Working Group # 6 Working Group 6: Epigenomics Elena Colicino - - PowerPoint PPT Presentation

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Working Group # 6 Working Group 6: Epigenomics Elena Colicino - - PowerPoint PPT Presentation

Jul 06, 2023 180 likes •626 views

Bhramar Mukherjee, PhD Professor of Biostatistics and Epidemiology University of Michigan School of Public Health bhramar@umich.edu SAMSI-SAVI Workshop, Mumbai, 2016 Working Group # 6 Working Group 6: Epigenomics Elena Colicino Sudha

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Bhramar Mukherjee, PhD Professor of Biostatistics and Epidemiology University of Michigan School of Public Health bhramar@umich.edu SAMSI-SAVI Workshop, Mumbai, 2016 Working Group # 6

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Elena Colicino Sudha Ramalingam Working Group 6: Epigenomics Bhramar Mukherjee

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The Patriotic Peacocks

Bhramar Tanujit Rajani Prakash Mohan Dimple Sharayu

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 What is interaction?  Why measure it?

  • biology, sub-group identification, improving power

 How to measure it?

  • Choice of scale, method of analysis, coding

 When to report it?

  • public health relevance, biological significance, statistical

significance

Introduction

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 Interactions

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 “Interaction as statisticians think of it is a Weasel parameter.” –Professor David Clayton, JSM 2012  Weasel Word: “an informal term for words and phrases aimed at creating an impression that a specific and/or meaningful statement has been made, when only a vague or ambiguous claim has been communicated, enabling the specific meaning to be denied if the statement is challenged” (wikipedia)

Statistical Interaction

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Very few replicable interactions reported in human observational studies!

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Me, 1978 Me, 2016 Gene x Environment x Time

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Lead exposure among children in India: determinants, neurobehavioral effects and genetic susceptibility

Working Group 6: Data Example

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Environmental Health Perspective, 2011

Dataset

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Neurotoxicology, 2013

Dataset

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World blood lead levels among children

Burden of disease, 2010

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Lead levels and lead in gasoline

USA, NHANES II ( Annest et al. 1983)

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Sources of lead exposure

 Leaded gasoline phased later than in US  Leaded paint  Occupational:

  • Garage workers
  • Smelting and metal working operations
  • Jewelery workers
  • Industrial activity
  • Mining

 Cultural practices

  • Ayurvedic medication
  • Cosmetics (surma, sindhur)
  • Holi colors
  • Spices
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Cosmetics Religious powders Ayurvedic medication

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Lead in paint (2009)

Clark, C.S. et al, Lead levels in new enamel household paints from Asia, Africa and South America. Environ. Res. (2009), doi:10.1016/j.envres.2009.07.002.

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Lead Paint

New York Times 2007 NDTV 2010

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Electronic waste

 10-20,000 tonnes, employing

25,000 people, in New Delhi alone

 E waste management and

handling Rule 2011 ( new law MOEF, India)

 Needs implementation

Toxics link 2010

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Determinants of blood lead levels among 3-7 year old children in Chennai, India (2005-2006)

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India Lead Study (Chennai) Study population (N= 756)

  • Cross-sectional
  • 12 schools (3 in 4 zones)
  • 3-7 year old children

High industry Low Industry High traffic HT/HI (3 schools) HT/LI (3 schools) Low traffic LT/HI (3 schools) LT/LI (3 schools)

Chennai

  • Blood lead levels assessed by
  • LeadCare™ Analyzer
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1 . 5 4 . 5 7 . 5 1 0 . 5 1 3 . 5 1 6 . 5 1 9 . 5 2 2 . 5 2 5 . 5 2 8 . 5 3 1 . 5 3 4 . 5 3 7 . 5 4 0 . 5 5 1 0 1 5 2 0 2 5 3 0 P e r c e n t BL L

Distribution of blood lead levels (g/dl) in children in Chennai

N=756 Mean=11.5 g/dl Range=2.6-40.5 g/dl

55% > 10 µg/dl 2% > 10 µg/dl (NHANES III)

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Assessment of Predictors

Questionnaires (primary care givers : Tamil)

  • Socioeconomic status
  • Family income, parental education, occupation
  • Type of house
  • Possible sources of exposure
  • Residence (traffic and industry zone), parental occupation, presence of

lead based industry, traffic level near house

  • Type of paint
  • Sources and storage of drinking water
  • Surma and ayurvedic medication use
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Predictors of blood lead

Variables Estimate 95% CI Partial R2 ** Age (months) 0.002

  • 0.001 0.005

0.003 Sex

  • 0.028
  • 0.094 0.039

0.001 Average monthly family income (Rs)*** <2000 0.259 0.125 0.394 0.028 2000-4000 0.233 0.123 0.342 0.033 4000-6500 0.182 0.081 0.282 0.017 Drinking water storage vessel*** Brass/ Bronze 0.210 0.061 0.359 0.010 Residence *** High industry 0.074

  • 0.082 0.231

0.007 * accounting for clustering at school level using generalized estimating equations ** unadjusted for clustering using linear regression *** compared to >6500 Rs/ month, ** all other drinking water storage vessels, ***low industry area

Total model R2= 5.8%

0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 <2000 2000-4000 4000-6500 >6500 Brass/Bronze Other Income (Rs) DWV Odds ratio

(>10µg/dl)

DWV: Type of vessel used for storage of drinking water. Adjusted for age (months), sex p-values<0.05

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Conclusions

  • Blood lead levels
  • Lower socioeconomic status
  • Drinking water stored in brass or bronze vessels
  • Residence in a high industry zone (<5 year old)
  • No effect of use of ayurvedic medication,

surma, traffic, paint

  • Little variation in blood lead was explained
  • Need in depth exposure assessment

Predictors of blood lead

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Lead exposure and behavior, IQ, Visual Motor skills children in Chennai, India

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Lanphear et al. 2005

Lead and IQ

  • IQ is best characterized
  • (Needleman 1979,

Bellinger 1983)

  • No threshold
  • Non-linear dose-response
  • (Schwartz 1994)

Heated debate!!

Lanphear et al 2005

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Behavioral and cognitive assessment

Behavior: Questionnaires administered to the class teachers

Connors ADHD DSM IV Scales (CADS)

  • ADHD Index
  • DSM IV: Hyperactivity
  • DSM IV: Inattention

Behavior Rating Inventory of Executive Function (BRIEF)

  • Executive function composite
  • Behavioral regulation (inhibit, shift, emotional control)
  • Metacognition (Initiate, working memory, planning, organization of materials,

monitoring)

Connors Teacher Rating Scales (CTRS-39)

  • Anxiety, Sociability, (Aggression, Hyperactivity, Inattention)
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Behavioral and cognitive assessment (con’t)

Intelligence

  • Binet - Kamat Intelligence scales ( Tamil)
  • mental age/ chronological age= IQ
  • administered to children

Genotyping

  • Bioserve Hyderabad, India
  • Mass Array Iplex (Sequenom process)
  • PCR and mass spectrometry
  • Blood
  • Negative and positive controls
  • 24 DNA samples from the Coriell Discovery panel
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Effect of lead and hemoglobin (Hb) on IQ

Generalized estimating equations*

Roy et al pending publication

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Lead and Visual motor skills

Pallaniapan & Roy et al 2011

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Conclusions

Lead and behavior

  • Blood lead levels are associated with poorer

behavior and visual-motor skills

  • ADHD, internalizing behaviors and executive function
  • Executive function is most sensitive to lead (0.4 SD)
  • 4 IQ points (0.25 SD IQ)
  • In ADHD, inattention is most affected
  • No effect seen on hyperactivity
  • Dose-response relationships are linear for behavior
  • Blood lead levels are associated with poorer
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Lead exposure, iron and intelligence: genetic susceptibility

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Lead and IQ

Wide variation in effect estimates

  • Residual confounding
  • Measurement error
  • Different dose ranges
  • Effect modification
  • Nutritional differences
  • Genetic differences

Lanphear et al. 2005

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Effect modification by Transferrin C2 polymorphsim

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Effect modification by Transferrin C2 polymorphsim

Roy et al Pending publication

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Distribution of DRD2 Taq IA genotype

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Effect of lead and Hb on IQ by DRD2 genotype

Roy et al 2011

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Hemoglobin, Lead & IQ: genetic susceptibility

  • *

* * *

IQ IQ

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 Data consists of 159 variables, including genotype data on 18 genetic

polymorphisms

 We will try to reproduce the published analysis with one marker at a time:

  • Choice of confounders
  • Transformation of Y and X
  • Dose response relationship
  • Interpreting interaction on the transformed scale
  • Reporting of findings
  • How robust are the conclusions
  • Extend to incorporate multiple markers, calculate a polygenic risk

score.

  • Unexplored Associations (birth order related to IQ?)

Plan for Analysis Working Group

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Determinants Blood lead Behavior

ADHD Executive function Internalizing behavior

Cognition

IQ

Dopamine D2 receptor polymorphism

Iron

Hemoglobin SES Industrial activity Brass and bronze vessels

OVERARCHING PARADIGM

Transferrin C2 polymorphism

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Research Team Kalpana Balakrishnan Kavitha Palaniapan Padmavathi Ramaswamy Venkatesh S.M. Shankar K.M. BIOSERVE Rama Modali

AKNOWLEDGEMENTS

David C. Bellinger Joel Schwartz Robert Wright Ananya Roy

HSPH SRMC

Funding : NIH (R01 ES007821) , Fogarty grant (R03 TW005914)

University of Toronto

Howard Hu

YSPH

Adrienne Ettinger

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Study Participants!

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How do we translate all these findings of reported associations and interactions into Public Health action? Why Should Francesca care?