THE OMICS REVOLUTION

OMIC “All constituents considered

collectively”

OMIC OMICS

Integrating ‘Omics’ technologies

Lifecourse

Development

To

Ageing

Bioinformatics

& Statistics

Data

integration

Data mining

Analysis

methods

• SNPs

• CNVs

• mtDNA variants

• Rare variants

• Whole genome sequencing

• DNA methylation

• miRNA expression

• Chromatin analysis

• LCL gene

expression

• Tissue specific

expression

profiles

• NMR metabolites

• One carbon

intermediates

• Questionnaires

• Clinical assessment

• Health records

• Recall

• Imaging

• Educational

attainment

• Data capture

• Questionnaires

• Environmental

monitoring

• Biological

sample

analysis

• Orbitrap-MS

intact protein

analysis

TARGETED METABOLOMICS (AND

PROTEOMICS)

METABOLOMICS – MEASURING SMALL MOLECULES

Kettunen et al. Nature Genetics 2012; 44(3):269-76

Kettunen et al. Genome-wide association study identifies multiple loci influencing

human serum metabolite levels. Nature Genetics 2010; 44,: 269–276.

Varbo et al. Remnant cholesterol as a causal risk factor for ischemic heart disease. J AM Coll Cardiol

2013;61:427-36.

Varbo et al. Remnant cholesterol as a causal risk factor for ischemic heart disease. J AM Coll Cardiol

2013;61:427-36.

Varbo et al. Remnant cholesterol as a causal risk factor for ischemic heart disease. J AM Coll Cardiol

2013;61:427-36.

PLEIOTROPIC EFFECTS OF LIPID-RELATED

SNPS UTILISED IN MR STUDIES

Wurtz P, et al. Lipoprotein Subclass Profiling Reveals Pleiotropy in the Genetic Variants of Lipid Risk Factors for Coronary Heart Disease – A Note on Mendelian Randomization Studies, submitted.

PLEIOTROPIC EFFECTS OF LIPID-RELATED

SNPS UTILISED IN MR STUDIES

• Wurtz P, et al. Lipoprotein Subclass Profiling Reveals Pleiotropy in the Genetic Variants of Lipid Risk Factors for Coronary Heart Disease – A Note on Mendelian Randomization Studies, submitted.

GE

NE

TIC

EF

FE

CT

S VS.

C

RO

SS-S

EC

TIO

NA

L O

BS

ER

VA

TIO

NS

LO

NG

ITU

DIN

AL

CH

AN

GE

S

VS.

CR

OS

S-S

EC

TIO

NA

L O

BS

ER

VA

TIO

NS

UNTARGETED METABONOMICS

“ANONYMOUS MENDELIAN RANDOMIZATION”

Select participants by extremes of genetic risk

predictions scores

Samples from ages before disease develops

Untargeted metabolomic analysis

Anonymous metabolite peaks identified that

differ between groups categorized by allele scores

GWAS of these peaks

Relate (in e.g. GWAS consortia) peak-associated

SNPs with disease

Follow up characterising the peaks which

triangulate through MR analysis

MICROBIOME

Qin J et al. A human gut microbial gene catalogue established by metagenomic

sequencing. Nature 2010 464; 59-67

Qin J et al. A human gut microbial gene catalogue established by metagenomic

sequencing. Nature 2010 464; 59-67

THE DIET-MICROBE MORBID UNION

Rak K et al. Nature 2011; 472:40

EPIGENOME

Epigenetics: the confused epidemiologist's friend.

Davey Smith G Int. J. Epidemiol. 2012;41:303-308

Published by Oxford University Press on behalf of the International Epidemiological Association ©

The Author 2012; all rights reserved.

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)

Linked to extensive associated data on genetics, exposures and phenotypes

Methylome sequencing BS-seq* 10-mother-child pairs at 5 time points across the life course(s)

Tissue specific (and paired blood) genome-wide DNA methylation analysis Muscle, adipose, liver, cartilage, skin, brain, fetal tissues

HM450 analysis and BS-seq*

Data integration

Data visualisation and browsing

Reference resource for ALSPAC (and other?) case-control studies Additional nested case-control studies in progress and planned (conduct

problems, rheumatoid arthritis, asthma, breast cancer, assisted conception etc)

* Agilent SureSelect targeted BS-seq

SERIAL SAMPLES FROM THE SAME INDIVIDUALS

http://www.ariesepigenomics.org.uk/

TISSUE SPECIFIC ANALYSIS

Adult tissues Adrenal gland Adipose Bone Brain - Frontal cortex - Temporal cortex - Hippocampus - Substantia nigra - Dorsal raphe nucleus - Putamen - Hypothalamus - Amygdala - Cerebellum Cartilage Eye Gonad - Ovary - Testis Heart Intestine - Colon Kidney Liver Lung Muscle Palate Pancreas Skin Spleen Stomach Thyroid

Fetal tissues Adrenal gland Bone - Rib cage Brain - Fore - Mid - Cerebellum Eye Gonad - Ovary - Testis Heart Intestine - Large - Small Kidney Liver Lung Muscle - Leg Pancreas Skin Spleen Stomach Thymus Umbilical cord Chorionic villi Yolk sac

BIOINFORMATICS INFRASTRUCTURE

BIOINFORMATICS WORKFLOW

1) Windows

Scheduled Task

00:00 : Push data to

epi-linus

2) Chronjob

03:00 :

• Check for new

files

• Copies IDATs to

repository

3) Epi-linus

envokes PHP

script to

generate

manifest files

using LIMS

data

4) R: Run lumi script

•p-value plot

•QC probe plots

•Dump out control probe betas

5) R: Run minfi script

• PDF QC report

6) Epi-linus

pushes QC

plots to LIMS

interface

7) PERL:

Parses QC

probe data.

Connect to

LIMS database

LIMS VM

8) LIMS mysql

Receive QC data.

Website updated

9) Archive:

• Manifest files

• Logs

EPI-

LINUS

Lab/Scanner

PC

VISUALISATION AND COMPARISON OF EPIGENETIC AND RELATED DATA

• Genome browser visualisation

• Comparison with other data sources

• Comparison of tissues, mother-child pairs, time points, age groups, genotype groups, etc

• Alignment with gene expression and SNP data

INTEGRATION WITH DATA FROM OTHER SOURCES

• ENCODE: http://genome.ucsc.edu/ENCODE/ - especially methylation, open chromatin etc.

• Published GWAS results: http://www.genome.gov/gwastudies/ (also UCSC track)

• OMIM: http://www.ncbi.nlm.nih.gov/omim/ (also UCSC track)

• Other UCSC tracks: http://genome.ucsc.edu/cgi-bin/hgGateway

– CpG Islands (and possibly other "Regulation" tracks), %GC

• RoadMap Epigenomics tracks: http://www.ncbi.nlm.nih.gov/epigenomics/

• MethylomeDB: http://epigenomics.columbia.edu/methylomedb/index.html

• PubMeth: http://www.pubmeth.org/ (and de novo lit mining in non-cancer areas?)

• Imprinted Genes database: http://igc.otago.ac.nz/home.html

• MethDB: http://www.methdb.de/

• DiseaseMeth: http://bioinfo.hrbmu.edu.cn/diseasemeth

• MicroRNA target database: http://www.microrna.org/microrna/home.do or http://www.targetscan.org/index.html

• ChromDB: http://www.chromdb.org/

• GEO: http://www.ncbi.nlm.nih.gov/geo/

• KEGG: http://www.genome.jp/kegg/

• STRING: http://string-db.org/

ARIES GENOME BROWSER (PROTOTYPE)

A TWO-STEP APPROACH

Exposure Phenotype

SNP 1

CpG

(A) Step 1

Exposure Phenotype

SNP 2

CpG

(B) Step 2

Relton CL, Davey Smith G, Two step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol 2012;41:161-176

EPIGENETIC MEDIATION OF SMOKING AND CARDIOVASCULAR DISEASE

Identified through EWAS of smoking on DNA methylation

Smoking associated with methylation of the coagulation factor II receptor-like 3 gene (F2RL3)

expression of the protease-activated receptor-4 (PAR4)

Induces platelet activation (aggregation)

Plausible mechanism of smoking induced CVD

↑ smoking ↑ platelet aggregation

↑ risk CVD ↑ expression PAR4

↓ methylation F2RL3

Breitling LP et al. Am J Hum Genet 2011 N=177 discovery N=316 replication

SMOKING, F2RL3 METHYLATION AND PROGNOSIS IN STABLE CORONARY HEART DISEASE

Breitling LP et al. Eur Heart J Apr 2012

Methylation level

Secondary CVD event

CVD mortality Non-CVD mortality

All-cause mortality

>0.74 (Q4) 1 (ref) 1 (ref) 1 (ref) 1 (ref)

0.74 (Q3) 0.92 (0.57-1.50) 1.06 (0.46-2.46) 1.55 (0.59-4.12) 1.26 (0.67-2.37)

0.67 (Q2) 1.14 (0.71-1.84) 1.89 (0.85-4.16) 2.33 (0.88-6.19) 2.07 (1.12-3.83)

0.54 (Q1) 1.40 (0.83-2.36) 3.49 (1.51-8.04) 5.36 (1.93-14.8) 4.19 (2.20-8.00)

Cox’s regression model of F2RL3 (CpG_4) methylation and prognosis in stable coronary heart disease

SMOKING AND CARDIOVASCULAR DISEASE

Smoking

CVD

F2RL3

SNP

U

F2RL3

CpG E Y

G

U

X

(B) Step 2

E Y

G

U

X

(A) Step 1

Smoking

CVD

CHRN3/5

SNP

U

F2RL3

CpG

INDUCED PLURIPOTENT STEM

CELLS

IPSC IN POPULATION HEALTH SCIENCES

RESOURCE FOR INDUCED PLURIPOTENT

STEM CELL STUDIES

Establish an accessible and sustainable biobank of iPSC from

ALSPAC participants

Establish collaborative links for the differentiation of iPSC

into desired cell lineages using standardised protocols

Fuel research into

The biology of pluripotency

Disease modelling

Drug screening

Cell-based therapies

Functional characterisation of GWAS hits (select

individuals by genetic load/allele score for in depth

molecular phenotyping)

RE-PROGRAMMING OF EBV TRANSFORMED

LYMPHOBLASTOID CELL LINES

HARVARD

Episomal

plasmid

method BRISTOL

Lentiviralm

ethod

LONDON

Heterokaryon

method

NEWCASTLE

Sendai virus

method

Reprogramme Assess pluripotency Assess removal of Differentiate into

EBV genome desired lineage

DATA INTEGRATION

MAKING THE MOST OF MULTI-DIMENSIONAL DATA - INTEGRATION AND DATA MINING

MAJOR NEW DEVELOPMENTS IN OMICS IN

BRISTOL

MRC Integrative Epidemiology Unit (IEU)

Epigenomics lab

Metabolomics facility

ALSPAC Strategic Award

Epigenomics programme

Expansion of biobanking provision

Building on ARIES

Bioinformatics growth

MRC High-throughput sequencing ‘omics’ funding proposal

iPSC

Project grants

MRC IEU

MRC Integrative Epidemiology Unit (IEU)

Scientific Structure

EPIGENOMICS LAB

To support a wide range of activities in

epigenomics research

To follow up on observational associations

through functional studies; to validate

differentially methylated regions seen in studies

using the Illumina HM450K array

To assess functional consequences of differential

methylation

Manipulation of DNA methylation levels

Gene expression

METABOLOMICS FACILITY

To establish capacity for high throughput analysis of

multiple metabolites from serum samples using NMR

spectroscopy

Led by Prof Mika Ala-Korpela

Housed in School of Chemistry

Other metabolomic initiatives are being piloted (e.g. mass

spectrometry of serum, and of histone proteins to measure

epigenomic profiles)

ALSPAC STRATEGIC AWARD

CORE PROGRAMME FUNDING RENEWAL

To enhance ALSPAC to incorporate detailed annotation of

epigenomic features and additional epigenomic profiling.

To exploit the unique scientific opportunities afforded by

ALSPAC to identify epigenetic signatures of exposure,

track their persistence over time, across generations and

evaluate their relationship with development and disease.

To assess the genetic contribution to DNA methylation

variation through leadership of a Genome-Wide Association

Study Consortium.

LC-MS/MS method development for high throughput

histone modification analysis. Analysis of 100 samples.

miRNA profiling of 100 samples.

EXPANSION OF BIOBANKING PROVISION

MRC NSHD 1946 cohort

Millennium Cohort Study

Southall And Brent REvisited (SABRE)

Born in Bradford Study

Cleft Collective

Head & Neck 5000

Sub-samples of other estbalished cohort studies (e.g.

MOBA, Copenhagen City Heart Study)

Other funding applications (e.g.Bio.ME)

...

BUILDING ON ARIES

Bioinformatics growth

ARIES- Explorer (BBSRC £1M)

Links with EBI for more rapid and widespread omics

data release, in line with other large scale international

omics projects

MRC High-Throughput Sequencing and ‘Omics’ profiling

funding bid

Large scale profiling of ALSPAC LCLs (£2.2M)

Project grants

Lots, commonly requesting additional HM450K

profiling of selected samples plus validation using

Pyrosequencing

Non-ALSPAC studies requesting HM450K analysis

Lymphoblastoid cell lines, n=500 Existing data:GWAS ; HM450k peripheral blood ; WGS(UK10K)

Epigenomics Histone preparation

20µg

Transcriptomics RNA extraction

10µg

Epigenomics DNA extraction

6µg

Methylation HM450k

1µg/sample

Methylation BS-seq

5µg/sample

Expression RNA-seq

10µg/sample

Histone PTM ChIP-seq 4xIP 4µg/sample

In house: ALSPAC

Laboratory

Outsource: Babraham Institute

Outsource: UoB

Chemistry

Histone PTM LC-MS/MS

7.5µg/sample

Outsource: MRC HTS Hub

GenePool, Edinburgh

Future phenotyping LCL storage

In house: ALSPAC

Laboratory

ENHANCEMENT OF ALSPAC THROUGH LARGE

SCALE OMIC PROFILING OF LYMPHOBLASTOID

CELL LINES

MRC High Throughput Sequencing and Omics

Profiling.

Sept 2013-March 2014

PUMP-PRIMING FUNDING FOR IPSC WORK

1. Initial pilot and feasibility studies on iPSC derivation from LCLs. In progress.

2. Optimization of the necessary work flow and quality control procedures within the ALSPAC labs.

3. Establishment of differentiation of iPSC into various lineages and comparison with fibroblast derived iPSC.

4. Development of links with the European Bioinformatics Institute (EBI) for omics data management.

5. Exemplar ‘case studies’ to illustrate the value and utility of an ALSPAC iPSC resource. Sample selected based on genetic load , LCLs reprogrammed to iPSC, relevant lineages derived and functional studies conducted;

Schizophrenia

Glucose trafficking

Psoriasis

PROGRAMMES, PROJECTS AND OTHER

ACTIVITIES

Metabolomic profiling of 35,000 ALSPAC serum samples at

various ages

UK10K; Whole Genome Sequence data on 2,000 ALSPAC

individuals

Is DNA methylation influenced by...

Alcohol, smoking, heavy metals, folate and related

metabolites, lipids, glucose, insulin, assisted

reproductive technologies, social deprivation, ...etc

Does DNA methylation play a role in...

Cardiovascular disease, respiratory function, diabetes,

eating disorders, depression, neurodevelopment, autism,

intellectual disability, ... etc

ACKNOWLEDGEMENTS

Caroline Relton

Peter Wurtz