Call Girls Colaba Mumbai ❤️ 9920874524 👈 Cash on Delivery
Correlation globes of the exposome 2016
1. Development of exposome correlation
globes to map out exposure-phenotype
associations
Chirag J Patel (and Arjun K Manrai)
International Society of Exposure Science
Utrecht 2016
10/10/16
chirag@hms.harvard.edu
@chiragjp
www.chiragjpgroup.org
3. How is the exposome associated with the phenome?:
Searches for exposures in telomere-length
IJE, 2016
0
1
2
3
4
−0.2 −0.1 0.0 0.1 0.2
effect size
−log10(pvalue)
PCBs
FDR<5%
Trunk Fat
Alk. PhosCRP
Cadmium
Cadmium (urine)cigs per day
retinyl stearate
VO2 Maxpulse rate
shorter telomeres longer telomeres
adjusted by age, age2, race, poverty, education, occupation
median N=3000; N range: 300-7000
Co-exposure plays a role in signal and association
4. Number of potential correlates complicates the
association between exposure and phenome
IJE 2012 Sci Trans Med 2011
Pesticides
Pollutants
Vitamins
Nutrients
Infectious Agents
Diabetes
Body Mass Index
Time-to-Death
Gene expression
Telomere length
PhenomeExposome
7. Does Bradford-Hill apply?:
Sheer number of correlations of the exposome have
implications for causal research, for example:
(1) Strength of associations:
correlation & p-values
(2) Consistency:
observed in different situations?
(3) Specificity:
do one-to-one associations exist?
8. Estimating correlations in E:
What does this buy us in conducting EWAS-like
investigations?
(1) Effective number of
variables to test in EWAS
Criterion 1:
Significance (p-values)
(2) Mapping/documenting
EWAS associations
Criterion 3:
Specificity
(3) Assessing correlations
due to model choice
Criterion 2:
Consistency
9. Estimating exposome ρ:
NHANES participants have >250 quantitative exposures
assayed in serum and urine and >500 via self-report!
Nutrients and Vitamins
e.g., vitamin D, carotenes
Pesticides and pollutants
e.g., atrazine; cadmium; hydrocarbons
Infectious Agents
e.g., hepatitis, HIV, Staph. aureus
Plastics and consumables
e.g., phthalates, bisphenol A
Physical Activity
e.g., steps
10. Estimating exposome ρ:
Replicated rank correlations between exposures and
visualized with a globe
’99-’00
’01-’02
’03-’04
’05-’06
289
357
456
313
| E |
575
35,835
56,557
80,401
47,203
81,937
| ρ(e1,e2) |cohorts
N:10-10K
FDR(e1,e2) < 5% in >1 cohorts?
(Benjamini-Hochberg)
Permutation-based p-values
Replicated(e1,e2) are linked
e1
e2e4
e3
http://circos.ca
ρ>0ρ<0
11. Estimating exposome ρ:
E correlations are concordant between independent
cohorts
‘99-’00 ‘01-’02 ‘03-’04 ‘05-’06
‘99-’00 1 0.84 0.84 0.92
‘01-’02 1 0.82 0.93
‘03-’04 1 0.94
‘05-’06 1
2,656 out of 81,937 (3%) pair-wise correlations
(FDR < 5% in > 1 cohort)
N:10-10K
12. The E correlation globe is dense (2,700 out of 81K), but
correlations are modest in absolute value (median: 0.45).
0.00
0.25
0.50
0.75
1.00
0.0 0.4 0.8
|Correlation|
Cumulativefraction
all correlations q<=0.05 >1 surveys q<=0.05 >2 surveys (replicated)
FDR<5%
FDR<5% in
>1 cohort
13. Replicated E correlations are modest in size and are mostly
positive
0
5
10
15
-1.0 -0.5 0.0 0.5 1.0
Correlation
Percent
ρ>0ρ<0
14. Estimating correlations in E:
What does this buy us in conducting EWAS-like
investigations?
(1) Effective number of
variables to test in EWAS
(2) Mapping/documenting
EWAS associations
(3) Assessing correlations
due to model choice
Criterion 1:
Significance (p-values)
Criterion 3:
Specificity
Criterion 2:
Consistency
15. Estimating correlations in E:
Effective number of variables in your data -
You measure M: are they all independent?
Meff ≤ M
Meff : 1 + (M - 1) (1 - Variance(L)/M)
L: eigenvalues
JECH, 2014
M: number of variables Meff: effective number
co-exposure
correlation
17. Dense ρ influences the number
of effective variables (Meff) in NHANES
JECH, 2014
National Health and Nutrition Examination
Survey (NHANES)
18. Estimating correlations in E:
What does this buy us in conducting EWAS-like
investigations?
(1) Effective number of
variables to test in EWAS
(2) Mapping/documenting
EWAS associations
(3) Assessing correlations
due to model choice
Criterion 1:
Significance (p-values)
Criterion 3:
Specificity
Criterion 2:
Consistency
19. Estimating exposome ρ:
Replicated rank correlations between exposures and
visualized with a globe
’99-’00
’01-’02
’03-’04
’05-’06
289
357
456
313
| E |
575
35,835
56,557
80,401
47,203
81,937
| ρ(e1,e2) |cohorts
N:10-10K
FDR(e1,e2) < 5% in >1 cohorts?
(Benjamini-Hochberg)
Permutation-based p-values
Replicated(e1,e2) are linked
e1
e2e4
e3
http://circos.ca
ρ>0ρ<0
20. Visualizing replicated E correlations with an exposome globe
Arranging exposures by category
198
14
36
82
3
17
47
59
25
31
51
12
7
38
65
7
10
8
15
12 7 22017 6
21. Visualizing replicated E correlations with an exposome globe
exposures linked to cotinine, a metabolite of nicotine
ρ>0: red
ρ<0: blue
22. Visualizing replicated E correlations with an exposome globe
2,656 (out of 81,937) pair-wise correlations
ρ>0: red
ρ<0: blue
23. Telomere Length All-cause mortality
http://bit.ly/globebrowse
Interdependencies of the exposome:
Telomeres vs. all-cause mortality
24. Browse these and 82 other phenotype-exposome globes!
http://www.chiragjpgroup.org/exposome_correlation
https://github.com/chiragjp/exposome_correlation
25. Estimating correlations in E:
What does this buy us in conducting EWAS-like
investigations?
(1) Effective number of
variables to test in EWAS
(2) Mapping/documenting
EWAS associations
(3) Assessing correlations
due to model choice
Criterion 1:
Significance (p-values)
Criterion 3:
Specificity
Criterion 2:
Consistency
26. On dense ρ and exposome globes:
Discussion and Future Directions
•E ρ are dense (~3% of links
replicated!) but modest in correlation
size
•Visualize and identify co-occuring E
•Contextualize EWAS findings
Demographic
Food(Recall)
PhysicalActivity
Nutrients
Smoking
Drugs
Furans
Dioxins
PCBs
Pesticides
Diakyls
PFCs
Phenols
Phthalates
Bacteria
Virus
Urinary_Dim
ethylarsonic_acid
number_of_days_since_quit
Beta-hexachlorocyclohexane
trans-b-carotene
3,3,4,4,5,5-hxcb
M
ercury,_urine
Oxychlordane
um
,_urine
1,2,3,4,6,7,8,9-ocdd
ercury,_inorganic
1,2,3,4,6,7,8-hpcdd
Hexachlorobenzene
g-tocopherol
Retinyl_palm
itate
PCB138_&_158
PCB196_&_203
Vitam
in_D
ny,_urine
Vitam
in_C
1,2,3,4,7,8-hxcdd
1,2,3,6,7,8-hxcdd
1,2,3,7,8,9-hxcdd
Cadm
ium
,_urine
Retinyl_stearate
1,2,3,4,7,8-hxcdf
1,2,3,6,7,8-hxcdf
Trans-nonachlor
Heptachlor
Thallium
,_urine
b-cryptoxanthin
ury,_total
1,2,3,7,8-pncdd
Levofloxacin_1
2,3,4,7,8-pncdf
Folate,_serum
3,3,4,4,5-pncb
ine
a-Tocopherol
CD8_counts
PCB170
Lead,_urine
2,3,7,8-tcdd
Oxacillin_1
a-Carotene
Cadm
ium
p,p-DDT
p,p-DDE
PCB105
PCB118
PCB156PCB157
PCB167
PCB146PCB153PCB172
PCB177
PCB178
PCB180
PCB183PCB187 PCB194
PCB199
Dieldrin
PCB66PCB74
PCB99
Retinol
white
black
Lead
Age
•Estimate and report Meff, number of independent variables
•Estimate and report VoE, how correlations change due to
model choice
27. On dense ρ and exposome globes:
Discussion and Future Directions
•E ρ are dense (~3% of links
replicated!) but modest in correlation
size
•Identify confounding variables?
•Ascertain E globes with respect to time in different
populations!
Demographic
Food(Recall)
PhysicalActivity
Nutrients
Smoking
Drugs
Furans
Dioxins
PCBs
Pesticides
Diakyls
PFCs
Phenols
Phthalates
Bacteria
Virus
Urinary_Dim
ethylarsonic_acid
number_of_days_since_quit
Beta-hexachlorocyclohexane
trans-b-carotene
3,3,4,4,5,5-hxcb
M
ercury,_urine
Oxychlordane
um
,_urine
1,2,3,4,6,7,8,9-ocdd
ercury,_inorganic
1,2,3,4,6,7,8-hpcdd
Hexachlorobenzene
g-tocopherol
Retinyl_palm
itate
PCB138_&_158
PCB196_&_203
Vitam
in_D
ny,_urine
Vitam
in_C
1,2,3,4,7,8-hxcdd
1,2,3,6,7,8-hxcdd
1,2,3,7,8,9-hxcdd
Cadm
ium
,_urine
Retinyl_stearate
1,2,3,4,7,8-hxcdf
1,2,3,6,7,8-hxcdf
Trans-nonachlor
Heptachlor
Thallium
,_urine
b-cryptoxanthin
ury,_total
1,2,3,7,8-pncdd
Levofloxacin_1
2,3,4,7,8-pncdf
Folate,_serum
3,3,4,4,5-pncb
ine
a-Tocopherol
CD8_counts
PCB170
Lead,_urine
2,3,7,8-tcdd
Oxacillin_1
a-Carotene
Cadm
ium
p,p-DDT
p,p-DDE
PCB105
PCB118
PCB156PCB157
PCB167
PCB146PCB153PCB172
PCB177
PCB178
PCB180
PCB183PCB187 PCB194
PCB199
Dieldrin
PCB66PCB74
PCB99
Retinol
white
black
Lead
Age
•What are the essential nodes of the network?
28. On dense ρ and exposome globes:
For papers, see:
https://paperpile.com/shared/0SnSa9
Ioannidis, John P. A. 2016. Statistics in Medicine 35 (11): 1749–62.
Patel, Chirag J., et al 2013. International Journal of Epidemiology 42 (6). IEA: 1795–1810.
Patel, Chirag J., et al 2012. International Journal of Epidemiology 41 (3): 828–43.
Patel, Chirag J., and Arjun K. Manrai. 2015. Pacific Symposium on Biocomputing., 231–42.
Ioannidis, John P. A. 2009. Science Translational Medicine 1 (7).
Patel, Chirag J., et al. 2015. Journal of Clinical Epidemiology 68.
Patel, Chirag J., and John P. A. Ioannidis. 2014. JAMA: 311 (21). 2173–74.
Smith, G. D., et al. 2007. “PLoS Medicine 4: e352.
Tu-SY-A4: The Exposome: From concept to practice - IV
29. Harvard DBMI
Isaac Kohane
Susanne Churchill
Stan Shaw
Jenn Grandfield
Sunny Alvear
Michal Preminger
Harvard Chan
Hugues Aschard
Francesca Dominici
Chirag J Patel
chirag@hms.harvard.edu
@chiragjp
www.chiragjpgroup.org
NIH Common Fund
Big Data to Knowledge
Acknowledgements
Stanford
John Ioannidis
Atul Butte (UCSF)
RagGroup
Chirag Lakhani
Adam Brown
Danielle Rasooly
Arjun Manrai
Erik Corona
Nam Pho
Jake Chung
ISES Co-exposures
Tom Webster
Arjun Manrai