Identification of epigenetic patterns in birth cohorts

Allan Just PhD

Harvard School of Public Health

PPTOX IV: Boston 2014

Identifying epigenetic patterns in birth cohorts

Contact: [email protected]

New directions for epigenomics in epidemiology

1. Why epigenetics excites the PPTOX crowd

2. How high-throughput technologies are

changing the biomarker game

3. Strategies when features vastly outnumber

samples

Allan Just – PPTOX2014 2

Epigenetics in perinatal programming

Boekelheide et al. Environ Health Persp. 2012 Allan Just – PPTOX2014 3

Beyond candidate gene approaches: Epigenome-wide

flickr.com/photos/jgr/2771104231/


High throughput + quantitative epi

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Joubert et al. EHP 2012

Prenatal smoking has reproducible associations with the fetal methylome measured in blood

n=1062 Effects are small differences Sign differs within AHRR

Same top site seen in former vs never smoking adults

X-axis: Years since quitting (showing 432 former smoking males from the Normative Aging Study)

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line adds median from 187 never smokers

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Steps toward a successful epigenome-wide association study

Michels et al. Nature Methods 2013 Allan Just – PPTOX2014 11

Information/cost tradeoff Pyrosequencing few sites; ~$5/sample Sequenom MassArray Illumina 450k BeadChip 485K sites; ~$300/sample

RRBS 1M sites

CpGiant 5M sites

Whole Genome Bisulfite Seq 28M sites; $$$


High throughput technologies

Microarrays

Standardized assays

Measure the same thing

Less expensive

Less flexible

Less future development

Next Gen Sequencing

More sites per sample

Costs rapidly decreasing

Innovation in new assays

Bioinformatic challenges

Information per site varies

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Measuring DNA methylation with the Illumina 450K microarray

• bisulfite treated DNA hybridizes to targeted probes → fluorescence

• % methylation = (methylated / unmethylated + methylated fluorescence)

• 485,512 methylation sites

Image from illumina.com


The benefit of standards: widespread adoption of the 450k

• Shared methods – software for analysis

• Public Repositories

marmal-aid.org (Lowe and Rakyan 2013)

contains 14,586 samples as of 10/27/2014


Opportunities for cohort epigenomics

Standardization of laboratory assays

measurement becomes a service

But, overwhelming amounts of data

How do you process,

learn from,

communicate with bigger data


Big(ger) data: scaling our approach • Having more data doesn’t relieve assumptions

(e.g. confounding, linearity, etc)

flickr.com/photos/terryfreedman/15425678852/


Tradeoffs and consequences of the new epigenomics

Multiple Comparisons:

On the 450k Bonferroni significance is ~1e-7

What happens when n << p?

Can you look at everything and find something?


Wilhelm-Benartzi et al. Br J Cancer 2013

Advancing Epigenomics Theme 1: improved measurements


Advancing Epigenomics Theme 2: improved analytic approaches

Restrict the search:

• Subset to biologically relevant features

• Prioritize best measured features

Borrow information:

• Pathway analyses

• Bump hunting or adjacency clustering (Aclust)


Borrow from your neighbor: Aclust

• Cluster adjacent sites based on correlation (within nearby region, ~1kb)

• Model methylation in the cluster as a multivariate outcome

Sofer et al. Bioinformatics 2013 Allan Just – PPTOX2014 21

Advancing Epigenomics Theme 3: larger sample sizes

Meta-analysis:

Prenatal And Childhood Epigenetics (PACE) Consortium


Summary: New directions for epigenomics in epidemiology

1. Exciting prospects to discover broad links with

early exposures and later outcomes

2. Measurements are standardized; analyses are

complicated

3. Need care when n<<p; borrow information

using biology and statistics


Collaborators

• Andrea Baccarelli

• Robert Wright

• Roz Wright

• Joel Schwartz

• Xihong Lin

Computational Epigenomics working group: Amar Mehta

Elena Colicino

Golareh Agha

Richard Barfield

Grant Support from the NIEHS: K99 ES023450

Contact: [email protected]

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Identification of epigenetic patterns in birth cohorts

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