Transcript Examples of genetic variation

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
NIEHS’s Environmental Genome Project
Resequencing of ~500 Candidate Genes
Potentially Involved in Environmental Disease

Concept and rationale

Examples of gene-environment interaction

Resequencing studies, accomplishments, and
accessing data.
Modulation of Response to
Exposure
Exposure
Early
Effects
Disease
Genetic Susceptibility
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
Genetic Modulation of Exposure Risk
No
Exposure
Resistant
Genotype
Sensitive
Background
Risk Level
(low)
Genotype
Exposure
Resistant
Genotype
2-Fold Risk
Sensitive
Genotype
4-Fold Risk
Benzo[a]pyrene Metabolism
Glutathione
HO
GST
HO
+ Glutathione
Inactive
CYP450
PAH-oxide
DNA Reactive
Benzo[a]pyrene Metabolism
Glutathione
HO
GST
HO
+ Glutathione
Inactive
GSTM1
Null
CYP450
PAH-oxide
DNA Reactive
Bladder Cancer Risk Associated with
Smoking and GSTM1 Null Genotype
Nonsmokers
>50 Packyears
Smoking
*P<0.001;
Bell et al, JNCI 85:1559,1993
Exposure Risk
1- 50 Packyears
Smoking
GSTM1
(+)
GSTM1
null
1
1.3
2.2*
4.3*
3.5*
5.9*
Genetic Risk
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)
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Adverse drug response and CYP2D6 poor
metabolism.
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Alcohol intolerance and aldehyde dehydrogenase.
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Smoking, GSTM1 null, NAT2 slow genotypes, and
bladder cancer risk .
Variation in Risk Estimates in Human
Populations
Phenotypic variation
in response due to:
Risk
Physiology
Metabolism
Repair
Growth
Timing of Exposure
Exposure
Example: Metabolism Polymorphisms
frequency
Range of Enzyme Activity in
Human Populations
No Phenotypic
Polymorphism
Activity
Distribution of Polymorphic Enzyme Activity
in a Population
frequency
Low
-/-
High
+/- +/+
Activity
High
Low
-/-
+/-
Activity
Examples: N-Acetyltransferase 2, GSTM1, CYP2D6
+/+
frequency
How does frequency of a risk
factor impact exposure induced
(G x E) risk in the population?
5%
95%
Activity
Effects of Exposure in High and Low Risk
Human Populations
Risk
frequency
95%
100
High Risk
10
5%
Activity
Average
Low Risk
0
Exposure
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.
Risk Assessment Process
Hazard/Risk Assessment
Exposure
Assessment
Effects in
Humans ?
Animal
toxicology
Human
Genetic
Susceptibility
R
(dose/response)
Risk
Management
More/Less
Control
S
Risk Model
(Extrapolation to
humans)
Replace default
assumptions
about variability
Engineering design
Incorporating Human Genetic Polymorphism
Information Into Risk Assessment
Cancer - Yes/No
Chemical
X
Dose ?
Extrapolate to
Humans
• Biochemistry
Susceptible
human
subgroup?
• Mechanism of toxicity
• Genes, pathways
• Human genetics
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.
GST Theta 1 (GSTT1) - One gene with 2 effects
Detoxication
HO
Ethylene
oxide
GSTT1
H2C
Glutathione
CH2
Inactive
+ Glutathione
Activation
Methylene
chloride
GSTT1
(Unstable)
Glutathione
Cl- CH2
+ Glutathione
(also Methyl
chloride)
+
HCHO
DNA
DNA
DNA
Reactive
Cl
D.A.Bell NIEHS
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 Effect of GSTT1 Genotype on Metabolism of Methyl Chloride
T1 Null No
Metabolism
Measure
exhaled
methyl
chloride
From Lof, A. et al, Pharmacogenetics 10:645, 2000.
T1 +
Metabolism to
DNA reactive
forms
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
Effect of GSTT1 null Genotype:
Ethylene Oxide-Hemoglobin Adducts Vs
Cotinine
800
Series1
GST T1 Null
HEVal Adducts (fmol)
700
GSTT1 null
600
GST T1 +
Series2
500
Linear (Series1)
400
Linear (Series2)
300
GSTT1 +
Study Design:
16 nonsmokers
32 smokers
HEVal hemoglobin
adducts measure by
mass spectrometry
P = 0.001 for difference
in slopes;
Nonparametric analysis
similar.
200
100
0
0
200
400
Plasma Cotinine (ng/ml)
600
Fennel et al CEBP 9:705,2000
Incorporating Genetics Into Risk Assessment
Needs:

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Identify genes involved in toxicological response.
Detailed population genetic information including:

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Determine functional relationship between genotype and
phenotype

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Identify polymorphisms.
Determine frequency in populations.
Population-based risk estimates in large studies (n=2000).
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.
Environmental Genomics
Discovery:
Functional
Analysis
Phenotype-directed
Genotype-directed
Disease Risk
Characterization
CTTATGT A/C GGGTAT
Genotype
Phenotype
Altered Binding
Effects in
Populations
Polymorphism and Function
Transcription
Factors
Coding region changes:
aa subs, deletions, stops.
Promoter
Exon 1
Exon 2
3’ UTR
Regulatory polymorphisms alter
transcription factor binding and
mRNA/protein level.
Gene Deletions, Duplications
e.g. GSTM1, CYP2D6
Effects of Polymorphism:
 Altered function
 Quantity of protein
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
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 Consortium – ~1 million SNPs
across genome
 NIEHS – Environmental/toxicology
genes
 NHLBI – Heart disease genes,
inflammation
 NIGMS – Pharmacogenetic genes
 The
SNP data is entered into the NCBI dbSNP database
UCSC
Hapmap
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U Wash EGP
Website
HapMap Website
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Characterize the large scale genetic
structure across the genome.
Genotyping SNPs at 1 kb interval across
the genome in European, African, and
Asian populations.
Bioinformatic Tools Available For
Picking Haplotype Tagging SNPs
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HapMap Website
Seattle SNPs or EGP website
Many other freely available programs
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:

Gene-environment interaction affects disease risk.

Effects of G x E interactions can be complex.
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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
1. Whole genome association studies
Test 10,000-100,000 SNPs in case control studies.
Identify candidate regions, genes, followup with
candidate gene studies.
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2. High resolution candidate gene studies.
Test functional SNPs and additional haplotype tagging SNPs in
case/control or other design.
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Bioinformatics to identify 1500 SNPs, 150 genes (10 SNPs/gene).
Coding SNPs, regulatory SNPs, haplotype tag SNPs.
Bioinformatic Identification of SNPs
That Affect Gene Expression
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Application to p53 response elements
Application to NRF2 response
elements
p53 inducible genes contain p53
Response Elements.
Following UV exposure
p53 binds RE of target gene.
RNA Pol
p53
p53
RRRCWWGYYY
p53
SEI1
mRNA
p53
RRRCWWAYYY
RRRCWWGYYY
ATG
SEI1 gene
Using bioinformatic methods, identify SNPs that
disrupt p53 response elements.
dbSNP
Data
Binding Site
Consensus
NCBI/Ensemble
Genome Data
Test SNPs Against p53 Response
Element Consensus
RRRCWWGYYYRRRCWWGYYY
AAAGGACAAGTTGAAACTTGCACAAGCAGCCTCCATTCTG
DNA ambiguity code
R = A or G
Y = C or T
W = A or T
Filter:
Best Hits
Access
database
Build Table of
All Promoter SNPs
Dan Tomso
Mismatch
with
consensus
CWWG
motif
Dan Tomso
Do SNPs in putative p53 response elements
affect p53 induced expression in Saos2
cells?
Saos2 Osteosarcoma Cells (p53 null)
RELATIVE INDUCTION
25
Strong
Weak
Strong
20
15
Weak
10
5
0
p21-5'
ADARB1
DCC
ARHGEF7
RRM1
TLR8
EOMES
SEI-1
SCGB1D2
Mike Resnick, Alberto Inga, Daniel Menendez
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
Phylogenetic Footprinting of NRF2/ARE Genes
~4000 Human
ARE containing
genes
1000
human/mouse
~4000 Mouse
ARE containing
genes
Human/
mouse/rat
~380
~2100 Rat ARE
containing genes
Gene x Environment Interaction
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Pharmacogenetics:
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Environmental disease
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Adverse drug reactions (toxicity)
Reduced efficacy
Modification of exposure-induced toxicity
Modification of exposure-induced disease
Can we generalize about risk associated with a
specific gene?