how to review genetic association studies lavinia paternoster 3rd year phd student

How to review genetic association studies

Lavinia Paternoster3rd 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

Intervention Control

(or intervention 2)

Observe

Outcome 1 Outcome 2

Input variable to be tested

Outcome to test success of intervention

Traditional meta-analysis

Intervention

e.g. beta-blockers

Control

(or intervention 2)

Observe

Outcome 1

e.g. cardiovascular

disease

Outcome 2

e.g. no cardiovascular

disease

Input variable to be tested

Outcome to test success of intervention

Traditional meta-analysis

Intervention

(beta-blockers)

Control

Outcome 1

(e.g. CVD)

n n

Outcome 2

(e.g. no CVD)

n n

Calculate relative risk (or odds ratio) for each study

Pool relative risks by using weighting methods

Beta-blockers & cardiovascular disease

Traditional meta-analysis

Intervention Control

Observe

Mean value of those with intervention

Mean value of controls

Variable to be tested

Outcome to test success of intervention

Traditional meta-analysis

Intervention

e.g. exercise

Control

Observe

Mean value of those with intervention

e.g. mean fatigue scale value

Mean value of controls

e.g. mean fatigue scale value

Variable to be tested

Outcome to test success of intervention

Edmonds et al. 2004. Exercise for chronic fatigue syndrome. Cochrane

Traditional meta-analysis

Observations

(e.g. fatigue scale)

Intervention 1

(exercise)

n mean sd

control n mean sd

Calculate mean difference (and 95%CI) for each study

Pool mean differences by using weighting methods

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

Intervention

e.g. beta-blockers

Control

(or intervention 2)

Observe

Outcome 1

e.g. cardiovascular

disease

Outcome 2

e.g. no cardiovascular

disease

Input variable to be tested

Outcome to test success of intervention

Traditional meta-analysis

Genotype AA

Observe

Outcome 1

e.g. cardiovascular

disease

Outcome 2

e.g. no cardiovascular

disease

Input variable to be tested

Outcome to test success of intervention

Genotype AB Genotype BB

Traditional meta-analysis

Intervention Control

Observe

Mean value of those with intervention

Mean value of controls

Variable to be tested

Outcome to test success of intervention

Traditional meta-analysis

AA BB

Observe

Mean value of those with

genotype AA

Variable to be tested

Outcome to test success of intervention

AB

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

Analyse by carrying out ANOVA using ‘genotype’ and ‘study’ as categorical variables and weighting each observation

Test whether ‘genotype’ is a significant variable

P=0.026

Which genetic model?

• Recessive– TT shows effect, CT = CC– MD1 = 0, so λ=0

• Dominant– 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

λ = MD1/MD2

MD1 = CT – CC

MD2 = TT - CC

Can use a linear regression of MD1 against MD2, weighted by study to determine overall the most appropriate genetic model

-0.1

0.1

0.2

0.3

-0.1 0.1 0.2 0.3

0.201

MD1

MD2

0.2 (95%CI, 0 to 0.4)

λ = 0, so recessive

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