The niehs environmental genome project enabling studies of gene environment interaction
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The NIEHS Environmental Genome Project: Enabling Studies of Gene-Environment Interaction. Douglas A. Bell, Ph.D. Environmental Genomics Section National Institute of Environmental Health Sciences Professor, Dept of Epidemiology UNC School of Public Health.

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The NIEHS Environmental Genome Project: Enabling Studies of Gene-Environment Interaction

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The niehs environmental genome project enabling studies of gene environment interaction

The NIEHS Environmental Genome Project: Enabling Studies of Gene-Environment Interaction

Douglas A. Bell, Ph.D.

Environmental Genomics Section

National Institute of Environmental Health Sciences

Professor, Dept of Epidemiology

UNC School of Public Health


The niehs environmental genome project enabling studies of gene environment interaction

NIEHS’s Environmental Genome ProjectResequencing of ~500 Candidate Genes Potentially Involved in Environmental Disease

  • Concept and rationale

  • Examples of gene-environment interaction

  • Resequencing studies, accomplishments, and accessing data.


The niehs environmental genome project enabling studies of gene environment interaction

Disease

Modulation of Response to Exposure

Exposure

Early

Effects

Genetic Susceptibility


The niehs environmental genome project enabling studies of gene environment interaction

Genetic Modulation of Exposure, Damage, and Biological Response

Exposure

Target

tissue

Biological

Response

Disease

  • Genetic Variation in:

  • Metabolism, or distribution, affects dose to the tissue

  • Detection and repair of damage

  • Differences in growth and recovery from damage


The niehs environmental genome project enabling studies of gene environment interaction

Genetic Modulation of Exposure Risk

Resistant

Genotype

Background

Risk Level

(low)

No Exposure

Sensitive

Genotype

Resistant

Genotype

2-Fold Risk

Exposure

Sensitive

Genotype

4-Fold Risk


The niehs environmental genome project enabling studies of gene environment interaction

Glutathione

HO

HO

Benzo[a]pyrene Metabolism

GST

+ Glutathione

Inactive

CYP450

PAH-oxide

DNA Reactive


The niehs environmental genome project enabling studies of gene environment interaction

Glutathione

HO

HO

Benzo[a]pyrene Metabolism

GST

+ Glutathione

Inactive

GSTM1 Null

CYP450

PAH-oxide

DNA Reactive


Bladder cancer risk associated with smoking and gstm1 null genotype

Exposure Risk

Genetic Risk

Bladder Cancer Risk Associated with Smoking and GSTM1 Null Genotype

GSTM1 (+)

GSTM1 null

1 1.3

2.2* 4.3*

3.5* 5.9*

Nonsmokers

1- 50 Packyears Smoking

>50 Packyears

Smoking

*P<0.001;

Bell et al, JNCI 85:1559,1993


Examples of gene environment interaction gene modifies environmental effect

Examples of Gene-Environment Interaction (gene modifies environmental effect)

  • Malaria and Sickle Cell gene.

  • HIV infection and CCR5 receptor variant.

  • LPS sensitivity and Toll Receptor (TLR4)

  • Adverse drug response and CYP2D6 poor metabolism.

  • Alcohol intolerance and aldehyde dehydrogenase.

  • Smoking, GSTM1 null, NAT2 slow genotypes, and bladder cancer risk .


Variation in risk estimates in human populations

Variation in Risk Estimates in Human Populations

Phenotypic variation in response due to:

Physiology

Metabolism

Repair

Growth

Timing of Exposure

Risk

Exposure


Example metabolism polymorphisms

Example: Metabolism Polymorphisms

Range of Enzyme Activity in Human Populations

No Phenotypic Polymorphism

frequency

Activity


Distribution of polymorphic enzyme activity in a population

frequency

Activity

Distribution of Polymorphic Enzyme Activity in a Population

Low

High

High

Low

+/+

-/-

+/-

+/+

-/-

+/-

Activity

Examples: N-Acetyltransferase 2, GSTM1, CYP2D6


How does frequency of a risk factor impact exposure induced g x e risk in the population

95%

5%

frequency

Activity

How does frequency of a risk factor impact exposure induced (G x E) risk in the population?


Effects of exposure in high and low risk human populations

95%

5%

frequency

Activity

Effects of Exposure in High and Low Risk Human Populations

Risk

100

High Risk

10

Average

Low Risk

0

Exposure


How will genetic data be used in public health risk assessment

How will genetic data be used in public health risk assessment?

  • Given detailed information on the relationship between genotype and phenotype, more accurate risk assessments may be possible.


The niehs environmental genome project enabling studies of gene environment interaction

Human Genetic Susceptibility

Exposure

Assessment

Risk Model

(Extrapolation to humans)

Animal toxicology (dose/response)

Effects in Humans ?

Engineering design

S

R

Risk Assessment Process

Hazard/Risk Assessment

Risk Management

More/Less Control

Replace default assumptions about variability


Incorporating human genetic polymorphism information into risk assessment

Incorporating Human Genetic Polymorphism Information Into Risk Assessment

Cancer - Yes/No

Dose ?

Extrapolate to Humans

Chemical X

  • Biochemistry

  • Mechanism of toxicity

  • Genes, pathways

  • Human genetics

Susceptible human subgroup?


Incorporating genetics into risk assessment issues

Incorporating Genetics Into Risk Assessment: Issues

  • A polymorphism may have different effects depending on the chemical, the target organ/ disease, and the population being considered.

    Thus, a protective allele for one chemical may convey risk for a different chemical. Similarly one organ system may be protected at the risk of another; e.g. immune system response could increase DNA damage or neurotoxicity.


The niehs environmental genome project enabling studies of gene environment interaction

Glutathione

HO

H2C

CH2

Glutathione

Cl- CH2

+

Cl

GST Theta 1 (GSTT1) - One gene with 2 effects

Detoxication

Ethylene oxide

Inactive

GSTT1

+ Glutathione

Activation

(Unstable)

HCHO

DNA

Methylene chloride

GSTT1

+ Glutathione

DNA Reactive

DNA

(also Methyl chloride)

D.A.Bell NIEHS


Activation vs detoxication

Activation vs. Detoxication

Effects of polymorphism dependent on chemical and toxicity pathway:

  • Activation- If the activation pathway is missing (null genotypes), some individuals may have zero risk even if they have exposure.

  • Detoxication- Since this process will never be 100% efficient, both functional and low activity genotypes will exhibit risk associated with exposure.


The niehs environmental genome project enabling studies of gene environment interaction

The Effect of GSTT1 Genotype on Metabolism of Methyl Chloride

T1 Null No Metabolism

Measure exhaled methyl chloride

T1 + Metabolism to DNA reactive forms

From Lof, A. et al, Pharmacogenetics 10:645, 2000.


Smoking gstt1 polymorphism and markers of genotoxicity in erythrocytes

Smoking, GSTT1 Polymorphism, and Markers of Genotoxicity in Erythrocytes

Background: Ethylene oxide –hemoglobin adducts are a good measure of smoking exposure in blood.

Experiment: To test if GST genotypes modulated effects of smoking in erythrocytes, we measured ethylene oxide hemoglobin adducts in freshly collected human erythrocytes from nonsmokers and smokers.

Results:

  • Ethylene oxide adducts (HEV) were ~50% higher in GSTT1 null individuals.

D.A.Bell NIEHS


Gstt1 null genotypes have higher levels of smoking induced hemoglobin adducts

GSTT1 null genotypes have higher levels of smoking-induced hemoglobin adducts

  • Study Design:

  • 16 nonsmokers

  • 32 smokers

  • HEVal hemoglobin adducts measure by mass spectrometry

  • P = 0.001 for difference in slopes;

  • Nonparametric analysis similar.

Fennel et al CEBP 9:705,2000


Incorporating genetics into risk assessment needs

Incorporating Genetics Into Risk Assessment Needs:

  • Identify genes involved in toxicological response.

  • Detailed population genetic information including:

    • Identify polymorphisms.

    • Determine frequency in populations.

    • Population-based risk estimates in large studies (n=2000).

  • Determine functional relationship between genotype and phenotype

    • Biochemical

    • In vitro, in vivo quantitative measurements of a cellular phenotype (tumors, adducts, mutation, cell death, gene expression).

  • Consider role of multiple genes, multiple pathways, etc.

  • Incorporate kinetic or other functional data into risk model.


The niehs environmental genome project enabling studies of gene environment interaction

Environmental Genomics

Discovery:

Phenotype-directed

Genotype-directed

Functional

Analysis

Disease Risk Characterization

CTTATGT A/CGGGTAT

Effects in Populations

Phenotype

Genotype

Altered Binding


The niehs environmental genome project enabling studies of gene environment interaction

Transcription Factors

Coding region changes:

aa subs, deletions, stops.

Regulatory polymorphisms alter transcription factor binding and mRNA/protein level.

Polymorphism and Function

Exon 1

Exon 2

3’ UTR

Promoter

Gene Deletions, Duplications

e.g. GSTM1, CYP2D6

  • Effects of Polymorphism:

  • Altered function

  • Quantity of protein


The niehs environmental genome project enabling studies of gene environment interaction

Phenotype—Directed Approach to Find SNPs That Alter Gene Expression Level

C

TGGGCCCCGCCCCCTTATGTAGGGTATAAAGCCC …. CCCGTCACC ATG

  SP1/Oct

Liu, X. et al


Sequence directed approaches to catalogue all significant snps in the human population

Sequence-Directed Approaches to Catalogue All Significant SNPs In The Human Population

Resequencing Projects: Describing candidate gene polymorphisms in diverse populations.

~9 million SNPs in dbSNP now,

by 2006, expect ~20 million human SNPs.

  • A SNP every ~100 bases.

    Haplotype Map: Describing which SNPs occur together on chromosomes in populations (haplotypes).


Snp discovery projects

SNP Discovery Projects

  • The SNP Consortium – ~1 million SNPs across genome

  • NIEHS – Environmental/toxicology genes

  • NHLBI – Heart disease genes, inflammation

  • NIGMS – Pharmacogenetic genes

SNP data is entered into the NCBI dbSNP database


The niehs environmental genome project enabling studies of gene environment interaction

UCSC

Hapmap


The niehs environmental genome project enabling studies of gene environment interaction

  • U Wash EGP Website


The niehs environmental genome project enabling studies of gene environment interaction

HapMap Website

  • Characterize the large scale genetic structure across the genome.

  • Genotyping SNPs at 1 kb interval across the genome in European, African, and Asian populations.


The niehs environmental genome project enabling studies of gene environment interaction

Bioinformatic Tools Available For Picking Haplotype Tagging SNPs

  • HapMap Website

  • Seattle SNPs or EGP website

  • Many other freely available programs


Niehs environmental genome project

NIEHS Environmental Genome Project

Resequencing of candidate environmental disease genes

Accomplishments:

  • Total genes sequenced = 437

  • Total kilobases sequenced = 11,001 kb

  • Total SNPs identified = 59,475


Niehs s environmental genome project summary

NIEHS’s Environmental Genome ProjectSummary:

  • Gene-environment interaction affects disease risk.

  • Effects of G x E interactions can be complex.

  • Resequencing projects are providing many new candidate gene polymorphism.

  • Determining the important functional SNPs that affect disease risk is a difficult challenge.


Strategies for incorporating snps into epidemiology studies

Strategies For Incorporating SNPs Into Epidemiology Studies

  • Whole genome association studies

    • Test 10,000-100,000 SNPs in case control studies.

    • Identify candidate regions, genes, followup with candidate gene studies.

      2. High resolution candidate gene studies.

    • Test functional SNPs and additional haplotype tagging SNPs in case/control or other design.

      • Bioinformatics to identify 1500 SNPs, 150 genes (10 SNPs/gene).

      • Coding SNPs, regulatory SNPs, haplotype tag SNPs.


The niehs environmental genome project enabling studies of gene environment interaction

Bioinformatic Identification of SNPs That Affect Gene Expression

  • Application to p53 response elements

  • Application to NRF2 response elements


The niehs environmental genome project enabling studies of gene environment interaction

p53 inducible genes contain p53

Response Elements.

RRRCWWGYYY

RRRCWWAYYY

p53

p53

ATG

Following UV exposure

p53 binds RE of target gene.

RNA Pol

SEI1

mRNA

p53

p53

RRRCWWGYYY

SEI1 gene

Using bioinformatic methods, identify SNPs that disrupt p53 response elements.


The niehs environmental genome project enabling studies of gene environment interaction

Binding Site

Consensus

NCBI/Ensemble

Genome Data

dbSNP

Data

Test SNPs Against p53 Response Element Consensus

RRRCWWGYYYRRRCWWGYYY

AAAGGACAAGTTGAAACTTGCACAAGCAGCCTCCATTCTG

Build Table of

All Promoter SNPs

DNA ambiguity code

R = A or G

Y = C or T

W = A or T

Access database

Filter:

Best Hits

Dan Tomso


The niehs environmental genome project enabling studies of gene environment interaction

Mismatch with consensus

CWWG

motif

Dan Tomso


The niehs environmental genome project enabling studies of gene environment interaction

Do SNPs in putative p53 response elements affect p53 induced expression in Saos2 cells?

Saos2 Osteosarcoma Cells (p53 null)

Strong

Weak

Mike Resnick, Alberto Inga, Daniel Menendez


Environmental genomics section

Environmental Genomics Section

Douglas A. Bell

Gary S. Pittman

Merrill ‘Chip’ Miller, III

Daniel J. Tomso

Michelle R. Campbell

Xuemei Liu

Xuting Wang

Monica Horvath


The niehs environmental genome project enabling studies of gene environment interaction

Phylogenetic Footprinting of NRF2/ARE Genes

~4000 Mouse ARE containing genes

1000 human/mouse

~4000 Human ARE containing genes

Human/ mouse/rat

~380

~2100 Rat ARE containing genes


Gene x environment interaction

Gene x Environment Interaction

  • Pharmacogenetics:

    • Adverse drug reactions (toxicity)

    • Reduced efficacy

  • Environmental disease

    • Modification of exposure-induced toxicity

    • Modification of exposure-induced disease

  • Can we generalize about risk associated with a specific gene?


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