Nic Timpson - University of Bristol · - Conceptual approach to Mendelian randomisation should be taken from the point of view of conventional epidemiology. - Observational epidemiology

Mendelian randomisation

Nic Timpson

Epigenetic Epidemiology 2012

Objectives: Build on “epigenetic epidemiology” and “strengthening causal inference” sessions Introduce MR specifically as a means to strengthening causal inference Illustrate the process/underpinnings of MR give worked examples Should should be able to: Describe what MR is Describe why MR allows causal inference Describe an example of MR Formulate a crude MR experiment


Mendelian randomisation (“MR”)

D

B C

A

D.A.G


- Conceptual approach to Mendelian randomisation should be taken from the point of view of conventional epidemiology. - Observational epidemiology is limited, however problems can be avoided given the existence of “better measures”. - In an observational framework what could we use as a “better measure”? - Mendelian randomisation aims to use genetic variation related to risk factors of interest in efforts to re-assess observational estimates.

What is Mendelian randomisation?

Genetic association study or observational epidemiology?


Assumptions of MR analysis

• Z associated with X

• Z is independent of U

• Z is independent of Y given U and X

Z X

U

Y


http://csg.sph.umich.edu/locuszoom/

Why are genotypes good instruments?

An instrumental variable is a variable that is:

(i) Associated with an exposure of interest

(ii) NOT associated with the outcome of interest – except through its association with the exposure of interest.


Z X

U

Y

CETP variation & HDL

HDL & Ischaemic Heart Disease (IHD) ???

Number of comparisons

Expected number

Observed number

P (Ho: OBS=EXP)

SNP/NON-SNP

96 non-genetic variables

4560 228 2447 <p*10-6 23 SNPs and

96 variables 110/120 P=0.35

23 candidate loci

253 12.7 14 0.66

Correlations here taken at p≤0.05


p value 0.05 0.01 0.0001 6.88 x 10-6 * 1.37 x 10-6 * 1.37 x 10-8 *

Expected 363 72 0 0 0 0

Observed 1843 1281 684 568 504 421

Total number of phenotypes: 121 Total number of pairs: 7260

*bonferroni corrected results of significance values 0.05, 0.01 and 0.0001, respectively

• Phenotypes are highly over correlated, as expected.

p value 0.05 0.01 0.0001 3.69 x 10-6 * 7.40 x 10-7 * 7.40 x 10-9 *

Expected 678 136 1 0 0 0

Observed 709 167 4 1 1 0

Number of SNPs with r2<0.25 = 112 Number of phenotypes = 121

Number of pairs at r2<0.25 = 13552

*bonferroni corrected results of significance values 0.05, 0.01 and 0.0001, respectively


Michelle Taylor

Modifiable exposure (Phenotype, affected by

genotype and by environment,

e.g. CRP)

Outcome (Usually a disease or health status, e.g. coronary heart

disease)

Confounders (Factors associated with both exposure and

outcome, including unmeasured confounders and confounders measured with error, e.g. smoking, socioeconomic position, diet, physical activity)

Conventional observational epidemiology

It is often impossible to exclude unmeasured or residual confounding as an explanation for observed

exposure/outcome associations


Instrumental variable

(Genotype, randomly determined,

e.g. CRP)



e.g. CRP)


disease)



Mendelian randomisation approach


Instrumental variable

(Genotype, randomly determined,

e.g. CRP)



e.g. CRP)


disease)



Mendelian randomisation approach


Example – using instruments for alcohol consumption

Z X

U

Y ADH2 Alcohol


Metabolism of alcohol

Ethanol ADH

CYP2E1

Acetaldehyde Acetic acid ALDH

* Mainly occurs in the liver, but some activity is also present in the oral cavity and digestive tract


ALDH2 genotype by alcohol consumption, g/day: 5 studies, n=6815

0

10

20

30

40

50

60

*1*1 *1*2 *2*2

Alc

oh

ol g

/da

y

Men

Women

Chen, Davey Smith et al, PLoS Med 2008


Relationship between characteristics and ALDH2 genotype

Age Y

ear

s Smoker

Pe

rce

nt

BMI

kg/m

2

Cholesterol

mg

/dl


ALDH2 genotype and systolic blood pressure

(homozygote vs homozygote)


(heterozygote vs heterozygote)

Example – using instruments for adiposity

Z X

U

Y BMI loci Adiposity

Epigenetic Epidemiology

GIANT_3 (n~250,000) – Speliotes et al 2010


BUT, are these “genes for obesity”?

Copenhagen data – n=~38000 cross sectional

FTO effect ~0.1SD across the distribution

Effect mirrored in a simple Cumulative frequency plot


U

Traits of metabolism

CRP/BMI

IHD

FTO Adiposity


Example – using instruments for adiposity

Diabetes, 2008


Timpson et al Hypertension 2009


Components of analysis Observational effects/trend Linear regression result Adjusted linear regression result Instrumental variable regression result A test for difference (p(DWH))


AA Aa aa

AA

Aa

aa

AA/Aa/aa


MR not just with continuous outcomes…

BMI

genotype

BMI

Confounders

IHD ~25%

~50%

Nordestgaard et al, PLoS Med, 2012


(1) (2)

(2) Histograms of BMI by the two groups generated from the two groups from figure 1. Red lines indicate the mean BMI for each – these are ~20 and 23kg/m2 respectively

** A change of threshold to a score using the top 50, ooo markers from the GIANT GWAS for BMI takes advantage of extra variance explained (>3.5% - continuous)

MR not just with simple instruments…


(1) Basic score and BMI gradient in ALSPAC samples with GWAS Red lines indicate the cut off points for the top and bottom 5% Score from Speliotes et al 2010 explains ~2.4% variance

Limitations to Mendelian randomisation

1- Heterogeneity

- Genetic heterogenetity through linkage disequilibrium

or by pleiotrophy.

- Phenotypic heteogeneity & complexity (e.g. EC-SOD).

- Population genetic heterogeneity in instrument choice.


LMS allowing adjustment for age-specific heteroscedasticity and skewness. Median (or M) curves were modelled with 9 degrees of freedom, with corresponding curves for the coefficient of variation (S) with 5 d.f. and skewness (L) with 3.


1- Heterogeneity


or by pleiotrophy.



2- Population stratification



1- Heterogeneity


or by pleiotrophy.




3- Canalisation

C. Waddington, 1957

‘the cell can take specific permitted trajectories, leading to different cell fates’



1- Heterogeneity


or by pleiotrophy.




3- Canalisation

4- Power (instrument “strength” and basic power)




CEBP(2010)19:1341-48

“Importantly, despite its size… study had low power…” - relatively weak relationship between genotype and milk consumption (an often ignored characteristic in the examination of lactase persistence genotypes & with weak instruments comes instability and bias - Burgess) - modest observational association between milk consumption and RCC A study with ∼37,000 cases and 37,000 controls would be needed to achieve 80% power under the same framework. - Not to mention the possibly complicating nature of variation at MCM6


Objectives: Build on “epigenetic epidemiology” and “strengthening causal inference” sessions Introduce MR specifically as a means to strengthening causal inference Illustrate the process/underpinnings of MR give worked examples Should should be able to: Describe what MR is Describe why MR allows causal inference Describe an example of MR Formulate a crude MR experiment



- Davey Smith, G. Reflections on the limitations to epidemiology. Journal of Clinical Epidemiology 54, 325-31 (2001).

- Davey Smith, G. & Ebrahim, S. Mendelian Randomisation: prospects, potentials and limitations. International Jounal of Epidemiology 33, 30-42 (2004).

- Davey Smith, G. et al. Genetic epidemiology and public health: hope, hype, and future prospects. Lancet 366, 1484-98 (2005).

- Lawlor, D.A., Harbord, R., Sterne, J., Timpson, N.J. & Davey Smith, G. Mendelian randomisation: using genes as instruments for making causal inferences in epidemiology. Statistics in Medicine 27, 1133-1163 (2008).

- Davey Smith, G. et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Medicine 4, e352 (2007).

- Greenland, S. An introduction to instrumental variables for epidemiologists. International Jounal of Epidemiology 29, 722-729 (2000).

- Burgess, S., Thompson, S.G. & Collaboration, C.C.G. Avoiding bias from weak instruments in Mendelian randomization studies. International Jounal of Epidemiology 40, 755/764 (2011).

- Chen, L., Davey Smith, G., Harbord, R.M. & Lewis, S.J. Alcohol intake and blood pressure: a systematic review implementing a Mendelian randomization approach. PLoS Medicine 5, e52 (2008).

- Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42, 937-948 (2010).

- Freathy, R.M. et al. Common variation in the FTO gene has a metabolic impact consistent with its effect on BMI. Diabetes 57, 1419-1426 (2008).

- Timpson, N.J. et al. Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomisation using FTO/MC4R genotype. Hypertension 54, 84-90 (2009). - Nordestgaard, B.r.G. et al. The Effect of Elevated Body Mass Index on Ischemic Heart Disease Risk: Causal Estimates from a Mendelian Randomisation Approach. PLoS Medicine 9, e1001212 (2012). - Sovio, U. et al. Association between Common Variation at the FTO Locus and Changes in Body Mass Index from Infancy to Late Childhood: The Complex Nature of Genetic Association through Growth and Development. PLoS Genet 7, e1001307 (2011). - Waddington, C.H. Canalisation of development and the inheritance of acquireed characters. Nature 150, 563-565 (1942). - Waddington, C.H. The Strategy of the Genes, (George Allen & Unwin., London, 1957).

References

Nic Timpson - University of Bristol · - Conceptual approach to Mendelian randomisation should be taken from the point of view of conventional epidemiology. - Observational epidemiology

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