The Impact of Epidemiology on Advancing Genomic Technologies

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The Impact of Epidemiology on Advancing Genomic
Technologies

• Muin J. Khoury, MD, Ph.D.
• Director,
• CDC Office of Genomics and Disease Prevention

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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• HuGE Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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Welcome to the Genomic EraGuttmacher and
Collins, NEJM 2003349996

• DNA 50th Anniversary
• Human Genome Sequence

DNA Changed the World Now What?NY Times,
February 25, 2003
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How Will Genetics Change Our Lives 50 Years from
Now?

• We will have individualized, preventive medical
  care based on our own predicted risk of disease
  as assessed by looking at our DNA. By then each
  of us will have had our genomes sequenced because
  it will cost less than 100 to do that. And this
  information will be part of our medical record.
  Because we will still get sick, we'll still need
  drugs, but these will be tailored to our
  individual needs.
• F. Collins MD, PhD, TIME, the Future of Life,
  2003 

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Predicted Home Computers for 2004 !?! Popular
Mechanics Magazine (1954) 
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From Genetics to Genomics
Genetics
Genomics

• Disease
• Single Gene Disorders
• Mutations/One Gene
• Inherited
• High Disease Risk
• Environment Role /-
• Genetic Services

• Information
• All Diseases
• Variation/Multi Genes
• Inherited/somatic
• Low Disease Risk
• Environment Role
• General Practice

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Genetic Disorders Featuring CAD/MI

• Apolipoprotein(a) excess
• Apolipoprotein AI deficiency
• Autosomal recessive hypercholesterolemia
• Cerebrotendinous xanthomatosis
• Fabry disease
• Familial combined hyperlipidemia
• Familial defective apoB
• Familial hypercholesterolemia
• Familial partial lipodystrophy
• Familial pseudo hyper kalemia due to RBCl leak

• Heparin cofactor II deficiency
• Homocystinuria/homocysteinemia
• Niemann-Pick disease, type E
• Progeria
• Protein C deficiency
• Pseudoxanthoma elasticum
• Sitosterolemia
• Spontaneous coronary dissection
• Tangier disease
• Type III hyperlipoproteinemia
• Werner syndrome
• Williams syndrome

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Gene-Environment Interaction in Cardiovascular
Disease

• Some vegetarians with 'acceptable' cholesterol
  levels suffer myocardial infarction in the 30's.
  Other individuals...seem to live forever despite
  personal stress, smoking, obesity, and poor
  adherence to a Heart Association-approved diet"

R.A. Hegele (1992)
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Genetics and Cardiovascular Disease
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Prediction of Risk of Myocardial Infarction from
Polymorphisms in Candidate GenesYamada et al.
NEJM 20023471916-1923.

• Case-Control Study (5061 MI and 2242 Controls)
• Analysis of 71 candidate genes with 112
  polymorphisms (2-step process)
• A few associations were foundsmall odds ratios
• Accompanying editorial
• Findings should be used to initiate further
  research
• Recommendations for primary prevention cannot be
  based on these findings.

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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• HuGE Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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Gene-Based Medicine in 2010?Hypothetical
Genetic Test Report

• Condition Genes RR Lifetime
• Prostate Ca HPC1, 2, 3 0.4 7
• Alzheimers APOE,FAD3,XAD 0.3 10
• Heart disease APOB,CETP 2.5 70
• Colon Cancer FCC4,APC 4.0 23
• Lung Cancer NAT2 6.0 40

Collins FC, New Engl J Med 199934128-37.
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Epidemiology in the 21st Century Population
Impact of Human Genome VariationCalculation,
Communication, and InterventionJ Koplan (CDC
Director, 2000)

• The sequencing of the human genome offers the
  greatest opportunity for epidemiology since John
  Snow discovered the Broad Street pump
• Shpilberg et al. J Clin Epidemiol (1997)

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The Importance of Epidemiologic Data

• Prevalence
• Disease burden
• Relative risk
• Absolute risk
• Attributable fraction
• Gene-Environment interaction

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The Importance of Epidemiologic Methods

• Selection bias
• Confounding bias
• Information bias
• Statistical power
• Generalizability

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Epidemiological Quality in Molecular
GeneticResearch Need for Methodological
Standards

• Bogardus et al. JAMA 19992811919-26.
• Aim To examine clinical epi quality of recent
  papers in molecular genetic analysis
• 40 inferential articles in 4 clinical journals
  (1995)
• 38 articles passed 4 or less methodologic
  standards

• Methodologic Standard
  
• Reproducibility 38
• Objectivity 33
• Case Group 78
• Case Spectrum 88
• Comparison Group 70
• Comparison Spectrum 88
• Quantitative Summary 90

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The Importance of Population-based DataRisk
of breast cancer in women with BRCA1/2 mutations

• High risk families - (BRCA1/2)
• Voluntary survey, Wash DC - (BRCA1/2)
• Population-based sample, Australia - (BRCA1/2)
• Population-based sample, Iceland - (BRCA2)

• 85 (94)
• 56 (97)
• 40 (99)
• 37 (98)

Courtesy Dr Wylie Burke
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Systematic application of epidemiologic method
and approaches to assess the impact of human
genetic variation on health and disease
Khoury, Little and Burke, HuGE 2004

• Genotype prevalence
• Gene - disease association
• Gene - gene interactions
• Gene - environment interactions
• Assessment of Genetic tests

HuGE problem 30,000 genes, their combinations
and interactions with risk factors
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The End of Black Box Epidemiology?
Risk Factors
Demographics Diet Occupation Smoking Alcohol
Environment
Adverse Health Outcomes
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Pathological Uterine Distention
Inflammation
Activation of Maternal/Fetal HPA Axis
Decidual Hemorrhage Abruption
Infection - Chorion-Decidual - Systemic
Multifetal Pregnancy Polyhydramnios Uterine
abnormalities
Maternal-Fetal Stress Premature Onset of
Physiologic Initiators
Prothrombin G20210A Factor V Leiden Protein C,
Protein S Type 1 Plasminogen MTHFR
Interleukins TNF-a Fas L
Gap jct IL-8
PGE2 Oxytocin recep
CRH E1-E3
Mechanical stretch
Chorion Decidua

CRH
CYP1A1 GSTT1
Susceptibility to environmental toxins
MMPs

proteases
uterotonins
PROM
Cervical change
Uterine Contractions
Preterm Delivery
Courtesy S. Dolan MD
Adapted from C. J. Lockwood, Paediatr Perinat
Epidemiol 15, 78 (2001) X. Wangl. Paediatr
Perinat Epidemiol 15, 63 (2001)
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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• HuGE Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

22
Examples of Epidemiologic Efforts to
Characterize Gene Effects on Population Health

• Cohort Studies
• Case-Control Studies
• Acute Public Health Investigations
• Cross-Sectional Studies

• Biobanks
• National Birth Defects Study
• Leptospirosis outbreak
• NHANES

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Centers for Birth Defects Research and Prevention
Iowa Massachusetts New Jersey New York
Arkansas California Georgia
North Carolina Texas Utah
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National Birth Defects Prevention Study

• Case-control study of 24 major birth defects
  started in 1997
• Based on state surveillance systems
• Maternal interview
• Buccal cell (DNA) collection from infant and
  parents

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Studying Gene-Environment Interaction
Smoking, detoxification genes, and orofacial
clefts
DNA for examination of genes involved in
detoxification
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Integrating Human Genomics into Acute Public
Health Investigations
Environment
Susceptibility genes
Outbreaks
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1998 Springfield Ironhorse TriathlonLeptospiro
sis Outbreak

• 876 triathletes 12 reported illness
• Serum from 474 52 positive for leptospirosis
• Genetic studies TNF-a, HLA-DRB, HLA-DQB
• DNA from 85 anonymized blood samples
• HLA-DQ6 positive triathletes (compared to DQ6
  negatives) were
• - more likely be seropositive for leptospirosis
• (OR2.8, p0.04)
• - especially for those who reported swallowing
  lake water (OR8.5, p0.001)

Lingappa J. et al., Genes Immunity
20045197-202
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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• HuGE Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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Genetic Testing for Sale Vineis, P,
Christiani, D. Epidemiology Jan 2004
34
AmpliChip CYP450 Microarray ASR

• 2D6 and 2C19 genes - role in metabolism of
  25 of prescription drugs
• Chip microarray detection system to identify 29
  2D6 and 2 2C19 alleles
• Variations affect how common drugs are processed
  or metabolized
• Poor metabolizers ? adverse reactions
• Ultrarapid metabolizers ? non-responders
• .aid for physicians in individualizing
  treatment doses for patients on therapeutics
  metabolized through these genes.

FDA clearance (510K) for diagnostic use Dec,
2004 Clearance in EU
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Diagnosing ovarian cancer by proteomics

• Patterns of specific serum proteins can be used
  to detect OvCa, even in early stages
• Clinical trials in progress
• FDA review will follow
• OvaCheck technology interpretive software
  licensed
• Scheduled to be offered in 2004 but many
  questions
• Baggerly et al and Ransohoff JNCI, Feb 2005

Petricoin EF. Use of proteomic patterns in serum
to identify ovarian cancer. Lancet. 2002 Feb
16359(9306)572-7.
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Public Health Approach to Evaluation of Genetic
Tests (ACCE Model System)

• Disorder/Setting
• Analytic Validity
• Clinical Validity
• Clinical Utility
• ELSI

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• Analytic Validity
• Defines the ability of a test to accurately and
  reliably identify or measure the analyte or
  mutation of interest

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Analytic sensitivity specificity

• Sensitivity Proportion of positive results
• when variant/analyte is
  present
• Specificity Proportion of negative results
• when variant/analyte is
  absent
• Measures intrinsic performance of assay
  technology
• Part of laboratory validation before use
• Established using positive and negative control
  samples characterized using gold standard or by
  consensus

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• Clinical validity
• Defines the ability of a test to detect or
  predict the phenotype or particular clinical
  outcome
• Elements build upon analysis of analytic validity

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Clinical sensitivity specificity
Disease Phenotype Yes
No
Pos A B Neg C D
Test Result
Sensitivity Proportion of positive test results
in individuals who have the phenotype A /
(AC) Specificity Proportion of negative test
results in individuals who do not have the
phenotype D / (BD)
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Positive negative predictive values
Disease Phenotype Yes
No
Pos A B Neg C D
Test Result
Positive predictive value A /
(AB) Probability that person with positive test
will have the phenotype Negative predictive
value D / (CD) Probability that person with
negative test will not have the phenotype
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Effect of prevalence on Clinical PPV
50
45
PPV 45 NPV 99.9
100,000
99,950
100
Prevalence 5 in 10,000, Sens 90, Spec 99.9
500
450
PPV 82 NPV 99.9
100,000
99,500
100
Prevalence 50 in 10,000, Sens 90, Spec 99.9
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• Clinical Utility
• Defining the risks and benefits associated with
  introduction into routine clinical practice
• Likelihood of improved health outcome

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Case Studies of Clinical UtilityUsing
Epidemiologic Data for Policy

• Should we screen the general population for
  hereditary hemochromatosis?
• Should we screen women for Factor V Leiden before
  prescribing oral contraceptives?
• Should we screen children for TPMT deficiency
  before ALL rx with 6MP?

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Case Study 1Hereditary Hemochromatosis

• The Genetic Disorder of the 21st Century
• Iron Overload
• Multiple organ system
• Intervention simple
• Gene Chromosome 6
• 1997 Expert Panel on Population Screening
• Public health research Agenda
• Provider education campaign

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Prevalence of Hereditary Hemochromatosis
Mutations in the USA

• NHANES III
• Genotype Prevalence ()
• Genotype/Group White Black Hisp
• C282Y/C282Y 0.3 .06 .03
• H63D/H63D 2.2 0.3 1.1
• C282Y/H63D 2.4 .06 0.2

Steinberg KK et al., JAMA 20012852216
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Hemochromatosis-Associated Hospitalizations,
National Hospital Discharge Survey 1979-1997
5
4
3
Rate per 100,000 US residents
2
1
males
females
0
79 - 82 83 - 87 88 - 92 93 97
Years
Brown al et al. Genet Med 20013109-111
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Natural History of Hereditary Hemochromatosis
30
Early death
25
Bronze diabetes
20
Signs of organ damage
Body Iron Content in grams
15
Non-specific symptoms
10
Asymptomatic
5
Mutation
0
0
10
20
30
40
50
60
Age
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Case Study 2Incidence of Venous Thrombosis
Among Women by Factor V Leiden and Oral
Contraceptive Use
Incidence/10,000
(-) Mutation (-) OC
(-) Mutation () OC
() Mutation () OC
() Mutation (-) OC
Mutation Factor V Leiden OC Oral
Contraceptives
Source Adapted from Vandenbroucke JP, et al.
BMJ 1996 313 1127-1130
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Screening for factor V Leiden mutation before
prescribing oral contraceptives?

• Cost-effectiveness of screening for factor V
  Leiden mutation in women in the United States
• To prevent one venous thromboembolic death
  attributable to oral contraceptives in women with
  factor V Leiden mutation, gt92,000 carriers need
  to be identified and stopped from using these
  pills
• Estimated charge to prevent this one death
  exceeds 300 million
• Creinin MD et al. Fertil Steril 199972(4)646-51

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Case Study 3Genetic Testing (TPMT) Decision
Analysis Tree for 6-MP Therapy for Acute
Lymphoblastic Leukemia (Veenstra et al. AAPS
Pharmasci 20002)
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Influence of Cost of Genetic Test and Outcome
Severity on Hypothetical Cost-Effectiveness of
Genotyping (Veenstra et al) Genotype
Prevalence0.5
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Influence of Cost of Genetic Test and Outcome
Severity on Hypothetical Cost-Effectiveness of
Genotyping (Veenstra et al) Genotype
Prevalence1
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Influence of Cost of Genetic Test and Outcome
Severity on Hypothetical Cost-Effectiveness of
Genotyping (Veenstra) Genotype Prevalence0.3
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Framework for Evaluating the Potential
Cost-Effectiveness of Pharmacogenomics (Veenstra
et al. AAPS Pharmasci 20002)

• FACTORS
• Outcome severity
• Drug monitoring
• Geno-Pheno Corr
• Assay
• Polymorphism

• FACTORS for COST-EFFECTIVENESS
• NA/difficult
• Rapid, inexpensive
• High allele frequency

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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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Family history a genomic tool

• Most common diseases - interactions of multiple
  genes, behaviors, and environment
• Relatives share a lot of genes in common (half
  for 1st rel, quarter for 2nd rel, and so on)
• A long way to go for genetic risk factor testing
• Family medical history is a genomic tool that
  captures these interactions

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Family History of Common Diseases
No family history
One disorder
Three or more disorders
Two disorders
Scheuner et al. Am J Med Genet 199771315-324.
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Family history is a risk factor for almost all
common diseases
Relative Risk
Heart disease 2.0 5.4 Breast cancer 2.1
3.9 Colorectal cancer 1.7 4.9 Prostate
cancer 3.2 11.0 Melanoma 2.7 4.3 Type II
diabetes 2.4 4.0 Osteoporosis 2.0
2.4 Asthma 3.0 7.0
Am J Prev Med - February 2003
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Family History Public Health Initiative

• Why focus on family history?
• FHx is underutilized in preventive medicine
• Risk factor for most chronic diseases of PH
  significance

• How can we use family history?
• assess risk for common chronic diseases
• influence early screening for disease
• educate people about prevention measures

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Family History Collection by PCPs

• Family history collected at about 50 of new
  visits and 22 of established visits
• Average duration of visit, 10 minutes average
  duration of family history discussion, 2.5
  minutes
• Acheson et al., 2000
• Only 29 of PCPs feel prepared to take family
  history and draw pedigrees
• Suchard et al., 1999

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The Health Family Tree Study Utah, 1983-1996 - CHD
Includes data from 122,155 families 16,602 early
CHD cases 54,182 cases of CHD at any age
Williams, et al. Am J Cardiol 2001 87129-135
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Schema for Using Family History to Guide and
Inform Prevention Activities
Standard prevention recommendations
Average
Family History Tool
Moderate
Personalized prevention recommendations
High
Genetic Evaluation personalized prevention
recommendations
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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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Human Genome Epidemiology Network (HuGE Net)

• Global collaboration of individuals and
  organizations to assess population impact of
  genomics and how it can be used to improve health
  and prevent disease
• Information Exchange
• Training and Technical Assistance
• Knowledge Base Development
• Information Dissemination

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From HuGE Research to Synthesis Dissemination
for Policy and Practice
Primary HuGE Research Agenda and Funding
Study Design Single studies (case-control,
cohort biobanks) Consortia
Implementation
Candidate gene selection Risk factor data Outcome
data
Identify
Knowledge gaps Methodologic problems Research
Priorities
Analysis G-G, G-E
Interpretation
Causal inference Risk estimation
Synthesis
Policy Practice
Reporting
Appraisal
Reviews Meta-analysis
(Single study)
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From HuGE Research to Synthesis Dissemination
for Policy and Practice
Primary HuGE Research Agenda and Funding
Study Design Single studies (case-control,
cohort biobanks) Consortia
Implementation
Candidate gene selection Risk factor data Outcome
data
Identify
Knowledge gaps Methodologic problems Research
Priorities
HuGE Net
Analysis G-G, G-E
Interpretation
Causal inference Risk estimation
Synthesis
Policy Practice (EGAPP)
Reporting
Appraisal
Reviews Meta-analysis
(Single study)
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The Human Genome Epidemiology Network
Research agenda
Study design
Implementation

Analysis
Research priorities
Interpretation
Policy Practice
Dissemination
Appraisal
Synthesis
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Selected HuGE Net Activities

• HuGE Studies Database
• HuGE Reviews
• Methodology/Training Workshops
• International Biobank/Cohort Study meeting
• Network of Networks
• HuGE book
• HuGE journal

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Number of Published HuGE Papers 2001-2004

• Year Prevalence Associations Interactions
• 2001 308 2141 436
• 2002 349 2799 569
• 2003 323 3010 598
• 2004 368 3486 604

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Rank Gene Symbol Gene name of Papers 01-03
1 APOE apolipoprotein E 481
2 ACE angiotensin I converting enzyme (peptidyl-dipeptidase A) 1 398
3 MTHFR 5,10-methylenetetrahydrofolate reductase (NADPH) 377
4 HLA-DRB1 major histocompatibility complex, class II, DR beta 1 376
5 TNF tumor necrosis factor (TNF superfamily, member 2) 346
6 GSTM1 glutathione S-transferase M1 253
7 HLA-DQB1 major histocompatibility complex, class II, DQ beta 1 248
8 F5 coagulation factor V (proaccelerin, labile factor) 213
9 GSTT1 glutathione S-transferase theta 1 204
10 IL10 interleukin 10 177
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The Need for Integrating Epidemiologic
Evidenceon Genes and Population Health

• Unmanageable amounts of data
• Small sample size of individual studies
• Small effect size of gene-disease associations
• Replication of associations
• Publication bias
• Heterogeneity
• Generate and test hypotheses

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Small sample size of individual studies
Ioannidis, Trends Mol Med 2003
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Small effect sizes in individual studies
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Outline

• Genomics 2005 Science and Expectations
• Why do we need epidemiology?
• Characterizing genes in populations
• Epidemiologic assessment of genomic tests
• Epidemiologic assessment of family history as a
  tool for disease prevention
• Human Genome Epidemiology Network (HuGE Net)

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