Introduction to Genetic Epidemiology - McGill …Introduction to Genetic Epidemiology Erwin Schurr...
Transcript of Introduction to Genetic Epidemiology - McGill …Introduction to Genetic Epidemiology Erwin Schurr...
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Introduction to Genetic Epidemiology
Erwin Schurr McGill International TB Centre
McGill University
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Phenotype Rare (very severe forms)
Common (infection/affection status)
Sample Small Large
Causality monogenic complex
Main tools Mendelian Genetics Genetic Epidemiology
Methods of investigation in humans
Rare mutation Strong effect
Common polymorphism Modest effect
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• In contrast to monogenic disease
• Complex trait :
– Environmental factors – Genetic factors
• major gene • other genes
• Examples: – Cancers – Cardiovascular diseases – Neurological diseases – Infectious diseases …
Complex phenotypes
gene*environment interactions
gene*gene interactions
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Do genetic factors play a role ? ⇒ Epidemiological observations / Experimental model
What is their nature ? ⇒ Segregation analysis
What is their chromosomal location ? ⇒ Linkage analysis Which allelic variant is implicated ?
⇒ Association studies What is its function ?
⇒ Functional studies
Genetic epidemiology: objectives / tools
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Sample # affected sibs
DNA Markers Main goal
Segregation analysis
Families 0 → n No - genetic model
Linkage analysis
Families 2 → n
Yes Microsat/SNPs
candidate regions
Association studies
Families
Cases/controls
1 → n
-
Yes
Yes
SNPs
SNPs
candidate alleles
candidate alleles
Genetic epidemiology: overview
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Spectrum of genetic predisposition
Casanova, Abel EMBO J 2007
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Annu
al m
otra
ility
rate
<1
1- 4
5 - 1
4
15 -
24
25 -
34
35 -
44
45 -
54
55 -
64
65 -
74
Age class in years
Interplay of age and genetics
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Do genetic factors play a role?
What is their nature?
Epidemiological observations
What is their chromosomal location?
Segregation analysis
What is the causal variant?
Linkage analysis
Association studies
What is the function?
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Family level – Twin studies
Share 50% of genetic background
Share 100% of genetic background
DZ TWINS MZ TWINS
2 fertilizations 1 fertilization
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+ - + A B
- C D
Twin 1
Twin 2
MZ Twins DZ Twins
+ - + A B
- C D
Twin 1
Concordance Rate = 2A / (2A + B + C)
Genetic contribution: CMZ vs. CDZ
Twin 2
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G E MZ
DZ
Concordance Discordance
Genetic contribution: CMZ > CDZ
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Do genetic factors play a role?
What is their nature?
Epidemiological observations
What is their chromosomal location?
Segregation analysis
What is the causal variant?
Linkage analysis
Association studies
What is the function?
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Do genetic factors play a role?
What is their nature?
Epidemiological observations
What is their chromosomal location?
Segregation analysis
What is the causal variant?
Linkage analysis
Association studies
What is the function?
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Model-based linkage analysis
• Need to specify the relation between the phenotype and the genotype – frequency of the disease allele – probability to be affected given genotype and risk factors
• Most powerful method IF the genetic model is correct
• Estimation of the recombination fraction θ between the ‘phenotype’ locus (to
locate) and the marker locus (known location)
• Linkage test = θ < 0.5 ?
• Example: schistosomiasis (infection intensities, severe hepatic fibrosis)
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Model-free linkage analysis
1,2 3,4
1,3 2,4 2,3 1,4 1,3
Alleles shared Probability 0 ¼ 1 ¼ 1 ¼ 2 ¼
RESOLUTION FOR GENE LOCALIZATION: ~ 10 million base pairs
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Linkage only look at few meiosis
Can we look at more … can we see dead people ?
Yes … by studying Linkage Disequilibrium
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Single Nucleotide Polymorphism
C T T A G C T T C T T A G T T T
Common SNP
C T T A G C T T C T T A G T T T
Rare SNP
94%
6%
99.9%
0.1%
In a population the same building block (=nucleotide) of DNA can occur in two alternative forms – i.e. at a given DNA position two different nucleotides (=alleles) can occur. If in a population the less frequent allele occurs with >2% we call it common variation.
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One binary phenotype
One candidate SNP in a candidate gene
One association study
Univariate analysis
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cases controls
AA c0 t0
AB c1 t1
BB c2 t2
Goodness-of-fit test = Chi-square 2 df
Genotypic analysis
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1. Formulate null (H0) and alternative (H1) hypothesis
2. Build a test statistic according to the data to come
3. Identify distribution of the test statistic under H0
4. Define a decision rule (i.e. type I error)
5. Make the experiment and compute the test statistic
6. Conclude, i.e. reject or not H0 and precise p-value
7. Interpret the conclusion
Hypothesis testing – general strategy
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Hypothesis testing – genetic association
1. H0 : cases = controls H1: cases ≠ controls
3. Under H0, χ² is distributed as a chi-square with 2 df
4. Type I error 5% ⇔ reject H0 if χ² > 5.99
5. χ² = 348
6. χ² > 5.99 therefore we reject H0 (p-value<0.001)
7. The genotypic distribution is significantly different in cases and in controls
2.
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cases controls
AA 200 500 700
AB 200 300 500
BB 600 200 200
1,000 1,000 2,000
Genotypic analysis
Expected AA cases = 1,000*700 / 2,000 =350
Expected AB cases = 1,000*500 / 2,000 =250
Etc …
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Goodness-of-fit test = Chi-square 2 df
X²=[(200-350)²/350] + [(200-250)²/250] + [(600-400)²/400] + [(500-350)²/350] + [(300-250)²/250] + [(200-400)²/400]
X²=348.5 with 2 df
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cases controls
AA c0 t0
AB c1 t1
BB c2 t2
Odds ratio AB vs. AA = c1*t0 / c0*t1
Genotypic analysis
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cases controls
AA 200 500
AB 200 300
BB 600 200
Genotypic analysis
Odds ratio AB vs. AA = 200*500 / 200*300=1.66
Odds ratio BB vs. AA = 600*500 / 200*200=7.5
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cases controls
AA c0 t0
AB c1 t1
BB c2 t2
Estimate OR
Estimate OR
Optimize coding scheme
Genotypic analysis – general strategy
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cases controls
AA c0 t0
AB or BB c1+ c2 t1+t2
Goodness-of-fit test = chi-square 1 df
cases controls
AA+AB c0+ c1 t0+t1
BB c2 t2
Genotypic analysis – dominance effect
B dominant B recessive
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Example
cases controls OR P-value
AA 100 200 1.00
AB+BB 200 100 4.00 <0.001
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Interpretation ?
Type I error
Allele B ⇒ phenotype = B is the causal allele
Allele B is in linkage disequilibrium with the causal allele
Genetic linkage between disease locus and marker locus
PLUS Allele B is preferentially associated with the causal allele
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Linkage is a relation between loci
Linkage disequilibrium is a relation between alleles
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Descriptors of Linkage Disequilibrium
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Linkage equilibrium (expected for distant loci)
PAB = PAPB ≠ PAPB
PAb = PAPb ≠ PAPb
PaB = PaPB ≠ PaPB
Pab = PaPb ≠ PaPb
Linkage disequilibrium (expected for nearby loci)
DAB = PAB – PAPB
D’
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DAB = PAB – PAPB
Hardly allows comparisons
D’AB = DAB / Dmax
r²AB = D²AB / (PAPB PaPb)
sign is arbitrary range ∝ allele frequencies
Scaled version
Dmax = min (PAPb; PaPB)
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Inflated Type I Error: Stratification
Replicate the results in an independent sample/population
Incorporate genomic information in the analysis
genome records demography history
use genome to discover hidden structure
by looking at ‘null’ markers
Use familial controls = family-based association studies
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Allelic controls – TDT
M1 M2 M2 M2
M1 M2
Transmitted alleles vs. non-transmitted alleles
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Transmitted alleles vs. non-transmitted alleles
Non-Transmitted Allele Transmitted M1 M2
M1 n11 n12
M2 n21 n22
(n12 - n21)2
(n12 + n21) TDT = ~ χ² (1 df)
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Genome-wide association studies (GWAS)
“Study of the common genetic variation across the entire human genome designed to identify genetic association
with observable traits”
NIH 2006
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NLP
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Chromosome
GWAS in leprosy
Zhang et al N Engl J Med 2009;361:2609-18.
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GWAS in Infectious Diseases