Recent advances in genomic technologies have made it feasible to measure, on the same individual, multiple types of genomic activity such as genotypes, gene expression, DNA copy number, methylation and microRNA expression. However, in order to benefit from the increasing amounts of heterogeneous data and to obtain a more complete view of genomic functions, there is a great need for statistical and computationally efficient methods that allow us to combine this information in an intelligent way. Challenges with prediction models in this setting arise from the high-dimensional non-linear nature of the data, the large number of measurements compared to the few samples for whom they are collected, and the presence of complex interactions between the different types of data. Methods such as sparse regression, hierarchical clustering and principal component analysis can address any one of these challenges, but can not do so simultaneously. Kernel methods, which use matrices measuring the similarity between two individuals, offer a powerful way of simultaneously addressing these challenges without significantly increasing the computational burden. In this work, we investigate the benefits and challenges that arise from using kernel methods in the context of integrating DNA methylation, gene expression and phenotypic data in a sample of mother-child pairs from a prospective birth cohort. The goal of this study is to identify epigenetic marks observed at birth that help predict childhood obesity.

1. Analysis of DNA Methylation and Gene
Expression data in Placenta tissue to
predict childhood obesity
An Integrative Approach
Bhatnagar SR1,2
, Houde A4,5
, Voisin G2
,
Bouchard L4,5
, Greenwood CMT1,2,3
1
Department of Epidemiology, Biostatistics and Occupational Health, McGill University
2
Lady Davis Institute, Jewish General Hospital, Montr´eal, QC
3
Departments of Oncology and Human Genetics, McGill University
4
Department of Biochemistry, Universit´e de Sherbrooke, QC
5
ECOGENE-21 and Lipid Clinic, Chicoutimi Hospital, QC
sahirbhatnagar.com/talks
Poster Session B, # 56

2. Motivating Question # 1
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3. Motivation
1 in 4 adult Canadians and 1 in 10 children are clinically obese.
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
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4. Motivating Question # 2
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5. Motivating Question
sample size
genomic data
25 50
Gene
Expression

6. Motivating Question
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression

7. Motivating Question
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression
??
?
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8. The Data
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9. The Data
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |

10. The Data
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |
X

11. The Data
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |
X
7 Fat
Measures
Child
n = 23
GD = 16

12. The Data
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |
X
7 Fat
Measures
Child
n = 23
GD = 16
Y

13. The Data
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |
X
7 Fat
Measures
Child
n = 23
GD = 16
Y
?
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14. Summarizing Expression,
Methylation and Gestational
Diabetes Phenotype in Placenta
Tissue
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15. Sparse Canonical Correlation Analysis (sCCA)
CCA requires calculation of XT
X
−1
and YT
Y
−1
When p + q >> n, these matrices are singular
sCCA applies an L1 penalty to the canonical vectors to obtain sparse
solutions (Witten et al., 2009; Parkhomenko et al., 2009)
Assumes XT
X = Ip, YT
Y = Iq
maximizeu,v uT
XT
Yv
subject to
u 2
2 ≤ 1, v 2
2 ≤ 1
and
P1(u) ≤ λ1, P2(v) ≤ λ2
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16. Supervised Sparse CCA
Main idea:
1. The features that are most associated with the outcome Q are
identified to form the reduced matrices X and Y
2. sCCA is performed on X and Y
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17. Importance of Gestational Diabetes Phenotype
0.88
0.90
0.92
0.94
0.96
0.98
#non−0expressionprobes
# non−0 methylation probes
Correlation
Gestational Diabetes Status Used in Sparse CCA
0.88
0.90
0.92
0.94
0.96
0.98
#non−0expressionprobes
# non−0 methylation probes
Correlation
Gestational Diabetes Status Not Used
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18. GO Stat Analysis for Enrichment
Enrichment Analysis based on non zero vector of 1st component from
the Supervised sCCA analysis
Genes associated with inflammatory processes
Table : Top list of enriched GO terms
GOBPID FDR OR E.Count Count Size Term
0002376 < 10−14
2.1 131.6 227 2178 immune system process
0006955 < 10−13
2.3 78.7 153 1303 immune response
0002252 < 10−9
2.7 34.1 80 565 immune effector process
0045087 < 10−8
2.3 49.0 99 811 innate immune response
0002682 < 10−8
2.1 66.56 122 1102 regulation of immune system process
0002684 < 10−8
2.4 40.1 84 664 positive regulation of immune system proces
0006952 < 10−8
1.9 84.5 144 1399 defense response
0050776 < 10−8
2.3 44.5 90 738 regulation of immune response
0050778 < 10−7
2.6 28.5 65 473 positive regulation of immune response
0006950 < 10−7
1.6 196.8 271 3258 response to stress
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19. Summarizing Bodyfat Measures
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20. Cluster 6 Bodyfat measures in 2 groups
34
14
8
16
7
6
38
30
20
25
13
3
12
11
17
21
39
31
19
37
28
32
18
Zscore BMI
percent fat
subscapularis
bicep
tricep
iliacus
−2 0 2
Value
Color Key
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21. Circle of Correlations
−1.0 −0.5 0.0 0.5 1.0
−1.0−0.50.00.51.0
Variables factor map (PCA)
Dim 1 (50.68%)
Dim2(15.41%)
Zscore BMI
percent fat
tricep
bicep
subscapularis
iliacus
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22. Combining Both Data
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23. Regression via Elastic Net
Expression
HT-12 v4
p = 46, 889
Methylation
Illumina 450k
p = 375, 561
Gestational
Diabetes
n = 45
GD = 29
Placenta
n = 45
time
at birth age 5
| |
X
7 Fat
Measures
Child
n = 23
GD = 16
Y
?
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24. 1st PC as Summary Bodyfat Measure
3
8
32
14294
102
1443
187
12853
124
375563
36
37513
81
338052
197
75115
30
7505
188
67612
196
84503
37
9380
202
75125
1
2
3
4
data used to predict 1st PC of bodyfat measures
LOOCVmeansquarederror
data.type
Canonical Variables
Expr+Methy non 0 CCA factors
Expr non 0 CCA factors
Methy non 0 CCA factors
Expr+Methy Filter
Expr Filter low means
Methy Filter low var
Expr+Methy Filter low+t.test
Expr Filter low+t.test
Methy Filter low+t.test
Expr+Methy Filter t.test
Expr Filter t.test
Methy Filter t.test
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25. Ward Clustering Groups
1
8
22
14294
1
1443
20
12853
331
375563
1
37513
54
338052
6
75115
1
7505
6
67612
7
84503
1
9380
30
75125
0.0
0.1
0.2
0.3
0.4
0.5
data used to predict Ward clustering groups
LOOCVmisclassificationerror
data.type
Canonical Variables
Expr+Methy non 0 CCA factors
Expr non 0 CCA factors
Methy non 0 CCA factors
Expr+Methy Filter
Expr Filter low means
Methy Filter low var
Expr+Methy Filter low+t.test
Expr Filter low+t.test
Methy Filter low+t.test
Expr+Methy Filter t.test
Expr Filter t.test
Methy Filter t.test
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26. Neuropeptide Y Receptor (NPY1R)
From OMIM:
One of the most abundant neuropeptides in the mammalian
nervous system
Exhibits a diverse range of important physiologic activities,
including effects on food intake
Have been identified in a variety of tissues, including
placenta (Herzog et al., 1992).
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27. Motivating Question #2: My Answer
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression
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28. Big Data
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29. Big Data
Data Integration
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30. Big Data
Data Integration
Machine Learning
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31. Smalln Data
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32. Acknowledgements
Celia Greenwood and
Mathieu Blanchette
Greg Voisin, Andr´ee-Anne
Houde, Luigi Bouchard
All the mothers and children
that took part in this study
You
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33. References
Principal component analysis plots and beamer template. URL
http://gastonsanchez.com/.
Elena Parkhomenko, David Tritchler, and Joseph Beyene. Sparse canonical
correlation analysis with application to genomic data integration.
Statistical Applications in Genetics and Molecular Biology, 8(1):1–34,
2009.
Daniela M Witten and Robert J Tibshirani. Extensions of sparse canonical
correlation analysis with applications to genomic data. Statistical
applications in genetics and molecular biology, 8(1):1–27, 2009.
Daniela M Witten, Robert Tibshirani, and Trevor Hastie. A penalized
matrix decomposition, with applications to sparse principal components
and canonical correlation analysis. Biostatistics, page kxp008, 2009.
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