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Epidemiology 217 Molecular and Genetic Epidemiology I

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Title: Epidemiology 217 Molecular and Genetic Epidemiology I


1
Epidemiology 217 Molecular and Genetic
Epidemiology I
  • Course Director
  • John Witte
  • Professor of Epidemiology Biostatistics

2
Course Goals
  • Develop a framework for interpreting and
    incorporating genetic information in your
    research
  • Learn
  • Common genetic measures
  • Approaches to search for disease-causing genes,
    and their interaction with environmental factors
  • Pharmacogenomics
  • Genetic testing, ethics.

3
Course Details
  • Class meets for 10 Tuesdays, 115-245 pm
  • Course Director John Witte (jwitte_at_ucsf.edu)
  • Co-Director Eric Jorgenson (eric.jorgenson_at_ucsf.e
    du)
  • Lecturers Neil Risch, Bernie Lo
  • website
  • www.epibiostat.ucsf.edu/courses/schedule/mol_metho
    dsi.html
  • (Lectures, homework assignments keys, readings)

4
Assignments
  • Four problem sets
  • 15 of grade for each (60 total)
  • Due at noon on Mondays to eric.jorgenson_at_ucsf.edu
  • Reading assignments / class participation
  • 10 of grade
  • Final project (design study)
  • 30 of grade (due Monday 3/8 at Noon)

5
Date Topic / Content Lecturer Reading Assignment
1/5 Introduction Is the Disease Genetic? J Witte
1/12 Overview of Fine Mapping and Testing J Witte Lancet 1 1
1/19 Molecular and Genetic Measures J Wiemels JAMA 1
1/26 Genome-Wide Studies Linkage E Jorgenson Lancet 2 2
2/2 Association Studies Direct J Witte Lancet 3
2/9 Association Studies Indirect J Witte Lancet 4 3
2/16 Admixture and Stratification N Risch JAMA 2
2/23 Pharmacogenomics E Jorgenson JAMA 3 5
3/2 Genetic Testing Ethical Issues B Lo Lancet 7
3/9 Putting it all Together J Witte Final Project
6
Resources
  • Videos from UAB Short course on statistical
    genetics http//www.soph.uab.edu/ssg_content.asp?
    id1174
  • Doraks notes on genetics http//dorakmt.tripod.c
    om/genetics/
  • Strachan Reads Human Molecular Genetics
  • http//www.ncbi.nlm.nih.gov/books/bv.fcgi?ridhmg

7
TICR Professional Conduct Statement
  • I will
  • maintain the highest standards of academic
    honesty
  • neither give nor receive aid in examinations or
    assignments unless such cooperation is expressly
    permitted by the instructor
  • conduct research in an unbiased manner, reports
    results truthfully, and credit ideas developed
    and work done by others
  • not use answer keys from prior years
  • write answers in my own words, and, when
    collaboration is permitted, acknowledge
    collaborators when answers are jointly formulated

8
Molecular Genetic Epidemiology
  • Distinction
  • Molecular measurement of non-genetic factors in
    biospecimens (e.g., selenium in toenails)
  • Genetic role of inherited factors in disease
  • Focus of course
  • Genetic epidemiology
  • Initially studied single gene disorders
  • Now more complex genetic disorders and
    environment
  • Many designs same as epidemiology (e.g.,
    case-control)
  • Some specialized analysis methods.
  • Population genetics increasingly important
  • Aims
  • Detect genetic causes of disease
  • Understand biological process
  • Prevention strategies, lifestyle intervention
  • Improved therapeutic strategies, personalized
    medicine

9
DNA
10
Human Chromosomes
11
Human Chromosome 21
Telomeres Centromere p petit arm q queue
(tail) or long arm 21q22.1 is pronounced
twenty-one q two two point one
12
Chromosome Bands
  • Stain chromosomes so they can be seen by
    microscope
  • e.g., Giesma.
  • Appear as alternating bands
  • e.g., dark/G-band and light band.
  • With low resolution, few bands seen
  • p2, p1 centromere q1, q2, (count out from
    centromere).
  • With higher resolution sub bands seen
  • p12, p11 centromere q11, q12

13
Variation in Genome
  • Mutation
  • When event first occurs in an individual
  • genetic change due to internal events (e.g., copy
    errors during cell division) or external agents
    (e.g., radiation, mutagens).
  • Can end with one generation, or be passed on
    (germline mutations)
  • But frequency remains lt 1 in a given population
  • Polymorphism
  • Means "many forms
  • Frequency gt 1
  • Generated by old mutations.
  • Common versus rare

14
Single Nucleotide Polymorphism (SNPs)
  • Change a single DNA letter
  • Most frequent genetic variant
  • 1/1300 base pairs
  • 10 million common SNPs (gt 1- 5 MAF) - 1/300 bp

15
Genotypes
Each somatic cell is diploid (two copies of each
autosome) Thus, 3 genotypes at locus 4
16
Types of Variants
  • Noncoding
  • Coding
  • Synonymous no change in amino acid
  • Nonsynonymous/nonsense change to stop codon
  • Nonsynonymous/missense change amino acid
  • MTHFR C677T SNP
  • Normal (wild-type) allele
  • Gene sequence ..GCG GGA GCC
    GAT
  • Protein Sequence Ala Gly Ala
    Asp
  • Variant allele
  • Gene Sequence ..GCG GGA GTC
    GAT.
  • Protein Sequence Ala Gly Val Asp
    ..

17
Human Genome Statistics (old)
  • 3,253,037,807 basepairs
  • SNPs 11,772,162
  • 21,667 known genes
  • 1,040 pseudogenes (defunct genes)
  • 269,405 exons
  • Mutation rate 10-8 per bp per generation
  • In each person
  • 65 new mutations expected
  • 1 variant per 1,331 basepairs
  • 2,444,055 variants
  • Most variants are old

http//www.ensembl.org/Homo_sapiens
18
Process of Genetic Epidemiology
Defining the Phenotype
Migrant Studies
Familial Aggregation
Segregation
Association Studies
Linkage Analysis
Cloning
Fine Mapping
Characterization
19
First Define the Phenotype!
Gleason DF. In Urologic Pathology The Prostate.
1977 171-198.
20
Migrant Studies
Weeks, Population. 1999
21
Example Standardized Mortality Ratios
Japanese
Cancer Site Japan Not US Born US Born US Caucasians
Stomach (M) 100 72 38 17
Colorectal (F) 100 218 209 483
Breast 100 166 136 591
MacMahon B, Pugh TF. Epidemiology. 1970178.
22
Familial Aggregation
  • Does the phenotype tend to run in families?

23
Recurrence Risks
  • lR kR/k,
  • kR is the risk to relatives of type R
  • k is the population risk
  • lS recurrence risk to siblings of probands
    versus the general population risk.
  • The higher the value of l, the stronger the
    genetic effect

24
Examples of ?s
  • Alzheimer Disease 3-4
  • Rheumatoid Arthritis 12
  • Schizophrenia 13
  • Type I Diabetes 15
  • Multiple Sclerosis 20-30
  • Neural Tube Defects 25-50
  • Autism 75-150

25
Familial Aggregation
  • One can calculate the familial risk ratio (FRR)
    from observational data.
  • With case-control data, calculate FRR as
  • cases among relatives of the cases (observed) /
  • cases among relatives of controls (expected)
  • For first-degree relatives, FRR equals l1.

26
Familial Aggregation Familial Risk Ratios (FRR)
Utah
Sweden
Site FRR
Prostate 2.21 2.21
Breast 1.83 1.83
Colorectal 2.54 2.54
Lung 2.55 2.55
Uterine 1.32 1.32
Melanoma 2.10 2.10
Bladder 1.53 1.53
Non-Hodgkins lymphoma 1.68 1.68
Brain/CNS 1.97 1.97
Risch CEBP 200110733-741

27
FRR versus GRR
  • How well does FRR estimate the genetic risk
    ratio?
  • FRR
  • Pr(DisFH) / Pr(Dis.FH-) ? Pr(DisG) /
    Pr(DisG-)
  • GRR

28
Twin Studies
  • Compare the disease concordance rates of MZ
    (identical) and DZ (fraternal) twins.
  • Then one can estimate heritability of a
    phenotype.

Twin 1
29
MZ Twins (Identical)
Twin 1
Both alleles are shared identical by descent (IBD)
Twin 2
30
DZ Twins (Fraternal)
Twin 1
Twin 2 any of the four IBD can be 2, 1, or 0
2 1 1 0
31
DZ Twins (Fraternal)
Twin 1
Average sharing is 50
100 50 50 0
32
IBD Sharing
  • of alleles shared IBD
  • 2 1 0
  • Pr(2) Pr(1) Pr(0) Prop IBD
  • Relationship
  • Self, MZ twins 1 0 0 1
  • Parent, Offspring 0 1 0 1/2
  • Full siblings 1/4 1/2 1/4 1/2
  • Gr-child, Gr-prt 0 1/4 3/4 1/4
  • First cousins 0 1/4 3/4 1/8
  • Proportion of alleles shared IBD alleles x
    Pr( alleles) / 2

33
Analysis of Twin Studies
  • Compare the disease concordance rates of MZ
    (identical) and DZ (fraternal) twins.

Twin 1
Disease Yes No
Yes A B
No C D
Concordance 2A/(2ABC)
Twin 2
Then one can estimate heritability of a
phenotype.
34
Example of Twin Study PCa
  • Twin registry (Sweden, Denmark, and Finland)
  • 7,231 MZ and 13,769 DZ Twins (male)

Twin Concordant pairs (A) Discordant pairs (BC) Concordance
MZ 40 299 0.21
DZ 20 584 0.06
Heritability 0.42 (0.29-0.50) Non-shared
Environment 0.58 (0.50-0.67) Lichtenstein et al
NEJM 2000 1334378-85.
35
Models of Genetic Susceptibility
  • Study families.
  • Estimate mode of inheritance what type of
    genetic variant might be causal.
  • Determine whether the disease appears to follow
    particular patterns across generations.
  • Estimate whether variants are rare or common, etc.

36
Segregation Harry Potters Pedigree
Muggle
Wizard / Witch
Lily Evans
James Potter
Vernon Dursley
Petunia Dursley
Harry Potter
Dudley Dursley
37
Segregation Analysis
  • What is the best model of inheritance for
    observed families?
  • Dominant
  • Recessive
  • Additive
  • Disease allele frequency?
  • Magnitude of risk?
  • Fit formal genetic models to data on disease
    phenotypes of family members.
  • The parameters of the model are generally fitted
    finding the values that maximize the probability
    (likelihood) of the observed data.
  • This information is useful in parametric linkage
    analysis, which assumes a defined model of
    inheritance.

38
Assignment 1
  • Due by Noon on Monday (1/11/10) to
    eric.jorgenson_at_ucsf.edu
  • 1) Complete Reading 1 Genetic Epidemiology 1
    Key concepts in genetic epidemiology. Burton PR,
    Tobin MD, Hopper JL. Lancet.
  • 2) Select disease you are interested in and
  • Define its clinical characteristics
  • Determine support for a genetic basis by
    searching pubmed for disease and major terms
    covered in first lecture. Note of articles and
    glance at some abstracts to qualitatively assess
    the support.
  • Search term Articles Support - Support
  • Genetic Epidemiology
  • Migrant Study
  • Familial Aggregation
  • Twin Studies
  • Segregation Analysis
  • Based on the above table, do you feel there is a
    genetic basis for this disease?
  • Is this for all (homogeneous) groupings of the
    disease, or particular subtypes?

39
Process of Genetic Epidemiology
Defining the Phenotype
Migrant Studies
Familial Aggregation
Segregation
Association Studies
Linkage Analysis
Cloning
Fine Mapping
Characterization
40
Linkage Harry Potters Pedigree
Measure co-segregation in pedigree Based on
detection of recombination events (meiosis)
Muggle
Wizard / Witch
Lily Evans
James Potter
Vernon Dursley
Petunia Dursley
or
Harry Potter
Dudley Dursley
or
41
Affected sib-pair Linkage
D
M1
M2
D
D
M1
M1
M2
42
Association Studies
ROCHE Genetic Education (www)
43
Linkage Disequilibrium
Hirschhorn Daly, Nat Rev Genet 2005
44
Genome-wide Association Studies (GWAS)
Altshuler Clark, Science 2005 3071052-3.
45
Multi-stage Study Designs
Hirschhorn Daly, Nat Rev Genet 2005
46
Admixture Mapping
  • Potentially powerful approach to searching for
    disease-causing genes
  • Requires
  • Two populations with naturally occurring
    phenotypic and genetic differences.
  • Recent gene flow between the populations (e.g.,
    within 10 generations).
  • Markers in the vicinity of the trait locus will
    also show excess ancestry from the population
    with the higher allele frequency

47
Admixture Mapping
Nature Genetics 37, 118 - 119 (2005)
48
Summary of Main Mapping Approaches
Linkage Analysis Admixture Mapping Admixture Mapping Association Analysis
Power Low Low Low High High
SNPs required for scan Low Low Low High Low
Sensitivity to genetic heterogeneity Low Low Low High Moderate
Mapping resolution Poor Poor Poor Good Intermediate





Nature Genetics 37, 118 - 119 (2005)
49
Cloning a Gene
  • Showing that it is clearly causal for disease.
  • Generally requires experiments beyond those
    undertaken by a genetic epidemiologist.

50
Characterization
  • Once genes are identified, molecular methods are
    used to determine the structure of the gene,
    identification of regulatory elements, etc.
  • Use epidemiologic studies to distinguish public
    health implications
  • Determine frequencies of causal alleles and
  • Characterize their effectsand interacting
    environmental factorson disease rates.

51
Pharmacogenomics
  • Determining how drug efficacy and toxicity are
    affected by genes.
  • Goal base prescription on genetic variants.
  • Example warfarin dose

52
Genetic Testing?
53
Large RR ? Good Prediction
Witte, Nat Rev Genet, 2009
54
Genetic Testing Based on GWAS?
  • Multiple companies marketing direct to consumer
    genetic test kits.
  • Send in spit.
  • Array technology (Illumina / Affymetrix).
  • Many results based on GWAS.
  • Companies
  • 23andMe
  • deCODEme
  • Navigenics

55
(No Transcript)
56
Test to Play
NY Times, 11/30/08
57
(No Transcript)
58
(No Transcript)
59
Taste Project
  • Strips coated with Phenylthiocarbamide (PTC, or
    phenylthiourea).
  • Bitter or tasteless, depending on variants in the
    taste receptor TAS2R.
  • What do you think your phenotype is?
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