correlations

• If measurement noise of SD 0.3 degrades
  expression measures, then true correlations of
  variables will be mostly closer to 0 than
  correlations of measures
• Given a correlation and measured standard
  deviations, what are most likely true standard
  deviations and true correlation?

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MLE of Noisy Correlations

• Noise can be estimated from replicates
• If N large can estimate
• SD of originals can be estimated by ML
• Given s and e, the MLE of correlation can be
  inferred
• For NCI 60 median MLE correlation 0.65

Epigenomic Analysis
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Correlations between epigenetic and expression
measures II

• Chromatin and Expression

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Do Epigenetic Marks Regulate Transcription?

• Several studies finding only weak evidence by
  correlation analysis
• Same technical issue S/N ratio
• Questions
• Does methylation shut down most genes?
• Which histone marks indicate active
  transcription?

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Methylation and Expression

• HELP assay gives genome-wide sampling of
  methylation sites at 15K genes
• Select genes with S/N gt 2 in both measures
• Correlations with gene expression values are
  bi-modal

Epigenomic Analysis
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Interpretation of Meth-Expr Corrs

• MLE of negative mode -0.8
• 2/3 of genes under that hump
• Unclear whether positive hump is real or an
  artifact of small sample size
• Possible explanations
• True induction by methylation
• Methylation of insulator
• Irrelevant CpG site

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Acetylation and Expression

• Histones often acetylated during expression
• Histone 3 lysine 9 (H3K9) acetylation measured
• Measures corrupted by noise
• Blue S/N gt 2.5
• Red S/N gt 2
• Black S/N gt 1.5

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Biological Prediction

• H3K9 acetylation gene expression
• Is this real?
• Experimental test find genes with high
  acetylation variance, and little expression
  variance by microarray
• Results (7 genes)
• Confirm hypothesis
• Implies
• Expression arrays are not sensitive

Epigenomic Analysis