Summary of Molecular Cancer Epidemiology - PowerPoint PPT Presentation

Loading...

PPT – Summary of Molecular Cancer Epidemiology PowerPoint presentation | free to download - id: 7db605-NTc3Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Summary of Molecular Cancer Epidemiology

Description:

Summary of Molecular Cancer Epidemiology EPI243: Molecular Cancer Epidemiology Zuo-Feng Zhang,MD, PhD Molecular Epidemiology The goal of molecular epidemiology is to ... – PowerPoint PPT presentation

Number of Views:133
Avg rating:3.0/5.0
Slides: 107
Provided by: Zhan1154
Learn more at: http://www.ph.ucla.edu
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Summary of Molecular Cancer Epidemiology


1
Summary of Molecular Cancer Epidemiology
  • EPI243 Molecular Cancer Epidemiology
  • Zuo-Feng Zhang,MD, PhD

2
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.

3
Molecular Epidemiology
  • studies utilizing biological markers of exposure,
    disease and susceptibility
  • studies which apply current and future
    generations of biomarkers in epidemiologic
    research.

4
(No Transcript)
5
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.

6
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.

7
(No Transcript)
8
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 protein-chip 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

9
(No Transcript)
10
Fig 1
A, GTE (20?g/ml)
pI
9
MW (kDa)
8
8
9
10
2
2
1
10
1
5
5
11
11
13
13
7
17
6
7
6
17
18
16
16
18
12
12
14
14
3
3
15
15
4
4
B, GTE (40?g/ml)
pI
20
19
MW (kDa)
1
1
10
5
10
5
11
11
13
13
17
17
18
12
18
16
16
12
14
14
15
15
4
Time
48 hr
48 hr
24 hr
-


GTE
11
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.

12
Bladder Array
Gelsolin
HE
13
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

14
DNA Methylation (Contd)
  • Aberrant DNA methylation are one of the most
    common features of human neoplasia
  • Two major potential mechanisms for aberrant DNA
    methylation in tumor carcinogenesis

Silencing tumor suppressor genes (e.g. p16 gene)
Point mutation C to T transition (e.g. P53 gene)
SourceRoyal Society of Chemistry
15
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

16
DNA methyltransferases
  • DNMTs catalyze the transfer of a methyl group
    (CH3) from S-adenosylmethionine (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

17
NORMAL CIN 1 CIN 2
CIN 3
NORMAL LGSIL HG SIL
HGSIL
18
Additional Molecular Event
Exposure to Carcinogen
Precancerous Intraepithelial Lesions, (PIN,
CIN, PaIN..)
Cancer
Birth
Surrogate End Point Markers
Markers for Exposure
Markers of Effect
Tumor Markers
Genetic Suscep. Marker
CHEMOPREVENTION
19
(No Transcript)
20
(No Transcript)
21
(No Transcript)
22
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

23
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

24
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

25
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

26
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.

27
Intervention Studies
  • Susceptibility markers (GSTM1) can also be used
    to determine whether the randomization is
    successful (comparable intervention and control
    arms)

28
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?

29
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.

30
Power or Sample Size Estimate for Case-Control
Studies
  • Alpha-level (false positive) 0.05
  • Beta-level (false negative level 1-betapower)
    0.20
  • Delta-level Proportion of exposure in controls
    and exposure in cases or expected odds ratio

31
Interaction Assessment
Factor A
Absent Present
Factor A Absent RR00 RR01
Present RR10 RR11
32
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.

33
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

34
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

35
Quality Assurance
  • Systematic Application of optimum procedures to
    ensure valid, reproducible, and accurate results

36
-70 freezers
37
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.

38
Types of Biospecimens Blood
  • Plasma
  • Serum
  • Lymphocytes
  • Erythrocytes
  • Platelets

39
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.

40
Tissue Collections
  • Confirming clinical diagnosis by histological
    analysis
  • Examining tumor characteristics at chromosome and
    molecular level

41
Laboratory Techniques with Tissue
tissue
RT-PCR
42
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.

43
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.

44
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)

45
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

46
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

47
Buccal cells
  • No invasive
  • Good for PCR-analysis
  • Can measure both germline and somatic mutations

48
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

49
Breast Milk
  • Measuring hormones, exposures to chemicals and
    biological contaminants (Aflatoxin), selenium
    levels
  • Cells of interests

50
Feaces
  • Certain cells of interest
  • Infectious markers
  • Oncogenes

51
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.

52
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.

53
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.

54
Safety
  • Protect specimen from contamination
  • Workers safety, HIV, HBV

55
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

56
(No Transcript)
57
(No Transcript)
58
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.

59
(No Transcript)
60
BackgroundMetabolism of aflatoxin B1
61
Main Effects of HBsAg, AFB1 levels, and IFNA17 on
liver cancer development
Variables Variables Case Case Control Control Crude Age Sex Adjusted Fully Adjusted
  N () N () N () N () OR (95CI) OR (95CI) OR (95CI)
HBsAg - 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)
AFB1 Mean (SD) 508.1 (328.7) 426.2 (250.4)      
  lt247 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)
  gt545.1 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
IFNA17 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(HW)0.878 p(HW)0.878 p(trend)0.104 p(trend)0.102 p(trend)0.037
  RIRR 157 (82.6)  289   (75.5)  1.55 (1.00-2.41) 1.52 (0.97-2.38) 1.77 (1.04-3.03)
Model includes age, sex, BMI, education,
alcohol consumption, tobacco smoking, HBsAg,
imputed AFB1 levels, anti-HCV
62
Interaction between HBV and AFB1 and IFNA17
    HBsAg HBsAg HBsAg Case Case Control Control Crude Age Sex Adjusted Fully Adjusted
          N () N () N () N () OR (95CI) OR (95CI) OR (95CI)
AFB1 AFB1 AFB1                  
    lt247 - - 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)
    gt545.1 - - 17 (9.3) 77 (20.5) 1.27 (0.57-2.85) 1.26 (0.56-2.82) 1.15 (0.48-2.74)
    lt247 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)
    gt545.1 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 (95CI) 1ORint (95CI) 1ORint (95CI) 0.73 (0.24-2.24) 0.70 (0.23-2.18) 0.42 (0.12-1.45)
            2ORint (95CI) 2ORint (95CI) 2ORint (95CI) 1.10 (0.35-3.49) 1.10 (.35-3.52) 0.77 (0.22-2.70)
            3ORint (95CI) 3ORint (95CI) 3ORint (95CI) 2.58 (0.82-8.12) 2.38 (0.75-7.55) 2.27 (0.65-7.92)
IFNA17 IFNA17 IFNA17 IFNA17 IFNA17              
  II II II - 13 (6.8) 66 (17.3) 1 1 1
  RIRR RIRR RIRR - 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 II 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)
  RIRR RIRR RIRR 107 (56.3) 69 (18.1) 7.87 (4.04-15.34) 7.17 (3.66-14.06) 9.18 (4.34-19.43)
            ORint (95CI) ORint (95CI) ORint (95CI) 1.81 (0.71-4.62) 1.81 (0.71-4.63) 1.71 (0.60-4.92)
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
gt545.1 fmol/mg) and HBsAg
63
Interaction between HBsAg and IFNA17 stratified
by AFB1
AFB1 HBsAg IFNA17 Case Control Crude Age Sex Adjusted Fully Adjusted
      N N OR (95CI) OR (95CI) OR (95CI)
               
lt388.9 - II 8 26 1 1 1
  - RIRR 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)
  RIRR 37 37 3.25 (1.30-8.11) 2.81 (1.10-7.19) 3.45 (1.21-9.83)
    ORint (95CI) ORint (95CI) ORint (95CI) 2.20 (0.58-8.38) 2.20 (0.56-8.70) 2.39 (0.50-11.45)
               
gt388.9 - II 5 34 1 1 1
  - RIRR 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)
  RIRR 57 27 14.35 (5.05-40.77) 13.88 (4.80-40.09) 21.80 (6.36-74.75)
    ORint (95CI) ORint (95CI) ORint (95CI) 1.06 (0.25-4.44) 1.06 (0.25-4.45) 1.13 (0.22-5.81)
Model includes age, sex, BMI, education,
alcohol consumption, tobacco smoking, HCV
64
(No Transcript)
65
(No Transcript)
66

67
(No Transcript)
68
(No Transcript)
69
(No Transcript)
70
(No Transcript)
71
(No Transcript)
72
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

73
(No Transcript)
74
(No Transcript)
75
(No Transcript)
76
Main component of green Tea Catechins 
(-)-Epigallocatechin gallate ((-)EGCg)
77
PHIP DNA Adducts
78
P32 postlabeling
79
(No Transcript)
80
(No Transcript)
81
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.

82
Biomarker of Genetic Susceptibility
  • High risk genes
  • Low risk genes

83
Genetic Susceptibility to Cancer
010205
84
McCarthy MI, Nature Review Genetics, 2008
85
DNA damage repaired
Defected DNA repair gene
If DNA damage not repaired
If loose cell cycle control
86
Non-homologous Recombination
homologous recombination
BRCA1
BRCA2
Damage recognition cell cycle delay response
(DRCCD )
ATM
CHEK2(RAD53
BRCA1
87
(No Transcript)
88
Baseline characteristics of each study
LA Study LA Study LA Study Taixing City Study Taixing City Study Taixing City Study Taixing City Study MSKCC study MSKCC study
Lung Cancer Cases () UADT cancer Cases () Controls () Stomach Cancer Cases () Esophageal Cancer Cases () Liver Cancer Cases () Controls () Bladder Cancer Cases () Controls ()
Total Total 611 601 1040 206 218 204 415 233 204
Age range Age range 32-59 20-59 17-65 30-82 30 84 22-83 21-84 32-84 17-80
Age, mean Age, mean 52.2 50.3 49.9 61.5 60.6 53.8 57.7 64.8 42.0
Gender Gender
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)
Females 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)
Education Education
lt 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)
gtHigh 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)
Smoking Smoking
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)
89
Associations between 8q24 SNPs and smoking
related cancers
LA Lung UADT (squam) Oroph. Larynx
Naso.
90
Associations between 8q24 SNPs and smoking
related cancers
Taixing Esoph. Stomach Liver MSKCC
Bladder
91
Association between 8q24 and 7 smoking related
cancer sites, stratified by smoking status
92
(No Transcript)
93
(No Transcript)
94
(No Transcript)
95
(No Transcript)
96
(No Transcript)
97
(No Transcript)
98
TP53 Mutations in Bladder Cancer
BP changes Reported, n200 Current study
Transitions
GC ?AT 41.0 37.5
(at CpG) 14.0 12.5
AT?GC 10.0 15.0
Transversions
GC?TA 13.0 12.5
GC?CG 19.0 10.0
AT?TA 3.0 0.0
AT?CG 2.0 2.5
Deletion/Insert. 12.0 10.0
99
Smoking and TP53 Mutations in Bladder Cancer
Smoking TP53 TP53- OR 95CI
No 8 24 1.00
Yes 58 83 6.27 1.29-30.2
Adjusted for age, gender, and education
100
Cigarettes/day and TP53 Mutations in Bladder
Cancer
Cig/day TP53 TP53- OR 95CI
No 8 24 1.00
1-20 8 21 2.07 0.22-19.9
21-40 36 47 5.50 1.08-28.2
gt40 17 18 10.4 1.90-56.8
Trend P0.003
Adjusted for age, gender, and education
101
Years of Smoking and TP53 Mutations in Bladder
Cancer
Years of smoking TP53 TP53- OR 95CI
No 8 24 1.00
1-20 5 10 5.64 0.82-38.7
21-40 42 58 6.45 1.24-33.4
gt40 14 18 6.20 1.17-32.8
Trend P0.041
Adjusted for age, gender and education
102
Association Studies of Genetic Factors
  • 1st generation
  • Very small studies (lt100 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 (gt500 cases)
  • Proper epi design pathways
  • 4th generation
  • Consortium-based pooled analyses (gt2000 cases)
  • GxE analyses
  • 5th generation
  • Post-GWS studies

Boffeta, 2007
103
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

104
Potential of GWAS
105
Kingsmore, 2008
106
Post-GWAS Epidemiology
  • Functional SNP analysis
  • Pathway-based analysis
  • Deep sequencing and fine mapping
  • Gene-Environmental Interaction
About PowerShow.com