Epigenetic Studies in ALSPAC

Report
Epigenetic Studies in ALSPAC
Caroline Relton
CAiTE Symposium
12th January 2010
Objectives
• Define DNA methylation variation due to DNA source and
extraction method
• Quantify changes in DNA methylation over time
• Validate differential DNA methylation at selected target loci in serial
DNA samples
• Undertake pilot MeDIP-sequencing of the entire methylome
• Quantify differential DNA methylation in pre- and post-menopausal
women
• Apply a Mendelian randomisation approach to strengthen evidence
for a causal relationship between environmental exposures, DNA
methylation and childhood outcomes
• Develop bioinformatic and statistical approaches for handling DNA
methylation data
Define DNA methylation variation due
to DNA source and extraction method
Time
Tube
Extraction
T1
Birth
Heparin
Phenol
T2
43 months
EDTA
Phenol
T3
61 months
EDTA
Phenol
T4
7 years
EDTA
Salting out
T5a
9 years
CPD/ACD
Guanidine hydrochloride
T5b
9 years
Cell ine
Guanidine hydrochloride
T0
Pregnancy
EDTA or heparin
Phenol
T0+17
+17y follow-up
EDTA
Guanidine hydrochloride
Avg Beta, Phenol
Average beta (Me/unMe)
#
Buffy
coat
White
cells
Whole
blood
114 probes more
methylated in guanidine
extracted DNA (> 1.5-fold)
Avg Beta, Guanidine
Quantify changes in DNA methylation
over time
• Sequenom EpiTyper
• 6 amplicons
• 12-21 CpG sites per
amplicon
• Birth and 7y DNA
• N=90
• Correlation is much
lower than that
observed in adults at 2
time points
• Additional samples are
being analysed
Highest intra-probe correlations
Amplicon
Spearman’s
rho (B)
95% CI (B)
Spearman
p
FTO_16
0.341
0.099, 0.568
0.006
P16_38
0.292
0.075, 0.490
0.008
P16_4
0.265
0.058, 0.458
0.014
IGF2BP2_11
-0.242
-0.439, -0.04
0.024
IGF2BP2_29
0.237
0.003, 0.464
0.039
PPARg_29
0.215
0.020, 0.398
0.040
P16_25
0.211
0.014, 0.397
0.047
IGF2BP2_26
0.216
-0.017, 0.443
0.047
B = bootstrapped
Validate differential DNA methylation
at selected target loci
BMI
Fat mass
SNP-dependent locus associated
with insulin resistance
Methylation at CpG vs rs231840
Lean mass
CDKN1C
MMP9
MPL
CDKN1C
EPHA1
HLA-DOB
NID1
Methylation (%)
6.0
5.0
4.0
3.0
2.0
1.0
0.0
-1.0
-2.0
-3.0
CASP10
CDKN1C
EPHA1
Change in outcome / 1%  in methylation
Methylation at birth and body
composition in childhood
Genotype (rs231840)
Undertake pilot MeDIP-sequencing of
the entire methylome
• High vs normal BMI
• Aged 0y, 7y and 15y
• N=10 per group
• MeDIP-seq pilot
• Illumina 27K array
Quantitative comparison of genome-wide
DNA methylation mapping technologies
Christoph Bock, Eleni M Tomazou, Arie B Brinkman,
Fabian Müller, Femke Simmer, Hongcang Gu, Natalie
Jäger, Andreas Gnirke, Hendrik G Stunnenberg &
Alexander Meissner
Nature Biotechnology : 28: 1106–1114 (2010)
Quantify differential DNA methylation
in pre- and post-menopausal women
Pregnancy vs +17y
Avg beta Post MP
▫ 1032 CpG sites differ +/- 5%
Avg beta Pre MP
Pre vs Post menopause
▫ 199 CpG sites differ +/- 5%
Average methylation for 5 largest methylation
shifts in each direction with SD error bars
Apply a Mendelian randomisation
approach
CVD
BMI
CpG
ADH1B
Reverse
causation
CVD
Confounded
Alcohol
CpG
HNSCC
CpG
BMI
CpG
CVD
On causal
pathway
Socio-economic
position
Nutritional status
Smoking
BMI
CVD
CpG
Independent
and both causal
Alternative non-epigenetic pathway
Develop bioinformatic and statistical
approaches
Defining where in the genome
to look for differential
methylation
• In silico tools
▫ CGI Explorer
▫ Data mining tools
▫ Transcription factor binding
sites
• Gene expression data
• Whole methylome analysis
▫ MeDIP-seq
• Genome-wide site-specific
analysis
▫ Illumina 450k array
• Targeted approaches
▫ Illumina VeraCode
Analysing DNA methylation
data
• Large data sets
• Highly correlated
• Non-normal distribution
• Outlier effects
• Temporal variation
• Tissue specificity
• Differences in DNA source and
method used
▫ Illumina
▫ Sequenom
▫ Pyrosequencing
• Relationship between genotype
and epigenotype
Grant submissions and future plans
• Grants awarded
▫
▫
▫
WT/MRC Strategic Award (GDS)
WT ALSPAC Mums (DAL)
MRC Fellowship (LZ)
• Grant submitted
▫
▫
▫
▫
NIH Conduct problem trajectories (EB)
NIH Obesity and epigenetics (CR)
MRC ALSPAC Mums (DAL)
BBSRC BBR (GDS)
• Grants in preparation
▫
▫
MRC Obesity and epigenetics (CR)
NIH methylation trajectories in development and ageing (CR)
• Future directions
▫
▫
▫
▫
▫
▫
Prostate cancer (RM)
Insulin resistance/T2D (CR)
Air pollution and respiratory phenotypes (JH, PV, PE)
UV exposure (JT, AS)
Genetical epigenomics (GDS)
Other longitudinal studies (MCS, NSHD (1946), Bto20, IMS, APCAPS, Barshi)
Acknowledgements
•
•
•
•
•
•
•
•
•
•
•
•
George DS
Debbie L
Sue R
Wendy M
Beate StP
Tom G
Adrian S
Jon T
Luisa Z
Nic T
Kate T
Kate N
• Hannah Elliott
• Alix Groom

similar documents