Correlation globes of the exposome 2016

Development of exposome correlation globes to map out exposure-phenotype

associationsChirag J Patel (and Arjun K Manrai)

International Society of Exposure ScienceUtrecht 2016

10/10/16

[email protected]@chiragjp

www.chiragjpgroup.org

mailto:[email protected]?subject=

http://www.chiragjpgroup.org

Pesticides Pollutants Vitamins Nutrients Infectious Agents

Diabetes Body Mass Index Time-to-Death Gene expression Telomere length

PhenomeExposome

How is the exposome associated with the phenome?

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 0.2effect size

−log

10(p

valu

e)

PCBs

FDR<5%

Trunk Fat

Alk. PhosCRP

Cadmium

Cadmium (urine)cigs per dayretinyl stearate

VO2 Maxpulse rate

shorter telomeres longer telomeres

adjusted by age, age2, race, poverty, education, occupationmedian N=3000; N range: 300-7000

Co-exposure plays a role in signal and association

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

Arjun Manrai

“This is what I call ‘indecent exposome'”

Does Bradford-Hill apply?:Sheer number of correlations of the exposome have

implications for causal research

Stat Med, 2015

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?

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

Estimating exposome ρ: NHANES participants have >250 quantitative exposures assayed in serum and urine and >500 via self-report!

Nutrients and Vitaminse.g., vitamin D, carotenes

Pesticides and pollutantse.g., atrazine; cadmium; hydrocarbons

Infectious Agentse.g., hepatitis, HIV, Staph. aureus

Plastics and consumablese.g., phthalates, bisphenol A Physical Activity

e.g., steps

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 linkede1

e2e4

e3http://circos.ca

ρ>0ρ<0

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

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|

Cum

ulat

ive

fract

ion

all correlations q<=0.05 >1 surveys q<=0.05 >2 surveys (replicated)

FDR<5%

FDR<5% in >1 cohort

Replicated E correlations are modest in size and are mostly positive

0

5

10

15

-1.0 -0.5 0.0 0.5 1.0Correlation

Percent

ρ>0ρ<0


investigations?







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

Meff influences signal to noise and power!

Dense ρ influences the numberof effective variables (Meff) in NHANES

JECH, 2014National Health and Nutrition Examination

Survey (NHANES)


investigations?







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 linkede1

e2e4

e3http://circos.ca

ρ>0ρ<0

Visualizing replicated E correlations with an exposome globe Arranging exposures by category

198

1436

82

3

17

4759

25

36

31

51

127

38

65

710

815

12 7 22017 6

Visualizing replicated E correlations with an exposome globe exposures linked to cotinine, a metabolite of nicotine

ρ>0: redρ<0: blue

Visualizing replicated E correlations with an exposome globe 2,656 (out of 81,937) pair-wise correlations

ρ>0: redρ<0: blue

Telomere Length All-cause mortality

http://bit.ly/globebrowse

Interdependencies of the exposome: Telomeres vs. all-cause mortality

Browse these and 82 other phenotype-exposome globes! http://www.chiragjpgroup.org/exposome_correlation

https://github.com/chiragjp/exposome_correlation

http://www.chiragjpgroup.org/exposome_correlation

http://www.chiragjpgroup.org/exposome_correlation


investigations?







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

Demographics

Food(Recall)

PhysicalActivity

Nutrients

Smoking

Drugs

Phytoestrogens

Hormones

Allergens

Acryl.Mel.

Perchlorate

PBDEs

Hydrocarbons

VoCs

Metals

Furans

Dioxins

PCBs

Pesticides

Diakyls

PFCs

Phenols

Phthalates

Bacteria

Virus

Urinary_Dim

ethylarsonic_acid

number_

of_days_

since_qu

it

Beta-hexachlorocyclohexane

trans-b-carotene

3,3,4,4,5,5

-hxcb

Mercury,_urine

Oxychlordane

Molybdenum,_urine

1,2,3,4,

6,7,8,9-

ocdd

Mercury,_inorganic

1,2,3,4,

6,7,8-h

pcdd

Hexachlorobenzene

g-tocopherol

Retinyl_palmitate

PCB138_&_1

58

PCB196_&_203

Vitamin_D

Antimony,_urine

Vitamin_C

1,2,3,4,

7,8-hxc

dd

1,2,3,6,

7,8-hxc

dd1,2,

3,7,8,9-

hxcdd

Cadmium,_urine

Retinyl_stearate

1,2,3,4

,7,8-hx

cdf

1,2,3,6

,7,8-hx

cdf

Trans-nonachlorHeptachlor

Thallium,_urine

b-cryptoxanthin

Mercury,_total

1,2,3,7

,8-pncd

d

Levofloxacin_12,3,4,7

,8-pncdf

Folate,_serum

Cesium,_urine

3,3,4,4,5-

pncb

Cobalt,_urine

a-Tocopherol

CD8_counts

PCB170

Lead,_urine

2,3,7,8-

tcdd

Oxacillin_1

a-Carotene

Cadmium

p,p-DDTp,p-DDE

PCB105PCB1

18

PCB156PCB157

PCB167

PCB146PCB153

PCB172PCB177

PCB178

PCB180PCB183PCB187

PCB194PCB199

Dieldrin

PCB66PCB74

PCB99

Retinol

Insulin

white

black

Lead

Age

•Estimate and report Meff, number of independent variables

•Estimate and report VoE, how correlations change due to model choice

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!

Demographics

Food(Recall)

PhysicalActivity

Nutrients

Smoking

Drugs

Phytoestrogens

Hormones

Allergens

Acryl.Mel.

Perchlorate

PBDEs

Hydrocarbons

VoCs

Metals

Furans

Dioxins

PCBs

Pesticides

Diakyls

PFCs

Phenols

Phthalates

Bacteria

Virus

Urinary_Dim

ethylarsonic_acid

number_

of_days_

since_qu

it

Beta-hexachlorocyclohexane

trans-b-carotene

3,3,4,4,5,5

-hxcb

Mercury,_urine

Oxychlordane

Molybdenum,_urine

1,2,3,4,

6,7,8,9-

ocdd

Mercury,_inorganic

1,2,3,4,

6,7,8-h

pcdd

Hexachlorobenzene

g-tocopherol

Retinyl_palmitate

PCB138_&_1

58

PCB196_&_203

Vitamin_D

Antimony,_urine

Vitamin_C

1,2,3,4,

7,8-hxc

dd

1,2,3,6,

7,8-hxc

dd1,2,

3,7,8,9-

hxcdd

Cadmium,_urine

Retinyl_stearate

1,2,3,4

,7,8-hx

cdf

1,2,3,6

,7,8-hx

cdf

Trans-nonachlorHeptachlor

Thallium,_urine

b-cryptoxanthin

Mercury,_total

1,2,3,7

,8-pncd

d

Levofloxacin_12,3,4,7

,8-pncdf

Folate,_serum

Cesium,_urine

3,3,4,4,5-

pncb

Cobalt,_urine

a-Tocopherol

CD8_counts

PCB170

Lead,_urine

2,3,7,8-

tcdd

Oxacillin_1

a-Carotene

Cadmium

p,p-DDTp,p-DDE

PCB105PCB1

18

PCB156PCB157

PCB167

PCB146PCB153

PCB172PCB177

PCB178

PCB180PCB183PCB187

PCB194PCB199

Dieldrin

PCB66PCB74

PCB99

Retinol

Insulin

white

black

Lead

Age

•What are the essential nodes of the network?

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

Harvard DBMI Isaac KohaneSusanne ChurchillStan ShawJenn GrandfieldSunny AlvearMichal Preminger

Harvard Chan Hugues AschardFrancesca Dominici

Chirag J [email protected]

@chiragjpwww.chiragjpgroup.org

NIH Common FundBig Data to Knowledge

AcknowledgementsStanford John IoannidisAtul Butte (UCSF)

RagGroup Chirag Lakhani Adam Brown Danielle RasoolyArjun ManraiErik CoronaNam PhoJake Chung

ISES Co-exposures Tom Webster

Arjun Manrai

mailto:[email protected]

http://www.chiragjpgroup.org

Correlation globes of the exposome 2016

Health & Medicine

Transcript of Correlation globes of the exposome 2016