1. Genetics as a determinant of health: new challenges for epidemiology
Julian Higgins
Senior Investigator Scientist, MRC Biostatistics Unit
and
Senior Epidemiologist, PHGU
Cambridge

2. Most diseases have a genetic component
Totally
Genetic
Environmental
Struck by
lightning
Motor
vehicle
accident
Duchenne
muscular
dystrophy
Cystic
fibrosis
Heart
disease
Cancer
Schizophrenia
Diabetes
Obesity
Asthma
Rheumatoid
arthritis
PKU
Alzheimers
Fragile X
Autism TB
Meningococcus
Multiple
sclerosis
With this understanding, it is easy to see that very few diseases are either totally genetic or totally environmental.
Case of PKU
Infectious diseases - TB and the triad of agent, nutrition and constitution

3. Genetics in epidemiology
Understanding genetic components gives clues to biological mechanisms
Predicting disease
population relevance
targeting preventive strategies
More effective therapy
biological understanding to develop new interventions
individualising treatments
early diagnosis
Information now available, post Human Genome Project
Genotyping technology

4. Outline Human genome epidemiology Some obstacles
Overcoming the obstacles
HuGENetTM and the road ahead
Example: bladder cancer
Concluding remarks

5. Human genome epidemiology
Gene-disease association U
C G
Cases
Controls
OR = 1.51
(95% CI to 2.44)
NAT2 carrier 37 89
NAT2 non-carr 74
118
Total
111
207

6. Human genome epidemiology
Gene-disease association U
C G
smoker
NAT2
Cases
Control
yes
carrier 29 63
non 37 73 no 74 76 35 56
Total
175
268
Gene-environment interaction U
C G ×

7. Human genome epidemiology
Gene-disease association U
C G
Gene-gene interaction U
C G
A T ×
Gene-environment interaction U
C G × U
Gene prevalence

8. Human genome epidemiology
Functional effect
Gene-based analysis
Mendelian deconfounding
use genetic determinants of biomarkers to determine the effects of the biomarkers

9. Human genome epidemiology
The full picture U

10. Some obstacles to the new epidemiology
Sample size
small effects are expected
variants may be uncommon
interactions require very large samples
Too many exposures
how to choose candidate genes?
many negative findings (less likely to be published)
spurious positive findings (more likely to be published)
major reporting biases

11. small effects are expected
Relative risks tend to be small
less than 1.5
Pro12Ala polymorphism in PPARg2 gene has RR = 1.23 for type 2 diabetes
(an often-quoted ‘established association’)
20 genes with common variants can explain 50% of common disease burden, even if RR =
Yang et al (2005)

12. Some obstacles to the new epidemiology
Sample size
small effects are expected
variants may be uncommon
interactions require very large samples
Too many exposures
how to choose candidate genes?
many negative findings (less likely to be published)
spurious positive findings (more likely to be published)
major reporting biases

13. interactions require very large samples
5% prevalence of gene variant
20% prevalence of environmental factor
RR = 1.5 for gene variant (generous)
RR = 2 for environmental factor
Sample size to detect interaction RR = 2 in case-control study with 80% power:
2742 cases and 2742 controls

14. Some obstacles to the new epidemiology
Sample size
small effects are expected
variants may be uncommon
interactions require very large samples
Too many exposures
how to choose candidate genes?
many negative findings (less likely to be published)
spurious positive findings (more likely to be published)
major reporting biases

15. major reporting biases
>10,000,000 Gene variants
 >1000 Diseases
 >10 Outcomes
 >10 Subgroups
 >5 Genetic contrasts
 >10 Investigators
= > 5 trillion candidate analyses!
after Ioannidis (2003)

16. Some obstacles to the new epidemiology
Other biases
genotyping errors
choice of controls (population stratification)
other standard biases
Variation and poor replicability
reporting and other biases
sample size
markers with different degrees of linkage to functional variant
other population characteristics

17. Overcoming the obstacles
Lots of data
large cohort and case-control studies, e.g. EPIC, NHANES
UK Biobank
Honest publication
negative results
web databases
Collaboration and synthesis
consortia of investigators
meta-analyses
Methods development
integrating multiple exposures

18. Human Genome Epidemiology Network (HuGENetTM)
A global collaboration of individuals and organizations committed to
the assessment of the impact of human genome variation on population health
how genetic information can be used to improve health and prevent disease
Undertaking systematic reviews and meta-analyses
Collating evidence to inform policy, practice and research

19. SINGLE TEAMS SINGLE STUDIES PUBLISHED AND UNPUBLISHED DATA
The roadmap
SINGLE TEAMS SINGLE STUDIES
Reporting
PUBLISHED AND UNPUBLISHED DATA
Feedback
HuGENet
Network of
Networks
Grading
FIELD-WIDE SYNOPSES
Synthesis
SYSTEMATIC REVIEWS
META-ANALYSES
Ioannidis et al (2006)

20. A systematic review
Joint effects of NAT1, NAT2 and smoking on bladder cancer risk
NAT2 gene: ‘rapid’ acetylator version metabolises aromatic amines in tobacco smoke quicker
NAT1 gene: believed to activate aromatic amines (so ‘slow’ version would be better)

21. Rather disappointing

22. Gene-gene-environment joint effects in bladder cancer: a single study
Smoking
NAT1
NAT2
Cases
Controls OR No
Slow
Rapid 6 13 1 16 31
1.12 (0.36, 3.5) 8
1.08 (0.30, 3.9) 10
1.30 (0.32, 5.3)
Yes 42 32
2.84 (0.97, 8.3) 61 51
2.59 (0.92, 7.3) 41 26
3.42 (1.2, 10.1) 35 12
6.32 (2.0, 20.3)
Taylor et al (1998)

23. Complex evidence synthesis
It turns out we can learn about the joint effects using studies of bladder cancer and…
NAT1
NAT2
NAT1 and NAT2
NAT1 and smoking
NAT2 and smoking
smoking
with assumptions

24. More exciting

25. Single study vs synthesis of 28 studies
Smoking
NAT1
NAT2
OR-Taylor
OR-synthesis No
Slow
Rapid 1
1.12 (0.36, 3.5)
0.98 (0.52, 1.8)
1.08 (0.30, 3.9)
0.70 (0.28, 1.6)
1.30 (0.32, 5.3)
1.12 (0.52, 2.2)
Yes
2.84 (0.97, 8.3)
1.53 (0.75, 3.0)
2.59 (0.92, 7.3)
2.23 (1.3, 3.8)
3.42 (1.15, 10.1)
1.37 (0.74, 2.4)
6.32 (2.0, 20.3)
2.88 (1.6, 5.0)

26. Concluding remarks: New challenges for epidemiology
Large amounts of data
big studies
collaborative, coordinated research
Investigating vast numbers of exposures
methods to home in on the truth
integrating multiple genes and environmental factors
Evaluating the technologies
in partnership with UK GTN etc

28. Warfarin This week’s BMJ: Can human genome epidemiology help?
Warfarin is underprescribed to patients with atrial fibrillation
Physicians are less likely to prescribe warfarin after one of their patients has a major adverse bleeding event associated with warfarin
Can human genome epidemiology help?
Choudhry et al (2006)

29. CYP2C9 variants and warfarin metabolism
Mean difference in daily dose: 2C9*2 carriers versus non-carriers -2 -1 1 2
Aithal
Taube
Furuya
Margaglione
Loebstein
Tabrizi
Scordo
Higashi
–0.85 (– 1.11, – 0.60)
Meta-analysis
Difference in mean warfarin dose (mg per day)
Lower dose for carriers
Sanderson et al (2005)