Contribution of Epigenetic Variation to Expression Changes Among Tissues and

Genotypes

Steve Eichten – Springer LabPAG iPlant Workshop1/17/12

Outline

• Questions we are interested in

• Using iPlant to assist in answering these questions

• Current and future analysis

Epigenetics & Gene Expression

• Heritable variation not due to sequence variation– DNA methylation, histone modification,

etc– Classic examples: imprinting,

paramutationAreas of research:• Epigenomic variation across genotypes &

development• Relationship between genetic & epigenetic

variation• Role of epigenetic variation in phenotype (Chandler & Stam, 2004)

Epigenetic variation• Across genotypes:

– Maize Nested Association Mapping population

(26 inbreds)• Across development:

– 5 tissues• Embryo• Endosperm• Leaf• Immature ear• tassel

25 DL SSD

B97

CML103

CML228

CML247

CML277

CML322

CML333

CML52

CML69

Hp301

Il14H

Ki11

Ki3

Ky21

M162W

M37W

Mo18W

MS71

NC350

NC358

Oh43

Oh7B

P39

Tx303

Tzi8

To answer these questions…• Genome-wide methylation assessment

– meDIP-chip (2.1M probe platform)– BS-seq (for B73 and Mo17 inbreds)

• Genome-wide expression assessment– RNA-seq (~20M reads x 25 inbreds)

Technology Leaf Embryo Endosperm Ear Tassel TOTAL

meDIP-chip 75 6 6 6 6 99 arrays

BS-seq 179,109,340 179,109,340 reads

RNA-seq 558,210,732 42,581,924 43,419,680 85,671,330 73,105,335 802,989,001 reads

~98,200,000,000 bases209,949,399 array data points

• Bioinformatic knowhow• Computational power and storage

– iPlant!

Old way1. Buy larger hard drives2. Upgrade computers3. Buy more hard drives4. Gain basic understanding of UNIX / Perl /

R / etc…5. Lots of command line work / installation /

troubleshooting- Array normalization / fastqc / tophat / cufflinks / DEGseq /

……

6. Realize you want it run analyses a different way. {GOTO 4}

7. Realize you really need more computing power to do what you want. {GOTO 2 or make some friends at a supercomputing institute}

8. Train others in your lab group how to do it! {Document everything and GOTO 1}

New way• Move sequence data to iPlant DE• Select apps for desired analysis• Run software faster than you can

locally• Quickly adapt methods to find

optimum• Develop analysis pipeline for

others to use• Store large files for others in your

lab group to access

Larger storage + Faster Computing + Faster Training + Faster Adaptation + Faster Implementation

= more user-friendly, more powerful computing

Applying iPlant to maize epigenomics

fastq files

Tophat aligner to pre-indexed maize reference genome

Cufflinks transcript assembly against maize

gene models

iRODS file transfer to iPlant

iPlant integrated Apps

Download files from iPlant, display in IGV

Assess read quality (FastQC)iPlant integrated Apps

Methylation variation in B73 and Mo17

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• Most locations show similar methylation profiles

• Identified ~700 differentially methylated regions (DMRs) in B73 and Mo17 inbreds

Eichten et al., 2011. PloS Genetics

(B73 methylated, Mo17 not)(Mo17 methylated, B73 not)

Examples of expression changes correlated with epigenetic stateChromosome 6

Methylation inversely correlated with expression state

Inbred-specific examples of methylation & expression variation

Hundreds of genes correlated with epigenetic state

Methylation & Expression variation across NAMChromosome 4

Putative targets of heritable epigenetic variation

Analysis of DNA methylation patterns across additional tissues and genotypes

B73 embryo

B73 endosperm

B73 leaf

Mo17 embryo

Mo17 endosperm

Mo17 leaf

Ki11 leaf

Mo18w leaf

NC358 leaf

Oh7b leaf

• DNA methylation patterns are generally quite similar among genotypes and tissues.

• However, there are ~1000 DMRs between any two genotypes.

• Variation frequently acts equally upon all tissues.

• Few Tissue specific DMRs and rarely conserved between genotypes

Wrap Up• Epigenetics

– Epigenetic variation exists in maize– Examples of gene expression states

correlated with epigenetic state can be identified

– Few tissue-specific methylation variants• Utility of iPlant

– Fast & remote location for storing large amounts of data

– Fluid analysis of sequence data to develop transcript alignment and quantification

• Springer Lab– Amanda Waters– Ruth Swanson-Wagner– Peter Hermanson– Nathan Springer

• iPlant & TACC– Matthew Vaughn

• NSF

Thanks!