3. Introduction
Methods
Results
Future Directions
Motivation
The data
Visual Representations
Motivation
1 in 4 adult Canadians and 1 in 10 children are clinically obese.
6 million Canadians are at higher risk for type 2 diabetes, high blood
pressure, cardiovascular disease.
Overweight and obesity related health care costs ≈ $6 billion, or
4.1% of Canada’s total health care budget
Events during pregnancy are suspected to play a role in childhood
obesity → we don’t know about the mechanisms involved
Children born to women who had a gestational diabetes
mellitus-affected pregnancy are more likely to be overweight and
obese
Evidence suggests epigenetic factors are important piece of the
puzzle
3 / 27
5. Introduction
Methods
Results
Future Directions
Motivation
The data
Visual Representations
Research Question(s)
Objectives
1 Identify epigenetic marks observed at birth that help predict
childhood obesity
2 Determine if these epigenetic changes are associated with specific
maternal factors (GD, weight gain during pregnancy)
4 / 27
6. Introduction
Methods
Results
Future Directions
Motivation
The data
Visual Representations
Research Question(s)
Objectives
1 Identify epigenetic marks observed at birth that help predict
childhood obesity
2 Determine if these epigenetic changes are associated with specific
maternal factors (GD, weight gain during pregnancy)
3 Impact of these epigenetic changes on gene expression levels
4 / 27
17. Introduction
Methods
Results
Future Directions
Motivation
The data
Visual Representations
mean methylation values for each probe by tissue
Density
0
1
2
3
0.0 0.2 0.4 0.6 0.8 1.0
cord
0.0 0.2 0.4 0.6 0.8 1.0
placenta
Figure 4 : Density plot of Mean methylation values for each probe by tissue
9 / 27
23. Introduction
Methods
Results
Future Directions
Adjusting for Cell type mixtures
Regression forms
q-value
Methylation (M) and Expression (E) for Cord blood and
Placenta
M or E ∼ Gestational Diabetes + Gestational Age + Cell Mixture (1)
M or E ∼ Body Fat Measures + Gestational Age+
Sex and Age of child + Cell Mixture (2)
note: The 7 body fat measures were modelled separately
note: n=45 for model (1), n=23 for model (2)
12 / 27
24. Introduction
Methods
Results
Future Directions
Adjusting for Cell type mixtures
Regression forms
q-value
Reporting Evidence
Evidence reported in terms of the p-value and q-value
The q-value is an extension of the False Discovery Rate (FDR), by
giving each feature its own individual measure of significance.
The q-value for a CpG site is the expected proportion of false
positives incurred when calling that site significant.
Whereas the p-value is a measure of significance in terms of the
false positive rate, the q-value is a measure in terms of the FDR.
Example: if 10 CpG sites with q-values ≤ 5% are called significant
in an EWAS, 1 of these 10 sites is a false positive
The q-value methodology estimates the proportion of features that
are truly null (from the given p-values) denoted by π0 whereas the
FDR methodology assumes π0 = 1.
We calculated the q-values using the qvalue package in R.
13 / 27
25. Introduction
Methods
Results
Future Directions
Methylation ∼ Gestational Diabetes
Methylation ∼ Body Fat measures
Gene Expression ∼ Body Fat measures
Cord blood and Placenta
Table 1 : The number of differentially methylated CpG sites in cord blood and
placenta DNA samples from newborns with or without exposure to gestational
diabetes mellitus, for unadjusted, age adjusted, age and cell mixture adjusted
models at different p and q value thresholds.
Threshold 1 × 10−3
0.01 0.025 0.05 0.10
Criteria p q p q p q p q p q
Model
Cordblood
Unadj 389 0 3,961 0 10,321 0 21,620 0 44,988 4
Age 253 1 2,648 1 6,904 1 14,457 1 31,250 1
Cell-adj 575 1 4,150 1 9,531 3 18,365 5 36,100 9
Placenta
Unadj 260 0 2,520 0 6,437 0 13,445 0 28,571 0
Age 259 0 2,492 0 6,493 0 13,425 0 28,692 0
Cell-adj 451 0 3,368 1 7,997 2 15,919 6 32,333 7
14 / 27