How to review genetic
association studies
Lavinia Paternoster
3rd year PhD student
Outline
• Traditional meta-analyses
• Why are genetic studies unique?
• Methods
–
–
–
–
choosing a genetic model
Multiple testing
Overall association
Per-allele mean differences
• Other things to consider
Research Question
• Does having gene “X” increase the risk of
disease/trait “Y”?
• Same as:
• Does intervention “X” increase the risk of
outcome “Y”?
BUT……….
Traditional
meta-analysis
Input variable to be
tested
Intervention
Control
(or intervention 2)
Observe
Outcome to test
success of
intervention
Outcome 1
Outcome 2
Traditional
meta-analysis
Input variable to be
tested
Intervention
Control
e.g. beta-blockers
(or intervention 2)
Observe
Outcome to test
success of
intervention
Outcome 1
Outcome 2
e.g.
cardiovascular
disease
e.g. no
cardiovascular
disease
Traditional metaanalysis
Outcome 1
(e.g. CVD)
Outcome 2
(e.g. no CVD)
Intervention
(beta-blockers)
n
Control
n
n
Calculate relative risk (or odds ratio) for each study
Pool relative risks by using weighting methods
n
Beta-blockers &
cardiovascular disease
Traditional
meta-analysis
Variable to be
tested
Intervention
Control
Observe
Outcome to test
success of
intervention
Mean value of those
with intervention
Mean value of controls
Traditional
meta-analysis
Variable to be
tested
Intervention
Control
e.g. exercise
Observe
Outcome to test
success of
intervention
Mean value of those
with intervention
e.g. mean fatigue
scale value
Mean value of controls
e.g. mean fatigue
scale value
Edmonds et al. 2004. Exercise for chronic fatigue syndrome. Cochrane
Traditional metaanalysis
Intervention 1
(exercise)
control
n
n
Observations
(e.g. fatigue scale)
mean
sd
mean
Calculate mean difference (and 95%CI) for each study
Pool mean differences by using weighting methods
sd
Exercise & Fatigue
Genetic Associations
• The simplest mutation (a→b) gives 3
genotypes: aa, ab, bb
• Comparing 3 groups not 2
• Conventional meta-analysis methods not
suitable
Traditional
meta-analysis
Input variable to be
tested
Intervention
Control
e.g. beta-blockers
(or intervention 2)
Observe
Outcome to test
success of
intervention
Outcome 1
Outcome 2
e.g.
cardiovascular
disease
e.g. no
cardiovascular
disease
Traditional
meta-analysis
Input variable to be
tested
Genotype AA
Genotype AB
Genotype BB
Observe
Outcome to test
success of
intervention
Outcome 1
Outcome 2
e.g.
cardiovascular
disease
e.g. no
cardiovascular
disease
Traditional
meta-analysis
Variable to be
tested
Intervention
Control
Observe
Outcome to test
success of
intervention
Mean value of those
with intervention
Mean value of controls
Traditional
meta-analysis
Variable to be
tested
AA
AB
BB
Observe
Outcome to test
success of
intervention
Mean value of
those with
genotype AA
Mean value of
those with
genotype AB
Mean value of
those with
genotype BB
My Research
• Meta-analysis of
association between
Carotid intima-media
thickness and several
genes
• Here I’ll show MTHFR
example
CC / CT / TT
Data
CC
CT
TT
Methods in the
literature
• Collapse into 2 groups
– Assume genetic model
• Dominant (tt+ct v cc)
• Recessive (tt v ct+cc)
– Multiple pairwise comparisons
• tt v cc, tt v ct, ct v cc
• dominant and recessive
Methods in the
literature
• Analyse as 3 groups
– Analyse as co-dominant (per-allele difference)
– Meta-ANOVA
My Method
• 3 stage approach
– Meta-ANOVA
• Looks for overall association between gene and
trait but does not indicate which alleles
increase/decrease
– Determine genetic model use linear
regression
– Estimate mean differences using chosen
genetic model
Meta- ANOVA
P=0.026
Analyse by carrying out ANOVA using ‘genotype’ and ‘study’ as
categorical variables and weighting each observation
Test whether ‘genotype’ is a significant variable
Which genetic
model?
• Recessive
– TT shows effect, CT = CC
– MD1 = 0, so λ=0
• Dominant
λ = MD1/MD2
MD1 = CT – CC
MD2 = TT - CC
– TT = CT and both show effect
– MD1 = MD2, so λ=1
• Co-dominant
– CT will be half way between CC
and TT
– MD1/MD2 = 0.5
Can use a linear regression of MD1 against MD2, weighted by study to
determine overall the most appropriate genetic model
0.3
0.2
0.2 (95%CI, 0 to 0.4)
MD1
λ = 0, so recessive
0.1
0.201
-0.1
0.1
0.2
MD2
-0.1
0.3
Mean differences
• For dominant and recessive genetic models
combine 2 genotypes and use methods
previously described
– Recessive
• combine CT and CC, compare with TT
– Dominant
• Combine TT and CT, compare with CC
• For co-dominant models use per-allele
difference
– Assumes same difference between TT & CT, and CT
& CC
Mean differences
• MTHFR was associated when analysed by
meta-ANOVA (p = 0.026)
• MTHFR was recessive (λ = 0.2)
• Mean difference between TT and CT/CC
is: 20μm (95%CI 10 to 30)
Summary
• Genetic association studies have at least 3
groups
– Chose a model based on previous evidence
– Multiple comparisons
– Overall association
– Novel 3 stage approach
Other issues
•
•
•
•
Other genetic models?
Different polymorphisms within gene
LD between genes?
Whole genome meta-analysis