Introduction to Genetic Epidemiology

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Introduction to Genetic Epidemiology

• 5th Annual Interdisciplinary Genetic Research
  Course
• Medical, Public Health, Biostatistical
  Bioethical Approaches
• October 6, 2008
• T.H. Beaty, Johns Hopkins School of Public Health

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Genetic Epidemiology
A hybrid science focusing on complex diseases
(where both genetic environmental factors
contribute to etiology of disease)
Parent sciences (genetics epidemiology) share
common goals but they differ in their histories
perspectives.
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Landmarks in Genetics
Year Event
1865 Gregor Mendel publishes work on peas describing fundamentals of inheritance
1871 DNA is isolated from the cell nucleus
1900 3 people independently re-discover Mendels work (Correns, DeVries vonTschermak)
1901-02 Garrod discovers human example of Mendelian disease (alkaptonuria) Landsteiner discovers 1st genetic marker (ABO)
1908 Hardy Weinberg lay the foundation for modeling genes in populations
1918 Fisher describes how Mendelian genes can account for quantitative phenotypes
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Landmarks in Genetics (contd)
1930s Biometrical school of genetics develops statistical models for genes in families populations
1944 One gene-one protein model is developed
1953 Double helix structure of DNA identified by Watson Crick ( R. Franklin )
1966 Genetic code established (3 nucleotides per codon)
1972 Recombinant DNA techniques developed
1987 Human Genome Project proposed
2001 Draft sequence of human genome available
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Summarizing Genetic History
Century 19th 20th 20th 20th 20th 21st 21st
Landmarks Mendel Mendel Rediscd DNA Structure Human Genome Proj.
Disease Rare Mendelian Rare Mendelian Multifactorial or Complex Multifactorial or Complex All? ?
Mapping Linkage (2 point ? genome wide) Linkage (2 point ? genome wide) LD mapping Sequencing ?
Genes Simple genetic models (4) One gene, one protein Models for complex traits One gene, gt1 proteins Multiple genes interaction ?
Gene Expression Gene defined by mutants Gene expression (mRNA) Gene Regulation ?
Testing Neonatal Screening Diagnostic Testing Susceptibility ?
Public Health applications
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Landmarks in Epidemiology
Year Event
1832 Cholera epidemic in Britain prompted epidemiologic studies by T. Proudfoot H. Gaulter
1843 W. Farr develops epidemiologic concepts based on vital statistics
1854 J. Snow defines transmission mechanism for cholera
1900s As part of bacteriologic revolution, epidemiology evolves from a descriptive to analytical science
1920s Epidemiologic methods are applied to chronic diseases
1935 Descriptive studies of lung cancer patterns implicate cigarette smoking
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Landmarks in Epidemiology
Year Event
1948 Large cohorts established to study cardiovascular disease (Framingham)
1950 Definitive case-control studies of lung cancer smoking published in US UK
1960s Large scale studies (both cohort trials) develop statistical tools for multiple risk factors
1979 Eradication of smallpox achieved
1980s HIV emerges as major public health threat
2000- Epidemiology faces a new millenium A mix of science, public health practice policy
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50 years of genetic epidemiologyMorton (2006) J
HUM GENET 51269-277

• Before DNA polymorphisms (1956-1979)
• Linkage analysis (LOD scores) began with very few
  markers
• Population genetics measured linkage
  disequilibrium (LD) by D r2
• Heritability (h2) began with twin studies
  evolved into path analysis with genetic
  non-genetic causal factors
• Pre-genome period (1980-2001)
• Linkage analysis expanded to multipoint analysis
• Even for Mendelian diseases, peaks are hard to
  narrow
• With complex phenotypes, it is worse
• Association studies proposed as alternative
  (Risch Merikangas 1996 SCIENCE 2731516-1517)
• Family based association tests are useful
  alternative

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50 years of genetic epidemiology (contd)

• Post-genome period (gt2002)
• Sequence of the human genome allows greater
  understanding of structure of genes, their
  physical position (maybe) their function
• HapMap offers virtually unlimited markers, but
  SNPs are not equally polymorphic in all
  populations
• LD haplotype blocks vary among populations
• Associated markers are only predictive, but can
  identify causal genes
• Study design will be critical
• Is 500K always better than 100K?

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Comparing genetics epidemiology
Genetics Epidemiology
Origins Biologic science of inheritance Methodologic science of Public Health
Breeding Experimental Descriptive Analytic
Focus Mechanisms of inheritance gene expression Etiologic factors in disease methods of control
Often on rare diseases Mostly on common diseases
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Comparing genetics epidemiology (contd)
Tools Family Studies Laboratory Methods Statistical Models Population Models Descriptive Studies Case/Control Cohort Designs Clinical Trials
Goals Understand mechanisms of inheritance Understand disease etiology distribution
Over-lap Diseases of interest (heart disease, cancer, diabetes) Prevent/control disease Diseases of interest (heart disease, cancer, diabetes) Prevent/control disease
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Central questions in Genetic Epidemiology

1. Does the trait cluster in families?
2. Can familial clustering be explained by genes or
   shared environment?
3. What is the best model of inheritance?
4. Can we locate genes for complex diseases/traits?
5. How does the gene control risk of
   disease?

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Extending basic questions in genetic
epidemiology(Burton et al. 2005 Lancet
366941-951)
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Useful References from Lancet 2005

1. Sharp D. Genetic epidemiology strengths,
   weaknesses, and opportunities. Lancet 366880,
   2005.
2. Burton P, Tobin MD, Hopper JL. Genetic
   Epidemiology 1 Key concepts in genetic
   epidemiology. Lancet 366941-951, 2005.
3. Teare MD, Barrett JH. Genetic Epidemiology 2
   Genetic linkage studies. Lancet 3661036-1044,
   2005.
4. Cordell HJ, Clayton DG. Genetic Epidemiology 3
   Genetic association studies. Lancet
   3661121-1131, 2005.
5. Palmer LJ, Cardon LR. Genetic Epidemiology 4
   Shaking the tree mapping complex disease genes
   with linkage disequilibrium. Lancet
   3661223-1234, 2005.
6. Hattersley AT, McCarthy MI. Genetic Epidemiology
   5 What makes a good association study? Lancet
   3661315-1323, 2005.
7. Hopper JL, Bishop DT, Easton DF. Genetic
   Epidemiology 6 Population-based family studies
   in genetic epidemiology. Lancet 3661397-1406,
   2005.
8. Davey-Smith G, Ebrahim S, Lewis S, Hansell AL,
   Palmer LJ, Burton PR. Genetic Epidemiology 7
   Genetic epidemiology and public health hope,
   hype, and future prospects. Lancet 3661484-1498,
   2005.

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Study designs for central questions
Does the disease cluster in families? Case-control studies familial correlation
Is this due to genes or shared environments? Heritability variance components
What is the best model of inheritance? 3. Segregation analysis
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Study design for central questions (contd)
4. Can we locate causal genes for complex diseases? Linkage analysis linkage disequilibrium studies
How does the gene control risk? Gene-environment interaction
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Nature is not linear
Case-Control Designs
Familial Correlation/ Aggregation
Gene-Environment Interaction
Linkage Analysis Linkage Disequilibrium
Heritability/Variance Components
Segregation Analysis
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Different levels of study

• Population comparisons (ecological studies) can
  suggest a role for genes
• Cohorts of individuals or complete populations
• Case-control studies can be used to test how
  genes affect risk
• information about genetic risk factors in complex
  diseases may reflect a direct/indirect effect on
  risk
• test for interactions or heterogeneity
• Family studies are generally more informative
  about genetic mechanisms, but you must know how
  families were sampled

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Study designs can ( do) overlap
1. Population Studies Ecological
comparisons Migrant studies Admixture
2. Case-control studies Case only Case-unrelated
control
3. Family Studies Twins sibs Nuclear families
Pedigrees Linkage studies in multiplex families
Case-related control Family based association
Outcrossing studies Genealogic registries Adoption
registries
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1. Population based designs

• Population comparisons (ecological design)
• Migrant studies
• Do people who move from a low risk environment to
  a high risk environment change their risk?
• Consider issues of self-selection, assimilation,
  etc.
• Admixture studies
• Does disease risk parallel genetic admixture (
  of genes of distinct ancestry)?
• Admixture is only estimated
• Human populations are not constant
• Vital records can be an important resource,
  especially birth defects disease registries
• Does risk of disease change among offspring of
  incross vs. outcross matings?

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2. Case-Control Designs

• Case-unrelated control can identify genetic risk
  factors
• Genetic index (e.g. inbreeding)
• Genetic marker
• Genetic marker can be a risk factor due to
• Direct effect of marker in causal pathway
• Indirect effect due to linkage disequilibrium
  (LD)
• association between a high risk allele at an
  unobserved causal gene observed marker allele

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2. Case-control designs (contd)

• Conventional case-control design
• Representative sample from case control
  populations
• Tests for difference in allele or genotypic
  frequencies
• Problems with confounding (population
  stratification)
• Case-related control design
• Representative sample of cases their unaffected
  sibs (or cousins)
• Minimize chances of confounding
• Overmatched for genetic background less
  statistical power
• Can test for linkage directly

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Variations on case-control design

• Incomplete case-controls designs can test for
  Gene-Environment interaction (GxE)
• Case-only designs
• Incomplete variations (G E on cases, only E on
  controls, etc.)
• Family based controls
• Create controls from parental mating type
• Under Ho, marker alleles are transmitted to case
  as often as not
• Rejecting Ho implies linkage linkage
  disequilibrium (association)
• Simplex families can now contribute to tests for
  linkage

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3. Family Designs

• Family designs
• Fixed sets of relatives
• Twins
• Adoption studies
• Nuclear families (parents offspring)
• Pedigrees of arbitrary structure

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Families come in different shapes sizes

• Sample fixed sets of relatives
• Adoption studies address fundamental questions
  about genes vs. environment
• Adoptee, adoptive parents, biological parents,
  unrelated sibs in adoptive family
• Twins estimate heritability by comparing MZ DZ
  twins
• Affected sib pairs (parents) to test for linkage
  
• Are these representative of all families?

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Families come in different shapes sizes (contd)

• Sample nuclear families (parents offspring)
• Measure familial aggregation/correlation
• Fit models of inheritance
• Collect data on family history in extended
  families
• Expected risk of disease can be computed as
  (person-years at risk) (age specific risk)
• Requires good information on baseline incidence
  rates
• Expected number of cases (E) based on population
  risk per person-year
• Observed number of cases (O) typically by report
• Compute Family History Score as Poisson statistic

Observed
Expected
See also Silberberg et al (1999 GENET EPI
16344-355)
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Family History Scores

• Summarize familial risk in families ascertained
  through probands (cases/controls)
• Kerber (1995 GENET EPI 12291-301) Breast cancer
  cases controls drawn from the Utah Population
  Data Base
• Can be used to identify highest risk families
• Schwartz et al (1988 AM J EPI 128524-535) Cancer
  risk in families of cases drawn from a cancer
  registry
• Can be useful for public health

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Public Health uses for family history
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CDC resources www.cdc.gov/genomics
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CDC resources
Another CDC resource
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CDC resourceshttp//hugenavigator.net/
Search by gene or phenotype
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If you sample families in a representative
manner,

• Quantitative traits or a common qualitative
  phenotype can be used to
• Estimate heritability (h2) or
• Find best fitting model of inheritance
  (segregation analysis)
• If genetic markers are available, these families
  can be used to
• Test for linkage to unobserved genes controlling
  qualitative phenotype
• Drs. Liang Xu will discuss this tomorrow
• Search for quantitative traits loci (QTL) that
  control quantitative phenotypes

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Family studies representative sampling (contd)

• Joint models for segregation analysis linkage
  are feasible
• Linkage analysis is still limited to families
  informative for meiosis
• Multiplex families with gt1 affected
• Simplex families have only 1 affected member
• Linkage will always reflect a subset of all
  families
• Heterogeneity between simplex multiplex
  families should be considered

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Families ascertained through proband

• Proband (typically affected) brings the rest of
  family into the study
• Segregation analysis can identify the best
  model of inheritance if ascertainment is
  considered
• Models have many parameters to estimate
• Even so they may not completely correct
• Families vary considerably in information
  content
• Correcting for ascertainment bias is necessary

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Linkage vs. Association

• Requires multiplex families
• Bigger is better
• Guaranteed to work for Mendelian diseases
• Genome wide studies are feasible
• Still useful for complex diseases
• Locus heterogeneity (linked unlinked families)
  is a problem
• Meta-analysis may strengthen evidence but
  narrowing peaks is still hard

• Unrelated cases controls can be used
• Can incorporate tests for G, E, GxE, GxG, etc.
• Meta-analysis can measure consistency across
  studies
• Or lack thereof
• Allelic heterogeneity is a problem
• Different high risk alleles
• Genome wide studies are now feasible (but
  expensive)
• Interpreting them is a challenge

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Genes as risk factors

• Epidemiology study designs treat genetic markers
  as a risk factor
• Test Ho Genotype (G) is independent of risk,
  P(case)
• Odds ratio (OR) measures association between
  marker risk of disease
• OR(caseG)(AD)/(CB)
• Dr. Liang will discuss this

Case Control
G A B
G- C D
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What can you do with a genetic risk factor?

• Are genes just inherited risk factors?
• How can you use genetic risk factors in public
  health?
• Causal mutations can be used to screen
• Women at high risk of breast cancer for BRCA1 2
  mutants
• Couples at risk of having CF child
• Linked markers can be used for genetic counseling
  or mapping
• Genetic markers that are true risk factors can be
  used in screening
• But you must be confident in the estimated risks
• e4 for Alzheimiers Diseae?
• Is there an intervention?
• These may depend on population or environment

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Big PicturePublic Health Genetics is
different from Genetic Epidemiology

• Public health genetics is broader than genetic
  epidemiology
• Application vs. Research
• Screening, intervention, treatment are part of
  public health genetics
• Policy is key part of public health genetics

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Public Health Genetics (contd)

• Deals with both Mendelian complex diseases
• Mendelian diseases in the aggregate are a major
  public health burden
• Screening the population can identify high risk
  individuals or groups
• Screening for complex diseases will be more
  demanding will require greater efforts to
  validate estimates of risk

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Trends in science Genomic Medicine Human
Genome Epidemiology

• Khoury, Little Burke (2004) Human Genome
  Epidemiology Oxford Univ Press
• Recent advances in genetics hold considerable
  promise for medicine public health
• Many reports of genes for common diseases, few
  are consistent
• There is some hype involved
• What to do with new information as it emerges?
• How to validate them?
• How to act on them?

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Trends in science (contd)

• Genomic medicine could predict risk of common
  diseases based on genotypes
• Genetic vs genomic ?One gene vs. many genes
• Pharmacogenomics could tailor pharmaceutical
  treatment based on genotypes
• Both require solid epidemiologic data to generate
  confirm predictive value of genotype on risk
• This requires many studies, not one
• This may vary among populations
• This may depend on environment

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Continuum from gene discovery to disease
prevention (Khoury et al, 2004)
Generalized intervention
Assessing impact of Genes on health
Developing evaluating Intervention
Integrating evidence
Gene discovery
Genotype specific Intervention (screening,
treatment, etc.)
Biology of gene?disease
Shaded box denotes steps where good epidemiologic
approach is critical
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Summary of introduction

• Genetic epidemiology is a wide ranging scientific
  discipline
• Focus on identifying genes involved in complex
  diseases
• Variety of study designs are used
• Variety of statistical methods are available
• Complex diseases are complex
• Nature has many surprises awaiting us