Comparative eQTL analyses within and between seven tissue …bee/pubs/ashg-bee-2012.pdf · 2013. 5. 2. · Casey Brown (UChicago, Penn), Lara Mangravite (Sage Bionetworks), Matthew

Comparative eQTL analyses within and betweenseven tissue types suggest mechanisms underlying

cell type specificity of eQTLs

Barbara Engelhardt, Duke UniversityChristopher D Brown, University of Pennsylvania

November 9th, 2012

Barbara Engelhardt Mechanisms underlying eQTL cell type specificity

Motivation: Predicting functional SNPs

Most functional nucleotides in vertebrate genomes arenon-coding

> 85% of common disease associations with non-coding SNPs

We would like to know whether any non-coding SNP in celltype of interest is biochemically functional to study:

genome-wide association study hitsde novo mutations involved in highly penetrant diseasesomatic mutations involved in cancer

Current functional SNP analyses are limited by our narrowunderstanding of the functional constraints of most of thegenome


Functional SNPs: Expression Quantitative Trait Loci

eQTLs are genetic variants that are associated withdifferences in mRNA transcription levels

Current eQTL studies do not go far enough:

cell specificity across relevant cell types unclearLD-linked SNPs instead of causal SNPoften one local, most significantly associated eQTL-SNP

Study goal: quantify, identify possible mechanisms for, andpredict cell type specific eQTLs

Results will enable functional interpretations of SNPs in a celltype specific way


Comparison of eQTLs: eleven studies, seven cell types

Used gene expression and genotype data from 11 publiclyavailable studies on 7 different cell types

Analysis pipeline was uniform across studies:

Remapped expression probes to unique genes in EnsemblRemoved unexpressed probes, probes containing SNPsRemoved principal components to account for study-specificconfoundersImputed genotypes to CEPH HapMap phase 2 panel

Evaluated eQTLs using Bayes factors (BFs)

Single permutation to evaluate FDR

Only considered cis-eQTLs (SNPs within 1Mb of TSS or TES)


eQTLs across studies: by the numbers

Study Code Tissue N N genesCAP CPL LCLs 480 18718

HapMap 2 STL LCLs 210 15752

Harvard HCE Cerebellum 540 18263

Harvard HPC Prefrontal cortex 678 18257

Harvard HVC Visual cortex 463 18263

GenCord GCF Blood fibroblasts 83 16691

GenCord GCL LCLs 85 16691

GenCord GCT Blood t cells 85 16691

UChicago CLI Liver 206 16236

Merck MLI Liver 266 18234

Myers MBR Brain 193 11707


Sample size versus fraction of genes with eQTLs

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Studies with duplicate arrays have substantially more power

Study size and replicate arrays account for 98% of thevariability in fraction of genes with eQTLs


Allelic heterogeneity

Allelic heterogeneity: variants at a genomic locusindependently regulate the same biological process.

ENCODE: > 400, 000 regulatory elements for ∼ 23, 000 genes

Most significant eQTL is often not the only eQTL

Used LD-block method to identify allelic heterogeneity

Followed identification with a test for independent effects


Allelic heterogeneity across eleven studies

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Sample size well correlated withlevels of allelic heterogeneity

Gene Ontology analysis showsno distinction between geneswith primary eQTLs and thosewith secondary or more eQTLs

We hypothesize that allelicheterogeneity is ubiquitous


eQTLs across cell types: locations

eQTLs enriched relative tobackground at TSS, TES

TSS, TES enrichment extendsto eQTLs in all tiers

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Replication within and between cell types

eQTL replication entails log10 BF > 1.0 in target data set forall eQTLs in discovery data at FDR < 5%

Blue lines show within cell type replication; red lines showbetween cell type replication

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Liver + LiverLiver + LCL

Brain + BrainBrain + LCL




False positives: small percentage of replicating eQTLs

False negatives: due to study design, lack of power, etc.


Incorporating ENCODE data: functional interpretability

ENCODE project has extensive genomic data for cell typespecific genomic featuresUnderstand how eQTL regulates transcription

Figure from ENCODE projectBarbara Engelhardt Mechanisms underlying eQTL cell type specificity

Allelic heterogeneity and insulators

CTCF is the best characterizedinsulator protein, conserved infunction across metazoans

If two SNPs independentlyregulate transcription, we mightexpect an enrichment of CTCFbetween them

In Drosophila melanogaster,recent work showed insulatorsare enriched between alternativepromoters [Negre, 2010]

We see this same enrichment inhumans

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Background SNPsIndependent eQTL SNPs

Figure 3. Insulators are enriched between SNPs independently associated with the samegene expression trait.Barbara Engelhardt Mechanisms underlying eQTL cell type specificity

eQTLs and overlap with DHS sites

DNAse I hypersensitive (DHS)sites: indicate histone-depletedopen chromatin; classic feature ofactive regulatory elements

Clear enrichment in eQTL overlap

Significant enrichment forreplicating eQTLs versusnon-replicating eQTLs (not shown)

Significant enrichment for LCLeQTLs in DHS sites in LCLs versusDHS sites in Hepg2 cells (notshown)

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HepG2 CREsLCL CREs



HepG2 CREsLCL CREs



HepG2 CREsLCL CREs


eQTLs and overlap with heterochromatin

Heterochromatin (facultative):tightly packed, cell specific form ofDNA; regulatory elements inheterochromatin regions areinaccessible to transcriptionalregulators

Clear depletion in eQTL overlap

Significant depletion for replicatingeQTLs versus non-replicatingeQTLs (not shown)

Significant depletion for LCLeQTLs in heterochromatin in LCLsversus heterochromatin in Hepg2cells (not shown)

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HepG2 CREsLCL CREs



HepG2 CREsLCL CREs



HepG2 CREsLCL CREs


Predicting replication of eQTLs

Built random forest classifier to predict whether a specificeQTL would replication in a second study

Class was whether an eQTL replicated or not

Features included:

genomic information (e.g., distance to TSS of SNP)non-cell type specific regulatory elements (e.g., GERP scores)cell type specific regulatory elements (e.g., DHS sites, TFBS)

Considered predicting replication:

within cell type using cell type specific CRE informationbetween cell type using target cell type specific CRE data

Validated accuracy using 10-fold cross validation


Predicting replication of eQTLs: ROC curves

Receiver Operating Characteristic (ROC) curves compare the rateof false positives versus the rate of true positives as the cutoffmoves from most to least restrictiveRed lines: within cell type replicability; blue lines: between celltype replicability.

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Area under the ROC Curve (AUC): quantifies improvementover random guessing

For LCL eQTLs, AUCs are 0.79 and 0.73, respectively, forwithin LCL and between LCL and liver eQTL replication


Predicting replication of eQTLs: Gini scores

How predictive is each feature for whether the eQTLreplicates?

Across all training sets, biggest contributors:

eQTL discovery significanceSNP to TSS distance,gene expression level

Cis-regulatory elements vary considerably in the degree towhich they are useful in predicting replication

Intervening insulators contribute substantially to within celltype predictions

Heterochromatin states contribute substantially to betweencell type predictions


Summary and Conclusions

We leveraged eQTLs found in both within and between celltypes and extensive ENCODE data in this large comparativestudy to quantify, describe mechanistically, and predict celltype specific eQTL SNPs

With an SNP and a cell type of interest:

identify an eQTL well correlated (in high LD) with the hitcompute probability that it will replicate in cell type of interestconsider the location of the hit relative to cell type specific andprediction-informative CREsmake a more informed hypothesis about mechanism ofphenotype (validate via experiments)


Acknowledgements

Casey Brown (UChicago, Penn), Lara Mangravite (SageBionetworks), Matthew Stephens (University of Chicago)

Greg Crawford (Duke University), all the ENCODE data

eQTL studies: GenCord, CAP, Harvard Brain, HapMap phase2, Merck liver, Myers brain, UChicago liver

Funding: NIH NHGRI K99/R00

Paper on arXiv, Haldane’s Sieve

Graphics: R package ggplot2


Documents

Comparative eQTL analyses within and between seven tissue …bee/pubs/ashg-bee-2012.pdf · 2013. 5. 2. · Casey Brown (UChicago, Penn), Lara Mangravite (Sage Bionetworks), Matthew