Download error, bias, problems and pitfalls in epigenetic epidemiology

ERROR, BIAS, PROBLEMS AND PITFALLS IN
EPIGENETIC EPIDEMIOLOGY
DR JONATHAN MILL
PSYCHIATRIC EPIGENETICS GROUP
MRC SGDP CENTRE
INSTITUTE OF PSYCHIATRY
KING’S COLLEGE LONDON
[email protected]
www.epigenomicslab.com
Newcastle, 2012
An exponential increase in published epigenetics research…
1997: first year with >100 publications
2010: >2,000 publications
2011: almost 2,500 publications
But is there is more “interest” than “information”???
NEUROSCIENCE
MENTAL HEALTH
REVIEWS
RESEARCH
ARTICLES
STEM CELLS
CANCER
“…The victory
over the genes…
Smarter, healthier,
happier...
How we can
outwit our
genome…”
“…Roll over, Mendel. Watson and Crick? They
are so your old man's version of DNA…”
“…The integration of standard massage
practices and the knowledge of the biology of
adversity will change our minds, our
physiology, our epigenetics—and hence our
massage practice…”
Would you trust
these guys with
your money?
Behavioural epigenetics is highly controversial
NATURE Vol 467|9 September 2010
AGTGCCTCAGCCTCCCTAGTAGCTGGGATTACAGGTGCCCTCCACAATGCCCAGCTAATTT
TTGTGTTTTTAGTAGACACAAGATTTCACTATGTTGCCCAGGCTGGTCTCAACCCCTGACC
TCAAGTGATCCACCTGCCTCAGTCTCCCAGAGTGCTGGGACTGCAGGCGTGAGCCAACAAG
CCCAGGCCACGATGTCTTACTTTTCACCTAAAACCTGCCTAAATGGCATGCCCAGTTAAAA
CAATCTTTTTCTGTTACAATAATCCATGTAAGAGTATGACACATTTTCTGAAAGATTTGTC
TAAAAAAGAGCCTGGTATGTTTACTGTTGCTGCTGAATTGGATTTGACTCTGCTGCTGTAT
CAGGGCCCCTTCTGACAATTCACCTCTTGCTTCCTTTCCTGCTAATTGTCCTGTTGACTAC
– 1 body, 1 genome: a blood sample is all you need!
TATTTTTTTTTTTTTTTGGTAACAGTGTCTGGCTCTGTCACCCAGCCTAGAGTGCAGTGGC
– 1 life, 1 genome: you are born with the genome
ACAATCTTGGCTCACTACAACCTCCATCTTCTGGGCTCAAGCTATTCTTCCACCTCAGCCT
CCCAAGTAGCTGAGACTACAGGCATGTGCCACCACACCCAGCTAGATTTTGTATTTTTTGT
you die with
AGAGACGGGGTCTTGTGATGTTGCCCAGGCTGGTCTTGAACTCCTGGGCTCAAAGCAATCC
– Any lifestyle, 1 genome: it doesn’t matter what
GCCCGCCTCCGCCTCCCAAAGTGCTGAGATGACAGGCGTGAGCAACTGCGCCCAGCCTTGT
GTACTTCTTAGGGCTCTTTTACATGCCTTTCTTTTTTTAACAGCCTTCCCACCACTACCTT
you’re exposed to
TTACATGTCTTGAGATTTTCCTGTATGCATGTGTATGCGTGCACGTGCACGCACGCACACA
– Any disease, 1 genome: no reverse causation
CACACACACACCTGATTTTGTCATTCTGGTGTTTAAAGCATATCATAGTCCTACTTCCAGA
AATACATCCAATGCAATGAACCTGGTAGCCAACACTGCTGAGAAATGACCCAAGGGTCTAC
– A nicely annotated reference genome and
CTTGAGTAGCCAGCCCCCAAATCCAAAGAATAGCTCCAGACCCCATAGTTTTCTCACCCAC
TAGGTCATGGGACCATGGCAAGAGTGAGAGAGTTCCACTTCCCAGAGGATGCCTGTTATTA
catalogue of SNPs is freely available
CCTTACCTCAATTTGAAATCTGTACTAAGGTTGAACACATGCATTCTCCTCCTTGACCTCC
– Methods that do as they say on the box and give
ACATCCCCTGTTGTTTCCTTTTTTTGTTGTTTTTGTTTTTTGTTTTTGTTTTGAGACAGAG
TCTCGCTCTGTCGCCCAGGCTGGAGTGCAGTGGCACGATCTCGGCTCACTGCAGTCTCTGC
results that are easy to interpret
CTCCCGGGCTCAAGCAATTCTCCTGCCTCAGCCTCCTGAGTAACTGGGATTACAGGTGTGT
Some of the (many) issues in
epigenetic epidemiology
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Technical / methodological
Sample related issues
Study design
Analysis and interpretation
Over-interpretation
Over-simplification
Biological Significance?
Confounding factors
Cause vs effect
Schizophrenia
Bipolar disorder
Autism Spectrum Disorders
Wong (in prep)
Alzheimer’s Disease
QDP
ITFG2
Schalkwyk et al (in prep)
How do different
disease-relevant
regions of the brain
differ epigenetically?
What is a ‘normal’
brain methylome?
Are there marked
epigenetic differences
between the major
cell-types in the
brain?
How is the brain
methylome
influenced by factors
such as age, sex and
medication?
What is the location
of functionallyrelevant DMRs?
Can peripheral tissues
be used as a ‘proxy’ for
the brain in epigenetic
epidemiology?
IJE, 2012
Biological, Technological, and
Methodological issues
1. We do not know where in the genome to look and what to look for
2. We have to rely on imperfect technology
3. We may be limited by available sample sizes that are optimal for epigenetic
epidemiology
4. Whatever we do, it may never be enough to fully account for epigenetic
differences between tissues and cells
5. We may be trying to detect inherently small effect sizes using sub-optimal
methods and sample cohorts
6. We lack a framework for the analysis of genome-wide epigenetic data
7. We have to manage high expectations
1. We do not really know
where to look, or what to
look for
Promoter CpG islands!!!
CpG Island ‘Shores’
Irizarry et al, Nature Genetics, 2009
Most tissue-variable CGI DMRs:
enrichment for intragenic CGIs
1000
900
Χ2 p = 1E-246
O/E=2.37
800
700
600
O/E=0.09
500
OBS
EXP
400
300
200
100
0
Promoter
Davies et al (in press)
Intragenic
3'UTR
Intergenic
HCP
Davies et al (in press)
LCP
And there’s more to epigenetic gene regulation than DNA methylation!
Zhou et al, Nat Rev Genet, 2011
How many DNA modifications are there??
5-hydroxymethylcytosine (5hmC)
5-formylcytosine (5fC)
5-carboxylcytosine (5caC)
5hmC appears to be
particularly important in ES
cell differentiation and the
CNS and is implicated in
postnatal neurodevelopment
and aging
Traditional bisulfite-based methods do
not distinguish between 5-mC and 5hmC
CMS (the product of bisulfite
conversion of 5-hmC) tends to stall
DNA polymerases during PCR densely hydroxymethylated regions of
DNA may be underrepresented in
quantitative methylation analyses
Existing 5-mC data sets may require
re-evaluation in the context of the
possible presence of 5-hmC
Antibodies against 5-mC and 5-hmC
can pull out fragments enriched for
each mark separately – but can’t
quantify at base-pair resolution
Oxidative bisulfite-sequencing (ox-BSseq) (Booth et al, Science, 2012)
Huang et al, PLoS ONE, 2010
Lunnon et al (in prep)
The relationship between DNA methylation and gene
expression is not necessarily straightforward…
(SAM)
Beyond transcriptional silencing:
functions of DNA methylation
• Chromatin compaction
• Genome stability
• Suppression of homologous recombination
between repeats
• Genome defense against retroviruses
• Genetic recombination & DNA mutability
• X-chromosome inactivation (in females)
• Genomic imprinting
Gene-body DNA methylation
Madeleine et al, Nature Biotechnology, 2009
41 (82%) out of the top 50 ranked cerebellum-cortex DMRs
are mirrored by significant gene expression differences
p = 1.90E-33
Cerebellum
Frontal cortex
DNA methylation
Log2 expression
EOMES
p = 1.39E-34
Cerebellum
Frontal cortex
DNA methylation
Log2 expression
GRM4
2. We have to reply upon
imperfect technology
Measuring DNA methylation is not like measuring genotype
Genome-scale assessment now feasible
Compromise between coverage and precision
Huge range of methods (enrichment, measurement and analysis)
No consensus on analysis method
There is a huge number of methylomic profiling methodologies
Sensitivity
Cost
Reproducibility
Coverage & Throughput
Sample requirements
Laird et al, 2010
Illumina 450K
methylation array
and EWAS
Still very much focused on CpG Islands….
High correlation across genome-wide
platforms…..
But…mainly driven by the fact that the
large majority of the genome is either
unmethylated or fully methylated
…substantial discrepancies between
platforms may exist for intermediate
level methylation
Can these genome-based approaches detect a small % change?
Many stages when inaccuracies in
measuring DNA methylation may occur
• Error (variation) can be introduced during
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Tissue / cell processing
DNA preparation / storage
Enrichment / conversion
Amplification
Measurement
QC
Analysis
• Accurate measurement may be vital if DNA methylation
is to be used for clinical (i.e. diagnostic or prognostic)
purposes
• But is ‘accuracy’ vital for epidemiological studies?
• Consistency / reliability more important?
Some inconvenient truths
• nothing you can do with normalization replaces
careful experimental design
• a nuisance variable confounded with what you
want to test can’t be fixed
– eg case and control in different batches
• nothing you can do with normalization replaces
rigorous QC
– samples with unusual raw intensity distributions
– multivariate methods such as PCA often identify
mislabeled samples
• Problems with outsourcing and core facilities
• Bad data will always be bad data!!
PCR bias: the effect of annealing temperature
Make sure you PCR machines are calibrated!!
PCR block Illumina batch effects
Block 1
Block 2
Block 3
Block 4
The Illumina 450K array – some
problems…
• Normalisation issues
– Type 1 and 2 probes
– Batch effects
– Array position
• Colour collection
• SNPs on probes
• Cross-hybridising probes
– Sex chromosomes very obvious
6-10% probes are non-specific
SNPs in array probes common
Features and sources of bias for
DNA methylation technologies
Laird, Nat Rev Genet, 2010
No method is perfect…
• Bisulfite-based methods: PCR biases, bisulfite / PCR
batch effects, hydroxymethylation
• Affinity-based methods – CG density, IP efficiency /
non-specificity, resolution, quantification, CNV
confounds
• MSRE-based methods – limited resolution, SNPs in
MSRE sites
• And it’s not all bad news especially for bigger effects.
• For smaller differences – does non-verification negate
a true difference?
• Data integration and meta-analysis across studies
MeDIP-seq vs 450K Illumina data
Cerebellum
Frontal cortex
Whole blood
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
P = 6.73E-104
Cerebellum
Frontal Cortex
Blood
Illumina 450K replication of MeDIP-seq identified DMRs
[but NB 20% of TS-DMRs not covered with any probes, and many only by single probes]
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Cerebellum
Frontal Cortex
Blood
MeDIP-seq vs bisulfite pyrosequencing data
• Low-frequency DNA methylation states
may not be optimally detected by genomewide (sequencing-based) approaches
because sensitivity is a function of readdepth
• They may also not be detected via sitespecific approaches such as
Pyrosequencing or Sequenom EpiTYPER
(sensitivity 2-5%)
• Ultra-deep bisulfite-sequencing of targeted
regions?
If something seems too good
to be true….it probably is
Things to look for when
interpreting published data…
• What enrichment/discrimination method has
been used?
• Have the relevant controls been used?
• Have reactions been done in replicate
(especially sodium bisulfite conversion,
bisulfite PCR)
• What magnitude of difference is being
reported?
• Do the differences reported exceed the
sensitivity of the platform?
F2RL3 encodes a protein that has functions
which are relevant to cardiovascular disease
Implications for confounding effects in
epigenetic epidemiological analyses
Sample-related technical issues…
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•
•
•
•
Limited number of samples – overlap between studies?
Pre-mortem factors and pH
Cause of death
Peri-mortem factors
Post-mortem factors
After a typical group lunch in the Mill lab…
DNA preparation (phenol/chloroform, columns, etc)
DNA storage (TE, Te, water, -80, -20, 4)
Importance to keep consistent across samples (confounding
case-control differences, longitudinal changes, etc)
26,320 years ago….
“…Our results suggest
that as long as ancient
nuclear DNA remains
amplifiable, cytosine
methylation patterns
can be
assessed…methylation
has been faithfully
retained along with the
DNA over evolutionary
timescales…” Llamas et
al, PLoS ONE, 2012
3. We may be limited by available
sample sizes that are optimal for
epigenetic epidemiology
The simple brute-force approach that
has been used (relatively) successfully in
GWAS is not valid for EWAS
Simply running Illumina 450K arrays on
your GWAS samples is potentially a
waste of ££
Rakyan et al, 2011
Discordant Monozygotic Twins – a powerful tool for
epigenetic studies of complex disease
Control for: age, sex,
genetics, pre-/perinatal environment,
parental origin
Verification of array data
Replication in brain tissue
Significant hypomethylation
(20-30%) in 15% of SZ brains
Dempster et al (2011)
Chloe Wong
Virtually Identical
Large Changes
DRD4
SERT
 Discordance for
disease phenotypes??
MAOA
Wong et al 2010
Longitudinal sampling…
Most existing cohorts were not designed with epigenetics in mind
Do sequential samples (of relevant tissues/cells) exist in ongoing
longitudinal cohorts / biobanks?
DNA methylation at three CpG
sites—in the promoters of the
EDARADD, TOM1L1 and NPTX2
genes—is linear with age over a
range of five decades.
Regression model that explains 73%
of the variance in age, and is able to
predict the age of an individual with
an average accuracy of 5.2 years!
Age is a huge potential confounder in epigenetic studies
Ruth Pidsley (unpublished)
Bell et al, PLoS Genetics, 2012
Slide No. 62
Boks et al (Epigenetics, in press)
CRESTAR Kick-off Meeting 2011
4. Whatever we do, it may never
be enough to fully account for
epigenetic differences between
tissues and cells
Davies et al, in press
Machine learning  100% tissue discrimination
HOXA gene cluster
Blood
BA9
BA10
BA8
EntCtx
STG
Cerebellum
Individual differences – conserved
across blood and brain?
r=0.87, p<0.0001
Much more data needed!
Cell-type-specific methylomes: neurons, astrocytes, glia
Katie Lunnon, Jon Cooper
5. We may be trying to detect
inherently small effect sizes using
sub-optimal methods and sample
cohorts
Do the small DNA methylation differences often observed between groups translate
into differences in gene expression in the relevant tissue??
1500bp upstream of transcription start siteP<1x10-8
P-Value
HOXB8
NKX2-5
C17orf100
SGCE
C4orf38
HAUS2
LOC399815
LRP1B
FAM92B
4.10E-06
2.54E-05
8.10E-05
0.000114176
0.0001863
0.000211401
0.000267029
0.000327942
0.000347768
Mean meth difference=-0.02
Adjusted P-value Methylation
difference
0.08
-0.02
0.26
-0.03
0.53
-0.01
0.53
-0.01
0.53
-0.01
0.53
-0.01
0.53
0.04
0.53
0.04
0.53
0.02
Confirming findings in cleaner model
systems (e.g. cell and animal models)
Control for confounding factors and environmental influences
But a mouse is not a man, and a cell-line is not a body
6. We lack a framework for the
analysis of genome-wide epigenetic
data
Reference epigenomes – across cells and tissues – what is
normal?
Cataloging regions of high inter-individual variation
Integrating epigenomic data with genetics and other –omics
information
Reference Epigenomes
Technology Development
Novel Epigenetic Marks
Epigenomics of Human Health & Disease
Neurodegeneration
Bipolar disorder
Schizophrenia
Autism
Atherosclerosis
Hypertension
SLE
Kidney disease
Asthma
Insulin Resistence
Click here to browse data
http://www.roadmapepigenomics.org
Where is the data: sites with unique features
Consortium homepage http://roadmapepigenomics.org
• View data on genome
• protocols
• standards
NCBI
http://ncbi.nlm.nih.gov/epigenomics
http://ncbi.nlm.nih.gov/geo/roadmap/epigenomics
• View data
• Download data
• Compare samples
Human Epigenome Atlas http://epigenomeatlas.org
• View data on genome or with Atlas gene browser
• Download data
• Tools at Genboree Workbench
WashU VizHub
http://vizhub.wustl.edu
• Next-gen browser http://epigenomegateway.wustl.edu
• UCSC visualization hub at http://genome.ucsc.edu
What data are available to me?
Range of cells/tissues covered:
Currently 125 cell/tissue types represented including….
iPS and ES cells, some differentiated forms
Fetal tissues (heart, brain, kidney, lung, others)
Adult primary cells and tissues (hematopoietic, brain regions, breast cell types,
liver, kidney, colon, muscle, adipocytes, others)
Most samples will have:
DNA methylation data (RRBS, MRE-seq, MeDIP-seq, whole genome bisulfite seq)
ChIP-seq data (currently H3K27me3, H3K36me3, H3K4me1, H3K4me3, H3K9me3)
DNase I hypersensitivity data
Gene expression data (arrays or RNA-seq)
Some samples will have:
Expanded panel of histone modifications (currently 20+)
Can download:
.wig, .bed, some .bam, SRA, peak calls
Two ways to browse: Data Table
Type search terms here to narrow list
Search isn’t literal (…type “lung”, “blood”)
Two ways to browse: Visual Browser
Mouse over sites, click for table
Genome-wide epigenetic data at NCBI: Epigenomics Gateway
Tutorials
Compare
samples
Text search or
browser search
http://www.ncbi.nlm.nih.gov/epigenomics
Compare Samples: Identify genes with significant
epigenetic differences
GO Terms and
pathways
found most
frequently
The Human Epigenome Atlas
Genboree
workbench
Click for
data
Click to view
selected data
sets
Click to download
data, or for metadata
A new epigenomics browser: data and metadata
together
Click here
for browser
http://epigenomegateway.wustl.edu
significance
What can I do with the data: interpret GWAS hits
Genomic locus
Data from ENCODE:
Human and mouse:
http://genome.ucsc.edu/ENCODE
Fly and worm:
http://modencode.org
Top SNPs linked to cell type
specific enhancer states in
disease relevant cell types
Ernst et al, Nature 2011
Beyond GWAS: Integrated genetic-epigenetic
approach to common disease
7. Back to hype and bad
science reporting –
especially with regard to
“transgenerational
epigenetic inheritance”
We need to manage expectations
Epigenetic profile erased and reset
de novo during gametogenesis
Transgenerational longitudinal cohort studies
Questions?
Lecturer in Epigenetics
Bioinformatic approaches and computational epigenomics
Environmental epigenomics
Functional epigenomics
Postdoctoral Research Workers
Laboratory Technician
PhD students
Contact: [email protected]
www.epigenomicslab.com