Designing epigenetic epidemiology studies

Caroline Relton

Institute of Genetic Medicine

Newcastle University, UK

Contents

• Defining the question

• Study designs used in epigenetic epidemiology and their applications

• Measuring epigenetic marks

• General considerations

The hypothesis

The study design chosen depend upon the hypothesis being addressed;

• Exposure X influences the epigenome

• Exposure X during pregnancy influences offspring epigenome

• The epigenome influences risk of disease Y

• The epigenome predicts risk of disease Y (independent of causality)

• Exposure X causes disease Y and this is mediated (in part) by changes to the epigenome

• Ability to establish (or maintain) epigenetic signatures is inherited

Study designs used in epigenetic epidemiology and their applications

Study design Application

Cross-sectional study Prevalence of an epigenetic mark in a well-defined population

Retrospective case-control study

Permanent epigenetic marks among individuals with and without disease

Cohort study Epigenetic marks underlying a risk factor-disease association

Nested (prospective) case-control study

Epigenetic marks predisposing to disease Biomarkers for early disease detection Biomarkers for disease risk

Intervention study Effect of intervention on epigenetic pattern Effect of epigenetic therapies on disease

Family-based studies Transgenerational inheritance of epigenetic traits

Birth cohort Influence of pre-conceptional and prenatal factors on establishment of the epigenome

Twin study Identifying the environmental influences on the epigenome

Migrant studies Epigenetic mechanisms in ethnic disease discordance

Adapted from Karin B Michels (ed) Epigenetic Epidemiology, doi 10.10007/978-94-007-2495-2_1, Springer Science+Business Media B.V. 2012

Cross-sectional study

Retrospective case-control study

Cognitive Function after Stroke (COGFAST)

• Aim: To identify predictive epigenetic biomarkers of post-stroke dementia

• Hospital-based stroke registers utilised to recruit non-demented stroke survivors three months post stroke

• Cognitive assessment & blood samples taken at base-line

• Annual neuropsychological assessment

• Brain neuropathology on death

• Comparison of methylation patterns in demented vs non-demented stroke survivors

Similar to case-control study except selection of controls which should come from the same source population as the cases but be disease-free

Cohort study

Nested (prospective) case-control study

European Prospective Investigation into Cancer & Nutrition (EPIC)

• Aim: To identify predictive epigenetic biomarkers of [lung] cancer

• 520,000 Europeans

• Biological samples taken at baseline (1992-2000)

• All cancers reported via registries

• Case-control comparison of methylation patterns possible using base-line pre-diagnostic DNA samples

23 centres, 10 European countries

Intervention study

No change in global methylation observed in response to 4,000µg/day for 6 months from un-coagulated blood BUT decreased global methylation observed in coagulated blood samples

Family based study

A study of DNA methylation in families harbouring pathogenic mitochondrial mutations for Leber’s Hereditary Optic Neuropathy

Twin study

SABRE: a multi-generational study to investigate migration, acculturation and disease risk

G1: 1st generation migrants Child environment ≠ Adults environment Age 60-84

G2: 2nd generation offspring Age mid 40s

G3: 3rd generation offspring School age

SABRE: 4,800 Europeans, Indian Asians and African Caribbeans Age 40-64, recruited 1988-1990 Followed up 2008-2011

Birth cohort study

Accessible Resource for Integrated Epigenomic Studies (ARIES)

• Genome-wide DNA methylation analysis in a longitudinal cohort study (ALSPAC)

– 1000 mother-child pairs at 5 time points across the life course (HM450)

– Extensive associated data on exposures and phenotypes from birth onwards

– Genome-wide SNP data

– Whole genome sequence and LCL gene expression data (sub-set)

• Methylome sequencing – 10-mother-child pairs at 5 time points across the life course

– 5-methyl cytosine & 5-hydroxymethylcytosine

• Tissue specific (and paired blood) genome-wide DNA methylation analysis

– Muscle, adipose, liver, cartilage, skin, brain …

• Data integration

• Data visualisation and browsing

ARIES Bioinformatics and Biological Resource

• Gene views

• Genome browser

• Query facility

• Analysis graphs and tables

Tom R Gaunt 15

Accessible Resource for Integrated Epigenomic Studies (ARIES)

Other methodological considerations in epigenetic epidemiology

• Choice of study population – Recruitment bias

– Can results be generalised to the population from which they were drawn?

• Tissue to analyse – Limitations of reliance upon accessible tissues

• Exposure measures – Temporality, cross-sectional, “aggregate” (e.g. ‘childhood adversity’)

– Plausibility of mechanistic link to the epigenome

• Selecting the epigenetic mark to study – DNA methylation dominates the literature

• Sample size – Most exposure-induced epigenetic changes detected in populations will be

modest in size requiring reasonable sample sizes

– Sample sizes are much lower than those required for GWAS

– Power calculations are essential

Other methodological considerations in epigenetic epidemiology

• Effect modification – Stratification by sub-groups may be required if there is reason to believe that

some exposures will act differently upon the epigenome in different individuals. Sub-group analysis needs to be well-powered.

• Confounding – All potential confounders should be assessed in any given study context.

Statistical adjustment does not fully remove the effect of (known) confounders.

• Reverse causation – A major issue in epigenetic epidemiology and a possibility that should always be

considered

• Misclassification – Measurement error. Technologies are generally robust to misclassification but

duplicates should be run on many platforms.

Measuring epigenetic marks

• What are we measuring?

• How reliable is the measure?

• What factors influence the measurement?

• How do we interpret the measure?

• Epigenetic marks are a phenotype – Blood lipid profiles

– Glucose and insulin levels

– Serum vitamin concentrations

– C-reactive protein

– DNA methylation patterns

– Histone modifications

– microRNA levels

DNA methylation – a binary phenomenon

• Beads represent ‘methylatable’ DNA positions • ‘C’s can be methylated or unmethylated • % measure is the proportion of copies of

methylated DNA in any given sample at a given site (or averaged across a region)

• methylated state may be transient

How are epigenetic patterns measured?

Important issues in study design covered in other talks

• Tissue specificity

• Power in epigenetic epidemiology studies

• Pitfalls and problems

• How to measure epigenetic marks

• Consideration of genetic architecture

• Using in silico (bioinformatic) tools

• Using multiple approaches to establish causality

• Quality control

• Validation and replication

• Statistical methods

Harmonization

• The value of international Consortia

• Consensus on pre-processing and quality control, thresholds etc

• Comparability between studies

• Development of guidelines e.g. for EWAS

• Data repository (akin to gene expression data)

References

• Allan LM et al. Long term incidence of dementia, predictors of mortality and pathological diagnosis in older stroke survivors. Brain 2011; 134(12):3716-27.

• Johansson M et al. Serum B vitamin levels and risk of lung cancer. JAMA 2010;303(23):2377-85.

• Tillin T et al. Southall and Brent Revisited: Cohort profile of SABRE, a UK population-based comparison of people of European, Indian Asian and African Caribbean origins. Int J Epidemiol 2012; 41(1):33-42.