Transcript Pitfalls in Genetic Association Studies [M.Tevfik DORAK]

Pitfalls in Genetic
Association Studies
M. Tevfik DORAK
Paediatric and Lifecourse Epidemiology Research Group
Sir James Spence Institute
Newcastle University, U.K.
Clinical Studies & Objective Medicine
Bodrum, 15-16 April 2006
Incident or prevalent cases
Comparable controls or convenience samples
Population based?
Confounding by ethnicity / Population stratification
Confounding by locus / Linkage disequilibrium
Statistical power
Multiple comparisons
No adjustment for known associations
Effect modification by sex
Different genetic models
Overinterpretation
LD ruled out?
Biological plausibility (a priori hypothesis)?
Replication / Consistency
Publication bias
Tabor, Risch & Myers. Nat Rev Genet 2002
(www)
Internal Validity of an Association Study
Avoid "BIAS"
Control for "CONFOUNDING"
Rule out "CHANCE"
Internal Validity of an Association Study
Avoid "BIAS"
Be careful!
Control for "CONFOUNDING"
Matching, Stratification, MV Analysis
Rule out "CHANCE"
Large sample, Replication
Special Kinds of Confounding in
Genetic Epidemiology
CONFOUNDING by Locus (LD)
MV Analysis, LD Analysis
CONFOUNDING by Ethnicity
(Population Stratification)
Matching, Stratification, MV Analysis
Family-Based Association Studies
Genomic Controls and Specially Designed GE Analysis
Special Kinds of Confounding in
Genetic Epidemiology
CONFOUNDING by Locus (LD)
MV Analysis, LD Analysis
CONFOUNDING by Ethnicity
(Population Stratification)
Matching, Stratification, MV Analysis
Family-Based Association Studies
Genomic Controls and Specially Designed GE Analysis
Mapping Disease Susceptibility Genes by Association Studies
Plot of minus log of P value for case-control test for allelic association with AD, for SNPs immediately
surrounding APOE (<100 kb)
Martin, 2000 (www)
Example: Linkage Disequilibrium
HLA-B47 association with congenital adrenal
hyperplasia (Dupont et al, Lancet 1977)
HLA-B14 association with late-onset adrenal
hyperplasia (Pollack et al, Am J Hum Genet 1981)
Is congenital adrenal hyperplasia an immune
system-mediated disease?
Example: Linkage Disequilibrium
HLA-B47 association with congenital adrenal
hyperplasia is due to deletion of CYP21A2 on
HLA-B47DR7 haplotype
HLA-B14 association with late-onset adrenal
hyperplasia is due to an exon 7 missense
mutation (V281L) in CYP21A2 on HLA-B14DR1
haplotype
Preliminary evidence of an association between HLADPB1*0201 and childhood common ALL supports an
infectious aetiology
Leukemia 1995;9(3):440-3
Evidence that an HLA-DQA1-DQB1 haplotype influences
susceptibility to childhood common ALL in boys provides
further support for an infection-related aetiology
Br J Cancer 1998;78(5):561-5
Why not LD?
Publication Bias
Negative studies do not get published NIH Genetic Associations Database ?
A different kind of publication bias?
Preliminary evidence of an association between HLADPB1*0201 and childhood common ALL supports an
infectious aetiology
Leukemia 1995;9(3):440-3
Evidence that an HLA-DQA1-DQB1 haplotype influences
susceptibility to childhood common ALL in boys provides
further support for an infection-related aetiology
Br J Cancer 1998;78(5):561-5
Special Kinds of Confounding in
Genetic Epidemiology
CONFOUNDING by Locus (LD)
MV Analysis, LD Analysis
CONFOUNDING by Ethnicity
(Population Stratification)
Matching, Stratification, MV Analysis
Family-Based Association Studies
Genomic Controls and Specially Designed GE Analysis
Wacholder, 2002 (www)
Population Stratification
Marchini, 2004 (www)
Cardon & Palmer, 2003 (www)
Internal Validity of an Association Study
Avoid "BIAS"
Be careful!
Control for "CONFOUNDING"
Matching, Stratification, MV Analysis
Rule out "CHANCE"
Large sample, Replication
Example: Functional Correlation
Example: Replication
HFE-C282Y Association in Childhood ALL
30
%
25
%
Patients
Controls
50
45
40
Patients
Controls
35
20
30
15
25
20
10
15
10
5
5
0
0
All patients
Males Only
Males (cALL)
All patients
Males Only
Males (cALL)
WELSH GROUP
SCOTTISH GROUP
117 patients - 415 newborns
P = 0.005; OR = 2.8 (1.4 to 5.4)
In cALL: P = 0.02; OR = 2.9 (1.4 to 6.4)
135 patients - 238 newborns
P = 0.0004; OR = 3.0 (1.7 to 5.4)
In cALL: P < 0.0001; OR = 4.7 (2.5 to 8.9)
Dorak et al, Blood 1999
Palmer LJ. Webcast
(www)
Multiple Comparisons & Spurious Associations
Diepstra, Lancet 2005 (www)
Genetic Models and
Case-Control Association Data Analysis
The data may also be analysed assuming a prespecified
genetic model. For example, with the hypothesis that
carrying allele B increased risk of disease (dominant
model), the AB and BB genotypes are pooled giving a
2x3x2 table. This is particularly relevant when allele B is
rare, with few BB observations in cases and controls.
Alternatively, under a recessive model for allele B, cells AA
and AB would be pooled. Analysing by alleles provides an
alternative perspective for case control data. This breaks
down genotypes to compare the total number of A and B
alleles in cases and controls, regardless of the genotypes
from which these alleles are constructed. This analysis is
counter-intuitive, since alleles do not act independently, but
it provides the most powerful method of testing under a
multiplicative genetic model, where risk of developing a
disease increases by a factor r for each B allele carried: risk
r for genotype AB and r2 for genotype BB. If a multiplicative
genetic model is appropriate, both case and control
genotypes will be in Hardy–Weinberg equilibrium, and this
can be tested for. A fourth possible genetic model is
additive, with an increased disease risk of r for AB
genotypes, and 2r for BB genotypes. This model shows a
clear trend of an increased number of AB and BB
genotypes, with the risk for AB genotypes approximately
half that for BB genotypes. The additive genetic model can
be tested for using Armitage’s test for trend.
Lewis CM. Brief Bioinform 2002 (www)
HLA-DRB4 Association in Childhood ALL
40
25
%
%
*
Patients
30
Controls
Boys, n=64
Patients
20
15
Controls
Girls, n=53
20
10
*
10
5
0
0
DRB5
DRB3
DRB4
DRB5
DRB3
DRB4
Homozygosity for HLA-DRB4 family is associated with susceptibility to childhood ALL
in boys only (P < 0.0001, OR = 6.1, 95% CI = 2.9 to 12.6 )
Controls are an unselected group of local newborns (201 boys & 214 girls)
* Case-only analysis P = 0.002 (OR = 5.6; 95% CI = 1.8 to 17.6)
This association extends to a DRB4-HSP70 haplotype (OR = 8.3; 95% CI = 3.0 to 22.9)
This association has been replicated in Scotland and Turkey
HLA-DRB4 ASSOCIATION
ADDITIVE MODEL
Linear Model
Logit estimates
Number of obs
=
265
LR chi2(1)
=
14.24
Prob > chi2
=
0.0002
Log likelihood = -139.37794
Pseudo R2
=
0.0486
-----------------------------------------------------------------------------caco | Common Odds Ratio
Std. Err.
z
P>|z|
[95% CI]
-------------+---------------------------------------------------------------drb4add |
2.208651
.4734163
3.70 0.000
1.45103 - 3.36186
------------------------------------------------------------------------------
Heterozygosity and Homozygosity
Logit estimates
Number of obs
=
265
LR chi2(2)
=
22.00
Prob > chi2
=
0.0000
Log likelihood = -135.49623
Pseudo R2
=
0.0751
-----------------------------------------------------------------------------caco | Odds Ratio
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------Wild-type
|
1.00 (ref)
Heterozygosity |
1.060652
.3557426
0.18
0.861
.549642
2.04676
Homozygosity
|
6.258503
2.65464
4.32
0.000
2.72534
14.37211
------------------------------------------------------------------------------
HLA-DRB4 - HSPA1B HAPLOTYPE ASSOCIATION
EFFECT MODIFICATION
Logit estimates
Number of obs
=
532
LR chi2(3)
=
23.97
Prob > chi2
=
0.0000
Log likelihood = -268.27826
Pseudo R2
=
0.0428
-----------------------------------------------------------------------------caco |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------sex | -.0299037
.2229554
-0.13
0.893
-.4668883
.4070808
hsp53 |
2.530033
.5929603
4.27
0.000
1.367852
3.692214
_IsexXhsp5~2 | -2.758189
.8812645
-3.13
0.002
-4.485436
-1.030943
_cons | -1.321474
.3517969
-3.76
0.000
-2.010984
-.6319651
------------------------------------------------------------------------------
CONFOUNDING BY SEX
Logit estimates
Number of obs
=
532
LR chi2(2)
=
11.99
Prob > chi2
=
0.0025
Log likelihood = -274.26995
Pseudo R2
=
0.0214
-----------------------------------------------------------------------------caco | Odds Ratio
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------hsp53 |
3.32777
1.191429
3.36
0.001
1.649684
6.712832
sex |
.7693041
.1636106
-1.23
0.218
.5070725
1.167148
-----------------------------------------------------------------------------Adjusted for sex?
HLA-DRB4 - HSPA1B HAPLOTYPE ASSOCIATION
BOYS ONLY
Logit estimates
Number of obs
=
265
LR chi2(1)
=
22.41
Prob > chi2
=
0.0000
Log likelihood = -135.29119
Pseudo R2
=
0.0765
-----------------------------------------------------------------------------caco | Odds Ratio
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------hsp53 |
12.55392
7.444028
4.27
0.000
3.926876
40.13392
------------------------------------------------------------------------------
GIRLS ONLY
Logit estimates
Number of obs
=
267
LR chi2(1)
=
0.13
Prob > chi2
=
0.7205
Log likelihood = -132.98706
Pseudo R2
=
0.0005
-----------------------------------------------------------------------------caco | Odds Ratio
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------hsp53 |
0.796
.5189439
-0.35
0.726
.22181
2.856571
------------------------------------------------------------------------------
The association is modified by sex
Cardon & Bell. Nat Rev Genet 2001
(www)
Current Criteria for Good Association Studies
Cardon & Bell. Nat Rev Genet 2001
(www)
Statistical checklist for genetic association studies
- In a case-control study:
Cases and controls derive from the same study base
There are more controls than cases (up to 5-to-1, for increased statistical power)
There are at least 100 cases and 100 controls
- Statistical power calculations are presented
- Hardy-Weinberg equilibrium (HWE) is checked and appropriate tests are used
- If HWE is violated, allelic association tests are not used
- Possible genotyping errors and counter-measures are discussed
- All statistical tests are two-tailed
- Alternative genetic models of association considered
- The choice of marker/allele/genotype frequency (for comparisons) is justified
- For HLA associations, a global test for association (G-test, RxC exact test) for each locus is used
(if necessary, with correction for multiple testing)
- Chi-squared and Fisher tests are NOT used interchangeably
- P values are presented without spurious accuracy (with two decimal places)
- Strength of association has been measured (usually odds ratio and its 95% CI)
- In a retrospective case-control study, ORs are presented (as opposed to RRs)
- Multiple comparisons issue is handled appropriately (this does not necessarily mean Bonferroni
corrections)
- Alternative explanations for the observed associations (chance, bias, confounding) are
discussed
http://www.dorak.info/hla/stat.html
Multifactorial Etiology
ROCHE Genetic Education (www)
Models of gene–environment interactions
Hunter, 2005 (www)
Generating Protein Diversity from the 'Small' Genome
Banks, 2000 (www)
Generating Protein Diversity from the 'Small' Genome
Alternative Splicing Can Generate Very Large Numbers of
Related Proteins From a Single Gene
Most extreme example is the Drosophila Dscam Gene:
12 x 48 x 33 x 2 = 38,016 alternative splice variants
Black, Cell 2000 (www)
Wojtowicz, Cell 2004 (www)
DSCAM = Down syndrome cell adhesion molecule
Generating Protein Diversity from the 'Small' Genome
Alternative Splicing Can be Tissue or Cell-Specific
Lodish et al. Molecular Cell Biology, 5th Ed, WH Freeman (www)
Generating Protein Diversity from the 'Small' Genome
mRNA editing (base modification) is a different
mechanism of alternative splicing
Lodish et al. Molecular Cell Biology, 5th Ed, WH Freeman (www)
OMIM 107730 (www)
Chen & Chan, 1996 (www)
Wedekind, 2003 (www)
RNA Editing in The Cell – NCBI Online (www)
Integration of proteomics in genetic
epidemiology studies would eliminate a lot of
obstacles arising from the following
Only <2% of the genome is protein-coding and most sequence
variants are silent changes
Even genome-wide sequence variant studies cannot identify
the genomic counterparts of 1.5 million proteins
Epigenetic changes, alternative transcription/splicing and
posttranslational modifications cannot be predicted by study of
sequence variants
Genomic DNA studies does not take into account selective
expression of genes in certain cell or tissues
No genetic association is complete without demonstration of
the functional relevance
50s Rule
Genome - 1:50 coding
Gene:Protein - 1:50 ratio
Overall efficiency of pure genomic studies
1:2500
http://www.dorak.info