Epigenetics and Developmental
Origins of Health and Disease
Caroline Relton
Institute of Genetic Medicine
Newcastle University, UK
Aim
• To highlight important issues relating to the epidemiological
investigation of epigenetic mechanisms in the context of
developmental origins of health and disease
Overview
• Change over time in the epigenome
• Evidence for the influence of early life exposures on the epigenome
• Inter-generational exposure versus trans-generational effects
• Persistent versus transient epigenetic change
• Temporal relationships
• The problem of confounding in a DOHaD context
Epigenetic mechanisms and developmental programming
Many diseases of maturity have their origins early in life
Rheumatoid
arthritis
Obesity
Hypertension
Early
development
Ischaemic heart
disease
Diabetes
mellitus
Stroke
The dynamic epigenome
Germline Parental genomic
epimutation demethylation
Epigenetic drift / somatic epimutation
Developmental epigenetic programming
Waterland RA. Nutr Rev 2008
Age 5
Age 10
• 3 gene loci analysed (DRD4,
SERT, MAOA)
• 46 MZ twin pairs
• 45 DZ twin pairs
• Total n = 182
• Sampled at 5 and 10 years
• [Modest] differences observed
between genetically identical
individuals
• Variation not consistent across
all loci
Age-related change in methylation
Manhattan plot showing association between methylation at individual CpG sites and
chronological age. Plotted are P-values indicating strength of association between DNA
methylation levels at >27 000 CpG sites and age in cerebellum (purple), frontal cortex
(green), pons (blue) and temporal cortex (red). For each point, a positive association
between DNA methylation and chronological age is indicated by upward pointing
triangles; a negative association is indicated by downward pointing triangles.
Note! p-values give no indication of magnitude of change
Hernandez DG et al. Hum Mol Genet 2011
Studies linking early life exposures to changes
in DNA methylation using animal models
Early life exposure
Animal
model
Epigenetic change
Disease
association
Maternal nutrition
Low Protein
Rat, Mouse,  and  DNA methylation,  and  histone
Pig
acetylation and histone methylation
Calorie restriction
Sheep, Rat
 DNA methylation,  histone acetylation and 
histone methylation
Obesity,
Diabetes
Periconceptional restriction B12,
folate, methionine
Sheep
Altered DNA methylation
Obesity
High fat
Macaque,
Mouse
 and  DNA methylation,  and  histone
acetylation and  and  histone methylation
Obesity
Rat
Altered DNA methylation,  histone acetylation
Diabetes
Mouse
 DNA methylation
Diabetes
Mouse
 DNA methylation
Obesity
Rat
Rat
 DNA methylation
Hyperacetylation
Obesity
Diabetes
Avy mouse
 DNA methylation
Obesity
Genistein supplementation +FA
Avy mouse
 DNA methylation
Obesity
Protein restriction + FA
Rat
Prevented or reversed hypomethylation
Obesity
Obesity
Surgical models
IUGR ( uterine artery ligation)
Environmental toxin
Arsenic
Paternal effect
Low protein
Neonatal diet
Leptin treatment
Extendin-4
Reversal with folic acid
Methyl supplementation
Seki Y et al. Endocrinology 2012
The component parts of a gene
Enhancer
Promoter
Transcription factor
binding sites
Transcription
start site
Exon
Intron
Gene body
Enhancer
Promoter
Environmentally induced epigenetic changes to
promoter-enhancer interaction
•
Sub-optimal nutrition in
early life modifies a
promoter-enhancer
interaction at the Hnf4
locus
•
Role in fetal pancreatic
development
•
Implicated in type 2
diabetes aetiology
•
Modest impact upon DNA
methylation
•
Pronounced effects upon
histone marks
Sandovici I et al. Proc Natl Acad Sci 2011
Dietary influences on epigenetic variance in
isogenic mice
Methylation levels
are unchanged after
methyl donor
supplementation
Whole-genome 5methylcytosine (m5C)
content in liver DNA
from control, F1
supplemented and F6
supplemented mice
Li CC et al PLoS Genetics 2011
Methyl donor
supplementation
increases epigenetic
variation in exposed
mice
Pseudo three-dimensional
plot showing PCA of
microarray data from
control and F1 and F6
supplemented mice. The
ellipsoids around the PCA
scores of each group were
determined by standard
deviations, so that their
size is indicative of the
overall variance within the
group.
Li CC et al. PLoS Genetics 2011
Evidence from human studies
Trans-generational effects vs inter-generational
exposure
• DOHaD is largely concerned with inter-generational exposure i.e.
exposure of the developing fetus whilst in utero via dietary, lifestyle
and behavioural exposures to the mother
• A lack of clarity in the literature has led to mis-interpretation of
inter-generational exposures as trans-generational effects i.e. those
inherited through altered germ line epigenetics
• Interest in epigenetics in the context of evolution, adaptation and
selection means language is used across disciplines but with
differing definitions and in different contexts
• Trans-generational effects are likely to play an extremely small role
in disease pathogenesis
Genome Res 2010; 2(12): 1623-8.
Int J Epidemiol 2012; 41(1): 236-47.
Persistence versus transient epigenetic changes
• Metabolic programming
…the concept that a stimulus or insult operating at a critical or
sensitive period of development could result in a long-standing or lifelong effect on the structure or function of the organism.
Methylation change
Lucas A. Human milk and infant feeding. In: Battaglia F, Boyd R, eds. Perinatal medicine.
London: Butterworths, 1983:172–200.
Persistent
Transient
Time
• Acute or chronic exposure
• Long term epigenetic change
required
• Transient epigenetic change
with lasting physiological
impact
• Implications for the age of
population studied and the
inferences that can be made
Temporal relationships between exposures and
epigenetic patterns
• The DOHaD paradigm is based upon the premise of a temporal
relationship between exposure (in early life) and an outcome (later
in the life course)
• Longitudinal studies can assist in defining whether methylation
changes occur before the onset of phenotype
• HOWEVER, a temporal relationship does not necessarily infer
causation (but it helps)
• Confounding structures within data can persist across the lifecourse
Temporal relationships in epigenetics:
the problem of confounding
Debbie Lawlor
Centre for Causal Analyses in Translational
Epidemiology
University of Bristol, UK
Confounding
• Affects / is associated with exposure
• Affects outcome
• Is not on the causal pathway between exposure and
outcome
• Fools (confounds) us into believing an association is
causal
• Can distort associations in either direction e.g. smoking
may mask (negative confounding) a stronger effect of BMI
on CHD
Confounding
Confounders
e.g. Dietary fat
Cigarette smoking
Physical activity
Risk / protective factor
Disease outcome
e.g. Vitamin C
e.g. CHD
What this means
• If interested in best causal estimate must:
• Have knowledge of all possible confounders
• Measure these accurately
• Correctly control for them (e.g. correctly modelled in
multivariable analyses)
• Ideally, should be measured before (or at same time) as exposure –
since to confound the confounder had to influence the exposure &
outcome
• But also need to understand plausible confounding / causal pathways
– e.g. smoking as confounder between birthweight & CHD
Offspring smoking
in later life
Maternal smoking
in pregnancy
Lower birth weight
Increased CHD
Good causal evidence that:
- Smoking in pregnancy causes low birth weight
- Parental smoking increases the likelihood of offspring (own) smoking
- Smoking causally increases the risk of CHD
So the association of lower birth weight with increased CHD could be confounded by a
pathway from maternal smoking through offspring smoking to their CHD
Note: maternal pregnancy smoking occurs BEFORE the lower birth weight (exposure)
Ideally to capture this confounding pathway fully one would want accurately measured
maternal pregnancy smoking and offspring smoking in later life. But many historical cohorts
do not have data on smoking in pregnancy, in which case adjusting only for offspring smoking
is better than no adjustment, even though offspring smoking occurs after birth weight
Difficulties in controlling confounding
• Unmeasured confounding
• Very difficult to measure all factors associated with both
treatment and outcome (confounders from across the life
course)
• Residual confounding
• If confounders are measured with error then they won’t be
fully controlled in regression models
• ‘Associational world’: difficult to think of every possible
confounder and include in statistical model
• May model confounders incorrectly
Associational World
96 non-genetic
traits*
Pair-wise
associations
Expected
significant at
p < 0.01
Observed
significant at
p < 0.01
P for null:
observed =
expected
4560
45.6 (1%)
2036 (45%)
< 0.000001
Including traits from across the life course e.g. birth weight, childhood social
class, leg length (marker of childhood nutrition) associated with various adult
‘risk factors’ including HRT use, serum vitamin C & E levels etc.
Davey Smith G, Lawlor DA, Harbord R et al, PLoS-Med 2007
Real / sensitivity analyses
• Characteristics with ‘small’ associations in data driven
confounder selection often excluded from final model
• Sensitivity analyses examine how strong one potential
confounder would need to be related to exposure and
outcome to nullify the best adjusted association
• BUT the more plausible situation is that many many
confounders from across the life course, each with ‘small’
association combine to produce a big confounding effect
Difference between
top and bottom ¼
Vit C
Independent OR for
CHD
Predicted OR
comparing top to
bottom ¼ Vit C
Child NM social class
9.5
0.79
0.98
Child car access
7.6
0.75
0.98
Full time education > 18
11.3
0.65
0.95
Adult NM social class
17.0
0.77
0.96
Not living in council house
1.5
0.64
0.99
Adult car access
13.2
0.77
0.97
State plus other pension
12.3
0.88
0.99
None smoker
11.2
0.68
0.96
Regular activity
11.8
0.67
0.95
Low fat diet
6.2
0.63
0.97
High fibre diet
2.2
0.86
0.99
Not obese
10.4
0.76
0.97
Reg. Moderate alcohol
11.1
0.80
0.98
0.095
0.75
0.97
0.19
0.55
0.89
Leg length per cm
FEV1 per litre
Total confounding
effect
0.60
Observed vitamin C - CHD association in
a cohort and an RCT
HR (95%CI) incident CHD per 15.7µmol/l
Cohort no
adjustment
Cohort adult
confounder
adjustment
Cohort adult &
childhood
confounder
adjustment
RCT
0.88 (0.80, 0.97)
0.90 (0.82, 0.99)
0.96 (0.85, 1.05)
1.02 (0.94, 1.11)
• 15.7µmol/l – is the difference in vitamin C achieved in the RCT by supplementation
• Associations in the cohort study progressively attenuate from 12% reduction per
dose to 4% reduction as go from no adjustment to adjustment for all available
confounders from childhood and adulthood
• Given ‘associational world’ cannot be certain that residual confounding remains
• Well conducted RCTs will not be confounded because randomisation breaks the
association of confounder with exposure (so do not need to measure all confounders)
… back to …
Caroline Relton
Institute of Genetic Medicine
Newcastle University, UK
Epidemiological strategies for strengthening
causality in a DOHaD context
• Observed associations between an exposure/DNA methylation and
DNA methylation/outcome represent a first step in identifying a
robust mechanistic pathway
• Additional strategies can be applied using epidemiological
approaches
– Replication in an independent sample
– Cohort comparison, in particular where the second cohort is not subject to the
same confounding influences
– Paternal versus maternal associations to decipher true in utero effects
– Using genetic proxies for exposure and/or methylation levels (Mendelian
randomization)
• As well as other tools
– More details later
Using genetic information
Diabetes 2012; 61(2): 391-400
TACSTD2
SNP
Postnatal growth
TACSTD2
methylation
TACSTD2
expression
Childhood adiposity
Differential gene expression and DNA methylation
are associated with postnatal growth and
childhood adiposity
0-12 weeks
Differential
postnatal growth
11 years
Adiposity
n = 20
Rho = 0.44
p = 0.061
Differential gene
expression
n = 20
Rho = -0.55
p = 0.016
Differential gene
methylation
n = 91
Rho = -0.22
p = 0.037
Reverse causation and confounding
• Are changes in methylation caused by childhood phenotype?
• Are changes in methylation caused by early growth patterns?
0-12 weeks
Differential
postnatal growth
11 years
Adiposity
Differential gene
expression
Differential gene
methylation
Summary
• Evidence suggests that epigenetic processes are likely to play a role
in developmental programming
• Animal evidence is more compelling than human
• Care is required not to confuse inter-generational exposure with
trans-generational inheritance
• We know little about the persistence of epigenetic marks
• Identifying the role of epigenetic processes in the context of
developmental programming faces all of the same challenges as
other epidemiological analyses
• Temporal relationships between exposure, mediator and outcome
are a pre-requisite but not a guarantee of an un-confounded
association
References
•
Waterland RA. Epigenetic epidemiology of obesity: application of epigenomic technology. Nutr Rev 2008; 66 (Suppl 1):
S21-3.
•
Wong C et al. A longitudinal study of epigenetic variation in twins. Epigenetics 2010; 5(6): 516-26.
•
Hernandez DG et al. Distinct DNA methylation changes highly correlated with chronological age in the human brain.
Hum Mol Genet 2011; 20(6): 1164-72.
•
Seki Y et al. Minireview: Epigenetic programming of diabetes and obesity: Animal models. Endocrinology 2012; 153(3):
1031-8.
•
Sandovici et al. Maternal diet and aging alter the epigenetic control of a promoter-enhancer interaction at the Hnf4a gene
in rat pancreatic islets. Proc Natl Acad Sci USA 2011; 108(13): 5449-54.
•
Li CC et al. A sustained dietary change increases epigenetic variation in isogenic mice. PLoS Genet 2011; 7(4): e1001380.
•
Tobi EW et al. Prenatal famine and genetic variation are independently and additively associated with DNA methylation
and regulatory loci within IGF2/H19. PLoS ONE 2012; 7(5); e37933.
•
Relton CL et al. DNA methylation patterns in cord blood DNA and body size in childhood. PLoS ONE 2012; 7(3): e31821.
•
Godfrey KM et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes
2011;60(5):1528-34.
•
McKay JA et al. Genetic and non-genetic influences during pregnancy on infant global and site specific DNA methylation:
role for folate variants and vitamin B12. PLoS ONE 2012; 7(3): e33290.
•
Groom A et al. Postnatal growth and DNA methylation are associated with differential gene expression of the TACSTD2
gene and childhood fat mass. Diabetes 2012; 61(2): 391-400.
•
Daxinger L, Whitelaw E. Transgenerational epigenetic inheritance: more questions than answers. Genome Res 2010;
2(12): 1623-8.
•
Davey Smith G. Epigenesis for epidemiologists: does evo-devo have implications for population health research and
practice. Int J Epidemiol 2012;41(1):236-47.
•
Davey Smith G et al. Clustered environments and randomized genes: a fundamental distinction between conventional and
genetic epidemiology. PLoS Med 2007; 4(12): e352.