How to review genetic association studies

1. How to review genetic association studies Lavinia Paternoster 3rd year PhD student

2. 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

3. 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……….

4. Traditional meta-analysis Input variable to be tested Intervention Control (or intervention 2) Observe Outcome 1 Outcome 2 Outcome to test success of intervention

5. Traditional meta-analysis Input variable to be tested Intervention e.g. beta-blockers Control (or intervention 2) Observe Outcome 1 e.g. cardiovascular disease Outcome 2 e.g. no cardiovascular disease Outcome to test success of intervention

6. Traditional meta-analysis Calculate relative risk (or odds ratio) for each study Pool relative risks by using weighting methods

7. Beta-blockers & cardiovascular disease

8. Traditional meta-analysis Variable to be tested Intervention Control Observe Mean value of those with intervention Mean value of controls Outcome to test success of intervention

9. Traditional meta-analysis Variable to be tested 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 Outcome to test success of intervention Edmonds et al. 2004. Exercise for chronic fatigue syndrome. Cochrane

10. Traditional meta-analysis Calculate mean difference (and 95%CI) for each study Pool mean differences by using weighting methods

11. Exercise & Fatigue

12. Genetic Associations • The simplest mutation (a→b) gives 3 genotypes: aa, ab, bb • Comparing 3 groups not 2 • Conventional meta-analysis methods not suitable

13. Traditional meta-analysis Input variable to be tested Intervention e.g. beta-blockers Control (or intervention 2) Observe Outcome 1 e.g. cardiovascular disease Outcome 2 e.g. no cardiovascular disease Outcome to test success of intervention

14. Traditional meta-analysis Input variable to be tested Genotype AA Genotype AB Genotype BB Observe Outcome 1 e.g. cardiovascular disease Outcome 2 e.g. no cardiovascular disease Outcome to test success of intervention

15. Traditional meta-analysis Variable to be tested Intervention Control Observe Mean value of those with intervention Mean value of controls Outcome to test success of intervention

16. Traditional meta-analysis Variable to be tested AA AB BB Observe Mean value of those with genotype AA Mean value of those with genotype AB Mean value of those with genotype BB Outcome to test success of intervention

17. My Research • Meta-analysis of association between Carotid intima-media thickness and several genes • Here I’ll show MTHFR example CC / CT / TT

18. Data CC CT TT

19. 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

20. Methods in the literature • Analyse as 3 groups • Analyse as co-dominant (per-allele difference) • Meta-ANOVA

21. 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

22. 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

23. 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

24. 0.3 0.2 MD1 0.1 0.201 -0.1 0.1 0.2 0.3 MD2 -0.1 0.2 (95%CI, 0 to 0.4) λ = 0, so recessive

25. 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

26. 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)

27. Summary • Genetic association studies have at least 3 groups • Chose a model based on previous evidence • Multiple comparisons • Overall association • Novel 3 stage approach

28. Other issues • Other genetic models? • Different polymorphisms within gene • LD between genes? • Whole genome meta-analysis