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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
Motivating Question # 1
sahirbhatnagar.com Data Integration CHSGM 2015 2 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 3 / 25
Motivating Question # 2
sahirbhatnagar.com Data Integration CHSGM 2015 4 / 25
Motivating Question
sample size
genomic data
25 50
Gene
Expression
Motivating Question
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression
Motivating Question
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression
??
?
sahirbhatnagar.com Data Integration CHSGM 2015 5 / 25
The Data
sahirbhatnagar.com Data Integration CHSGM 2015 6 / 25
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
| |
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
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
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
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
?
sahirbhatnagar.com Data Integration CHSGM 2015 7 / 25
Summarizing Expression,
Methylation and Gestational
Diabetes Phenotype in Placenta
Tissue
sahirbhatnagar.com Data Integration CHSGM 2015 8 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 9 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 10 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 11 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 12 / 25
Summarizing Bodyfat Measures
sahirbhatnagar.com Data Integration CHSGM 2015 13 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 14 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 15 / 25
Combining Both Data
sahirbhatnagar.com Data Integration CHSGM 2015 16 / 25
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
?
sahirbhatnagar.com Data Integration CHSGM 2015 17 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 18 / 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
sahirbhatnagar.com Data Integration CHSGM 2015 19 / 25
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).
sahirbhatnagar.com Data Integration CHSGM 2015 20 / 25
Motivating Question #2: My Answer
sample size
genomic data
25 50
Gene
Expression
DNA
Methylation
DNA
Methylation
Gene
Expression
sahirbhatnagar.com Data Integration CHSGM 2015 21 / 25
Big Data
sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
Big Data
Data Integration
sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
Big Data
Data Integration
Machine Learning
sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
Smalln Data
sahirbhatnagar.com Data Integration CHSGM 2015 23 / 25
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
sahirbhatnagar.com Data Integration CHSGM 2015 24 / 25
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.
sahirbhatnagar.com Data Integration CHSGM 2015 25 / 25

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Analysis of DNA methylation and Gene expression to 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 sahirbhatnagar.com Data Integration CHSGM 2015 2 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 3 / 25
  • 4. Motivating Question # 2 sahirbhatnagar.com Data Integration CHSGM 2015 4 / 25
  • 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 ?? ? sahirbhatnagar.com Data Integration CHSGM 2015 5 / 25
  • 8. The Data sahirbhatnagar.com Data Integration CHSGM 2015 6 / 25
  • 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 ? sahirbhatnagar.com Data Integration CHSGM 2015 7 / 25
  • 14. Summarizing Expression, Methylation and Gestational Diabetes Phenotype in Placenta Tissue sahirbhatnagar.com Data Integration CHSGM 2015 8 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 9 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 10 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 11 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 12 / 25
  • 19. Summarizing Bodyfat Measures sahirbhatnagar.com Data Integration CHSGM 2015 13 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 14 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 15 / 25
  • 22. Combining Both Data sahirbhatnagar.com Data Integration CHSGM 2015 16 / 25
  • 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 ? sahirbhatnagar.com Data Integration CHSGM 2015 17 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 18 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 19 / 25
  • 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). sahirbhatnagar.com Data Integration CHSGM 2015 20 / 25
  • 27. Motivating Question #2: My Answer sample size genomic data 25 50 Gene Expression DNA Methylation DNA Methylation Gene Expression sahirbhatnagar.com Data Integration CHSGM 2015 21 / 25
  • 28. Big Data sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
  • 29. Big Data Data Integration sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
  • 30. Big Data Data Integration Machine Learning sahirbhatnagar.com Data Integration CHSGM 2015 22 / 25
  • 31. Smalln Data sahirbhatnagar.com Data Integration CHSGM 2015 23 / 25
  • 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 sahirbhatnagar.com Data Integration CHSGM 2015 24 / 25
  • 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. sahirbhatnagar.com Data Integration CHSGM 2015 25 / 25