Transcript Summary of Molecular Cancer Epidemiology

Summary of Molecular Cancer
Epidemiology
EPI243: Molecular Cancer
Epidemiology
Zuo-Feng Zhang,MD, PhD
Molecular Epidemiology
• The goal of molecular epidemiology is to
supplement and integrate, not to replace,
existing methods
• Molecular epidemiology can be utilized to
enhance capacity of epidemiology to
understand disease in terms of the
interaction of the environment and
heredity.
Molecular Epidemiology
• studies utilizing biological markers
of exposure, disease and
susceptibility
• studies which apply current and
future generations of biomarkers
in epidemiologic research.
Tasks for Molecular
Epidemiologist
The major tasks are
• to reduce misclassification of exposure,
• to assess effect of exposure on the target tissue,
• to measure susceptibility/inherited predisposition
to cancer,
• to establish the link between environmental
exposures and gene mutations,
• to assess gene-environment interaction.
• To set up prevention/intervention strategies.
High Throughput Techniques
• Microarray technology
– DNA chips
• cDNA array format
• in situ synthesized oligonucleotide format (Affymetrix)
– Proteomics
– Tissue arrays
• These are powerful tools and high through put methods
to study gene expression, but they are not the answers
themselves
• Individual targets/patterns identified need to be validated
• In epidemiological studies, these methods can be used
to identify specific exposure induced molecular changes,
individual risk assessments, etc.
An example of our 9000 gene mouse-arrays using
differential expression analysis with Cy3 and Cy5
fluorescent dyes.
Proteomics
• Examine protein level expression in a high throughput
manner
• Used to identify protein markers/patterns associated with
disease/function
• Different formats:
– SELDI-TOF (laser desorption ionization time-of-flight): the proteinchip arrays, the mass analyzer, and the data-analysis software
– 2D Page coupled with MALDI-TOF (matrix-assisted laser desorption
ionization time-of-flight)
– Antibody based formats
A, GTE (20g/ml)
3.5 4.5 5.1 5.5
6.0
7.0
8.4
Fig 1
pI
9.5 3.5 4.5 5.1 5.5
6.0
7.0
9.53.5 4.5 5.1 5.5
8.4
6.0
7.0
8.4
9.5
MW (kDa)
217
116
98
8
55
2 10
7
17
6
30
13
7
16
12
5
11
13
2 10 1
9
8
1
5
11
37
9
6
17
16
18
12
14
14
3
20
15
B, GTE (40g/ml)
3.5 4.5 5.1 5.5
6.0
7.0
8.4
18
3
15
4
4
pI
9.5 3.5 4.5 5.1 5.5
6.0
7.0
8.4
9.53.5 4.5 5.1 5.5
6.0
7.0
8.4
9.5
MW (kDa)
217
116
98
55
5
1
10
11
20
19
13
37
10
5
11
17
12
14
30
17
18
16
12
14
15
20
48 hr
GTE:
-
16
15
4
Time:
1
24 hr
48 hr
+
+
13
18
Tissue Array
• Provide a new high-throughput tool for the study of gene
dosage and protein expression patterns in a large number
of individual tissues for rapid and comprehensive molecular
profiling of cancer and other diseases, without exhausting
limited tissue resources.
• A typical example of a tissue array application is in
searching for oncogenes amplifications in vast tumor tissue
panels. Large-scale studies involving tumors encompassing
differing stages and grades of disease are necessary to
more efficiently validate putative markers and ultimately
correlate genotypes with phenotypes.
• Also applicable to any medical research discipline in which
paraffin-embedded tissues are utilized, including structural,
developmental, and metabolic studies.
Bladder Array
Gelsolin
HE
DNA Methylation
DNA methylation plays an important role in normal cellular
processes, including X chromosome inactivation, imprinting
control and transcriptional regulation of genes
It predominantly found on cytosine residues in CpG
dinucleotide, CpG island, to producing 5-Methylcytosine
CpG islands frequently located in or around the
transcription sites
DNA Methylation (Cont’d)
Aberrant DNA methylation are one of the most common
features of human neoplasia
Two major potential mechanisms for aberrant DNA
methylation in tumor carcinogenesis
Point mutation: C to T transition
(e.g. P53 gene)
Silencing tumor suppressor
genes (e.g. p16 gene)
Source:Royal Society of Chemistry
Promoter-Region Methylation
Promoter-region CpG islands methylation
• Is rare in normal cells
• Occur virtually in every type of human neoplasm
• Associate with inappropriate transcriptional silence
• Early event in tumor progression
In tumor suppressor genes
Most of the tumor suppressor genes are under-methylated
in normal cells but methylated in tumor cells. Methylation
is often correlated with an decreasing level of gene
expression and can be found in premalignant lesions
DNA methyltransferases
DNMTs catalyze the transfer of a methyl group (CH3) from Sadenosylmethionine (SAM) to the carbon-5 position of
cytosine producing the 5-methylcytosine
There are several DNA methyltransferases had been
discovered, including DNMT1, 3a, and 3b
NORMAL
CIN 1
NORMAL
LGSIL
CIN 2
HG SIL
CIN 3
HGSIL
Exposure to Carcinogen
Birth
Precancerous
Intraepithelial
Lesions,
(PIN, CIN, PaIN..)
Additional
Molecular Event
Cancer
Surrogate End Point Markers
Genetic Suscep.
Marker
Markers for
Exposure
Markers of
Effect
CHEMOPREVENTION
Tumor
Markers
Case-Control Studies
• Disease end-point as a major interest
• Clinical (Hospital)-based or population-based
case-control studies
• Inclusion of both questionnaire data and
biological specimens
• Biological markers can be measured and
compared between cases and controls when
other variables can be used as either
confounding factors or effect modifiers
Prospective Cohort Studies
• Exposure is measured before the outcome
• The source population is defined
• The participation rate is high if specimen
are available for all subjects and follow-up
is complete
Nested Case-Control Study
• The biomarker can be measured in
specimens matched on storage duration
• The case-control set can be analyzed in
the same laboratory batch, reducing the
potential for bias introduced by sample
degradation and laboratory drift
Case-Case Study Design
• Case-only, Case-series, etc.
• Studies with cases without using controls
• Can be employed to evaluate the
etiological heterogeneity when studying
tumor markers and exposure
• May be used to assess the statistical
gene-environment or gene-gene
interactions
Intervention Studies
• In studies of smoking cessation intervention, we
can measure either serum cotinine or protein or
DNA adducts (exposure) or p53 mutation,
dysplasia and cell proliferation (intermediate
markers for disease)
• Measure compliance with the intervention such
as assaying serum b-carotene in a randomized
trial of b-carotene.
Intervention Studies
Susceptibility markers (GSTM1) can also
be used to determine whether the
randomization is successful (comparable
intervention and control arms)
Family Studies
• Does familial aggregation exist for a specific
disease or characteristic?
• Is the aggregation due to genetic factors or
environmental factors, or both?
• If a genetic component exists, how many genes
are involved and what is their mode of
inheritance?
• What is the physical location of these genes and
what is their function?
Sample Size and Power
• False positive (alpha-level, or Type I
error). The alpha-level used and accepted
traditionally are 0.01 or 0.05. The smaller
the level of alpha, the larger the sample
size.
Power or Sample Size Estimate
for Case-Control Studies
• Alpha-level (false positive): 0.05
• Beta-level (false negative level; 1beta=power): 0.20
• Delta-level: Proportion of exposure in
controls and exposure in cases or
expected odds ratio
Interaction Assessment
Factor A
Factor A
Absent
Present
Absent
RR00
RR01
Present
RR10
RR11
Sample Size Consideration for
Interaction Assessment
• Evaluation of interaction requires a
substantial increase in study size. For
example, in a case-control study involves
comparing the sizes of the odds ratios
(relating exposure and disease) in different
strata of the effect modifier, rather than
merely testing whether the overall odds
ratio is different from the null value of 1.0.
Introduction
• Sample Collection, such as handling,
labeling, processing, aliquoting, storage,
and transportation, may affect the results
of the study
• If case sample are handled differently from
controls samples, differential
misclassification may occur
Information linked to Sample
• Time and date of collection
• Recent diet and supplement use,
• Reproductive information (menstrual
cycle)
• Recent smoking
• current medication use
• Recent medical illness
• Storage conditions
Quality Assurance
Systematic Application of optimum
procedures to ensure valid, reproducible,
and accurate results
-70 freezers
Types of Biospecimens: Blood
The use of skilled technicians and precise
procedures when perform phlebotomy are
important because painful, prolonged or
repeated attempts at venepuncture can
cause patient discomfort or injury and
result in less than optimum quality or
quantity of sample.
Types of Biospecimens: Blood
•
•
•
•
•
Plasma
Serum
Lymphocytes
Erythrocytes
Platelets
Urine Collection
Urine is an ultrafiltrate of the plasma. It can
be used to evaluate and monitor body
metabolic disease process, exposure to
xenobiotic agents, mutagenicity, exfoliated
cells, DNA adducts, etc.
Tissue Collections
• Confirming clinical diagnosis by
histological analysis
• Examining tumor characteristics at
chromosome and molecular level
Laboratory Techniques with Tissue
tissue
RT-PCR
Adipose Tissue
• Adipose tissue may be quite feasible for
subject and involve low risk. The tissue
offers a relatively stable deposit of
triglyceride and fat-soluble substances
such as fat-soluble vitamins (vitamins A
and D). It represents the greatest reservoir
of carotenoids and reflect long-term
dietary intake of essential fatty acids.
Bronchoalveolar Lavage (BAL)
• BAL is used to assess and quantify
asbestos exposures
• Induced sputum sample and BALF can
also provide sufficient DNA for PCR
assays.
Exhaled Air
• To evaluate exposure to different
substances, particularly solvents such as
benzene, styrene
• To be used as a source of exposure and
susceptibility markers (caffeine breath test
for p4501A2 activity)
• Breath urea (presence of urease positive
organisms such as H. pylori)
Hair
• Easy available biological tissue whose
typical morphology may reflect disease
conditions within the body
• Provides permanent record of trace
elements associated with normal and
abnormal metabolism
• A source for occupational and
environmental exposure to toxic metals
Nail Clippings
• Toenail or fingernail clippings are obtained
in a very easy and comfortable way.
• They do not require processing, storage
and shipping condition and thus suitable
for large epidemiological studies
Buccal cells
• No invasive
• Good for PCR-analysis
• Can measure both germline and somatic
mutations
Saliva
• It is an efficient, painless and relatively
inexpensive source of biological materials
for certain assays
• It provides a useful tool for measuring
endogenous and xenobiotic compounds
Breast Milk
• Measuring hormones, exposures to
chemicals and biological contaminants
(Aflatoxin), selenium levels
• Cells of interests
Feaces
• Certain cells of interest
• Infectious markers
• Oncogenes
Semen
• Evaluate the effects of exposures on
endocrine and reproductive factors.
• Sexual abstinence for at least 2 days but
not exceeding 7 days.
• Should reach the lab within one hour.
Storage
• Freezers may fail, leading to the necessity
for 24 hour monitoring for the facility
through a computerized alarm system to
alter personnel and activate backup
equipment.
• Monitoring fire, power loss, leakage, etc.
Shipping
• Sample shipping requirements depends on the
time, distance, climate, season, method of
transport, applicable regulations, type of
specimen and markers to be assayed.
• Polyurethane boxes containing dye ice are used
to ship and transport samples that require low
temperature. For samples require very low
temperature, liquid nitrogen container can be
used
• The quantity of dry ice should be carefully
calculated, based on estimated time of trip.
Safety
• Protect specimen from contamination
• Workers safety, HIV, HBV
Biomarker in Epidemiology:
Biomarkers of Biological Agents
• HPV DNA by PCR-based assays
HPV infection is often transient, especially
in young women so that repeated
sampling is required to assess persistent
HPV infections
Biomarker in Epidemiology:
Biomarkers of Biological Agents
HBV infection by serological assays.
• There are serological markers that
distinguish between past and persistent
infections. HBV DNA detection in sera
further refines the assessment of
exposure.
Background:
Metabolism of aflatoxin B1
dietary intake
GST-μ,
(GST-θ)
+
glutathione
CYP3A4
(CYP1A2)
AFB1
AFB1-exo-8,9epoxide
CYPs
H 2O
(mEH)
glutathione-AFB1
conjugate
excretion
AFB1-8,9dihydrodiol
excretion
[phenolate resonance
form]
AFM1
AFQ1
AFB1-endo-8,9epoxide
DNAadducts
protein
adducts
Main Effects of HBsAg, AFB1 levels, and IFNA17
on liver cancer development
Variables
HBsAg
AFB1
IFNA17
Case
Control
Crude
Age & Sex Adjusted
Fully Adjusted**
N (%)
N (%)
OR (95%CI)
OR (95%CI)
OR (95%CI)
-
72
(35.3)
312
(75.4)
1
1
1
+
132
(64.7)
102
(24.6)
5.61 (3.90-8.07)
5.21 (3.60-7.53)
5.68 (3.80-8.51)
Mean (SD)
508.1
(328.7)
426.2
(250.4)
<247
33
(18.1)
94
(24.9)
1
1
1
247.1-388.8
46
(25.3)
94
(24.9)
1.39 (0.82-2.37)
1.38 (0.81-2.37)
1.15 (0.61-2.14)
388.9-545
42
(23.1)
95
(25.2)
1.26 (0.74-2.16)
1.27 (0.74-2.20)
1.19 (0.64-2.21)
>545.1
61
(33.5)
94
(24.9)
1.85 (1.11-3.08)
1.75 (1.04-2.94)
1.63 (0.90-2.96)
p(trend)=0.031
p(trend)=0.055
p(trend)=0.109
II
33
(17.4)
94
(24.5)
1
1
1
RI
104
(54.7)
193
(50.4)
1.54 (0.97-2.44)
1.49 (0.93-2.38)
1.67 (0.95-2.93)
RR
53
(27.9)
96
(25.1)
1.57 (0.94-2.64)
1.58 (0.93-2.68)
1.99 (1.06-3.73)
p(trend)=0.104
p(trend)=0.102
p(trend)=0.037
1.55 (1.00-2.41)
1.52 (0.97-2.38)
1.77 (1.04-3.03)
p(HW)=0.878
RI&RR
157
(82.6)
289
(75.5)
**Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, HBsAg, imputed AFB1 levels, anti-HCV
Interaction between HBV and AFB1 and IFNA17
HBsAg
Case
Control
Crude
Age & Sex Adjusted
Fully Adjusted**
N (%)
N (%)
OR (95%CI)
OR (95%CI)
OR (95%CI)
AFB1
<247
-
12
(6.6)
69
(18.4)
1
1
1
247.1-388.8
-
19
(10.4)
67
(17.8)
1.63 (0.74-3.62)
1.64 (0.73-3.65)
1.72 (0.73-4.08)
388.9-545
-
15
(8.2)
71
(18.9)
1.22 (0.53-2.78)
1.22 (0.53-2.80)
1.34 (0.55-3.27)
>545.1
-
17
(9.3)
77
(20.5)
1.27 (0.57-2.85)
1.26 (0.56-2.82)
1.15 (0.48-2.74)
<247
+
21
(11.5)
25
(6.6)
4.83 (2.08-11.23)
4.61 (1.97-10.80)
6.43 (2.56-16.16)
247.1-388.8
+
27
(14.8)
27
(7.2)
5.75 (2.55-12.96)
5.30 (2.34-12.02)
4.68 (1.92-11.38)
388.9-545
+
27
(14.8)
24
(6.4)
6.47 (2.84-14.74)
6.20 (2.70-14.21)
6.65 (2.72-16.25)
>545.1
+
44
(24.2)
16
(4.3)
15.82 (6.84-36.57)
13.75 (5.90-32.06)
16.72 (6.60-42.38)
1ORint
(95%CI)=
0.73 (0.24-2.24)
0.70 (0.23-2.18)
0.42 (0.12-1.45)
2ORint
(95%CI)=
1.10 (0.35-3.49)
1.10 (.35-3.52)
0.77 (0.22-2.70)
3ORint
(95%CI)=
2.58 (0.82-8.12)
2.38 (0.75-7.55)
2.27 (0.65-7.92)
IFNA17
II
-
13
(6.8)
66
(17.3)
1
1
1
RI&RR
-
50
(26.3)
220
(57.6)
1.15 (0.59-2.25)
1.14 (0.58-2.23)
1.34 (0.64-2.82)
II
+
20
(10.5)
27
(7.1)
3.76 (1.64-8.62)
3.49 (1.51-8.04)
3.99 (1.54-10.32)
RI&RR
+
107
(56.3)
69
(18.1)
7.87 (4.04-15.34)
7.17 (3.66-14.06)
9.18 (4.34-19.43)
1.81 (0.71-4.62)
1.81 (0.71-4.63)
1.71 (0.60-4.92)
ORint (95%CI)=
**Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, imputed AFB1 levels, anti-HCV; 1ORint for
AFB1 (247.1-388.8 fmol/mg) and HBsAg; 2ORint for AFB1 (388.9-545 fmol/mg) and HBsAg; 3ORint for AFB1 >545.1 fmol/mg)
and HBsAg
Interaction between HBsAg and IFNA17 stratified
by AFB1
AFB1
<388.9
HBsAg
IFNA17
Case
Control
Crude
Age & Sex Adjusted
Fully Adjusted**
N
N
OR (95%CI)
OR (95%CI)
OR (95%CI)
-
II
8
26
1
1
1
-
RI&RR
20
99
0.66 (0.26-1.66)
0.63 (0.24-1.62)
0.70 (0.24
+
II
9
13
2.25 (0.70-7.19)
2.04 (0.62-6.74)
2.07 (0.52-8.18)
+
RI&RR
37
37
3.25 (1.30-8.11)
2.81 (1.10-7.19)
3.45 (1.21-9.83)
2.20 (0.58-8.38)
2.20 (0.56-8.70)
2.39 (0.50-11.45)
ORint (95%CI)=
>388.9
-
II
5
34
1
1
1
-
RI&RR
25
104
1.63 (0.58-4.60)
1.62 (0.58-4.59)
2.09 (0.64-6.86)
+
II
11
9
8.31 (2.29-30.10)
8.07 (2.21-29.42)
9.22 (2.08-40.86)
+
RI&RR
57
27
14.35 (5.05-40.77)
13.88 (4.80-40.09)
21.80 (6.36-74.75)
1.06 (0.25-4.44)
1.06 (0.25-4.45)
1.13 (0.22-5.81)
ORint (95%CI)=
**Model includes age, sex, BMI, education, alcohol consumption, tobacco smoking, HCV
Biomarker of Dietary Intake
• Whether it is a good indicator of intake
• Whether it is a long- or short-term marker
• Whether there is a need for multiple
measurements
• Whether it is acceptable for researcher
and the subject
• Whether it is compatible with study design
Main component of green Tea Catechins:
(-)-Epigallocatechin gallate ((-)EGCg)
P32
postlabel
ing
Susceptibility Markers
• Susceptibility markers represent a group
of biological markers, which may make an
individual susceptible to cancer.
• These markers may be genetically
inherited or determined or acquired.
• They are independent of environmental
exposures.
Biomarker of Genetic
Susceptibility
• High risk genes
• Low risk genes
Genetic Susceptibility to Cancer
•Mutations with strong influence on risk
•Variations with weak functional effect
•Rare in the population (<1%)
•Low to high frequency in the
population (1-50%)
•Results in familial clustering
•Can be studied in families
•Limited familial clustering
•Can be studied in populations
•e.g. BRCA germline mutations
010205
McCarthy MI, Nature Review Genetics, 2008
2-1. Background: Theoretical model of gene-gene/environmental interaction pathway
Tobacco consumption
Occupational
Exposures
Environmental Carcinogens /
Procarcinogens Exposures
Ile105Val 
Ala114Val
Environmental Exposure
Null 
GSTP1
GSTM1
CYP1A1
MspI
Ile462Val 
Tyr113His
His139Arg
PAHs,
Xenobiotics,
Arene,
Alkine, etc
Detoxified
carcinogens
Active carcinogens
Pro187Ser
mEH
mEH
NQO1
DNA damage
repaired
DNA Damage
Tyr113His
His139Arg
Normal cell
Defected DNA
repair gene
If DNA damage not
repaired
XRCC1
Arg194Trp,
Arg399Gln,
Arg280His
M
G
G2
P53
P16
Arg72Pro
Ala146Thr
S
G870A
Cyclin D1
If loose cell cycle
control
Carcinogenesis
Programmed cell
death
Non-homologous
Recombination
homologous
recombination
BRCA1
Damage recognition
ATM CHEK2(RAD53
cell cycle delay
BRCA1
response (DRCCD )
BRCA2
Baseline characteristics of each study
LA Study
Taixing City Study
MSKCC study
Lung
Cancer
Cases (%)
UADT
cancer
Cases (%)
Controls
(%)
Stomach
Cancer
Cases (%)
Esophage
al Cancer
Cases (%)
Liver
Cancer
Cases (%)
Controls
(%)
Bladder
Cancer
Cases (%)
Controls
(%)
Total
611
601
1040
206
218
204
415
233
204
Age range
32-59
20-59
17-65
30-82
30 – 84
22-83
21-84
32-84
17-80
Age, mean
52.2
50.3
49.9
61.5
60.6
53.8
57.7
64.8
42.0
Males
303 (49.6)
391 (74.2)
623 (59.9)
138 (67.0)
141 (64.7)
159 (77.9)
287 (69.2)
206 (83.4)
156 (77.2)
Female
s
308 (50.4)
136 (25.8)
417 (40.1)
68 (33.0)
77 (35.3)
45 (22.1)
128 (30.8)
41 (16.6)
46 (22.8)
< High
school
265 (43.4)
240 (45.5)
300 (28.9)
204 (99.5)
215
(100.0)
204
(100.0)
405 (97.6)
95 (40.8)
34 (16.7)
>High
School
346 (56.6)
287 (54.5)
739 (71.1)
1 (0.5)
0 (0.0)
0 (0.0)
10 (2.4)
138 (59.2)
170 (83.3)
Never
110 (18.0)
164 (31.1)
491 (47.3)
92 (45.8)
94 (43.1)
85 (44.3)
217 (52.4)
42 (17.3)
92 (46.0)
Ever
501 (82.0)
363 (68.9)
548 (52.7)
109 (54.2)
117 (53.7)
107 (55.7)
197 (47.9)
201 (82.7)
108 (54)
Gender
Education
Smoking
Associations between 8q24 SNPs and smoking related cancers
LA
Lung
UADT
(squam)
Oroph.
Larynx
Naso.
Associations between 8q24 SNPs and smoking related cancers
Taixing
Esoph.
Stomach
Liver
MSKCC
Bladder
Association between 8q24 and 7 smoking
related cancer sites, stratified by smoking
status
TP53 Mutations in Bladder Cancer
BP changes
Transitions
GC AT
(at CpG)
ATGC
Transversions
GCTA
GCCG
ATTA
ATCG
Reported,
n=200
Current study
41.0%
14.0%
10.0%
37.5%
12.5%
15.0%
13.0%
19.0%
3.0%
2.0%
12.5%
10.0%
0.0%
2.5%
Smoking and TP53 Mutations in
Bladder Cancer
Smoking TP53+
TP53-
OR
No
8
24
1.00
Yes
58
83
6.27
Adjusted for age, gender, and education
95%CI
1.2930.2
Cigarettes/day and TP53
Mutations in Bladder Cancer
Cig/day
TP53+
TP53-
OR
No
8
24
1.00
1-20
8
21
2.07
21-40
36
47
5.50
>40
17
18
10.4
Trend
P=0.003
Adjusted for age, gender, and education
95%CI
0.2219.9
1.0828.2
1.9056.8
Years of Smoking and TP53
Mutations in Bladder Cancer
Years of TP53+
smoking
No
8
TP53-
OR
24
1.00
1-20
5
10
5.64
21-40
42
58
6.45
>40
14
18
6.20
Adjusted for age, gender and education
Trend
P=0.041
95%CI
0.8238.7
1.2433.4
1.1732.8
Association Studies of Genetic
Factors
•
•
•
•
•
1st generation
– Very small studies (<100 cases)
– Usually not epidemiologic study design; 1-2 SNPs
2nd generation
– Small studies (100-500 cases)
– More epi focus; a few SNPs
3rd generation
– Large molecular epi studies (>500 cases)
– Proper epi design; pathways
4th generation
– Consortium-based pooled analyses (>2000 cases)
– GxE analyses
5th generation
– Post-GWS studies
Boffeta, 2007
Issues in genetic association studies
• Many genes
– ~25,000 genes, many can be candidates
• Many SNPs
– ~12,000,000 SNPs, ability to predict functional SNPs is limited
• Methods to select SNPs:
– Only functional SNPs in a candidate gene
– Systematic screen of SNPs in a candidate gene
– Systematic screen of SNPs in an entire pathway
– Genomewide screen
– Systematic screen for all coding changes
Potential of GWAS
Kingsmore, 2008
Post-GWAS Epidemiology
•
•
•
•
Functional SNP analysis
Pathway-based analysis
Deep sequencing and fine mapping
Gene-Environmental Interaction