Lessons Learned in a Multi-Tissue Genome Wide Methylation Study

Elissa Wilker, ScDBeth Israel Deaconess Medical Center

Outline

Participant Recruitment

Data Collection

Sample Processing

QC Assessment

Data Analysis

•Pilot Study Background

•Methods

•Evaluating Data Quality

Key Points

Atrial Fibrillation

http://www.porthuronhospital.org/

Atrial Fibrillation (AF)

Magnani J W et al. Circulation 2011

• Epigenetics refers to factors influencing phenotype of a specific cell type that are not caused by changes in the DNA sequence

• DNA Methylation may influence atrial function– 5‑methylcytosine (5mC)– In promoter --> repression– More complex relationship in genomic regions– Can be measured through sequencing and microarray technology

• However, the relationship of leukocyte methylation to target tissue of the atrium is unknown

DNA Methylation

www.illumina.com

• Using genome wide methylation to examine the following in a population of Coronary Artery Bypass Graft (CABG) patients:

– Association (correlation) between methylation in the atrial tissue, internal mammary artery tissue, and peripheral leukocytes

– Methylation patterns predictive of post operative atrial fibrillation

Study Objectives!

Methods

• 18 patients underwent coronary artery bypass graft (CABG) at BIDMC

• Recruited at pre-operative testing or if admitted the day prior to surgery

• Clinical data abstracted from the medical record

• Followed over index hospitalization for incidence of AF

• 6 of the 18 developed AF

Participant Recruitment

Data Collection

Sample Processing

QC Assessment

Data Analysis

Methods

• Peripheral leukocytes collected prior to anesthesia induction

• Right atrial appendage and left internal mammary artery (LIMA) collected and transferred to laboratory for tissue sectioning and storage -80⁰C

• DNA extracted from leukocytes and tissue samples (PAXgene kits) by laboratory staff

Participant Recruitment

Data Collection

Sample Processing

QC Assessment

Data Analysis

Methods

• Concentration determined and duplicates selected

• Bisulfite (HSO3-) treatment to identify non-

methylated cytosine bases in genetic code

• CpG methylation evaluated with the 450K Infinium Methylation BeadChip (Illumina, San Diego, CA)

Participant Recruitment

Data Collection

Sample Processing

QC Assessment

Data Analysis

• 450,000 DNA methylation sites covering 14,000 genes • Major advance from : uses 2 different assays– Infinium I vs. Infinium II

• The % methylation of cytosine bases is reported as the beta value• Methylated / (Methylated + Unmethylated + 100)

• Raw data received• QC info– Average loci detected– 485,395 out of 485,577 loci (99.9%)

– Average beta (0-100% methylation):• 0.49

– 6 samples out of 56 run in duplicate:• Concordance rate for 4 out of the 6 samples and their

respective duplicates gave an r2 value above 0.99. – Sample 9-blood & 9-blood-D r2 value was 0.95.– Sample 18-atrium & 18-atrium-D gave low concordance of 0.85

Sample Processing: Infinium 450K

Methods

• Plot raw data

• Sample validation

• Begin to answer key data analysis questions

Is methylation preserved across tissue types?

Participant Recruitment

Data Collection

Sample Processing

QC Assessment

Data Analysis

Spatial Artifact

Study Samples(blood, atrium, and artery)

Hierarchical Clustering

Hierarchical Clustering

Blood Artery Atrium

Hierarchical Clustering

Blood Artery Atrium

Correlation Matrix

4-blood

4-blood-D

18-Atrium

18-Atrium-D

18-Artery

18-Artery-D

9-Blood

9-Blood-D

Correlation Matrix

17

4-blood

4-blood-D

18-Atrium

18-Atrium-D

18-Artery

18-Artery-D

9-Blood

9-Blood-D

Correlation Matrix

4-blood

4-blood-D

18-Atrium

18-Atrium-D

18-Artery

18-Artery-D

9-Blood

9-Blood-D

Reanalysis

SNP Profile

• Beta values will cluster differently depending on whether comparison is within or between individuals

Same Participant Different Participants

Data AnalysisGenes with Differentially Methylated Probes by AF Status

Data Analysis• DNA methylation was largely conserved across tissue status

• Linear regression and Support Vector Machine (SVM) models for each CpG site to predict methylation level in a target tissue based on the methylation level in a surrogate tissue

• New method shows promise to improve accuracy for cross tissue prediction• R2 increases from from 0.83 to 0.99 for blood-atrium prediction

Ma et al., in review